Sales Forecasting .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "029fdcf7",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "66980dfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15, 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c0ae933e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Sakshi'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display (os.getcwd())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61f761b1",
   "metadata": {},
   "source": [
    "# Read Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a5b0333",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047   \n",
       "1           DRC01         5.92          Regular         0.019278   \n",
       "2           FDN15        17.50          Low Fat         0.016760   \n",
       "3           FDX07        19.20          Regular         0.000000   \n",
       "4           NCD19         8.93          Low Fat         0.000000   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092            OUT049   \n",
       "1            Soft Drinks   48.2692            OUT018   \n",
       "2                   Meat  141.6180            OUT049   \n",
       "3  Fruits and Vegetables  182.0950            OUT010   \n",
       "4              Household   53.8614            OUT013   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998         NaN               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales  \n",
       "0  Supermarket Type1          3735.1380  \n",
       "1  Supermarket Type2           443.4228  \n",
       "2  Supermarket Type1          2097.2700  \n",
       "3      Grocery Store           732.3800  \n",
       "4  Supermarket Type1           994.7052  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = pd.read_csv(\"D:\\\\Acmegrade\\\\Sales Forecasting\\\\Train.csv\")\n",
    "display (dt.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c3551b",
   "metadata": {},
   "source": [
    "# Display shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6def03c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8523, 12)\n"
     ]
    }
   ],
   "source": [
    "print (dt.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91ec00bc",
   "metadata": {},
   "source": [
    "# Display the column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60481b81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Item_Identifier', 'Item_Weight', 'Item_Fat_Content', 'Item_Visibility',\n",
       "       'Item_Type', 'Item_MRP', 'Outlet_Identifier',\n",
       "       'Outlet_Establishment_Year', 'Outlet_Size', 'Outlet_Location_Type',\n",
       "       'Outlet_Type', 'Item_Outlet_Sales'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display (dt.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "096b54b7",
   "metadata": {},
   "source": [
    "# Describe the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a3cfaea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7060.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.857645</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>140.992782</td>\n",
       "      <td>1997.831867</td>\n",
       "      <td>2181.288914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.643456</td>\n",
       "      <td>0.051598</td>\n",
       "      <td>62.275067</td>\n",
       "      <td>8.371760</td>\n",
       "      <td>1706.499616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.555000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.290000</td>\n",
       "      <td>1985.000000</td>\n",
       "      <td>33.290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.773750</td>\n",
       "      <td>0.026989</td>\n",
       "      <td>93.826500</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>834.247400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.053931</td>\n",
       "      <td>143.012800</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>1794.331000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.850000</td>\n",
       "      <td>0.094585</td>\n",
       "      <td>185.643700</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>3101.296400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21.350000</td>\n",
       "      <td>0.328391</td>\n",
       "      <td>266.888400</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>13086.964800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Item_Weight  Item_Visibility     Item_MRP  Outlet_Establishment_Year  \\\n",
       "count  7060.000000      8523.000000  8523.000000                8523.000000   \n",
       "mean     12.857645         0.066132   140.992782                1997.831867   \n",
       "std       4.643456         0.051598    62.275067                   8.371760   \n",
       "min       4.555000         0.000000    31.290000                1985.000000   \n",
       "25%       8.773750         0.026989    93.826500                1987.000000   \n",
       "50%      12.600000         0.053931   143.012800                1999.000000   \n",
       "75%      16.850000         0.094585   185.643700                2004.000000   \n",
       "max      21.350000         0.328391   266.888400                2009.000000   \n",
       "\n",
       "       Item_Outlet_Sales  \n",
       "count        8523.000000  \n",
       "mean         2181.288914  \n",
       "std          1706.499616  \n",
       "min            33.290000  \n",
       "25%           834.247400  \n",
       "50%          1794.331000  \n",
       "75%          3101.296400  \n",
       "max         13086.964800  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display (dt.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04b75738",
   "metadata": {},
   "source": [
    "# Display Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e2f5f3c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8523 entries, 0 to 8522\n",
      "Data columns (total 12 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Item_Identifier            8523 non-null   object \n",
      " 1   Item_Weight                7060 non-null   float64\n",
      " 2   Item_Fat_Content           8523 non-null   object \n",
      " 3   Item_Visibility            8523 non-null   float64\n",
      " 4   Item_Type                  8523 non-null   object \n",
      " 5   Item_MRP                   8523 non-null   float64\n",
      " 6   Outlet_Identifier          8523 non-null   object \n",
      " 7   Outlet_Establishment_Year  8523 non-null   int64  \n",
      " 8   Outlet_Size                6113 non-null   object \n",
      " 9   Outlet_Location_Type       8523 non-null   object \n",
      " 10  Outlet_Type                8523 non-null   object \n",
      " 11  Item_Outlet_Sales          8523 non-null   float64\n",
      "dtypes: float64(4), int64(1), object(7)\n",
      "memory usage: 799.2+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display (dt.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f79f627a",
   "metadata": {},
   "source": [
    "# Display the Unique Values for each column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b591b9fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier              1559\n",
       "Item_Weight                   416\n",
       "Item_Fat_Content                5\n",
       "Item_Visibility              7880\n",
       "Item_Type                      16\n",
       "Item_MRP                     5938\n",
       "Outlet_Identifier              10\n",
       "Outlet_Establishment_Year       9\n",
       "Outlet_Size                     4\n",
       "Outlet_Location_Type            3\n",
       "Outlet_Type                     4\n",
       "Item_Outlet_Sales            3493\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display (dt.apply(lambda x: len(x.unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc7c1f44",
   "metadata": {},
   "source": [
    "# Check for Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a63587a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier                 0\n",
       "Item_Weight                  1463\n",
       "Item_Fat_Content                0\n",
       "Item_Visibility                 0\n",
       "Item_Type                       0\n",
       "Item_MRP                        0\n",
       "Outlet_Identifier               0\n",
       "Outlet_Establishment_Year       0\n",
       "Outlet_Size                  2410\n",
       "Outlet_Location_Type            0\n",
       "Outlet_Type                     0\n",
       "Item_Outlet_Sales               0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display (dt.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ec56e34",
   "metadata": {},
   "source": [
    "# Store the Categorical columns in a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "832f31a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Item_Identifier',\n",
       " 'Item_Fat_Content',\n",
       " 'Item_Type',\n",
       " 'Outlet_Identifier',\n",
       " 'Outlet_Size',\n",
       " 'Outlet_Location_Type',\n",
       " 'Outlet_Type']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_col = []\n",
    "for x in dt.dtypes.index:\n",
    "    if dt.dtypes[x] == 'object':\n",
    "        cat_col.append(x)\n",
    "display (cat_col)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fa8c0a1",
   "metadata": {},
   "source": [
    "# Delete the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f9f314ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Item_Fat_Content',\n",
       " 'Item_Type',\n",
       " 'Outlet_Size',\n",
       " 'Outlet_Location_Type',\n",
       " 'Outlet_Type']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_col.remove('Item_Identifier')\n",
    "cat_col.remove('Outlet_Identifier')\n",
    "display (cat_col)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29ba6ed7",
   "metadata": {},
   "source": [
    "# Display the Unique Values in category columns – Count "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8f417d28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item_Fat_Content 5\n",
      "Item_Type 16\n",
      "Outlet_Size 4\n",
      "Outlet_Location_Type 3\n",
      "Outlet_Type 4\n"
     ]
    }
   ],
   "source": [
    "for col in cat_col:\n",
    "    print(col , len(dt[col].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac9632e4",
   "metadata": {},
   "source": [
    "# Unique values in each category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c325b349",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item_Fat_Content\n",
      "Low Fat    5089\n",
      "Regular    2889\n",
      "LF          316\n",
      "reg         117\n",
      "low fat     112\n",
      "Name: Item_Fat_Content, dtype: int64\n",
      "\n",
      "**************************************************\n",
      "Item_Type\n",
      "Fruits and Vegetables    1232\n",
      "Snack Foods              1200\n",
      "Household                 910\n",
      "Frozen Foods              856\n",
      "Dairy                     682\n",
      "Canned                    649\n",
      "Baking Goods              648\n",
      "Health and Hygiene        520\n",
      "Soft Drinks               445\n",
      "Meat                      425\n",
      "Breads                    251\n",
      "Hard Drinks               214\n",
      "Others                    169\n",
      "Starchy Foods             148\n",
      "Breakfast                 110\n",
      "Seafood                    64\n",
      "Name: Item_Type, dtype: int64\n",
      "\n",
      "**************************************************\n",
      "Outlet_Size\n",
      "Medium    2793\n",
      "Small     2388\n",
      "High       932\n",
      "Name: Outlet_Size, dtype: int64\n",
      "\n",
      "**************************************************\n",
      "Outlet_Location_Type\n",
      "Tier 3    3350\n",
      "Tier 2    2785\n",
      "Tier 1    2388\n",
      "Name: Outlet_Location_Type, dtype: int64\n",
      "\n",
      "**************************************************\n",
      "Outlet_Type\n",
      "Supermarket Type1    5577\n",
      "Grocery Store        1083\n",
      "Supermarket Type3     935\n",
      "Supermarket Type2     928\n",
      "Name: Outlet_Type, dtype: int64\n",
      "\n",
      "**************************************************\n"
     ]
    }
   ],
   "source": [
    "for col in cat_col:\n",
    "    print(col)\n",
    "    print(dt[col].value_counts())\n",
    "    print()\n",
    "    print ('*' *50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac5c1d8e",
   "metadata": {},
   "source": [
    "# Display the missing values - missing values will be True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ddd16638",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "8518    False\n",
       "8519    False\n",
       "8520    False\n",
       "8521    False\n",
       "8522    False\n",
       "Name: Item_Weight, Length: 8523, dtype: bool"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "miss_bool = dt['Item_Weight'].isnull()\n",
    "display (miss_bool)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1283e6c",
   "metadata": {},
   "source": [
    "# Missing value count - column - Item_Weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1ea95931",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1463"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display (dt['Item_Weight'].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc088134",
   "metadata": {},
   "source": [
    "# Display all NULL Records "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d1016291",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FDP10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.127470</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>107.7622</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>4022.7636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>DRI11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.034238</td>\n",
       "      <td>Hard Drinks</td>\n",
       "      <td>113.2834</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>2303.6680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>FDW12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.035400</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>144.5444</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>4064.0432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>FDC37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.057557</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>107.6938</td>\n",
       "      <td>OUT019</td>\n",
       "      <td>1985</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>214.3876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>FDC14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.072222</td>\n",
       "      <td>Canned</td>\n",
       "      <td>43.6454</td>\n",
       "      <td>OUT019</td>\n",
       "      <td>1985</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>125.8362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8485</th>\n",
       "      <td>DRK37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.043792</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>189.0530</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>6261.8490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8487</th>\n",
       "      <td>DRG13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.037006</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>164.7526</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>4111.3150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8488</th>\n",
       "      <td>NCN14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.091473</td>\n",
       "      <td>Others</td>\n",
       "      <td>184.6608</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>2756.4120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8490</th>\n",
       "      <td>FDU44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.102296</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>162.3552</td>\n",
       "      <td>OUT019</td>\n",
       "      <td>1985</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>487.3656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8504</th>\n",
       "      <td>NCN18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.124111</td>\n",
       "      <td>Household</td>\n",
       "      <td>111.7544</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>4138.6128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1463 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "7              FDP10          NaN          Low Fat         0.127470   \n",
       "18             DRI11          NaN          Low Fat         0.034238   \n",
       "21             FDW12          NaN          Regular         0.035400   \n",
       "23             FDC37          NaN          Low Fat         0.057557   \n",
       "29             FDC14          NaN          Regular         0.072222   \n",
       "...              ...          ...              ...              ...   \n",
       "8485           DRK37          NaN          Low Fat         0.043792   \n",
       "8487           DRG13          NaN          Low Fat         0.037006   \n",
       "8488           NCN14          NaN          Low Fat         0.091473   \n",
       "8490           FDU44          NaN          Regular         0.102296   \n",
       "8504           NCN18          NaN          Low Fat         0.124111   \n",
       "\n",
       "                  Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "7               Snack Foods  107.7622            OUT027   \n",
       "18              Hard Drinks  113.2834            OUT027   \n",
       "21             Baking Goods  144.5444            OUT027   \n",
       "23             Baking Goods  107.6938            OUT019   \n",
       "29                   Canned   43.6454            OUT019   \n",
       "...                     ...       ...               ...   \n",
       "8485            Soft Drinks  189.0530            OUT027   \n",
       "8487            Soft Drinks  164.7526            OUT027   \n",
       "8488                 Others  184.6608            OUT027   \n",
       "8490  Fruits and Vegetables  162.3552            OUT019   \n",
       "8504              Household  111.7544            OUT027   \n",
       "\n",
       "      Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "7                          1985      Medium               Tier 3   \n",
       "18                         1985      Medium               Tier 3   \n",
       "21                         1985      Medium               Tier 3   \n",
       "23                         1985       Small               Tier 1   \n",
       "29                         1985       Small               Tier 1   \n",
       "...                         ...         ...                  ...   \n",
       "8485                       1985      Medium               Tier 3   \n",
       "8487                       1985      Medium               Tier 3   \n",
       "8488                       1985      Medium               Tier 3   \n",
       "8490                       1985       Small               Tier 1   \n",
       "8504                       1985      Medium               Tier 3   \n",
       "\n",
       "            Outlet_Type  Item_Outlet_Sales  \n",
       "7     Supermarket Type3          4022.7636  \n",
       "18    Supermarket Type3          2303.6680  \n",
       "21    Supermarket Type3          4064.0432  \n",
       "23        Grocery Store           214.3876  \n",
       "29        Grocery Store           125.8362  \n",
       "...                 ...                ...  \n",
       "8485  Supermarket Type3          6261.8490  \n",
       "8487  Supermarket Type3          4111.3150  \n",
       "8488  Supermarket Type3          2756.4120  \n",
       "8490      Grocery Store           487.3656  \n",
       "8504  Supermarket Type3          4138.6128  \n",
       "\n",
       "[1463 rows x 12 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Item_Weight_null = dt[dt['Item_Weight'].isna()]\n",
    "display (Item_Weight_null)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99295e1c",
   "metadata": {},
   "source": [
    "# NULL Records by Item Identifier column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b78b98c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FDK08    2\n",
       "FDA08    2\n",
       "FDV23    2\n",
       "FDY56    2\n",
       "FDI04    2\n",
       "        ..\n",
       "FDM44    1\n",
       "FDZ48    1\n",
       "FDK41    1\n",
       "FDD57    1\n",
       "NCN18    1\n",
       "Name: Item_Identifier, Length: 1142, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Item_Weight_null['Item_Identifier'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b21ce66",
   "metadata": {},
   "source": [
    "# Find the mean for the column – Item Weight group by Item Identifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f9dbc800",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DRA12</th>\n",
       "      <td>11.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRA24</th>\n",
       "      <td>19.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRA59</th>\n",
       "      <td>8.270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRB01</th>\n",
       "      <td>7.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRB13</th>\n",
       "      <td>6.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ30</th>\n",
       "      <td>6.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ41</th>\n",
       "      <td>19.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ42</th>\n",
       "      <td>10.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ53</th>\n",
       "      <td>9.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCZ54</th>\n",
       "      <td>14.650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1555 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Item_Weight\n",
       "Item_Identifier             \n",
       "DRA12                 11.600\n",
       "DRA24                 19.350\n",
       "DRA59                  8.270\n",
       "DRB01                  7.390\n",
       "DRB13                  6.115\n",
       "...                      ...\n",
       "NCZ30                  6.590\n",
       "NCZ41                 19.850\n",
       "NCZ42                 10.500\n",
       "NCZ53                  9.600\n",
       "NCZ54                 14.650\n",
       "\n",
       "[1555 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "item_weight_mean = dt.pivot_table(values = \"Item_Weight\", index = 'Item_Identifier')\n",
    "display (item_weight_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14e4c6f4",
   "metadata": {},
   "source": [
    "# Display Item Identifier column  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e2686897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       FDA15\n",
       "1       DRC01\n",
       "2       FDN15\n",
       "3       FDX07\n",
       "4       NCD19\n",
       "        ...  \n",
       "8518    FDF22\n",
       "8519    FDS36\n",
       "8520    NCJ29\n",
       "8521    FDN46\n",
       "8522    DRG01\n",
       "Name: Item_Identifier, Length: 8523, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display (dt['Item_Identifier'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29a855f8",
   "metadata": {},
   "source": [
    "# Fill the missing values (Item Weight) with mean, the mean calculated by group by Item identifier  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5b19477f",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, item in enumerate(dt['Item_Identifier']):\n",
    "    if miss_bool[i]:\n",
    "        if item in item_weight_mean.index:\n",
    "            dt['Item_Weight'][i] = item_weight_mean.loc[item]['Item_Weight']\n",
    "        else:\n",
    "            dt['Item_Weight'][i] = np.mean(dt['Item_Weight'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f69ad5fd",
   "metadata": {},
   "source": [
    "# Check the Null values again – Same column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7591e2e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = dt['Item_Weight'].isnull().sum()\n",
    "display (result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a04d3c9",
   "metadata": {},
   "source": [
    "# Record count based on 'Outlet_Size'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c62a091f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Outlet_Size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medium</th>\n",
       "      <td>2793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small</th>\n",
       "      <td>2388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Outlet_Size\n",
       "Outlet_Size             \n",
       "High                 932\n",
       "Medium              2793\n",
       "Small               2388"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = dt.groupby('Outlet_Size').agg({'Outlet_Size': np.size})\n",
    "display (result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a71f6e1",
   "metadata": {},
   "source": [
    "# NULL Record based on 'Outlet_Size'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f7b8e610",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2410"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result= dt['Outlet_Size'].isnull().sum()\n",
    "display (result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae8ff26e",
   "metadata": {},
   "source": [
    "# Display all NULL Records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "63de637f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.200</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FDH17</td>\n",
       "      <td>16.200</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.016687</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>96.9726</td>\n",
       "      <td>OUT045</td>\n",
       "      <td>2002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>1076.5986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FDU28</td>\n",
       "      <td>19.200</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.094450</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>187.8214</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>4710.5350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NCD06</td>\n",
       "      <td>13.000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.099887</td>\n",
       "      <td>Household</td>\n",
       "      <td>45.9060</td>\n",
       "      <td>OUT017</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>838.9080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>FDE51</td>\n",
       "      <td>5.925</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.161467</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>45.5086</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>178.4344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8502</th>\n",
       "      <td>NCH43</td>\n",
       "      <td>8.420</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.070712</td>\n",
       "      <td>Household</td>\n",
       "      <td>216.4192</td>\n",
       "      <td>OUT045</td>\n",
       "      <td>2002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3020.0688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8508</th>\n",
       "      <td>FDW31</td>\n",
       "      <td>11.350</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.043246</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>199.4742</td>\n",
       "      <td>OUT045</td>\n",
       "      <td>2002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2587.9646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8509</th>\n",
       "      <td>FDG45</td>\n",
       "      <td>8.100</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.214306</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>213.9902</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>424.7804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8514</th>\n",
       "      <td>FDA01</td>\n",
       "      <td>15.000</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.054489</td>\n",
       "      <td>Canned</td>\n",
       "      <td>57.5904</td>\n",
       "      <td>OUT045</td>\n",
       "      <td>2002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>468.7232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8519</th>\n",
       "      <td>FDS36</td>\n",
       "      <td>8.380</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.046982</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>108.1570</td>\n",
       "      <td>OUT045</td>\n",
       "      <td>2002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>549.2850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2410 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "3              FDX07       19.200          Regular         0.000000   \n",
       "8              FDH17       16.200          Regular         0.016687   \n",
       "9              FDU28       19.200          Regular         0.094450   \n",
       "25             NCD06       13.000          Low Fat         0.099887   \n",
       "28             FDE51        5.925          Regular         0.161467   \n",
       "...              ...          ...              ...              ...   \n",
       "8502           NCH43        8.420          Low Fat         0.070712   \n",
       "8508           FDW31       11.350          Regular         0.043246   \n",
       "8509           FDG45        8.100          Low Fat         0.214306   \n",
       "8514           FDA01       15.000          Regular         0.054489   \n",
       "8519           FDS36        8.380          Regular         0.046982   \n",
       "\n",
       "                  Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "3     Fruits and Vegetables  182.0950            OUT010   \n",
       "8              Frozen Foods   96.9726            OUT045   \n",
       "9              Frozen Foods  187.8214            OUT017   \n",
       "25                Household   45.9060            OUT017   \n",
       "28                    Dairy   45.5086            OUT010   \n",
       "...                     ...       ...               ...   \n",
       "8502              Household  216.4192            OUT045   \n",
       "8508  Fruits and Vegetables  199.4742            OUT045   \n",
       "8509  Fruits and Vegetables  213.9902            OUT010   \n",
       "8514                 Canned   57.5904            OUT045   \n",
       "8519           Baking Goods  108.1570            OUT045   \n",
       "\n",
       "      Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "3                          1998         NaN               Tier 3   \n",
       "8                          2002         NaN               Tier 2   \n",
       "9                          2007         NaN               Tier 2   \n",
       "25                         2007         NaN               Tier 2   \n",
       "28                         1998         NaN               Tier 3   \n",
       "...                         ...         ...                  ...   \n",
       "8502                       2002         NaN               Tier 2   \n",
       "8508                       2002         NaN               Tier 2   \n",
       "8509                       1998         NaN               Tier 3   \n",
       "8514                       2002         NaN               Tier 2   \n",
       "8519                       2002         NaN               Tier 2   \n",
       "\n",
       "            Outlet_Type  Item_Outlet_Sales  \n",
       "3         Grocery Store           732.3800  \n",
       "8     Supermarket Type1          1076.5986  \n",
       "9     Supermarket Type1          4710.5350  \n",
       "25    Supermarket Type1           838.9080  \n",
       "28        Grocery Store           178.4344  \n",
       "...                 ...                ...  \n",
       "8502  Supermarket Type1          3020.0688  \n",
       "8508  Supermarket Type1          2587.9646  \n",
       "8509      Grocery Store           424.7804  \n",
       "8514  Supermarket Type1           468.7232  \n",
       "8519  Supermarket Type1           549.2850  \n",
       "\n",
       "[2410 rows x 12 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Outlet_Size_null= dt[dt['Outlet_Size'].isna()]\n",
    "display (Outlet_Size_null)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f666347c",
   "metadata": {},
   "source": [
    "# Null Record count based on -Outlet Type "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a4862fe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Supermarket Type1    1855\n",
       "Grocery Store         555\n",
       "Name: Outlet_Type, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = Outlet_Size_null['Outlet_Type'].value_counts()\n",
    "display (result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "054bcbfb",
   "metadata": {},
   "source": [
    "# Group by based on Outlet_Type and Outlet_Size to find the most repeated value, this is to fill missing value by Outlet Type "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ffbed88e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Outlet_Type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Grocery Store</th>\n",
       "      <th>Small</th>\n",
       "      <td>528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Supermarket Type1</th>\n",
       "      <th>High</th>\n",
       "      <td>932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medium</th>\n",
       "      <td>930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small</th>\n",
       "      <td>1860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket Type2</th>\n",
       "      <th>Medium</th>\n",
       "      <td>928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket Type3</th>\n",
       "      <th>Medium</th>\n",
       "      <td>935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Outlet_Type\n",
       "                                     size\n",
       "Outlet_Type       Outlet_Size            \n",
       "Grocery Store     Small               528\n",
       "Supermarket Type1 High                932\n",
       "                  Medium              930\n",
       "                  Small              1860\n",
       "Supermarket Type2 Medium              928\n",
       "Supermarket Type3 Medium              935"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result= dt.groupby (['Outlet_Type','Outlet_Size'] ).agg({'Outlet_Type':[np.size]})\n",
    "display (result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "121f309d",
   "metadata": {},
   "source": [
    "# Alternate way to identify most repeated value – Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "61c28fc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Grocery Store</th>\n",
       "      <th>Supermarket Type1</th>\n",
       "      <th>Supermarket Type2</th>\n",
       "      <th>Supermarket Type3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Outlet_Size</th>\n",
       "      <td>Small</td>\n",
       "      <td>Small</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Outlet_Type Grocery Store Supermarket Type1 Supermarket Type2  \\\n",
       "Outlet_Size         Small             Small            Medium   \n",
       "\n",
       "Outlet_Type Supermarket Type3  \n",
       "Outlet_Size            Medium  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outlet_size_mode = dt.pivot_table(values='Outlet_Size', columns='Outlet_Type', aggfunc=(lambda x: x.mode()[0]))\n",
    "display (outlet_size_mode)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fb6bc7d",
   "metadata": {},
   "source": [
    "# Use Mode to fill missing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a5b16f69",
   "metadata": {},
   "outputs": [],
   "source": [
    "miss_bool = dt['Outlet_Size'].isnull()\n",
    "dt.loc[miss_bool, 'Outlet_Size'] = dt.loc[miss_bool, 'Outlet_Type'].apply(lambda x: outlet_size_mode[x])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f69f7b53",
   "metadata": {},
   "source": [
    "# Equivalent For loop for the above statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "239d0ad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "miss_bool = dt['Outlet_Size'].isnull()\n",
    "for i, item in enumerate (dt['Outlet_Size']):\n",
    "    if miss_bool[i]:\n",
    "        dt['Outlet_Size'][i] = outlet_size_mode.loc['Outlet_Size',dt['Outlet_Type'][i] ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdc51b95",
   "metadata": {},
   "source": [
    "# Check the Null values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d19a260a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display (dt['Outlet_Size'].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fa63d1e",
   "metadata": {},
   "source": [
    "# Check the group by count to see if the count increased "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fbe16cfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Outlet_Type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Grocery Store</th>\n",
       "      <th>Small</th>\n",
       "      <td>1083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Supermarket Type1</th>\n",
       "      <th>High</th>\n",
       "      <td>932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medium</th>\n",
       "      <td>930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small</th>\n",
       "      <td>3715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket Type2</th>\n",
       "      <th>Medium</th>\n",
       "      <td>928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket Type3</th>\n",
       "      <th>Medium</th>\n",
       "      <td>935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Outlet_Type\n",
       "                                     size\n",
       "Outlet_Type       Outlet_Size            \n",
       "Grocery Store     Small              1083\n",
       "Supermarket Type1 High                932\n",
       "                  Medium              930\n",
       "                  Small              3715\n",
       "Supermarket Type2 Medium              928\n",
       "Supermarket Type3 Medium              935"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = dt.groupby (['Outlet_Type','Outlet_Size'] ).agg({'Outlet_Type':[np.size]})\n",
    "display (result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7f688fe",
   "metadata": {},
   "source": [
    "# Check Item visibility column with value - 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1a7f2359",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "526"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display (sum(dt['Item_Visibility']==0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9c5a78d",
   "metadata": {},
   "source": [
    "# Replace zeros with mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "728ab4c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt.loc[:, 'Item_Visibility'].replace([0], [dt['Item_Visibility'].mean()], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0a76c46",
   "metadata": {},
   "source": [
    "# Check any value with 0 again "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7f065879",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(sum(dt['Item_Visibility']==0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3945ffa8",
   "metadata": {},
   "source": [
    "# Check distinct values - Item_Fat_Content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6e11e44e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat    5089\n",
       "Regular    2889\n",
       "LF          316\n",
       "reg         117\n",
       "low fat     112\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = dt['Item_Fat_Content'].value_counts()\n",
    "display (result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9b2195d",
   "metadata": {},
   "source": [
    "# Consolidate similar Column Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "557fb61a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat    5517\n",
       "Regular    3006\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt['Item_Fat_Content'] = dt['Item_Fat_Content'].replace({'LF':'Low Fat', 'reg':'Regular', 'low fat':'Low Fat'})\n",
    "result = dt['Item_Fat_Content'].value_counts()\n",
    "display (result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e580d59e",
   "metadata": {},
   "source": [
    "# Create new attributes with first two characters of item identifier column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "51869ccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       FD\n",
       "1       DR\n",
       "2       FD\n",
       "3       FD\n",
       "4       NC\n",
       "        ..\n",
       "8518    FD\n",
       "8519    FD\n",
       "8520    NC\n",
       "8521    FD\n",
       "8522    DR\n",
       "Name: New_Item_Type, Length: 8523, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt['New_Item_Type'] = dt['Item_Identifier'].apply(lambda x: x[:2])\n",
    "display (dt['New_Item_Type'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2269b33",
   "metadata": {},
   "source": [
    "# Display Number of records in each category \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f0527a64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FD    6125\n",
       "NC    1599\n",
       "DR     799\n",
       "Name: New_Item_Type, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display (dt['New_Item_Type'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "905422b4",
   "metadata": {},
   "source": [
    "# Map the values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3d291144",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Food              6125\n",
       "Non-Consumable    1599\n",
       "Drinks             799\n",
       "Name: New_Item_Type, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt['New_Item_Type'] = dt['New_Item_Type'].map({'FD':'Food', 'NC':'Non-Consumable', 'DR':'Drinks'})\n",
    "display (dt['New_Item_Type'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28e0da2a",
   "metadata": {},
   "source": [
    "# Display distinct values in Item_Fat_Content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1a553f67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat    5517\n",
       "Regular    3006\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display (dt['Item_Fat_Content'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3652215b",
   "metadata": {},
   "source": [
    "# Display the count based on New_Item_Type and Item_Fat_Content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4458b6c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Outlet_Type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New_Item_Type</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Drinks</th>\n",
       "      <th>Low Fat</th>\n",
       "      <td>728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Regular</th>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Food</th>\n",
       "      <th>Low Fat</th>\n",
       "      <td>3190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Regular</th>\n",
       "      <td>2935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-Consumable</th>\n",
       "      <th>Low Fat</th>\n",
       "      <td>1599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Outlet_Type\n",
       "                                       size\n",
       "New_Item_Type  Item_Fat_Content            \n",
       "Drinks         Low Fat                  728\n",
       "               Regular                   71\n",
       "Food           Low Fat                 3190\n",
       "               Regular                 2935\n",
       "Non-Consumable Low Fat                 1599"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = dt.groupby (['New_Item_Type','Item_Fat_Content'] ).agg({'Outlet_Type':[np.size]})\n",
    "display (result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25551ff5",
   "metadata": {},
   "source": [
    "# Update Item_Fat_Content  to ‘Non Edible’  where New_Item_Type = Non-Consumable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2bdc829f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat       3918\n",
       "Regular       3006\n",
       "Non-Edible    1599\n",
       "Name: Item_Fat_Content, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt.loc[dt['New_Item_Type']=='Non-Consumable', 'Item_Fat_Content'] = 'Non-Edible'\n",
    "result =  (dt['Item_Fat_Content'].value_counts())\n",
    "display (result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc35b871",
   "metadata": {},
   "source": [
    "# Display the count based on New_Item_Type and Item_Fat_Content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6ade9621",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Outlet_Type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New_Item_Type</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Drinks</th>\n",
       "      <th>Low Fat</th>\n",
       "      <td>728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Regular</th>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Food</th>\n",
       "      <th>Low Fat</th>\n",
       "      <td>3190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Regular</th>\n",
       "      <td>2935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-Consumable</th>\n",
       "      <th>Non-Edible</th>\n",
       "      <td>1599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Outlet_Type\n",
       "                                       size\n",
       "New_Item_Type  Item_Fat_Content            \n",
       "Drinks         Low Fat                  728\n",
       "               Regular                   71\n",
       "Food           Low Fat                 3190\n",
       "               Regular                 2935\n",
       "Non-Consumable Non-Edible              1599"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = dt.groupby (['New_Item_Type','Item_Fat_Content'] ).agg({'Outlet_Type':[np.size]})\n",
    "display (result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bdf5acb",
   "metadata": {},
   "source": [
    "# Display how many years the outlet is present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7af68e09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       25\n",
      "1       15\n",
      "2       25\n",
      "3       26\n",
      "4       37\n",
      "        ..\n",
      "8518    37\n",
      "8519    22\n",
      "8520    20\n",
      "8521    15\n",
      "8522    27\n",
      "Name: Outlet_Years, Length: 8523, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "dt['Outlet_Years'] = 2024 - dt['Outlet_Establishment_Year']\n",
    "print (dt['Outlet_Years'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa3e1a0f",
   "metadata": {},
   "source": [
    "# Display Top 5 Records "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5400c71e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>New_Item_Type</th>\n",
       "      <th>Outlet_Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "      <td>Food</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "      <td>Drinks</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "      <td>Food</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "      <td>Food</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Non-Edible</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "      <td>Non-Consumable</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047   \n",
       "1           DRC01         5.92          Regular         0.019278   \n",
       "2           FDN15        17.50          Low Fat         0.016760   \n",
       "3           FDX07        19.20          Regular         0.066132   \n",
       "4           NCD19         8.93       Non-Edible         0.066132   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092            OUT049   \n",
       "1            Soft Drinks   48.2692            OUT018   \n",
       "2                   Meat  141.6180            OUT049   \n",
       "3  Fruits and Vegetables  182.0950            OUT010   \n",
       "4              Household   53.8614            OUT013   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998       Small               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales   New_Item_Type  Outlet_Years  \n",
       "0  Supermarket Type1          3735.1380            Food            25  \n",
       "1  Supermarket Type2           443.4228          Drinks            15  \n",
       "2  Supermarket Type1          2097.2700            Food            25  \n",
       "3      Grocery Store           732.3800            Food            26  \n",
       "4  Supermarket Type1           994.7052  Non-Consumable            37  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display (dt.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b43294a",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a669f0ec",
   "metadata": {},
   "source": [
    "# Create Dist Plot – Item Weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "913668e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dt['Item_Weight'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bc9e034",
   "metadata": {},
   "source": [
    "# Create Dist Plot – Item Visibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "86a7c4b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dt['Item_Visibility'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4353c53f",
   "metadata": {},
   "source": [
    "# Create Dist Plot – Item MRP "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "463840c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dt['Item_MRP'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9488c568",
   "metadata": {},
   "source": [
    "# Create Dist Plot – Item Outlet Sales \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f4691b62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dt['Item_Outlet_Sales'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70a1fa50",
   "metadata": {},
   "source": [
    "# Log Transformation to reduce Outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "da44af49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       8.225808\n",
       "1       6.096776\n",
       "2       7.648868\n",
       "3       6.597664\n",
       "4       6.903451\n",
       "          ...   \n",
       "8518    7.929984\n",
       "8519    6.310436\n",
       "8520    7.085159\n",
       "8521    7.521100\n",
       "8522    6.642056\n",
       "Name: Item_Outlet_Sales, Length: 8523, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt['Item_Outlet_Sales'] = np.log(1+dt['Item_Outlet_Sales'])\n",
    "display (dt['Item_Outlet_Sales'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa2e81df",
   "metadata": {},
   "source": [
    "# Create Dist Plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "60a9b5a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dt['Item_Outlet_Sales'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6997f66c",
   "metadata": {},
   "source": [
    "# Create Count Plot – Number of records in each category "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1b67cda4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = dt[\"Item_Fat_Content\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6941f7ee",
   "metadata": {},
   "source": [
    "# Create Count Plot – Item Type \n",
    "l is the list of unique Item Types - This is used to display X-Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5569f4f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "l = list(dt['Item_Type'].unique()) \n",
    "chart = sns.countplot(x =dt[\"Item_Type\"])\n",
    "chart.set_xticklabels(labels=l, rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69a924fa",
   "metadata": {},
   "source": [
    "# Create Count Plot – Establishment year \n",
    "Number of stores started per year  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9de33f67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x= dt['Outlet_Establishment_Year'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34b378ea",
   "metadata": {},
   "source": [
    "# Count Plot Outlet Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "86f905e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABjI3CKwqt5RVQ9W1a0zY8dU1XVVded0f/TMcxdV1a6quqOqXjEzfnpV7Zyeu6Sqal5zBgAAWO/meSTwsiRnLRq7MMn27t6cZPv0OFV1SpKtSZ4zbfO2qjps2ubtSc5Psnm6Ld4nAAAAyzS3COzu30vyp4uGz05y+bR8eZJzZsav7O6Hu/uuJLuSnFFVJyQ5qrtv6O5OcsXMNgAAAKzQwf5M4PHdfX+STPfHTeMbk9w7s97uaWzjtLx4HAAAgCfgULkwzFKf8+t9jC+9k6rzq2pHVe3Ys2fPAZscAADAenGwI/CB6RTPTPcPTuO7k5w4s96mJPdN45uWGF9Sd1/a3Vu6e8uGDRsO6MQBAADWg4Mdgdck2TYtb0ty9cz41qp6alWdnIULwNw4nTL6UFWdOV0V9LyZbQAAAFihw+e146p6b5KXJDm2qnYn+ckkb0xyVVW9Jsk9Sc5Nku6+raquSnJ7kkeSXNDdj067el0WrjR6RJJrpxsAAABPwNwisLtf+ThPvexx1r84ycVLjO9IcuoBnBoAAMCw5haBAAAcml70Cy9a7SnAkD7y+o+s9hSSHDpXBwUAAOAgEIEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADEYEAAAADWTMRWFVnVdUdVbWrqi5c7fkAAACsRWsiAqvqsCS/lOSfJTklySur6pTVnRUAAMDasyYiMMkZSXZ19x93918nuTLJ2as8JwAAgDVnrUTgxiT3zjzePY0BAACwAoev9gSWqZYY68esVHV+kvOnh5+rqjvmOivWi2OTfGa1J8ETU2/ettpTgMfjvWUt+8mlfvWAQ4L3ljWs/vVBf2/5mqUG10oE7k5y4szjTUnuW7xSd1+a5NKDNSnWh6ra0d1bVnsewPrivQWYB+8tHAhr5XTQjyXZXFUnV9VTkmxNcs0qzwkAAGDNWRNHArv7kar6wSQfSHJYknd0922rPC0AAIA1Z01EYJJ09/uTvH+158G65BRiYB68twDz4L2Fr1h1P+b6KgAAAKxTa+UzgQAAABwAIpA1qaq6qt418/jwqtpTVe9b4X6ur6ot0/L7q+qZB3iqwDpRVT9RVbdV1S1VdXNVfcMB2OfnpvuTqurWr3yWwFq19/1g5vGrq+oXp+XXVtV5+9n+b9aH/VkznwmERT6f5NSqOqK7/yrJP03yf7+SHXb3txyQmQHrTlV9Y5JvS/KC7n64qo5N8pRVnhYwiO7+5dWeA+uLI4GsZdcm+dZp+ZVJ3rv3iap6elW9o6o+VlWfqKqzp/EjqurK6S/5v5bkiJlt7q6qYxf/Rb6qfqSq3jAtX19V/6Wqfq+qPllVL6yq36yqO6vqPx2EnxlYHSck+Ux3P5wk3f2Z7r5vet/42aq6oap2VNULquoDVfWpqnptklTVkVW1vao+XlU7974fASxXVb2hqn5kWn7h9HvMDVX1pkVnETyrqn53+r3kP6/SdFkDRCBr2ZVJtlbV05I8L8lHZ577iSQf6u4XJvmmJG+qqqcneV2S/9fdz0tycZLTn8Dr/nV3vzjJLye5OskFSU5N8uqq+uon/NMAh7IPJjmxqv6oqt5WVf9k5rl7u/sbk/yvJJcl+Z4kZyb56en5LyT5zu5+QRbej95SVXXwpg6sEUdMp5rfXFU352/fQxZ7Z5LXTu87jy567rQk35vkuUm+t6pOnNdkWducDsqa1d23VNVJWTgKuPjrQ16e5Dv2/tUsydOSPDvJi5NcMrP9LU/gpa+Z7ncmua2770+SqvrjJCcm+ewT2CdwCOvuz1XV6Un+cRZC7teq6sLp6dn3hCO7+6EkD1XVF6bPGX8+yc9W1YuTfCnJxiTHJ/mTg/kzAIe8v+ru0/Y+qKpXJ9kyu8L0nvKM7v79aeg9WThVfa/t3f0X07q3J/maJPfOb8qsVSKQte6aJG9O8pIks0fhKsl3d/cdsytPf3zf3/eiPJIvP0r+tEXPPzzdf2lmee9j/03BOtXdjya5Psn1VbUzybbpqf29J3xfkg1JTu/uL1bV3Xns+wrAcuzvLILZ96BH4/cSHofTQVnr3pHkp7t756LxDyR5/d5Trqrq+dP472XhF7JU1alZOI10sQeSHFdVX11VT82X/4UNGFBVfX1VbZ4ZOi3Jp5e5+d9J8uAUgN+Uhb/MA6xYd/9ZFs40OHMa2rqa82HtEoGsad29u7vfusRTP5PkyUlumT4w/TPT+NuTHDmdBvpjSW5cYp9fzMJ5+B9N8r4kfziPuQNrypFJLq+q26f3j1OSvGGZ2747yZaq2pGFP0J5TwG+Eq9JcmlV3ZCFI4N/scrzYQ2q7v2dGQcAABwKqurI7t77HaMXJjmhu39olafFGuM8YQAAWDu+taouysLv8Z9O8urVnQ5rkSOBAAAAA/GZQAAAgIGIQAAAgIGIQAAAgIGIQAAAgIGIQACGUlWbqurqqrqzqj5VVW+tqqfsZ5sfX/T4c/tZ/5lV9QP7WedJVXVJVd1aVTur6mNVdfL03Pur6pnL/JEAYEVEIADDqKpK8ptJfru7Nyf5uix8EfzF+9n0x/fz/GLPTLLPCEzyvUmeleR53f3cJN+Z5M+TpLu/pbv/fIWvCQDLIgIBGMlLk3yhu9+ZJN39aJJ/k+T7q+oHquoX965YVe+rqpdU1RuTHFFVN1fVuxfvsKp+dDqKd0tV/dQ0/MYkXztt86bHmcsJSe7v7i9Nc9nd3X827fPuqjq2ql477ePmqrqrqj48Pf/yqrqhqj5eVf+jqo48MP88AIxABAIwkuckuWl2oLv/Msk9Wfji5cfo7guT/FV3n9bd3zf7XFW9PMnmJGckOS3J6VX14iQXJvnUtM2PPs5crkry7VPgvaWqnr/Ea/9yd5+W5IVJdif5+ao6Nsl/SPLN3f2CJDuS/Ntl/fQAkMf5Hx4ArFOVpFcwvj8vn26fmB4fmYUovGd/G3b37qr6+iwcnXxpku1VdW53b19i9bcm+VB3/05VfVuSU5J8ZOHs1jwlyQ1PYO4ADEoEAjCS25J89+xAVR2V5MQkf5EvP0PmacvYXyX5ue7+lUX7PGk5k+nuh5Ncm+TaqnogyTlJviwCq+rVSb4myQ/OvOZ13f3K5bwGACzmdFAARrI9yVdV1XlJUlWHJXlLksuS/HGS06ardp6YhVM89/piVT15if19IAufJzxy2t/GqjouyUNJnrGviVTVC6rqWdPyk5I8L8mnF61zepIfSfKqvZ8dTPIHSV5UVX9vWuerqurrlvsPAAAiEIBhdHdn4Sqc51bVnUn+KMkXsnD1z48kuSvJziRvTvLxmU0vTXLL4gvDdPcHk7wnyQ1VtTPJryd5Rnd/Nguna966jwvDHJfkd6rq1iS3JHkkyS8uWucHkxyT5MPTZwd/tbv3JHl1kvdW1S1ZiMK/v/J/DQBGVQv/PwQAAGAEjgQCAAAMxIVhAGCOquq5Sd61aPjh7v6G1ZgPADgdFAAAYCBOBwUAABiICAQAABiICAQAABiICAQAABiICAQAABjI/wfGhnjAd7MnsQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=dt['Outlet_Size'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a841fe9",
   "metadata": {},
   "source": [
    "# Count Plot Outlet Location Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "cac4a906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=dt['Outlet_Location_Type'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82a4107f",
   "metadata": {},
   "source": [
    "# Count Plot Outlet Type "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "abba4713",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x= dt['Outlet_Type'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "307d3550",
   "metadata": {},
   "source": [
    "# Co-relation Matrix\n",
    "Display top 3 records to check columns with numeric values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "89bc3a5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>New_Item_Type</th>\n",
       "      <th>Outlet_Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>8.225808</td>\n",
       "      <td>Food</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>6.096776</td>\n",
       "      <td>Drinks</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>7.648868</td>\n",
       "      <td>Food</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility    Item_Type  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047        Dairy   \n",
       "1           DRC01         5.92          Regular         0.019278  Soft Drinks   \n",
       "2           FDN15        17.50          Low Fat         0.016760         Meat   \n",
       "\n",
       "   Item_MRP Outlet_Identifier  Outlet_Establishment_Year Outlet_Size  \\\n",
       "0  249.8092            OUT049                       1999      Medium   \n",
       "1   48.2692            OUT018                       2009      Medium   \n",
       "2  141.6180            OUT049                       1999      Medium   \n",
       "\n",
       "  Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales New_Item_Type  \\\n",
       "0               Tier 1  Supermarket Type1           8.225808          Food   \n",
       "1               Tier 3  Supermarket Type2           6.096776        Drinks   \n",
       "2               Tier 1  Supermarket Type1           7.648868          Food   \n",
       "\n",
       "   Outlet_Years  \n",
       "0            25  \n",
       "1            15  \n",
       "2            25  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(dt.head(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3edc109d",
   "metadata": {},
   "source": [
    "# Create Data Frame with numeric columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "987bc816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>Outlet_Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.300</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>1999</td>\n",
       "      <td>8.225808</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.920</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>2009</td>\n",
       "      <td>6.096776</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.500</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>1999</td>\n",
       "      <td>7.648868</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.200</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>1998</td>\n",
       "      <td>6.597664</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.930</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>1987</td>\n",
       "      <td>6.903451</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8518</th>\n",
       "      <td>6.865</td>\n",
       "      <td>0.056783</td>\n",
       "      <td>214.5218</td>\n",
       "      <td>1987</td>\n",
       "      <td>7.929984</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8519</th>\n",
       "      <td>8.380</td>\n",
       "      <td>0.046982</td>\n",
       "      <td>108.1570</td>\n",
       "      <td>2002</td>\n",
       "      <td>6.310436</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8520</th>\n",
       "      <td>10.600</td>\n",
       "      <td>0.035186</td>\n",
       "      <td>85.1224</td>\n",
       "      <td>2004</td>\n",
       "      <td>7.085159</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8521</th>\n",
       "      <td>7.210</td>\n",
       "      <td>0.145221</td>\n",
       "      <td>103.1332</td>\n",
       "      <td>2009</td>\n",
       "      <td>7.521100</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8522</th>\n",
       "      <td>14.800</td>\n",
       "      <td>0.044878</td>\n",
       "      <td>75.4670</td>\n",
       "      <td>1997</td>\n",
       "      <td>6.642056</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8523 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Item_Weight  Item_Visibility  Item_MRP  Outlet_Establishment_Year  \\\n",
       "0           9.300         0.016047  249.8092                       1999   \n",
       "1           5.920         0.019278   48.2692                       2009   \n",
       "2          17.500         0.016760  141.6180                       1999   \n",
       "3          19.200         0.066132  182.0950                       1998   \n",
       "4           8.930         0.066132   53.8614                       1987   \n",
       "...           ...              ...       ...                        ...   \n",
       "8518        6.865         0.056783  214.5218                       1987   \n",
       "8519        8.380         0.046982  108.1570                       2002   \n",
       "8520       10.600         0.035186   85.1224                       2004   \n",
       "8521        7.210         0.145221  103.1332                       2009   \n",
       "8522       14.800         0.044878   75.4670                       1997   \n",
       "\n",
       "      Item_Outlet_Sales  Outlet_Years  \n",
       "0              8.225808            25  \n",
       "1              6.096776            15  \n",
       "2              7.648868            25  \n",
       "3              6.597664            26  \n",
       "4              6.903451            37  \n",
       "...                 ...           ...  \n",
       "8518           7.929984            37  \n",
       "8519           6.310436            22  \n",
       "8520           7.085159            20  \n",
       "8521           7.521100            15  \n",
       "8522           6.642056            27  \n",
       "\n",
       "[8523 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dtc= dt.iloc[:,[1,3,5,7,11,13]]\n",
    "display (dtc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1e69f1a",
   "metadata": {},
   "source": [
    "# Print Co relation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "41acbbdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>Outlet_Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Item_Weight</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.017477</td>\n",
       "      <td>0.025972</td>\n",
       "      <td>-0.013418</td>\n",
       "      <td>0.007675</td>\n",
       "      <td>0.013418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Visibility</th>\n",
       "      <td>-0.017477</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005259</td>\n",
       "      <td>-0.078355</td>\n",
       "      <td>-0.202466</td>\n",
       "      <td>0.078355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_MRP</th>\n",
       "      <td>0.025972</td>\n",
       "      <td>-0.005259</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005020</td>\n",
       "      <td>0.509886</td>\n",
       "      <td>-0.005020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <td>-0.013418</td>\n",
       "      <td>-0.078355</td>\n",
       "      <td>0.005020</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.077032</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <td>0.007675</td>\n",
       "      <td>-0.202466</td>\n",
       "      <td>0.509886</td>\n",
       "      <td>0.077032</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.077032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outlet_Years</th>\n",
       "      <td>0.013418</td>\n",
       "      <td>0.078355</td>\n",
       "      <td>-0.005020</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.077032</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Item_Weight  Item_Visibility  Item_MRP  \\\n",
       "Item_Weight                   1.000000        -0.017477  0.025972   \n",
       "Item_Visibility              -0.017477         1.000000 -0.005259   \n",
       "Item_MRP                      0.025972        -0.005259  1.000000   \n",
       "Outlet_Establishment_Year    -0.013418        -0.078355  0.005020   \n",
       "Item_Outlet_Sales             0.007675        -0.202466  0.509886   \n",
       "Outlet_Years                  0.013418         0.078355 -0.005020   \n",
       "\n",
       "                           Outlet_Establishment_Year  Item_Outlet_Sales  \\\n",
       "Item_Weight                                -0.013418           0.007675   \n",
       "Item_Visibility                            -0.078355          -0.202466   \n",
       "Item_MRP                                    0.005020           0.509886   \n",
       "Outlet_Establishment_Year                   1.000000           0.077032   \n",
       "Item_Outlet_Sales                           0.077032           1.000000   \n",
       "Outlet_Years                               -1.000000          -0.077032   \n",
       "\n",
       "                           Outlet_Years  \n",
       "Item_Weight                    0.013418  \n",
       "Item_Visibility                0.078355  \n",
       "Item_MRP                      -0.005020  \n",
       "Outlet_Establishment_Year     -1.000000  \n",
       "Item_Outlet_Sales             -0.077032  \n",
       "Outlet_Years                   1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = dtc.corr()\n",
    "display (corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e415d0ad",
   "metadata": {},
   "source": [
    "# Print Co Relation Matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ce3ad1a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr, annot=True, cmap='coolwarm')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a4c55e4",
   "metadata": {},
   "source": [
    "# Display Top 5 Records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e5425bcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>New_Item_Type</th>\n",
       "      <th>Outlet_Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>8.225808</td>\n",
       "      <td>Food</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>6.096776</td>\n",
       "      <td>Drinks</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>7.648868</td>\n",
       "      <td>Food</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>6.597664</td>\n",
       "      <td>Food</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Non-Edible</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>6.903451</td>\n",
       "      <td>Non-Consumable</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047   \n",
       "1           DRC01         5.92          Regular         0.019278   \n",
       "2           FDN15        17.50          Low Fat         0.016760   \n",
       "3           FDX07        19.20          Regular         0.066132   \n",
       "4           NCD19         8.93       Non-Edible         0.066132   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092            OUT049   \n",
       "1            Soft Drinks   48.2692            OUT018   \n",
       "2                   Meat  141.6180            OUT049   \n",
       "3  Fruits and Vegetables  182.0950            OUT010   \n",
       "4              Household   53.8614            OUT013   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998       Small               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales   New_Item_Type  Outlet_Years  \n",
       "0  Supermarket Type1           8.225808            Food            25  \n",
       "1  Supermarket Type2           6.096776          Drinks            15  \n",
       "2  Supermarket Type1           7.648868            Food            25  \n",
       "3      Grocery Store           6.597664            Food            26  \n",
       "4  Supermarket Type1           6.903451  Non-Consumable            37  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display (dt.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "664c54c9",
   "metadata": {},
   "source": [
    "# Label Encoding \n",
    "Label Encoding – Column Outlet Identifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f42d2be5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       9\n",
       "1       3\n",
       "2       9\n",
       "3       0\n",
       "4       1\n",
       "       ..\n",
       "8518    1\n",
       "8519    7\n",
       "8520    6\n",
       "8521    3\n",
       "8522    8\n",
       "Name: Outlet, Length: 8523, dtype: int32"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "dt['Outlet'] = le.fit_transform(dt['Outlet_Identifier'])\n",
    "display (dt['Outlet'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e56a01e",
   "metadata": {},
   "source": [
    "# Label Encoding – Remaining columns with For loop "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "eba61228",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>New_Item_Type</th>\n",
       "      <th>Outlet_Years</th>\n",
       "      <th>Outlet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>4</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.225808</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>2</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>14</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6.096776</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>10</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.648868</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>2</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>6</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6.597664</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>1</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>9</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6.903451</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight  Item_Fat_Content  Item_Visibility  Item_Type  \\\n",
       "0           FDA15         9.30                 0         0.016047          4   \n",
       "1           DRC01         5.92                 2         0.019278         14   \n",
       "2           FDN15        17.50                 0         0.016760         10   \n",
       "3           FDX07        19.20                 2         0.066132          6   \n",
       "4           NCD19         8.93                 1         0.066132          9   \n",
       "\n",
       "   Item_MRP Outlet_Identifier  Outlet_Establishment_Year  Outlet_Size  \\\n",
       "0  249.8092            OUT049                       1999            1   \n",
       "1   48.2692            OUT018                       2009            1   \n",
       "2  141.6180            OUT049                       1999            1   \n",
       "3  182.0950            OUT010                       1998            2   \n",
       "4   53.8614            OUT013                       1987            0   \n",
       "\n",
       "   Outlet_Location_Type  Outlet_Type  Item_Outlet_Sales  New_Item_Type  \\\n",
       "0                     0            1           8.225808              1   \n",
       "1                     2            2           6.096776              0   \n",
       "2                     0            1           7.648868              1   \n",
       "3                     2            0           6.597664              1   \n",
       "4                     2            1           6.903451              2   \n",
       "\n",
       "   Outlet_Years  Outlet  \n",
       "0            25       9  \n",
       "1            15       3  \n",
       "2            25       9  \n",
       "3            26       0  \n",
       "4            37       1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_col = ['Item_Fat_Content', 'Item_Type', 'Outlet_Size', 'Outlet_Location_Type', 'Outlet_Type', 'New_Item_Type']\n",
    "for col in cat_col:\n",
    "    dt[col] = le.fit_transform(dt[col])\n",
    "display (dt.head()) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c2cfa5d",
   "metadata": {},
   "source": [
    "# One hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "98b0507d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "      <th>Outlet_Years</th>\n",
       "      <th>Outlet</th>\n",
       "      <th>...</th>\n",
       "      <th>Outlet_Location_Type_0</th>\n",
       "      <th>Outlet_Location_Type_1</th>\n",
       "      <th>Outlet_Location_Type_2</th>\n",
       "      <th>Outlet_Type_0</th>\n",
       "      <th>Outlet_Type_1</th>\n",
       "      <th>Outlet_Type_2</th>\n",
       "      <th>Outlet_Type_3</th>\n",
       "      <th>New_Item_Type_0</th>\n",
       "      <th>New_Item_Type_1</th>\n",
       "      <th>New_Item_Type_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>4</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>8.225808</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>14</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>6.096776</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>10</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>7.648868</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>6</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>6.597664</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>9</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>6.903451</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight  Item_Visibility  Item_Type  Item_MRP  \\\n",
       "0           FDA15         9.30         0.016047          4  249.8092   \n",
       "1           DRC01         5.92         0.019278         14   48.2692   \n",
       "2           FDN15        17.50         0.016760         10  141.6180   \n",
       "3           FDX07        19.20         0.066132          6  182.0950   \n",
       "4           NCD19         8.93         0.066132          9   53.8614   \n",
       "\n",
       "  Outlet_Identifier  Outlet_Establishment_Year  Item_Outlet_Sales  \\\n",
       "0            OUT049                       1999           8.225808   \n",
       "1            OUT018                       2009           6.096776   \n",
       "2            OUT049                       1999           7.648868   \n",
       "3            OUT010                       1998           6.597664   \n",
       "4            OUT013                       1987           6.903451   \n",
       "\n",
       "   Outlet_Years  Outlet  ...  Outlet_Location_Type_0  Outlet_Location_Type_1  \\\n",
       "0            25       9  ...                       1                       0   \n",
       "1            15       3  ...                       0                       0   \n",
       "2            25       9  ...                       1                       0   \n",
       "3            26       0  ...                       0                       0   \n",
       "4            37       1  ...                       0                       0   \n",
       "\n",
       "   Outlet_Location_Type_2  Outlet_Type_0  Outlet_Type_1  Outlet_Type_2  \\\n",
       "0                       0              0              1              0   \n",
       "1                       1              0              0              1   \n",
       "2                       0              0              1              0   \n",
       "3                       1              1              0              0   \n",
       "4                       1              0              1              0   \n",
       "\n",
       "   Outlet_Type_3  New_Item_Type_0  New_Item_Type_1  New_Item_Type_2  \n",
       "0              0                0                1                0  \n",
       "1              0                1                0                0  \n",
       "2              0                0                1                0  \n",
       "3              0                0                1                0  \n",
       "4              0                0                0                1  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = pd.get_dummies(dt, columns=['Item_Fat_Content', 'Outlet_Size', 'Outlet_Location_Type', 'Outlet_Type', 'New_Item_Type'],dtype = int )\n",
    "display (dt.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e28726a9",
   "metadata": {},
   "source": [
    "# Create X – Remove un used columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ebb0ca27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Years</th>\n",
       "      <th>Outlet</th>\n",
       "      <th>Item_Fat_Content_0</th>\n",
       "      <th>Item_Fat_Content_1</th>\n",
       "      <th>Item_Fat_Content_2</th>\n",
       "      <th>Outlet_Size_0</th>\n",
       "      <th>...</th>\n",
       "      <th>Outlet_Location_Type_0</th>\n",
       "      <th>Outlet_Location_Type_1</th>\n",
       "      <th>Outlet_Location_Type_2</th>\n",
       "      <th>Outlet_Type_0</th>\n",
       "      <th>Outlet_Type_1</th>\n",
       "      <th>Outlet_Type_2</th>\n",
       "      <th>Outlet_Type_3</th>\n",
       "      <th>New_Item_Type_0</th>\n",
       "      <th>New_Item_Type_1</th>\n",
       "      <th>New_Item_Type_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.30</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>4</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.92</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>14</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.50</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>10</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.20</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>6</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.93</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>9</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Weight  Item_Visibility  Item_Type  Item_MRP  Outlet_Years  Outlet  \\\n",
       "0         9.30         0.016047          4  249.8092            25       9   \n",
       "1         5.92         0.019278         14   48.2692            15       3   \n",
       "2        17.50         0.016760         10  141.6180            25       9   \n",
       "3        19.20         0.066132          6  182.0950            26       0   \n",
       "4         8.93         0.066132          9   53.8614            37       1   \n",
       "\n",
       "   Item_Fat_Content_0  Item_Fat_Content_1  Item_Fat_Content_2  Outlet_Size_0  \\\n",
       "0                   1                   0                   0              0   \n",
       "1                   0                   0                   1              0   \n",
       "2                   1                   0                   0              0   \n",
       "3                   0                   0                   1              0   \n",
       "4                   0                   1                   0              1   \n",
       "\n",
       "   ...  Outlet_Location_Type_0  Outlet_Location_Type_1  \\\n",
       "0  ...                       1                       0   \n",
       "1  ...                       0                       0   \n",
       "2  ...                       1                       0   \n",
       "3  ...                       0                       0   \n",
       "4  ...                       0                       0   \n",
       "\n",
       "   Outlet_Location_Type_2  Outlet_Type_0  Outlet_Type_1  Outlet_Type_2  \\\n",
       "0                       0              0              1              0   \n",
       "1                       1              0              0              1   \n",
       "2                       0              0              1              0   \n",
       "3                       1              1              0              0   \n",
       "4                       1              0              1              0   \n",
       "\n",
       "   Outlet_Type_3  New_Item_Type_0  New_Item_Type_1  New_Item_Type_2  \n",
       "0              0                0                1                0  \n",
       "1              0                1                0                0  \n",
       "2              0                0                1                0  \n",
       "3              0                0                1                0  \n",
       "4              0                0                0                1  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = dt.drop(columns=['Outlet_Establishment_Year', 'Item_Identifier', 'Outlet_Identifier', 'Item_Outlet_Sales'])\n",
    "display (X.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e231d3b4",
   "metadata": {},
   "source": [
    "# Create y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "65a3e7f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    8.225808\n",
       "1    6.096776\n",
       "2    7.648868\n",
       "3    6.597664\n",
       "4    6.903451\n",
       "Name: Item_Outlet_Sales, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = dt['Item_Outlet_Sales']\n",
    "display (y.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1428c8ae",
   "metadata": {},
   "source": [
    "# Train Test Split  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0c6a2054",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8523, 22) (8523,)\n",
      "(5966, 22) (2557, 22) (5966,) (2557,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "print (X.shape, y.shape)\n",
    "print (X_train.shape, X_test.shape ,  y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abc2f914",
   "metadata": {},
   "source": [
    "# Function to create Model \n",
    "Display all Scoring options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "7a3fa22b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "def train(model, X, y):\n",
    "    # training the model\n",
    "    model.fit(X, y)\n",
    "    \n",
    "    pred = model.predict(X)\n",
    "    # perform cross-validation\n",
    "    cv_score = cross_val_score(model, X, y, scoring='neg_mean_squared_error', cv=5)\n",
    "    print(\"Model Report\")\n",
    "    print ('Scoring - neg_mean_squared_error')\n",
    "    print ( cv_score )    \n",
    "    cv_score = np.abs(np.mean(cv_score))    \n",
    "    print ('ABS Average of - neg_mean_squared_error',cv_score )       \n",
    "    cv_score = cross_val_score(model, X, y,  cv=5)\n",
    "    print ()\n",
    "    print ('R2 Score ')\n",
    "    print ( cv_score )    \n",
    "    cv_score = np.mean(cv_score)     \n",
    "    print ('Average R2 Score ',cv_score)    \n",
    "    print ()\n",
    "    print ('Accuracy for full Data')\n",
    "    print('R2_Score:', r2_score(y,pred))\n",
    "    print ()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9c24817",
   "metadata": {},
   "source": [
    "# Create Linear Regression Model  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d0a19a83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Report\n",
      "Scoring - neg_mean_squared_error\n",
      "[-0.29271824 -0.27380022 -0.28650685 -0.28450916 -0.28157839]\n",
      "ABS Average of - neg_mean_squared_error 0.2838225748921782\n",
      "\n",
      "R2 Score \n",
      "[0.69534635 0.73185961 0.7163057  0.73463311 0.72780316]\n",
      "Average R2 Score  0.7211895851380341\n",
      "\n",
      "Accuracy for full Data\n",
      "R2_Score: 0.7232624990443841\n",
      "\n",
      "Outlet_Type_0            -1.254723\n",
      "Outlet_Size_1            -0.735141\n",
      "Outlet_Size_2            -0.575278\n",
      "Outlet_Type_1            -0.476526\n",
      "Outlet_Location_Type_2   -0.383803\n",
      "Outlet_Type_2            -0.293352\n",
      "Outlet_Years             -0.073229\n",
      "Item_Visibility          -0.038618\n",
      "New_Item_Type_0          -0.026509\n",
      "Item_Fat_Content_0       -0.010449\n",
      "Item_Weight              -0.001516\n",
      "Item_Type                 0.000923\n",
      "Item_Fat_Content_2        0.005071\n",
      "Item_Fat_Content_1        0.005377\n",
      "New_Item_Type_2           0.005377\n",
      "Item_MRP                  0.008172\n",
      "New_Item_Type_1           0.021131\n",
      "Outlet_Location_Type_1    0.030356\n",
      "Outlet                    0.061955\n",
      "Outlet_Location_Type_0    0.353446\n",
      "Outlet_Size_0             1.310419\n",
      "Outlet_Type_3             2.024600\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "model = LinearRegression()\n",
    "train(model, X_train, y_train)\n",
    "coef = pd.Series(model.coef_, X.columns).sort_values()\n",
    "print (coef)\n",
    "coef.plot(kind='bar', title=\"Model Coefficients\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba176614",
   "metadata": {},
   "source": [
    "# Create Ridge Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "981f527a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Report\n",
      "Scoring - neg_mean_squared_error\n",
      "[-0.29241213 -0.27413941 -0.2869929  -0.28451826 -0.2818739 ]\n",
      "ABS Average of - neg_mean_squared_error 0.2839873202494144\n",
      "\n",
      "R2 Score \n",
      "[0.69566494 0.73152743 0.71582442 0.73462463 0.7275175 ]\n",
      "Average R2 Score  0.7210317841099348\n",
      "\n",
      "Accuracy for full Data\n",
      "R2_Score: 0.7230806625087227\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Ridge()\n",
    "train(model, X_train, y_train)\n",
    "coef = pd.Series(model.coef_, X.columns).sort_values()\n",
    "coef.plot(kind='bar', title=\"Model Coefficients\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b28c887",
   "metadata": {},
   "source": [
    "# Create  Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "612d346e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Report\n",
      "Scoring - neg_mean_squared_error\n",
      "[-0.72314998 -0.75701947 -0.75847666 -0.74093272 -0.78787083]\n",
      "ABS Average of - neg_mean_squared_error 0.7534899315973709\n",
      "\n",
      "R2 Score \n",
      "[0.24736402 0.25862917 0.24896908 0.30891853 0.23837923]\n",
      "Average R2 Score  0.2604520071591053\n",
      "\n",
      "Accuracy for full Data\n",
      "R2_Score: 0.26227683517122646\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Lasso()\n",
    "train(model, X_train, y_train)\n",
    "coef = pd.Series(model.coef_, X.columns).sort_values()\n",
    "coef.plot(kind='bar', title=\"Model Coefficients\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99ca483a",
   "metadata": {},
   "source": [
    "# Decision Tree Regression  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "fbeb98af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Report\n",
      "Scoring - neg_mean_squared_error\n",
      "[-0.55131298 -0.55526722 -0.53499615 -0.56029039 -0.5632742 ]\n",
      "ABS Average of - neg_mean_squared_error 0.5530281887748169\n",
      "\n",
      "R2 Score \n",
      "[0.40865509 0.45338006 0.47567821 0.48091345 0.4641306 ]\n",
      "Average R2 Score  0.4565514817892781\n",
      "\n",
      "Accuracy for full Data\n",
      "R2_Score: 1.0\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "model = DecisionTreeRegressor()\n",
    "train(model,X_train, y_train)\n",
    "coef = pd.Series(model.feature_importances_, X.columns).sort_values(ascending=False)\n",
    "coef.plot(kind='bar', title=\"Feature Importance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b78d46b",
   "metadata": {},
   "source": [
    "# Random Forest Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "5568c23e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Report\n",
      "Scoring - neg_mean_squared_error\n",
      "[-0.30798721 -0.2834677  -0.30824842 -0.29188489 -0.30487587]\n",
      "ABS Average of - neg_mean_squared_error 0.2992928200219576\n",
      "\n",
      "R2 Score \n",
      "[0.67944616 0.721592   0.69611674 0.72971253 0.7050306 ]\n",
      "Average R2 Score  0.7063796092632957\n",
      "\n",
      "Accuracy for full Data\n",
      "R2_Score: 0.9591060387161355\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "model = RandomForestRegressor()\n",
    "train(model, X_train, y_train)\n",
    "coef = pd.Series(model.feature_importances_, X.columns).sort_values(ascending=False)\n",
    "coef.plot(kind='bar', title=\"Feature Importance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef6ce4f1",
   "metadata": {},
   "source": [
    "# Extra Tree Regression  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "9b8c2fcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Report\n",
      "Scoring - neg_mean_squared_error\n",
      "[-0.33198671 -0.31335497 -0.3202753  -0.31640409 -0.3208199 ]\n",
      "ABS Average of - neg_mean_squared_error 0.3205681952013939\n",
      "\n",
      "R2 Score \n",
      "[0.65590251 0.6965063  0.67793744 0.70502044 0.69561247]\n",
      "Average R2 Score  0.686195829625093\n",
      "\n",
      "Accuracy for full Data\n",
      "R2_Score: 1.0\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "model = ExtraTreesRegressor()\n",
    "train(model, X_train, y_train)\n",
    "coef = pd.Series(model.feature_importances_, X.columns).sort_values(ascending=False)\n",
    "coef.plot(kind='bar', title=\"Feature Importance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54e64194",
   "metadata": {},
   "source": [
    "# LGBMRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "9df5331b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001112 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 806\n",
      "[LightGBM] [Info] Number of data points in the train set: 5966, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.295854\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000264 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 813\n",
      "[LightGBM] [Info] Number of data points in the train set: 4772, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.292207\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000429 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 813\n",
      "[LightGBM] [Info] Number of data points in the train set: 4773, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.293465\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000381 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 4773, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.297274\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000575 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 807\n",
      "[LightGBM] [Info] Number of data points in the train set: 4773, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.289242\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000429 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 809\n",
      "[LightGBM] [Info] Number of data points in the train set: 4773, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.307082\n",
      "Model Report\n",
      "Scoring - neg_mean_squared_error\n",
      "[-0.28764356 -0.26745894 -0.2822739  -0.27688429 -0.28678444]\n",
      "ABS Average of - neg_mean_squared_error 0.2802090265209983\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000498 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 813\n",
      "[LightGBM] [Info] Number of data points in the train set: 4772, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.292207\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000443 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 813\n",
      "[LightGBM] [Info] Number of data points in the train set: 4773, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.293465\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000402 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 4773, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.297274\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000731 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 807\n",
      "[LightGBM] [Info] Number of data points in the train set: 4773, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.289242\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000459 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 809\n",
      "[LightGBM] [Info] Number of data points in the train set: 4773, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.307082\n",
      "\n",
      "R2 Score \n",
      "[0.70062795 0.73806981 0.7204971  0.74174497 0.72277056]\n",
      "Average R2 Score  0.7247420773391064\n",
      "\n",
      "Accuracy for full Data\n",
      "R2_Score: 0.8159406776859842\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lightgbm import LGBMRegressor\n",
    "model = LGBMRegressor()\n",
    "train(model, X_train, y_train)\n",
    "coef = pd.Series(model.feature_importances_, X.columns).sort_values(ascending=False)\n",
    "coef.plot(kind='bar', title=\"Feature Importance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2918de13",
   "metadata": {},
   "source": [
    "# XG Boost Regressor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "adbf6527",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Report\n",
      "Scoring - neg_mean_squared_error\n",
      "[-0.34171575 -0.29940184 -0.32970416 -0.31770428 -0.32953668]\n",
      "ABS Average of - neg_mean_squared_error 0.3236125411964996\n",
      "\n",
      "R2 Score \n",
      "[0.644351   0.70678721 0.67353245 0.70367142 0.68144273]\n",
      "Average R2 Score  0.6819569626413553\n",
      "\n",
      "Accuracy for full Data\n",
      "R2_Score: 0.9120531911594594\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "model = XGBRegressor()\n",
    "train(model, X_train, y_train)\n",
    "coef = pd.Series(model.feature_importances_, X.columns).sort_values(ascending=False)\n",
    "coef.plot(kind='bar', title=\"Feature Importance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b25b58",
   "metadata": {},
   "source": [
    "# Random Search CV  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "30c0a1a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7723e3d8",
   "metadata": {},
   "source": [
    "# Parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b2bb695b",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_features = ['auto', 'sqrt']\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "min_samples_leaf = [1, 2, 5, 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e71f948",
   "metadata": {},
   "source": [
    "# Param Grid    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "77862230",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_features': ['auto', 'sqrt'], 'max_depth': [5, 10, 15, 20, 25, 30], 'min_samples_split': [2, 5, 10, 15, 100], 'min_samples_leaf': [1, 2, 5, 10]}\n"
     ]
    }
   ],
   "source": [
    "random_grid = {\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}\n",
    "\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2aaa421",
   "metadata": {},
   "source": [
    "# Random Forest Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "7792b5e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=-1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100]},\n",
       "                   random_state=42, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestRegressor()\n",
    "rf=RandomizedSearchCV(estimator = rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = -1)\n",
    "display (rf.fit(X_train, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0be1af5b",
   "metadata": {},
   "source": [
    "# Best Parameters  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "582252c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_samples_split': 100, 'min_samples_leaf': 2, 'max_features': 'auto', 'max_depth': 5}\n",
      "-0.26859497870247956\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7266222967648222"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([7.72028979, 7.58652431, 7.53424513, ..., 7.47246072, 7.69085582,\n",
       "       7.86103705])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(rf.best_params_)\n",
    "print(rf.best_score_)\n",
    "predictions=rf.predict(X_test)\n",
    "display (r2_score (y_test,predictions))\n",
    "display (predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0601769f",
   "metadata": {},
   "source": [
    "# Create the Dist plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "a37d8c82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-predictions)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3273b15a",
   "metadata": {},
   "source": [
    "# Parameter for LGBM Regressor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "404391d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import uniform, randint\n",
    "params = {\n",
    "    \"gamma\": uniform(0, 0.5),\n",
    "    \"learning_rate\": uniform(0.03, 0.3), # default 0.1 \n",
    "    \"max_depth\": randint(2, 6), # default 3\n",
    "    \"n_estimators\": randint(100, 150), # default 100\n",
    "    \"subsample\": uniform(0.6, 0.4)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adcc4fdf",
   "metadata": {},
   "source": [
    "# Model LGBM Regressor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e72515a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001300 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296077\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.18727005942368125, learning_rate=0.3152142919229748, max_depth=4, n_estimators=107, subsample=0.8394633936788146; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000695 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303069\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.18727005942368125, learning_rate=0.3152142919229748, max_depth=4, n_estimators=107, subsample=0.8394633936788146; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000578 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.289657\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.18727005942368125, learning_rate=0.3152142919229748, max_depth=4, n_estimators=107, subsample=0.8394633936788146; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000513 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296582\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.18727005942368125, learning_rate=0.3152142919229748, max_depth=4, n_estimators=107, subsample=0.8394633936788146; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000563 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 812\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303930\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.18727005942368125, learning_rate=0.3152142919229748, max_depth=4, n_estimators=107, subsample=0.8394633936788146; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000457 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296077\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.07800932022121826, learning_rate=0.07679835610086079, max_depth=4, n_estimators=110, subsample=0.9464704583099741; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000524 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303069\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.07800932022121826, learning_rate=0.07679835610086079, max_depth=4, n_estimators=110, subsample=0.9464704583099741; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000516 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.289657\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.07800932022121826, learning_rate=0.07679835610086079, max_depth=4, n_estimators=110, subsample=0.9464704583099741; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000625 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296582\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.07800932022121826, learning_rate=0.07679835610086079, max_depth=4, n_estimators=110, subsample=0.9464704583099741; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000349 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 812\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303930\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.07800932022121826, learning_rate=0.07679835610086079, max_depth=4, n_estimators=110, subsample=0.9464704583099741; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000334 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296077\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.3005575058716044, learning_rate=0.24242177333881365, max_depth=3, n_estimators=101, subsample=0.8887995089067299; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000514 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303069\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.3005575058716044, learning_rate=0.24242177333881365, max_depth=3, n_estimators=101, subsample=0.8887995089067299; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000456 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.289657\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.3005575058716044, learning_rate=0.24242177333881365, max_depth=3, n_estimators=101, subsample=0.8887995089067299; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000571 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296582\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.3005575058716044, learning_rate=0.24242177333881365, max_depth=3, n_estimators=101, subsample=0.8887995089067299; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000587 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 812\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303930\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.3005575058716044, learning_rate=0.24242177333881365, max_depth=3, n_estimators=101, subsample=0.8887995089067299; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000350 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296077\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[CV] END gamma=0.4692763545078751, learning_rate=0.030233629752304297, max_depth=5, n_estimators=120, subsample=0.8469926038510867; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000440 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303069\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[CV] END gamma=0.4692763545078751, learning_rate=0.030233629752304297, max_depth=5, n_estimators=120, subsample=0.8469926038510867; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000521 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.289657\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[CV] END gamma=0.4692763545078751, learning_rate=0.030233629752304297, max_depth=5, n_estimators=120, subsample=0.8469926038510867; total time=   0.1s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000510 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296582\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[CV] END gamma=0.4692763545078751, learning_rate=0.030233629752304297, max_depth=5, n_estimators=120, subsample=0.8469926038510867; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000610 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 812\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303930\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[CV] END gamma=0.4692763545078751, learning_rate=0.030233629752304297, max_depth=5, n_estimators=120, subsample=0.8469926038510867; total time=   0.1s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000320 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296077\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.30582658024414044, learning_rate=0.03211989156591522, max_depth=2, n_estimators=148, subsample=0.8099098641033556; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000469 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303069\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.30582658024414044, learning_rate=0.03211989156591522, max_depth=2, n_estimators=148, subsample=0.8099098641033556; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000486 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.289657\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.30582658024414044, learning_rate=0.03211989156591522, max_depth=2, n_estimators=148, subsample=0.8099098641033556; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000547 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296582\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.30582658024414044, learning_rate=0.03211989156591522, max_depth=2, n_estimators=148, subsample=0.8099098641033556; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000660 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 812\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303930\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.30582658024414044, learning_rate=0.03211989156591522, max_depth=2, n_estimators=148, subsample=0.8099098641033556; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000334 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296077\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[CV] END gamma=0.19993048585762774, learning_rate=0.04399969896408463, max_depth=5, n_estimators=115, subsample=0.6931085361721216; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000456 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303069\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[CV] END gamma=0.19993048585762774, learning_rate=0.04399969896408463, max_depth=5, n_estimators=115, subsample=0.6931085361721216; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000479 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.289657\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[CV] END gamma=0.19993048585762774, learning_rate=0.04399969896408463, max_depth=5, n_estimators=115, subsample=0.6931085361721216; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000279 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296582\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[CV] END gamma=0.19993048585762774, learning_rate=0.04399969896408463, max_depth=5, n_estimators=115, subsample=0.6931085361721216; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000466 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 812\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303930\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[CV] END gamma=0.19993048585762774, learning_rate=0.04399969896408463, max_depth=5, n_estimators=115, subsample=0.6931085361721216; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000470 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296077\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[CV] END gamma=0.0453032172664104, learning_rate=0.2155158027999262, max_depth=5, n_estimators=102, subsample=0.9439761626945282; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000499 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303069\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[CV] END gamma=0.0453032172664104, learning_rate=0.2155158027999262, max_depth=5, n_estimators=102, subsample=0.9439761626945282; total time=   0.1s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000615 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.289657\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[CV] END gamma=0.0453032172664104, learning_rate=0.2155158027999262, max_depth=5, n_estimators=102, subsample=0.9439761626945282; total time=   0.1s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000827 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296582\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[CV] END gamma=0.0453032172664104, learning_rate=0.2155158027999262, max_depth=5, n_estimators=102, subsample=0.9439761626945282; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000581 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 812\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303930\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[CV] END gamma=0.0453032172664104, learning_rate=0.2155158027999262, max_depth=5, n_estimators=102, subsample=0.9439761626945282; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000572 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296077\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.34015376929388985, learning_rate=0.16514977559086289, max_depth=3, n_estimators=103, subsample=0.9768807022739411; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000538 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303069\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.34015376929388985, learning_rate=0.16514977559086289, max_depth=3, n_estimators=103, subsample=0.9768807022739411; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000385 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.289657\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.34015376929388985, learning_rate=0.16514977559086289, max_depth=3, n_estimators=103, subsample=0.9768807022739411; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000544 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296582\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.34015376929388985, learning_rate=0.16514977559086289, max_depth=3, n_estimators=103, subsample=0.9768807022739411; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000505 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 812\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303930\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.34015376929388985, learning_rate=0.16514977559086289, max_depth=3, n_estimators=103, subsample=0.9768807022739411; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000501 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296077\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.28164410892276964, learning_rate=0.14562495076197482, max_depth=3, n_estimators=101, subsample=0.8736932106048627; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000474 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303069\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.28164410892276964, learning_rate=0.14562495076197482, max_depth=3, n_estimators=101, subsample=0.8736932106048627; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000468 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.289657\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.28164410892276964, learning_rate=0.14562495076197482, max_depth=3, n_estimators=101, subsample=0.8736932106048627; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000571 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296582\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.28164410892276964, learning_rate=0.14562495076197482, max_depth=3, n_estimators=101, subsample=0.8736932106048627; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000588 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 812\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303930\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.28164410892276964, learning_rate=0.14562495076197482, max_depth=3, n_estimators=101, subsample=0.8736932106048627; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000541 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296077\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.22007624686980065, learning_rate=0.06661147045343364, max_depth=2, n_estimators=107, subsample=0.6137554084460873; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000692 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303069\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.22007624686980065, learning_rate=0.06661147045343364, max_depth=2, n_estimators=107, subsample=0.6137554084460873; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000549 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6818, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.289657\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.22007624686980065, learning_rate=0.06661147045343364, max_depth=2, n_estimators=107, subsample=0.6137554084460873; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000311 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.296582\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.22007624686980065, learning_rate=0.06661147045343364, max_depth=2, n_estimators=107, subsample=0.6137554084460873; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000587 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 812\n",
      "[LightGBM] [Info] Number of data points in the train set: 6819, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.303930\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[CV] END gamma=0.22007624686980065, learning_rate=0.06661147045343364, max_depth=2, n_estimators=107, subsample=0.6137554084460873; total time=   0.0s\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000345 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 811\n",
      "[LightGBM] [Info] Number of data points in the train set: 8523, number of used features: 22\n",
      "[LightGBM] [Info] Start training from score 7.297863\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LGBMRegressor(), n_jobs=1,\n",
       "                   param_distributions={'gamma': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000002471199C9D0>,\n",
       "                                        'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000024711791730>,\n",
       "                                        'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000247080D0250>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000247080D2880>,\n",
       "                                        'subsample': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000002470609D5E0>},\n",
       "                   random_state=42, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgb=LGBMRegressor()\n",
    "lgb = RandomizedSearchCV(estimator = lgb, param_distributions = params,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)\n",
    "lgb.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8be3a83",
   "metadata": {},
   "source": [
    "# Best Parameter "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "f947b8ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'gamma': 0.22007624686980065, 'learning_rate': 0.06661147045343364, 'max_depth': 2, 'n_estimators': 107, 'subsample': 0.6137554084460873}\n",
      "-0.270486817430124\n",
      "[LightGBM] [Warning] Unknown parameter: gamma\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7378840825312187"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([7.76943348, 7.43403433, 7.51033891, ..., 7.39307919, 7.75267129,\n",
       "       7.85537919])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(lgb.best_params_)\n",
    "print(lgb.best_score_)\n",
    "predictions=lgb.predict(X_test)\n",
    "display (r2_score (y_test,predictions))\n",
    "display (predictions)    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acb03f07",
   "metadata": {},
   "source": [
    "# Create Dist Plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "3c11d5e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-predictions)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17a4a4d8",
   "metadata": {},
   "source": [
    "# Model XG Boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "2bfe582e",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    \"gamma\": uniform(0, 0.5),\n",
    "    \"learning_rate\": uniform(0.03, 0.3), # default 0.1 \n",
    "    \"max_depth\": randint(2, 6), # default 3\n",
    "    \"n_estimators\": randint(100, 150), # default 100\n",
    "    \"subsample\": uniform(0.6, 0.4)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8dea0ca",
   "metadata": {},
   "source": [
    "# XG Boost Regressor  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ea619f97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END gamma=0.18727005942368125, learning_rate=0.3152142919229748, max_depth=4, n_estimators=107, subsample=0.8394633936788146; total time=   0.1s\n",
      "[CV] END gamma=0.18727005942368125, learning_rate=0.3152142919229748, max_depth=4, n_estimators=107, subsample=0.8394633936788146; total time=   0.0s\n",
      "[CV] END gamma=0.18727005942368125, learning_rate=0.3152142919229748, max_depth=4, n_estimators=107, subsample=0.8394633936788146; total time=   0.0s\n",
      "[CV] END gamma=0.18727005942368125, learning_rate=0.3152142919229748, max_depth=4, n_estimators=107, subsample=0.8394633936788146; total time=   0.0s\n",
      "[CV] END gamma=0.18727005942368125, learning_rate=0.3152142919229748, max_depth=4, n_estimators=107, subsample=0.8394633936788146; total time=   0.0s\n",
      "[CV] END gamma=0.07800932022121826, learning_rate=0.07679835610086079, max_depth=4, n_estimators=110, subsample=0.9464704583099741; total time=   0.0s\n",
      "[CV] END gamma=0.07800932022121826, learning_rate=0.07679835610086079, max_depth=4, n_estimators=110, subsample=0.9464704583099741; total time=   0.1s\n",
      "[CV] END gamma=0.07800932022121826, learning_rate=0.07679835610086079, max_depth=4, n_estimators=110, subsample=0.9464704583099741; total time=   0.0s\n",
      "[CV] END gamma=0.07800932022121826, learning_rate=0.07679835610086079, max_depth=4, n_estimators=110, subsample=0.9464704583099741; total time=   0.1s\n",
      "[CV] END gamma=0.07800932022121826, learning_rate=0.07679835610086079, max_depth=4, n_estimators=110, subsample=0.9464704583099741; total time=   0.0s\n",
      "[CV] END gamma=0.3005575058716044, learning_rate=0.24242177333881365, max_depth=3, n_estimators=101, subsample=0.8887995089067299; total time=   0.0s\n",
      "[CV] END gamma=0.3005575058716044, learning_rate=0.24242177333881365, max_depth=3, n_estimators=101, subsample=0.8887995089067299; total time=   0.0s\n",
      "[CV] END gamma=0.3005575058716044, learning_rate=0.24242177333881365, max_depth=3, n_estimators=101, subsample=0.8887995089067299; total time=   0.0s\n",
      "[CV] END gamma=0.3005575058716044, learning_rate=0.24242177333881365, max_depth=3, n_estimators=101, subsample=0.8887995089067299; total time=   0.0s\n",
      "[CV] END gamma=0.3005575058716044, learning_rate=0.24242177333881365, max_depth=3, n_estimators=101, subsample=0.8887995089067299; total time=   0.0s\n",
      "[CV] END gamma=0.4692763545078751, learning_rate=0.030233629752304297, max_depth=5, n_estimators=120, subsample=0.8469926038510867; total time=   0.1s\n",
      "[CV] END gamma=0.4692763545078751, learning_rate=0.030233629752304297, max_depth=5, n_estimators=120, subsample=0.8469926038510867; total time=   0.1s\n",
      "[CV] END gamma=0.4692763545078751, learning_rate=0.030233629752304297, max_depth=5, n_estimators=120, subsample=0.8469926038510867; total time=   0.1s\n",
      "[CV] END gamma=0.4692763545078751, learning_rate=0.030233629752304297, max_depth=5, n_estimators=120, subsample=0.8469926038510867; total time=   0.1s\n",
      "[CV] END gamma=0.4692763545078751, learning_rate=0.030233629752304297, max_depth=5, n_estimators=120, subsample=0.8469926038510867; total time=   0.1s\n",
      "[CV] END gamma=0.30582658024414044, learning_rate=0.03211989156591522, max_depth=2, n_estimators=148, subsample=0.8099098641033556; total time=   0.0s\n",
      "[CV] END gamma=0.30582658024414044, learning_rate=0.03211989156591522, max_depth=2, n_estimators=148, subsample=0.8099098641033556; total time=   0.0s\n",
      "[CV] END gamma=0.30582658024414044, learning_rate=0.03211989156591522, max_depth=2, n_estimators=148, subsample=0.8099098641033556; total time=   0.0s\n",
      "[CV] END gamma=0.30582658024414044, learning_rate=0.03211989156591522, max_depth=2, n_estimators=148, subsample=0.8099098641033556; total time=   0.0s\n",
      "[CV] END gamma=0.30582658024414044, learning_rate=0.03211989156591522, max_depth=2, n_estimators=148, subsample=0.8099098641033556; total time=   0.0s\n",
      "[CV] END gamma=0.19993048585762774, learning_rate=0.04399969896408463, max_depth=5, n_estimators=115, subsample=0.6931085361721216; total time=   0.1s\n",
      "[CV] END gamma=0.19993048585762774, learning_rate=0.04399969896408463, max_depth=5, n_estimators=115, subsample=0.6931085361721216; total time=   0.1s\n",
      "[CV] END gamma=0.19993048585762774, learning_rate=0.04399969896408463, max_depth=5, n_estimators=115, subsample=0.6931085361721216; total time=   0.0s\n",
      "[CV] END gamma=0.19993048585762774, learning_rate=0.04399969896408463, max_depth=5, n_estimators=115, subsample=0.6931085361721216; total time=   0.0s\n",
      "[CV] END gamma=0.19993048585762774, learning_rate=0.04399969896408463, max_depth=5, n_estimators=115, subsample=0.6931085361721216; total time=   0.1s\n",
      "[CV] END gamma=0.0453032172664104, learning_rate=0.2155158027999262, max_depth=5, n_estimators=102, subsample=0.9439761626945282; total time=   0.1s\n",
      "[CV] END gamma=0.0453032172664104, learning_rate=0.2155158027999262, max_depth=5, n_estimators=102, subsample=0.9439761626945282; total time=   0.2s\n",
      "[CV] END gamma=0.0453032172664104, learning_rate=0.2155158027999262, max_depth=5, n_estimators=102, subsample=0.9439761626945282; total time=   0.0s\n",
      "[CV] END gamma=0.0453032172664104, learning_rate=0.2155158027999262, max_depth=5, n_estimators=102, subsample=0.9439761626945282; total time=   0.1s\n",
      "[CV] END gamma=0.0453032172664104, learning_rate=0.2155158027999262, max_depth=5, n_estimators=102, subsample=0.9439761626945282; total time=   0.0s\n",
      "[CV] END gamma=0.34015376929388985, learning_rate=0.16514977559086289, max_depth=3, n_estimators=103, subsample=0.9768807022739411; total time=   0.0s\n",
      "[CV] END gamma=0.34015376929388985, learning_rate=0.16514977559086289, max_depth=3, n_estimators=103, subsample=0.9768807022739411; total time=   0.0s\n",
      "[CV] END gamma=0.34015376929388985, learning_rate=0.16514977559086289, max_depth=3, n_estimators=103, subsample=0.9768807022739411; total time=   0.0s\n",
      "[CV] END gamma=0.34015376929388985, learning_rate=0.16514977559086289, max_depth=3, n_estimators=103, subsample=0.9768807022739411; total time=   0.0s\n",
      "[CV] END gamma=0.34015376929388985, learning_rate=0.16514977559086289, max_depth=3, n_estimators=103, subsample=0.9768807022739411; total time=   0.0s\n",
      "[CV] END gamma=0.28164410892276964, learning_rate=0.14562495076197482, max_depth=3, n_estimators=101, subsample=0.8736932106048627; total time=   0.0s\n",
      "[CV] END gamma=0.28164410892276964, learning_rate=0.14562495076197482, max_depth=3, n_estimators=101, subsample=0.8736932106048627; total time=   0.0s\n",
      "[CV] END gamma=0.28164410892276964, learning_rate=0.14562495076197482, max_depth=3, n_estimators=101, subsample=0.8736932106048627; total time=   0.0s\n",
      "[CV] END gamma=0.28164410892276964, learning_rate=0.14562495076197482, max_depth=3, n_estimators=101, subsample=0.8736932106048627; total time=   0.0s\n",
      "[CV] END gamma=0.28164410892276964, learning_rate=0.14562495076197482, max_depth=3, n_estimators=101, subsample=0.8736932106048627; total time=   0.0s\n",
      "[CV] END gamma=0.22007624686980065, learning_rate=0.06661147045343364, max_depth=2, n_estimators=107, subsample=0.6137554084460873; total time=   0.0s\n",
      "[CV] END gamma=0.22007624686980065, learning_rate=0.06661147045343364, max_depth=2, n_estimators=107, subsample=0.6137554084460873; total time=   0.0s\n",
      "[CV] END gamma=0.22007624686980065, learning_rate=0.06661147045343364, max_depth=2, n_estimators=107, subsample=0.6137554084460873; total time=   0.0s\n",
      "[CV] END gamma=0.22007624686980065, learning_rate=0.06661147045343364, max_depth=2, n_estimators=107, subsample=0.6137554084460873; total time=   0.0s\n",
      "[CV] END gamma=0.22007624686980065, learning_rate=0.06661147045343364, max_depth=2, n_estimators=107, subsample=0.6137554084460873; total time=   0.0s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                          callbacks=None,\n",
       "                                          colsample_bylevel=None,\n",
       "                                          colsample_bynode=None,\n",
       "                                          colsample_bytree=None, device=None,\n",
       "                                          early_stopping_rounds=None,\n",
       "                                          enable_categorical=False,\n",
       "                                          eval_metric=None, feature_types=None,\n",
       "                                          gamma=None, grow_policy=None,\n",
       "                                          importance_type=None,\n",
       "                                          interaction_constraints=None,\n",
       "                                          learning_rate=...\n",
       "                                        'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000002470658ACD0>,\n",
       "                                        'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000247116FE0D0>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000247080EF580>,\n",
       "                                        'subsample': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000247116FE4C0>},\n",
       "                   random_state=42, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb = RandomizedSearchCV(estimator = model, param_distributions = params,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = 1)\n",
    "xgb.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd153d48",
   "metadata": {},
   "source": [
    "# Print Best Parameter  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "5eeb2b74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'gamma': 0.22007624686980065, 'learning_rate': 0.06661147045343364, 'max_depth': 2, 'n_estimators': 107, 'subsample': 0.6137554084460873}\n",
      "-0.26945651877072835\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7391462915552081"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([7.7549877, 7.417403 , 7.5525136, ..., 7.356347 , 7.753353 ,\n",
       "       7.8522286], dtype=float32)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(xgb.best_params_)\n",
    "print(xgb.best_score_)\n",
    "predictions=xgb.predict(X_test)\n",
    "display (r2_score (y_test,predictions))\n",
    "display (predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38231071",
   "metadata": {},
   "source": [
    "# Create Dist plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "0fcb9067",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test-predictions)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23908767",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}