Netflix_appetency_consumer_classification.ipynb

{
 "cells": [
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "3e42b06e",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a7a65c0",
   "metadata": {},
   "source": [
    "## OBJECTIVE:-\n",
    "   - Classify consumers according to their appetite to subscribe to Netflix.\n",
    "## DATA:-\n",
    "\n",
    "    For reasons of confidentiality, the data is anonymized and augmented.\n",
    "\n",
    "        - train.csv - the training set. it consists of an id column, the customers features, and a target column: target.\n",
    "        - test.csv - the test set. it consists of everything in train.csv except target.\n",
    "        - sample_submission.csv - a sample submission file in the correct format target=1 means that the customer subscribes               to Netflix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e782281",
   "metadata": {},
   "source": [
    "## IMPORTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7ed7f972",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2022-03-28T17:45:52.948894Z",
     "iopub.status.busy": "2022-03-28T17:45:52.947513Z",
     "iopub.status.idle": "2022-03-28T17:45:55.433887Z",
     "shell.execute_reply": "2022-03-28T17:45:55.432912Z",
     "shell.execute_reply.started": "2022-03-28T17:42:31.228625Z"
    },
    "papermill": {
     "duration": 2.554501,
     "end_time": "2022-03-28T17:45:55.434075",
     "exception": false,
     "start_time": "2022-03-28T17:45:52.879574",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from catboost import CatBoostClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import cross_val_score, KFold, RepeatedStratifiedKFold,StratifiedKFold,cross_val_predict\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e077e674",
   "metadata": {},
   "source": [
    "- train.csv - the training set. it consists of an id column, the customers features, and a target column: target.\n",
    "- test.csv - the test set. it consists of everything in train.csv except target.\n",
    "- sample_submission.csv - a sample submission file in the correct format target=1 means that the customer subscribes to Netflix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3319aadb",
   "metadata": {},
   "source": [
    "##### IMPORTING THE DATASET "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "46b8ba5c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:45:55.560994Z",
     "iopub.status.busy": "2022-03-28T17:45:55.560309Z",
     "iopub.status.idle": "2022-03-28T17:46:03.441593Z",
     "shell.execute_reply": "2022-03-28T17:46:03.441006Z",
     "shell.execute_reply.started": "2022-03-28T17:42:33.913403Z"
    },
    "papermill": {
     "duration": 7.946307,
     "end_time": "2022-03-28T17:46:03.441747",
     "exception": false,
     "start_time": "2022-03-28T17:45:55.495440",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train=pd.read_csv('train.csv')\n",
    "df_test=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1734203f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:46:03.573892Z",
     "iopub.status.busy": "2022-03-28T17:46:03.572246Z",
     "iopub.status.idle": "2022-03-28T17:46:03.577311Z",
     "shell.execute_reply": "2022-03-28T17:46:03.576766Z",
     "shell.execute_reply.started": "2022-03-28T17:42:41.902935Z"
    },
    "papermill": {
     "duration": 0.076493,
     "end_time": "2022-03-28T17:46:03.577465",
     "exception": false,
     "start_time": "2022-03-28T17:46:03.500972",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%config Completer.use_jedi = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c877cfb5",
   "metadata": {},
   "source": [
    "###### BASIC ANALYSIS"
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   "id": "e97842a6",
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       "   id  target feature_0 feature_1 feature_2 feature_3 feature_4  feature_5  \\\n",
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       "   id feature_0 feature_1 feature_2 feature_3 feature_4  feature_5  feature_6  \\\n",
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       "      <td>27792.000000</td>\n",
       "      <td>25424.000000</td>\n",
       "      <td>26389.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>70000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50025.281314</td>\n",
       "      <td>0.298186</td>\n",
       "      <td>40.124969</td>\n",
       "      <td>39.975888</td>\n",
       "      <td>38.937569</td>\n",
       "      <td>32.120109</td>\n",
       "      <td>31.533970</td>\n",
       "      <td>30.517150</td>\n",
       "      <td>2.891956</td>\n",
       "      <td>2.847829</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.138300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.138300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.138300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.138300</td>\n",
       "      <td>-3.138300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.138300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>28878.675532</td>\n",
       "      <td>0.457465</td>\n",
       "      <td>21.415242</td>\n",
       "      <td>20.591512</td>\n",
       "      <td>20.364727</td>\n",
       "      <td>13.766195</td>\n",
       "      <td>13.330736</td>\n",
       "      <td>13.692785</td>\n",
       "      <td>2.294987</td>\n",
       "      <td>2.267976</td>\n",
       "      <td>...</td>\n",
       "      <td>17.344944</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.344944</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.344944</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.344944</td>\n",
       "      <td>17.344944</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.344944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-217.460000</td>\n",
       "      <td>-89.690000</td>\n",
       "      <td>-66.497000</td>\n",
       "      <td>-219.180000</td>\n",
       "      <td>-89.690000</td>\n",
       "      <td>-49.390000</td>\n",
       "      <td>-30.850000</td>\n",
       "      <td>-19.813333</td>\n",
       "      <td>...</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24999.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32.470000</td>\n",
       "      <td>32.470000</td>\n",
       "      <td>31.923333</td>\n",
       "      <td>30.750000</td>\n",
       "      <td>28.600000</td>\n",
       "      <td>27.215000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50030.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.480000</td>\n",
       "      <td>37.560000</td>\n",
       "      <td>37.030667</td>\n",
       "      <td>34.160000</td>\n",
       "      <td>33.320000</td>\n",
       "      <td>31.710000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75027.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>48.062750</td>\n",
       "      <td>48.490000</td>\n",
       "      <td>47.726458</td>\n",
       "      <td>35.830000</td>\n",
       "      <td>35.830000</td>\n",
       "      <td>35.830000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99999.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>403.880000</td>\n",
       "      <td>639.756667</td>\n",
       "      <td>511.091667</td>\n",
       "      <td>148.360000</td>\n",
       "      <td>136.030000</td>\n",
       "      <td>136.030000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>53.556667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 417 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id        target     feature_5     feature_6     feature_7  \\\n",
       "count  70000.000000  70000.000000  25424.000000  26389.000000  27792.000000   \n",
       "mean   50025.281314      0.298186     40.124969     39.975888     38.937569   \n",
       "std    28878.675532      0.457465     21.415242     20.591512     20.364727   \n",
       "min        0.000000      0.000000   -217.460000    -89.690000    -66.497000   \n",
       "25%    24999.750000      0.000000     32.470000     32.470000     31.923333   \n",
       "50%    50030.500000      0.000000     37.480000     37.560000     37.030667   \n",
       "75%    75027.250000      1.000000     48.062750     48.490000     47.726458   \n",
       "max    99999.000000      1.000000    403.880000    639.756667    511.091667   \n",
       "\n",
       "          feature_8     feature_9    feature_10    feature_11    feature_12  \\\n",
       "count  25424.000000  26389.000000  27792.000000  25424.000000  26389.000000   \n",
       "mean      32.120109     31.533970     30.517150      2.891956      2.847829   \n",
       "std       13.766195     13.330736     13.692785      2.294987      2.267976   \n",
       "min     -219.180000    -89.690000    -49.390000    -30.850000    -19.813333   \n",
       "25%       30.750000     28.600000     27.215000      2.500000      2.500000   \n",
       "50%       34.160000     33.320000     31.710000      2.500000      2.500000   \n",
       "75%       35.830000     35.830000     35.830000      2.500000      2.500000   \n",
       "max      148.360000    136.030000    136.030000     47.000000     53.556667   \n",
       "\n",
       "       ...   feature_497  feature_498   feature_499  feature_500  \\\n",
       "count  ...  70000.000000      70000.0  70000.000000      70000.0   \n",
       "mean   ...     -3.138300          0.0     -3.138300          0.0   \n",
       "std    ...     17.344944          0.0     17.344944          0.0   \n",
       "min    ...    -99.000000          0.0    -99.000000          0.0   \n",
       "25%    ...      0.000000          0.0      0.000000          0.0   \n",
       "50%    ...      0.000000          0.0      0.000000          0.0   \n",
       "75%    ...      0.000000          0.0      0.000000          0.0   \n",
       "max    ...      0.000000          0.0      0.000000          0.0   \n",
       "\n",
       "        feature_501  feature_502   feature_503   feature_504  feature_505  \\\n",
       "count  70000.000000      70000.0  70000.000000  70000.000000      70000.0   \n",
       "mean       3.138300          0.0      3.138300     -3.138300          0.0   \n",
       "std       17.344944          0.0     17.344944     17.344944          0.0   \n",
       "min        0.000000          0.0      0.000000    -99.000000          0.0   \n",
       "25%        0.000000          0.0      0.000000      0.000000          0.0   \n",
       "50%        0.000000          0.0      0.000000      0.000000          0.0   \n",
       "75%        0.000000          0.0      0.000000      0.000000          0.0   \n",
       "max       99.000000          0.0     99.000000      0.000000          0.0   \n",
       "\n",
       "        feature_506  \n",
       "count  70000.000000  \n",
       "mean       3.138300  \n",
       "std       17.344944  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max       99.000000  \n",
       "\n",
       "[8 rows x 417 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33969592",
   "metadata": {},
   "source": [
    "###### FINDING MISSING DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c43c7b4b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:46:05.860757Z",
     "iopub.status.busy": "2022-03-28T17:46:05.859750Z",
     "iopub.status.idle": "2022-03-28T17:46:05.862451Z",
     "shell.execute_reply": "2022-03-28T17:46:05.862921Z",
     "shell.execute_reply.started": "2022-03-28T17:42:43.730288Z"
    },
    "papermill": {
     "duration": 0.071563,
     "end_time": "2022-03-28T17:46:05.863093",
     "exception": false,
     "start_time": "2022-03-28T17:46:05.791530",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_missings(df):\n",
    "    labels,values = list(),list()\n",
    "    if df.isna().sum().sum()>0:\n",
    "        for column in df.columns:\n",
    "            if df[column].isnull().sum():\n",
    "                labels.append(column)\n",
    "                values.append((df[column].isnull().sum() / len(df[column]))*100)\n",
    "        missings=pd.DataFrame({'Feature':labels,'MissingPercent':values }).sort_values(by='MissingPercent',ascending=False)\n",
    "        return missings\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1ebe1418",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:46:05.993327Z",
     "iopub.status.busy": "2022-03-28T17:46:05.991994Z",
     "iopub.status.idle": "2022-03-28T17:46:07.642797Z",
     "shell.execute_reply": "2022-03-28T17:46:07.643359Z",
     "shell.execute_reply.started": "2022-03-28T17:42:43.739867Z"
    },
    "papermill": {
     "duration": 1.718555,
     "end_time": "2022-03-28T17:46:07.643542",
     "exception": false,
     "start_time": "2022-03-28T17:46:05.924987",
     "status": "completed"
    },
    "tags": []
   },
   "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>Feature</th>\n",
       "      <th>MissingPercent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>feature_193</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>feature_196</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>feature_197</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>feature_198</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>feature_83</td>\n",
       "      <td>98.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Feature  MissingPercent\n",
       "52  feature_193          100.00\n",
       "55  feature_196          100.00\n",
       "56  feature_197          100.00\n",
       "57  feature_198          100.00\n",
       "21   feature_83           98.32"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Missings_df=get_missings(df_train)\n",
    "Missings_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3bb56d7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:46:07.779600Z",
     "iopub.status.busy": "2022-03-28T17:46:07.778550Z",
     "iopub.status.idle": "2022-03-28T17:46:08.108508Z",
     "shell.execute_reply": "2022-03-28T17:46:08.107603Z",
     "shell.execute_reply.started": "2022-03-28T17:42:45.427168Z"
    },
    "papermill": {
     "duration": 0.404086,
     "end_time": "2022-03-28T17:46:08.108663",
     "exception": false,
     "start_time": "2022-03-28T17:46:07.704577",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'The number of features')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Fx8fr7bff1sqVK7V7926tX7/ekYcHAABwSQ4LYKdPn1ZiYqIGDRokScrIyFC9evVUt25deXl5KSIiQunp6Y46PAAAgMvyclTDY8eOVWxsrI4fPy5JOnHihMxms217QECAcnJyyt2uv7/vdauxLN7eDjstNwSz2c/ZJVzEFWsC/eKK6BPXRL+4Jmf2i0OSxpIlS1SzZk0FBwdr6dKlkqSSkhKZTCbbPlartdTylTp5MlclJdbrVutfmc1+Kiwsclj7NwKL5Tdnl1CK2ezncjWBfnFF9Ilrol9ck6P7xcPDdNmLRg4JYCtXrpTFYlH37t115swZ5eXlKTs7W56enrZ9LBaLAgICHHF4AAAAl+aQADZv3jzbz0uXLtXWrVs1btw4hYaGKisrS3Xq1FFaWpp69uzpiMMDAAC4NMMmO/n4+GjKlCmKiYlRfn6+QkJC1LlzZ6MODwAA4DIcHsCioqIUFRUlSQoODlZKSoqjDwkAAODSuBM+AACAwQhgAAAABiOAAQAAGIwABgAAYDACGAAAgMEIYAAAAAYjgAEAABiMAAYAAGAwAhgAAIDBCGAAAAAGI4ABAAAYjAAGAABgMAIYAACAwQhgAAAABiOAAQAAGIwABgAAYDACGAAAgMEIYAAAAAYjgAEAABiMAAYAAGAwAhgAAIDBCGAAAAAGI4ABAAAYjAAGAABgMAIYAACAwQhgAAAABiOAAQAAGIwABgAAYDC7AezcuXP67rvvJEkLFy5UfHy8jh075ui6AAAA3JbdADZy5EitWbNGGRkZev/991WzZk2NGTPGiNoAAADckt0AduTIEf3P//yPvvjiC/Xo0UMxMTE6ffq0AaUBAAC4J7sBrKioSJK0ceNGtW7dWsXFxcrLy3N4YQAAAO7Ky94OLVq0UFhYmDw9PdWyZUs9/fTTevjhh42oDQAAwC3ZDWBjxozRjh07FBgYKA8PDw0YMEBt27Y1ojYAAAC3ZHcI0tPTU7/88ovef/99nTt3Trm5ufLw4O4VAAAAV8tuknr33Xe1cOFCpaen6/z580pKStJbb71lRG0AAABuyW4AW7Fihd577z1VrlxZt912mxYvXqy0tDQjagMAAHBLdgOYl5eXKlWqZFu+5ZZb5OVld+oYAAAAymA3SdWsWVPr1q2TyWRSQUGB5syZo9q1axtRGwAAgFu6om9BDh8+XD/88IOaN2+uoKAgvfHGG0bUBgAA4JbsBrBdu3bpgw8+0Llz51RcXCxfX18j6gIAAHBbdueAJSYmSpIqV65M+AIAALgO7F4Ba9Sokf71r3/pgQceUJUqVWzrmzRp4tDCAAAA3JXdALZz507t3LlTS5Yssa0zmUxas2aNQwsDAABwV3YD2Nq1a42oAwAAoMKwG8DmzZt3yfXPPPPMdS8GAACgIrAbwH788UfbzwUFBdq2bZuCg4MdWhQAAIA7sxvAJk+eXGo5JydHo0aNclhBAAAA7s7ubSj+qnr16srOzr6ifWfMmKGwsDCFh4fbhjI3b96siIgIhYaG2m5xAQAAUJGUaw6Y1WrV7t275e/vb7fhrVu36quvvlJKSoqKiooUFham4OBgxcfH66OPPlLNmjU1cOBArV+/XiEhIdf2LgAAAG4g5ZoDJv3xbMgRI0bYbfjBBx/Uhx9+KC8vL+Xk5Ki4uFhnz55VvXr1VLduXUlSRESE0tPTCWAAAKBCsRvAOnTooI4dO5Zal5ycrMjISLuNe3t7a+bMmZo7d646d+6sEydOyGw227YHBAQoJyen/FUDAADcwMoMYGvXrlVRUZGmTp0qq9Uqq9UqSSoqKtKsWbOuKIBJ0pAhQ/Tcc89p0KBByszMlMlksm2zWq2llq+Ev7/jH4fk7W03l7o1s9nP2SVcxBVrAv3iiugT10S/uCZn9kuZSWPv3r366quvdPLkSX344Yf/fYGXl/r162e34QMHDqigoED33HOPKleurNDQUKWnp8vT09O2j8ViUUBAQLkKPnkyVyUl1nK9pjzMZj8VFhY5rP0bgcXym7NLKMVs9nO5mkC/uCL6xDXRL67J0f3i4WG67EWjMgPY4MGDNXjwYC1YsEB9+vQp94GPHj2qmTNnauHChZKkNWvWKDo6WlOnTlVWVpbq1KmjtLQ09ezZs9xtAwAA3MjsjrU9/vjj+vzzz/X7779LkoqLi3X48GHFxsZe9nUhISHKyMhQZGSkPD09FRoaqvDwcFWrVk0xMTHKz89XSEiIOnfufH3eCQAAwA3CbgCLjY3VkSNHZLFYdO+992rnzp168MEHr6jxmJgYxcTElFoXHByslJSUq6sWAADADdi9EevevXu1dOlSdejQQfHx8Vq4cKHOnDljRG0AAABuyW4ACwgIkJeXl+688079+OOPatiwoX77jcmEAAAAV8tuAKtSpYpSU1PVuHFjrVq1Sj/88IPy8vKMqA0AAMAt2Q1gY8eO1d69e9WmTRt5eHiob9++GjBggBG1AQAAuCW7k/DvvPNODR8+XGfPntWbb75pQEkAAADuze4VsIMHDyosLEzh4eHKyclRly5ddODAASNqAwAAcEt2A9jEiRM1atQo+fv7q3r16urbt6/Gjh1rRG0AAABuyW4AO336tNq0aWNb7tOnj3Jzcx1aFAAAgDuzG8AkKT8/3/bQbIvFopKSEocWBQAA4M7sTsLv3bu3BgwYoJMnT+qNN97QihUr9OyzzxpRGwAAgFsqM4AdO3ZMtWrV0uOPP64777xT69atU1FRkSZMmFBqSBIAAADlU2YAi4mJ0aeffqphw4Zp2rRpatWqlZF1AQAAuK0yA9jZs2c1ZswYbdq0SRMnTrxo++jRox1aGAAAgLsqM4DNmjVLa9askYeHh6pWrWpgSQAAAO6tzADWuHFjNW7cWHfccYciIiKMrAkAAMCt2b0NBeELAADg+rqi+4ABAADg+ikzgP3nP/+RJBUUFBhWDAAAQEVQZgCbMWOGJKlXr16GFQMAAFARlDkJ/+abb1anTp2Uk5NzyXlgqampDi0MAADAXZUZwN5//33t3btXo0aN0pgxY4ysCQAAwK2VGcB8fX3VqlUrzZ49WwEBAfr+++9VVFSkZs2aydfX18gaAQAA3Irdh3H/9ttvevLJJ3X77beruLhYOTk5euedd9SyZUsj6gMAAHA7dgNYQkKCpk+frtatW0uStmzZoilTpmjx4sUOLw4AAMAd2b0P2O+//24LX5IUHBysc+fOObQoAAAAd2Y3gJlMJmVnZ9uWjx49Kk9PT4cWBQAA4M7sDkEOHjxYvXr1UnBwsEwmkzZu3KhXX33ViNoAAADckt0A1rFjR9WvX19fffWVSkpKNHDgQDVo0MCI2gAAANyS3QAmSfXr11f9+vUdXQsAAECFwMO4AQAADEYAAwAAMJjdADZ8+HAj6gAAAKgw7AawvXv3ymq1GlELAABAhWB3En5AQIDCw8MVFBSkm2++2bZ+9OjRDi0MAADAXdkNYC1atFCLFi2MqAUAAKBCsBvAXnrpJZ0/f15ZWVlq2LCh8vPzVblyZSNqAwAAcEt254Dt3LlTHTt21MCBA3XixAn9/e9/1/bt242oDQAAwC3ZDWAJCQmaP3++qlatqho1amjq1Kl6/fXXjagNAADALdkNYOfPn9fdd99tWw4JCVFxcbFDiwIAAHBndgOYl5eXzpw5I5PJJEk6ePCgw4sCAABwZ3Yn4b/wwgvq27evLBaLXn75ZW3atEnjx483ojYAAAC3ZDeAtWvXTvXr19emTZtUUlKiwYMHq0GDBkbUBgAA4Jau6FmQRUVFKikpkZeXl7y87GY2AAAAXIbdAPbpp5/qqaee0q5du/TNN9+oT58+Wr16tRG1AQAAuCW7l7Pmz5+vZcuWKSAgQJJ07NgxDRw4UJ06dXJ4cQAAAO7I7hUwb29vW/iSpFq1asnb29uhRQEAALizMq+Aff/995KkwMBAjR8/Xr169ZKnp6eWLl2qli1bGlYgAACAuykzgMXExJRaXrdune1nk8mk0aNHO6woAAAAd1ZmAFu7dq2RdQAAAFQYdifhWywWLVu2TKdPny61fvjw4XYbT0pK0qpVqyT98Qij4cOHa/PmzZo8ebLy8/PVpUsXxcbGXl3lAAAANyi7k/BfeOEFZWRkyGq1lvpnz+bNm7Vx40YtW7ZMycnJ+v7775WWlqb4+Hi9/fbbWrlypXbv3q3169dflzcCAABwo7B7BaywsFBJSUnlbthsNisuLk6VKlWSJDVo0ECZmZmqV6+e6tatK0mKiIhQenq6QkJCyt0+AADAjcpuAGvSpIl+/PFHNWrUqFwNN2zY0PZzZmamVq1apb59+8psNtvWBwQEKCcnp1zt+vv7lmv/q+HtXbHv9m82+zm7hIu4Yk2gX1wRfeKa6BfX5Mx+sZs0WrZsqcjISJnN5lKPIVqzZs0VHWD//v0aOHCghg8fLk9PT2VmZtq2Wa1WmUymchV88mSuSkrsD4FeLbPZT4WFRQ5r/0Zgsfzm7BJKMZv9XK4m0C+uiD5xTfSLa3J0v3h4mC570chuAJszZ46mT5+uO+64o9wH//bbbzVkyBDFx8crPDxcW7dulcVisW23WCylbvIKAABQEdgNYLfccovCwsLK3fDx48c1ePBgJSYmKjg4WJIUFBSkQ4cOKSsrS3Xq1FFaWpp69uxZ/qoBAABuYHYDWOvWrZWQkKDQ0FDbhHrpj7lhlzNnzhzl5+drypQptnXR0dGaMmWKYmJilJ+fr5CQEHXu3PkaygcAALjx2A1gqampkqTVq1fb1plMJrtzwEaPHl3m3fJTUlLKUyMAAIBbsRvAuCM+AADA9WU3gM2bN++S65955pnrXgwAAEBFYDeA/fjjj7afCwoKtG3bNtukegAAAJSf3QA2efLkUss5OTkaNWqUwwoCAABwd3afBflX1atXV3Z2tiNqAQAAqBDKNQfMarVq9+7d8vf3d2hRAAAA7qxcc8AkqWbNmho+fLjDCgIAAHB35Z4DBgAAgGtjN4Dt2LFD//znP3XmzBlZrf99CPaFG7QCAACgfOwGsLFjxyoqKkr33nuvTCaTETUBAAC4NbsBzMvLi5uuAgAAXEd2b0PRsGFD/fDDD0bUAgAAUCHYvQJ25MgR9ezZU7Vq1ZKPj49tPXPAAAAAro7dABYbG2tEHQAAABWG3QD24IMPGlEHAABAhVHuRxEBAADg2hDAAAAADHbFAezs2bOOrAMAAKDCsBvADh48qLCwMIWHhysnJ0ddunTRgQMHjKgNAADALdkNYBMnTtSoUaPk7++v6tWrq2/fvho7dqwRtQEAALgluwHs9OnTatOmjW25T58+ys3NdWhRAAAA7uyK5oDl5+fbngNpsVhUUlLi0KIAAADcmd37gD3xxBMaMGCATp48qTfeeEMrVqzQs88+a0RtAAAAbsluAHvsscd0xx13aP369SoqKtKECRNKDUkCAACgfOwGMEm6//77FRgYKKvVKumPeWFVq1Z1ZF0AAABuy24AW7BggRISElRYWChJslqtMplM2rt3r8OLAwAAcEd2A9jcuXP18ccf65577jGiHgAAALdn91uQt956K+ELAADgOiozgJ0+fVqnT59W8+bNNX/+fP3yyy+2dadPnzawRAAAAPdS5hBk69atZTKZbBPvp0yZYtvGHDAAAICrV2YA27lzp3x8fIysBQAAoEIocwgyOjrayDoAAAAqjDID2IWhRwAAAFxfZQ5B5ufna8+ePWUGsSZNmjisKAAAAHdWZgA7cuSIYmJiLhnATCaT1qxZ49DCAAAA3FWZAezuu+9WcnKygaUAAABUDHZvxAoAAIDrq8wA9sADDxhZBwAAQIVRZgAbPXq0kXUAAABUGAxBAgAAGIwABgAAYDACGAAAgMEIYAAAAAYjgAEAABiMAAYAAGAwAhgAAIDBynwUEQBUFLfcWlk+ldzj49Bs9ivX/vkFRTp75pyDqgFQFvf4xAGAa+BTyUvDZqx3dhnXzNvbS4WFReV6zbShIQ6qBsDlOHQIMjc3V127dtXRo0clSZs3b1ZERIRCQ0OVmJjoyEMDAAC4LIcFsJ07d6p3797KzMyUJJ0/f17x8fF6++23tXLlSu3evVvr19/4/8cJAABQXg4LYIsXL9arr76qgIAASVJGRobq1aununXrysvLSxEREUpPT3fU4QEAAFyWw+aAvf7666WWT5w4IbPZbFsOCAhQTk6Oow4PAADgsgybhF9SUiKTyWRbtlqtpZavlL+/7/Us65K8vSv2dxPK+y0qI7hiTXCvfnGX3/ureR/u1I+uinPsmpzZL4Z94tSoUUMWi8W2bLFYbMOT5XHyZK5KSqzXs7RSzGa/cn+LyN1YLL85u4RSzGY/l6sJ7tUv7vJ7fzXfgpRc73fe3bjT74o7cXS/eHiYLnvRyLAbsQYFBenQoUPKyspScXGx0tLS1LZtW6MODwAA4DIMuwLm4+OjKVOmKCYmRvn5+QoJCVHnzp2NOjwAAIDLcHgAW7t2re3n4OBgpaSkOPqQAAAALo1nQQIAABiMAAYAAGAwAhgAAIDBCGAAAAAGI4ABAAAYjAAGAABgMAIYAACAwQhgAAAABiOAAQAAGIwABgAAYDACGAAAgMEIYAAAAAYjgAEAABiMAAYAAGAwAhgAAIDBCGAAAAAGI4ABAAAYjAAGAABgMC9nFwAAcJ7CohKZzX7OLsNp8guKdPbMOWeXgQqIAAYAFZi3l4eGzVjv7DKcZtrQEGeXgAqKIUgAAACDEcAAAAAMRgADAAAwGHPAAACogG65tbJ8KlXcGFBYVOLU41fcMw8AQAXmU8mLL2A4EUOQAAAABiOAAQAAGIwABgAAYDACGAAAgMGYhA9A0tV9I6oiP8IGAK4FAQyApPJ/I8rb20uFhUUOrMg4zv42FICKhyFIAAAAgxHAAAAADEYAAwAAMBgBDAAAwGAEMAAAAIMRwAAAAAxGAAMAADAYAQwAAMBgBDAAAACDEcAAAAAMRgADAAAwGAEMAADAYAQwAAAAgxHAAAAADEYAAwAAMBgBDAAAwGBezi4ArqWwqERms5+zy7iIETUVFhXL28vT4ccBAIAAhlK8vTw0bMZ6Z5dRire3lwoLixx+nGlDQ1zuvRtp2tAQZ5cAABWGU4YgU1NTFRYWptDQUC1YsMAZJQAAADiN4VfAcnJylJiYqKVLl6pSpUqKjo7WQw89pLvvvtvoUgAAAJzC8AC2efNmtW7dWlWrVpUkderUSenp6XrppZeu6PUeHiYHVveH2/x8HH4MV+Zq79/L20tFhcbMzXK192608rx/I/vFCO7Q91fbJ+7w3q+FEX9XjDjG1aDvHdcv9to2Wa1Wq8OOfgmzZ89WXl6eYmNjJUlLlixRRkaGJkyYYGQZAAAATmP4HLCSkhKZTP9NhVartdQyAACAuzM8gNWoUUMWi8W2bLFYFBAQYHQZAAAATmN4AHv44Ye1ZcsWnTp1SufOndNnn32mtm3bGl0GAACA0xg+Cb969eqKjY3VU089pcLCQj322GNq1qyZ0WUAAAA4jeGT8AEAACo6ngUJAABgMAIYAACAwQhgAAAABiOAAQAAGIwA9v94QLhrSEpKUnh4uMLDwzV16lRJfzy+KiIiQqGhoUpMTHRyhRVbQkKC4uLiJNEvrmDt2rWKiopSly5dNHHiREn0i7MtX77c9hmWkJAgiT5xptzcXHXt2lVHjx6VVHZf7N27V1FRUerUqZNGjRqloqIixxdnhfXnn3+2tmvXzvrrr79af//9d2tERIR1//79zi6rwtm0aZO1V69e1vz8fGtBQYH1qaeesqamplpDQkKshw8fthYWFlr79+9vXbdunbNLrZA2b95sfeihh6wjRoywnjt3jn5xssOHD1sfeeQR6/Hjx60FBQXW3r17W9etW0e/OFFeXp61VatW1pMnT1oLCwutjz32mHXNmjX0iZN899131q5du1qbNGliPXLkyGU/t8LDw607duywWq1W68iRI60LFixweH1cAVPpB4RXqVLF9oBwGMtsNisuLk6VKlWSt7e3GjRooMzMTNWrV09169aVl5eXIiIi6BsnOH36tBITEzVo0CBJUkZGBv3iZJ9//rnCwsJUo0YNeXt7KzExUZUrV6ZfnKi4uFglJSU6d+6cioqKVFRUJF9fX/rESRYvXqxXX33V9rSdsj63srOzdf78eTVv3lySFBUVZUgfGX4jVld04sQJmc1m23JAQIAyMjKcWFHF1LBhQ9vPmZmZWrVqlfr27XtR3+Tk5DijvApt7Nixio2N1fHjxyVd+neGfjFWVlaWvL29NWjQIB0/flx///vf1bBhQ/rFiXx9fTV06FB16dJFlStXVqtWrfhdcaLXX3+91HJZffHX9Waz2ZA+4gqYeEC4q9m/f7/69++v4cOHq27duvSNky1ZskQ1a9ZUcHCwbR2/M85XXFysLVu2aNKkSfr444+VkZGhI0eO0C9OtG/fPn366af64osvtGHDBnl4eCgzM5M+cRFlfW456/OMK2D64wHh33zzjW2ZB4Q7z7fffqshQ4YoPj5e4eHh2rp1Kw9vd7KVK1fKYrGoe/fuOnPmjPLy8pSdnS1PT0/bPvSL8W6//XYFBwerWrVqkqSOHTsqPT2dfnGijRs3Kjg4WP7+/pL+GMqaM2cOfeIiatSoccm/J39d/8svvxjSR1wBEw8IdxXHjx/X4MGDNX36dIWHh0uSgoKCdOjQIWVlZam4uFhpaWn0jcHmzZuntLQ0LV++XEOGDFH79u31/vvv0y9O1q5dO23cuFFnz55VcXGxNmzYoM6dO9MvTtS4cWNt3rxZeXl5slqtWrt2LZ9hLqSsvqhdu7Z8fHz07bffSvrjm6xG9BFXwMQDwl3FnDlzlJ+frylTptjWRUdHa8qUKYqJiVF+fr5CQkLUuXNnJ1YJSfLx8aFfnCwoKEjPPvusnnjiCRUWFqpNmzbq3bu36tevT784ySOPPKI9e/YoKipK3t7eatq0qWJiYtSmTRv6xAVc7nNr+vTpGj16tHJzc9WkSRM99dRTDq+Hh3EDAAAYjCFIAAAAgxHAAAAADEYAAwAAMBgBDAAAwGAEMAAAAIMRwAAAAAxGAAPgcEePHlVgYKD69u170ba4uDgFBgZq165dGjJkyFW1P2PGDCUnJ191fXFxcfrb3/6m7t27KzIyUl27dtULL7ygkydPXnWb18O6des0Y8YMp9YAwDEIYAAM4ePjo0OHDik7O9u2Li8vT9u3b5ck1a5dWzNnzryqtocOHarIyMhrqq9fv35avny5kpOTlZaWpnr16mncuHHX1Oa12rVrl86cOePUGgA4BnfCB2AIT09PdenSRampqRo0aJAk6bPPPlOHDh00d+5cbd26VUlJSUpLS9M333yjKVOmqKSkRJI0cOBAderUqcz1cXFxatiwoQYMGKCmTZvq+eef16ZNm3TixAnb3eKLi4s1depUrV27Vn5+fmrWrJkOHDigjz766JL1BgcHa9q0aZKknJwcjR8/XsePH1dhYaHCw8M1aNAgHT16VH369FGDBg2UnZ2tjz76SN9//73efPNNlZSUqEqVKho3bpwaN26s7du3a/r06Tp37pw8PDz00ksvqV27dlq6dKk+//xzeXh4KCsrSzfddJMSEhKUm5urRYsWqbi4WH5+foqNjTWglwAYhStgAAwTGRmp5cuX25aTk5PVo0ePi/abNWuWnnnmGS1dulSTJk3SV199ddn1f1ZQUKDbbrtNixYt0syZMzV58mTl5+dryZIl+v7775WWlqZFixbpyJEjZdZ5/vx5JScn66GHHpIkDRs2TD179tTSpUv1ySefaPPmzVq5cqUk6eeff9aLL76o1atXy8PDQ8OGDdPkyZOVmpqqAQMGaPr06Tpz5oxGjhypqVOnatmyZXr77bf12muv6dixY5Kkbdu2acyYMUpLS1NQUJDeffddBQUFKTo6WmFhYYQvwA1xBQyAYe677z55enpq9+7d8vf31++//65GjRpdtF+XLl00fvx4rV27Vg8//LBefvnly67/qw4dOkiSmjRpooKCAuXl5Wn9+vXq3r27fHx8JEm9evUqdfVr/vz5SklJkSQVFxerVatWevnll5WXl6dt27bpzJkztvlYeXl52rdvn5o1ayYvLy81b95ckrR9+3Y1bNhQ9957ryQpNDRUoaGhWr9+vSwWiwYPHmw7nslk0g8//GCrs0aNGpKke++9V59//vnVnWAANwwCGABDdevWTSkpKapWrZq6d+9+yX2io6PVrl07bdq0SRs2bFBSUpLS09PLXP9XF0KWyWSSJFmtVnl5lf648/AoPQDQr18/DRgw4KK2cnNzZbVatWjRIlWuXFmSdOrUKfn4+OjXX39VpUqVbG17enrajnnhuD/88IOKi4vVoEEDLVmyxLYtJydH1apVU2pqqm666SbbepPJJB7RC7g/hiABGKp79+5KT0/XypUr1bVr10vuEx0drb179yoqKkoTJkzQ2bNnZbFYylx/JUJCQpSSkqKCggIVFRVp2bJlV/Q6X19fNW/eXPPmzZMknT17Vr1799aaNWsu2jcoKEgHDhzQ/v37JUlr1qzRsGHD1Lx5c2VlZWnbtm2SpL1796pTp07Kycm57LE9PT1VVFR0RXUCuLFwBQyAoapXr64GDRrIz89PVatWveQ+r7zyiiZNmqQ333xTJpNJL730kurUqVPm+isRFRWlQ4cOKTIyUlWqVFGdOnVsV7TsmT59uiZMmKCIiAgVFBSoa9eu6tatm44ePVpqv9tvv13Tp0/XiBEjVFxcLF9fXyUmJqpatWqaOXOmpk6dqvz8fFmtVk2dOlV16tTR1q1byzxu69at9corr2jChAkaM2bMFdUK4MZgsnKtG0AFsHHjRp08edI27Dlx4kT5+Pho2LBhTq4MQEVEAANQIeTk5CguLk6//PKLSkpK1LhxY7322mvy8/NzdmkAKiACGAAAgMGYhA8AAGAwAhgAAIDBCGAAAAAGI4ABAAAYjAAGAABgsP8D0FYmIX2XfI8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.histplot(x=Missings_df.MissingPercent).set_title('Missing values distribution',size=15)\n",
    "plt.ylabel('The number of features')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4667bc8a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:46:08.273891Z",
     "iopub.status.busy": "2022-03-28T17:46:08.268984Z",
     "iopub.status.idle": "2022-03-28T17:46:08.590636Z",
     "shell.execute_reply": "2022-03-28T17:46:08.591332Z",
     "shell.execute_reply.started": "2022-03-28T17:42:45.785927Z"
    },
    "papermill": {
     "duration": 0.419682,
     "end_time": "2022-03-28T17:46:08.591519",
     "exception": false,
     "start_time": "2022-03-28T17:46:08.171837",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'The number of features')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "data = Missings_df[Missings_df.MissingPercent<25]\n",
    "sns.histplot(x=data.MissingPercent).set_title('Missing values distribution',size=15)\n",
    "plt.ylabel('The number of features')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "947db4a3",
   "metadata": {},
   "source": [
    "- Get columns with more than 25% missing values\n",
    "- Drop from train and test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "85a27553",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:46:08.724765Z",
     "iopub.status.busy": "2022-03-28T17:46:08.724125Z",
     "iopub.status.idle": "2022-03-28T17:46:08.912560Z",
     "shell.execute_reply": "2022-03-28T17:46:08.911794Z",
     "shell.execute_reply.started": "2022-03-28T17:42:46.157222Z"
    },
    "papermill": {
     "duration": 0.2553,
     "end_time": "2022-03-28T17:46:08.912726",
     "exception": false,
     "start_time": "2022-03-28T17:46:08.657426",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "Missings_custom=Missings_df[Missings_df.MissingPercent>25]\n",
    "\n",
    "df_train=df_train.drop(list(Missings_custom.Feature),axis=1)\n",
    "df_test=df_test.drop(list(Missings_custom.Feature),axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd2b0e22",
   "metadata": {},
   "source": [
    "Print categorical features name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ce68839d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:46:09.126358Z",
     "iopub.status.busy": "2022-03-28T17:46:09.084774Z",
     "iopub.status.idle": "2022-03-28T17:46:09.130529Z",
     "shell.execute_reply": "2022-03-28T17:46:09.130014Z",
     "shell.execute_reply.started": "2022-03-28T17:42:46.357859Z"
    },
    "papermill": {
     "duration": 0.153061,
     "end_time": "2022-03-28T17:46:09.130685",
     "exception": false,
     "start_time": "2022-03-28T17:46:08.977624",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['feature_0', 'feature_1', 'feature_2', 'feature_3', 'feature_4',\n",
       "       'feature_16', 'feature_17', 'feature_18', 'feature_19', 'feature_20',\n",
       "       'feature_21', 'feature_22', 'feature_23', 'feature_24', 'feature_27',\n",
       "       'feature_28', 'feature_29', 'feature_30', 'feature_31', 'feature_32',\n",
       "       'feature_33', 'feature_34', 'feature_41', 'feature_42', 'feature_54',\n",
       "       'feature_55', 'feature_58', 'feature_97', 'feature_133', 'feature_134',\n",
       "       'feature_135', 'feature_152', 'feature_163', 'feature_164',\n",
       "       'feature_165', 'feature_185', 'feature_186', 'feature_187',\n",
       "       'feature_188', 'feature_189', 'feature_190', 'feature_191',\n",
       "       'feature_192', 'feature_199', 'feature_200', 'feature_201',\n",
       "       'feature_205', 'feature_239', 'feature_240', 'feature_241',\n",
       "       'feature_242', 'feature_243', 'feature_244', 'feature_246',\n",
       "       'feature_247', 'feature_248', 'feature_249', 'feature_250',\n",
       "       'feature_251', 'feature_270', 'feature_271', 'feature_272',\n",
       "       'feature_273', 'feature_274', 'feature_275', 'feature_276',\n",
       "       'feature_277', 'feature_278', 'feature_279', 'feature_280',\n",
       "       'feature_281', 'feature_282', 'feature_283', 'feature_284',\n",
       "       'feature_285', 'feature_286', 'feature_287', 'feature_288',\n",
       "       'feature_289', 'feature_290', 'feature_291', 'feature_292',\n",
       "       'feature_293', 'feature_336', 'feature_337', 'feature_338',\n",
       "       'feature_371'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Categorical_Features=df_train.select_dtypes(include=['object']).columns\n",
    "Categorical_Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c14f9691",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:46:09.352211Z",
     "iopub.status.busy": "2022-03-28T17:46:09.351541Z",
     "iopub.status.idle": "2022-03-28T17:46:09.358146Z",
     "shell.execute_reply": "2022-03-28T17:46:09.357662Z",
     "shell.execute_reply.started": "2022-03-28T17:42:46.450532Z"
    },
    "papermill": {
     "duration": 0.163333,
     "end_time": "2022-03-28T17:46:09.358321",
     "exception": false,
     "start_time": "2022-03-28T17:46:09.194988",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['feature_14', 'feature_15', 'feature_25', 'feature_26', 'feature_35',\n",
       "       'feature_36', 'feature_37', 'feature_38', 'feature_39', 'feature_40',\n",
       "       ...\n",
       "       'feature_497', 'feature_498', 'feature_499', 'feature_500',\n",
       "       'feature_501', 'feature_502', 'feature_503', 'feature_504',\n",
       "       'feature_505', 'feature_506'],\n",
       "      dtype='object', length=393)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Numeric_Features=df_train.select_dtypes(exclude=['object']).columns[2:] #excluding id--> which will be dropeed latter stage\n",
    "                                                                     #target will be assigned to y\n",
    "Numeric_Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70a7004f",
   "metadata": {},
   "source": [
    "Plot a pichart to show the percentage of numeric and categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3b514109",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:46:09.503839Z",
     "iopub.status.busy": "2022-03-28T17:46:09.503061Z",
     "iopub.status.idle": "2022-03-28T17:46:09.614251Z",
     "shell.execute_reply": "2022-03-28T17:46:09.615078Z",
     "shell.execute_reply.started": "2022-03-28T17:42:46.544497Z"
    },
    "papermill": {
     "duration": 0.192355,
     "end_time": "2022-03-28T17:46:09.615397",
     "exception": false,
     "start_time": "2022-03-28T17:46:09.423042",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [len(Categorical_Features),len(Numeric_Features)]\n",
    "colors = sns.color_palette('pastel')\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.title('The percentage of numeric and categorical features',size=15)\n",
    "plt.pie(data, colors = colors, labels=['Categorical','Numeric'] ,autopct = '%0.0f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed5b416f",
   "metadata": {},
   "source": [
    "Fill with median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f5ff6e32",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:46:09.787567Z",
     "iopub.status.busy": "2022-03-28T17:46:09.786878Z",
     "iopub.status.idle": "2022-03-28T17:46:10.283674Z",
     "shell.execute_reply": "2022-03-28T17:46:10.284278Z",
     "shell.execute_reply.started": "2022-03-28T17:42:46.680048Z"
    },
    "papermill": {
     "duration": 0.569459,
     "end_time": "2022-03-28T17:46:10.284454",
     "exception": false,
     "start_time": "2022-03-28T17:46:09.714995",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for col in Numeric_Features:\n",
    "    df_train[col].fillna(df_train[col].median(),inplace=True)\n",
    "    df_test[col].fillna(df_test[col].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17ae7f5d",
   "metadata": {},
   "source": [
    "Fill with mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7de5128c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:46:10.422207Z",
     "iopub.status.busy": "2022-03-28T17:46:10.421545Z",
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     "shell.execute_reply": "2022-03-28T17:46:12.944625Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_mode(feature,df):\n",
    "    return df[feature].mode()[0]\n",
    "\n",
    "for col in Categorical_Features:\n",
    "    df_train[col].fillna(get_mode(col,df_train),inplace=True)\n",
    "    df_test[col].fillna(get_mode(col,df_test),inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c244d8b1",
   "metadata": {},
   "source": [
    "Find columns that contain date objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ab6af26d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:46:13.082382Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "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>feature_191</th>\n",
       "      <th>feature_192</th>\n",
       "      <th>feature_199</th>\n",
       "      <th>feature_200</th>\n",
       "      <th>feature_201</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11/10/2017</td>\n",
       "      <td>11/10/2017</td>\n",
       "      <td>11/10/2017</td>\n",
       "      <td>17/01/2016</td>\n",
       "      <td>11/10/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25/03/2015</td>\n",
       "      <td>25/03/2015</td>\n",
       "      <td>02/04/2017</td>\n",
       "      <td>25/03/2015</td>\n",
       "      <td>25/03/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27/01/2015</td>\n",
       "      <td>27/01/2015</td>\n",
       "      <td>24/03/2016</td>\n",
       "      <td>27/01/2015</td>\n",
       "      <td>27/01/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>05/02/2014</td>\n",
       "      <td>01/04/2014</td>\n",
       "      <td>01/04/2014</td>\n",
       "      <td>01/04/2014</td>\n",
       "      <td>01/04/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20/07/2009</td>\n",
       "      <td>10/09/2013</td>\n",
       "      <td>10/09/2013</td>\n",
       "      <td>24/01/2016</td>\n",
       "      <td>10/09/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69995</th>\n",
       "      <td>27/09/2010</td>\n",
       "      <td>01/02/2014</td>\n",
       "      <td>01/02/2014</td>\n",
       "      <td>14/02/2014</td>\n",
       "      <td>01/02/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69996</th>\n",
       "      <td>19/09/2016</td>\n",
       "      <td>19/09/2016</td>\n",
       "      <td>19/09/2016</td>\n",
       "      <td>19/09/2016</td>\n",
       "      <td>19/09/2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69997</th>\n",
       "      <td>14/09/2005</td>\n",
       "      <td>25/07/2015</td>\n",
       "      <td>25/07/2015</td>\n",
       "      <td>09/04/2016</td>\n",
       "      <td>25/07/2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69998</th>\n",
       "      <td>29/11/2017</td>\n",
       "      <td>29/11/2017</td>\n",
       "      <td>29/11/2017</td>\n",
       "      <td>17/01/2016</td>\n",
       "      <td>29/11/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69999</th>\n",
       "      <td>23/01/2018</td>\n",
       "      <td>23/01/2018</td>\n",
       "      <td>23/01/2018</td>\n",
       "      <td>17/01/2016</td>\n",
       "      <td>23/01/2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      feature_191 feature_192 feature_199 feature_200 feature_201\n",
       "0      11/10/2017  11/10/2017  11/10/2017  17/01/2016  11/10/2017\n",
       "1      25/03/2015  25/03/2015  02/04/2017  25/03/2015  25/03/2015\n",
       "2      27/01/2015  27/01/2015  24/03/2016  27/01/2015  27/01/2015\n",
       "3      05/02/2014  01/04/2014  01/04/2014  01/04/2014  01/04/2014\n",
       "4      20/07/2009  10/09/2013  10/09/2013  24/01/2016  10/09/2013\n",
       "...           ...         ...         ...         ...         ...\n",
       "69995  27/09/2010  01/02/2014  01/02/2014  14/02/2014  01/02/2014\n",
       "69996  19/09/2016  19/09/2016  19/09/2016  19/09/2016  19/09/2016\n",
       "69997  14/09/2005  25/07/2015  25/07/2015  09/04/2016  25/07/2015\n",
       "69998  29/11/2017  29/11/2017  29/11/2017  17/01/2016  29/11/2017\n",
       "69999  23/01/2018  23/01/2018  23/01/2018  17/01/2016  23/01/2018\n",
       "\n",
       "[70000 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "mask = df_train[Categorical_Features][:10].astype(str).apply(lambda x : x.str.match('(\\d{2,4}(-|\\/|\\\\|\\.| )\\d{2}(-|\\/|\\\\|\\.| )\\d{2,4})+').any())\n",
    "datetime_Features=df_train[Categorical_Features].loc[:,mask]\n",
    "\n",
    "datetime_Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
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    "execution": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        11/10/2017\n",
       "1        25/03/2015\n",
       "2        27/01/2015\n",
       "3        05/02/2014\n",
       "4        20/07/2009\n",
       "            ...    \n",
       "69995    27/09/2010\n",
       "69996    19/09/2016\n",
       "69997    14/09/2005\n",
       "69998    29/11/2017\n",
       "69999    23/01/2018\n",
       "Name: feature_191, Length: 70000, dtype: object"
      ]
     },
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     "metadata": {},
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    "df_train.feature_191"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41589cf7",
   "metadata": {},
   "source": [
    "- Apply datetime format\n",
    "- Show datetime features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c557f120",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
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   },
   "outputs": [
    {
     "data": {
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       "    }\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>feature_191</th>\n",
       "      <th>feature_192</th>\n",
       "      <th>feature_199</th>\n",
       "      <th>feature_200</th>\n",
       "      <th>feature_201</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-11-10</td>\n",
       "      <td>2017-11-10</td>\n",
       "      <td>2017-11-10</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>2017-11-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-03-25</td>\n",
       "      <td>2015-03-25</td>\n",
       "      <td>2017-02-04</td>\n",
       "      <td>2015-03-25</td>\n",
       "      <td>2015-03-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-27</td>\n",
       "      <td>2015-01-27</td>\n",
       "      <td>2016-03-24</td>\n",
       "      <td>2015-01-27</td>\n",
       "      <td>2015-01-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-05-02</td>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>2014-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-07-20</td>\n",
       "      <td>2013-10-09</td>\n",
       "      <td>2013-10-09</td>\n",
       "      <td>2016-01-24</td>\n",
       "      <td>2013-10-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69995</th>\n",
       "      <td>2010-09-27</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>2014-02-14</td>\n",
       "      <td>2014-01-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69996</th>\n",
       "      <td>2016-09-19</td>\n",
       "      <td>2016-09-19</td>\n",
       "      <td>2016-09-19</td>\n",
       "      <td>2016-09-19</td>\n",
       "      <td>2016-09-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69997</th>\n",
       "      <td>2005-09-14</td>\n",
       "      <td>2015-07-25</td>\n",
       "      <td>2015-07-25</td>\n",
       "      <td>2016-09-04</td>\n",
       "      <td>2015-07-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69998</th>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>2017-11-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69999</th>\n",
       "      <td>2018-01-23</td>\n",
       "      <td>2018-01-23</td>\n",
       "      <td>2018-01-23</td>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>2018-01-23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      feature_191 feature_192 feature_199 feature_200 feature_201\n",
       "0      2017-11-10  2017-11-10  2017-11-10  2016-01-17  2017-11-10\n",
       "1      2015-03-25  2015-03-25  2017-02-04  2015-03-25  2015-03-25\n",
       "2      2015-01-27  2015-01-27  2016-03-24  2015-01-27  2015-01-27\n",
       "3      2014-05-02  2014-01-04  2014-01-04  2014-01-04  2014-01-04\n",
       "4      2009-07-20  2013-10-09  2013-10-09  2016-01-24  2013-10-09\n",
       "...           ...         ...         ...         ...         ...\n",
       "69995  2010-09-27  2014-01-02  2014-01-02  2014-02-14  2014-01-02\n",
       "69996  2016-09-19  2016-09-19  2016-09-19  2016-09-19  2016-09-19\n",
       "69997  2005-09-14  2015-07-25  2015-07-25  2016-09-04  2015-07-25\n",
       "69998  2017-11-29  2017-11-29  2017-11-29  2016-01-17  2017-11-29\n",
       "69999  2018-01-23  2018-01-23  2018-01-23  2016-01-17  2018-01-23\n",
       "\n",
       "[70000 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[datetime_Features.columns]= df_train[datetime_Features.columns].apply(pd.to_datetime,dayfirst=False)\n",
    "df_test[datetime_Features.columns]= df_test[datetime_Features.columns].apply(pd.to_datetime,dayfirst=False)\n",
    "\n",
    "datetime_tmp=df_train.select_dtypes(include=['datetime'])\n",
    "datetime_tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95fbcc09",
   "metadata": {},
   "source": [
    "Get each part of datetime using pandas DatetimeIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bf654e57",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:46:14.891211Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for col in datetime_tmp.columns:\n",
    "    df_train[col+\"_year\"]=pd.DatetimeIndex(df_train[col]).year\n",
    "    df_train[col+\"_month\"]=pd.DatetimeIndex(df_train[col]).month\n",
    "    df_train[col+\"_week\"]=pd.DatetimeIndex(df_train[col]).week\n",
    "    df_train[col+\"_day\"]=pd.DatetimeIndex(df_train[col]).day\n",
    "\n",
    "    \n",
    "    df_test[col+\"_year\"]=pd.DatetimeIndex(df_test[col]).year\n",
    "    df_test[col+\"_month\"]=pd.DatetimeIndex(df_test[col]).month\n",
    "    df_test[col+\"_week\"]=pd.DatetimeIndex(df_test[col]).week\n",
    "    df_test[col+\"_day\"]=pd.DatetimeIndex(df_test[col]).day"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44a0f5cc",
   "metadata": {},
   "source": [
    "- Drop from train/test\n",
    "- Update Categorical_Features list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f70d1ff2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:46:15.448452Z",
     "iopub.status.busy": "2022-03-28T17:46:15.447788Z",
     "iopub.status.idle": "2022-03-28T17:46:15.823407Z",
     "shell.execute_reply": "2022-03-28T17:46:15.822735Z",
     "shell.execute_reply.started": "2022-03-28T17:42:51.646302Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train.drop(list(datetime_tmp.columns),axis=1,inplace=True)\n",
    "df_test.drop(list(datetime_tmp.columns),axis=1,inplace=True)\n",
    "\n",
    "Categorical_Features = [e for e in Categorical_Features\n",
    "                        if e not in datetime_tmp.columns]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1467c56",
   "metadata": {},
   "source": [
    "Create a copy of datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "365cb4f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:46:15.964595Z",
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     "shell.execute_reply.started": "2022-03-28T17:42:52.036067Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train_copy= df_train.copy()\n",
    "df_test_copy= df_test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ed4fbb18",
   "metadata": {
    "execution": {
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    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>target</th>\n",
       "      <th>feature_0</th>\n",
       "      <th>feature_1</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_3</th>\n",
       "      <th>feature_4</th>\n",
       "      <th>feature_14</th>\n",
       "      <th>feature_15</th>\n",
       "      <th>feature_16</th>\n",
       "      <th>...</th>\n",
       "      <th>feature_199_week</th>\n",
       "      <th>feature_199_day</th>\n",
       "      <th>feature_200_year</th>\n",
       "      <th>feature_200_month</th>\n",
       "      <th>feature_200_week</th>\n",
       "      <th>feature_200_day</th>\n",
       "      <th>feature_201_year</th>\n",
       "      <th>feature_201_month</th>\n",
       "      <th>feature_201_week</th>\n",
       "      <th>feature_201_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C0</td>\n",
       "      <td>C0</td>\n",
       "      <td>C1</td>\n",
       "      <td>C5</td>\n",
       "      <td>C11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>C0</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>10</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>45</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>C0</td>\n",
       "      <td>C0</td>\n",
       "      <td>C3</td>\n",
       "      <td>C5</td>\n",
       "      <td>C1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>C2</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>C0</td>\n",
       "      <td>C0</td>\n",
       "      <td>C3</td>\n",
       "      <td>C5</td>\n",
       "      <td>C2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>C0</td>\n",
       "      <td>C0</td>\n",
       "      <td>C1</td>\n",
       "      <td>C5</td>\n",
       "      <td>C1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>C0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>C0</td>\n",
       "      <td>C0</td>\n",
       "      <td>C3</td>\n",
       "      <td>C3</td>\n",
       "      <td>C11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.258</td>\n",
       "      <td>C2</td>\n",
       "      <td>...</td>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>2013</td>\n",
       "      <td>10</td>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 497 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  target feature_0 feature_1 feature_2 feature_3 feature_4  feature_14  \\\n",
       "0   0       0        C0        C0        C1        C5       C11         0.0   \n",
       "1   1       0        C0        C0        C3        C5        C1         0.0   \n",
       "2   2       0        C0        C0        C3        C5        C2         0.0   \n",
       "3   3       0        C0        C0        C1        C5        C1         0.0   \n",
       "4   4       1        C0        C0        C3        C3       C11         0.0   \n",
       "\n",
       "   feature_15 feature_16  ... feature_199_week feature_199_day  \\\n",
       "0       0.000         C0  ...               45              10   \n",
       "1       0.000         C2  ...                5               4   \n",
       "2       0.000         C1  ...               12              24   \n",
       "3       0.000         C0  ...                1               4   \n",
       "4       0.258         C2  ...               41               9   \n",
       "\n",
       "  feature_200_year feature_200_month feature_200_week feature_200_day  \\\n",
       "0             2016                 1                2              17   \n",
       "1             2015                 3               13              25   \n",
       "2             2015                 1                5              27   \n",
       "3             2014                 1                1               4   \n",
       "4             2016                 1                3              24   \n",
       "\n",
       "  feature_201_year feature_201_month  feature_201_week  feature_201_day  \n",
       "0             2017                11                45               10  \n",
       "1             2015                 3                13               25  \n",
       "2             2015                 1                 5               27  \n",
       "3             2014                 1                 1                4  \n",
       "4             2013                10                41                9  \n",
       "\n",
       "[5 rows x 497 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_copy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3295148f",
   "metadata": {},
   "source": [
    "Get the number of unique values for each feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c5d29110",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_high_cardinality(df,ftype):\n",
    "    tmp=[]\n",
    "    for col in ftype:\n",
    "        tmp.append( df[col].nunique() )\n",
    "\n",
    "    High_cardinality=pd.DataFrame({'Features':ftype,'Cardinality':tmp})\n",
    "    return High_cardinality.sort_values(by='Cardinality',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfaacc62",
   "metadata": {},
   "source": [
    "Categorical Features Sorted by cardinality "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d73a8d19",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Features</th>\n",
       "      <th>Cardinality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>feature_18</td>\n",
       "      <td>15858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>feature_133</td>\n",
       "      <td>7126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>feature_20</td>\n",
       "      <td>5841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>feature_22</td>\n",
       "      <td>2339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>feature_21</td>\n",
       "      <td>2327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>feature_19</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>feature_54</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>feature_185</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>feature_246</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>feature_279</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Features  Cardinality\n",
       "7    feature_18        15858\n",
       "28  feature_133         7126\n",
       "9    feature_20         5841\n",
       "11   feature_22         2339\n",
       "10   feature_21         2327\n",
       "8    feature_19          260\n",
       "24   feature_54          260\n",
       "35  feature_185          255\n",
       "48  feature_246          147\n",
       "63  feature_279          136"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cardinality=get_high_cardinality(df_train_copy,Categorical_Features).head(10)\n",
    "cardinality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "75c973b3",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for col in list(get_high_cardinality(df_train_copy,Categorical_Features).Features):\n",
    "        # get a list of categories which contain one value\n",
    "        tmp=list(df_train_copy[col].value_counts()[df_train_copy[col].value_counts()<2].index)\n",
    "        # Replace with 'other'\n",
    "        df_train_copy[col]=df_train_copy[col].apply(lambda x:'other' if x in tmp else x)\n",
    "\n",
    "# Do the same for test dataset    \n",
    "for col in list(get_high_cardinality(df_test_copy,Categorical_Features).Features):\n",
    "        tmp=list(df_test_copy[col].value_counts()[df_test_copy[col].value_counts()<2].index)\n",
    "        df_test_copy[col]=df_test_copy[col].apply(lambda x:'other' if x in tmp else x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f13d2ca2",
   "metadata": {},
   "source": [
    "###### Import the chi-square "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "40eb7d28",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>Column</th>\n",
       "      <th>Hypothesis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>feature_0</td>\n",
       "      <td>Fail to Reject Null Hypothesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>feature_1</td>\n",
       "      <td>Fail to Reject Null Hypothesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>feature_2</td>\n",
       "      <td>Reject Null Hypothesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>feature_3</td>\n",
       "      <td>Reject Null Hypothesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>feature_4</td>\n",
       "      <td>Reject Null Hypothesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>feature_293</td>\n",
       "      <td>Reject Null Hypothesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>feature_336</td>\n",
       "      <td>Reject Null Hypothesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>feature_337</td>\n",
       "      <td>Fail to Reject Null Hypothesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>feature_338</td>\n",
       "      <td>Fail to Reject Null Hypothesis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>feature_371</td>\n",
       "      <td>Reject Null Hypothesis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Column                      Hypothesis\n",
       "0     feature_0  Fail to Reject Null Hypothesis\n",
       "1     feature_1  Fail to Reject Null Hypothesis\n",
       "2     feature_2          Reject Null Hypothesis\n",
       "3     feature_3          Reject Null Hypothesis\n",
       "4     feature_4          Reject Null Hypothesis\n",
       "..          ...                             ...\n",
       "77  feature_293          Reject Null Hypothesis\n",
       "78  feature_336          Reject Null Hypothesis\n",
       "79  feature_337  Fail to Reject Null Hypothesis\n",
       "80  feature_338  Fail to Reject Null Hypothesis\n",
       "81  feature_371          Reject Null Hypothesis\n",
       "\n",
       "[82 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "chi2_check = []\n",
    "for i in Categorical_Features:\n",
    "    if chi2_contingency(pd.crosstab(df_train_copy['target'], df_train_copy[i]))[1] < 0.05:\n",
    "        chi2_check.append('Reject Null Hypothesis')\n",
    "    else:\n",
    "        chi2_check.append('Fail to Reject Null Hypothesis')\n",
    "res = pd.DataFrame(data = [Categorical_Features, chi2_check] \n",
    "             ).T \n",
    "res.columns = ['Column', 'Hypothesis']\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e870045",
   "metadata": {},
   "source": [
    "- add unwanted categorical features to drop list\n",
    "- Add to Drop list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6694b9ab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:47:00.805631Z",
     "iopub.status.busy": "2022-03-28T17:47:00.804734Z",
     "iopub.status.idle": "2022-03-28T17:47:00.808222Z",
     "shell.execute_reply": "2022-03-28T17:47:00.808968Z",
     "shell.execute_reply.started": "2022-03-28T17:43:36.487476Z"
    },
    "papermill": {
     "duration": 0.08101,
     "end_time": "2022-03-28T17:47:00.809212",
     "exception": false,
     "start_time": "2022-03-28T17:47:00.728202",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14  features added to drop list\n"
     ]
    }
   ],
   "source": [
    "Unwanted_catFeatures= res[res.Hypothesis=='Fail to Reject Null Hypothesis'].Column\n",
    "\n",
    "\n",
    "Features_toDrop=[]\n",
    "Features_toDrop+=list(Unwanted_catFeatures)\n",
    "print(len(Unwanted_catFeatures),' features added to drop list')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b32f8c7",
   "metadata": {},
   "source": [
    "- Get correlations with target (sorted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "71870c86",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:47:00.957151Z",
     "iopub.status.busy": "2022-03-28T17:47:00.956127Z",
     "iopub.status.idle": "2022-03-28T17:47:01.546536Z",
     "shell.execute_reply": "2022-03-28T17:47:01.545846Z",
     "shell.execute_reply.started": "2022-03-28T17:43:36.502157Z"
    },
    "papermill": {
     "duration": 0.665328,
     "end_time": "2022-03-28T17:47:01.546688",
     "exception": false,
     "start_time": "2022-03-28T17:47:00.881360",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "corr_df=df_train_copy.drop(['target'],axis=1).corrwith(df_train_copy['target']).abs().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14dd9aa9",
   "metadata": {},
   "source": [
    "##### Plot features which have more than 0.08 correlation with the target\n",
    "- Plot - sorted correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e0aa22eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:47:01.692609Z",
     "iopub.status.busy": "2022-03-28T17:47:01.691600Z",
     "iopub.status.idle": "2022-03-28T17:47:02.211734Z",
     "shell.execute_reply": "2022-03-28T17:47:02.211110Z",
     "shell.execute_reply.started": "2022-03-28T17:43:37.105397Z"
    },
    "papermill": {
     "duration": 0.593449,
     "end_time": "2022-03-28T17:47:02.211878",
     "exception": false,
     "start_time": "2022-03-28T17:47:01.618429",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Correlation')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "sns.barplot(x=corr_df[corr_df>0.08],y=corr_df[corr_df>0.08].index).set_title('Correlation between numeric features and target')\n",
    "plt.xlabel('Correlation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b2bfd8e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:47:02.366129Z",
     "iopub.status.busy": "2022-03-28T17:47:02.365484Z",
     "iopub.status.idle": "2022-03-28T17:47:02.686513Z",
     "shell.execute_reply": "2022-03-28T17:47:02.685845Z",
     "shell.execute_reply.started": "2022-03-28T17:43:37.641952Z"
    },
    "papermill": {
     "duration": 0.400162,
     "end_time": "2022-03-28T17:47:02.686663",
     "exception": false,
     "start_time": "2022-03-28T17:47:02.286501",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.xlabel('Correlation with target')\n",
    "plt.ylabel('The number of numeric features')\n",
    "sns.histplot(corr_df).set_title('Distribution of numeric features in terms of correlation with the target',size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bc95f68f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:47:02.843092Z",
     "iopub.status.busy": "2022-03-28T17:47:02.842369Z",
     "iopub.status.idle": "2022-03-28T17:47:04.842042Z",
     "shell.execute_reply": "2022-03-28T17:47:04.841515Z",
     "shell.execute_reply.started": "2022-03-28T17:43:37.977515Z"
    },
    "papermill": {
     "duration": 2.081586,
     "end_time": "2022-03-28T17:47:04.842195",
     "exception": false,
     "start_time": "2022-03-28T17:47:02.760609",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x864 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3,3,figsize=(17,12))\n",
    "sns.countplot(df_train_copy.feature_307,hue=df_train_copy.target, ax=ax[0][0])\n",
    "sns.countplot(df_train_copy.feature_297,hue=df_train_copy.target, ax=ax[0][1])\n",
    "sns.histplot(df_train_copy.feature_81, ax=ax[0][2])\n",
    "sns.histplot(df_train_copy.feature_82, ax=ax[1][0])\n",
    "sns.countplot(df_train_copy.feature_108,hue=df_train_copy.target, ax=ax[1][1])\n",
    "sns.countplot(df_train_copy.feature_110,hue=df_train_copy.target, ax=ax[1][2])\n",
    "sns.countplot(df_train_copy.feature_470,hue=df_train_copy.target, ax=ax[2][0])\n",
    "sns.countplot(df_train_copy.feature_264,hue=df_train_copy.target, ax=ax[2][1])\n",
    "sns.countplot(df_train_copy.feature_263,hue=df_train_copy.target, ax=ax[2][2])\n",
    "\n",
    "plt.suptitle('Important numeric features')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2e65554f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:47:05.004824Z",
     "iopub.status.busy": "2022-03-28T17:47:05.004081Z",
     "iopub.status.idle": "2022-03-28T17:47:05.076512Z",
     "shell.execute_reply": "2022-03-28T17:47:05.075804Z",
     "shell.execute_reply.started": "2022-03-28T17:43:40.083629Z"
    },
    "papermill": {
     "duration": 0.155697,
     "end_time": "2022-03-28T17:47:05.076658",
     "exception": false,
     "start_time": "2022-03-28T17:47:04.920961",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Taking sqrt of feature_81\n",
    "df_train_copy['feature_81_sqrt']= df_train_copy.feature_81.apply(np.sqrt)\n",
    "df_test_copy['feature_81_sqrt']= df_test_copy.feature_81.apply(np.sqrt)\n",
    "\n",
    "# Binarization feature_82\n",
    "df_train_copy['feature_82_binary']=df_train_copy['feature_82'].apply(lambda x: 0 if x==0 else 1)\n",
    "df_test_copy['feature_82_binary']= df_test_copy['feature_82'].apply(lambda x: 0 if x==0 else 1)\n",
    "\n",
    "# Multiplication of feature_307 and feature_82_binary\n",
    "df_train_copy['feature_82_307']= df_train_copy['feature_307']*df_train_copy['feature_82_binary']\n",
    "df_test_copy['feature_82_307']= df_test_copy['feature_307']*df_test_copy['feature_82_binary']\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39872747",
   "metadata": {},
   "source": [
    "- Find corr less than 0.002 with target.\n",
    "- Add to drop list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "703e6b26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:47:05.232164Z",
     "iopub.status.busy": "2022-03-28T17:47:05.231492Z",
     "iopub.status.idle": "2022-03-28T17:47:05.238396Z",
     "shell.execute_reply": "2022-03-28T17:47:05.237801Z",
     "shell.execute_reply.started": "2022-03-28T17:43:40.165423Z"
    },
    "papermill": {
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     "end_time": "2022-03-28T17:47:05.238547",
     "exception": false,
     "start_time": "2022-03-28T17:47:05.152863",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33  features added to drop list\n"
     ]
    }
   ],
   "source": [
    "low_corr=list(corr_df[corr_df<0.002].index)\n",
    "\n",
    "\n",
    "Features_toDrop+=low_corr\n",
    "print(len(low_corr),' features added to drop list')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "290b47c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:47:05.399492Z",
     "iopub.status.busy": "2022-03-28T17:47:05.398771Z",
     "iopub.status.idle": "2022-03-28T17:47:06.364366Z",
     "shell.execute_reply": "2022-03-28T17:47:06.363805Z",
     "shell.execute_reply.started": "2022-03-28T17:43:40.175411Z"
    },
    "papermill": {
     "duration": 1.046036,
     "end_time": "2022-03-28T17:47:06.364521",
     "exception": false,
     "start_time": "2022-03-28T17:47:05.318485",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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      ],
      "text/plain": [
       "      feature_55  feature_100  feature_149 feature_187 feature_188  \\\n",
       "0             C0            0            0          C0          C0   \n",
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       "3             C0            0            0          C0          C0   \n",
       "4             C0            0            0          C0          C0   \n",
       "...          ...          ...          ...         ...         ...   \n",
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       "69999         C0            0            0          C0          C0   \n",
       "\n",
       "      feature_190  feature_227  feature_228 feature_248 feature_249  ...  \\\n",
       "0              C0            0            0          C0          C1  ...   \n",
       "1              C0            0            0          C0          C1  ...   \n",
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       "...           ...          ...          ...         ...         ...  ...   \n",
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       "69998          C0            0            0          C0          C1  ...   \n",
       "69999          C0            0            0          C0          C1  ...   \n",
       "\n",
       "       feature_484  feature_487  feature_489  feature_491  feature_493  \\\n",
       "0                0            0            0            0            0   \n",
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       "\n",
       "[70000 rows x 59 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp=[]\n",
    "for col in df_train_copy.columns:\n",
    "    #If column contains one unique value\n",
    "    if (( df_train_copy[col].nunique() <=1 )):\n",
    "        #add to list\n",
    "        tmp.append(col)\n",
    "#Filter dataframe\n",
    "df_train_copy[tmp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "92b6f0da",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59  features added to drop list\n"
     ]
    }
   ],
   "source": [
    "#Add to drop list\n",
    "Features_toDrop+=list(tmp)\n",
    "\n",
    "print(len(tmp),' features added to drop list')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "67afe6b1",
   "metadata": {
    "execution": {
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   "source": [
    "\n",
    "\n",
    "def get_highly_correlated(df, features, threshold=0.5):\n",
    "    corr_df = df[features].corr() # get correlations\n",
    "    correlated_features = np.where(np.abs(corr_df) > threshold) # select ones above the abs threshold\n",
    "    correlated_features = [(corr_df.iloc[x,y], x, y) for x, y in zip(*correlated_features) if x != y and x < y] # avoid duplication\n",
    "    s_corr_list = sorted(correlated_features, key=lambda x: -abs(x[0])) # sort by correlation value\n",
    "    if s_corr_list == []:\n",
    "        print(\"There are no highly correlated features with correlation above\", threshold)\n",
    "    else:\n",
    "        col1,col2,col3=list(),list(),list()\n",
    "        for v, i, j in s_corr_list:\n",
    "            col1.append(corr_df.index[i])\n",
    "            col2.append(corr_df.columns[j])\n",
    "            col3.append(v)\n",
    "        col_list=[col1,col2,col3]\n",
    "        return pd.DataFrame(col_list, index=['F1', 'F2','Correlation']).T.sort_values(by='Correlation',ascending=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9ee31f78",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2022-03-28T17:47:36.243785Z",
     "shell.execute_reply": "2022-03-28T17:47:36.243202Z",
     "shell.execute_reply.started": "2022-03-28T17:43:41.220044Z"
    },
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      "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>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>feature_46</td>\n",
       "      <td>feature_140</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>feature_448</td>\n",
       "      <td>feature_464</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>feature_444</td>\n",
       "      <td>feature_503</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>feature_444</td>\n",
       "      <td>feature_506</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>feature_448</td>\n",
       "      <td>feature_450</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>feature_420</td>\n",
       "      <td>feature_504</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>feature_420</td>\n",
       "      <td>feature_499</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>feature_420</td>\n",
       "      <td>feature_497</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>feature_420</td>\n",
       "      <td>feature_490</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>feature_473</td>\n",
       "      <td>feature_492</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2352 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              F1           F2 Correlation\n",
       "0     feature_46  feature_140           1\n",
       "520  feature_448  feature_464           1\n",
       "511  feature_444  feature_503           1\n",
       "513  feature_444  feature_506           1\n",
       "514  feature_448  feature_450           1\n",
       "..           ...          ...         ...\n",
       "419  feature_420  feature_504          -1\n",
       "416  feature_420  feature_499          -1\n",
       "415  feature_420  feature_497          -1\n",
       "411  feature_420  feature_490          -1\n",
       "787  feature_473  feature_492          -1\n",
       "\n",
       "[2352 rows x 3 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F_corr_df= get_highly_correlated(df_train_copy[Numeric_Features],Numeric_Features,threshold=0.9)\n",
    "F_corr_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "26604f4a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:47:36.404527Z",
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     "shell.execute_reply": "2022-03-28T17:47:36.413308Z",
     "shell.execute_reply.started": "2022-03-28T17:44:11.661794Z"
    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "188  features added to drop list\n"
     ]
    }
   ],
   "source": [
    "#Using set() to get distinct features from the column F1\n",
    "drop_F1=list( set(F_corr_df.F1[F_corr_df.Correlation>0.9]) ) + list( set(F_corr_df.F1[F_corr_df.Correlation<-0.9]) )\n",
    "\n",
    "#Drop\n",
    "Features_toDrop += drop_F1\n",
    "print(len(drop_F1),' features added to drop list')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d29692e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:47:36.578397Z",
     "iopub.status.busy": "2022-03-28T17:47:36.577662Z",
     "iopub.status.idle": "2022-03-28T17:47:36.773903Z",
     "shell.execute_reply": "2022-03-28T17:47:36.774431Z",
     "shell.execute_reply.started": "2022-03-28T17:44:11.674878Z"
    },
    "papermill": {
     "duration": 0.27977,
     "end_time": "2022-03-28T17:47:36.774607",
     "exception": false,
     "start_time": "2022-03-28T17:47:36.494837",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "218  Features dropped\n"
     ]
    }
   ],
   "source": [
    "Features_toDrop=list(set(Features_toDrop))\n",
    "\n",
    "df_train_copy.drop(Features_toDrop,axis=1,inplace=True)\n",
    "df_test_copy.drop(Features_toDrop,axis=1,inplace=True)\n",
    "\n",
    "#Remove from Categorical_Features list\n",
    "Categorical_Features = [e for e in Categorical_Features\n",
    "                        if e not in Features_toDrop]\n",
    "#Remove from Numeric features list\n",
    "Numeric_Features=[e for e in Numeric_Features\n",
    "                  if e not in Features_toDrop]\n",
    "\n",
    "print(len(Features_toDrop),' Features dropped')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a4558cc7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:47:36.944752Z",
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    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70000, 282), (30000, 281))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_copy.shape, df_test_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "20170c3a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:47:37.112726Z",
     "iopub.status.busy": "2022-03-28T17:47:37.111987Z",
     "iopub.status.idle": "2022-03-28T17:47:41.217750Z",
     "shell.execute_reply": "2022-03-28T17:47:41.218341Z",
     "shell.execute_reply.started": "2022-03-28T17:44:11.935339Z"
    },
    "papermill": {
     "duration": 4.189449,
     "end_time": "2022-03-28T17:47:41.218521",
     "exception": false,
     "start_time": "2022-03-28T17:47:37.029072",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Create copy\n",
    "train_e=df_train_copy.copy()\n",
    "test_e=df_test_copy.copy()\n",
    "\n",
    "for col in list(Categorical_Features):\n",
    "    le = LabelEncoder()\n",
    "    #fit encoder on train/test at the same time\n",
    "    le.fit(pd.concat([train_e[col],test_e[col]], ignore_index=True))\n",
    "    #transform \n",
    "    train_e[col]=le.transform(train_e[col])\n",
    "    test_e[col]=le.transform(test_e[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3b353dd7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:47:41.386619Z",
     "iopub.status.busy": "2022-03-28T17:47:41.385714Z",
     "iopub.status.idle": "2022-03-28T17:47:41.673509Z",
     "shell.execute_reply": "2022-03-28T17:47:41.672845Z",
     "shell.execute_reply.started": "2022-03-28T17:44:16.201490Z"
    },
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     "end_time": "2022-03-28T17:47:41.673661",
     "exception": false,
     "start_time": "2022-03-28T17:47:41.302400",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "toDrop=['target'] + ['feature_82','feature_192_year','feature_199_year','feature_200_year','feature_201_year']\n",
    "\n",
    "#Variables\n",
    "X=train_e.drop(toDrop,axis=1)\n",
    "y=train_e['target']\n",
    "\n",
    "X_train,X_valid,y_train,y_valid = train_test_split(X, y, test_size=0.3,random_state=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7a75e791",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:47:41.868391Z",
     "iopub.status.busy": "2022-03-28T17:47:41.867659Z",
     "iopub.status.idle": "2022-03-28T17:47:41.881648Z",
     "shell.execute_reply": "2022-03-28T17:47:41.882220Z",
     "shell.execute_reply.started": "2022-03-28T17:44:16.518071Z"
    },
    "papermill": {
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     "end_time": "2022-03-28T17:47:41.882435",
     "exception": false,
     "start_time": "2022-03-28T17:47:41.764332",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Parameter grids\n",
    "LGBM_params = {\n",
    "    'n_estimators': 10000,\n",
    "    'learning_rate': 0.01346129943895438,\n",
    "    'num_leaves': 1230,\n",
    "    'max_depth': 5,\n",
    "    'lambda_l1': 9.014424962435049,\n",
    "    'lambda_l2': 5.09941903202415,\n",
    "    'min_child_samples': 56,\n",
    "    'subsample': 0.8210241479916653,\n",
    "    'bagging_fraction': 0.8,\n",
    "    'bagging_freq': 5,\n",
    "    'feature_fraction': 0.6000000000000001,\n",
    "}\n",
    "\n",
    "CATBOOST_params = {\n",
    "    'subsample': 0.50,\n",
    "    'learning_rate': 0.03,\n",
    "    \"max_depth\": 6,\n",
    "    \"min_data_in_leaf\": 50,\n",
    "    \"colsample_bylevel\": 0.5,\n",
    "    'random_state':42,\n",
    "    'num_boost_round': 2000,\n",
    "    'l2_leaf_reg': 1.0,\n",
    "    'eval_metric': \"AUC\",\n",
    "}\n",
    "\n",
    "XGB_params = {\n",
    "   'n_estimators': 14140,\n",
    "    'learning_rate': 0.0111538499996725174,\n",
    "    'max_depth': 4,\n",
    "    'reg_alpha': 2.9735993984217104,\n",
    "    'reg_lambda': 2.214625977393439,\n",
    "    'scale_pos_weight': 0.9,\n",
    "    'subsample': 0.7768428006197691,\n",
    "    'gamma': 0.5\n",
    "}\n",
    "\n",
    "#LGBM\n",
    "LGBM = LGBMClassifier(**LGBM_params)\n",
    "\n",
    "#Catboost\n",
    "CATBOOST = CatBoostClassifier(**CATBOOST_params)\n",
    "\n",
    "#XGBoost\n",
    "XGB = XGBClassifier(**XGB_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "81b5f77e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:47:42.047284Z",
     "iopub.status.busy": "2022-03-28T17:47:42.046534Z",
     "iopub.status.idle": "2022-03-28T17:47:42.050527Z",
     "shell.execute_reply": "2022-03-28T17:47:42.051073Z",
     "shell.execute_reply.started": "2022-03-28T17:44:16.534435Z"
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     "status": "completed"
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   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#Returns validation score\n",
    "def get_val_score(model):\n",
    "    val_pred = model.predict_proba(X_valid)\n",
    "    return roc_auc_score(y_valid,val_pred[:,1])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c0e62410",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:47:42.548543Z",
     "iopub.status.busy": "2022-03-28T17:47:42.547845Z",
     "iopub.status.idle": "2022-03-28T17:48:16.717465Z",
     "shell.execute_reply": "2022-03-28T17:48:16.718366Z",
     "shell.execute_reply.started": "2022-03-28T17:44:16.578384Z"
    },
    "papermill": {
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     "exception": false,
     "start_time": "2022-03-28T17:47:42.465485",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] feature_fraction is set=0.6000000000000001, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.6000000000000001\n",
      "[LightGBM] [Warning] lambda_l1 is set=9.014424962435049, reg_alpha=0.0 will be ignored. Current value: lambda_l1=9.014424962435049\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=0.8210241479916653 will be ignored. Current value: bagging_fraction=0.8\n",
      "[LightGBM] [Warning] lambda_l2 is set=5.09941903202415, reg_lambda=0.0 will be ignored. Current value: lambda_l2=5.09941903202415\n",
      "[LightGBM] [Warning] bagging_freq is set=5, subsample_freq=0 will be ignored. Current value: bagging_freq=5\n",
      "[100]\tvalid_0's auc: 0.767228\tvalid_0's binary_logloss: 0.527574\n",
      "[200]\tvalid_0's auc: 0.77347\tvalid_0's binary_logloss: 0.51101\n",
      "[300]\tvalid_0's auc: 0.777242\tvalid_0's binary_logloss: 0.505458\n",
      "[400]\tvalid_0's auc: 0.779645\tvalid_0's binary_logloss: 0.502765\n",
      "[500]\tvalid_0's auc: 0.781046\tvalid_0's binary_logloss: 0.501318\n",
      "[600]\tvalid_0's auc: 0.782368\tvalid_0's binary_logloss: 0.500065\n",
      "[700]\tvalid_0's auc: 0.782968\tvalid_0's binary_logloss: 0.499475\n",
      "[800]\tvalid_0's auc: 0.783431\tvalid_0's binary_logloss: 0.499072\n",
      "[900]\tvalid_0's auc: 0.783712\tvalid_0's binary_logloss: 0.498837\n",
      "[1000]\tvalid_0's auc: 0.783997\tvalid_0's binary_logloss: 0.498593\n",
      "[1100]\tvalid_0's auc: 0.784139\tvalid_0's binary_logloss: 0.498462\n",
      "[1200]\tvalid_0's auc: 0.784343\tvalid_0's binary_logloss: 0.498295\n",
      "[1300]\tvalid_0's auc: 0.784467\tvalid_0's binary_logloss: 0.49821\n",
      "[1400]\tvalid_0's auc: 0.784496\tvalid_0's binary_logloss: 0.498218\n",
      "====================\n",
      "LGBM Validation AUC :  0.7844935103604445\n"
     ]
    }
   ],
   "source": [
    "LGBM.fit(X_train,y_train,\n",
    "         eval_set=[(X_valid, y_valid)],\n",
    "         early_stopping_rounds=200,verbose=100,eval_metric='auc')\n",
    "#Get score\n",
    "print(\"=\"*20)\n",
    "print(\"LGBM Validation AUC : \", get_val_score(LGBM))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b860fbc2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:48:16.899252Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'LGBM Feature importance')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FI=pd.DataFrame({'Features':X.columns,'Importance':LGBM.feature_importances_})\n",
    "FI=FI.sort_values(by='Importance',ascending=False)[:20]\n",
    "#plot\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.barplot(x=FI.Importance,y=FI.Features).set_title('LGBM Feature importance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ac80992f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T17:48:17.885799Z",
     "iopub.status.busy": "2022-03-28T17:48:17.885099Z",
     "iopub.status.idle": "2022-03-28T17:48:19.228744Z",
     "shell.execute_reply": "2022-03-28T17:48:19.229371Z",
     "shell.execute_reply.started": "2022-03-28T17:44:51.381378Z"
    },
    "papermill": {
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     "exception": false,
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     "status": "completed"
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   "outputs": [],
   "source": [
    "toDrop=['feature_82','feature_192_year','feature_199_year','feature_200_year','feature_201_year']\n",
    "\n",
    "LGBM_pred=LGBM.predict_proba(test_e.drop(toDrop,axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2b7ed053",
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    "execution": {
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     "iopub.status.busy": "2022-03-28T17:48:19.411782Z",
     "iopub.status.idle": "2022-03-28T17:48:19.413951Z",
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     "shell.execute_reply.started": "2022-03-28T17:44:52.792360Z"
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     "status": "completed"
    },
    "tags": []
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   "outputs": [],
   "source": [
    "test_predictions = 1 * LGBM_pred[:,1] \n",
    "# + 0.5 * CATBOOST_pred[:,1] + 0.2 * XGB_pred[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0104bfa4",
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     "status": "completed"
    },
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   },
   "outputs": [
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       "      <th>target</th>\n",
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       "   id    target\n",
       "0   5  0.417238\n",
       "1   7  0.325637\n",
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       "3  11  0.120411\n",
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   "source": [
    "sample=pd.read_csv('sample_submission.csv')\n",
    "sample['target']=test_predictions\n",
    "sample.to_csv('final.csv',index=False)\n",
    "sample.head(10)"
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