Catboost Damage Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c6c91c8e",
   "metadata": {},
   "source": [
    "# Insurance Company's Accident Damage Prediction Using Catboost\n",
    "In this notebook we are going to predict accident damage using catboost regressor.\n",
    "\n",
    "### About dataset\n",
    "Dataset consists of two parts, train and test. There are 132 features.\n",
    "\n",
    "\n",
    "We have categorical and numerical features. categorical features are coded and we dont have its actuall values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6e30de3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import pickle\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "500e26a6",
   "metadata": {},
   "source": [
    "### Train data\n",
    "loss column is the target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "91656d5e",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.43919</td>\n",
       "      <td>0.338312</td>\n",
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       "      <td>0.611431</td>\n",
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       "      <td>0.315545</td>\n",
       "      <td>0.27320</td>\n",
       "      <td>0.26076</td>\n",
       "      <td>0.32446</td>\n",
       "      <td>0.381398</td>\n",
       "      <td>0.373424</td>\n",
       "      <td>0.195709</td>\n",
       "      <td>0.774425</td>\n",
       "      <td>3005.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
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       "      <td>B</td>\n",
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       "      <td>0.391128</td>\n",
       "      <td>0.31796</td>\n",
       "      <td>0.32128</td>\n",
       "      <td>0.44467</td>\n",
       "      <td>0.327915</td>\n",
       "      <td>0.321570</td>\n",
       "      <td>0.605077</td>\n",
       "      <td>0.602642</td>\n",
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       "      <td>0.247408</td>\n",
       "      <td>0.24564</td>\n",
       "      <td>0.22089</td>\n",
       "      <td>0.21230</td>\n",
       "      <td>0.204687</td>\n",
       "      <td>0.202213</td>\n",
       "      <td>0.246011</td>\n",
       "      <td>0.432606</td>\n",
       "      <td>2763.85</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 132 columns</p>\n",
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       "4  11    A    B    A    B    A    A    A    A    B  ...  0.178193  0.247408   \n",
       "\n",
       "     cont8    cont9   cont10    cont11    cont12    cont13    cont14     loss  \n",
       "0  0.30260  0.67135  0.83510  0.569745  0.594646  0.822493  0.714843  2213.18  \n",
       "1  0.60087  0.35127  0.43919  0.338312  0.366307  0.611431  0.304496  1283.60  \n",
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       "\n",
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     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train = pd.read_csv('train.csv')\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "92ef11f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(188318, 132)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c08d9ee",
   "metadata": {},
   "source": [
    "No Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b2dd65fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1837d3d",
   "metadata": {},
   "source": [
    "### Test data\n",
    "No target here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6a8f841b",
   "metadata": {},
   "outputs": [
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       "   id cat1 cat2 cat3 cat4 cat5 cat6 cat7 cat8 cat9  ...     cont5     cont6  \\\n",
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       "1   6    A    B    A    B    A    A    A    A    B  ...  0.836443  0.482425   \n",
       "2   9    A    B    A    B    B    A    B    A    B  ...  0.718531  0.212308   \n",
       "3  12    A    A    A    A    B    A    A    A    A  ...  0.397069  0.369930   \n",
       "4  15    B    A    A    A    A    B    A    A    A  ...  0.302678  0.398862   \n",
       "\n",
       "      cont7    cont8    cont9   cont10    cont11    cont12    cont13    cont14  \n",
       "0  0.317681  0.61229  0.34365  0.38016  0.377724  0.369858  0.704052  0.392562  \n",
       "1  0.443760  0.71330  0.51890  0.60401  0.689039  0.675759  0.453468  0.208045  \n",
       "2  0.325779  0.29758  0.34365  0.30529  0.245410  0.241676  0.258586  0.297232  \n",
       "3  0.342355  0.40028  0.33237  0.31480  0.348867  0.341872  0.592264  0.555955  \n",
       "4  0.391833  0.23688  0.43731  0.50556  0.359572  0.352251  0.301535  0.825823  \n",
       "\n",
       "[5 rows x 131 columns]"
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     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test = pd.read_csv('test.csv')\n",
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ab74f3d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125546, 131)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66a592fb",
   "metadata": {},
   "source": [
    "No missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "265c03b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "167e0c35",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train['isTrain'] = True\n",
    "data_test['isTrain'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1b23374d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([data_train, data_test], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "efa12ee1",
   "metadata": {},
   "outputs": [
    {
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       "      <th>cont9</th>\n",
       "      <th>cont10</th>\n",
       "      <th>cont11</th>\n",
       "      <th>cont12</th>\n",
       "      <th>cont13</th>\n",
       "      <th>cont14</th>\n",
       "      <th>loss</th>\n",
       "      <th>isTrain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.335060</td>\n",
       "      <td>0.30260</td>\n",
       "      <td>0.67135</td>\n",
       "      <td>0.83510</td>\n",
       "      <td>0.569745</td>\n",
       "      <td>0.594646</td>\n",
       "      <td>0.822493</td>\n",
       "      <td>0.714843</td>\n",
       "      <td>2213.18</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.436585</td>\n",
       "      <td>0.60087</td>\n",
       "      <td>0.35127</td>\n",
       "      <td>0.43919</td>\n",
       "      <td>0.338312</td>\n",
       "      <td>0.366307</td>\n",
       "      <td>0.611431</td>\n",
       "      <td>0.304496</td>\n",
       "      <td>1283.60</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.315545</td>\n",
       "      <td>0.27320</td>\n",
       "      <td>0.26076</td>\n",
       "      <td>0.32446</td>\n",
       "      <td>0.381398</td>\n",
       "      <td>0.373424</td>\n",
       "      <td>0.195709</td>\n",
       "      <td>0.774425</td>\n",
       "      <td>3005.09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.391128</td>\n",
       "      <td>0.31796</td>\n",
       "      <td>0.32128</td>\n",
       "      <td>0.44467</td>\n",
       "      <td>0.327915</td>\n",
       "      <td>0.321570</td>\n",
       "      <td>0.605077</td>\n",
       "      <td>0.602642</td>\n",
       "      <td>939.85</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247408</td>\n",
       "      <td>0.24564</td>\n",
       "      <td>0.22089</td>\n",
       "      <td>0.21230</td>\n",
       "      <td>0.204687</td>\n",
       "      <td>0.202213</td>\n",
       "      <td>0.246011</td>\n",
       "      <td>0.432606</td>\n",
       "      <td>2763.85</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 133 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id cat1 cat2 cat3 cat4 cat5 cat6 cat7 cat8 cat9  ...     cont7    cont8  \\\n",
       "0   1    A    B    A    B    A    A    A    A    B  ...  0.335060  0.30260   \n",
       "1   2    A    B    A    A    A    A    A    A    B  ...  0.436585  0.60087   \n",
       "2   5    A    B    A    A    B    A    A    A    B  ...  0.315545  0.27320   \n",
       "3  10    B    B    A    B    A    A    A    A    B  ...  0.391128  0.31796   \n",
       "4  11    A    B    A    B    A    A    A    A    B  ...  0.247408  0.24564   \n",
       "\n",
       "     cont9   cont10    cont11    cont12    cont13    cont14     loss isTrain  \n",
       "0  0.67135  0.83510  0.569745  0.594646  0.822493  0.714843  2213.18    True  \n",
       "1  0.35127  0.43919  0.338312  0.366307  0.611431  0.304496  1283.60    True  \n",
       "2  0.26076  0.32446  0.381398  0.373424  0.195709  0.774425  3005.09    True  \n",
       "3  0.32128  0.44467  0.327915  0.321570  0.605077  0.602642   939.85    True  \n",
       "4  0.22089  0.21230  0.204687  0.202213  0.246011  0.432606  2763.85    True  \n",
       "\n",
       "[5 rows x 133 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1f4fa4f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(313864, 133)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff0e52f4",
   "metadata": {},
   "source": [
    "Extracting categorical indices for catboost regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c7263184",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a04a46ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_pattern = re.compile(\"^cat([1-9]|[1-9][0-9]|[1-9][0-9][0-9])$\")\n",
    "cont_pattern = re.compile(\"^cont([1-9]|[1-9][0-9]|[1-9][0-9][0-9])$\")\n",
    "cat_col = sorted([cat for cat in data_train.columns if 'cat' in cat], key=lambda s: int(s[3:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "806d287a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cat_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a2d79389",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_index = [i for i in range(0, len(data_train.columns)) if cat_pattern.match(data_train.columns[i])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2ff9a603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cat_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faec8a38",
   "metadata": {},
   "source": [
    "### Visualize target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4687a672",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "sns.histplot(data_train['loss'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26c077fa",
   "metadata": {},
   "source": [
    "The target is skewed and we apply log function to normalize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "adecd897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "sns.histplot(np.log(data_train['loss']))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ad489c0",
   "metadata": {},
   "source": [
    "It is normal now."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8473f647",
   "metadata": {},
   "source": [
    "### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e52a2f1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\xgboost\\compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    }
   ],
   "source": [
    "from catboost import CatBoostRegressor\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "01456c20",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_train.drop(['id', 'loss', 'isTrain'], axis=1)\n",
    "y = np.log(data_train['loss'])\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b544df3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(141238, 130)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bd1b8fe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(141238,)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c95b9cd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "del X\n",
    "del y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f4bc1b6",
   "metadata": {},
   "source": [
    "---\n",
    "# Modeling\n",
    "After testing defferent hyperparameters, we now build our model with the following values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "867b82be",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = CatBoostRegressor(iterations=100, learning_rate=0.1, depth=12, eval_metric='MAE', verbose=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e65c5def",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "66f0213495124c7881116ae87f2a562e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.6321233\ttest: 0.6339025\tbest: 0.6339025 (0)\ttotal: 1.61s\tremaining: 2m 39s\n",
      "10:\tlearn: 0.4921812\ttest: 0.4962813\tbest: 0.4962813 (10)\ttotal: 13.8s\tremaining: 1m 51s\n",
      "20:\tlearn: 0.4475441\ttest: 0.4542978\tbest: 0.4542978 (20)\ttotal: 25.4s\tremaining: 1m 35s\n",
      "30:\tlearn: 0.4309422\ttest: 0.4402376\tbest: 0.4402376 (30)\ttotal: 36.5s\tremaining: 1m 21s\n",
      "40:\tlearn: 0.4224745\ttest: 0.4334991\tbest: 0.4334991 (40)\ttotal: 47.8s\tremaining: 1m 8s\n",
      "50:\tlearn: 0.4167667\ttest: 0.4297962\tbest: 0.4297962 (50)\ttotal: 58.7s\tremaining: 56.4s\n",
      "60:\tlearn: 0.4124589\ttest: 0.4272250\tbest: 0.4272250 (60)\ttotal: 1m 9s\tremaining: 44.6s\n",
      "70:\tlearn: 0.4092618\ttest: 0.4257567\tbest: 0.4257567 (70)\ttotal: 1m 20s\tremaining: 32.9s\n",
      "80:\tlearn: 0.4061605\ttest: 0.4244975\tbest: 0.4244975 (80)\ttotal: 1m 31s\tremaining: 21.5s\n",
      "90:\tlearn: 0.4036346\ttest: 0.4236056\tbest: 0.4236056 (90)\ttotal: 1m 43s\tremaining: 10.2s\n",
      "99:\tlearn: 0.4012561\ttest: 0.4228345\tbest: 0.4228345 (99)\ttotal: 1m 54s\tremaining: 0us\n",
      "\n",
      "bestTest = 0.422834465\n",
      "bestIteration = 99\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x1c70cb9d9d0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, np.asarray(cat_index) - 1, eval_set=(X_test, y_test), plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "799d9117",
   "metadata": {},
   "source": [
    "Best score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0033089d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4228344650030103"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best = model.best_score_.get('validation').get('MAE')\n",
    "best"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5240153a",
   "metadata": {},
   "source": [
    "Actual value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7a696084",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1.5262816220301358\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', np.exp(best))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b34356a4",
   "metadata": {},
   "source": [
    "---\n",
    "# Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8af00967",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test.drop(['id'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3dfb530b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ee97c02a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = np.exp(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "89bc0a10",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test['loss'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c96f9925",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cat1</th>\n",
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       "      <th>...</th>\n",
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       "      <th>cont10</th>\n",
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       "      <th>cont12</th>\n",
       "      <th>cont13</th>\n",
       "      <th>cont14</th>\n",
       "      <th>isTrain</th>\n",
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       "      <td>0.369858</td>\n",
       "      <td>0.704052</td>\n",
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       "      <td>0.71330</td>\n",
       "      <td>0.51890</td>\n",
       "      <td>0.60401</td>\n",
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       "      <td>0.675759</td>\n",
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       "      <td>0.29758</td>\n",
       "      <td>0.34365</td>\n",
       "      <td>0.30529</td>\n",
       "      <td>0.245410</td>\n",
       "      <td>0.241676</td>\n",
       "      <td>0.258586</td>\n",
       "      <td>0.297232</td>\n",
       "      <td>False</td>\n",
       "      <td>7337.218584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
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       "      <td>0.40028</td>\n",
       "      <td>0.33237</td>\n",
       "      <td>0.31480</td>\n",
       "      <td>0.348867</td>\n",
       "      <td>0.341872</td>\n",
       "      <td>0.592264</td>\n",
       "      <td>0.555955</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.23688</td>\n",
       "      <td>0.43731</td>\n",
       "      <td>0.50556</td>\n",
       "      <td>0.359572</td>\n",
       "      <td>0.352251</td>\n",
       "      <td>0.301535</td>\n",
       "      <td>0.825823</td>\n",
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       "<p>5 rows × 132 columns</p>\n",
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       "3    A    A    A    A    B    A    A    A    A     A  ...  0.342355  0.40028   \n",
       "4    B    A    A    A    A    B    A    A    A     A  ...  0.391833  0.23688   \n",
       "\n",
       "     cont9   cont10    cont11    cont12    cont13    cont14 isTrain  \\\n",
       "0  0.34365  0.38016  0.377724  0.369858  0.704052  0.392562   False   \n",
       "1  0.51890  0.60401  0.689039  0.675759  0.453468  0.208045   False   \n",
       "2  0.34365  0.30529  0.245410  0.241676  0.258586  0.297232   False   \n",
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       "\n",
       "          loss  \n",
       "0  1558.910027  \n",
       "1  1949.891908  \n",
       "2  7337.218584  \n",
       "3  5534.420985  \n",
       "4   849.462900  \n",
       "\n",
       "[5 rows x 132 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a6719bd",
   "metadata": {},
   "source": [
    "Saving new dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "831a6ccb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test.to_csv('test_predictions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "969bc3a3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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