Fraud_Detection_Starter.ipynb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RqLQ-hbyb5jH"
   },
   "source": [
    "# Fraud Detection in Electricity and Gas Consumption Challenge\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"gasvelectric.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jy_tdygDJm-k"
   },
   "source": [
    "# Data Prep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GKfpS4UVJ4Wj"
   },
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "OJMD_I3EJWP4"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import lightgbm\n",
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "exEGyf6vJ_4T"
   },
   "source": [
    "## Read the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "I2oIx7IuKCJm"
   },
   "outputs": [],
   "source": [
    "client_train = pd.read_csv('train/client_train.csv', low_memory=False)\n",
    "invoice_train = pd.read_csv('train/invoice_train.csv', low_memory=False)\n",
    "\n",
    "client_test = pd.read_csv('test/client_test.csv', low_memory=False)\n",
    "invoice_test = pd.read_csv('test/invoice_test.csv', low_memory=False)\n",
    "sample_submission = pd.read_csv('SampleSubmission.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cDRp9i5ZO9mz"
   },
   "source": [
    "## Data Understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "h4zQzvLjKZ6E",
    "outputId": "deb14c51-2f5d-40cd-aa35-771477e044e0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(135493, 6) (4476749, 16) (58069, 5) (4476749, 16)\n"
     ]
    }
   ],
   "source": [
    "#compare size of the various datasets\n",
    "print(client_train.shape, invoice_train.shape, client_test.shape, invoice_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 270
    },
    "id": "Tw1On2leQsxU",
    "outputId": "4bbe6376-2007-4ea4-cd6f-e53e4c0bcb33"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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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>client_id</th>\n",
       "      <th>invoice_date</th>\n",
       "      <th>tarif_type</th>\n",
       "      <th>counter_number</th>\n",
       "      <th>counter_statue</th>\n",
       "      <th>counter_code</th>\n",
       "      <th>reading_remarque</th>\n",
       "      <th>counter_coefficient</th>\n",
       "      <th>consommation_level_1</th>\n",
       "      <th>consommation_level_2</th>\n",
       "      <th>consommation_level_3</th>\n",
       "      <th>consommation_level_4</th>\n",
       "      <th>old_index</th>\n",
       "      <th>new_index</th>\n",
       "      <th>months_number</th>\n",
       "      <th>counter_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_Client_0</td>\n",
       "      <td>2014-03-24</td>\n",
       "      <td>11</td>\n",
       "      <td>1335667</td>\n",
       "      <td>0</td>\n",
       "      <td>203</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14302</td>\n",
       "      <td>14384</td>\n",
       "      <td>4</td>\n",
       "      <td>ELEC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_Client_0</td>\n",
       "      <td>2013-03-29</td>\n",
       "      <td>11</td>\n",
       "      <td>1335667</td>\n",
       "      <td>0</td>\n",
       "      <td>203</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1200</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12294</td>\n",
       "      <td>13678</td>\n",
       "      <td>4</td>\n",
       "      <td>ELEC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_Client_0</td>\n",
       "      <td>2015-03-23</td>\n",
       "      <td>11</td>\n",
       "      <td>1335667</td>\n",
       "      <td>0</td>\n",
       "      <td>203</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14624</td>\n",
       "      <td>14747</td>\n",
       "      <td>4</td>\n",
       "      <td>ELEC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_Client_0</td>\n",
       "      <td>2015-07-13</td>\n",
       "      <td>11</td>\n",
       "      <td>1335667</td>\n",
       "      <td>0</td>\n",
       "      <td>207</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14747</td>\n",
       "      <td>14849</td>\n",
       "      <td>4</td>\n",
       "      <td>ELEC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_Client_0</td>\n",
       "      <td>2016-11-17</td>\n",
       "      <td>11</td>\n",
       "      <td>1335667</td>\n",
       "      <td>0</td>\n",
       "      <td>207</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>572</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15066</td>\n",
       "      <td>15638</td>\n",
       "      <td>12</td>\n",
       "      <td>ELEC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        client_id invoice_date  tarif_type  counter_number counter_statue  \\\n",
       "0  train_Client_0   2014-03-24          11         1335667              0   \n",
       "1  train_Client_0   2013-03-29          11         1335667              0   \n",
       "2  train_Client_0   2015-03-23          11         1335667              0   \n",
       "3  train_Client_0   2015-07-13          11         1335667              0   \n",
       "4  train_Client_0   2016-11-17          11         1335667              0   \n",
       "\n",
       "   counter_code  reading_remarque  counter_coefficient  consommation_level_1  \\\n",
       "0           203                 8                    1                    82   \n",
       "1           203                 6                    1                  1200   \n",
       "2           203                 8                    1                   123   \n",
       "3           207                 8                    1                   102   \n",
       "4           207                 9                    1                   572   \n",
       "\n",
       "   consommation_level_2  consommation_level_3  consommation_level_4  \\\n",
       "0                     0                     0                     0   \n",
       "1                   184                     0                     0   \n",
       "2                     0                     0                     0   \n",
       "3                     0                     0                     0   \n",
       "4                     0                     0                     0   \n",
       "\n",
       "   old_index  new_index  months_number counter_type  \n",
       "0      14302      14384              4         ELEC  \n",
       "1      12294      13678              4         ELEC  \n",
       "2      14624      14747              4         ELEC  \n",
       "3      14747      14849              4         ELEC  \n",
       "4      15066      15638             12         ELEC  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print top rows of dataset\n",
    "invoice_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "jUcrU9IYSMZJ",
    "outputId": "e70e5da0-7451-48ca-8c3a-643524f3139c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disrict</th>\n",
       "      <th>client_id</th>\n",
       "      <th>client_catg</th>\n",
       "      <th>region</th>\n",
       "      <th>creation_date</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>train_Client_0</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "      <td>31/12/1994</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69</td>\n",
       "      <td>train_Client_1</td>\n",
       "      <td>11</td>\n",
       "      <td>107</td>\n",
       "      <td>29/05/2002</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>train_Client_10</td>\n",
       "      <td>11</td>\n",
       "      <td>301</td>\n",
       "      <td>13/03/1986</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69</td>\n",
       "      <td>train_Client_100</td>\n",
       "      <td>11</td>\n",
       "      <td>105</td>\n",
       "      <td>11/07/1996</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>train_Client_1000</td>\n",
       "      <td>11</td>\n",
       "      <td>303</td>\n",
       "      <td>14/10/2014</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   disrict          client_id  client_catg  region creation_date  target\n",
       "0       60     train_Client_0           11     101    31/12/1994     0.0\n",
       "1       69     train_Client_1           11     107    29/05/2002     0.0\n",
       "2       62    train_Client_10           11     301    13/03/1986     0.0\n",
       "3       69   train_Client_100           11     105    11/07/1996     0.0\n",
       "4       62  train_Client_1000           11     303    14/10/2014     0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print top rows of dataset\n",
    "client_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "fFBTeG8YSicC",
    "outputId": "551f42dc-8b1c-42a9-9a60-f6dc9279911b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>tarif_type</th>\n",
       "      <th>counter_number</th>\n",
       "      <th>counter_code</th>\n",
       "      <th>reading_remarque</th>\n",
       "      <th>counter_coefficient</th>\n",
       "      <th>consommation_level_1</th>\n",
       "      <th>consommation_level_2</th>\n",
       "      <th>consommation_level_3</th>\n",
       "      <th>consommation_level_4</th>\n",
       "      <th>old_index</th>\n",
       "      <th>new_index</th>\n",
       "      <th>months_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.476749e+06</td>\n",
       "      <td>4.476749e+06</td>\n",
       "      <td>4.476749e+06</td>\n",
       "      <td>4.476749e+06</td>\n",
       "      <td>4.476749e+06</td>\n",
       "      <td>4.476749e+06</td>\n",
       "      <td>4.476749e+06</td>\n",
       "      <td>4.476749e+06</td>\n",
       "      <td>4.476749e+06</td>\n",
       "      <td>4.476749e+06</td>\n",
       "      <td>4.476749e+06</td>\n",
       "      <td>4.476749e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.012804e+01</td>\n",
       "      <td>1.230587e+11</td>\n",
       "      <td>1.724884e+02</td>\n",
       "      <td>7.321702e+00</td>\n",
       "      <td>1.003040e+00</td>\n",
       "      <td>4.109795e+02</td>\n",
       "      <td>1.093225e+02</td>\n",
       "      <td>2.030620e+01</td>\n",
       "      <td>5.292588e+01</td>\n",
       "      <td>1.776700e+04</td>\n",
       "      <td>1.834970e+04</td>\n",
       "      <td>4.483095e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.347256e+01</td>\n",
       "      <td>1.657267e+12</td>\n",
       "      <td>1.338871e+02</td>\n",
       "      <td>1.571654e+00</td>\n",
       "      <td>3.083466e-01</td>\n",
       "      <td>7.573080e+02</td>\n",
       "      <td>1.220123e+03</td>\n",
       "      <td>1.574239e+02</td>\n",
       "      <td>8.754725e+02</td>\n",
       "      <td>4.036693e+04</td>\n",
       "      <td>4.095321e+04</td>\n",
       "      <td>3.128335e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>1.211080e+05</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7.900000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.791000e+03</td>\n",
       "      <td>2.056000e+03</td>\n",
       "      <td>4.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>4.945610e+05</td>\n",
       "      <td>2.030000e+02</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.740000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.690000e+03</td>\n",
       "      <td>8.192000e+03</td>\n",
       "      <td>4.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>1.115161e+06</td>\n",
       "      <td>2.070000e+02</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.000000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.166000e+04</td>\n",
       "      <td>2.234300e+04</td>\n",
       "      <td>4.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.500000e+01</td>\n",
       "      <td>2.798115e+13</td>\n",
       "      <td>6.000000e+02</td>\n",
       "      <td>4.130000e+02</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>9.999100e+05</td>\n",
       "      <td>9.990730e+05</td>\n",
       "      <td>6.449200e+04</td>\n",
       "      <td>5.479460e+05</td>\n",
       "      <td>2.800280e+06</td>\n",
       "      <td>2.870972e+06</td>\n",
       "      <td>6.366240e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         tarif_type  counter_number  counter_code  reading_remarque  \\\n",
       "count  4.476749e+06    4.476749e+06  4.476749e+06      4.476749e+06   \n",
       "mean   2.012804e+01    1.230587e+11  1.724884e+02      7.321702e+00   \n",
       "std    1.347256e+01    1.657267e+12  1.338871e+02      1.571654e+00   \n",
       "min    8.000000e+00    0.000000e+00  0.000000e+00      5.000000e+00   \n",
       "25%    1.100000e+01    1.211080e+05  5.000000e+00      6.000000e+00   \n",
       "50%    1.100000e+01    4.945610e+05  2.030000e+02      8.000000e+00   \n",
       "75%    4.000000e+01    1.115161e+06  2.070000e+02      9.000000e+00   \n",
       "max    4.500000e+01    2.798115e+13  6.000000e+02      4.130000e+02   \n",
       "\n",
       "       counter_coefficient  consommation_level_1  consommation_level_2  \\\n",
       "count         4.476749e+06          4.476749e+06          4.476749e+06   \n",
       "mean          1.003040e+00          4.109795e+02          1.093225e+02   \n",
       "std           3.083466e-01          7.573080e+02          1.220123e+03   \n",
       "min           0.000000e+00          0.000000e+00          0.000000e+00   \n",
       "25%           1.000000e+00          7.900000e+01          0.000000e+00   \n",
       "50%           1.000000e+00          2.740000e+02          0.000000e+00   \n",
       "75%           1.000000e+00          6.000000e+02          0.000000e+00   \n",
       "max           5.000000e+01          9.999100e+05          9.990730e+05   \n",
       "\n",
       "       consommation_level_3  consommation_level_4     old_index     new_index  \\\n",
       "count          4.476749e+06          4.476749e+06  4.476749e+06  4.476749e+06   \n",
       "mean           2.030620e+01          5.292588e+01  1.776700e+04  1.834970e+04   \n",
       "std            1.574239e+02          8.754725e+02  4.036693e+04  4.095321e+04   \n",
       "min            0.000000e+00          0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%            0.000000e+00          0.000000e+00  1.791000e+03  2.056000e+03   \n",
       "50%            0.000000e+00          0.000000e+00  7.690000e+03  8.192000e+03   \n",
       "75%            0.000000e+00          0.000000e+00  2.166000e+04  2.234300e+04   \n",
       "max            6.449200e+04          5.479460e+05  2.800280e+06  2.870972e+06   \n",
       "\n",
       "       months_number  \n",
       "count   4.476749e+06  \n",
       "mean    4.483095e+01  \n",
       "std     3.128335e+03  \n",
       "min     0.000000e+00  \n",
       "25%     4.000000e+00  \n",
       "50%     4.000000e+00  \n",
       "75%     4.000000e+00  \n",
       "max     6.366240e+05  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get a summary for all numerical columns\n",
    "invoice_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "egcBZ2ysSkji",
    "outputId": "37102972-554b-4063-ce3b-14dae74b3540"
   },
   "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>disrict</th>\n",
       "      <th>client_catg</th>\n",
       "      <th>region</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>135493.000000</td>\n",
       "      <td>135493.000000</td>\n",
       "      <td>135493.000000</td>\n",
       "      <td>135493.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>63.511222</td>\n",
       "      <td>11.512506</td>\n",
       "      <td>206.159809</td>\n",
       "      <td>0.055841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.354400</td>\n",
       "      <td>4.423761</td>\n",
       "      <td>104.207044</td>\n",
       "      <td>0.229614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>69.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>307.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>69.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             disrict    client_catg         region         target\n",
       "count  135493.000000  135493.000000  135493.000000  135493.000000\n",
       "mean       63.511222      11.512506     206.159809       0.055841\n",
       "std         3.354400       4.423761     104.207044       0.229614\n",
       "min        60.000000      11.000000     101.000000       0.000000\n",
       "25%        62.000000      11.000000     103.000000       0.000000\n",
       "50%        62.000000      11.000000     107.000000       0.000000\n",
       "75%        69.000000      11.000000     307.000000       0.000000\n",
       "max        69.000000      51.000000     399.000000       1.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get a summary for all numerical columns\n",
    "client_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-8gQmkajSnXs",
    "outputId": "fef429ad-2fd5-46e3-9d3b-32def757324c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4476749 entries, 0 to 4476748\n",
      "Data columns (total 16 columns):\n",
      " #   Column                Dtype \n",
      "---  ------                ----- \n",
      " 0   client_id             object\n",
      " 1   invoice_date          object\n",
      " 2   tarif_type            int64 \n",
      " 3   counter_number        int64 \n",
      " 4   counter_statue        object\n",
      " 5   counter_code          int64 \n",
      " 6   reading_remarque      int64 \n",
      " 7   counter_coefficient   int64 \n",
      " 8   consommation_level_1  int64 \n",
      " 9   consommation_level_2  int64 \n",
      " 10  consommation_level_3  int64 \n",
      " 11  consommation_level_4  int64 \n",
      " 12  old_index             int64 \n",
      " 13  new_index             int64 \n",
      " 14  months_number         int64 \n",
      " 15  counter_type          object\n",
      "dtypes: int64(12), object(4)\n",
      "memory usage: 546.5+ MB\n"
     ]
    }
   ],
   "source": [
    "#Get concise information of each column in dataset\n",
    "invoice_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VII8aa8CSoed",
    "outputId": "330bd35b-fd37-422b-b718-c34d354661db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 135493 entries, 0 to 135492\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   disrict        135493 non-null  int64  \n",
      " 1   client_id      135493 non-null  object \n",
      " 2   client_catg    135493 non-null  int64  \n",
      " 3   region         135493 non-null  int64  \n",
      " 4   creation_date  135493 non-null  object \n",
      " 5   target         135493 non-null  float64\n",
      "dtypes: float64(1), int64(3), object(2)\n",
      "memory usage: 6.2+ MB\n"
     ]
    }
   ],
   "source": [
    "#Get concise information of each column in dataset\n",
    "client_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iiJ3e_pFSOWg",
    "outputId": "d074cb2a-d009-471f-f124-466db367df68"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "client_id - 135493\n",
      "invoice_date - 8275\n",
      "tarif_type - 17\n",
      "counter_number - 201893\n",
      "counter_statue - 12\n",
      "counter_code - 42\n",
      "reading_remarque - 8\n",
      "counter_coefficient - 16\n",
      "consommation_level_1 - 8295\n",
      "consommation_level_2 - 12576\n",
      "consommation_level_3 - 2253\n",
      "consommation_level_4 - 12075\n",
      "old_index - 155648\n",
      "new_index - 157980\n",
      "months_number - 1370\n",
      "counter_type - 2\n"
     ]
    }
   ],
   "source": [
    "#Getting unique values on the invoice train data\n",
    "for col in invoice_train.columns:\n",
    "    print(f\"{col} - {invoice_train[col].nunique()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uMC16gFETUAF",
    "outputId": "0e363f7b-35c4-45c2-c763-acf80585ef7c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "disrict - 4\n",
      "client_id - 135493\n",
      "client_catg - 3\n",
      "region - 25\n",
      "creation_date - 8088\n",
      "target - 2\n"
     ]
    }
   ],
   "source": [
    "#Getting unique values on the invoice train data\n",
    "for col in client_train.columns:\n",
    "    print(f\"{col} - {client_train[col].nunique()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "h10LPXsfVHhu",
    "outputId": "332ac9e1-02e3-4a3a-a8d6-1c8d706b9601"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "client_id               0\n",
       "invoice_date            0\n",
       "tarif_type              0\n",
       "counter_number          0\n",
       "counter_statue          0\n",
       "counter_code            0\n",
       "reading_remarque        0\n",
       "counter_coefficient     0\n",
       "consommation_level_1    0\n",
       "consommation_level_2    0\n",
       "consommation_level_3    0\n",
       "consommation_level_4    0\n",
       "old_index               0\n",
       "new_index               0\n",
       "months_number           0\n",
       "counter_type            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for missing values\n",
    "invoice_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b3XyoiIbVO-b",
    "outputId": "9200e12f-66fd-43a4-edf4-daa1e1cfdd11"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "disrict          0\n",
       "client_id        0\n",
       "client_catg      0\n",
       "region           0\n",
       "creation_date    0\n",
       "target           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for missing values\n",
    "client_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i3oZLTnSVSOT"
   },
   "source": [
    "No missing values in train set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical Analysis and EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Work_Space import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = pd.to_datetime(client_train['creation_date'], dayfirst=True)\n",
    "client_train['creation_year'] = L.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_client(client_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "years=set(L.dt.year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_client_per_year(client_train,years)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of customers increased over the years 1997 and 2019 especially for client category 11, however, we have up and down from 1999 to 2005 and then starting from 2006 the increase of the number of customers is noticeable, and we noticed the same for the client categories 12 and 51."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vVKiOeZn8YOBn+B8X1Zvypb/mAccXcum/uA5Sz1SoDnmZ/M7ATfgfGhAZKIv/qasf6igCbqhQHfAy+SRwHv6OBW0rlbC9CBxRyKVrZkLzeqFCrbLgDfq3AxX5bL9IYAVwWiGX/r3rIPVEhVpFXib/GeAOYCPXWaRu3IH/ToCi6yD1QDtBqsTL5M/Cn4lKZSrV9A1gppfJ7+Y6SD3QCLXCvEx+BP7boQ5xnUXqWjPwnUIufavrIHGmQq0gL5PfDvgjsKXrLCKBnwPnF3Jp/eFXgAq1QrxM/kDgVmCI6ywindwLHK1pAcOnbagV4GXyp+CPTFWmUosOAp7wMvlNXQeJGxVqyLxM/ufAb4Ck6ywiPdgReM7L5PdwHSROtMofEi+TbwCuBb7lOotIH6wGjizk0ne7DhIHKtQQeJn8cPxV/H1cZxHphzbg24Vc+mbXQaJOq/wD5GXyGwGPoTKV6EoC071M/iTXQaJOhToAXiY/EXgcmOI4ishAGeDq4AMo0k8q1H7yMvkJ+J982sJ1FpEQXeJl8lnXIaJK21D7IZjgZAYw2XEUkUq5tJBLn+06RNSoUPvIy+Q3wC9TzWEqcffrQi59husQUaJV/j7wMvlx+Kv5KlOpB6d7mfz5rkNEiUaoZfIy+TH4ZbqD6ywiVXZKIZe+2nWIKFChlsHL5IcBfwd2cp1FxIF24PBCLv0H10FqnVb5exEcruR2VKZSvxLAzV4m/0XXQWqdCrV3vwbSrkOIODYIuEcTVfdMhdoDL5M/A/iu6xwiNWIY8ICXyevtgt3QNtRuBPOZ3o3+6Yh09iawm45TtS6VRRe8TH5n/Mmh9fyIrGtL/G2qxnWQWqPC6CSYdPdPaHJokZ58Gfix6xC1Rqv8JbxMfhDwJLCz6ywiEWCBQwq59D2ug9QKjVDXdikqU5FyGfxp/7ZxHaRWaIQa8DL5Q4C7XOcQiaC38HdSLXMdxDUVKuBl8pOAF4GRrrOIRNQDwJfr/fDUdb/KH2w3vROVqchAHAD8l+sQrtV9oQKXoO2mImH4hZfJ1/VMbHW9yu9l8gfjH1xPRMLxAjC1kEu3uA7iQt2OUL1MfkPgGtc5RGJmZ+BC1yFcqdtCBa4ERrsOIRJDGS+Tn+o6hAt1ucqvVX2Rinsb2LGQS69wHaSa6m6E6mXy6+OPTkWkcrYAfuU6RLXVXaECvwTGuw4hUgdO8DL5vVyHqKa6WuX3Mvl9gL+4zhG29uYmljx4OWs+eAeAsQecjkk1suT/rsS2rcEkkoze5xQaJ6w7jeVHz91L08y/gIGGcR5jDzgDkxrE0hnXs+rfLzBog80Z++WzAGh69VHam5czYpcDq/r4JNJm46/618Ve/7oZoXqZ/FDgd65zVMKHj/yOwZN2ZuMTrmbCcVfQMGYTls64nlHTjmDCsVcwas+jWDrj+nWu17r8Az564U+MP+ZXTPjOb6C9nRWzH6N99QpWvzubCcf9L9a2s2ZxgfaW1ax49WGGf0oHL5A+2Ro423WIaqmbQsWfasxzHSJs7atX0jxvFsN22BcAk2wgMXiYf9malf9ZJjlsTDc30IZtXYNtb8O2riY5bDRgsG2tWGuxrf4I96Nn72b4zl/FJFPVeFgSLz/yMnnPdYhqqIu/Di+T/wTw/1znqITWZQtJDhnBkgcuY837c2gc/wnW/8KJjP7CiSy68wKW/u33YNsZf/Ql61w3NXwsI3b7Gu9edSwmNYjBm3+K9Tb3j0U4ZPIeLLjhNAZvtiOmcShrFrzJqGlHVPvhSTysh7/v4mDXQSqtLrahepn8XcAhrnNUwuoFb7HwprMYf/TFNE6YzIcP/5bEoCG0r1lJ4ybbMXTyNFbMfpymmQ+x4eE/W+u6bc1NLL7n54w78FwSjUNZfF+OIZOnMWzbvddabsmDlzN8pzSrF/6L5jkv0bCBx6g9Dq/mw5R4+GIhl37EdYhKiv0qv5fJTyOmZQr+KDM5fOx/djgNmTyNNYvepumVRxiy5R7+eVvtyeoFb65z3ebCy6RGbkhyyEhMMsWQLaey+t3Zay2zZtHb/v2svzErXn2UcQdlaFk8l5YP363wI5MY+rWXycd6rTjWhRoc8+ZS1zkqKTlsfVIjxtKyZD4AzXNn0jB2U5LDRrN63isfn7f+hHWumxoxjjXvvUF7SzPWWn+5MZustcyyx29m5J5HQXsr2Hb/TJPAtq6u7AOTONoWONl1iEqK9Sq/l8kfDtzmOkelrVn0b5Y8dDm2rZXUqPGMOeAMWj6Yy9KHf4dtb8OkBjF631NpHP8JWpcvYclDl7PhYf8NwLLHb2HF649jEgkGbbgFY/Y/DZNqAGDlm0+x5v05jNrzSACWPnodq+a8SMMGHuO+8n1nj1cibSEwqZBLr3IdpBJiW6heJt8IvE4M9+yLRNzZhVw6lmuOcV7lPw2VqUgtOjd4X3jsxLJQvUx+BPAD1zlEpEvjiOnbGGNZqMB3gfVdhxCRbn0/GPjESuwK1cvkhwBnus4hIj0aDZzhOkTYYleo+G/LGOc6hIj06kwvkx/lOkSYYlWowRFMv+c6h4iUZRT+zuPYiFWhAkcCG7sOISJlO8XL5BtchwhLbAo1+FRU3UwTJhIT44Gvuw4RltgUKvAl/I+2iUi0xGa1P06FerrrACLSL7t5mfxurkOEIRaFGkxeu4/rHCLSb7EYpcaiUIHjAOM6hIj022FeJh/5g2dGvlC9TD4BHOs6h4gMyCBiMLVf5AsV2B+Y6DqEiAzYiV4mn3QdYiDiUKjHuw4gIqHYCPi86xADEelC9TL5DYEvu84hIqE50nWAgYh0oQLHALH5lIWIcLCXyQ92HaK/4lCoIhIfI4ADXIfor8gWqpfJTwa2cZ1DREIX2dX+yBYq8DXXAUSkItJRnXw6yoV6kOsAIlIRg4GDXYfoj0gWqpfJTwBi8dlfEenSEa4D9EckCxV/dKqPmorE115RPDJqlAtVROJrELCX6xB9FblCDY5Bs5fjGCJSefu6DtBXkStU/Peo6c38IvG3n+sAfRXFQv2i6wAiUhWTvUx+M9ch+iKKhfpZ1wFEpGoiNUqNVKEGb5fawnUOEakaFWoFaXQqUl++4GXyKdchyqVCFZFaNhKY4jpEuVSoIlLrdnUdoFyRKVQvkx+DZpcSqUcq1Ar4DPq4qUg92sV1gHJFqVCnug4gIk5s42XyQ1yHKEeUCnV71wFExIkksJPrEOWIUqFu5zqAiDgTidX+SBRqMHv3Jq5ziIgzkdgxFYlCRaNTkXqnEWqIVKgi9W0LL5Ov+VnmVKgiEgVJoOZnnlKhikhU1PzESFEp1G1dBxAR5ya5DtCbmi9UL5MfDmzgOoeIOKcRaggmuA4gIjVBhRoCFaqIgAo1FBu7DiAiNUHbUEOgEaqIAAz1Mvma3p8ShULVCFVEOoxxHaAnUShUjVBFpMP6rgP0JAqFqhGqiHQY7TpAT6JQqBu5DiAiNUMj1AEa7jqAiNQMjVAHaD3XAUSkZmiEOkAqVBHpoBFqf3mZ/GB0pFMR+ZhGqAOg0amIlBrhOkBPVKgiEiVJ1wF6okIVkShRoQ6AClVEStV0oaZcB+hFo+sAUhv2SLw6ayzFZtc5xK1WUu9B2nWMbtV6oa5xHUDcG8Xypbc0/HwLYxjsOos4txR+7DpDt2p9lV8jEuGc1B3/VJlKoN11gJ6oUKXGWXto8u+e6xRSM9pcB+iJClVq2lcST704yLTV/PHYpWpUqAOgQq1z5zbc3uo6g9SUJtcBelLrhbrKdQBxZ6JZ/N7GfLCL6xxSU4quA/Skpgu1kEu3UuNDfKmc81M3vWVMbb/vUKpumesAPanpQg1otb8OpWht2Sfxwtauc0jNWeY6QE+iUKg1vc1EKuOo5MPPJ42t6SNcihPLXAfoSRQK9X3XAaT6Tk/do48dS1e0DXWAVKh1Zmsz9+3RZvkU1zmkJi1zHaAnUSjURa4DSHVd2DB9vusMUrOWug7QExWq1JT1WL3y02b2FNc5pGa96zpAT6JQqDX9BEq4Tk3d94IxjHSdQ2pSCzXeB1EoVK3+1ZHjkg+OdZ1BatZ8skVNjjJA81wHkOqYmpg1a6hZrfeeSncKrgP0RoUqNeOC1E01vcNBnCu4DtCbKBTqe8Bq1yGkskaxfOlW5h19bl96Mtd1gN7UfKEWcuk24HXXOaSyvq9JpKV3BdcBelPzhRp41XUAqSRrD9Mk0tK7Oa4D9CYqhTrLdQCpnHTiGU0iLeWo+YGVClWcy6Ru0yTS0pv5ZIsfug7Rm6gUas3/Z5L+2ZjFCyaaxdoZJb152XWAckSlUOcAK12HkPCd13DLG5pEWsow03WAckSiUAu5tAVec51DwpWkrXW/xHN6I7+U42XXAcoRiUINaLU/Zo5KPvxc0tgNXeeQSNAINWTPuA4g4To9dbcmkZZyNAH/ch2iHFEq1MddB5DwbGXe+fcYTSIt5XmZbNG6DlGOKBXqa8AS1yEkHBembtQcDVKuyAymIlOowY6pf7jOIQO3HqtX7p7QJNJSthmuA5QrMoUaiMx/Kuneyan7NYm0lKsFeMJ1iHKpUKXqjtck0lK+58gWV7gOUa6oFeqLQGSeXFnX7olZrw01zXrvqZRrhusAfRGpQi3k0q3AU65zSP9dkLqp5j+PLTVlhusAfRGpQg38zXUA6Z+RNC3b2ryzs+scEhmR2n4K0SzU+10HkP75fuqOmcagN/NLuZ4kW4zUHB6RK9RCLv0qEfnUhJSy9uvJv2vOU+mLe1wH6KvIFWrgPtcBpG8OSDz70iDT6rnOIZFyr+sAfRXVQr3XdQDpm0zq1hbXGSRSXiRbrPmD8nUW1UJ9EljsOoSUZwIfLNhEk0hL30RudR8iWqiFXLod+JPrHFIeTSIt/aBCrbJ7XQeQ3iVpa90/8exWrnNIpLxFthjJ48hFuVD/ij9PotSwI5OPPJ80drzrHBIpkRydQoQLtZBLNwN3uc4hPTs9dfcg1xkkcm52HaC/IluogWtdB5DuTTbvzBnDR59ynUMi5XmyxVdch+ivSBdqIZd+AnjddQ7p2oWp6e8Yg3GdQyLlOtcBBiLShRr4vesAsq7BrF61e+K1HV3nkEhZBdzmOsRAxKFQb8SfREFqyMmpP72QMIxynUMi5Y9ki0XXIQYi8oVayKXfB/7sOoes7fjkA6NdZ5DIifTqPsSgUAOR/0XEyW5m9mvDTPM2rnNIpLwN/N11iIGKS6E+BLzrOoT4LmyYrkmkpa+uicqhonsSi0It5NJtwG9c5xB/EultzFxNIi190QT81nWIMMSiUANXAh+5DlHvzk7dqUmkpa+uIVtc5jpEGGJTqIVcukhM/stFl7XfSM7Y1HUKiZRW4DLXIcISm0IN/ApY7TpEvdrfn0R6c9c5JFLuIFt8x3WIsMSqUAu59ALgJtc56tUPUretcZ1BIudi1wHCFKtCDVwEtLsOUW82YsnCTc37mkRa+uKvZIszXYcIU+wKtZBLv0WEp/+KqvMabn7dGFKuc0ikXOQ6QNhiV6iBnOsA9SRJW+uXEs9Ndp1DIuUxssWHXYcIWywLtZBLP4+OjFo1RyQffT5p2jdynUMi5TzXASohzqto5wJp4v0Ya8IZqT/GchLp5lbLZ69fweo2aG2HQ7dO8d97D+YPs1rI/n01sxe38+wJQ9llwrqHy5pXbOdb965iYZMlYeDEnRo4ffdGAM79azMP/quVKeOTTP+a/5bdm2au4cNV9j/LxNyDZIv/cB2iEmI5QgUo5NJvAL9znSPutjTzYjuJdGMSHj1mKDNPHsbLJw3lobdbeXp+K9ttkODur6/HZzfr/riDqQRcuu9gZn93GE9/ZyhXPtfCa4vbKDZbnpzfxj9PGUabtbyyqI1VLZYbZrZw6q6x/L/UmQV+6DpEpcS2UANZ9OmpiorzJNLGGIYN8h9aSzu0tIEBth6XZPLYng/iutHwBDtt5C8zvNGw9bgE737kj1bXtFmstaxqgYYkXPzkGk7bbRANyVg+jZ3dTLb4susQlRLrQi3k0ouBX7jOEVeDWb1qamJWrCeRbmu3TLm6iQ0uXs4+k1J8emLftyAVlrXz0oI2Pj0xyfBGwyFbN/Cp365g81EJRjYannuvjQO3aqhA+prTDJzvOkQl1cP2xV8BpwATXQeJmxOTf34hYdjTdY5KSiYML588jGXNlq/dsZJX329juw16Hp2WalpjOeTOlVy2/2BGNPoj0HOmNXLONH9b6fH3r+LHezVy7Ytr+MvbreywYZLzPxvb7aiXx+lTUV2J9QgVoJBLryLm/xVdOTGVr5tJpEcNNuy1WYqH/tVa9nVa2vwyPWr7Bg7eet0R6EsL2gDYckyC6TNbuPOwIbz6fhtvLWkLLXcNmQ/8xHWISot9oQZuAl5yHSJOdjWvz477JNKLV7SzrNmfonNVi+XhOa1sNba8PxlrLd+5v5mtxyb53tSuR5w/+ttqfrx3Iy3t0BbMBJowsDKeB/Q5nWyxyXWISquLQi3k0u3ACUAs//W7cGHD9CWuM1TagibL3jeuYIermtj1mhXsMynFl7ds4J7ZLUz85XKemt9G+taV7HfzCgDeW97OAbesBOCJeW3c9M8WHp3TypSrm5hydRMPvPVxU977egu7TkgyYXiCUYMNUycm2f6qJoyBHceXv0khIvJki3e7DlENxtrIT5JdNi+Tvxg423WOqBtBU3Fm44kNxjDEdRapeauAbckW57gOUg11MUItcQH+sWtkAM5K3fWyylTK9NN6KVOos0INdlCd6DpH1B2efFSTSEs5ZhOz6fl6U1eFClDIpR9FR0ntt/0Sz77UqEmkpXftwMlki/HcxdaNuivUwNnAAtchougHqduaXWeQSPgl2eJjrkNUW10WaiGXXgb8l+scUTOeDxdtZhbt6jqH1LyZxHQ2qd7UZaECFHLpu4FbXOeIkvMabp6tSaSlF83AkWSLdXk4nLot1MApaK9/WZK0tR6QeFaTSEtvziVbfM11CFfqulALufRy4Aigrjac98c3kn/TJNLSm/8DrnAdwqW6LlSAQi79HHW6vacvzkz9sS6mQ5J+WwIcS7ZYP58U6kLdF2rgEuBPrkPUqk+a+YWxFHdynUNqVjvwTbLFun/njAoVKOTSFjgGKDiOUpMuTE2fG9dJpCUUPyJbfNB1iFpQV5/l742Xye8K/AOoi2NRlKORNc2zG7+9KmFY33UWqUl/JFs81HWIWqERaolge+qprnPUkmASaZWpdGUW8G3XIWqJCrWTQi59HTpsyn+cmMqPcp1BatIy4KB6mOO0L1SoXfsBcJfrEK7tYt6YPdys2tZ1Dqk57fhv3v+X6yC1RoXahWAn1TeBZ1xncakeJpGWfjlHO6G6pkLtRiGXbga+Sp3u+R9BU3E7M0dvlZLOLiVbvNR1iFqlQu1BIZd+H0gDRddZqu3M1B81ibR0djPwfdchapkKtReFXPo14BDq7OOpRyYf2cR1BqkpDwHH1fsnoXqjQi1DIZd+BDgSKP8YwhG2b+K5lxpN6yTXOaRmPAscWm+TRfeHCrVMhVz6LvwdVbE/cuoPUrdqEmnp8CaQJltc4TpIFKhQ+6CQS9+O/0bmdsdRKmY8Hy7yzKJdXOeQmvBvYB+yxQ9cB4kKFWofFXLpm4HjgVhuS/phwy2zjUEzS8lbwOfIFt9xHSRKVKj9UMilrwdOImalmqC97YDEM5pEWl7HL9P5roNEjQq1nwq59DXE7LhU30j+7YWUJpGud7Pwy7Tup+LrDxXqABRy6d8AJxKTHVVnpu5Kus4gTs0E9iJbfN91kKhSoQ5QMFL9GrDSdZaB2MK8O3ecJpGuZy8An9cOqIFRoYagkEv/CfgCENkX44Wp6QVNIl238vgj0w9dB4k6FWpICrn008A0YI7rLH3VyJrmPROv7OA6hzjxG+BATcMXDhVqiAq59JvAHsBLrrP0xQnJvCaRrj/twFlki98lW4zFPoBaoEINWSGXXgh8Dvir6yzlOin151GuM0hVrQIOI1v8pesgcaNCrYBCLr0cOAD4lessvdnJvPm6JpGuK+8De5Mt3u06SBzpIH0V5mXyXweuA4a5ztKV+wed9/gOiTmfcZ1DquJp4Otki/NcB4krjVArrJBL3wnsBsx2naWz4awobm/m7Ow6h1TFZcBnVaaVpUKtgkIuPRu/VO90naWUJpGuCx/hT713pqbfqzyt8leZl8mfAVwMpBxH4Y3GY95uNC1buM4hFTMTv0x1ML0q0Qi1ygq59GXAXvhTozmzT+L5l1WmsXYdsLvKtLpUqA4UcukngB2Bq3A0Y9UPUreucnG/UnGLgYPJFo8nW9RE4VWmVX7HvEz+i/ijiU2rdZ8b8uH7Tzf+1/qa9zR27gNO1OQm7miE6lghl34Y2B6/VKvihw23vqYyjZUPgW+RLR6kMnVLI9Qa4mXyXwKuATau1H0kaG97s/Fbi1KmfUKl7kOq6o/Ad8kWF7kOIhqh1pRCLv0gsB3+ttWKfL76sOSMF1SmsTAXf1vpoSrT2qERao3yMvkdgF/jvyMgNM82nvr8BmaZDsIXXauAXwAXkS1qx2KNUaHWOC+TPxS4BNhsoLc1ybw395FBZ2+qeU8j6w/A2TpwXu3SKn+NK+TSdwFbARcwwKMCaBLpyHoFf0KTr6tMa5tGqBHiZfIT8Vf3DqeP/wwbWdM8u/HYlQljR1cknFTCPOBnwLWaszQaVKgR5GXy2wDnA9+gzGI9NXnfE+c03DGtosEkLO8C/wNcQ7a4JswbNsZsiD+t5O7AUmANcJG19p7g8l8DhwKbWGvbg/OuBz5VcjOjgPWstRuGmS0OVKgR5mXyk/GL9QigxyOW/rPxO6+OMKu2q0ow6a8F+EX6O7LF1WHfuDHGAE8CN1prrw7O2wz4qrX2CmNMAigA7wEZa+2MLm4jAcwApltrrw07Y9SpUGPAy+Q/AZwHHE0Xk658yrz1xj2NF06uejAp10IgB/y2kh8XNcZ8AbjAWvu5Hi4/C7gD2MNae1IXy5wP7GStPbhSOaNMhRojXiY/CcjgF+t6HeffO+hHj09JvK1JpGvPi8DlwO2VGJF2Zow5DdjcWntmN5dfC/wd/yOsswHPWttScnnHFJQ7WWt1hNQuqFBjyMvkRwPHA6cMY+XoVxqPTxrDUNe5BIBW4G7gcrLFJ6p5x50L1RhzJbAn/nbUafir+5OttcuNMXcD11lr88Gyw4AXgFOttY9UM3eUqFBjzMvkE6ck79v33IY7TgP2Q2+Tc+kD4HfAVWSL810E6GqV3xgzFngeOA24DX+2KoAhwF+stUcHy10PfGCt/X51U0eLCrVeZEduhj9qPQ7QR0+rowV4CLgZuN/1dHrBTqmngRustVcF520KPAY8Bdxvrb0tOH8oMAfw8A84+UNgd2ttqO86iBsVar3JjkwAn8F/y9WhwDi3gWLpKfwSvYNscYnrMKWMMRvhv23q0/ij0RXADcF5nrX2o5Jl78bfQfVj/BFr58cy1Vqrj7+WUKHWs+zIJPB5/HI9GFjfbaBImw3cDtxCtvi26zDihgpVfNmRDcC+wFeDr57TPLWvGf/9mA8AebJFp4e0kdqgQpWuZUd+Er9Y98Of8Wq40zy1YR4dBQqPkC0OaG4FiR8VqvTOH71OBfYJvu4CjHSaqfLagdfwP1nkn7LFt9xGklqnQpW+y440wJbAbsCuwdcpQKPDVAO1GP+wyx0F+jTZYtFtJIkaFaqEwx/FbgtMxi/bTwZft6S2dna9i78D6bWS02yyxQ+cppJYUKFK5WVHjsEv2C2ADYLTuC6+9mc7rcXfQbQSWA4sKjktBOYHp3nAXLLFj7q5HZEBU6FK7ciOTAGDejgl8A8BsrLk1Ey2qBex1AQVqohISPTZbhGRkKhQRURCokIVEQmJClVEJCQqVBGRkKhQRURCokIVEQmJClVEJCQqVBGRkKhQRURCokIVEQmJClVEJCQqVBGRkKhQRURCokIVEQmJClVEJCQqVBGRkKhQRURCokIVEQmJClVEJCQqVBGRkKhQRURCokIVEQmJClVEJCQqVBGRkKhQRURCokIVEQmJClVEJCQqVBGRkKhQRURCokIVEQmJClVEJCQqVBGRkKhQRURCokIVEQmJClVEJCQqVBGRkKhQRURC8v8BWlN5eOeRdrUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_counter_type(invoice_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation:\n",
    "\n",
    "    68.8% for the counter type ELEC\n",
    "\n",
    "    31.2% for the counter type GAZ\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "id": "JOzSAlEWTR3j",
    "outputId": "97a46548-924e-44a4-9b85-e40d01979b99"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize fraudulent activities\n",
    "fraudactivities = client_train.groupby(['target'])['client_id'].count()\n",
    "plt.bar(x=fraudactivities.index, height=fraudactivities.values, tick_label = [0,1])\n",
    "plt.title('Fraud - Target Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fraud(client_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05584052312665599"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client_train['target'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a fraud is P(target=1)=0.05584052312665599"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fQb7RZf_Ufn9"
   },
   "source": [
    "Target is highly imbalanced with fewer cases of fraudulent activities. <strong>Let's specify</strong>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_distribution_by(client_train,'disrict',(2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disrict</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.035869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.051619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.065236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.071485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           target\n",
       "disrict          \n",
       "60       0.035869\n",
       "62       0.051619\n",
       "63       0.065236\n",
       "69       0.071485"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client_train[['disrict', 'target']].groupby(['disrict']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The disrict with the biggest fraud activity is 69 P(target=1|disrict=69)=0.071485"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_distribution_by(client_train,'client_catg',(2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_catg</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.054687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.039638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.168653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               target\n",
       "client_catg          \n",
       "11           0.054687\n",
       "12           0.039638\n",
       "51           0.168653"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client_train[['client_catg', 'target']].groupby(['client_catg']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The category with the biggest fraud activity is 51 P(target=1|client_catg=51)=0.168653"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0.035919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>0.102967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>0.055966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>0.055927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>0.075071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>0.065813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0.018868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>0.033005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>0.061703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>0.054497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>0.066876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>0.057806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>0.048643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>0.062204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>0.072533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>0.043064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>0.050917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>0.080445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>0.049783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>0.070809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>0.071671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>0.100932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>0.066895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>0.005714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          target\n",
       "region          \n",
       "101     0.035919\n",
       "103     0.102967\n",
       "104     0.055966\n",
       "105     0.055927\n",
       "106     0.075071\n",
       "107     0.065813\n",
       "199     0.000000\n",
       "206     0.018868\n",
       "301     0.033005\n",
       "302     0.061703\n",
       "303     0.054497\n",
       "304     0.066876\n",
       "305     0.057806\n",
       "306     0.048643\n",
       "307     0.062204\n",
       "308     0.072533\n",
       "309     0.043064\n",
       "310     0.050917\n",
       "311     0.080445\n",
       "312     0.049783\n",
       "313     0.070809\n",
       "371     0.071671\n",
       "372     0.100932\n",
       "379     0.066895\n",
       "399     0.005714"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client_train[['region', 'target']].groupby(['region']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The region with the biggest fraud activity is 103 P(target=1|disrict=103)=0.102967"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n0q42YdAduB840HcUkRJ7BdiXMIp8B0kzrT1STeIpkTtRYUtt2gF4kDDo7TtImqm0q0W8l74bgG/6jiJSRnsAf08+UUoHqLSrx1XASb5DiFTAAcCdySdLaSeVdjUIg0uAc33HEKmgbwI3JJ8wpR1U2r6FwfnAT33HEPHgJOJPmNIOKm2fwuBU4q3GROrVuYRB6DtEmmiVP1/C4CjiNUX0xikC5xFGGnUXQaXtQxgcCIxHx3IUWcEBJxNGN/sOUu1U2pUWBsOAl4ABnpOIVJsMsDdh9KzvINVMpV1J8UYFE4FtfEcRqVIzgR0Iozm+g1QrzadW1g2osEVaMxi4izDo6jtItVJpV0oYnAN8x3cMkRQYDfzad4hqpemRSgiDPYAn0BePIu1xJGF0j+8Q1UalXW5h8BXivZut6zuKSMosAnYhjCb7DlJNND1STmHQDbgHFbZIR/Ql3rlUP99BqolKu7yuAHb3HUIkxbYAtO52AZV2uYTBd4BzfMcQqQFHEAYX+g5RLTSnXQ5hMAR4C+jvO4pIjcgRz2+/4juIbxppl8e1qLBFSqkB+JPW31Zpl148LXKI7xgiNWgE8APfIXzT9EgphcFawGRgLd9RRGrUMmA7wug930F80Ui7tH6PCluknHoCN9bzEW9U2qUSBocAR/uOIVIH9gRO8x3CF02PlEIY9CdeW2SI7ygideILYEvC6BPfQSpNI+3S+DUqbJFK6k+8llbd0Ui7s8JgL+ApoG7n2EQ8Ooowust3iEpSaXdGGPQEXgc29R1FpE7NBYYTRp/7DlIpmh7pnHNRYYv4NAi4xHeIStJIu6PCYA1gKjrWo4hvGeIvJaf4DlIJGml33P+gwhapBt2Ay3yHqBSNtDsiDIYC7wLdPScRkZVGEkbP+w5Rbhppd8yvUGGLVJvLfQeoBJV2e4XBNmjLR5Gq4hzLnslt7Xb40V8O8J2l3Op+N4cdEKJ1skWqgnNkX3XDnv1e47mbzmSt0UAf4J++c5WT5rTbIwxGEB+kV6Ut4pFz5N936008PfP99T5wgzds8uvDpo8bc7+XYBWgkXb7hKiwRbya4dZ64YzGsQPfcBvv1sJVfg7UbGlrpF2sMNgBeNl3DJF69anr/8o5mbN6PJvfeqsirn7Q9HFjHi17KA800i7eT3wHEKlHC12vt36QOa3x0fwuO7TjZucDNVnaGmkXIz5Q73Ti49SJSAUsdd3fD7PHz78rt+8uHVzEiOnjxrxW0lBVQCPt4pyGClukIjKu4cMrskd+8sfcmJGOLp1ZLfk84IRS5aoWGmm3JQy6AR8D6/iOIlLLcq7LrOtzh0y5MvutUTkaSjGgzABDp48bM7MEy6oa2rimbUegwhYpm7yzz+7M7vv0lstvGnh59qg9S1TYEO+T5OwSLatqaKTdljCYQHxMOhEpIef44qH8yEk/zJy6w2J69SvT3cwH1p8+bsziMi2/4jSn3Zow2BoVtkhJOcfSCfltXxibOXOb+fQfXea7Gwh8G7ipzPdTMSrt1p3pO4BIrXCOzCS3ycTvNZ676SzWLHdZFzqJGiptTY+0JAz6ATOBvr6jiKSZc+Tfc0OeOz0zdv1pbvAGnmJsOn3cmJo4SIJG2i07HhW2SKd8nF/7+dMzY9d8y220u+coJxIfuCT1NNJuSRi8CRSzuayINDHPBa+c1Xh2z+fdllv6zpKYAWw4fdyYvO8gnaWRdnPCYFdU2CLt9oXr/cYFmdOyj+V3bs8m55UwBPgq8JjvIJ2l0m7e4b4DiKTJUtf9vZ9lT4zuye29s+8srTiJGihtTY80JwzeATb3HUOk2jW6rtMvz3575o25g0eBVftui5cBa6V9nW2NtJsKgy1QYYu0Kuu6zLou9/UpV2W/OSpHw1DfeYrUEzgQuNd3kM5Qaa/uMN8BRKpV3tmnd+b2e+sX2eNGNtItjRueHUrKS1vTI02FwURgV98xRKqJc0Tj86NevShzyo6L6ZXmVWE/B9aZPm5M1neQjtIOowqFwVeAju67V6TmOMeSJ3PbPb398uvz52bOHp3ywgZYA9irMwsws3PMbLKZ3VmiTCuWu7eZPdjW9TQ9sqpD0TEgRXCOzMtus+fOajxn89msUclNzivhUOCJTtz+TOAg59y0FReYWVfnXEVG7xppr+ow3wFEfHKO/OT8+v/dp/HK2d9sDPeazRq1uFviQzt6QzO7DtgYGG9mkZn90cweA24zs6Fm9oyZvZL87JbcZpURtJldbWYnJqcPNLN3zOw/wDeKyqA57UQY9AfmAd19RxHx4cP8oIlnZMau/bYbOsx3lgrYfvq4Ma925IZmNh3YCTgL+Dqwh3NuqZn1BvLOuWVmtinwF+fcTma2N3CBc+6Q5PZXAy8BfwXeB/YFpgB3Ab1XXK8lmh5Z6SBU2FKH5rrg5bMaz+n1ghs+0neWCvoa8GoJljPeObc0Od0NuNrMRgA5YLM2brsFMM059z6Amd0BfLetO1Rpr9Tqu5tIrYlc79fPy5yR/3d+xx19Z/FgH+DyEiyncEOd7wNzgO2Ip56XJZdnWXUqumfB6XZPdWhOeyXfeyETqYglrse75zWe/uJ2y2/c9t/5HUf4zuPJHkN/9FC3Ei8zAGY55/LAcaw8GPiHwJZm1sPMAmC/5PJ3gI3MbMV01NHF3IlG2gBhsDawke8YIuXU6LpOG5c9evZNuQNHpmCT83LrC+wMPFvCZV4D3Gtm3wKeJBmFO+c+NrO7gdeJ57AnJZcvM7PvAg+Z2afAf4Ct27oTfREJEAaHAA/4jiFSDlnX5ZM/5A6d9rvsEaPydGlo+xZ148fTx40Z5ztEe2mkHdMWkFJz8s7m3Zbbf/Kl2WN2baTber7zVKE9fAfoCM1px1TaUjOcI/pHbvent17+p95h9sS9GunWw3emKrXb0B89lLppIo20w8DQputSA5xjyRP57V88L3PGdhF9a20rxnIYSLza3WTfQdpDpR3vhjXwHUKko5yj8UW3+cSzG8/eYk7tbXJebiNQaadOPW1QIDXEOXJvuw2fOyMzduhHbp1O7QSpjm0L/MV3iPZQaWs+W1LGOdyHbp3nT898f9A7boNUfplWRbbzHaC9VNoqbUmROW7AS99rPLfPS25zfUIsjdSVdn2vpx0GvYAv0JuXVLkFrs/r38+c6Z7Mb1/yksl8NoN543/95fnsgtkM2ONY+u+8cmd40fP3svjtp+Iz+RyZz2Yw5Ow7weWZ9/dfkV++iAF7HkfvzUYBMPfeX7DG186ka781Sx23HNaePm7Mp75DFKvey2pL9BxIFVvsekz+Sebkxffl99ipXPfRbc0hDD7pfwFw+Rwzrjnhy/JdIdj1CIJdjwBgyZTn+eLF+2no1Y8vXhpPn633pc/wvZh7z8X03mwUS6Y8T/d1hqWlsCEebf/bd4hi1Xth1cMuKCWFlruuH1yaPWburbmv7VrJTc6Xffga3QZ8ha7BoBavs/jtCfQZHn/vaQ1dcdlGXC4DZrh8joUv3c/aR/ysUpFLYVtU2qmxie8AIoWyrsuM32cPn3517vBRebpsXOn7Xzx5Ar2Ht7wiSj6zjGXTXmaN/U8HoM+Wo/l0/G9Y/NYTDBh9IgtfeYg+W+1Hl249W1xGFRruO0B71Htpa6QtVSHnbN6tuQMmX5b9zsgMXYf4yOByGZZOeYGBo09o8TpLp7xAj/WG09CrHwBdevRh0LdCAHLLFvHF8/ey9uEX8dkjvye/bBH9dzmcHutVfSdu6DtAe9T7ZuwqbfEq71hwb27Pp7deflOfS7LH75Whq7cDcSz94GW6rzOMhj4DW7zO4skT6LNl89vvRP/9C8GoI1n89tN0X3cT1jx4LPMn3FauuKWk0k4RlbZ44RyL/5Xb8ekRy/9o52fOGL2UHr19Z1r89tNfzlU3J798Mcs/fpNem6y+tmHm80/ILfqcnhtsg8suB4urxWUby5a3hDbwHaA96re0w6ArMNh3DKkvzrF8Yn74hJ2XX7Pk1Mz5o7+gb1XsQiGfWcay6a/Se/Pdvrxs4aSHWTjp4S/PL3nvOXoO3Z4u3Vefr14w4XYG7HksAH2Gj2bxG48z+/YL6L9LUceq9a3X0B891PI3r1WmftfTDoP1gY98x5D64By5t9zQ587IjN3oYzdIu0mtPrtOHzfmBd8hilHPX0R6+bJH6otzuGlu3YlnZMau+642Oa9mGwIq7Sqn0Y6U1Sw38MUzG8f2n+Q2HdX2tcWz1HwZqdIWKbH5ru9r52a+ZxPy2+3sO4sULTVz2iptkRJZ7HpO/lHmlCUP5Hfb0XcWabcBvgMUq55Luyq+tZf0W+66Tf1l9th5t+e+OhJSd/QqiaWmD+q5tFO1na1Un4xrmPG77Dc+/EPu0FGOLlrnP90G+A5QLJW2SDvlnM29KXfQO5dnj/K2ybmUnEbaKaAjVEu75B3z/5Yb/frF2RN3XkoPHd6rtgzwHaBY9VzaGmlLUZxj0T/zO738g8xpIxbSRwfOrU0aaaeASlta5RzLn8tvOfHszNlbfUagsq5tKu0UUGlLs5wj+4bb6LkzM2OHzXBrq6zrQ2qmS1XaIgnncFPdV547I/P9we+7IXv6ziMVlZqd59XzDqPeATb3HUOqw0y35otnNp7b/1W3iV4T9ath+rgxed8h2qKRttQ151j4SH6Xlx/N7dxvA5uzaAOb87LvTOLHXAYajPEdo031PNKeDazjO4aIVI0GwqjqR9qpmccpg26+A4hI1XBpKGyo79Je4juAiFSNnO8Axarn0l7gO4CIVI2s7wDFqufSjnwHEJGqodJOAZW2iKyQ8R2gWPVc2gt8BxCRqvGp7wDFqufS1khbRFaY4ztAsVTaIiIq7VRY4DuAiFSN2b4DFKueS1sjbRFZQSPtFFBpi8gKKu0UWOA7gIhUDZV2CqRmFR8RKbvUzGnX865Zp/gO0JrfTVzODa9kcMCpO3Rj7Mge/OCxZTzwXpbuDTBsjS7cfGgvBvS0VW63LOvY6+bFLM9BNg/fHN6Vn+8T74X2h/9axiNTsoxYt4HbDu8FwO2vNfL5Use5I1Nz4A6RctBIu+qF0Xxgnu8YzXlzbo4bXsnwwql9eO30Pjz4Xpb3P8ux/7CuvHlmH14/oy+brdGFy55ZvtptezTAEyf04bXT+/LqaX14dGqWiTOyRMscz87I8foZfck5xxtzcizNOG55LcOZO3f38ChFqopKOyXe9R2gOZPn5Rk5pIHe3YyuXYzRG3blH+9k+dqwrnTtEo+sRw5pYMbC1fckaWb07R5fJ5OHTA4M6GLQmHM451iagW4N8JtnGzlnl+50a7DVliNSRxYSRkt9hyiWSrsKbT2oCxM+zPHZkjxLMo6Hp2T5uMmufm96NcNBmzQ/u5XLO0Zct4hBv1nI/ht3ZdchXenXwzhieDe2v34xGw3oQtDDeHFmjkO30G7Fpe7N8B2gPep5ThuqtLSHr93AD3fvzv63L6Fvd2O7dbp8OcIG+NWE5XTtAsds03zhNnQxXj29LwuWOQ6/awlvzs2x9aAGLty9BxfuHs9dnzJ+KZfs3YMbX2nksalZtl2ngf/ZS/PaUpfe8B2gPep9pP2e7wAtOXmH7rxyWl8mnNSHNXoZm64Z/6lufbWRB9/Pcuc3emHW+rTGgJ7G3ht25dEpq+51ctKseH/vm63Zhdtey3D3t3rz5twc73+Wmv3Ai5TSa74DtEe9l3ZVjrQB5i6Op0M+ivL8fXKWo7fuxqNTsvz6v42MP6oXvbs1X9jzFudZsCw+7ufSjOPxaVm2WGvVP/NPn1zOJfv0IJOHXHKI0C4GS1Kzc0qRkkpVadf79MhU4p2fV93zcMTdS/lsiaNbA/zh4J4M7GWc9fBSludg/9vjI6WNHNLAdYf0YubCPKeMX8bDx/Rm1iLHCfctIZeHvIMjt+rGIZutnEa5750MOw9uYHC/uMhHDWlgm2sXse06Xdhu3QYvj1XEs1SVdv0ejX2FMHgP2NR3DBHx4nPCaE3fIdqj3qdHoIqnSESk7F73HaC9VNoqbZF6lqqpEVBpQwr/aCJSMqn7/6/Shmd9BxARb1TaqRNGU0nRfgdEpGSywFu+Q7SXSjum0bZI/XmXMFp9r2tVTqUd+6/vACJScakcrJW8tM3sRDMb3MLvtjCzV81skpkNK8N9TzeztTpwU5W2SP151HeAjijHSPtEoNnSBg4D7nfObe+cm7riQov5HPW/DCzyeP8iUllZ4N++Q3REq0VpZkPNbLKZ3WBmb5nZY2bWK/ndCDObaGavm9k/zGygmX0T2Am4MxlR9ypY1sHAWOAUM3uyYNnXAK8A65vZtWb2UnJfPy+47ZcjaDPbycyeSk6vmWSaZGbXE+86uv3CKANM6NBtRSSNnieMUnlw72JGt5sCf3DObUV8MNwjkstvA37onNuWeNeGFzvn/ga8BBzjnBvhnPtyx+LOuYeB64CrnHP7JBdvDtyWjLw/BH7inNsJ2BYYbWbbtpHtYuA/zrntgfHABkU8npak8l1XRDoklVMjUFxpT3POvZqcfhkYamYBMMA593Ry+a3AXh24/w+dcxMLzh9pZq8Ak4CtgC3buP1ewB0AzrmHgPkdyLCCSlukfvzTd4COKqa0C1eJyVHaPeItXnHCzDYCLgD2S0bvDwE9k19nWZm1J6sq1R6vXqdKjxkpIiX1KfEANJU69OWfcy4C5pvZnslFxwErRt0LgX4dWGx/4hKPzGwd4KCC300HdkxOH1Fw+QTgGAAzOwgY2IH7jYWRAx7v8O1FJC3+RRitfoDVlOjMGhsnAL8xs9eBEcAlyeW3ANc1/SKyLc6514inRd4CbmLV1fB+DvzOzJ4hHu0XXr5XMqXyNeCjjj2UL/2tk7cXkeqX2qkR0P60VxUGPYk3ae/vO4qIlIUDBhNGs30H6ShtEVkojJYB9/uOISJl83qaCxtU2s35q+8AIlI2D/kO0Fkq7dX9C/jMdwgRKYs7fQfoLJV2U/HWkff6jiEiJTeJMHrbd4jOUmk3T1MkIrXndt8BSkGl3byngVm+Q4hIyeSAv/gOUQoq7ebEK97f4zuGiJTM42lfa2QFlXbLauJdWUQAuNl3gFJRabckjCYC03zHEJFOmwf8w3eIUlFpt+4a3wFEpNNuJYwafYcoFZV2624g3gGWiKTXDb4DlJJKuzXxkS1u9B1DRDrsacLoPd8hSkml3bbfseqeBUUkPa71HaDUVNptCaMP0S5bRdLofWrw/65KuzhX+g4gIu12GWFUc5+SVdrFCKMXgWd8xxCRok2nRjZbb0qlXTyNtkXSYxxhlPUdohxU2sV7gHiOTESq2yfU0BaQTam0ixXvj+Qq3zFEpE2X19LGNE2ptNvnFmCu7xAi0qI51NjGNE2ptNsjjJYCF/uOISItujL5f1qzVNrtdwPwlu8QIrKaz6jBjWmaUmm3V7ze5wW+Y4jIaq4ijBb5DlFuKu2OCKNHgUd9xxCRL80Efu87RCWotDvufLRPEpFqcT5hVBd75FRpd1R8VOea/pZaJCWeIIzq5mDcKu3O+Rnwhe8QInUsA5zlO0QlqbQ7I4zmAZf6jiFSx/4fYTTZd4hKUml33m/RsSRFfPgY+IXvEJWm0u6sMFoO/NB3DJE6NJYwWuw7RKWZc853htoQBg8Ah/iOIVInHiWMDvIdwgeNtEvnNGCB7xAidWA5cLbvEL6otEsljGYCY33HEKkDlxNGU3yH8EWlXUphdCvwoO8YIjXsbeAy3yF8UmmX3mnAfN8hRGrQUuDIWt+LX1tU2qUWT5Oc7juGSA0aSxjV/R42tfZIuYTBLcAJvmOI1Ii7CaNv+w5RDTTSLp+zgam+Q4jUgGnAqb5DVAuVdrnEexw7FqjJI0KLVEgGOIow0j5+EirtcgqjiejwZCKdcRFh9ILvENVEpV1+lwH3+A4hkkKPAFf6DlFt9EVkJYRBL+AZYEffUURSYhawXbInTSmgkXYlxOuVHkp8SCQRaV0OOFaF3TyVdqWE0SfExV3XGwaIFOEcwugJ3yGqlUq7ksLoJeAk3zFEqthvCaNrfIeoZirtSguju4BLfMcQqUL3Ex8wW1qh0vYjRGuUiBR6GfgOYZT3HaTaae0RX7RGicgK04DdCKPZvoOkgUbavqxco2SG7ygiHs0BvqbCLp5K26d4jZJ9gU98RxHx4AvgoHo+oEFHqLR9C6P3gX1QcUt9WQ4cRhhN8h0kbVTa1UDFLfUlS/yl45O+g6SRSrtarCxubTUptWwZcDhh9HffQdJKa49UmzDYFHgKGOw5iUipLQT+D2H0lO8gaabSrkYqbqk9nxN/6ajdrHaSSrtaqbildswG9ieM3vQdpBZoTrtaxXPce6M5bkm36cAeKuzSUWlXs7i4dwf0gpc0egfYkzDSsVJLSKVd7cJoOrAb8KDnJCLtMQnYizDSFr8lptJOg/ggwYcCV/iOIlKEZ4B9dBCD8tAXkWkTBicB1wHdfUcRacbVwHmEUcZ3kFql0k6jMNgT+Duwlu8oIoklwGmE0R2+g9Q6lXZahcFGwAPAVr6jSN2bCnyDMHrdd5B6oDnttAqjacAo4GHfUaSuPQjspMKuHJV2msVfUH4d+A2gj0xSSXngZ8SbpS/wnKWuaHqkVoTBAcAtwLqek0jtmw8cQxg94jtIPVJp15IwWBv4E/HoW6QcJgFHJNNz4oFKuxaFwZnE63T38h1FakYGuBz4BWG03HeYeqbSrlVhsAVwMzDSdxRJvZeAk/VlY3XQF5G1KozeAfYALiTe8bxIey0FfgCMVGFXD42060EYDCcede/qO4qkxpPAqdrZU/XRSLsehNFk4r0Fngcs8BtGqlwEfBfYT4VdnTTSrjdhsCYQAqcDXf2GkSpzP3AmYaR9uFcxlXa9ir+ovAIY4zuKeDcNuJAw+pvvINI2lXa9C4P9gSuBbXxHkYr7DPgFcC1h1Og7jBRHpS0QBg3AycAlwDqe00j5LQV+C/yaMIo8Z5F2UmnLSmHQD7gIGAv09BtGyqCReIvZXxFGn/gOIx2j0pbVhcH6wPeBU4B+ntNI52WIV/n8FWH0ke8w0jkqbWlZGATEq3+dAwzxnEbaLwPcSlzW0z1nkRJRaUvbwqAb8G3gfGCE3zBShE+APwI3EEazfIeR0lJpS/uEwX7E5X0gYJ7TyEoOeAK4BhhPGGU955EyUWlLx4TBlsRbWB4L9PCcpp4tIJ4CuZYwetdzFqkAlbZ0ThisARwBHA2MRrtGqJRXgGuBPxNGS3yHkcpRaUvphMFXgCOBo9AuYcvhE+JNzW8jjJ73HUb8UGlLeYTBUOLyPhrY1m+YVHuLuKjvA14ijPQfts6ptKX84l3DHkW8BsrmntNUuzzwHHFJ30cYTfEbR6qNSlsqKww2AL4K7Jf8aLP5+CAVjxMX9QOE0Vy/caSaqbTFrzDYGtiHeH/fewDr+Q1Udg54H3g++XkBeE07bJJiqbSluoTBhsQFvjuwA/F0ykCvmTrnU+JiXlnSYTTfbyRJM5W2VL8wWJu4vJv+DKN6DuTwOTC14Oct4HnC6AOvqaTmqLQlvcKgK7AxK0t8EDAACJr8u+J0e/Zc2Eg817w8+XcRMJN4tbvCn4+BqYTRgk49FpEiqbSlfoRBD1YWeT8gy6rFvOzL81q1TqqUSltEJEW0ybGISIqotEVEUkSlLSKSIiptEZEUUWmLiKSISltEJEVU2iIiKaLSFhFJEZW2iEiKqLRFRFJEpS0ikiIqbRGRFFFpi4ikiEpbRCRFVNoiIimi0hYRSRGVtohIiqi0RURSRKUtIpIiKm0RkRRRaYuIpIhKW0QkRVTaIiIpotIWEUkRlbaISIqotEVEUkSlLSKSIiptEZEUUWmLiKSISltEJEVU2iIiKaLSFhFJEZW2iEiKqLRFRFJEpS0ikiIqbRGRFFFpi4ikiEpbRCRF/j85G/9vqS0zOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_counter=farud_by_counter(client_train,invoice_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>counter_type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ELEC</th>\n",
       "      <td>0.076751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAZ</th>\n",
       "      <td>0.083561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                target\n",
       "counter_type          \n",
       "ELEC          0.076751\n",
       "GAZ           0.083561"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a fraud in Electricity is P(target=1|counter_type=ELEC)=0.076751"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a fraud in Gaz is P(target=1|counter_type=GAZ)=0.083561"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "client_train.drop(['creation_year'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0-LOrxdIU7-h"
   },
   "source": [
    "## Feature Engineering (Part 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "aVypp4ahU9tq"
   },
   "outputs": [],
   "source": [
    "#convert the column invoice_date to date time format on both the invoice train and invoice test\n",
    "for df in [invoice_train,invoice_test]:\n",
    "    df['invoice_date'] = pd.to_datetime(df['invoice_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "sW2W5i63VYyK"
   },
   "outputs": [],
   "source": [
    "#encode labels in categorical column\n",
    "d={\"ELEC\":0,\"GAZ\":1}\n",
    "invoice_train['counter_type']=invoice_train['counter_type'].map(d)\n",
    "invoice_test['counter_type']=invoice_test['counter_type'].map(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "NldYnn0GWHAk"
   },
   "outputs": [],
   "source": [
    "#convert categorical columns to int for model\n",
    "client_train['client_catg'] = client_train['client_catg'].astype(int)\n",
    "client_train['disrict'] = client_train['disrict'].astype(int)\n",
    "\n",
    "client_test['client_catg'] = client_test['client_catg'].astype(int)\n",
    "client_test['disrict'] = client_test['disrict'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "Bt7LJHhyXJji"
   },
   "outputs": [],
   "source": [
    "def aggregate_by_client_id(invoice_data):\n",
    "    aggs = {}\n",
    "    aggs['consommation_level_1'] = ['mean']\n",
    "    aggs['consommation_level_2'] = ['mean']\n",
    "    aggs['consommation_level_3'] = ['mean']\n",
    "    aggs['consommation_level_4'] = ['mean']\n",
    "\n",
    "    agg_trans = invoice_data.groupby(['client_id']).agg(aggs)\n",
    "    agg_trans.columns = ['_'.join(col).strip() for col in agg_trans.columns.values]\n",
    "    agg_trans.reset_index(inplace=True)\n",
    "\n",
    "    df = (invoice_data.groupby('client_id')\n",
    "            .size()\n",
    "            .reset_index(name='{}transactions_count'.format('1')))\n",
    "    return pd.merge(df, agg_trans, on='client_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "8gUjxEmKWZjp"
   },
   "outputs": [],
   "source": [
    "#group invoice data by client_id\n",
    "agg_train = aggregate_by_client_id(invoice_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "5cIpY1tGWrkw",
    "outputId": "c57de3d5-3efe-4e8a-9a0c-7362f05fca4c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(135493, 6)\n"
     ]
    },
    {
     "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>client_id</th>\n",
       "      <th>1transactions_count</th>\n",
       "      <th>consommation_level_1_mean</th>\n",
       "      <th>consommation_level_2_mean</th>\n",
       "      <th>consommation_level_3_mean</th>\n",
       "      <th>consommation_level_4_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_Client_0</td>\n",
       "      <td>35</td>\n",
       "      <td>352.400000</td>\n",
       "      <td>10.571429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_Client_1</td>\n",
       "      <td>37</td>\n",
       "      <td>557.540541</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_Client_10</td>\n",
       "      <td>18</td>\n",
       "      <td>798.611111</td>\n",
       "      <td>37.888889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_Client_100</td>\n",
       "      <td>20</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_Client_1000</td>\n",
       "      <td>14</td>\n",
       "      <td>663.714286</td>\n",
       "      <td>104.857143</td>\n",
       "      <td>117.357143</td>\n",
       "      <td>36.714286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           client_id  1transactions_count  consommation_level_1_mean  \\\n",
       "0     train_Client_0                   35                 352.400000   \n",
       "1     train_Client_1                   37                 557.540541   \n",
       "2    train_Client_10                   18                 798.611111   \n",
       "3   train_Client_100                   20                   1.200000   \n",
       "4  train_Client_1000                   14                 663.714286   \n",
       "\n",
       "   consommation_level_2_mean  consommation_level_3_mean  \\\n",
       "0                  10.571429                   0.000000   \n",
       "1                   0.000000                   0.000000   \n",
       "2                  37.888889                   0.000000   \n",
       "3                   0.000000                   0.000000   \n",
       "4                 104.857143                 117.357143   \n",
       "\n",
       "   consommation_level_4_mean  \n",
       "0                   0.000000  \n",
       "1                   0.000000  \n",
       "2                   0.000000  \n",
       "3                   0.000000  \n",
       "4                  36.714286  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(agg_train.shape)\n",
    "agg_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "beXOMOEMW97l"
   },
   "outputs": [],
   "source": [
    "#merge aggregate data with client dataset\n",
    "train = pd.merge(client_train,agg_train, on='client_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "y6SFawOVXFDX"
   },
   "outputs": [],
   "source": [
    "#aggregate test set\n",
    "agg_test = aggregate_by_client_id(invoice_test)\n",
    "test = pd.merge(client_test,agg_test, on='client_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dg0832jcXzkI",
    "outputId": "2c6ff557-0258-4310-d8f6-aad0627c9ddb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((135493, 12), (58069, 10))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "BwYBcKk8X3mr"
   },
   "outputs": [],
   "source": [
    "#drop redundant columns\n",
    "sub_client_id = test['client_id']\n",
    "drop_columns = ['client_id', 'creation_date']\n",
    "\n",
    "for col in drop_columns:\n",
    "    if col in train.columns:\n",
    "        train.drop([col], axis=1, inplace=True)\n",
    "    if col in test.columns:\n",
    "        test.drop([col], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iJ3KlVOCYfnZ"
   },
   "source": [
    "# Benchmark Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d1qXp8jYY30X"
   },
   "source": [
    "## Train LGBM *Classifier*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "QTr8Wbu1ZKTM"
   },
   "outputs": [],
   "source": [
    "x_train = train.drop(columns=['target'])\n",
    "y_train = train['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iXjUC4PZYMgF",
    "outputId": "3f132cea-1ef5-45ce-bdf0-45c76a62c84c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Unknown parameter: num_iterationss\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(num_iterationss=500)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(num_iterationss=500)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LGBMClassifier(num_iterationss=500)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LGBMClassifier(boosting_type='gbdt', num_iterationss=500)\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ddn7FuuCZHqr",
    "outputId": "e6d5735b-9abf-4e99-e83d-b04b7e63ac44"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([60, 69, 62, 63])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.disrict.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CKsrOiePadZ7"
   },
   "source": [
    "## Make Predictions on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nBww16tEajXs",
    "outputId": "a963cd4a-4aaf-4d1b-8524-0d7e67be1c48"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['disrict', 'client_catg', 'region', '1transactions_count',\n",
       "       'consommation_level_1_mean', 'consommation_level_2_mean',\n",
       "       'consommation_level_3_mean', 'consommation_level_4_mean'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "SGIjyZfmaf7C",
    "outputId": "b4bdd7a5-a496-405c-9651-ba482afd1046"
   },
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target\n",
       "0     0.0\n",
       "1     0.0\n",
       "2     0.0\n",
       "3     0.0\n",
       "4     0.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = model.predict(test)\n",
    "preds = pd.DataFrame(preds, columns=['target'])\n",
    "preds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "mX3rv6dwaw3-",
    "outputId": "6db1fc21-c545-4b99-a6f1-9357b2ef405d"
   },
   "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>client_id</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test_Client_0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test_Client_1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test_Client_10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>test_Client_100</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>test_Client_1000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          client_id  target\n",
       "0     test_Client_0     0.0\n",
       "1     test_Client_1     0.0\n",
       "2    test_Client_10     0.0\n",
       "3   test_Client_100     0.0\n",
       "4  test_Client_1000     0.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.DataFrame(\n",
    "    {\n",
    "        'client_id': sub_client_id,\n",
    "        'target': preds['target']\n",
    "    }\n",
    ")\n",
    "\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "O7_ANcWUboUB"
   },
   "outputs": [],
   "source": [
    "submission.to_csv('output/submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature engeneering (Part 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_change(cl, inv):\n",
    "\n",
    "    cl['client_catg'] = cl['client_catg'].astype('category')\n",
    "    cl['disrict'] = cl['disrict'].astype('category')\n",
    "    cl['region'] = cl['region'].astype('category')\n",
    "    cl['region_group'] = cl['region'].apply(lambda x: 100 if x<100 else 300 if x>300 else 200)\n",
    "    cl['creation_date'] = pd.to_datetime(cl['creation_date'])\n",
    "    \n",
    "    cl['coop_time'] = (2019 - cl['creation_date'].dt.year)*12 - cl['creation_date'].dt.month\n",
    "\n",
    "    inv['counter_type'] = inv['counter_type'].map({\"ELEC\":1,\"GAZ\":0})\n",
    "    inv['counter_statue'] = inv['counter_statue'].map({0:0,1:1,2:2,3:3,4:4,5:5,769:5,'0':0,'5':5,'1':1,'4':4,'A':0,618:5,269375:5,46:5,420:5})\n",
    "    \n",
    "    inv['invoice_date'] = pd.to_datetime(inv['invoice_date'], dayfirst=True)\n",
    "    inv['invoice_month'] = inv['invoice_date'].dt.month\n",
    "    inv['invoice_year'] = inv['invoice_date'].dt.year\n",
    "    inv['is_weekday'] = ((pd.DatetimeIndex(inv.invoice_date).dayofweek) // 5 == 1).astype(float)\n",
    "    inv['delta_index'] = inv['new_index'] - inv['old_index']\n",
    "    \n",
    "    return cl, inv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "client_train1, invoice_train1 = feature_change(client_train, invoice_train)\n",
    "client_test1, invoice_test1 = feature_change(client_test, invoice_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disrict</th>\n",
       "      <th>client_id</th>\n",
       "      <th>client_catg</th>\n",
       "      <th>region</th>\n",
       "      <th>creation_date</th>\n",
       "      <th>target</th>\n",
       "      <th>region_group</th>\n",
       "      <th>coop_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>train_Client_0</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "      <td>1994-12-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69</td>\n",
       "      <td>train_Client_1</td>\n",
       "      <td>11</td>\n",
       "      <td>107</td>\n",
       "      <td>2002-05-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>train_Client_10</td>\n",
       "      <td>11</td>\n",
       "      <td>301</td>\n",
       "      <td>1986-03-13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69</td>\n",
       "      <td>train_Client_100</td>\n",
       "      <td>11</td>\n",
       "      <td>105</td>\n",
       "      <td>1996-11-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>train_Client_1000</td>\n",
       "      <td>11</td>\n",
       "      <td>303</td>\n",
       "      <td>2014-10-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135488</th>\n",
       "      <td>62</td>\n",
       "      <td>train_Client_99995</td>\n",
       "      <td>11</td>\n",
       "      <td>304</td>\n",
       "      <td>2004-07-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135489</th>\n",
       "      <td>63</td>\n",
       "      <td>train_Client_99996</td>\n",
       "      <td>11</td>\n",
       "      <td>311</td>\n",
       "      <td>2012-10-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135490</th>\n",
       "      <td>63</td>\n",
       "      <td>train_Client_99997</td>\n",
       "      <td>11</td>\n",
       "      <td>311</td>\n",
       "      <td>2011-11-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135491</th>\n",
       "      <td>60</td>\n",
       "      <td>train_Client_99998</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "      <td>1993-12-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135492</th>\n",
       "      <td>60</td>\n",
       "      <td>train_Client_99999</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "      <td>1986-02-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135493 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       disrict           client_id client_catg region creation_date  target  \\\n",
       "0           60      train_Client_0          11    101    1994-12-31     0.0   \n",
       "1           69      train_Client_1          11    107    2002-05-29     0.0   \n",
       "2           62     train_Client_10          11    301    1986-03-13     0.0   \n",
       "3           69    train_Client_100          11    105    1996-11-07     0.0   \n",
       "4           62   train_Client_1000          11    303    2014-10-14     0.0   \n",
       "...        ...                 ...         ...    ...           ...     ...   \n",
       "135488      62  train_Client_99995          11    304    2004-07-26     0.0   \n",
       "135489      63  train_Client_99996          11    311    2012-10-25     0.0   \n",
       "135490      63  train_Client_99997          11    311    2011-11-22     0.0   \n",
       "135491      60  train_Client_99998          11    101    1993-12-22     0.0   \n",
       "135492      60  train_Client_99999          11    101    1986-02-18     0.0   \n",
       "\n",
       "        region_group  coop_time  \n",
       "0                200        288  \n",
       "1                200        199  \n",
       "2                300        393  \n",
       "3                200        265  \n",
       "4                300         50  \n",
       "...              ...        ...  \n",
       "135488           300        173  \n",
       "135489           300         74  \n",
       "135490           300         85  \n",
       "135491           200        300  \n",
       "135492           200        394  \n",
       "\n",
       "[135493 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client_train1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def agg_feature(invoice, client_df, agg_stat):\n",
    "    \n",
    "    invoice['delta_time'] = invoice.sort_values(['client_id','invoice_date']).groupby('client_id')['invoice_date'].diff().dt.days.reset_index(drop=True)\n",
    "    agg_trans = invoice.groupby('client_id')[agg_stat+['delta_time']].agg(['mean','std','min','max'])\n",
    "    \n",
    "    agg_trans.columns = ['_'.join(col).strip() for col in agg_trans.columns.values]\n",
    "    agg_trans.reset_index(inplace=True)\n",
    "\n",
    "    df = invoice.groupby('client_id').size().reset_index(name='transactions_count')\n",
    "    agg_trans = pd.merge(df, agg_trans, on='client_id', how='left')\n",
    "    \n",
    "    weekday_avg = invoice.groupby('client_id')[['is_weekday']].agg(['mean'])\n",
    "    weekday_avg.columns = ['_'.join(col).strip() for col in weekday_avg.columns.values]\n",
    "    weekday_avg.reset_index(inplace=True)\n",
    "    client_df = pd.merge(client_df, weekday_avg, on='client_id', how='left')\n",
    "    \n",
    "    full_df = pd.merge(client_df, agg_trans, on='client_id', how='left')\n",
    "    \n",
    "    full_df['invoice_per_cooperation'] = full_df['transactions_count'] / full_df['coop_time']\n",
    "    \n",
    "    return full_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_stat_columns = [\n",
    " 'tarif_type',\n",
    " 'counter_number',\n",
    " 'counter_statue',\n",
    " 'counter_code',\n",
    " 'reading_remarque',\n",
    " 'consommation_level_1',\n",
    " 'consommation_level_2',\n",
    " 'consommation_level_3',\n",
    " 'consommation_level_4',\n",
    " 'old_index',\n",
    " 'new_index',\n",
    " 'months_number',\n",
    " 'counter_type',\n",
    " 'invoice_month',\n",
    " 'invoice_year',\n",
    " 'delta_index'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df1 = agg_feature(invoice_train1, client_train1, agg_stat_columns)\n",
    "test_df1 = agg_feature(invoice_test1, client_test1, agg_stat_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disrict</th>\n",
       "      <th>client_id</th>\n",
       "      <th>client_catg</th>\n",
       "      <th>region</th>\n",
       "      <th>creation_date</th>\n",
       "      <th>target</th>\n",
       "      <th>region_group</th>\n",
       "      <th>coop_time</th>\n",
       "      <th>is_weekday_mean</th>\n",
       "      <th>transactions_count</th>\n",
       "      <th>...</th>\n",
       "      <th>invoice_year_max</th>\n",
       "      <th>delta_index_mean</th>\n",
       "      <th>delta_index_std</th>\n",
       "      <th>delta_index_min</th>\n",
       "      <th>delta_index_max</th>\n",
       "      <th>delta_time_mean</th>\n",
       "      <th>delta_time_std</th>\n",
       "      <th>delta_time_min</th>\n",
       "      <th>delta_time_max</th>\n",
       "      <th>invoice_per_cooperation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>train_Client_0</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "      <td>1994-12-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200</td>\n",
       "      <td>288</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>362.971429</td>\n",
       "      <td>341.553930</td>\n",
       "      <td>38</td>\n",
       "      <td>1386</td>\n",
       "      <td>144.147059</td>\n",
       "      <td>87.601070</td>\n",
       "      <td>40.0</td>\n",
       "      <td>508.0</td>\n",
       "      <td>0.121528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69</td>\n",
       "      <td>train_Client_1</td>\n",
       "      <td>11</td>\n",
       "      <td>107</td>\n",
       "      <td>2002-05-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200</td>\n",
       "      <td>199</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>37</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>557.540541</td>\n",
       "      <td>197.935960</td>\n",
       "      <td>190</td>\n",
       "      <td>1207</td>\n",
       "      <td>136.472222</td>\n",
       "      <td>92.492312</td>\n",
       "      <td>4.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>0.185930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>train_Client_10</td>\n",
       "      <td>11</td>\n",
       "      <td>301</td>\n",
       "      <td>1986-03-13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>393</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>836.500000</td>\n",
       "      <td>646.808386</td>\n",
       "      <td>188</td>\n",
       "      <td>3082</td>\n",
       "      <td>289.470588</td>\n",
       "      <td>250.701794</td>\n",
       "      <td>20.0</td>\n",
       "      <td>738.0</td>\n",
       "      <td>0.045802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69</td>\n",
       "      <td>train_Client_100</td>\n",
       "      <td>11</td>\n",
       "      <td>105</td>\n",
       "      <td>1996-11-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200</td>\n",
       "      <td>265</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>3.607011</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>140.210526</td>\n",
       "      <td>95.569974</td>\n",
       "      <td>4.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>0.075472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>train_Client_1000</td>\n",
       "      <td>11</td>\n",
       "      <td>303</td>\n",
       "      <td>2014-10-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>50</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>922.642857</td>\n",
       "      <td>633.485669</td>\n",
       "      <td>124</td>\n",
       "      <td>2382</td>\n",
       "      <td>121.923077</td>\n",
       "      <td>80.057752</td>\n",
       "      <td>4.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>0.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135488</th>\n",
       "      <td>62</td>\n",
       "      <td>train_Client_99995</td>\n",
       "      <td>11</td>\n",
       "      <td>304</td>\n",
       "      <td>2004-07-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>173</td>\n",
       "      <td>0.014085</td>\n",
       "      <td>71</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72.842857</td>\n",
       "      <td>72.195916</td>\n",
       "      <td>0.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.410405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135489</th>\n",
       "      <td>63</td>\n",
       "      <td>train_Client_99996</td>\n",
       "      <td>11</td>\n",
       "      <td>311</td>\n",
       "      <td>2012-10-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>74</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>41</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>186.609756</td>\n",
       "      <td>203.347840</td>\n",
       "      <td>0</td>\n",
       "      <td>831</td>\n",
       "      <td>57.675000</td>\n",
       "      <td>68.849292</td>\n",
       "      <td>0.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>0.554054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135490</th>\n",
       "      <td>63</td>\n",
       "      <td>train_Client_99997</td>\n",
       "      <td>11</td>\n",
       "      <td>311</td>\n",
       "      <td>2011-11-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>85</td>\n",
       "      <td>0.194444</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>273.083333</td>\n",
       "      <td>211.123441</td>\n",
       "      <td>33</td>\n",
       "      <td>1075</td>\n",
       "      <td>80.600000</td>\n",
       "      <td>101.504622</td>\n",
       "      <td>0.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>0.423529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135491</th>\n",
       "      <td>60</td>\n",
       "      <td>train_Client_99998</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "      <td>1993-12-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200</td>\n",
       "      <td>300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2005</td>\n",
       "      <td>370.500000</td>\n",
       "      <td>232.638131</td>\n",
       "      <td>206</td>\n",
       "      <td>535</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>0.006667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135492</th>\n",
       "      <td>60</td>\n",
       "      <td>train_Client_99999</td>\n",
       "      <td>11</td>\n",
       "      <td>101</td>\n",
       "      <td>1986-02-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200</td>\n",
       "      <td>394</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1996</td>\n",
       "      <td>459.333333</td>\n",
       "      <td>178.864008</td>\n",
       "      <td>259</td>\n",
       "      <td>603</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>2.828427</td>\n",
       "      <td>120.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>0.007614</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135493 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       disrict           client_id client_catg region creation_date  target  \\\n",
       "0           60      train_Client_0          11    101    1994-12-31     0.0   \n",
       "1           69      train_Client_1          11    107    2002-05-29     0.0   \n",
       "2           62     train_Client_10          11    301    1986-03-13     0.0   \n",
       "3           69    train_Client_100          11    105    1996-11-07     0.0   \n",
       "4           62   train_Client_1000          11    303    2014-10-14     0.0   \n",
       "...        ...                 ...         ...    ...           ...     ...   \n",
       "135488      62  train_Client_99995          11    304    2004-07-26     0.0   \n",
       "135489      63  train_Client_99996          11    311    2012-10-25     0.0   \n",
       "135490      63  train_Client_99997          11    311    2011-11-22     0.0   \n",
       "135491      60  train_Client_99998          11    101    1993-12-22     0.0   \n",
       "135492      60  train_Client_99999          11    101    1986-02-18     0.0   \n",
       "\n",
       "        region_group  coop_time  is_weekday_mean  transactions_count  ...  \\\n",
       "0                200        288         0.028571                  35  ...   \n",
       "1                200        199         0.054054                  37  ...   \n",
       "2                300        393         0.055556                  18  ...   \n",
       "3                200        265         0.050000                  20  ...   \n",
       "4                300         50         0.285714                  14  ...   \n",
       "...              ...        ...              ...                 ...  ...   \n",
       "135488           300        173         0.014085                  71  ...   \n",
       "135489           300         74         0.000000                  41  ...   \n",
       "135490           300         85         0.194444                  36  ...   \n",
       "135491           200        300         0.000000                   2  ...   \n",
       "135492           200        394         0.000000                   3  ...   \n",
       "\n",
       "        invoice_year_max  delta_index_mean  delta_index_std  delta_index_min  \\\n",
       "0                   2019        362.971429       341.553930               38   \n",
       "1                   2019        557.540541       197.935960              190   \n",
       "2                   2019        836.500000       646.808386              188   \n",
       "3                   2012          1.200000         3.607011                0   \n",
       "4                   2019        922.642857       633.485669              124   \n",
       "...                  ...               ...              ...              ...   \n",
       "135488              2019          0.000000         0.000000                0   \n",
       "135489              2019        186.609756       203.347840                0   \n",
       "135490              2019        273.083333       211.123441               33   \n",
       "135491              2005        370.500000       232.638131              206   \n",
       "135492              1996        459.333333       178.864008              259   \n",
       "\n",
       "        delta_index_max  delta_time_mean  delta_time_std  delta_time_min  \\\n",
       "0                  1386       144.147059       87.601070            40.0   \n",
       "1                  1207       136.472222       92.492312             4.0   \n",
       "2                  3082       289.470588      250.701794            20.0   \n",
       "3                    15       140.210526       95.569974             4.0   \n",
       "4                  2382       121.923077       80.057752             4.0   \n",
       "...                 ...              ...             ...             ...   \n",
       "135488                0        72.842857       72.195916             0.0   \n",
       "135489              831        57.675000       68.849292             0.0   \n",
       "135490             1075        80.600000      101.504622             0.0   \n",
       "135491              535       122.000000             NaN           122.0   \n",
       "135492              603       122.000000        2.828427           120.0   \n",
       "\n",
       "        delta_time_max  invoice_per_cooperation  \n",
       "0                508.0                 0.121528  \n",
       "1                372.0                 0.185930  \n",
       "2                738.0                 0.045802  \n",
       "3                442.0                 0.075472  \n",
       "4                301.0                 0.280000  \n",
       "...                ...                      ...  \n",
       "135488           250.0                 0.410405  \n",
       "135489           231.0                 0.554054  \n",
       "135490           395.0                 0.423529  \n",
       "135491           122.0                 0.006667  \n",
       "135492           124.0                 0.007614  \n",
       "\n",
       "[135493 rows x 79 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def new_features(df):\n",
    "    \n",
    "    for col in agg_stat_columns:\n",
    "        df[col+'_range'] = df[col+'_max'] - df[col+'_min']\n",
    "        df[col+'_max_mean'] = df[col+'_max']/df[col+'_mean']\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df2 = new_features(train_df1)\n",
    "test_df2 = new_features(test_df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(client_train.columns)+len(invoice_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_df2.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop(df):\n",
    "\n",
    "    col_drop = ['client_id', 'creation_date']\n",
    "    for col in col_drop:\n",
    "        df.drop([col], axis=1, inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = drop(train_df2)\n",
    "test_df = drop(test_df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train_df['target']\n",
    "X = train_df.drop('target',axis=1)\n",
    "\n",
    "feature_name = X.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_col=['reading_remarque_max','counter_statue_min','counter_type_min','counter_type_max','counter_type_range',\n",
    "          'tarif_type_max', 'delta_index_min', 'consommation_level_4_mean']\n",
    "\n",
    "X = X.drop(drop_col, axis=1)\n",
    "test_df = test_df.drop(drop_col, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from optuna import Trial\n",
    "import gc\n",
    "import optuna\n",
    "from sklearn.model_selection import train_test_split,StratifiedKFold\n",
    "import lightgbm as lgb\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "category_cols = ['disrict', 'client_catg', 'region']\n",
    "\n",
    "def objective(trial:Trial):\n",
    "    \n",
    "    gc.collect()\n",
    "    models=[]\n",
    "    validScore=0\n",
    "   \n",
    "    model,log = fitLGBM(trial,X,y)\n",
    "    \n",
    "    models.append(model)\n",
    "    gc.collect()\n",
    "    validScore+=log\n",
    "    validScore/=len(models)\n",
    "    \n",
    "    return validScore\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fitLGBM(trial,X, y):\n",
    "    \n",
    "    params={\n",
    "      'n_estimators':trial.suggest_int('n_estimators', 0, 1000), \n",
    "      'num_leaves':trial.suggest_int('num_leaves', 2, 512),\n",
    "      'max_depth':trial.suggest_int('max_depth', 2, 128),\n",
    "      'learning_rate': trial.suggest_loguniform('learning_rate', 0.001, 0.15),\n",
    "      'min_split_gain': trial.suggest_loguniform('min_split_gain', 0.001, 0.1),\n",
    "      'feature_fraction':trial.suggest_uniform('feature_fraction',0.1, 1.0),\n",
    "      'bagging_freq':trial.suggest_int('bagging_freq',0.1,10),\n",
    "      'verbosity': -1,\n",
    "      'random_state':seed\n",
    "            }\n",
    "    stkfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=seed)\n",
    "    model = LGBMClassifier(**params)\n",
    "    \n",
    "    res=[]\n",
    "    for i, (tdx, vdx) in enumerate(stkfold.split(X, y)):\n",
    "        X_train, X_valid, y_train, y_valid = X.iloc[tdx], X.iloc[vdx], y[tdx], y[vdx]\n",
    "        model.fit(X_train, y_train,\n",
    "                 eval_set=[(X_train, y_train), (X_valid, y_valid)],\n",
    "                 early_stopping_rounds=30, verbose=False)\n",
    "        preds = model.predict_proba(X_valid)\n",
    "        res.append(roc_auc_score(y_valid, preds[:,1]))\n",
    "    \n",
    "    err = np.mean(res)\n",
    "    \n",
    "    return model, err"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LGBMClassifier(random_state=0, n_estimators=830,num_leaves=454, max_depth=61,\n",
    "                       learning_rate=0.006910869038433314, min_split_gain=0.00667926424629105, \n",
    "                       feature_fraction=0.3764303138879782, bagging_freq=8)\n",
    "\n",
    "stkfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)\n",
    "\n",
    "def calc(X, y, model, cv):\n",
    "    res=[]\n",
    "    local_probs=pd.DataFrame()\n",
    "    probs = pd.DataFrame()\n",
    "\n",
    "    for i, (tdx, vdx) in enumerate(cv.split(X, y)):\n",
    "        X_train, X_valid, y_train, y_valid = X.iloc[tdx], X.iloc[vdx], y[tdx], y[vdx]\n",
    "        model.fit(X_train, y_train,\n",
    "                 eval_set=[(X_train, y_train), (X_valid, y_valid)],\n",
    "                 early_stopping_rounds=30, verbose=False)\n",
    "        \n",
    "        preds = model.predict_proba(X_valid)\n",
    "        oof_predict = model.predict_proba(test_df)\n",
    "        local_probs['fold_%i'%i] = oof_predict[:,1]\n",
    "        res.append(roc_auc_score(y_valid, preds[:,1]))\n",
    "\n",
    "    print('ROC AUC:', round(np.mean(res), 6))    \n",
    "    local_probs['res'] = local_probs.mean(axis=1)\n",
    "    probs['target'] = local_probs['res']\n",
    "    \n",
    "    return probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] feature_fraction is set=0.3764303138879782, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.3764303138879782\n",
      "[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8\n",
      "[LightGBM] [Warning] feature_fraction is set=0.3764303138879782, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.3764303138879782\n",
      "[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8\n",
      "[LightGBM] [Warning] feature_fraction is set=0.3764303138879782, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.3764303138879782\n",
      "[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8\n",
      "[LightGBM] [Warning] feature_fraction is set=0.3764303138879782, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.3764303138879782\n",
      "[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8\n",
      "[LightGBM] [Warning] feature_fraction is set=0.3764303138879782, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.3764303138879782\n",
      "[LightGBM] [Warning] bagging_freq is set=8, subsample_freq=0 will be ignored. Current value: bagging_freq=8\n",
      "ROC AUC: 0.884778\n"
     ]
    }
   ],
   "source": [
    "probs = calc(X, y, model, stkfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"client_id\": sample_submission[\"client_id\"],\n",
    "        \"target\": probs['target']\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('output/submission1.csv', index=False)"
   ]
  }
 ],
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