Desicion_Tree_Random_Forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### banking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp_var_rate</th>\n",
       "      <th>cons_price_idx</th>\n",
       "      <th>cons_conf_idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr_employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.444</td>\n",
       "      <td>-36.1</td>\n",
       "      <td>4.963</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.021</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jun</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>success</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>94.055</td>\n",
       "      <td>-39.8</td>\n",
       "      <td>0.729</td>\n",
       "      <td>4991.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>apr</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>93.075</td>\n",
       "      <td>-47.1</td>\n",
       "      <td>1.405</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>92.201</td>\n",
       "      <td>-31.4</td>\n",
       "      <td>0.869</td>\n",
       "      <td>5076.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital          education  default housing loan  \\\n",
       "0   44  blue-collar  married           basic.4y  unknown     yes   no   \n",
       "1   53   technician  married            unknown       no      no   no   \n",
       "2   28   management   single  university.degree       no     yes   no   \n",
       "3   39     services  married        high.school       no      no   no   \n",
       "4   55      retired  married           basic.4y       no     yes   no   \n",
       "\n",
       "    contact month day_of_week  ...  campaign  pdays  previous     poutcome  \\\n",
       "0  cellular   aug         thu  ...         1    999         0  nonexistent   \n",
       "1  cellular   nov         fri  ...         1    999         0  nonexistent   \n",
       "2  cellular   jun         thu  ...         3      6         2      success   \n",
       "3  cellular   apr         fri  ...         2    999         0  nonexistent   \n",
       "4  cellular   aug         fri  ...         1      3         1      success   \n",
       "\n",
       "  emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  nr_employed  y  \n",
       "0          1.4          93.444          -36.1      4.963       5228.1  0  \n",
       "1         -0.1          93.200          -42.0      4.021       5195.8  0  \n",
       "2         -1.7          94.055          -39.8      0.729       4991.6  1  \n",
       "3         -1.8          93.075          -47.1      1.405       5099.1  0  \n",
       "4         -2.9          92.201          -31.4      0.869       5076.2  1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_bank = pd.read_csv('banking.csv')\n",
    "data_bank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             41188 non-null  int64  \n",
      " 1   job             41188 non-null  object \n",
      " 2   marital         41188 non-null  object \n",
      " 3   education       41188 non-null  object \n",
      " 4   default         41188 non-null  object \n",
      " 5   housing         41188 non-null  object \n",
      " 6   loan            41188 non-null  object \n",
      " 7   contact         41188 non-null  object \n",
      " 8   month           41188 non-null  object \n",
      " 9   day_of_week     41188 non-null  object \n",
      " 10  duration        41188 non-null  int64  \n",
      " 11  campaign        41188 non-null  int64  \n",
      " 12  pdays           41188 non-null  int64  \n",
      " 13  previous        41188 non-null  int64  \n",
      " 14  poutcome        41188 non-null  object \n",
      " 15  emp_var_rate    41188 non-null  float64\n",
      " 16  cons_price_idx  41188 non-null  float64\n",
      " 17  cons_conf_idx   41188 non-null  float64\n",
      " 18  euribor3m       41188 non-null  float64\n",
      " 19  nr_employed     41188 non-null  float64\n",
      " 20  y               41188 non-null  int64  \n",
      "dtypes: float64(5), int64(6), object(10)\n",
      "memory usage: 6.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data_bank.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### balance-scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>1</th>\n",
       "      <th>1.1</th>\n",
       "      <th>1.2</th>\n",
       "      <th>1.3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   B  1  1.1  1.2  1.3\n",
       "0  R  1    1    1    2\n",
       "1  R  1    1    1    3\n",
       "2  R  1    1    1    4\n",
       "3  R  1    1    1    5\n",
       "4  R  1    1    2    1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_bal = pd.read_csv('balance-scale.csv')\n",
    "data_bal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Balance</th>\n",
       "      <th>Left_Weight</th>\n",
       "      <th>Left_Distance</th>\n",
       "      <th>Right_Weight</th>\n",
       "      <th>Right_Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Balance  Left_Weight  Left_Distance  Right_Weight  Right_Distance\n",
       "0       R            1              1             1               2\n",
       "1       R            1              1             1               3\n",
       "2       R            1              1             1               4\n",
       "3       R            1              1             1               5\n",
       "4       R            1              1             2               1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_bal.columns = ['Balance', 'Left_Weight', 'Left_Distance', 'Right_Weight', 'Right_Distance']\n",
    "data_bal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 624 entries, 0 to 623\n",
      "Data columns (total 5 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   Balance         624 non-null    object\n",
      " 1   Left_Weight     624 non-null    int64 \n",
      " 2   Left_Distance   624 non-null    int64 \n",
      " 3   Right_Weight    624 non-null    int64 \n",
      " 4   Right_Distance  624 non-null    int64 \n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 24.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data_bal.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['R', 'L', 'B'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_bal['Balance'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Slicing (Train and Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_bal.values[:,1:5]\n",
    "Y = data_bal.values[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 2],\n",
       "       [1, 1, 1, 3],\n",
       "       [1, 1, 1, 4],\n",
       "       ...,\n",
       "       [5, 5, 5, 3],\n",
       "       [5, 5, 5, 4],\n",
       "       [5, 5, 5, 5]], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R',\n",
       "       'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'L', 'B',\n",
       "       'R', 'R', 'R', 'B', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R',\n",
       "       'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'L', 'L', 'B',\n",
       "       'R', 'R', 'L', 'R', 'R', 'R', 'R', 'B', 'R', 'R', 'R', 'R', 'R',\n",
       "       'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'L', 'L', 'L', 'B',\n",
       "       'R', 'L', 'B', 'R', 'R', 'R', 'L', 'R', 'R', 'R', 'R', 'B', 'R',\n",
       "       'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'L', 'L', 'L', 'L', 'B',\n",
       "       'L', 'L', 'R', 'R', 'R', 'L', 'R', 'R', 'R', 'R', 'L', 'R', 'R',\n",
       "       'R', 'R', 'B', 'R', 'R', 'R', 'R', 'L', 'B', 'R', 'R', 'R', 'B',\n",
       "       'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R',\n",
       "       'R', 'R', 'R', 'R', 'R', 'R', 'L', 'L', 'L', 'B', 'R', 'L', 'B',\n",
       "       'R', 'R', 'R', 'L', 'R', 'R', 'R', 'R', 'B', 'R', 'R', 'R', 'R',\n",
       "       'R', 'R', 'R', 'R', 'R', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'B',\n",
       "       'R', 'R', 'L', 'B', 'R', 'R', 'R', 'L', 'R', 'R', 'R', 'R', 'L',\n",
       "       'R', 'R', 'R', 'R', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'B',\n",
       "       'R', 'L', 'L', 'R', 'R', 'R', 'L', 'B', 'R', 'R', 'R', 'L', 'R',\n",
       "       'R', 'R', 'R', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'B',\n",
       "       'L', 'L', 'L', 'R', 'R', 'L', 'L', 'R', 'R', 'R', 'L', 'B', 'R',\n",
       "       'R', 'R', 'L', 'L', 'B', 'R', 'R', 'L', 'R', 'R', 'R', 'R', 'B',\n",
       "       'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R',\n",
       "       'R', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'B', 'R', 'R', 'L', 'B',\n",
       "       'R', 'R', 'R', 'L', 'R', 'R', 'R', 'R', 'L', 'R', 'R', 'R', 'R',\n",
       "       'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'R', 'L', 'L', 'B',\n",
       "       'R', 'R', 'L', 'L', 'R', 'R', 'R', 'L', 'R', 'R', 'R', 'R', 'L',\n",
       "       'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'B',\n",
       "       'R', 'L', 'L', 'B', 'R', 'R', 'L', 'L', 'R', 'R', 'R', 'L', 'L',\n",
       "       'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'B',\n",
       "       'L', 'L', 'L', 'R', 'R', 'L', 'L', 'B', 'R', 'R', 'L', 'L', 'L',\n",
       "       'B', 'R', 'L', 'B', 'R', 'R', 'R', 'L', 'R', 'R', 'R', 'R', 'B',\n",
       "       'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'L', 'L', 'L', 'L',\n",
       "       'L', 'L', 'L', 'L', 'B', 'R', 'L', 'L', 'R', 'R', 'R', 'L', 'B',\n",
       "       'R', 'R', 'R', 'L', 'R', 'R', 'R', 'R', 'L', 'L', 'L', 'L', 'L',\n",
       "       'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'B', 'R', 'L', 'L', 'B',\n",
       "       'R', 'R', 'L', 'L', 'R', 'R', 'R', 'L', 'L', 'L', 'L', 'L', 'L',\n",
       "       'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'B',\n",
       "       'R', 'L', 'L', 'L', 'R', 'R', 'L', 'L', 'L', 'L', 'L', 'L', 'L',\n",
       "       'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'B',\n",
       "       'L', 'L', 'L', 'B', 'R', 'L', 'L', 'L', 'L', 'B', 'L', 'L', 'R',\n",
       "       'R', 'R', 'L', 'R', 'R', 'R', 'R', 'L', 'R', 'R', 'R', 'R', 'B',\n",
       "       'R', 'R', 'R', 'R', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L',\n",
       "       'B', 'L', 'L', 'L', 'R', 'R', 'L', 'L', 'R', 'R', 'R', 'L', 'B',\n",
       "       'R', 'R', 'R', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L',\n",
       "       'L', 'L', 'L', 'L', 'B', 'L', 'L', 'L', 'R', 'R', 'L', 'L', 'B',\n",
       "       'R', 'R', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L',\n",
       "       'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'B', 'L', 'L', 'L', 'B',\n",
       "       'R', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L',\n",
       "       'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'B'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Run The Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_entropy = DecisionTreeClassifier(criterion = 'entropy')\n",
    "clf_entropy.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf_entropy.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['L', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'B', 'R', 'R', 'L', 'R',\n",
       "       'L', 'L', 'L', 'R', 'R', 'L', 'L', 'L', 'L', 'R', 'L', 'L', 'R',\n",
       "       'B', 'B', 'L', 'L', 'L', 'L', 'L', 'L', 'R', 'L', 'L', 'L', 'L',\n",
       "       'L', 'L', 'B', 'B', 'R', 'L', 'L', 'B', 'R', 'L', 'R', 'R', 'B',\n",
       "       'L', 'R', 'L', 'L', 'L', 'B', 'R', 'L', 'L', 'B', 'L', 'L', 'R',\n",
       "       'B', 'R', 'L', 'R', 'L', 'R', 'R', 'L', 'L', 'L', 'L', 'R', 'R',\n",
       "       'R', 'R', 'R', 'B', 'R', 'R', 'L', 'L', 'R', 'L', 'B', 'L', 'L',\n",
       "       'R', 'R', 'R', 'R', 'R', 'L', 'L', 'B', 'L', 'R', 'L', 'R', 'B',\n",
       "       'R', 'R', 'R', 'R', 'L', 'R', 'L', 'B', 'R', 'R', 'R', 'L', 'R',\n",
       "       'L', 'R', 'R', 'R', 'B', 'L', 'R', 'R', 'L', 'L', 'L', 'R', 'R',\n",
       "       'L', 'L', 'R', 'L', 'R', 'R', 'R', 'L', 'L', 'L', 'B', 'L', 'R',\n",
       "       'L', 'R', 'L', 'R', 'B', 'L', 'R', 'R', 'L', 'L', 'L', 'R', 'L',\n",
       "       'B', 'L', 'L', 'L', 'R', 'L', 'B', 'L', 'B', 'L', 'R', 'B', 'R',\n",
       "       'B', 'B', 'R', 'R', 'R', 'L', 'L', 'R', 'R', 'L', 'L', 'L', 'B',\n",
       "       'R', 'R', 'L', 'L', 'R', 'L'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracu Score: 0.7712765957446809\n"
     ]
    }
   ],
   "source": [
    "print('Accuracu Score:', accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25081198, 0.24942478, 0.26926441, 0.23049884])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_entropy.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp_var_rate</th>\n",
       "      <th>cons_price_idx</th>\n",
       "      <th>cons_conf_idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr_employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.444</td>\n",
       "      <td>-36.1</td>\n",
       "      <td>4.963</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.021</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jun</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>success</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>94.055</td>\n",
       "      <td>-39.8</td>\n",
       "      <td>0.729</td>\n",
       "      <td>4991.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>apr</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>93.075</td>\n",
       "      <td>-47.1</td>\n",
       "      <td>1.405</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>92.201</td>\n",
       "      <td>-31.4</td>\n",
       "      <td>0.869</td>\n",
       "      <td>5076.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital          education  default housing loan  \\\n",
       "0   44  blue-collar  married           basic.4y  unknown     yes   no   \n",
       "1   53   technician  married            unknown       no      no   no   \n",
       "2   28   management   single  university.degree       no     yes   no   \n",
       "3   39     services  married        high.school       no      no   no   \n",
       "4   55      retired  married           basic.4y       no     yes   no   \n",
       "\n",
       "    contact month day_of_week  ...  campaign  pdays  previous     poutcome  \\\n",
       "0  cellular   aug         thu  ...         1    999         0  nonexistent   \n",
       "1  cellular   nov         fri  ...         1    999         0  nonexistent   \n",
       "2  cellular   jun         thu  ...         3      6         2      success   \n",
       "3  cellular   apr         fri  ...         2    999         0  nonexistent   \n",
       "4  cellular   aug         fri  ...         1      3         1      success   \n",
       "\n",
       "  emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  nr_employed  y  \n",
       "0          1.4          93.444          -36.1      4.963       5228.1  0  \n",
       "1         -0.1          93.200          -42.0      4.021       5195.8  0  \n",
       "2         -1.7          94.055          -39.8      0.729       4991.6  1  \n",
       "3         -1.8          93.075          -47.1      1.405       5099.1  0  \n",
       "4         -2.9          92.201          -31.4      0.869       5076.2  1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_bank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             41188 non-null  int64  \n",
      " 1   job             41188 non-null  object \n",
      " 2   marital         41188 non-null  object \n",
      " 3   education       41188 non-null  object \n",
      " 4   default         41188 non-null  object \n",
      " 5   housing         41188 non-null  object \n",
      " 6   loan            41188 non-null  object \n",
      " 7   contact         41188 non-null  object \n",
      " 8   month           41188 non-null  object \n",
      " 9   day_of_week     41188 non-null  object \n",
      " 10  duration        41188 non-null  int64  \n",
      " 11  campaign        41188 non-null  int64  \n",
      " 12  pdays           41188 non-null  int64  \n",
      " 13  previous        41188 non-null  int64  \n",
      " 14  poutcome        41188 non-null  object \n",
      " 15  emp_var_rate    41188 non-null  float64\n",
      " 16  cons_price_idx  41188 non-null  float64\n",
      " 17  cons_conf_idx   41188 non-null  float64\n",
      " 18  euribor3m       41188 non-null  float64\n",
      " 19  nr_employed     41188 non-null  float64\n",
      " 20  y               41188 non-null  int64  \n",
      "dtypes: float64(5), int64(6), object(10)\n",
      "memory usage: 6.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data_bank.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-Hot Encoding for Categorical Data\n",
    "cat_vars = ['job','marital','education','default','housing','loan', 'contact','month','day_of_week','poutcome']\n",
    "for var in cat_vars:\n",
    "    cat_list = pd.get_dummies(data_bank[var], prefix=var)\n",
    "    data1 = data_bank.join(cat_list)\n",
    "    data_bank = data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>month_oct</th>\n",
       "      <th>month_sep</th>\n",
       "      <th>day_of_week_fri</th>\n",
       "      <th>day_of_week_mon</th>\n",
       "      <th>day_of_week_thu</th>\n",
       "      <th>day_of_week_tue</th>\n",
       "      <th>day_of_week_wed</th>\n",
       "      <th>poutcome_failure</th>\n",
       "      <th>poutcome_nonexistent</th>\n",
       "      <th>poutcome_success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</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",
       "      <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>53</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jun</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>apr</td>\n",
       "      <td>fri</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>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>55</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</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>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 × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital          education  default housing loan  \\\n",
       "0   44  blue-collar  married           basic.4y  unknown     yes   no   \n",
       "1   53   technician  married            unknown       no      no   no   \n",
       "2   28   management   single  university.degree       no     yes   no   \n",
       "3   39     services  married        high.school       no      no   no   \n",
       "4   55      retired  married           basic.4y       no     yes   no   \n",
       "\n",
       "    contact month day_of_week  ...  month_oct  month_sep  day_of_week_fri  \\\n",
       "0  cellular   aug         thu  ...          0          0                0   \n",
       "1  cellular   nov         fri  ...          0          0                1   \n",
       "2  cellular   jun         thu  ...          0          0                0   \n",
       "3  cellular   apr         fri  ...          0          0                1   \n",
       "4  cellular   aug         fri  ...          0          0                1   \n",
       "\n",
       "   day_of_week_mon day_of_week_thu  day_of_week_tue  day_of_week_wed  \\\n",
       "0                0               1                0                0   \n",
       "1                0               0                0                0   \n",
       "2                0               1                0                0   \n",
       "3                0               0                0                0   \n",
       "4                0               0                0                0   \n",
       "\n",
       "   poutcome_failure  poutcome_nonexistent  poutcome_success  \n",
       "0                 0                     1                 0  \n",
       "1                 0                     1                 0  \n",
       "2                 0                     0                 1  \n",
       "3                 0                     1                 0  \n",
       "4                 0                     0                 1  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_bank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove Categorical Data\n",
    "data_vars = data_bank.columns.values.tolist()\n",
    "to_keep = [i for i in data_vars if i not in cat_vars]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define new DataFrame without Categorical Data\n",
    "data_bank2 = data_bank[to_keep]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>age</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp_var_rate</th>\n",
       "      <th>cons_price_idx</th>\n",
       "      <th>cons_conf_idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr_employed</th>\n",
       "      <th>...</th>\n",
       "      <th>month_oct</th>\n",
       "      <th>month_sep</th>\n",
       "      <th>day_of_week_fri</th>\n",
       "      <th>day_of_week_mon</th>\n",
       "      <th>day_of_week_thu</th>\n",
       "      <th>day_of_week_tue</th>\n",
       "      <th>day_of_week_wed</th>\n",
       "      <th>poutcome_failure</th>\n",
       "      <th>poutcome_nonexistent</th>\n",
       "      <th>poutcome_success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44</td>\n",
       "      <td>210</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.444</td>\n",
       "      <td>-36.1</td>\n",
       "      <td>4.963</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>...</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",
       "      <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>53</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.021</td>\n",
       "      <td>5195.8</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>339</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>94.055</td>\n",
       "      <td>-39.8</td>\n",
       "      <td>0.729</td>\n",
       "      <td>4991.6</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>185</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>93.075</td>\n",
       "      <td>-47.1</td>\n",
       "      <td>1.405</td>\n",
       "      <td>5099.1</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>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>55</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>92.201</td>\n",
       "      <td>-31.4</td>\n",
       "      <td>0.869</td>\n",
       "      <td>5076.2</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>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 × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  duration  campaign  pdays  previous  emp_var_rate  cons_price_idx  \\\n",
       "0   44       210         1    999         0           1.4          93.444   \n",
       "1   53       138         1    999         0          -0.1          93.200   \n",
       "2   28       339         3      6         2          -1.7          94.055   \n",
       "3   39       185         2    999         0          -1.8          93.075   \n",
       "4   55       137         1      3         1          -2.9          92.201   \n",
       "\n",
       "   cons_conf_idx  euribor3m  nr_employed  ...  month_oct  month_sep  \\\n",
       "0          -36.1      4.963       5228.1  ...          0          0   \n",
       "1          -42.0      4.021       5195.8  ...          0          0   \n",
       "2          -39.8      0.729       4991.6  ...          0          0   \n",
       "3          -47.1      1.405       5099.1  ...          0          0   \n",
       "4          -31.4      0.869       5076.2  ...          0          0   \n",
       "\n",
       "   day_of_week_fri  day_of_week_mon  day_of_week_thu  day_of_week_tue  \\\n",
       "0                0                0                1                0   \n",
       "1                1                0                0                0   \n",
       "2                0                0                1                0   \n",
       "3                1                0                0                0   \n",
       "4                1                0                0                0   \n",
       "\n",
       "   day_of_week_wed  poutcome_failure  poutcome_nonexistent  poutcome_success  \n",
       "0                0                 0                     1                 0  \n",
       "1                0                 0                     1                 0  \n",
       "2                0                 0                     0                 1  \n",
       "3                0                 0                     1                 0  \n",
       "4                0                 0                     0                 1  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_bank2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Split (Train and Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_vars2 = data_bank2.columns.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = ['y']\n",
    "X = [i for i in data_vars2 if i not in y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age',\n",
       " 'duration',\n",
       " 'campaign',\n",
       " 'pdays',\n",
       " 'previous',\n",
       " 'emp_var_rate',\n",
       " 'cons_price_idx',\n",
       " 'cons_conf_idx',\n",
       " 'euribor3m',\n",
       " 'nr_employed',\n",
       " 'job_admin.',\n",
       " 'job_blue-collar',\n",
       " 'job_entrepreneur',\n",
       " 'job_housemaid',\n",
       " 'job_management',\n",
       " 'job_retired',\n",
       " 'job_self-employed',\n",
       " 'job_services',\n",
       " 'job_student',\n",
       " 'job_technician',\n",
       " 'job_unemployed',\n",
       " 'job_unknown',\n",
       " 'marital_divorced',\n",
       " 'marital_married',\n",
       " 'marital_single',\n",
       " 'marital_unknown',\n",
       " 'education_basic.4y',\n",
       " 'education_basic.6y',\n",
       " 'education_basic.9y',\n",
       " 'education_high.school',\n",
       " 'education_illiterate',\n",
       " 'education_professional.course',\n",
       " 'education_university.degree',\n",
       " 'education_unknown',\n",
       " 'default_no',\n",
       " 'default_unknown',\n",
       " 'default_yes',\n",
       " 'housing_no',\n",
       " 'housing_unknown',\n",
       " 'housing_yes',\n",
       " 'loan_no',\n",
       " 'loan_unknown',\n",
       " 'loan_yes',\n",
       " 'contact_cellular',\n",
       " 'contact_telephone',\n",
       " 'month_apr',\n",
       " 'month_aug',\n",
       " 'month_dec',\n",
       " 'month_jul',\n",
       " 'month_jun',\n",
       " 'month_mar',\n",
       " 'month_may',\n",
       " 'month_nov',\n",
       " 'month_oct',\n",
       " 'month_sep',\n",
       " 'day_of_week_fri',\n",
       " 'day_of_week_mon',\n",
       " 'day_of_week_thu',\n",
       " 'day_of_week_tue',\n",
       " 'day_of_week_wed',\n",
       " 'poutcome_failure',\n",
       " 'poutcome_nonexistent',\n",
       " 'poutcome_success']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['y']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data_bank2[X], data_bank2[y], test_size = 0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Run The Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_clf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-35-152d25007fe9>:1: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  rf_clf.fit(X_train, y_train)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_scoreRF = rf_clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy: 0.9138949583232177\n"
     ]
    }
   ],
   "source": [
    "print('Model Accuracy:', model_scoreRF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importance Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DjaY1R/1a4PvtqLuZmTWAG/HqinGjywvvl5N+b98h9X4PzZff7yls/1Kdx6hVRl31i4jlkpZG6xOKy4FVJa1O6jWPjIhn8iX4YjxqeR0r1XkV4IWIGF6lDu16KtKxq2ZmjeXL6R03mHT/GVKCWj1eJEW6dqaMepUa7Odzj7rd0agR8R/gKUlHACjZPq+eRprJDVaOpjUzs27gRrzjfgB8T9I00uXsetwNbJXn6z6yg2XUJSJeAC4nXV7/FTCzg0UdBRyXo1wXku7BA3wZOEnSTNKXETMz62YeJ27dxrGrZmbtV2ucuHviZmZmTcoPtvViksaQLlsXTYuIk3qiPp3V8uxihp5xe7v3c2KbmVllDemJSzpW0oWNKKtQ5iGStiq8P0vSvo08Rgfq1KV1kHSQpDPy60OAB/OY8uJPmw14V3weZmbW+/TmnvghpKSzRwAi4swerU0D6yBpQEQsq1D+rcCt+e0hFM6/JygNDldELO+pOpiZWXV19cQlfVrSQ/mp6kslDZA0RtLjku6lMIuXpHF5zu3S+yWF11+R1CJpnqRz8rLjJc3My26WtKakXYCDgHPzMYcVy5W0j6SHc1lXShqYly+S9G1Jc/K6LWuc01hJpxXeL5A0NP/8QdLlkhZKmixpjeK5Sdpf0i8L++4p6Tf59X6SHsh1mJCHd5Xqdqak+4EjJJ0s6RFJ8yX9Im9zrKQLq5z/nMLxNpM0u+x8qn0eG+Tf68z8M7qw/M5cz0slPS1p/cL5XwzMAd4j6fS873xJ3671d1Hh93yCpFmSZi17eXG1j8PMzDqgzUZc0vuAI4HROfRjGfBpUo74aOCDwFZVC2gtZ39S73LniNieNLwK4JYcDbo98AfguIiYTuqRnp4vIf+pUM7qwDjgyBwzuirw+cKhno+IHYGfkWJCO2Iz4KKI2Bp4gdZkspI7gQ9IWiu/PxK4UdL6wDeAfXMdZpFyzUtejYhdI+IXwBnADhGxHXBisfAq579YaRY0gDH5dwCApCFU/zzOB87L8aiHAVfk5d8Cfp/rORHYqLDPFsA1OQ52i/z72Ik0gcsISbtX+btYaby4Y1fNzLpOPZfT9wFGADPT1VXWIEVx3hMR/wCQdCNpIo5a9gWuioiXAQqxpNtI+j9gXWAQcEcb5WxBikR9PL+/GjiJNJEIwC3539nAx9ooq5qnImJuoZyhxZUR8YakScCBkm4iZZV/BdiD1IBOy7+rtwAPFHYtRpvOB8ZL+hVpHHdbrgDGSDqV1HjuVFi3M9U/j31JY9NL266jNMHJrsCh+XwmSfp3obynI2JGfr1f/nk4vx9EatS3Y+W/i+fqOA8zM2uQehpxAVdHxP++uSA9dHVole3fIPfwlf7v/pZCOZUGpY8DDomIeUozgO1ZR31qKcWjLqP2+b1Zz6wYSVqMXF1GaqDK3Uj68vAvYGZEvJjP986I+GSVYxajTQ8AdiddNv+mpK1r1BXgZnLvGZgdEf8sW19twP8qwKiIeKW4UIVWvY16CvheRFxatv+XKPu7aItjV83MGquee+JTgMMlvR1A0nqkXtmekt4maTXgiML2i0g9NEjpXqVJPSYDn5W0ZqEcSDGkf83lFC/HlkeUljwKDJW0aX5/NHBvHedRbhFpelAk7Uia2rM97sn7H09rD3sGMLpUN6X7+ytdoZC0CvCeiLib1INfl9TDLVrh/CPiVdJVip+RpkUtepDqn8dk4IuFYw/PL+8HPp6X7Qe8tcp53kH63Er39t+V/xZW+ruQtHGVMszMrAu02YhHxCOk+7yTJc0n3Q8eAowlXSq+i/QAVMnlwB6SHiJd5n0plzOJdJ93lqS5tN6v/iapEbqT1ECX/AI4XekBtmGF+rxKuic8QWkGr+XAJe066+RmYL1cl88Dj9fefEX56fLbSHN435aX/YOUgX5D/l3NIE1jWm4AcF2u/8Oke9YvlG1T6fzHk3rckwFy3YmIv1L98zgZGJkfSnuE1vvv3wb2yw/M7Q/8lfTFofw8J5PmDX8g1/cmYO0afxdmZtZNHLvaRJSeph8cEd9sQFkDgWX5/v4o0tzgwztbbi2OXTUzaz/ViF3tzePErUDSRGAYsHeDitwI+GW+tP866baAmZk1kT7fE1cfiy5tZgOHbBZDjvlJu/dz7KqZ9We1euJ9fgKUiLiqI9Gl3UHS9Brr9pR0W4OP1+4yJV2hQvytmZn1Hr6c3oMiYpeerkNbIuJzPV0HMzOrrM/3xHszSUuUnKsU+9oi6cjCJutImqgUz3pJvn9drayf5XjThWXRqB+W9KhS3OvHCsvHSrpaKVZ2kaSPSfpBrsOkPFQNSfdIGlmo73eVInJnSNqwjnN07KqZWRdxI97zPkaKM92elK52bo5RhZTK9j/AtqSH2mol0H093zPZjjTEbzuliNrLgQOB3YB3lO0zjBQ6czBwHXB3jrJ9JS8vtxYwI0fkTqWOh+Ecu2pm1nXciPe8XYEbImJZRPydFFzz/rzuoYh4Mo9JvyFvW83H85jvh4GtSfGvW5IiZJ+I9ATjdWX7/C4ilgItpLHrk/LyFsqiZrPXyWPiqRBHa2Zm3cv3xHterfjT8qEDFYcSSNqEFJ7z/oj4t6RxtMbI1hp+8BpARCyXtDRahyosp/LfRnGbtmJtV+LYVTOzxnJPvOdNBY5Umt51A1Ke+kN53U6SNsn3wo8kRaVWsg4pGW9xvk+9f17+KLBJIfGtWqa7mZk1ITfiPStI04DOB+aRJjf5SkT8La9/ADgHWAA8lbdduZCIeaTL6AuBK4FpefmrwAnA7fnBtqe74iQkHSTprK4o28zMquvzYS+9laS3AXMiot9MGuLYVTOz9uvXYS+9kaR3knrZP+zpupiZWfPyg209ICL+Aqw0RWk9JD0IDCxbfHREtHS0PpJ+C3yqwkxqDdXy7GKGnnF7h/Z19KqZ2crciDeZiNi51npJA/KQtPaU+ZHO1crMzHqCL6c3EUlDc/ra1Xl+8JskrZkT187MD68dIWk/SQ9ImiNpgqRBkvaX9MtCWXtK+k1+vUjS+vn1qTk9boGkUwrHXVDY9zRJY/Prk3Oi3HxJv+jGX4eZWb/nnnjz2QI4LiKmSboS+EJe/mpE7Job41uAfSPiJUlfBU4FzgYulbRWRLxEGrJ2Y7FgSSOAMcDOpPHrD0q6F/h3jfqcAWwSEa9JWrd8paQTSE/IM2CdDTp80mZmtjL3xJvPMxExLb++jtYUt1KD/AFSWts0SXOBY4CNI+INUiLbgZJWJcWq/rqs7F2BiRHxUkQsIX0Z2K2N+swHxkv6NPBG+UrHrpqZdR33xJtPtRS3l/K/Au6MiErBLjcCJwH/AmZGxItl66ulx73Bil/4Vi+8PoAUUHMQ8E1JW+cvDGZm1sXciDefjSSNiogHSAls9wM7FNbPAC6StGlE/FHSmsC7I+Jx4B7g56SJS25kZVOBcZLOITXohwJHA38H3p7Hti8BPgpMykly74mIu/P9+E8Bg4AXKlXcsatmZo3ly+nN5w/AMZLmA+sBPyuujIh/AMcCN+RtZpAmQiE/tX4bKZb1NspExBxgHCn29UHgioh4OE+SclZedhspzhXSpCnXSWohJcad19XD1MzMrJUT25qIpKHAbRGxTU/XpSOc2GZm1n5ObDMzM+uDfE+8iUTEIqApe+FmZtZ4bsSt23QmdhUcvWpmVs6X07tZefpZF5Q/vavKNjOz3sWNeB8TEbv0dB3MzKx7uBHvGQMkXS5poaTJktaQNFzSjJxBPlHSWwEk3SNpZH69vqRF+fXWkh6SNDfvs1leviT/u2fe96actz5ekvK6j+Rl90u6QNJKw81KJI2VdGUu60lJJxfWrZSzXmH/EyTNkjRr2cuLG/PbMzMzwI14T9kMuCgitiYFoxwGXAN8NSK2A1qAb7VRxonA+RExHBgJ/LnCNjsAp5BiWN8LjJa0OnApsH9E7ArUE2i+JfAhYCfgW5JWK8tZ/wBwvKQdynd07KqZWdfxg20946mImJtfzwaGAetGxL152dXAhDbKeAD4uqR3A7dExBMVtnkoIv4MkHPUh5IS156MiKfyNjeQJyip4faIeA14TdJzwIYUctZz+aWc9YerFeLENjOzxnJPvGe8Vni9DFi3xrbF3PI3M8sj4npSXvkrwB2S9q7jOKtSPR+9PfXtaDlmZtZAbsR7h8XAvyWVZgw7Gij1yhcBI/Lrw0s7SHovqUd9AXArsF2dx3oUeG9Of4M0JWlHTAUOyfOZr0XKWb+vg2WZmVkH+HJ673EMcEmesORJ0v1mgB8Cv5R0NPD7wvZHAp+WtBT4GynbvE0R8YqkL5AmMHmelJPebhExR9K4wv5XRETVS+lmZtZ4zk7vhyQNiogl+Wn1i4AnIuK8rj6us9PNzNrP2elW7vj8oNtCYDDpaXUzM2sy7ol3oa6edUzS9EaFu0gaA3y5bPG0iDipEeUDDByyWQw55icd3t+xq2bWH9XqifueeBNrZDpbRFwFXNWo8szMrOv5cnrXa4p0NkmrSHpC0gaF93/M9dhA0s2SZuaf0XmbPXKd5kp6WNLaXfy7NDOzAjfiXa8p0tkiYjlwHXBUXrQvMC8ingfOB86LiPfn+l+RtzkNOCnXazfSmPUVOHbVzKzruBHvevWks+3eRhkPAF+T9FVg44hYqbEkp7PlxnguKZ1tS1ZOZ6vlSuAz+fVnab28vi9wYX4Y7lZgndzrngb8OOeprxsRb5QX6NhVM7Ou43viXa8h6WySHgQOIKWzfS4ifl+2b6dT1SLiGUl/z+lvO9PaK18FGFXhy8M5km4HPgLMkLRvRDxarXzHrpqZNZZ74t2vt6ezXUG6rP7LiFiWl00Gvlioz/D877CIaImI7wOzSD1/MzPrJm7Ee8YxwLmS5gPDaU1b+yHweUnTgfUL2x8JLMiXs7ck3VNvU+45l9LZ7gf+TvoSUcutwCBWfFL9ZGBkfqjuEdI9eoBT8jSk80j3w39XT73MzKwxPE68j2tvOlt+Ov68iNit2jYd5cQ2M7P2c2Jb/1Z3OpukM4Cbgf/tnqqZmVln+MG2Pi73ulfoebeRznZOd9XNzMw6x5fT+whJJwIvR0Rd98vrKK9ipGueuey2iLipvWV2Nna1LY5lNbO+yLGrfZykVSPikkaW2chIVzMz6xq+J94Bkj6Tn9SeJ+laSRtLmpKXTZG0Ud5uXI46nS7pSUmH5+VDJE3NcaULCsPNKh1riaQfSZqTyy7Fot4j6WxJ9wJfljRW0ml53aaS7sr1myNpWF5+eo5NnS/p222cYynSVZIulPRIHhP+9rx8sKTHJG2R398g6fjO/m7NzKx+bsTbSdLWwNeBvSNie9K95QuBa3KM6njggsIuQ4BdgY/Ser/5U8AdOa50e1LCWjVrAXMiYkfSePJiROu6EbFHRPyobJ/xpKjX7YFdgL9K2o8UAbsTaVjbCEltJcUBHApsAWwLHJ/LIyIWk8aOj5P0CeCtEXF5+c6OXTUz6zpuxNtvb+CmnClORPwLGAVcn9dfS2q0S34VEcsj4hFgw7xsJjBG0lhg24h4scbxlgM35tfXlZV9Y/nGOQ71XRExMdfv1Yh4Gdgv/zwMzCGNN9+sjvPdHbghIpZFxF+AN5PiIuJOUvb7RcDnKu3s2FUzs67je+LtJ6CtpwGL64txqAKIiKm5F3wAcK2kc9vxQFqx7Jeq1K8SAd+LiKpDzOo8ZmuB0irA+0hBL+tReWKWNzl21cyssdwTb78pwMclvQ1A0nrAdOATef1RwP21CpC0MfBcvvz8c2DHGpuvQmsE66faKjsi/gP8WdIh+VgDJa0J3AF8VtKgvPxdkt5eq6xsKvAJSQMkDQH2Kqz7b+APwCeBKyWtVkd5ZmbWIO6Jt1NELJT0XeBeSctIl6dPJjVipwP/AMa0UcyewOmSlgJLaJ05rJKXgK0lzSZFptaTf340cKmks4ClwBERMVnS+4AHUngbS4BPA8+1UdZE0i2EFuBxcs67pM1Jl9B3iogXJU0FvkHb06qamVmDeJx4LydpSUQM6ul6NIJjV83M2s+xq2ZmZn2QL6f3Enm+8IFli4/uyl54vq8/pcKqfSLin2XbHgtMzk+om5lZL+BGvJeIiJ27quw8g5kiYnnZMf9JGjNe2m5AYQ7xcscCC4AON+Itzy5m6Bm3d3T3dnEEq5n1B76c3gCSPi3poZzAdml+knuJpO9Lmp3T03bKKWtPSjoo73espF9LmpTTz6o+FJbL+kLh/VhJ/yNpUE5ymyOpRdLBef1QSX+QdDFpXPh7qpS7RNJZ+UrAKEln5lS3BZIuy4lthwMjgfH5HNeQNELSvfn87shPrpuZWTdyI95J+YnvI4HROYFtGWmY2VrAPRExAngR+D/gg6QEtLMKReyUtx8OHKE0n3clv2DFJ9M/DkwAXgUOzYluewE/yj1vSElr10TEDhHxdJVy1wIWRMTOEXE/cGFEvD8itgHWAD6aJzuZBRyVz/EN4KfA4fn8rgS+W+X348Q2M7Mu4svpnbcPMAKYmdvONUjDtl4HJuVtWoDXImKppBZgaGH/O0v3nyXdQkpkW+kR7oh4WNLbJb0T2AD4d0T8vzw2++wcHrMceBetyXBPR8SMNuq/jDSHeMlekr4CrEkKcFkI/KZsny2AbYA78zkPAP5aqfCIuAy4DNIsZm3UxczM2sGNeOcJuDoi/neFhdJp0Tp+bzk5uS0ilksq/t7LG7ZaDd1NpOCXd5B65pB68RsAI/KXhEXA6nldpUS3cq+W7oNLWh24GBgZEc/kWNjVK+wjYGFEjKqjfDMz6yJuxDtvCvBrSedFxHM5wW3tduz/wbzPK8AhwGdrbPsL4HJgfWCPvGwwKf1tqaS9gI3bewIFpQb7+ZzsdjjpiwOkWwKl83oM2EDSqIh4IF8N2DwiFtYq3LGrZmaN5Ua8kyLiEUnfACbnLPGlwEntKOJ+0qQpmwLXR0TVNJScFrc28GxElC5fjwd+I2kWaTa0RztwGqXyX5B0Oeny/yLSRC0l44BLJL1CmvDlcOACSYNJf0c/IV16NzOzbuLEth6Ux16PjIgv9nRduoMT28zM2s+JbWZmZn2QL6f3oIgYR7pM/ab2pKi1R41EuJaOlmlmZj3LjXgdJO0JvB4R07v6WOUpag0st8sS4czMrGe4Ea/PnqSpO7u8Ee/LujN2tZxjWM2sL+rT98Rz9Oijkq6WNF/STZLWlLSPpIdzTOmVkgbm7RdJWj+/HpljUocCJwL/nSNHd5O0oaSJkubln13yPqfmuNIFkk4pq8MVefl4SftKmibpCUk75e3WynWZmet2cI3z2lqtMa/zJW2Wj7OgsM1peZw3kjZVin6dl+NZh+XlX8m/g3mSzsnLhinFwM6WdJ+kLfPyI3L95ynNHV6xHg39AM3MrKb+0BPfAjguIqZJuhI4Ffgv0j3mxyVdA3yeNERqJRGxSNIlwJKI+CGApBuBeyPiUEkDgEGSRgBjgJ1JYSgPSroX+Ddp+NgRwAmkYVufIiWzHQR8jTQ+/OvA7yPis5LWBR6SdFdEVApsORE4PyLGS3oLKTFtwwrblYwHzomIiTnQZRVJ++fj7hwRL+ex6pDS1U6MiCck7UwKf9kbOBP4UEQ8m+tXrR4rkHRCPm8GrLNBjSqamVl79emeePZMREzLr68jxaQ+FRGP52VXA7u3s8y9gZ8BRMSyiFhMapQnRsRLEbEEuAXYLW//VES05FnEFgJTcppbMYJ1P+AMSXOBe0jBKxtVOf4DwNckfRXYOCJeqVbRPK78XRExMdf31Yh4GdgXuCq/JiL+lQNedgEm5HpcCpQmNpkGjJN0PK2NdZv1iIjLImJkRIwcsObgatU0M7MO6A898fYMhH+D1i82leJGa1GNda8VXi8vvF9O62cg4LCIeKytA0XE9flp8wOAOyR9DnicFb+UlepfrV5i5d/NKsALeZKT8mOemHvmBwBzJQ2vVI+I+H1b9Tczs8boD434RqV4UOCTwF3Af0naNCL+CBwN3Ju3XUSazOR3wGGFMl4E1im8n0K+BJ8vp68FTCX1VM8hNZCH5rLrdQfwJUlfioiQtENEPFxpQ0nvBZ6MiAvy6+2A+4C35yFqS4CPApMi4j+S/izpkIj4Vb7/PwCYDJwp6frS5fTcG39K0hERMUGSgO0iYp6kYRHxIOk2wYHAe5TS2srrUbURd+yqmVlj9YfL6X8AjpE0nzQr13mke9cTlGYUWw5ckrf9NnC+pPtIs3uV/AY4tPRgG/Bl0mxfLcBsYOuImEMa8/0Q8CBwRbVGuIrvAKsB8/MDat+pse2RwIJ8yXtL0nSjS0lTnD4I3MaK8atHAyfn38F04B0RMQm4FZiVyzktb3sUcJykeaRL/6UH7M7ND8EtIH1hmVepHu04XzMz66Q+Hbuq9GT5bXlubOthjl01M2s/OXbVzMys7+nT98QjYhHQtL1wSR8Cvl+2+KmIOLQn6mNmZr1Ln76cbm1TipQ9LSI+2tXHGjhksxhyzE+6+jBtcnqbmTUTX043MzPrg9yI92GqHjv74bz8fuBjhe13kjQ9x75Ol7RFXn6fpOGF7aZJ2k7SHvmJ/bl5n7W7/yzNzPovN+J93xbAZRGxHfAfUuzs5cCBpES5dxS2fRTYPSJ2IMWsnp2XXwEcCyBpc2BgRMwnDUs7KYfD7AaslNgm6QRJsyTNWvby4safnZlZP+ZGvO8rj50dSXo47okc/XpdYdvBpPHzC0jj6bfOyycAH5W0GvBZWudAnwb8WNLJwLoR8Ub5wR27ambWddyI933lTy4OrrCs5DvA3Xlc/YHk6Nacr34nKfjl48D1efk5wOeANYAZpRnPzMyse/TpIWYGVI+dHRYRf8rLSgYDz+bXx5aVcwUpue6+iPgXpGlLI6IFaJE0ipTa9ihVOHbVzKyx3BPv+yrFzp4A3J4fbHu6sO0PgO9JmkbZtKIRMZt0T/2qwuJTSnOMk+6H/67rTsPMzMq5J973LY+IE8uWTSL1mleQe+ubFxZ9s/RC0jtJX/omF7b/UmOramZm7eGeuLVJ0mdIE6t8Pc+JbmZmvYB74n1Yo2JnI+IaPEOZmVmv40bcuk3Ls4sZesbtPV2NNzl+1cyanS+n90OSTpb0B0njq6w/SNIZVdYt6dramZlZvdwT75++AOwfEU9VWhkRtwK3dm+VzMysvdwT7yRJn8m55PMkXStpY0lT8rIpkjbK242TdEHOJH9S0uF5+RBJU3P++AJJu9U41oclzcnHmpKXrSfpV/l4MyRtl5ePlXSlpHvy8U7Oyy8B3gvcKum/qxznWEkX5tebSHpA0kxJ3ylsc6iku5QMkfS4pHdUKMuxq2ZmXcSNeCdI2hr4OrB3RGwPfBm4ELgmZ5WPBy4o7DIE2BX4KHBOXvYp4I6cP749MLfKsTYgZZ4flo91RF71beDhfLyvseIDaFsCHwJ2Ar4labU83OwvwF4RcV4dp3k+8LOIeD/wt9LCiJiY35+U6/WtiPhb+c6OXTUz6zpuxDtnb+CmiHgeICeZjSLHkgLXkhrtkl9FxPKIeATYMC+bCYyRNBbYNiJerHKsDwBTS5fAS6lpufxr87LfA2+TVGotb4+I13L9niscsz1GAzcUzqfoS8D/Aq9FxA2YmVm38j3xzhHVc8hLiutfK9uXiJgqaXfgAOBaSefmIV31Hks1jlk83jI6/nlXO8d3AcuBDSWt0tYYcseumpk1lnvinTMF+Likt0G6Pw1MBz6R1x8F3F+rAEkbA89FxOXAz4Edq2z6ALCHpE0KxwKYmo+DpD2B5yPiPx08n0qmseL5lOq9KimC9VOkaNdTG3hMMzOrg3vinRARCyV9F7hX0jLgYeBk4EpJpwP/AMa0UcyewOmSlgJLgM9UOdY/JJ0A3CJpFdLl8Q8CY4Grcjb6y8AxnT6xFX0ZuF7Sl4GbC8u/RpoM5T5Jc4GZkm6PiD80+PhmZlaF0pTSZl1v5MiRMWvWrJ6uhplZU5E0OyJGVlrny+lmZmZNypfTeyFJDwIDyxYfnefubuRxxpAulxdNA+4AtoqIc1beq+N6W+xqvRzPama9lRvxXigidu6m41zFivODFzmxzcysl/Pl9F6oQgrcgZIelPRwTknbMG83VtLVkiZLWiTpY5J+IKlF0iRJq+XtFkn6vqSH8s+meXm1couJbcNyEtxMSWeVstMl7ZnT4G6S9Kik8ZIqDXczM7Mu4ka8l6mSAnc/8IGI2AH4BfCVwi7DSGPMDwauA+6OiG2BV/Lykv9ExE6kRLmf5GW1yi05Hzg/J7b9pWzdDsApwFakKNfRFc7HsatmZl3EjXjvUykF7t3AHZJagNOBrQvb/y4ilgItwABgUl7eAgwtbHdD4d9R+XWtcktGARPy6+vL1j0UEX/OIS9zy45Hrr9jV83Muogb8d6nUjLbT4ELcw/7v4DVC+teA8gN6dJoHTO4nBWfeYgKr2uVW49GJcKZmVkH+H+6vc8UYKKk8yLinzmZbTDwbF7f0TCXI0mTrhxJSn+jznJnAIcBN9Ka3NYhjl01M2ssN+K9TJUUuLHABEnPkhrVTTpQ9MA8dG0V4JN5WT3lngJcJ+l/gNsB39g2M+slnNjWD0haBIws3Wdv575rAq9EREj6BPDJiDi4I/VwYpuZWfvVSmxzT9zaMgK4MA8fewH4bM9Wx8zMStyI9wMRMbQT+94HbN+42piZWaO4Ee8HJI0FlkTED7uo/KHAbRGxTa3tmjV2tcgRrGbWm3iIWQ/Ic3GbmZl1ihvxBpM0VNIfJF0uaWGORF0jR5SeLeleVp50pLTvBpJuzhGnMyWNzss7Fa9adozhOUZ1vqSJkt6ao1XnFLbZTNLs/HqEpHslzZZ0h6QhheXzJD0AnNT436SZmbXFjXjX2Ay4KCK2Jj0Mdlhevm5E7BERP6qy3/nAeTni9DDgisK6zsSrFl0DfDUitiOlun0rIv4ELJY0PG8zBhiXvxz8FDg8IkYAVwLfzdtcBZwcEaOowbGrZmZdx5d1u8ZTETE3v55NaxzpjW3sty+wVWEekXUkrZ1f/y4iluaI1HrjVc8rFi5pMOmLxL150dW0RqpeAYyRdCopEGYnYAtgG+DOXKcBwF8rlHMtsH+lE4qIy4DLAAYO2czjGc3MGsiNeNcojyNdI79+qY39VgFGRcQrxYW5AX0zXlVSe+NV63Ez8C3g98DsnBb3TmBheW9b0rrtLBtwYpuZWaP5cnrvMhn4YulN4fJ2exxZ+PeB4oqIWAz8W9JuedHRwL153avAHcDPaJ1j/DFgA0mjcn1Wk7R1RLxAuvy+a97uqA7U08zMOsk98d7lZOAiSfNJn81U4MR2llEpXrXoGOCSnMT2JOn+d8l44GOkLxNExOuSDgcuyJfQVyXdZ1+Y97tS0sukxt/MzLqZY1f7kM7Eq+b9TwMGR8Q3G1qxzLGrZmbt59hVa5OkiaQn4Pfu6bqYmVl93Ij3AElfB44oWzwhIr5baft6dTJe9dDOHNvMzLqfL6dbtxk4ZLMYcsxPeroafZ6jYc36llqX0/10ur1J0q9yMttCSSfkZcdJejwnzl0u6cK8vGK6nJmZdR9fTreiz0bEvyStAcyUdDvwTWBH4EXSGPJ5edtSutz9kjYiPaH+vp6otJlZf+VG3IpOllS6N/4e8jjyiPgXgKQJwOZ5fcV0uYh4sVhg7tGfADBgnQ26uPpmZv2LG3EDQNKepIZ5VES8LOkeUthLtd51xXS5co5dNTPrOm7ErWQw8O/cgG8JfAC4HNhD0ltJl9MPI2W1Q2u63LmQ0uUKefEVOXbVzKyx/GCblUwCVs1pcd8BZgDPAmcDDwJ3AY8ApanITgZG5ilNH6H9yXJmZtZJ7okbABHxGhVmIpM0KyIuk7QqMJHWSNbnac1pNzOzHuCeuLVlrKS5wALgKeBXPVobMzN7k3viVlNEnNbTdTAzs8rcE+8DJB0k6Yz8elyeeayjZW2cA1/m5tAX3+s2M+ul3BNvcpJWjYhbgVsbURbwV2CXiHhN0iBggaRbI+IvnS2/5dnFDD3j9s4WY93A0a1mzcE98V5C0qclPZR7wJdKGiBpSWH94ZLG5dfjJP1Y0t3A9yUdW4pDzfaVdF+OS/1o3md1SVdJapH0sKS98vJjJU2Q9BtgckS8nh9yAxhI4W9E0iJJZ0t6QNIsSTtKukPSn9xjNzPrfu6J9wKS3kd60nt0RCyVdDFwVBu7bQ7sGxHLJB1btm4osAdpatG7JW0KnAQQEdvmceCTJZXS10YB2xWS2d4D3A5sCpxe1gt/JiJGSToPGAeMBlYHFgKXVDg3J7aZmXURN+K9wz7ACFJeOcAawHNt7DMhIpZVWffLiFgOPCHpSWBLYFfgpwAR8aikp2mNUL2z1IDn9c8A20l6J/ArSTdFxN/z6tJl+xZgUI5ZfVHSq5LWjYgXihVxYpuZWdfx5fTeQcDVETE8/2wREWOBYqO3etk+L9Uor7yxjHyMaiqWlXvgC4HdCotLl9qXF16X3vtLoZlZN/L/dHuHKcCvJZ0XEc9JWg9YG/h7vtT+GHAoKfq0HkdIuhrYBHhv3n8q6RL97/Nl9I3y8h2LO0p6N/DPiHglx62OBn7c6TPEsatmZo3mRrwXiIhHJH2DdJ96FWAp6R72GcBtwDOksJVBdRb5GHAvsCFwYkS8mu+zXyKpBXgDODY/gV6+7/uAH0kq9d5/GBEt5RuZmVnPU4RvU1r3GDlyZMyaNaunq2Fm1lQkzY6IkZXW+Z64mZlZk3IjbmZm1qTciDcxSWMldTrbXNK6kr5QeP9OSTd1tlwzM+tafrCtn8jxrG9UWb0u8AXgYnhzaFmH89erceyqmfVHXRlj7J54k5H0dUmPSboL2CIvu0fSyPx6fUmL8usVIlUlDZI0RdKcHL96cC72HGBYjnw9V9JQSQtyGbXiWm+RNEnSE5J+0M2/CjOzfs898SYiaQTwCWAH0mc3B5jdxm5vRqrmCU4OjYj/SFofmCHpVtJQtm0iYng+ztDC/rXiWofnurwGPCbppzntrVhnx66amXUR98Sby27AxIh4OSL+Q30zlxUjVQWcLWk+cBfwLtJY8lp2Ba6FFNcKFONap0TE4oh4FXgE2Lh854i4LCJGRsTIAWsOrqO6ZmZWL/fEm0+lgf1v0PqFrFY861HABsCIPNHKogrbl6sV11qMXV2G/57MzLqV/6fbXKYC4ySdQ/rsDgQuBRaRJlB5iNoPpA0GnssN+F609pxfJMW8Vjtmm3Gt9XDsqplZY/lyehOJiDnAjcBc4Gbgvrzqh8DnJU0H1q9RxHhgpKRZpIb50VzuP4FpkhZIOrdsn4uBATmu9UZyXGuDTsnMzDrBsavWbRy7ambWfrViV92IW7eR9CLpUnxftz7wfE9Xoov5HPuG/nCO0PznuXFEVBze43vi1p0eq/Ztsi+RNKuvn6fPsW/oD+cIffs8fU/czMysSbkRNzMza1JuxK07XdbTFegm/eE8fY59Q384R+jD5+kH28zMzJqUe+JmZmZNyo24mZlZk3Ijbg0h6cN5itQ/SjqjwnpJuiCvny9px3r37S06eY6L8nSuc3NiXq9UxzluKekBSa9JOq09+/YmnTzPvvJZHpX/TudLmi5p+3r37S06eY5N8Tm2KSL8459O/QADgD8B7wXeAswDtirb5iPA70gTqnwAeLDefXvDT2fOMa9bBKzf0+fRgHN8O/B+4LvAae3Zt7f8dOY8+9hnuQvw1vx6/z7632TFc2yWz7GeH/fErRF2Av4YEU9GxOvAL4CDy7Y5GLgmkhnAupKG1Llvb9CZc2wWbZ5jRDwXETOBpe3dtxfpzHk2i3rOcXpE/Du/nQG8u959e4nOnGOf4UbcGuFdwDOF93/Oy+rZpp59e4POnCOkKWQnS5ot6YQuq2XndOazaJbPETpf1774WR5HuorUkX17SmfOEZrjc2yTY1etESrNOV4+drHaNvXs2xt05hwBRkfEXyS9HbhT0qMRMbWhNey8znwWzfI5Qufr2qc+yzwt8XHAru3dt4d15hyhOT7HNrknbo3wZ+A9hffvBv5S5zb17NsbdOYciYjSv88BE0mXAnubznwWzfI5Qifr2pc+S0nbAVcAB0eakrjufXuBzpxjs3yObXIjbo0wE9hM0iaS3gJ8Ari1bJtbgc/kJ7g/ACyOiL/WuW9v0OFzlLSWpLUBJK0F7Acs6M7K16kzn0WzfI7Qibr2pc9S0kbALcDREfF4e/btJTp8jk30ObbJl9Ot0yLiDUlfBO4gPTF6ZUQslHRiXn8J8FvS09t/BF4GxtTatwdOo6bOnCOwITBREqT/5q6PiEndfAptquccJb0DmAWsAyyXdArpieD/NMPnCJ07T9KUln3iswTOBN4GXJzP542IGNnH/puseI40yX+T9XDsqpmZWZPy5XQzM7Mm5UbczMysSbkRNzMza1JuxM3MzJqUG3EzM7Mm5UbczMysSbkRNzMza1L/HzBSavnOBqKPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importances = pd.Series(rf_clf.feature_importances_, index = X)\n",
    "feature_importances.nlargest(20).plot(kind = 'barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import f1_score, confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rf_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is  0.9138949583232177\n"
     ]
    }
   ],
   "source": [
    "ac = accuracy_score(y_test, y_pred)\n",
    "print('Accuracy is ', ac)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization (Confusion Matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot = True, fmt = 'd')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACC               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.97      0.95     10991\n",
      "           1       0.66      0.45      0.54      1366\n",
      "\n",
      "    accuracy                           0.91     12357\n",
      "   macro avg       0.80      0.71      0.74     12357\n",
      "weighted avg       0.90      0.91      0.91     12357\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('ACC', classification_report(y_test, y_pred))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}