projet_fraud.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Projet Fraude \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas import DataFrame, Series\n",
    "import matplotlib\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import classification_report, roc_curve, auc, confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importer les dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>signup_time</th>\n",
       "      <th>purchase_time</th>\n",
       "      <th>purchase_value</th>\n",
       "      <th>device_id</th>\n",
       "      <th>source</th>\n",
       "      <th>browser</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>ip_address</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22058</td>\n",
       "      <td>2015-02-24 22:55:49</td>\n",
       "      <td>2015-04-18 02:47:11</td>\n",
       "      <td>34</td>\n",
       "      <td>QVPSPJUOCKZAR</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>M</td>\n",
       "      <td>39</td>\n",
       "      <td>7.327584e+08</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>333320</td>\n",
       "      <td>2015-06-07 20:39:50</td>\n",
       "      <td>2015-06-08 01:38:54</td>\n",
       "      <td>16</td>\n",
       "      <td>EOGFQPIZPYXFZ</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>F</td>\n",
       "      <td>53</td>\n",
       "      <td>3.503114e+08</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1359</td>\n",
       "      <td>2015-01-01 18:52:44</td>\n",
       "      <td>2015-01-01 18:52:45</td>\n",
       "      <td>15</td>\n",
       "      <td>YSSKYOSJHPPLJ</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Opera</td>\n",
       "      <td>M</td>\n",
       "      <td>53</td>\n",
       "      <td>2.621474e+09</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150084</td>\n",
       "      <td>2015-04-28 21:13:25</td>\n",
       "      <td>2015-05-04 13:54:50</td>\n",
       "      <td>44</td>\n",
       "      <td>ATGTXKYKUDUQN</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>41</td>\n",
       "      <td>3.840542e+09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>221365</td>\n",
       "      <td>2015-07-21 07:09:52</td>\n",
       "      <td>2015-09-09 18:40:53</td>\n",
       "      <td>39</td>\n",
       "      <td>NAUITBZFJKHWW</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>4.155831e+08</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id          signup_time        purchase_time  purchase_value  \\\n",
       "0    22058  2015-02-24 22:55:49  2015-04-18 02:47:11              34   \n",
       "1   333320  2015-06-07 20:39:50  2015-06-08 01:38:54              16   \n",
       "2     1359  2015-01-01 18:52:44  2015-01-01 18:52:45              15   \n",
       "3   150084  2015-04-28 21:13:25  2015-05-04 13:54:50              44   \n",
       "4   221365  2015-07-21 07:09:52  2015-09-09 18:40:53              39   \n",
       "\n",
       "       device_id source browser sex  age    ip_address  class  \n",
       "0  QVPSPJUOCKZAR    SEO  Chrome   M   39  7.327584e+08      0  \n",
       "1  EOGFQPIZPYXFZ    Ads  Chrome   F   53  3.503114e+08      0  \n",
       "2  YSSKYOSJHPPLJ    SEO   Opera   M   53  2.621474e+09      1  \n",
       "3  ATGTXKYKUDUQN    SEO  Safari   M   41  3.840542e+09      0  \n",
       "4  NAUITBZFJKHWW    Ads  Safari   M   45  4.155831e+08      0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#target --> class : 1 Fraud | 0 ok \n",
    "data_fraud = pd.read_csv(\"Fraud_Data.csv\")\n",
    "data_fraud.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lower_bound_ip_address</th>\n",
       "      <th>upper_bound_ip_address</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16777216.0</td>\n",
       "      <td>16777471</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16777472.0</td>\n",
       "      <td>16777727</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16777728.0</td>\n",
       "      <td>16778239</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16778240.0</td>\n",
       "      <td>16779263</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16779264.0</td>\n",
       "      <td>16781311</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   lower_bound_ip_address  upper_bound_ip_address    country\n",
       "0              16777216.0                16777471  Australia\n",
       "1              16777472.0                16777727      China\n",
       "2              16777728.0                16778239      China\n",
       "3              16778240.0                16779263  Australia\n",
       "4              16779264.0                16781311      China"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ip = pd.read_csv(\"Ipaddress_to_Country.csv\")\n",
    "data_ip.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Créer une nouvelle colonne `country` en effectuant une jointure entre les deux dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ip2country(ip: int):\n",
    "    return data_ip.loc[(data_ip['lower_bound_ip_address'] <= ip) & (ip <= data_ip['upper_bound_ip_address']), \"country\"].squeeze()\n",
    "\n",
    "\n",
    "data_fraud[\"country\"] = data_fraud[\"ip_address\"].map(ip2country)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>signup_time</th>\n",
       "      <th>purchase_time</th>\n",
       "      <th>purchase_value</th>\n",
       "      <th>device_id</th>\n",
       "      <th>source</th>\n",
       "      <th>browser</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>ip_address</th>\n",
       "      <th>class</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22058</td>\n",
       "      <td>2015-02-24 22:55:49</td>\n",
       "      <td>2015-04-18 02:47:11</td>\n",
       "      <td>34</td>\n",
       "      <td>QVPSPJUOCKZAR</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>M</td>\n",
       "      <td>39</td>\n",
       "      <td>7.327584e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>333320</td>\n",
       "      <td>2015-06-07 20:39:50</td>\n",
       "      <td>2015-06-08 01:38:54</td>\n",
       "      <td>16</td>\n",
       "      <td>EOGFQPIZPYXFZ</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>F</td>\n",
       "      <td>53</td>\n",
       "      <td>3.503114e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1359</td>\n",
       "      <td>2015-01-01 18:52:44</td>\n",
       "      <td>2015-01-01 18:52:45</td>\n",
       "      <td>15</td>\n",
       "      <td>YSSKYOSJHPPLJ</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Opera</td>\n",
       "      <td>M</td>\n",
       "      <td>53</td>\n",
       "      <td>2.621474e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150084</td>\n",
       "      <td>2015-04-28 21:13:25</td>\n",
       "      <td>2015-05-04 13:54:50</td>\n",
       "      <td>44</td>\n",
       "      <td>ATGTXKYKUDUQN</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>41</td>\n",
       "      <td>3.840542e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>Series([], Name: country, dtype: object)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>221365</td>\n",
       "      <td>2015-07-21 07:09:52</td>\n",
       "      <td>2015-09-09 18:40:53</td>\n",
       "      <td>39</td>\n",
       "      <td>NAUITBZFJKHWW</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>4.155831e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id          signup_time        purchase_time  purchase_value  \\\n",
       "0    22058  2015-02-24 22:55:49  2015-04-18 02:47:11              34   \n",
       "1   333320  2015-06-07 20:39:50  2015-06-08 01:38:54              16   \n",
       "2     1359  2015-01-01 18:52:44  2015-01-01 18:52:45              15   \n",
       "3   150084  2015-04-28 21:13:25  2015-05-04 13:54:50              44   \n",
       "4   221365  2015-07-21 07:09:52  2015-09-09 18:40:53              39   \n",
       "\n",
       "       device_id source browser sex  age    ip_address  class  \\\n",
       "0  QVPSPJUOCKZAR    SEO  Chrome   M   39  7.327584e+08      0   \n",
       "1  EOGFQPIZPYXFZ    Ads  Chrome   F   53  3.503114e+08      0   \n",
       "2  YSSKYOSJHPPLJ    SEO   Opera   M   53  2.621474e+09      1   \n",
       "3  ATGTXKYKUDUQN    SEO  Safari   M   41  3.840542e+09      0   \n",
       "4  NAUITBZFJKHWW    Ads  Safari   M   45  4.155831e+08      0   \n",
       "\n",
       "                                    country  \n",
       "0                                     Japan  \n",
       "1                             United States  \n",
       "2                             United States  \n",
       "3  Series([], Name: country, dtype: object)  \n",
       "4                             United States  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_fraud.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Créer une nouvelle variable `time_diff` qui sera la différence entre les variables `purchase_time` et `signup_time`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_fraud[['signup_time','purchase_time']] = data_fraud[['signup_time','purchase_time']].apply(pd.to_datetime)\n",
    "data_fraud['time_diff'] = (data_fraud['purchase_time'] - data_fraud['signup_time']).dt.total_seconds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>signup_time</th>\n",
       "      <th>purchase_time</th>\n",
       "      <th>purchase_value</th>\n",
       "      <th>device_id</th>\n",
       "      <th>source</th>\n",
       "      <th>browser</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>ip_address</th>\n",
       "      <th>class</th>\n",
       "      <th>country</th>\n",
       "      <th>time_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22058</td>\n",
       "      <td>2015-02-24 22:55:49</td>\n",
       "      <td>2015-04-18 02:47:11</td>\n",
       "      <td>34</td>\n",
       "      <td>QVPSPJUOCKZAR</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>M</td>\n",
       "      <td>39</td>\n",
       "      <td>7.327584e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4506682.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>333320</td>\n",
       "      <td>2015-06-07 20:39:50</td>\n",
       "      <td>2015-06-08 01:38:54</td>\n",
       "      <td>16</td>\n",
       "      <td>EOGFQPIZPYXFZ</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>F</td>\n",
       "      <td>53</td>\n",
       "      <td>3.503114e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>17944.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1359</td>\n",
       "      <td>2015-01-01 18:52:44</td>\n",
       "      <td>2015-01-01 18:52:45</td>\n",
       "      <td>15</td>\n",
       "      <td>YSSKYOSJHPPLJ</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Opera</td>\n",
       "      <td>M</td>\n",
       "      <td>53</td>\n",
       "      <td>2.621474e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150084</td>\n",
       "      <td>2015-04-28 21:13:25</td>\n",
       "      <td>2015-05-04 13:54:50</td>\n",
       "      <td>44</td>\n",
       "      <td>ATGTXKYKUDUQN</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>41</td>\n",
       "      <td>3.840542e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>Series([], Name: country, dtype: object)</td>\n",
       "      <td>492085.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>221365</td>\n",
       "      <td>2015-07-21 07:09:52</td>\n",
       "      <td>2015-09-09 18:40:53</td>\n",
       "      <td>39</td>\n",
       "      <td>NAUITBZFJKHWW</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>4.155831e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>4361461.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id         signup_time       purchase_time  purchase_value  \\\n",
       "0    22058 2015-02-24 22:55:49 2015-04-18 02:47:11              34   \n",
       "1   333320 2015-06-07 20:39:50 2015-06-08 01:38:54              16   \n",
       "2     1359 2015-01-01 18:52:44 2015-01-01 18:52:45              15   \n",
       "3   150084 2015-04-28 21:13:25 2015-05-04 13:54:50              44   \n",
       "4   221365 2015-07-21 07:09:52 2015-09-09 18:40:53              39   \n",
       "\n",
       "       device_id source browser sex  age    ip_address  class  \\\n",
       "0  QVPSPJUOCKZAR    SEO  Chrome   M   39  7.327584e+08      0   \n",
       "1  EOGFQPIZPYXFZ    Ads  Chrome   F   53  3.503114e+08      0   \n",
       "2  YSSKYOSJHPPLJ    SEO   Opera   M   53  2.621474e+09      1   \n",
       "3  ATGTXKYKUDUQN    SEO  Safari   M   41  3.840542e+09      0   \n",
       "4  NAUITBZFJKHWW    Ads  Safari   M   45  4.155831e+08      0   \n",
       "\n",
       "                                    country  time_diff  \n",
       "0                                     Japan  4506682.0  \n",
       "1                             United States    17944.0  \n",
       "2                             United States        1.0  \n",
       "3  Series([], Name: country, dtype: object)   492085.0  \n",
       "4                             United States  4361461.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_fraud.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regroupper les `user_id` par `device_id`  afin d'avoir le nombre de device par client  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>signup_time</th>\n",
       "      <th>purchase_time</th>\n",
       "      <th>purchase_value</th>\n",
       "      <th>device_id</th>\n",
       "      <th>source</th>\n",
       "      <th>browser</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>ip_address</th>\n",
       "      <th>class</th>\n",
       "      <th>country</th>\n",
       "      <th>time_diff</th>\n",
       "      <th>device_user_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22058</td>\n",
       "      <td>2015-02-24 22:55:49</td>\n",
       "      <td>2015-04-18 02:47:11</td>\n",
       "      <td>34</td>\n",
       "      <td>QVPSPJUOCKZAR</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>M</td>\n",
       "      <td>39</td>\n",
       "      <td>7.327584e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4506682.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>333320</td>\n",
       "      <td>2015-06-07 20:39:50</td>\n",
       "      <td>2015-06-08 01:38:54</td>\n",
       "      <td>16</td>\n",
       "      <td>EOGFQPIZPYXFZ</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>F</td>\n",
       "      <td>53</td>\n",
       "      <td>3.503114e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>17944.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1359</td>\n",
       "      <td>2015-01-01 18:52:44</td>\n",
       "      <td>2015-01-01 18:52:45</td>\n",
       "      <td>15</td>\n",
       "      <td>YSSKYOSJHPPLJ</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Opera</td>\n",
       "      <td>M</td>\n",
       "      <td>53</td>\n",
       "      <td>2.621474e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150084</td>\n",
       "      <td>2015-04-28 21:13:25</td>\n",
       "      <td>2015-05-04 13:54:50</td>\n",
       "      <td>44</td>\n",
       "      <td>ATGTXKYKUDUQN</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>41</td>\n",
       "      <td>3.840542e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>Series([], Name: country, dtype: object)</td>\n",
       "      <td>492085.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>221365</td>\n",
       "      <td>2015-07-21 07:09:52</td>\n",
       "      <td>2015-09-09 18:40:53</td>\n",
       "      <td>39</td>\n",
       "      <td>NAUITBZFJKHWW</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>4.155831e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>4361461.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id         signup_time       purchase_time  purchase_value  \\\n",
       "0    22058 2015-02-24 22:55:49 2015-04-18 02:47:11              34   \n",
       "1   333320 2015-06-07 20:39:50 2015-06-08 01:38:54              16   \n",
       "2     1359 2015-01-01 18:52:44 2015-01-01 18:52:45              15   \n",
       "3   150084 2015-04-28 21:13:25 2015-05-04 13:54:50              44   \n",
       "4   221365 2015-07-21 07:09:52 2015-09-09 18:40:53              39   \n",
       "\n",
       "       device_id source browser sex  age    ip_address  class  \\\n",
       "0  QVPSPJUOCKZAR    SEO  Chrome   M   39  7.327584e+08      0   \n",
       "1  EOGFQPIZPYXFZ    Ads  Chrome   F   53  3.503114e+08      0   \n",
       "2  YSSKYOSJHPPLJ    SEO   Opera   M   53  2.621474e+09      1   \n",
       "3  ATGTXKYKUDUQN    SEO  Safari   M   41  3.840542e+09      0   \n",
       "4  NAUITBZFJKHWW    Ads  Safari   M   45  4.155831e+08      0   \n",
       "\n",
       "                                    country  time_diff  device_user_count  \n",
       "0                                     Japan  4506682.0                  1  \n",
       "1                             United States    17944.0                  1  \n",
       "2                             United States        1.0                 12  \n",
       "3  Series([], Name: country, dtype: object)   492085.0                  1  \n",
       "4                             United States  4361461.0                  1  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Different user id's using the same device\n",
    "device_user_count = len(data_fraud) * [0]\n",
    "device_count = data_fraud.groupby('device_id')['user_id'].count()\n",
    "device_user_count = device_count[data_fraud['device_id']]\n",
    "device_user_count = device_user_count.reset_index().drop('device_id', axis = 1)\n",
    "device_user_count.columns = ['device_user_count']\n",
    "data_fraud = pd.concat([data_fraud, device_user_count], axis = 1)\n",
    "data_fraud.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Faire de même pour les `ip_address` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>signup_time</th>\n",
       "      <th>purchase_time</th>\n",
       "      <th>purchase_value</th>\n",
       "      <th>device_id</th>\n",
       "      <th>source</th>\n",
       "      <th>browser</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>ip_address</th>\n",
       "      <th>class</th>\n",
       "      <th>country</th>\n",
       "      <th>time_diff</th>\n",
       "      <th>device_user_count</th>\n",
       "      <th>ip_count</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>22058</td>\n",
       "      <td>2015-02-24 22:55:49</td>\n",
       "      <td>2015-04-18 02:47:11</td>\n",
       "      <td>34</td>\n",
       "      <td>QVPSPJUOCKZAR</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>M</td>\n",
       "      <td>39</td>\n",
       "      <td>7.327584e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4506682.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>16</td>\n",
       "      <td>EOGFQPIZPYXFZ</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>F</td>\n",
       "      <td>53</td>\n",
       "      <td>3.503114e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>17944.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1359</td>\n",
       "      <td>2015-01-01 18:52:44</td>\n",
       "      <td>2015-01-01 18:52:45</td>\n",
       "      <td>15</td>\n",
       "      <td>YSSKYOSJHPPLJ</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Opera</td>\n",
       "      <td>M</td>\n",
       "      <td>53</td>\n",
       "      <td>2.621474e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150084</td>\n",
       "      <td>2015-04-28 21:13:25</td>\n",
       "      <td>2015-05-04 13:54:50</td>\n",
       "      <td>44</td>\n",
       "      <td>ATGTXKYKUDUQN</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>41</td>\n",
       "      <td>3.840542e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>Series([], Name: country, dtype: object)</td>\n",
       "      <td>492085.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>221365</td>\n",
       "      <td>2015-07-21 07:09:52</td>\n",
       "      <td>2015-09-09 18:40:53</td>\n",
       "      <td>39</td>\n",
       "      <td>NAUITBZFJKHWW</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>4.155831e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>4361461.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id         signup_time       purchase_time  purchase_value  \\\n",
       "0    22058 2015-02-24 22:55:49 2015-04-18 02:47:11              34   \n",
       "1   333320 2015-06-07 20:39:50 2015-06-08 01:38:54              16   \n",
       "2     1359 2015-01-01 18:52:44 2015-01-01 18:52:45              15   \n",
       "3   150084 2015-04-28 21:13:25 2015-05-04 13:54:50              44   \n",
       "4   221365 2015-07-21 07:09:52 2015-09-09 18:40:53              39   \n",
       "\n",
       "       device_id source browser sex  age    ip_address  class  \\\n",
       "0  QVPSPJUOCKZAR    SEO  Chrome   M   39  7.327584e+08      0   \n",
       "1  EOGFQPIZPYXFZ    Ads  Chrome   F   53  3.503114e+08      0   \n",
       "2  YSSKYOSJHPPLJ    SEO   Opera   M   53  2.621474e+09      1   \n",
       "3  ATGTXKYKUDUQN    SEO  Safari   M   41  3.840542e+09      0   \n",
       "4  NAUITBZFJKHWW    Ads  Safari   M   45  4.155831e+08      0   \n",
       "\n",
       "                                    country  time_diff  device_user_count  \\\n",
       "0                                     Japan  4506682.0                  1   \n",
       "1                             United States    17944.0                  1   \n",
       "2                             United States        1.0                 12   \n",
       "3  Series([], Name: country, dtype: object)   492085.0                  1   \n",
       "4                             United States  4361461.0                  1   \n",
       "\n",
       "   ip_count  \n",
       "0         1  \n",
       "1         1  \n",
       "2        12  \n",
       "3         1  \n",
       "4         1  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of users' using a given ip address\n",
    "\n",
    "ip_count = data_fraud.groupby('ip_address')['user_id'].count()\n",
    "ip_count = ip_count[data_fraud['ip_address']].reset_index().drop('ip_address', axis = 1)\n",
    "ip_count.columns = ['ip_count']\n",
    "data_fraud = pd.concat([data_fraud, ip_count], axis = 1)\n",
    "data_fraud.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Traiter les variables `signup_time` & `purchase_time` afin de les différentier suivant la semaine et le week end  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>user_id</th>\n",
       "      <th>signup_time</th>\n",
       "      <th>purchase_time</th>\n",
       "      <th>purchase_value</th>\n",
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       "      <th>class</th>\n",
       "      <th>country</th>\n",
       "      <th>time_diff</th>\n",
       "      <th>device_user_count</th>\n",
       "      <th>ip_count</th>\n",
       "      <th>signup_day</th>\n",
       "      <th>signup_week</th>\n",
       "      <th>purchase_day</th>\n",
       "      <th>purchase_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22058</td>\n",
       "      <td>2015-02-24 22:55:49</td>\n",
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       "      <td>0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4506682.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>333320</td>\n",
       "      <td>2015-06-07 20:39:50</td>\n",
       "      <td>2015-06-08 01:38:54</td>\n",
       "      <td>16</td>\n",
       "      <td>EOGFQPIZPYXFZ</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>F</td>\n",
       "      <td>53</td>\n",
       "      <td>3.503114e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>17944.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1359</td>\n",
       "      <td>2015-01-01 18:52:44</td>\n",
       "      <td>2015-01-01 18:52:45</td>\n",
       "      <td>15</td>\n",
       "      <td>YSSKYOSJHPPLJ</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Opera</td>\n",
       "      <td>M</td>\n",
       "      <td>53</td>\n",
       "      <td>2.621474e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150084</td>\n",
       "      <td>2015-04-28 21:13:25</td>\n",
       "      <td>2015-05-04 13:54:50</td>\n",
       "      <td>44</td>\n",
       "      <td>ATGTXKYKUDUQN</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>41</td>\n",
       "      <td>3.840542e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>Series([], Name: country, dtype: object)</td>\n",
       "      <td>492085.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>221365</td>\n",
       "      <td>2015-07-21 07:09:52</td>\n",
       "      <td>2015-09-09 18:40:53</td>\n",
       "      <td>39</td>\n",
       "      <td>NAUITBZFJKHWW</td>\n",
       "      <td>Ads</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>4.155831e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>4361461.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>151107</th>\n",
       "      <td>345170</td>\n",
       "      <td>2015-01-27 03:03:34</td>\n",
       "      <td>2015-03-29 00:30:47</td>\n",
       "      <td>43</td>\n",
       "      <td>XPSKTWGPWINLR</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>M</td>\n",
       "      <td>28</td>\n",
       "      <td>3.451155e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>5261233.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151108</th>\n",
       "      <td>274471</td>\n",
       "      <td>2015-05-15 17:43:29</td>\n",
       "      <td>2015-05-26 12:24:39</td>\n",
       "      <td>35</td>\n",
       "      <td>LYSFABUCPCGBA</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Safari</td>\n",
       "      <td>M</td>\n",
       "      <td>32</td>\n",
       "      <td>2.439047e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>931270.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151109</th>\n",
       "      <td>368416</td>\n",
       "      <td>2015-03-03 23:07:31</td>\n",
       "      <td>2015-05-20 07:07:47</td>\n",
       "      <td>40</td>\n",
       "      <td>MEQHCSJUBRBFE</td>\n",
       "      <td>SEO</td>\n",
       "      <td>IE</td>\n",
       "      <td>F</td>\n",
       "      <td>26</td>\n",
       "      <td>2.748471e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>6681616.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151110</th>\n",
       "      <td>207709</td>\n",
       "      <td>2015-07-09 20:06:07</td>\n",
       "      <td>2015-09-07 09:34:46</td>\n",
       "      <td>46</td>\n",
       "      <td>CMCXFGRHYSTVJ</td>\n",
       "      <td>SEO</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>M</td>\n",
       "      <td>37</td>\n",
       "      <td>3.601175e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>5146119.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151111</th>\n",
       "      <td>138208</td>\n",
       "      <td>2015-06-10 07:02:20</td>\n",
       "      <td>2015-07-21 02:03:53</td>\n",
       "      <td>20</td>\n",
       "      <td>ZINIADFCLHYPG</td>\n",
       "      <td>Direct</td>\n",
       "      <td>IE</td>\n",
       "      <td>M</td>\n",
       "      <td>38</td>\n",
       "      <td>4.103825e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>Series([], Name: country, dtype: object)</td>\n",
       "      <td>3524493.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>151112 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id         signup_time       purchase_time  purchase_value  \\\n",
       "0         22058 2015-02-24 22:55:49 2015-04-18 02:47:11              34   \n",
       "1        333320 2015-06-07 20:39:50 2015-06-08 01:38:54              16   \n",
       "2          1359 2015-01-01 18:52:44 2015-01-01 18:52:45              15   \n",
       "3        150084 2015-04-28 21:13:25 2015-05-04 13:54:50              44   \n",
       "4        221365 2015-07-21 07:09:52 2015-09-09 18:40:53              39   \n",
       "...         ...                 ...                 ...             ...   \n",
       "151107   345170 2015-01-27 03:03:34 2015-03-29 00:30:47              43   \n",
       "151108   274471 2015-05-15 17:43:29 2015-05-26 12:24:39              35   \n",
       "151109   368416 2015-03-03 23:07:31 2015-05-20 07:07:47              40   \n",
       "151110   207709 2015-07-09 20:06:07 2015-09-07 09:34:46              46   \n",
       "151111   138208 2015-06-10 07:02:20 2015-07-21 02:03:53              20   \n",
       "\n",
       "            device_id  source browser sex  age    ip_address  class  \\\n",
       "0       QVPSPJUOCKZAR     SEO  Chrome   M   39  7.327584e+08      0   \n",
       "1       EOGFQPIZPYXFZ     Ads  Chrome   F   53  3.503114e+08      0   \n",
       "2       YSSKYOSJHPPLJ     SEO   Opera   M   53  2.621474e+09      1   \n",
       "3       ATGTXKYKUDUQN     SEO  Safari   M   41  3.840542e+09      0   \n",
       "4       NAUITBZFJKHWW     Ads  Safari   M   45  4.155831e+08      0   \n",
       "...               ...     ...     ...  ..  ...           ...    ...   \n",
       "151107  XPSKTWGPWINLR     SEO  Chrome   M   28  3.451155e+09      1   \n",
       "151108  LYSFABUCPCGBA     SEO  Safari   M   32  2.439047e+09      0   \n",
       "151109  MEQHCSJUBRBFE     SEO      IE   F   26  2.748471e+09      0   \n",
       "151110  CMCXFGRHYSTVJ     SEO  Chrome   M   37  3.601175e+09      0   \n",
       "151111  ZINIADFCLHYPG  Direct      IE   M   38  4.103825e+09      0   \n",
       "\n",
       "                                         country  time_diff  \\\n",
       "0                                          Japan  4506682.0   \n",
       "1                                  United States    17944.0   \n",
       "2                                  United States        1.0   \n",
       "3       Series([], Name: country, dtype: object)   492085.0   \n",
       "4                                  United States  4361461.0   \n",
       "...                                          ...        ...   \n",
       "151107                             United States  5261233.0   \n",
       "151108                               Netherlands   931270.0   \n",
       "151109                                     Japan  6681616.0   \n",
       "151110                             United States  5146119.0   \n",
       "151111  Series([], Name: country, dtype: object)  3524493.0   \n",
       "\n",
       "        device_user_count  ip_count  signup_day  signup_week  purchase_day  \\\n",
       "0                       1         1           1            9             5   \n",
       "1                       1         1           6           23             0   \n",
       "2                      12        12           3            1             3   \n",
       "3                       1         1           1           18             0   \n",
       "4                       1         1           1           30             2   \n",
       "...                   ...       ...         ...          ...           ...   \n",
       "151107                  2         1           1            5             6   \n",
       "151108                  1         1           4           20             1   \n",
       "151109                  1         1           1           10             2   \n",
       "151110                  2         1           3           28             0   \n",
       "151111                  1         1           2           24             1   \n",
       "\n",
       "        purchase_week  \n",
       "0                  16  \n",
       "1                  24  \n",
       "2                   1  \n",
       "3                  19  \n",
       "4                  37  \n",
       "...               ...  \n",
       "151107             13  \n",
       "151108             22  \n",
       "151109             21  \n",
       "151110             37  \n",
       "151111             30  \n",
       "\n",
       "[151112 rows x 19 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_fraud['signup_day'] = data_fraud['signup_time'].apply(lambda x: x.dayofweek)\n",
    "data_fraud['signup_week'] = data_fraud['signup_time'].apply(lambda x: x.week)\n",
    "data_fraud['purchase_day'] = data_fraud['purchase_time'].apply(lambda x: x.dayofweek)\n",
    "data_fraud['purchase_week'] = data_fraud['purchase_time'].apply(lambda x: x.week)\n",
    "data_fraud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Définir vos variables de travail (X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(151112, 11) (151112,)\n"
     ]
    }
   ],
   "source": [
    "#features\n",
    "X= data_fraud[['device_id','source'   ,'browser' ,'sex', 'age','purchase_value',\n",
    "       'ip_address', 'country','time_diff','ip_count', 'device_user_count']]\n",
    "#Response Variable\n",
    "y = data_fraud['class']\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Afficher les informations du dataset afin de vizualiser les variables numeriques et categoriques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 151112 entries, 0 to 151111\n",
      "Data columns (total 19 columns):\n",
      " #   Column             Non-Null Count   Dtype         \n",
      "---  ------             --------------   -----         \n",
      " 0   user_id            151112 non-null  int64         \n",
      " 1   signup_time        151112 non-null  datetime64[ns]\n",
      " 2   purchase_time      151112 non-null  datetime64[ns]\n",
      " 3   purchase_value     151112 non-null  int64         \n",
      " 4   device_id          151112 non-null  object        \n",
      " 5   source             151112 non-null  object        \n",
      " 6   browser            151112 non-null  object        \n",
      " 7   sex                151112 non-null  object        \n",
      " 8   age                151112 non-null  int64         \n",
      " 9   ip_address         151112 non-null  float64       \n",
      " 10  class              151112 non-null  int64         \n",
      " 11  country            151112 non-null  object        \n",
      " 12  time_diff          151112 non-null  float64       \n",
      " 13  device_user_count  151112 non-null  int64         \n",
      " 14  ip_count           151112 non-null  int64         \n",
      " 15  signup_day         151112 non-null  int64         \n",
      " 16  signup_week        151112 non-null  int64         \n",
      " 17  purchase_day       151112 non-null  int64         \n",
      " 18  purchase_week      151112 non-null  int64         \n",
      "dtypes: datetime64[ns](2), float64(2), int64(10), object(5)\n",
      "memory usage: 21.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data_fraud.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Traiter les variables catégoriques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(151112, 11)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Label Encoding string variables\n",
    "lb =  LabelEncoder() \n",
    "X['device_id'] = lb.fit_transform(X['device_id'])\n",
    "X['source'] = lb.fit_transform(X['source'])\n",
    "X['browser'] = lb.fit_transform(X['browser'])\n",
    "X['sex'] = lb.fit_transform(X['sex'])\n",
    "X['country'] = lb.fit_transform(X['country'].astype(str))\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>device_id</th>\n",
       "      <th>source</th>\n",
       "      <th>browser</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>purchase_value</th>\n",
       "      <th>ip_address</th>\n",
       "      <th>country</th>\n",
       "      <th>time_diff</th>\n",
       "      <th>ip_count</th>\n",
       "      <th>device_user_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>89215</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>34</td>\n",
       "      <td>7.327584e+08</td>\n",
       "      <td>165</td>\n",
       "      <td>4506682.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24078</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>16</td>\n",
       "      <td>3.503114e+08</td>\n",
       "      <td>82</td>\n",
       "      <td>17944.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>131216</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>15</td>\n",
       "      <td>2.621474e+09</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3977</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>44</td>\n",
       "      <td>3.840542e+09</td>\n",
       "      <td>54</td>\n",
       "      <td>492085.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68757</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>39</td>\n",
       "      <td>4.155831e+08</td>\n",
       "      <td>82</td>\n",
       "      <td>4361461.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151107</th>\n",
       "      <td>125335</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>43</td>\n",
       "      <td>3.451155e+09</td>\n",
       "      <td>82</td>\n",
       "      <td>5261233.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151108</th>\n",
       "      <td>63001</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>35</td>\n",
       "      <td>2.439047e+09</td>\n",
       "      <td>22</td>\n",
       "      <td>931270.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151109</th>\n",
       "      <td>64204</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>40</td>\n",
       "      <td>2.748471e+09</td>\n",
       "      <td>165</td>\n",
       "      <td>6681616.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151110</th>\n",
       "      <td>13118</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>46</td>\n",
       "      <td>3.601175e+09</td>\n",
       "      <td>82</td>\n",
       "      <td>5146119.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151111</th>\n",
       "      <td>134439</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>4.103825e+09</td>\n",
       "      <td>54</td>\n",
       "      <td>3524493.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>151112 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        device_id  source  browser  sex  age  purchase_value    ip_address  \\\n",
       "0           89215       2        0    1   39              34  7.327584e+08   \n",
       "1           24078       0        0    0   53              16  3.503114e+08   \n",
       "2          131216       2        3    1   53              15  2.621474e+09   \n",
       "3            3977       2        4    1   41              44  3.840542e+09   \n",
       "4           68757       0        4    1   45              39  4.155831e+08   \n",
       "...           ...     ...      ...  ...  ...             ...           ...   \n",
       "151107     125335       2        0    1   28              43  3.451155e+09   \n",
       "151108      63001       2        4    1   32              35  2.439047e+09   \n",
       "151109      64204       2        2    0   26              40  2.748471e+09   \n",
       "151110      13118       2        0    1   37              46  3.601175e+09   \n",
       "151111     134439       1        2    1   38              20  4.103825e+09   \n",
       "\n",
       "        country  time_diff  ip_count  device_user_count  \n",
       "0           165  4506682.0         1                  1  \n",
       "1            82    17944.0         1                  1  \n",
       "2            82        1.0        12                 12  \n",
       "3            54   492085.0         1                  1  \n",
       "4            82  4361461.0         1                  1  \n",
       "...         ...        ...       ...                ...  \n",
       "151107       82  5261233.0         1                  2  \n",
       "151108       22   931270.0         1                  1  \n",
       "151109      165  6681616.0         1                  1  \n",
       "151110       82  5146119.0         1                  2  \n",
       "151111       54  3524493.0         1                  1  \n",
       "\n",
       "[151112 rows x 11 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spliter vos variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['user_id', 'signup_time', 'purchase_time', 'purchase_value', 'device_id', 'source', 'browser', 'sex', 'age', 'ip_address', 'class', 'country', 'time_diff', 'device_user_count', 'ip_count', 'signup_day', 'signup_week', 'purchase_day', 'purchase_week']\n"
     ]
    }
   ],
   "source": [
    "#Predictors\n",
    "print(data_fraud.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting data into train and test dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.25, random_state= 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instancier un `RandomForestClassifier` et l'entrainer sur vos données "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_forest = RandomForestClassifier()\n",
    "clf_forest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Afficher l'accuracy moyenne pour 10 'fold cross validation'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(113334, 11) (37778, 11) (113334,) (37778,)\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      1.00      0.98     34215\n",
      "           1       0.99      0.54      0.70      3563\n",
      "\n",
      "    accuracy                           0.96     37778\n",
      "   macro avg       0.97      0.77      0.84     37778\n",
      "weighted avg       0.96      0.96      0.95     37778\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cross_val_score(rfc, X_train, y_train, scoring = \"accuracy\", cv = 10)\n",
    "X_train.shape\n",
    "print(X_train.shape,X_test.shape,y_train.shape,y_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Afficher les `feature_importances_`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time_diff            0.347227\n",
      "device_user_count    0.158655\n",
      "ip_count             0.128742\n",
      "ip_address           0.090908\n",
      "device_id            0.090580\n",
      "purchase_value       0.060951\n",
      "age                  0.051679\n",
      "country              0.032844\n",
      "browser              0.018858\n",
      "source               0.010379\n",
      "sex                  0.009177\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Variable importance\n",
    "feature_importances_ = pd.Series( clf_forest.feature_importances_,index = X.columns.values).sort_values(ascending=False)\n",
    "print(feature_importances_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vizualiser les sur un graphique "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Visualising important features'}, xlabel='features importance score', ylabel='features'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.title('Visualising important features')\n",
    "plt.xlabel('features importance score')\n",
    "plt.ylabel('features')\n",
    "sns.barplot(x = feature_importances_, y = feature_importances_.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Afficher le `Classification report`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      1.00      0.98     34215\n",
      "           1       0.99      0.54      0.70      3563\n",
      "\n",
      "    accuracy                           0.96     37778\n",
      "   macro avg       0.97      0.77      0.84     37778\n",
      "weighted avg       0.96      0.96      0.95     37778\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print( classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Traçer la courbe AUC "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "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": [
    "prob_score = clf_forest.predict_proba(X_test)\n",
    "prob_score = DataFrame(prob_score).iloc[:,0]\n",
    "fpr,tpr,thresholds = roc_curve(y_test,y_pred)\n",
    "#auc = auc(fpr,tpr)\n",
    "#Plotting the accuracy\n",
    "plt.plot(fpr,tpr, color = 'blue')\n",
    "plt.title('accuracy')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Afficher la matrice de confusion \n",
    "Qu'en pensez vous ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(c,annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Faire une selection de certaine variables et spliter vos varibales "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape du dataset d'entrainement :  (105778, 4) | shape du dataset de test: (45334,)\n"
     ]
    }
   ],
   "source": [
    "#features\n",
    "X1= data_fraud[[\n",
    "       'country','time_diff','ip_count', 'device_user_count']]\n",
    "X1['country'] = lb.fit_transform(X['country'].astype(str))\n",
    "#Response Variable\n",
    "y1 = data_fraud['class']\n",
    "#Splitting data into train and test dataset\n",
    "X_train1, X_test1, y_train1, y_test1 = train_test_split(X1,y1, test_size = 0.3, random_state= 42)\n",
    "print(\"Shape du dataset d'entrainement : \",X_train1.shape,\"| shape du dataset de test:\", y_test1.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entrainer un classifieur `LogisticRegression` sur ces variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 151112 entries, 0 to 151111\n",
      "Data columns (total 4 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   country            151112 non-null  int32  \n",
      " 1   time_diff          151112 non-null  float64\n",
      " 2   ip_count           151112 non-null  int64  \n",
      " 3   device_user_count  151112 non-null  int64  \n",
      "dtypes: float64(1), int32(1), int64(2)\n",
      "memory usage: 4.0 MB\n"
     ]
    }
   ],
   "source": [
    "X1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
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       "LogisticRegression()"
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     "execution_count": 61,
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   "source": [
    "cl_Logistic = LogisticRegression()\n",
    "cl_Logistic.fit(X_train1, y_train1)"
   ]
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   "source": [
    "### Afficher le Classification report"
   ]
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   "execution_count": 73,
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     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      1.00      0.95     41117\n",
      "           1       0.00      0.00      0.00      4217\n",
      "\n",
      "    accuracy                           0.91     45334\n",
      "   macro avg       0.45      0.50      0.48     45334\n",
      "weighted avg       0.82      0.91      0.86     45334\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = cl_Logistic.predict(X_test1)\n",
    "y_pred\n",
    "print(classification_report(y_test1, y_pred))"
   ]
  }
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