Fraud detection[#CC69845].ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "016a6cdf-ea8a-466d-b432-b54b03a9a95b",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "182de3df-5321-4b91-be4b-4e37a9e44118",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import pandas as pd\n",
    "pd.set_option(\"display.max_columns\",None)\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(rc={'figure.figsize':(18,8)},style='darkgrid')\n",
    "sns.set_palette('rocket')\n",
    "from time import time\n",
    "import pingouin\n",
    "from scipy.stats import ttest_ind\n",
    "from datasist.structdata import detect_outliers\n",
    "from geopy.distance import great_circle\n",
    "from category_encoders import WOEEncoder\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.metrics import *\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03465544-dbee-46b1-b6bb-beb051b30a0d",
   "metadata": {},
   "source": [
    "# Collect Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8cfe999b-dcb9-482e-af6c-770563be9aa5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>trans_date_trans_time</th>\n",
       "      <th>cc_num</th>\n",
       "      <th>merchant</th>\n",
       "      <th>category</th>\n",
       "      <th>amt</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>gender</th>\n",
       "      <th>street</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>city_pop</th>\n",
       "      <th>job</th>\n",
       "      <th>dob</th>\n",
       "      <th>trans_num</th>\n",
       "      <th>unix_time</th>\n",
       "      <th>merch_lat</th>\n",
       "      <th>merch_long</th>\n",
       "      <th>is_fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2019-01-01 00:00:18</td>\n",
       "      <td>2703186189652095</td>\n",
       "      <td>fraud_Rippin, Kub and Mann</td>\n",
       "      <td>misc_net</td>\n",
       "      <td>4.97</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Banks</td>\n",
       "      <td>F</td>\n",
       "      <td>561 Perry Cove</td>\n",
       "      <td>Moravian Falls</td>\n",
       "      <td>NC</td>\n",
       "      <td>28654</td>\n",
       "      <td>36.0788</td>\n",
       "      <td>-81.1781</td>\n",
       "      <td>3495</td>\n",
       "      <td>Psychologist, counselling</td>\n",
       "      <td>1988-03-09</td>\n",
       "      <td>0b242abb623afc578575680df30655b9</td>\n",
       "      <td>1325376018</td>\n",
       "      <td>36.011293</td>\n",
       "      <td>-82.048315</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01 00:00:44</td>\n",
       "      <td>630423337322</td>\n",
       "      <td>fraud_Heller, Gutmann and Zieme</td>\n",
       "      <td>grocery_pos</td>\n",
       "      <td>107.23</td>\n",
       "      <td>Stephanie</td>\n",
       "      <td>Gill</td>\n",
       "      <td>F</td>\n",
       "      <td>43039 Riley Greens Suite 393</td>\n",
       "      <td>Orient</td>\n",
       "      <td>WA</td>\n",
       "      <td>99160</td>\n",
       "      <td>48.8878</td>\n",
       "      <td>-118.2105</td>\n",
       "      <td>149</td>\n",
       "      <td>Special educational needs teacher</td>\n",
       "      <td>1978-06-21</td>\n",
       "      <td>1f76529f8574734946361c461b024d99</td>\n",
       "      <td>1325376044</td>\n",
       "      <td>49.159047</td>\n",
       "      <td>-118.186462</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:00:51</td>\n",
       "      <td>38859492057661</td>\n",
       "      <td>fraud_Lind-Buckridge</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>220.11</td>\n",
       "      <td>Edward</td>\n",
       "      <td>Sanchez</td>\n",
       "      <td>M</td>\n",
       "      <td>594 White Dale Suite 530</td>\n",
       "      <td>Malad City</td>\n",
       "      <td>ID</td>\n",
       "      <td>83252</td>\n",
       "      <td>42.1808</td>\n",
       "      <td>-112.2620</td>\n",
       "      <td>4154</td>\n",
       "      <td>Nature conservation officer</td>\n",
       "      <td>1962-01-19</td>\n",
       "      <td>a1a22d70485983eac12b5b88dad1cf95</td>\n",
       "      <td>1325376051</td>\n",
       "      <td>43.150704</td>\n",
       "      <td>-112.154481</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-01-01 00:01:16</td>\n",
       "      <td>3534093764340240</td>\n",
       "      <td>fraud_Kutch, Hermiston and Farrell</td>\n",
       "      <td>gas_transport</td>\n",
       "      <td>45.00</td>\n",
       "      <td>Jeremy</td>\n",
       "      <td>White</td>\n",
       "      <td>M</td>\n",
       "      <td>9443 Cynthia Court Apt. 038</td>\n",
       "      <td>Boulder</td>\n",
       "      <td>MT</td>\n",
       "      <td>59632</td>\n",
       "      <td>46.2306</td>\n",
       "      <td>-112.1138</td>\n",
       "      <td>1939</td>\n",
       "      <td>Patent attorney</td>\n",
       "      <td>1967-01-12</td>\n",
       "      <td>6b849c168bdad6f867558c3793159a81</td>\n",
       "      <td>1325376076</td>\n",
       "      <td>47.034331</td>\n",
       "      <td>-112.561071</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2019-01-01 00:03:06</td>\n",
       "      <td>375534208663984</td>\n",
       "      <td>fraud_Keeling-Crist</td>\n",
       "      <td>misc_pos</td>\n",
       "      <td>41.96</td>\n",
       "      <td>Tyler</td>\n",
       "      <td>Garcia</td>\n",
       "      <td>M</td>\n",
       "      <td>408 Bradley Rest</td>\n",
       "      <td>Doe Hill</td>\n",
       "      <td>VA</td>\n",
       "      <td>24433</td>\n",
       "      <td>38.4207</td>\n",
       "      <td>-79.4629</td>\n",
       "      <td>99</td>\n",
       "      <td>Dance movement psychotherapist</td>\n",
       "      <td>1986-03-28</td>\n",
       "      <td>a41d7549acf90789359a9aa5346dcb46</td>\n",
       "      <td>1325376186</td>\n",
       "      <td>38.674999</td>\n",
       "      <td>-78.632459</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 trans_date_trans_time            cc_num  \\\n",
       "0           0   2019-01-01 00:00:18  2703186189652095   \n",
       "1           1   2019-01-01 00:00:44      630423337322   \n",
       "2           2   2019-01-01 00:00:51    38859492057661   \n",
       "3           3   2019-01-01 00:01:16  3534093764340240   \n",
       "4           4   2019-01-01 00:03:06   375534208663984   \n",
       "\n",
       "                             merchant       category     amt      first  \\\n",
       "0          fraud_Rippin, Kub and Mann       misc_net    4.97   Jennifer   \n",
       "1     fraud_Heller, Gutmann and Zieme    grocery_pos  107.23  Stephanie   \n",
       "2                fraud_Lind-Buckridge  entertainment  220.11     Edward   \n",
       "3  fraud_Kutch, Hermiston and Farrell  gas_transport   45.00     Jeremy   \n",
       "4                 fraud_Keeling-Crist       misc_pos   41.96      Tyler   \n",
       "\n",
       "      last gender                        street            city state    zip  \\\n",
       "0    Banks      F                561 Perry Cove  Moravian Falls    NC  28654   \n",
       "1     Gill      F  43039 Riley Greens Suite 393          Orient    WA  99160   \n",
       "2  Sanchez      M      594 White Dale Suite 530      Malad City    ID  83252   \n",
       "3    White      M   9443 Cynthia Court Apt. 038         Boulder    MT  59632   \n",
       "4   Garcia      M              408 Bradley Rest        Doe Hill    VA  24433   \n",
       "\n",
       "       lat      long  city_pop                                job         dob  \\\n",
       "0  36.0788  -81.1781      3495          Psychologist, counselling  1988-03-09   \n",
       "1  48.8878 -118.2105       149  Special educational needs teacher  1978-06-21   \n",
       "2  42.1808 -112.2620      4154        Nature conservation officer  1962-01-19   \n",
       "3  46.2306 -112.1138      1939                    Patent attorney  1967-01-12   \n",
       "4  38.4207  -79.4629        99     Dance movement psychotherapist  1986-03-28   \n",
       "\n",
       "                          trans_num   unix_time  merch_lat  merch_long  \\\n",
       "0  0b242abb623afc578575680df30655b9  1325376018  36.011293  -82.048315   \n",
       "1  1f76529f8574734946361c461b024d99  1325376044  49.159047 -118.186462   \n",
       "2  a1a22d70485983eac12b5b88dad1cf95  1325376051  43.150704 -112.154481   \n",
       "3  6b849c168bdad6f867558c3793159a81  1325376076  47.034331 -112.561071   \n",
       "4  a41d7549acf90789359a9aa5346dcb46  1325376186  38.674999  -78.632459   \n",
       "\n",
       "   is_fraud  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=pd.read_csv(\"fraudTrain.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60d2a44a-215a-4cab-94ef-e74424640b1c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>trans_date_trans_time</th>\n",
       "      <th>cc_num</th>\n",
       "      <th>merchant</th>\n",
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       "      <th>gender</th>\n",
       "      <th>street</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>city_pop</th>\n",
       "      <th>job</th>\n",
       "      <th>dob</th>\n",
       "      <th>trans_num</th>\n",
       "      <th>unix_time</th>\n",
       "      <th>merch_lat</th>\n",
       "      <th>merch_long</th>\n",
       "      <th>is_fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-06-21 12:14:25</td>\n",
       "      <td>2291163933867244</td>\n",
       "      <td>fraud_Kirlin and Sons</td>\n",
       "      <td>personal_care</td>\n",
       "      <td>2.86</td>\n",
       "      <td>Jeff</td>\n",
       "      <td>Elliott</td>\n",
       "      <td>M</td>\n",
       "      <td>351 Darlene Green</td>\n",
       "      <td>Columbia</td>\n",
       "      <td>SC</td>\n",
       "      <td>29209</td>\n",
       "      <td>33.9659</td>\n",
       "      <td>-80.9355</td>\n",
       "      <td>333497</td>\n",
       "      <td>Mechanical engineer</td>\n",
       "      <td>1968-03-19</td>\n",
       "      <td>2da90c7d74bd46a0caf3777415b3ebd3</td>\n",
       "      <td>1371816865</td>\n",
       "      <td>33.986391</td>\n",
       "      <td>-81.200714</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-06-21 12:14:33</td>\n",
       "      <td>3573030041201292</td>\n",
       "      <td>fraud_Sporer-Keebler</td>\n",
       "      <td>personal_care</td>\n",
       "      <td>29.84</td>\n",
       "      <td>Joanne</td>\n",
       "      <td>Williams</td>\n",
       "      <td>F</td>\n",
       "      <td>3638 Marsh Union</td>\n",
       "      <td>Altonah</td>\n",
       "      <td>UT</td>\n",
       "      <td>84002</td>\n",
       "      <td>40.3207</td>\n",
       "      <td>-110.4360</td>\n",
       "      <td>302</td>\n",
       "      <td>Sales professional, IT</td>\n",
       "      <td>1990-01-17</td>\n",
       "      <td>324cc204407e99f51b0d6ca0055005e7</td>\n",
       "      <td>1371816873</td>\n",
       "      <td>39.450498</td>\n",
       "      <td>-109.960431</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2020-06-21 12:14:53</td>\n",
       "      <td>3598215285024754</td>\n",
       "      <td>fraud_Swaniawski, Nitzsche and Welch</td>\n",
       "      <td>health_fitness</td>\n",
       "      <td>41.28</td>\n",
       "      <td>Ashley</td>\n",
       "      <td>Lopez</td>\n",
       "      <td>F</td>\n",
       "      <td>9333 Valentine Point</td>\n",
       "      <td>Bellmore</td>\n",
       "      <td>NY</td>\n",
       "      <td>11710</td>\n",
       "      <td>40.6729</td>\n",
       "      <td>-73.5365</td>\n",
       "      <td>34496</td>\n",
       "      <td>Librarian, public</td>\n",
       "      <td>1970-10-21</td>\n",
       "      <td>c81755dbbbea9d5c77f094348a7579be</td>\n",
       "      <td>1371816893</td>\n",
       "      <td>40.495810</td>\n",
       "      <td>-74.196111</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2020-06-21 12:15:15</td>\n",
       "      <td>3591919803438423</td>\n",
       "      <td>fraud_Haley Group</td>\n",
       "      <td>misc_pos</td>\n",
       "      <td>60.05</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Williams</td>\n",
       "      <td>M</td>\n",
       "      <td>32941 Krystal Mill Apt. 552</td>\n",
       "      <td>Titusville</td>\n",
       "      <td>FL</td>\n",
       "      <td>32780</td>\n",
       "      <td>28.5697</td>\n",
       "      <td>-80.8191</td>\n",
       "      <td>54767</td>\n",
       "      <td>Set designer</td>\n",
       "      <td>1987-07-25</td>\n",
       "      <td>2159175b9efe66dc301f149d3d5abf8c</td>\n",
       "      <td>1371816915</td>\n",
       "      <td>28.812398</td>\n",
       "      <td>-80.883061</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2020-06-21 12:15:17</td>\n",
       "      <td>3526826139003047</td>\n",
       "      <td>fraud_Johnston-Casper</td>\n",
       "      <td>travel</td>\n",
       "      <td>3.19</td>\n",
       "      <td>Nathan</td>\n",
       "      <td>Massey</td>\n",
       "      <td>M</td>\n",
       "      <td>5783 Evan Roads Apt. 465</td>\n",
       "      <td>Falmouth</td>\n",
       "      <td>MI</td>\n",
       "      <td>49632</td>\n",
       "      <td>44.2529</td>\n",
       "      <td>-85.0170</td>\n",
       "      <td>1126</td>\n",
       "      <td>Furniture designer</td>\n",
       "      <td>1955-07-06</td>\n",
       "      <td>57ff021bd3f328f8738bb535c302a31b</td>\n",
       "      <td>1371816917</td>\n",
       "      <td>44.959148</td>\n",
       "      <td>-85.884734</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 trans_date_trans_time            cc_num  \\\n",
       "0           0   2020-06-21 12:14:25  2291163933867244   \n",
       "1           1   2020-06-21 12:14:33  3573030041201292   \n",
       "2           2   2020-06-21 12:14:53  3598215285024754   \n",
       "3           3   2020-06-21 12:15:15  3591919803438423   \n",
       "4           4   2020-06-21 12:15:17  3526826139003047   \n",
       "\n",
       "                               merchant        category    amt   first  \\\n",
       "0                 fraud_Kirlin and Sons   personal_care   2.86    Jeff   \n",
       "1                  fraud_Sporer-Keebler   personal_care  29.84  Joanne   \n",
       "2  fraud_Swaniawski, Nitzsche and Welch  health_fitness  41.28  Ashley   \n",
       "3                     fraud_Haley Group        misc_pos  60.05   Brian   \n",
       "4                 fraud_Johnston-Casper          travel   3.19  Nathan   \n",
       "\n",
       "       last gender                       street        city state    zip  \\\n",
       "0   Elliott      M            351 Darlene Green    Columbia    SC  29209   \n",
       "1  Williams      F             3638 Marsh Union     Altonah    UT  84002   \n",
       "2     Lopez      F         9333 Valentine Point    Bellmore    NY  11710   \n",
       "3  Williams      M  32941 Krystal Mill Apt. 552  Titusville    FL  32780   \n",
       "4    Massey      M     5783 Evan Roads Apt. 465    Falmouth    MI  49632   \n",
       "\n",
       "       lat      long  city_pop                     job         dob  \\\n",
       "0  33.9659  -80.9355    333497     Mechanical engineer  1968-03-19   \n",
       "1  40.3207 -110.4360       302  Sales professional, IT  1990-01-17   \n",
       "2  40.6729  -73.5365     34496       Librarian, public  1970-10-21   \n",
       "3  28.5697  -80.8191     54767            Set designer  1987-07-25   \n",
       "4  44.2529  -85.0170      1126      Furniture designer  1955-07-06   \n",
       "\n",
       "                          trans_num   unix_time  merch_lat  merch_long  \\\n",
       "0  2da90c7d74bd46a0caf3777415b3ebd3  1371816865  33.986391  -81.200714   \n",
       "1  324cc204407e99f51b0d6ca0055005e7  1371816873  39.450498 -109.960431   \n",
       "2  c81755dbbbea9d5c77f094348a7579be  1371816893  40.495810  -74.196111   \n",
       "3  2159175b9efe66dc301f149d3d5abf8c  1371816915  28.812398  -80.883061   \n",
       "4  57ff021bd3f328f8738bb535c302a31b  1371816917  44.959148  -85.884734   \n",
       "\n",
       "   is_fraud  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test=pd.read_csv('fraudTest.csv')\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "09d05c3c-35e3-48cc-9cb1-7320bf913f0c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#this test data also contains is_fraud it's not seperated to our test model\n",
    "#so we will concat them together to clean them,then make our predictions on test data without is_fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5cc89c73-d15a-4085-8ce6-6556146cebed",
   "metadata": {
    "tags": []
   },
   "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>Unnamed: 0</th>\n",
       "      <th>trans_date_trans_time</th>\n",
       "      <th>cc_num</th>\n",
       "      <th>merchant</th>\n",
       "      <th>category</th>\n",
       "      <th>amt</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>gender</th>\n",
       "      <th>street</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>city_pop</th>\n",
       "      <th>job</th>\n",
       "      <th>dob</th>\n",
       "      <th>trans_num</th>\n",
       "      <th>unix_time</th>\n",
       "      <th>merch_lat</th>\n",
       "      <th>merch_long</th>\n",
       "      <th>is_fraud</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2019-01-01 00:00:18</td>\n",
       "      <td>2703186189652095</td>\n",
       "      <td>fraud_Rippin, Kub and Mann</td>\n",
       "      <td>misc_net</td>\n",
       "      <td>4.97</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Banks</td>\n",
       "      <td>F</td>\n",
       "      <td>561 Perry Cove</td>\n",
       "      <td>Moravian Falls</td>\n",
       "      <td>NC</td>\n",
       "      <td>28654</td>\n",
       "      <td>36.0788</td>\n",
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       "      <td>Psychologist, counselling</td>\n",
       "      <td>1988-03-09</td>\n",
       "      <td>0b242abb623afc578575680df30655b9</td>\n",
       "      <td>1325376018</td>\n",
       "      <td>36.011293</td>\n",
       "      <td>-82.048315</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01 00:00:44</td>\n",
       "      <td>630423337322</td>\n",
       "      <td>fraud_Heller, Gutmann and Zieme</td>\n",
       "      <td>grocery_pos</td>\n",
       "      <td>107.23</td>\n",
       "      <td>Stephanie</td>\n",
       "      <td>Gill</td>\n",
       "      <td>F</td>\n",
       "      <td>43039 Riley Greens Suite 393</td>\n",
       "      <td>Orient</td>\n",
       "      <td>WA</td>\n",
       "      <td>99160</td>\n",
       "      <td>48.8878</td>\n",
       "      <td>-118.2105</td>\n",
       "      <td>149</td>\n",
       "      <td>Special educational needs teacher</td>\n",
       "      <td>1978-06-21</td>\n",
       "      <td>1f76529f8574734946361c461b024d99</td>\n",
       "      <td>1325376044</td>\n",
       "      <td>49.159047</td>\n",
       "      <td>-118.186462</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:00:51</td>\n",
       "      <td>38859492057661</td>\n",
       "      <td>fraud_Lind-Buckridge</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>220.11</td>\n",
       "      <td>Edward</td>\n",
       "      <td>Sanchez</td>\n",
       "      <td>M</td>\n",
       "      <td>594 White Dale Suite 530</td>\n",
       "      <td>Malad City</td>\n",
       "      <td>ID</td>\n",
       "      <td>83252</td>\n",
       "      <td>42.1808</td>\n",
       "      <td>-112.2620</td>\n",
       "      <td>4154</td>\n",
       "      <td>Nature conservation officer</td>\n",
       "      <td>1962-01-19</td>\n",
       "      <td>a1a22d70485983eac12b5b88dad1cf95</td>\n",
       "      <td>1325376051</td>\n",
       "      <td>43.150704</td>\n",
       "      <td>-112.154481</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-01-01 00:01:16</td>\n",
       "      <td>3534093764340240</td>\n",
       "      <td>fraud_Kutch, Hermiston and Farrell</td>\n",
       "      <td>gas_transport</td>\n",
       "      <td>45.00</td>\n",
       "      <td>Jeremy</td>\n",
       "      <td>White</td>\n",
       "      <td>M</td>\n",
       "      <td>9443 Cynthia Court Apt. 038</td>\n",
       "      <td>Boulder</td>\n",
       "      <td>MT</td>\n",
       "      <td>59632</td>\n",
       "      <td>46.2306</td>\n",
       "      <td>-112.1138</td>\n",
       "      <td>1939</td>\n",
       "      <td>Patent attorney</td>\n",
       "      <td>1967-01-12</td>\n",
       "      <td>6b849c168bdad6f867558c3793159a81</td>\n",
       "      <td>1325376076</td>\n",
       "      <td>47.034331</td>\n",
       "      <td>-112.561071</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2019-01-01 00:03:06</td>\n",
       "      <td>375534208663984</td>\n",
       "      <td>fraud_Keeling-Crist</td>\n",
       "      <td>misc_pos</td>\n",
       "      <td>41.96</td>\n",
       "      <td>Tyler</td>\n",
       "      <td>Garcia</td>\n",
       "      <td>M</td>\n",
       "      <td>408 Bradley Rest</td>\n",
       "      <td>Doe Hill</td>\n",
       "      <td>VA</td>\n",
       "      <td>24433</td>\n",
       "      <td>38.4207</td>\n",
       "      <td>-79.4629</td>\n",
       "      <td>99</td>\n",
       "      <td>Dance movement psychotherapist</td>\n",
       "      <td>1986-03-28</td>\n",
       "      <td>a41d7549acf90789359a9aa5346dcb46</td>\n",
       "      <td>1325376186</td>\n",
       "      <td>38.674999</td>\n",
       "      <td>-78.632459</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 trans_date_trans_time            cc_num  \\\n",
       "0           0   2019-01-01 00:00:18  2703186189652095   \n",
       "1           1   2019-01-01 00:00:44      630423337322   \n",
       "2           2   2019-01-01 00:00:51    38859492057661   \n",
       "3           3   2019-01-01 00:01:16  3534093764340240   \n",
       "4           4   2019-01-01 00:03:06   375534208663984   \n",
       "\n",
       "                             merchant       category     amt      first  \\\n",
       "0          fraud_Rippin, Kub and Mann       misc_net    4.97   Jennifer   \n",
       "1     fraud_Heller, Gutmann and Zieme    grocery_pos  107.23  Stephanie   \n",
       "2                fraud_Lind-Buckridge  entertainment  220.11     Edward   \n",
       "3  fraud_Kutch, Hermiston and Farrell  gas_transport   45.00     Jeremy   \n",
       "4                 fraud_Keeling-Crist       misc_pos   41.96      Tyler   \n",
       "\n",
       "      last gender                        street            city state    zip  \\\n",
       "0    Banks      F                561 Perry Cove  Moravian Falls    NC  28654   \n",
       "1     Gill      F  43039 Riley Greens Suite 393          Orient    WA  99160   \n",
       "2  Sanchez      M      594 White Dale Suite 530      Malad City    ID  83252   \n",
       "3    White      M   9443 Cynthia Court Apt. 038         Boulder    MT  59632   \n",
       "4   Garcia      M              408 Bradley Rest        Doe Hill    VA  24433   \n",
       "\n",
       "       lat      long  city_pop                                job         dob  \\\n",
       "0  36.0788  -81.1781      3495          Psychologist, counselling  1988-03-09   \n",
       "1  48.8878 -118.2105       149  Special educational needs teacher  1978-06-21   \n",
       "2  42.1808 -112.2620      4154        Nature conservation officer  1962-01-19   \n",
       "3  46.2306 -112.1138      1939                    Patent attorney  1967-01-12   \n",
       "4  38.4207  -79.4629        99     Dance movement psychotherapist  1986-03-28   \n",
       "\n",
       "                          trans_num   unix_time  merch_lat  merch_long  \\\n",
       "0  0b242abb623afc578575680df30655b9  1325376018  36.011293  -82.048315   \n",
       "1  1f76529f8574734946361c461b024d99  1325376044  49.159047 -118.186462   \n",
       "2  a1a22d70485983eac12b5b88dad1cf95  1325376051  43.150704 -112.154481   \n",
       "3  6b849c168bdad6f867558c3793159a81  1325376076  47.034331 -112.561071   \n",
       "4  a41d7549acf90789359a9aa5346dcb46  1325376186  38.674999  -78.632459   \n",
       "\n",
       "   is_fraud  split  \n",
       "0         0  train  \n",
       "1         0  train  \n",
       "2         0  train  \n",
       "3         0  train  \n",
       "4         0  train  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['split']='train'\n",
    "test['split']='test'\n",
    "df=pd.concat([train,test],axis=0).reset_index(drop=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffea0b8f-4d19-49b0-8c44-d908b79f2df2",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ed52cec7-f96d-48a9-8cd8-76c4b168a7ff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1852394 entries, 0 to 1852393\n",
      "Data columns (total 24 columns):\n",
      " #   Column                 Dtype  \n",
      "---  ------                 -----  \n",
      " 0   Unnamed: 0             int64  \n",
      " 1   trans_date_trans_time  object \n",
      " 2   cc_num                 int64  \n",
      " 3   merchant               object \n",
      " 4   category               object \n",
      " 5   amt                    float64\n",
      " 6   first                  object \n",
      " 7   last                   object \n",
      " 8   gender                 object \n",
      " 9   street                 object \n",
      " 10  city                   object \n",
      " 11  state                  object \n",
      " 12  zip                    int64  \n",
      " 13  lat                    float64\n",
      " 14  long                   float64\n",
      " 15  city_pop               int64  \n",
      " 16  job                    object \n",
      " 17  dob                    object \n",
      " 18  trans_num              object \n",
      " 19  unix_time              int64  \n",
      " 20  merch_lat              float64\n",
      " 21  merch_long             float64\n",
      " 22  is_fraud               int64  \n",
      " 23  split                  object \n",
      "dtypes: float64(5), int64(6), object(13)\n",
      "memory usage: 339.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1cab8c72-23aa-48c3-91a3-5d67ce838624",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0               0\n",
       "trans_date_trans_time    0\n",
       "cc_num                   0\n",
       "merchant                 0\n",
       "category                 0\n",
       "amt                      0\n",
       "first                    0\n",
       "last                     0\n",
       "gender                   0\n",
       "street                   0\n",
       "city                     0\n",
       "state                    0\n",
       "zip                      0\n",
       "lat                      0\n",
       "long                     0\n",
       "city_pop                 0\n",
       "job                      0\n",
       "dob                      0\n",
       "trans_num                0\n",
       "unix_time                0\n",
       "merch_lat                0\n",
       "merch_long               0\n",
       "is_fraud                 0\n",
       "split                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check NUllS\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b961e84d-35c0-494e-9123-f2f7fc80f501",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c37c634e-c1b3-4d20-aa53-f481df9b0860",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'trans_date_trans_time', 'cc_num', 'merchant', 'category',\n",
       "       'amt', 'first', 'last', 'gender', 'street', 'city', 'state', 'zip',\n",
       "       'lat', 'long', 'city_pop', 'job', 'dob', 'trans_num', 'unix_time',\n",
       "       'merch_lat', 'merch_long', 'is_fraud', 'split'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b0e855db-b9de-4708-98dc-501a3efbfdf6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#drop unnecessary columns \n",
    "#we will drop customer location and merchant\n",
    "#we will drop location of customer as we will get the distance of between\n",
    "df.drop(columns=['Unnamed: 0','street','state','first','last','trans_num','unix_time'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "51da0a2d-fe21-43f6-ba4b-01cfbdec2f55",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trans_date_trans_time</th>\n",
       "      <th>cc_num</th>\n",
       "      <th>merchant</th>\n",
       "      <th>category</th>\n",
       "      <th>amt</th>\n",
       "      <th>gender</th>\n",
       "      <th>city</th>\n",
       "      <th>zip</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
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       "      <th>dob</th>\n",
       "      <th>merch_lat</th>\n",
       "      <th>merch_long</th>\n",
       "      <th>is_fraud</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01 00:00:18</td>\n",
       "      <td>2703186189652095</td>\n",
       "      <td>fraud_Rippin, Kub and Mann</td>\n",
       "      <td>misc_net</td>\n",
       "      <td>4.97</td>\n",
       "      <td>F</td>\n",
       "      <td>Moravian Falls</td>\n",
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       "      <td>1988-03-09</td>\n",
       "      <td>36.011293</td>\n",
       "      <td>-82.048315</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-01 00:00:44</td>\n",
       "      <td>630423337322</td>\n",
       "      <td>fraud_Heller, Gutmann and Zieme</td>\n",
       "      <td>grocery_pos</td>\n",
       "      <td>107.23</td>\n",
       "      <td>F</td>\n",
       "      <td>Orient</td>\n",
       "      <td>99160</td>\n",
       "      <td>48.8878</td>\n",
       "      <td>-118.2105</td>\n",
       "      <td>149</td>\n",
       "      <td>Special educational needs teacher</td>\n",
       "      <td>1978-06-21</td>\n",
       "      <td>49.159047</td>\n",
       "      <td>-118.186462</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-01 00:00:51</td>\n",
       "      <td>38859492057661</td>\n",
       "      <td>fraud_Lind-Buckridge</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>220.11</td>\n",
       "      <td>M</td>\n",
       "      <td>Malad City</td>\n",
       "      <td>83252</td>\n",
       "      <td>42.1808</td>\n",
       "      <td>-112.2620</td>\n",
       "      <td>4154</td>\n",
       "      <td>Nature conservation officer</td>\n",
       "      <td>1962-01-19</td>\n",
       "      <td>43.150704</td>\n",
       "      <td>-112.154481</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-01 00:01:16</td>\n",
       "      <td>3534093764340240</td>\n",
       "      <td>fraud_Kutch, Hermiston and Farrell</td>\n",
       "      <td>gas_transport</td>\n",
       "      <td>45.00</td>\n",
       "      <td>M</td>\n",
       "      <td>Boulder</td>\n",
       "      <td>59632</td>\n",
       "      <td>46.2306</td>\n",
       "      <td>-112.1138</td>\n",
       "      <td>1939</td>\n",
       "      <td>Patent attorney</td>\n",
       "      <td>1967-01-12</td>\n",
       "      <td>47.034331</td>\n",
       "      <td>-112.561071</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-01 00:03:06</td>\n",
       "      <td>375534208663984</td>\n",
       "      <td>fraud_Keeling-Crist</td>\n",
       "      <td>misc_pos</td>\n",
       "      <td>41.96</td>\n",
       "      <td>M</td>\n",
       "      <td>Doe Hill</td>\n",
       "      <td>24433</td>\n",
       "      <td>38.4207</td>\n",
       "      <td>-79.4629</td>\n",
       "      <td>99</td>\n",
       "      <td>Dance movement psychotherapist</td>\n",
       "      <td>1986-03-28</td>\n",
       "      <td>38.674999</td>\n",
       "      <td>-78.632459</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  trans_date_trans_time            cc_num                            merchant  \\\n",
       "0   2019-01-01 00:00:18  2703186189652095          fraud_Rippin, Kub and Mann   \n",
       "1   2019-01-01 00:00:44      630423337322     fraud_Heller, Gutmann and Zieme   \n",
       "2   2019-01-01 00:00:51    38859492057661                fraud_Lind-Buckridge   \n",
       "3   2019-01-01 00:01:16  3534093764340240  fraud_Kutch, Hermiston and Farrell   \n",
       "4   2019-01-01 00:03:06   375534208663984                 fraud_Keeling-Crist   \n",
       "\n",
       "        category     amt gender            city    zip      lat      long  \\\n",
       "0       misc_net    4.97      F  Moravian Falls  28654  36.0788  -81.1781   \n",
       "1    grocery_pos  107.23      F          Orient  99160  48.8878 -118.2105   \n",
       "2  entertainment  220.11      M      Malad City  83252  42.1808 -112.2620   \n",
       "3  gas_transport   45.00      M         Boulder  59632  46.2306 -112.1138   \n",
       "4       misc_pos   41.96      M        Doe Hill  24433  38.4207  -79.4629   \n",
       "\n",
       "   city_pop                                job         dob  merch_lat  \\\n",
       "0      3495          Psychologist, counselling  1988-03-09  36.011293   \n",
       "1       149  Special educational needs teacher  1978-06-21  49.159047   \n",
       "2      4154        Nature conservation officer  1962-01-19  43.150704   \n",
       "3      1939                    Patent attorney  1967-01-12  47.034331   \n",
       "4        99     Dance movement psychotherapist  1986-03-28  38.674999   \n",
       "\n",
       "   merch_long  is_fraud  split  \n",
       "0  -82.048315         0  train  \n",
       "1 -118.186462         0  train  \n",
       "2 -112.154481         0  train  \n",
       "3 -112.561071         0  train  \n",
       "4  -78.632459         0  train  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2ba1d6ae-f641-4885-a268-e687925123b8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#change Date type\n",
    "df['trans_date_trans_time']=pd.to_datetime(df['trans_date_trans_time'],format='mixed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b32e5a73-4bfc-48b7-b93d-2bf8d651f1d0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['hour']=df['trans_date_trans_time'].dt.hour\n",
    "df['day']=df['trans_date_trans_time'].dt.day_name()\n",
    "df['month']=df['trans_date_trans_time'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "29d9313b-1e0f-4a8c-b434-788632c177df",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#clean merchant column\n",
    "df['merchant']=df['merchant'].apply(lambda x : x.replace('fraud_',\"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9bcfdf60-2704-4096-b10c-20d7a0d0e0cd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>merchant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rippin, Kub and Mann</td>\n",
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       "      <th>1</th>\n",
       "      <td>Heller, Gutmann and Zieme</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lind-Buckridge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kutch, Hermiston and Farrell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Keeling-Crist</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       merchant\n",
       "0          Rippin, Kub and Mann\n",
       "1     Heller, Gutmann and Zieme\n",
       "2                Lind-Buckridge\n",
       "3  Kutch, Hermiston and Farrell\n",
       "4                 Keeling-Crist"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['merchant']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "98a586aa-0be8-4ad0-849d-587d610f7eca",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#date of birth => age of customer\n",
    "df['dob']=pd.to_datetime(df['dob'],format='mixed')\n",
    "df['age']=(df['trans_date_trans_time'].dt.year-df['dob'].dt.year).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fa242082-aa95-4a32-bcb3-e5123bad1437",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#location between customer and merchant\n",
    "df['distance_km']=df.apply(lambda col : round(great_circle((col['lat'],col['long']),(col['merch_lat'],col['merch_long'])).kilometers,2),axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f7515d44-c2cd-4a12-a14c-bc268bb90559",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.drop(columns=['dob','lat','long','merch_long','merch_lat'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4506f216-91c4-46bd-ace5-07f1fa54a7e8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trans_date_trans_time</th>\n",
       "      <th>cc_num</th>\n",
       "      <th>merchant</th>\n",
       "      <th>category</th>\n",
       "      <th>amt</th>\n",
       "      <th>gender</th>\n",
       "      <th>city</th>\n",
       "      <th>zip</th>\n",
       "      <th>city_pop</th>\n",
       "      <th>job</th>\n",
       "      <th>is_fraud</th>\n",
       "      <th>split</th>\n",
       "      <th>hour</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>age</th>\n",
       "      <th>distance_km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01 00:00:18</td>\n",
       "      <td>2703186189652095</td>\n",
       "      <td>Rippin, Kub and Mann</td>\n",
       "      <td>misc_net</td>\n",
       "      <td>4.97</td>\n",
       "      <td>F</td>\n",
       "      <td>Moravian Falls</td>\n",
       "      <td>28654</td>\n",
       "      <td>3495</td>\n",
       "      <td>Psychologist, counselling</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>78.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-01 00:00:44</td>\n",
       "      <td>630423337322</td>\n",
       "      <td>Heller, Gutmann and Zieme</td>\n",
       "      <td>grocery_pos</td>\n",
       "      <td>107.23</td>\n",
       "      <td>F</td>\n",
       "      <td>Orient</td>\n",
       "      <td>99160</td>\n",
       "      <td>149</td>\n",
       "      <td>Special educational needs teacher</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>30.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-01 00:00:51</td>\n",
       "      <td>38859492057661</td>\n",
       "      <td>Lind-Buckridge</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>220.11</td>\n",
       "      <td>M</td>\n",
       "      <td>Malad City</td>\n",
       "      <td>83252</td>\n",
       "      <td>4154</td>\n",
       "      <td>Nature conservation officer</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>108.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-01 00:01:16</td>\n",
       "      <td>3534093764340240</td>\n",
       "      <td>Kutch, Hermiston and Farrell</td>\n",
       "      <td>gas_transport</td>\n",
       "      <td>45.00</td>\n",
       "      <td>M</td>\n",
       "      <td>Boulder</td>\n",
       "      <td>59632</td>\n",
       "      <td>1939</td>\n",
       "      <td>Patent attorney</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>95.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-01 00:03:06</td>\n",
       "      <td>375534208663984</td>\n",
       "      <td>Keeling-Crist</td>\n",
       "      <td>misc_pos</td>\n",
       "      <td>41.96</td>\n",
       "      <td>M</td>\n",
       "      <td>Doe Hill</td>\n",
       "      <td>24433</td>\n",
       "      <td>99</td>\n",
       "      <td>Dance movement psychotherapist</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>77.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  trans_date_trans_time            cc_num                      merchant  \\\n",
       "0   2019-01-01 00:00:18  2703186189652095          Rippin, Kub and Mann   \n",
       "1   2019-01-01 00:00:44      630423337322     Heller, Gutmann and Zieme   \n",
       "2   2019-01-01 00:00:51    38859492057661                Lind-Buckridge   \n",
       "3   2019-01-01 00:01:16  3534093764340240  Kutch, Hermiston and Farrell   \n",
       "4   2019-01-01 00:03:06   375534208663984                 Keeling-Crist   \n",
       "\n",
       "        category     amt gender            city    zip  city_pop  \\\n",
       "0       misc_net    4.97      F  Moravian Falls  28654      3495   \n",
       "1    grocery_pos  107.23      F          Orient  99160       149   \n",
       "2  entertainment  220.11      M      Malad City  83252      4154   \n",
       "3  gas_transport   45.00      M         Boulder  59632      1939   \n",
       "4       misc_pos   41.96      M        Doe Hill  24433        99   \n",
       "\n",
       "                                 job  is_fraud  split  hour      day  month  \\\n",
       "0          Psychologist, counselling         0  train     0  Tuesday      1   \n",
       "1  Special educational needs teacher         0  train     0  Tuesday      1   \n",
       "2        Nature conservation officer         0  train     0  Tuesday      1   \n",
       "3                    Patent attorney         0  train     0  Tuesday      1   \n",
       "4     Dance movement psychotherapist         0  train     0  Tuesday      1   \n",
       "\n",
       "   age  distance_km  \n",
       "0   31        78.60  \n",
       "1   41        30.21  \n",
       "2   57       108.21  \n",
       "3   52        95.67  \n",
       "4   33        77.56  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29e28ccc-593a-4aaa-9bf4-8f4c23a2423f",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8d53577f-f0fa-42b6-9652-16dd008945cf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
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       "      <th>50%</th>\n",
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       "      <th>trans_date_trans_time</th>\n",
       "      <td>1852394</td>\n",
       "      <td>2020-01-20 21:31:46.801827328</td>\n",
       "      <td>2019-01-01 00:00:18</td>\n",
       "      <td>2019-07-23 04:13:43.750000128</td>\n",
       "      <td>2020-01-02 01:15:31</td>\n",
       "      <td>2020-07-23 12:11:25.249999872</td>\n",
       "      <td>2020-12-31 23:59:34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cc_num</th>\n",
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       "      <td>417386038393710400.0</td>\n",
       "      <td>60416207185.0</td>\n",
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       "      <td>1309115265318020352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amt</th>\n",
       "      <td>1852394.0</td>\n",
       "      <td>70.063567</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.64</td>\n",
       "      <td>47.45</td>\n",
       "      <td>83.1</td>\n",
       "      <td>28948.9</td>\n",
       "      <td>159.253975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zip</th>\n",
       "      <td>1852394.0</td>\n",
       "      <td>48813.258191</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>26237.0</td>\n",
       "      <td>48174.0</td>\n",
       "      <td>72042.0</td>\n",
       "      <td>99921.0</td>\n",
       "      <td>26881.845966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_pop</th>\n",
       "      <td>1852394.0</td>\n",
       "      <td>88643.674509</td>\n",
       "      <td>23.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>2443.0</td>\n",
       "      <td>20328.0</td>\n",
       "      <td>2906700.0</td>\n",
       "      <td>301487.618344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_fraud</th>\n",
       "      <td>1852394.0</td>\n",
       "      <td>0.00521</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.071992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <td>1852394.0</td>\n",
       "      <td>12.806119</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6.815753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>1852394.0</td>\n",
       "      <td>7.152067</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.424954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1852394.0</td>\n",
       "      <td>46.21138</td>\n",
       "      <td>14.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>17.395446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_km</th>\n",
       "      <td>1852394.0</td>\n",
       "      <td>76.111833</td>\n",
       "      <td>0.02</td>\n",
       "      <td>55.32</td>\n",
       "      <td>78.22</td>\n",
       "      <td>98.51</td>\n",
       "      <td>152.12</td>\n",
       "      <td>29.117007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count                           mean  \\\n",
       "trans_date_trans_time    1852394  2020-01-20 21:31:46.801827328   \n",
       "cc_num                 1852394.0           417386038393710400.0   \n",
       "amt                    1852394.0                      70.063567   \n",
       "zip                    1852394.0                   48813.258191   \n",
       "city_pop               1852394.0                   88643.674509   \n",
       "is_fraud               1852394.0                        0.00521   \n",
       "hour                   1852394.0                      12.806119   \n",
       "month                  1852394.0                       7.152067   \n",
       "age                    1852394.0                       46.21138   \n",
       "distance_km            1852394.0                      76.111833   \n",
       "\n",
       "                                       min                            25%  \\\n",
       "trans_date_trans_time  2019-01-01 00:00:18  2019-07-23 04:13:43.750000128   \n",
       "cc_num                       60416207185.0              180042946491150.0   \n",
       "amt                                    1.0                           9.64   \n",
       "zip                                 1257.0                        26237.0   \n",
       "city_pop                              23.0                          741.0   \n",
       "is_fraud                               0.0                            0.0   \n",
       "hour                                   0.0                            7.0   \n",
       "month                                  1.0                            4.0   \n",
       "age                                   14.0                           33.0   \n",
       "distance_km                           0.02                          55.32   \n",
       "\n",
       "                                       50%                            75%  \\\n",
       "trans_date_trans_time  2020-01-02 01:15:31  2020-07-23 12:11:25.249999872   \n",
       "cc_num                  3521417320836166.0             4642255475285942.0   \n",
       "amt                                  47.45                           83.1   \n",
       "zip                                48174.0                        72042.0   \n",
       "city_pop                            2443.0                        20328.0   \n",
       "is_fraud                               0.0                            0.0   \n",
       "hour                                  14.0                           19.0   \n",
       "month                                  7.0                           10.0   \n",
       "age                                   44.0                           57.0   \n",
       "distance_km                          78.22                          98.51   \n",
       "\n",
       "                                         max                    std  \n",
       "trans_date_trans_time    2020-12-31 23:59:34                    NaN  \n",
       "cc_num                 4992346398065154048.0  1309115265318020352.0  \n",
       "amt                                  28948.9             159.253975  \n",
       "zip                                  99921.0           26881.845966  \n",
       "city_pop                           2906700.0          301487.618344  \n",
       "is_fraud                                 1.0               0.071992  \n",
       "hour                                    23.0               6.815753  \n",
       "month                                   12.0               3.424954  \n",
       "age                                     96.0              17.395446  \n",
       "distance_km                           152.12              29.117007  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#SUMMARY STATS\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "22f2e2f1-88de-4c42-aea1-3850c161135c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>merchant</th>\n",
       "      <td>1852394</td>\n",
       "      <td>693</td>\n",
       "      <td>Kilback LLC</td>\n",
       "      <td>6262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <td>1852394</td>\n",
       "      <td>14</td>\n",
       "      <td>gas_transport</td>\n",
       "      <td>188029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>1852394</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>1014749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <td>1852394</td>\n",
       "      <td>906</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>8040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <td>1852394</td>\n",
       "      <td>497</td>\n",
       "      <td>Film/video editor</td>\n",
       "      <td>13898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split</th>\n",
       "      <td>1852394</td>\n",
       "      <td>2</td>\n",
       "      <td>train</td>\n",
       "      <td>1296675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>1852394</td>\n",
       "      <td>7</td>\n",
       "      <td>Monday</td>\n",
       "      <td>369418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count unique                top     freq\n",
       "merchant  1852394    693        Kilback LLC     6262\n",
       "category  1852394     14      gas_transport   188029\n",
       "gender    1852394      2                  F  1014749\n",
       "city      1852394    906         Birmingham     8040\n",
       "job       1852394    497  Film/video editor    13898\n",
       "split     1852394      2              train  1296675\n",
       "day       1852394      7             Monday   369418"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='object').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fd74f4ca-e198-4a5e-ba35-22f0d07f6938",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check corr between numeric values\n",
    "sns.heatmap(df.select_dtypes(include='number').corr(),annot=None,cmap='coolwarm',fmt='.2f',linewidths=0.5,cbar_kws={'shrink':0.8})\n",
    "plt.title('Correlation matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e6b1c3c7-285b-4e3e-b7af-aaa8bc5d37d7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cc_num</th>\n",
       "      <th>amt</th>\n",
       "      <th>zip</th>\n",
       "      <th>city_pop</th>\n",
       "      <th>is_fraud</th>\n",
       "      <th>hour</th>\n",
       "      <th>month</th>\n",
       "      <th>age</th>\n",
       "      <th>distance_km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cc_num</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001826</td>\n",
       "      <td>0.041504</td>\n",
       "      <td>-0.009118</td>\n",
       "      <td>-0.001125</td>\n",
       "      <td>-0.000902</td>\n",
       "      <td>-0.000188</td>\n",
       "      <td>-0.000131</td>\n",
       "      <td>0.003082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amt</th>\n",
       "      <td>0.001826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001979</td>\n",
       "      <td>0.004921</td>\n",
       "      <td>0.209308</td>\n",
       "      <td>-0.024891</td>\n",
       "      <td>-0.002593</td>\n",
       "      <td>-0.010695</td>\n",
       "      <td>-0.000538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zip</th>\n",
       "      <td>0.041504</td>\n",
       "      <td>0.001979</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.077601</td>\n",
       "      <td>-0.002190</td>\n",
       "      <td>0.005947</td>\n",
       "      <td>0.001160</td>\n",
       "      <td>0.010359</td>\n",
       "      <td>0.006750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_pop</th>\n",
       "      <td>-0.009118</td>\n",
       "      <td>0.004921</td>\n",
       "      <td>0.077601</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000325</td>\n",
       "      <td>0.019949</td>\n",
       "      <td>-0.000510</td>\n",
       "      <td>-0.090889</td>\n",
       "      <td>0.010989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_fraud</th>\n",
       "      <td>-0.001125</td>\n",
       "      <td>0.209308</td>\n",
       "      <td>-0.002190</td>\n",
       "      <td>0.000325</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013196</td>\n",
       "      <td>-0.016417</td>\n",
       "      <td>0.010927</td>\n",
       "      <td>0.000359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <td>-0.000902</td>\n",
       "      <td>-0.024891</td>\n",
       "      <td>0.005947</td>\n",
       "      <td>0.019949</td>\n",
       "      <td>0.013196</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000986</td>\n",
       "      <td>-0.173014</td>\n",
       "      <td>0.000391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>-0.000188</td>\n",
       "      <td>-0.002593</td>\n",
       "      <td>0.001160</td>\n",
       "      <td>-0.000510</td>\n",
       "      <td>-0.016417</td>\n",
       "      <td>-0.000986</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>-0.000067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.000131</td>\n",
       "      <td>-0.010695</td>\n",
       "      <td>0.010359</td>\n",
       "      <td>-0.090889</td>\n",
       "      <td>0.010927</td>\n",
       "      <td>-0.173014</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_km</th>\n",
       "      <td>0.003082</td>\n",
       "      <td>-0.000538</td>\n",
       "      <td>0.006750</td>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0.000391</td>\n",
       "      <td>-0.000067</td>\n",
       "      <td>-0.004155</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               cc_num       amt       zip  city_pop  is_fraud      hour  \\\n",
       "cc_num       1.000000  0.001826  0.041504 -0.009118 -0.001125 -0.000902   \n",
       "amt          0.001826  1.000000  0.001979  0.004921  0.209308 -0.024891   \n",
       "zip          0.041504  0.001979  1.000000  0.077601 -0.002190  0.005947   \n",
       "city_pop    -0.009118  0.004921  0.077601  1.000000  0.000325  0.019949   \n",
       "is_fraud    -0.001125  0.209308 -0.002190  0.000325  1.000000  0.013196   \n",
       "hour        -0.000902 -0.024891  0.005947  0.019949  0.013196  1.000000   \n",
       "month       -0.000188 -0.002593  0.001160 -0.000510 -0.016417 -0.000986   \n",
       "age         -0.000131 -0.010695  0.010359 -0.090889  0.010927 -0.173014   \n",
       "distance_km  0.003082 -0.000538  0.006750  0.010989  0.000359  0.000391   \n",
       "\n",
       "                month       age  distance_km  \n",
       "cc_num      -0.000188 -0.000131     0.003082  \n",
       "amt         -0.002593 -0.010695    -0.000538  \n",
       "zip          0.001160  0.010359     0.006750  \n",
       "city_pop    -0.000510 -0.090889     0.010989  \n",
       "is_fraud    -0.016417  0.010927     0.000359  \n",
       "hour        -0.000986 -0.173014     0.000391  \n",
       "month        1.000000  0.000060    -0.000067  \n",
       "age          0.000060  1.000000    -0.004155  \n",
       "distance_km -0.000067 -0.004155     1.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we can't detect clear corr between features\n",
    "df.select_dtypes(include='number').corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "714101a4-efae-4d72-8ce2-085249c7b3d6",
   "metadata": {},
   "source": [
    "### get in depth in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d998854b-1bc2-47a5-b262-eed59f1af9ba",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#function to visualize the determined data\n",
    "def bar_plot(col):\n",
    "    def top_fraud(col):\n",
    "        return pd.DataFrame(df.loc[df['is_fraud']==1,[col]].value_counts()).reset_index().head(10)\n",
    "    ax=sns.barplot(data=top_fraud(col),x=col,y='count',palette='bone')\n",
    "    ax.bar_label=(ax.containers[0])\n",
    "    plt.title(f'TOP 10 FRAUDS | {col}',fontsize=16,fontweight='bold')\n",
    "    plt.xticks(rotation=45,fontweight='bold')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b90f86db-1627-474c-b270-79f9da3e07d3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1700x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17,15))\n",
    "for idx,val in enumerate(['cc_num','merchant','category','city','job','age']):\n",
    "    plt.subplot(3,2,idx+1)\n",
    "    bar_plot(val)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d2f60059-ab78-4c00-8355-59828fff1351",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x177d3629690>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df,x='amt',col='is_fraud',kind='box',sharex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "119a4ec3-3898-4f95-b9d4-ddee0a2b941d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#we detect huge number of outliers in the amts of genuine transactions \n",
    "#we will handle that using the scaling process\n",
    "#since we can't remove them\n",
    "#Because they represent the real data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8d1846ff-cefa-479f-a63e-c792e96bc55e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender\n",
      "F    1014749\n",
      "M     837645\n",
      "Name: count, dtype: int64\n"
     ]
    },
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      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def pie_bar_plot(col):\n",
    "    print(df[col].value_counts())\n",
    "    sns.set_palette('viridis')\n",
    "    fig,axs=plt.subplots(1,2)\n",
    "    axs[0].pie(df[col].value_counts().values.tolist(),autopct='%.2f%%',textprops={'fontsize':25},explode=[0,0.05],shadow=True)\n",
    "    sns.countplot(data=df,x=col,ax=axs[1])\n",
    "    fig.legend(labels=df[col].value_counts().index.tolist(),loc='upper left',fontsize=20)\n",
    "    fig.tight_layout()\n",
    "    fig.show()\n",
    "pie_bar_plot('gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2144cfca-df52-4202-90c2-93702482754a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is_fraud\n",
      "0    1842743\n",
      "1       9651\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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QO7bs86AqAACAw9u1vUqu63pdBgAAAAAAQJcQ+KFXVVVWKxKJKis73GHf4KIi7WD9PgAAkIZ2ba+SZTlelwEAAAAAANAlBH7oVdXVtXIcR36/v912n89UYdEgAj8AAJCWdu+ols80vC4DAAAAAACgSwj80KuqKqs73V5YOEg+n6md2wj8AABA+inbWSXTx4/KAAAAAAAgM3AXA71q9649CoVCHbYPLi6SJAI/AACQlnbv6PxDSwAAAAAAAOmIwA+9xrIsle/Zq6zsrA77Bg0qUFNjVI31UQ8qAwAAOLyK8jrZNmv4AQAAAACAzEDgh15TU1On5uaosjsJ/PLyc1VZUe9BVQAAAEdmWw4/qwAAAAAAgIxB4IdeU11Vo0gkqqysTgK/vFztK+cmGgAASF87t1bKdVyvywAAAAAAADgiAj/0muqqGtmOrUDA32FfXl6eKisaPKgKAACga3Ztr5Jl216XAQAAAAAAcEQEfug1VVU10iE+FJ+fn6t9e+nwAwAA6WvP7hr5fD6vywAAAAAAADgiAj/0mt279ygUCnbYHgj4lZ2TpUoCPwAAkMaq9zXINA2vywAAAAAAADgiAj/0Ctu2tadsr7KyO1u/L0+SVFlB4AcAANJXTVWT1yUAAAAAAAB0CYEfekVdXb2amyPK7izwy8+VJEZ6AgCAtFZT1eh1CQAAAAAAAF1C4IdeUV/fqHgsrnA41GFffn6uHMdV9T5uogEAgPRVW93sdQkAAAAAAABdQuCHXtHY0KhEIqFgsOMafvn5eaqraZJl2R5UBgAA0DWWZaupIep1GQAAAAAAAEdE4Ide0VDf0r1nGEaHfXn5uazfBwAAMkJNNev4AQAAAACA9Efgh17R2Ngs9xD78vLytK+cwA8AAKS/qooGr0sAAAAAAAA4IgI/9Ira2jqZRud/vfLycrVvL4EfAABIf1WVDYwhBwAAAAAAaY/AD72icl+VQqGO6/dJLYFfdWVjH1cEAADQfTVVTXIPNbYAAAAAAAAgTRD4oce5rqvq6tpOAz/TNBUKB9VQH/GgMgAAgO6prmyUz8ePzAAAAAAAIL1x9wI9LhKJKhqJKhjsGPiFwyFJUlNDtK/LAgAA6LaaqkaZpuF1GQAAAAAAAIdF4Ice19jQqHg83mmH3/7Ar7Eh1tdlAQAAdFtNFWPIAQAAAABA+iPwQ49rbGxSPJ5QMBTqsC/UGvg1NxL4AQCA9FdT1eR1CQAAAAAAAEdE4Ice19DQJMuyFAj4O+xLjfRsZKQnAABIfw11rDsMAAAAAADSH4EfelxDQ6MkQ4bRcb0bRnoCAIBMEosmvC4BAAAAAADgiDq2YAHHqKmxSZLb6b5QOCzbdgbMzTPbjasxuVlRa4+STqMcWfIZIYXMIuUEjle2b3SnwejBXNdRk7VdEWuXEk6tbDchU34FzALl+McoNzBOphHog1ckNSQ3qSa+TJI0OHSm8gLju3RexNqjJmub4na1HDcuyZDPyFLYN0R5gRMU8hUd8Tlc11WTtUWNyS1KOg0y5FPIN1gFwZMV9g3tUh0V0fcUtcsUMAdpVPYnunQOAGDgchxXiXhSwVDffJ/FwDZjxgydf/75Kiws7LCvsrJSM2bM0Le//W0PKgMAAAAApDs6/NDjamvrZZq+TveFwyE1D5Bxns3WLpU1z1JdYo3iTrUcJSQ5st2oInaZKmPzVR59Q0mn8bDPk7DrVRZ5VdXxxYra5bLdmCRHjhKKO5WqSSxXWWSWIlZ5r78my2lWbXxVt86x3bj2Rt/RvthcRaxdst2IXNlyZclyG9VkbVV59HVVx5fJdZ3DPld1fLGq40uVcGrlypajhKJ2ufZG31ZDYtMRa0nYdYraZZKkQcFTuvU6AAADVyya9LoEDBDXXXeddu3a1em+DRs26M477zzq57733nv1la985bDH1NbW6kc/+pGmTZumadOm6Ze//KUikfZjbV999VVdfPHFOu200/SZz3xGc+fOPeqaAAAAAAA9hw4/9Limxmb5/Z3/1QqHQmpq7P/jPBuTW1UdX9xuW9AsUsDMl+MmFbP3yVVSCadG5ZE3NDxruoK+QR2eJ27Xam/0bbk6cKPRb+Qq5BssyVXMrpLtRmS7Me2LzVVx6CzlBo7vldfkuq6q4ovlyuryOY5rqyI6VwmnOrUtYOQr6CuS5KTql6TG5CY5blJDwmd3+lzN1i41WdskSYZ8CvuGy5WlmL1PkquaxHKFfIMP2ylYl1jbUoNZoGzf6C6/DgDAwBaLJpQ/KNvrMtBPXXHFFdq8ebOklp+3rrzySgWDwQ7HVVdXa+zYsUd1jUcffVR33nmnpk2bdtjjvv/97ysej+vRRx9VQ0ODrr/+et1www364x//KElauHChfvKTn+hnP/uZzjnnHD333HO68sorNWPGDE2YMOGoagMAAAAA9AwCP/S4puZm+f2H7vBrbOjfHX4Ju1bV8aWpr/1GroaEz2kN6Vo4blLV8WVqtrbLUUL7YvM0MvsTMg1/m2MsVcbmpcI+Qz4NDk1rF+i5rqvG5EbVJFZKagnkguagTsPDY9VobVHMrujWOQ3JDW3CPlPFoTMPqt9RfXJ9KohrtrYr2xqlHP+YDs9Vn9ggSTLk1/Cs6algL2KVaV9sniRX9Yn1Gpp1Xqe1JOx6RezdkqRBgVO6NEoVAABJikQGxihyeOOKK67Qs88+K0l64YUXNHnyZBUVtf8Ak2mays/P1yWXXNKt566oqND111+vZcuWady4cYc9dsWKFVq8eLFeeeWVVHh344036lvf+pb+7//+T8OGDdODDz6oiy66SP/zP/8jSbr22mu1YsUKPfbYY7rxxhu7VRsAAAAAoGcR+KHHRZoihwz8QuGQmmr7d4dfbWKNpJbRlD4jrOFZF8pvZrU7xjQCGhI+W27MUcTaKcttUkOyRIOCp6aOaUxuluU2t35laGj4I8ryD2/3PIZhKD94kmSYrevqOapJLNfwrOk9+ppaRnmu7NY5ruuqIXlgzGZR8AMdug8Nw9Sg4KmynIiarK2SpIZEaYfAz3KiSjg1kqTcwLh2XXzZ/lHK8o1Q1N6jqF0u13VkGB2nFdclW7v7jHxldxIoAgBwKJHmuNcloB+bOnWqpk6dmvr6e9/7nsaM6ZmfVdatW6eCggK99NJLuueee1RWVnbIY5cuXaohQ4a069Q788wzZRiGli1bpk984hNavny5fvazn7U776yzztIbb7zRI/UCAAAAAI4ea/ihR9m2rWgsdsiRnqFQ/+7ws924ovae1NeFwQ92CPvaKgp+UPv/GTYkNrZbw67J2p56nOsf3yHsays/cKIC5iBJUszep7hde3Qv4BAOjPI0FTQLu3RO0m2Q4+6/QWoqNzD+kMfmByamHsedarmu3W5/wqlLPQ6Zg3WwkK9YkuTKbhOStj2/QRGrpbuvIDiZ7j4AQLc0D4Bx5EgPN910U4+FfZI0ffp03XbbbV16zoqKCo0YMaLdtmAwqEGDBqm8vFwNDQ2KRCIaPrz9z6RDhw5VeXnvryUNAAAAADg8OvzQo2KxuKykJX8g0On+UD9fwy9mV6Yemwp0OpqyLb+ZpbBviGJ2hRwlFLMrleUfJtuNK9km5MoLHHlNlBz/GNUlWs6JWLsV8nUtmDuSxuTm1CjPgsDJstxmJZwjB4oHwj7JZ4TajSs9mN/MbfOVK9tNyG8cCEoPfq6Dtd3muB3HrtUn1kly5TfylOM/urVvAPQvruuq2dqpZmu74k6tHDchnxFseZ8IjFWu/3iZRuffy7prV/OLst2uf9glL3CiBofOOOR+202oKblVUbtcSadetpuQIZ/8RpZCviHKC4xvN0b6UBw3qbrE+tZO86h8RlBZvhEqCJ6iQLv35UOdb6ss8rJsN6a8wEQNDk094jmZKhqJy7EdmT4+K4feVVNTo9///vd65513FI1G5bpuu/2GYWj9+vW9cu1oNNrp2oGhUEjxeFyxWMvP8Acfs3//sfD70//flo9//wDQI3g/BQCg9xD4oUfF43FZtq1wVrjT/cFgoF+PxbKdSOpx0FfU6WjJgwXMglSgFneqlaVhsp22N4a71lUXMAtSj+OpdfOOjeU0qya+KvX8g4KTVRVf0qVzfW0CO9uNy3GtQ4Z+7W+EGx1CPaNdM3Jn3Xlum0ftb4wlnQY1WzslSQXBk7v0ZwKgf7PduPZF5ynuVB60PSbbjSker1RDolRDwuce84cnbDferbDvSJqtXaqKLU6t77qfK0dJN6mk1aAma4ty/MdrcGiaTKPzEduOm1R59K12Hy6x3ZiarG1qtnZrWNb5Crd2Tx9KU3KLbDcmyVRB4ORjfWlpLdKckOO6jMZAr/vNb36jd999V5/61Kc0fPhwmWbf/a0Lh8NKJDp+cCoejys7O1uhUMvPZwcfE4/HlZV16IkWR2KahgoLc476fABAZsnPP/rvGQAA4PAI/NCjYtG4bMs65EhPn8+nZNLudF9/4LS5AWt20onWGbPNP8OkU9/J8wS7NIKys+c5VlXxJa03lQ0Vh86ScYgbx53xGznyGeHWm8GOmpJblR+c2OmxjcnNqcchs7hDKGcaBz5J3lkHn32YDsC6xHq1dPflKNd/fJfrB9A/Oa6liuicg0YFF8tv5sp2o4rZ+yS5stwmVUTnaET2v3ep2+1QEvaB6/iNPIV9Q494Ttgc0un2puQOVcUXtNliKuwrlt/IkeNaijuVre+5UrO1XbYb07DwRzv9oENtYnUq7DONsMK+obKcRiWcWrlKqjI2X6OyLz5kl6Pr2qpPbpAk5QXGH3Z8dX8QjSY6dFoBveG9997Tz3/+c1122WV9fu3hw4frzTffbLctkUiorq5Ow4YN06BBg5Sdna19+/a1O2bfvn0dxnx2h+O4amiIHPlAj/l8JjepAaAHNDREZdvOkQ8EAACSWj4s09UOeQI/9KhYPC7LsuX3dx4M+XymrH4c+Jk6cGPUdZOHOfKAtgHW/i6QY3+emFzXPaa16hqTWxSz90qS8gMnKeQr6tb5hmEqPzBJtYmVkqTaxCr5jLByAgdGarquq8bkJjUkS/efpUHBUzs8V7B1fUJJijtVylH7sZxxu7r1bL/8xoEb80mnUc3WDkn71+6jNwMY6OoSa1Jhn88Ia2j4I+3GXyadRu2LzVfSqZOjhKpiCzQi+6Kjvl7bYDE3cLwGBU85quexnKiq44tTX2f5Rmhw6Mx2QZvrOmpIblRtYpUkVzF7r+qTJRoUnNzuuWw3ocbkFklSwByk4VkXpD4sURNfqYZkiWw3qqbktkN/UMPa2vo9q/9390lStDmuzjvMgZ4VDAZ7dA2/7pg2bZpuvfVW7dixQ8cdd5wkadGiRZKkqVOnyjAMTZ06VYsXL9Z//ud/ps5btGiRzjjj0GOIu8KyuPELAAOFbTu87wMA0Eu4+40eFY/FZVmWfL5DdPj5/f068PObeanHcbumS90Abcdv2q2hnd/M0f4bi67sdh0iXXme1uFuXaq5My2jPFe21GLkaVDwtKN6nvzAScoLnNBaka3K+Psqi7yiythCVcYWqCwyUzWJ5ZIkQz4Vh85Sln9Yh+fxmeHUWNPG5FbF7QNrCEasPYra5ZKkbP/IdiFnfWt3n8/IVq5/3FG9BgD9h+VE1JDclPr64LBPkgJmnoaHP5YaSxx3qhWx9hz1NduOzGz74YXuakhukCu79XmKNDT8kQ5ddYZhqiA4SYXB01Pb6hMb5LhWu+OiVrmklpssg4KntOuMLgyeLlMtXdURu/PX7bq26hMt3X25/nGt37P6t2g0oWP4DA3QZRdddJFmzpzZJ9eybVuVlZWptfmmTJmiqVOn6oc//KFWr16thQsX6te//rU+//nPa9iwlp/Pvv71r2vWrFl65JFHtGXLFt1yyy3asGGDLr/88j6pGQAAAABwaHT4oUfFYjE5jnPIFlOfz+zXIz1b1jsyJTlyFFeztV25gUMHTXG7WgnnQHjlui2/N6YRUMgsSoV4DclSFfvOOuTzOK6lJmt7u22uax91M8KBUZ5ScfjMQ64BdSSGYWhw6EPK9o1WVXyhbDempNOgpNPQ7ji/kaNhWR9ToE1gerCCwMmqjL8vV5b2Rt9U2DdcruzW9Q9dHdxlknSaUr8ndPcBkKQma6v2B11ZvlEdwr79fGZYg4Knqrp1zdLG5GZl+0ce1TUTPRT4NVu7Uo8Lg6cd9j0tP3CSGpKlLd3eSipmVyrbP6LTmkJm+98DwzAV9BUpZu895HjoJmubbDciyVBBsP9390lStDnRp2upYeCaPHmy/vKXv2jXrl2aMmWKwuH262IbhqErr7yyR65VXl6uCy+8UDfddJMuueQSGYahu+++WzfccIMuv/xyhUIhfeITn9B1112XOue8887TH/7wB9177726/fbbdcIJJ+j+++/XhAkTeqQmAAAAAMDRI/BDj4rHEpKMQ46SbFnDz+p0X39gGgHl+o9vvaks1cSXK2gOUtBX2OFY242pKrbooK0HxlrkBU5UPN4S+DVZ2xRKDlFeYHyH53FdR1XxxXLarGN38HN1R9tRnnmBExX2db6WVFc1JberNrE6ta5UZyy3WXujb6soNFU5/s7HWOUExirmVKoxuUmubEXtsjZ7W4LFtr/PB7r7spRHdx8ASRHrwPvGod5r2u6vji+V5Cpq75XjJg+5nt2huK6jRGtoZipw1J1wltOcGvlsyKewr2MndFuGYSpkDlak9X2y5UMWBwI/5zDrnrbd1tmaqa7rtOnuO/6Y1jfMJJZl0+GHPnHjjTdKkpYsWaIlS5Z02H8sgd/NN9/c7uvRo0ertLS03bbBgwfrzjvvPOzzfP7zn9fnP//5o6oBAAAAANB7CPzQo2Lxg0On9vx+X78e6SlJg4KnKmKXyXHjcpRUefQtFQRPVo5/rPxGjhw3oYi9R3WJtbLdiPxGriy3SVLLjdz9cvxj1ZjcorhTKUmqji9W3K5UXuBEBcwCSY5idpXqE+sUd6pkGmG5riVXVofn6irLibQZ5ZmjwuCUY/q9qImvUkNyQ+tXhvL8E5QbGK+AmS/JVdyuVkOyVFG7XLYbVWVsvuzQGcoPnNjp8w0OnaGQOVgNyY1KOvUyZCrkK1ZB8JTW7sr9r6P5QHdf4GQZB3Uouq6bCiB9RviY1joEkBkc12rX2Xak0Mw0ggqaBa3nOK1dct3r8ks6jdr/4YvAMXT3uXKU7R8j22npqutSx3KbY/Z/X0jtajfRvbP3P7f1fzuOpW6ytslym9XS3Te5w/7+6ljXxQW6qqSkxOsSAAAAAAAZisAPPSoWjR1yjOT+UVi23b8XZ/ab2RoaPk8V0blylZQrS3WJNapLrOlwbMAsUFFwqipicySpXTBlGKaGhD+sitg7qbFqTdY2NVnbOjyPoYCGhT+iiug7qduzB4dcXVHdZpTn4NA0mcbRv0VErLJ2Yd+Q8Ic7dNRk+Ycryz9ctfE1qk+uk9TSFRk2hyjoG9Tp8+YGjldu4PjDXrul+8SRzwgrN3BgxJTrOqpLrFNjcrMctYTTphFWfuDE1mCQcW1Af9XS5dbyDmko0GH9u84EzPxUSNjyPty9wK/tyOb9HcgJp14xqyIVmvmNbIX9wxQ0Cw5TR56Ghs/t3rXbrP16cBefaQRTjx03IZ/RfmSg3doBePB5Ld196yVJOf7jDjuGub9xnCOvyQsAAAAAAOAlAj/0qGTSktzOb4rt/2C8088DP0kK+4ZoRPa/qSa+TDF7XydHmMoLTFBhcIoSTs2BrQfdXPWbWRqRdaFq4itbg76Ov7dh33AVh6bJZ2TLSXVxmN0ePdeY3KqoXS5JyvWPV5Z/eLfOP1hd601hScoPnHjY8XmFodMUd6pbR4m6qk+u1xDfh4/qupYTUWPrSNX8wMnt1h+sii9Us7Wz3fGOG1NdYo3idrWGhj9CBwfQT7UEbC26OlrTZ2QfON9pPsyRnWvbUSjXUXnkrVTXdvsDWzoOi0JnKGjmd/s6B4vZlbLcxtTXB6/T13YtwbhTpWxz9IEyXUcJu7bDcZLUZG1PBZWDBlB3n9TS4Qf0hbbr5R3KTTfd1AeVAAAAAAAyDYEfepTjHHpc5/4gxRkgN82CZoGGZ01Xwq5T1N4r243KkE9+M1fZ/lGpzom2N5EP7rKQWjoxisNnapBzqqL2HiWdJkmu/EaOsvzDW8dj7n8e95DPcziWE1FtfEXruVkqCn2g+y+4DduNK+FUp77OO8SIzrYKAiel1g6MWOVHPT6tPtnS3WcaIeW16e5rtnanwr5s32gVhj4gQz7VJ9erMblJUXuPGpIbVRA8qdvXBJD+2q4j2tX3yLYdbrYOP7K6M20Dv0Zr82GPjdkVKo+8oaHh85TlP/y40cNxXVe1raOZJclv5HZYRzbsHybFTUmO6hJrFfINSb3WusQaOWpZuy/bP6rN8x5Yuy/HPyb1vWegcOnwQx9ZtOjg9Z2lSCSiuro6DRo0SKeddpoHVQEAAAAAMgGBH3qUZdmHavBLhTcD7aZZ0DfokOMppfY3hA830s1vZivPPOGYn6czUXuvnNZRnrYb1c7m57t0XnV8sarjiyW1dMKMyfmspPYhpqFAl8a+BX0HOlBcJeW48aMILqNqSrZ09xUEJrUbSdqQKJXUsjbhkPCHU+M7B4fOUNKpV8zep4ZkqfIDE+nyy2AFhV3r3MLAE28wtT+zCwaDXfq7kmzMVm1L9iWf3+3236/dkfp2X2cHh2lo/mnKDg6VzwwoYTWqLrJVlY1r5biWXCVVGZ+viYM+q1Cge+/j++2tX6Z4mw9cjCicqoKcg+vOUUQTVd1cooRTp/LobOWGhituNSqarJIkBX15GlE8WT6zpVu8tnmzrOaWrsHRxR9SODCw/q2Fs4NHPgjoAW+//Xan27du3aqrr75an//85/u2IAAAAABAxiDwQ49KJpOHDEsGauB3JDH7wHi3g8enefE8PcHVgbGtxqEWdTyIqfZrDrZ9jq6qT26QK1umgsoLHAhHHddS3Gm5iZ3lH9Vhrb5s32jF7H2y3Ygst0kBY+CsS9WfmD5Tz7z506M613Vdua7kqvW/A6QTeSC5/777dPfdSyRJH/7YKbr11h8d8Zxnn3lGN9ywQJI0ecoo/e3hI5+zX01NjT76kb+lvv7qVy/Xj3/yk9R6tm2VbNigb3zj62poaJDjJlQ4fo8efPDGLl9rvxdnzNAvfvFw6uuzzz5bf33w3k6v2dz8v/rOt7+lVatWyXKiqoseWB+2oKBADz70N02e3DK203Ecfe5zn5VqpH//93/Xn2//Xbdr6w8SSVtH+3mQgL/76+oCbY0fP15XXnml7rrrLn3qU5/yuhwAAAAAQBoi8EOPSiYtmeYh7oYNgJGeruuqJrFcthOVo4SGhS84bLeY5URSa/gZ8inkG5La15DYqLhTJcuJanBoaoeRbAeLWGWpx1n+Ed2qO2DmK9c/4cgHSorZ+1JrQ4V9Q+VvDcfajr5r25nnKCHbjbfb3xnLjbT7+kjHH8x2YmpKbpEk5QdPareGoeU0av+404CR2+Fcv3lgW9Jp6FJHItKP67i64cdPKRj0KxgKKBjyKxQKKBj0KxDyKxTyt9sXDPoVDLdsH31csfwBn3x+U36fT6bPlGEYLW9bhtEhADQNQ4ZhHPr9rhfFE5biSUuJpKVE0k59HU+0fJ1ofRxv8ziRtGU7/X/91MNZs7k89XjX3jo9+eqyI56zfO2O1OOKmqYunbOflUzoy/97neqq9ykei2rUaRfqn6+tOOTxF3z6v/Xik/dJkha8/75u/9vzGjbyuC5fb92KBXrpqftTf1dz8gp05r//92Gv+fH/+r4Khr+itcvnq76mSlk5uZowaYo+8u+XaOWOqFbuWJZ67m1bt0qGobGnnt/h98G2LDU11CkQCik7h/fPtsaNLNJHzzh0dz7QHbm5uSorKzvygQAAAACAAYnADz3KSloduqf2GwgdfoZhKGrtTQVicadaYV/xIY9vSJamHmf7R7UbQZlw6lJrzkXs8sMGfhGrLHVNn5GlkHnoa3Ym7Cs+bJ1tVcYWyrJarpXjP155gfEdjvEbOfIZ4dSaWc3WTuUfYR2/iLU79ThoFskwutcN0ba7Lz8wsd0+R1bqsWF0fNszdODvrOMmu3VdpA/XdfX+nJIef17DMFIBYSDoVyh84HE4K6Ds7JCyc0PKyg4qOzukcHbL43BWQOFwUKGsgEKt4eP+4DEQ8CsQ9CkQ8Msf8MnvN+Xzm/L5zFTYmHpd7WqR/D5TwUBIRndH3tq2kklbCctWojUQbBsWxhJJxRNtA8ODQsSkpXjCPhA2tj7e//X+Yw8Eki2Pk9ah13btS/G9e1KPN+6o0L3PzDvyORUbU49372vs0jkd+SXladmz8w97lOu6MoK5chNNkqR/PPOSwiM/2KUrxCtLFN02V6m/LWZAxtgL9fjstV04u1ga9znljWv5aoekHa+vb1dX49pnJUmBQcfpuXk7JbV8b3KSUUV3LlCyZqvktvw5m1mFCo/6kIJFHb83DEQXnjmRwA/dsmfPng7bbNvW3r179Ze//EUTJnTtA1oAAAAAgIGHwA89yrIO3eHntnaX+HydB4L9RZZ/uBqTLYFYY3LjIYO0uF2thuSm1Nf5gUkHPc8INVkt69E1JbeoIHBSpyGY7SZUE1/e5nlOOmTo2lcMw1CO/7hUoFmXWKsc3xj5zM4DiqTTpPrEhtTXuf7ju3U9242pMblZkpQfnNiuu09q6Z7cz1XH8KHttu4Gjej/XNdVPJZUPNb3YbDPbx7UsehPdSxmZQeVkxNWVk5Q2dlBZWWHlJUTVDgr2CZoDCgcbgkag8EDQWUg6FNWwKe83KyWoNFvymeaMkyztatR7dZjNSTJMGQeRVej47pKJm0lLftAV2LSUiJhKZZoEzYm9weRB8LIRJsw8eCOxYODxbZB5f5t7TrKfQfeF1zb6qTSjlynzYcFfIHDHHnsDMOQP2+4ktUt72V2tKZL58X2LFds95IDG8yAck+6WP6cIYc+qRuStVvlRGslSaGRU1PbHSuupg0vyYnVtTveidYqsvkNOWPOUnjEB3qkhkzmRRcwMtv06dM7nQ7huq6ysrJ01113eVAVAAAAACATEPihR9m2rUMtcJNMttw4DYT691+7vMAJreGTq2Zrp/zxPA0KntIuhItYu1UVWyy1rlOX6x+vkK+o3fNk+0bKb+TIcptluc3aF3tfxeFp7cZlJuw6VcYXyHKbJUkBI/+QnXSNya2qji9OfT0q+9MKmB3HW/aUguDJakpul6O4HDeu8ugbKg6fpbBvaLvjYvY+VcYWylEi9RryAt379Hp9okSubBkKdOjuk1q6HveznKYO+5NttrU9FvCabTmKWglFI4k+va5hGAoEfR1HoIZaOhyzc8LKzgm1ho0hZWWHFM4KKCurpbMxFA62dEK2Pbc1aPQHfMoPBuXPDnetq1GSYRqpMardYdtOS0dj0tIbb7yh63/2riRp/Ihc3Xj9F1vDxgPBYuygsahzZm3Xil0tz3X82JE69UMnthzXWZdj2+7H5NF1NZrBA+/JrhU77LGu6yi6Y74S+w5048kXVO7ET8qfN/yort/xGq5iZS0fKPEPGtsuRIyVLU2FfeFRH1Jw6GS5VkyR7XNlN+5VbNdi+fNH9VjwmKl8nayfCBzOH/7whw7vdYZhKDc3V2effbZyc3vvZzcAAAAAQGbr38kL+pzjuDrU7VjXdWXbjkKh3u2S8FrQLFBBYLLqk+skSfXJdWqytilkFskwTMXt2tT4TUkKmYNVFJra4XkMw6fBoQ+pItYypi1ql2l38z6FfIPlM8KynEbFnerU8aYR0tCs89KmQ81nhDU061xVRN+VK1uW26y90bcVMAsUNAskGUo4dUo69alzTHX/Ndhu/EB3X+BEmUawwzF+M0s+I0u2G1XE2qPC4JQOAez+CoLmoKN5uUC/4rquEnFLibglNR4+eOppps9ss95ia2jYut5iOCug7JxQS+CYHWwJHbODCoVbR6hmBRUM+RUOB9qFjYGgT6OHjU5do7a6UpPGDpHPd/iuxpL3/6X9K+D9xyfO1de//pkuv45EsmWcafvOxNaOxnhraJg40L0YT1p69fndWlC+UpI0aliRpk8/vcNajYmkrWg0pg3znlFi34EucTOQrbxJF8vIGnz0v/kHSdZuk9PaaRgeeUZqu2snlahsGZ8bGDxR4VGt+wJZyp34SdWv/IdkJxTfu0b+CdN7rJ5MRIcfuuuSSy7xugQAAAAAQIYi8EOPcuVKh4z8WkZ+BoP9/6/doOCpcmWrIdlyQ9R2I4rYkQ7H5fiP0+DQtHZr97WV5R+hIeFzVBVbLFeWXCUVs/d2OC5oFmpI+MMKmHk9+0KOUdg3VMOzLlRl7H1ZbksXXdKpbxfy7Rc0i1pfQ/c+ud6QKJErS4b8yg+edMjjWkaMlshyG1UZW6DC0BQZ8qk+uV5xp7L1mLGH/LMA0Dcc21E00vNdja7rSDIlOaqsrNRnz/tNuw8IGIYhf8DXEjaGWkafrt25KLV/3ms7tKf0mdaQsXWdxuygssJBhbMDqZAxFG4JKDdv3aDdZTvU2NigSSdP0kc/er782WH5/S0djb42XY37c8ZFrx34/nnmlJN07dcu7NDpE41GdcUVV6i+/EDYN3bsWD388MMaM2ZMqqsx2WbtxUTSbl2jsSVsjB80KvXA+NT2weKLj8xURNKQ0RM15cwzUvurdpeqvnXcaf7wE+UL+FJdjYYvqED+KCVrt8lqKOu5P8AM5e/nY8zRO2pqavTII49o0aJFamhoUGFhoT70oQ/pa1/7mgYP7rlQHwAAAADQv3BnGz3KdZxDTfSUJFlJq9+P9JRabhwXhT6gHP9YNSY3KWZXynajkiSfka2wb4hyA+MPub5fWzn+sQplF6shuUlRu1yW0yRXjnxGSCFzsHICxynbN7rbo+76SshXpFHZF6vZ2qGIVaa4UyPHjUtq6QIM+YqV7R+jbN+o7o/rcxOpdRDzAyfKZ4QOeWxB8GQ1W9tluzFF7F2KRHa1228qqMLg6d18dQAyhWGYCpmFqc7omF2lbP/I1H7XdZVMWEomLKmx5f2lOba/i9rQzo0J7d60rsvXq4otTq3DmuUbqWFZSw55rOlrWatxS807qW2rF9bomssfVCgcUE5uSNk5IQWz/Hr17Ye1fVdJ6rgxo8fph1f+Snt3xVS7b7sC+zsjAz75gz6FAn7lZPvlz8+Wb3/QaLaORz2oq7Hl98mQaUhz5szRY5Utgd2df7pBU6ce6ER/7LHH9IcFLY8fuekKnXRSy4ctkklbCcvSbbdG9Y+/b5ObjOjBX1wi0xdUbP/41MOEjPFORqXGDxqz2tm6jbZz0ItII36/Kcd1Zabp92ikn7179+qyyy5TTU2NPvCBD2jy5MmqrKzUI488ohkzZui5557TsGHDvC4TAAAAAJCG+n/ygj7lHHzn8CCWZff7kZ5thXxFCvnOOubn8ZvZKgpNkTTlqJ8jLzBeeYHxx1zLkPDZGqKzu3WOYZjKDYxTbmDcMV+/rYZEaZvuvkmHPdZnhDQs6wLti76X6jbcz2/kaEj4PPnN7B6tD0B6yfaPUTzREuI1WzvaBX4Hi1g7tb/3LuwbItPo3veukG9wKvCL2RWy3fghP5Tg2I5qGrcrae9/bzLVXJ2r0tr2HXLV8aWpEcaSFDKHyKidqjt++1q3amsrEGw7PvXA4yUlT0mShg8Zr3dn7tSiOXuVnR1SOCuoZYtKU+eXrClXc53/wHqPQZ8M90BXW7YpjRhe1LpWY0vQeHBno9S6VqNhpNZr7A7bdpRsXasx0ToeNRUixq32Xx8qbOxCsNh+PGvLdY4k6PfJdVzJR+CHrvnTn/4kv9+vV155RWPGjElt37Vrl77xjW/o9ttv18033+xhhQAAAACAdEXghx7lOu5hu7QGykhP9A1XjnL9E1rXNTx0d99+QbNAo7I/qWZrtxJOjSRDQbNQ2f7RMtNk7UMAvSfHP0a1idWSHDVbO5Vnn6Cwb0iH42wnprrEgW6+PP8J3b5Wtn+MauLL5cqWK1u18dUqDk/r9Fjbjasmviz1da7/OPnNrHbHRKw9B4V9gzUs6/xjHkO8v6uxuc3nICLWHjXEKlq+aDpes19c0e6c+sSW1OO//H5G67qsB9TEDxz/v5fd3+n7s+kzFQj4UiNQ2waO4aygsnNDysk5MDo1OzukrKygQlkBhcNBhcKt41PbhJSBoL/lOYM+5WSH5fP75PO3dDWaR+hqNAzJZ3Z//GaitSsxae0PHVtCwpaOxqSKC3KUvv2HSEfz5s3Tz3/+83ZhnySNGTNGV155pW655RaPKgMAAAAApDuSF/Qo94gdfgNjpCf6RkvXY/cYhk+5geP+n737jq+6PPj//z77nIyTvRNISEhCAoQVdtjIElHECS7c1nG3aqu3ve34tWrr6Ffbam2ts8NW69aqOOoWGaLIChsCmWTvnPH7A01LgZBgzjk5yev5ePBIcs71+Zz3574lnH7e57ouSYN7PxCAPs1sDJXTkv31/qpeVbR8oDj7ZDnMiZ1jOjyNqmj9sHMZ5sMfCkg76lyVrZ+qybVH0uGlmtNCTzvieZPBqgjrMNW2fyVJanTtlLHNrEjriCNKunZ3rSrbPpHL2yTpm+WFj/zd5vV6jigEjbIqzj7FZ3uOflN22oxxspvij3reZPh3GenyNB5V+HV4DreHBplk1LFnRnrcHrW5PWpr7eit2N1msZhksZllsx1dNjo6i0abQkKth/drdFg1euIQJaZE6PW3/yWL2SSTySyz2SR5pabmZs2ZWaT46IgTvzhwAm63W1FRUcd8Ljo6Wo2Njcd8DgAAAAAAmhf0KrPF3GXpd3hJT/6zAwAERqQ1Xy3uUnV46uRRu8pb/yWrMVoWo1Nub6ta3eX6ZrFJgyyKtU886T1SIyx5anVXqdVdJkmq79imxo49nUuEdnjqO/cUPPx6ZsU7pslktB9xnibX/s5CUDpcXP7nDMQTsZtiu72kcourVO1fZ4q05h9zjM0U3fl9s6tEIeaUzp893na1uiskSVZjtAyGns+a87WODrc6Otxqbmzr9jFXOxYoItqmXbv3HvF4a2urDpaWa/zYUYqKpPDDt5eTk6MXX3xR06ZNO+q5F154QdnZ2QFIBQAAAAAIBjQv6FU2q1Uej+e4z7vdbpb0BAAEjNFgUaJ9pipaP1Sbp0qS1O6p/nqZ338zGRyKt089avZaTxgMRsXbi1Tdtq5zPz+P2tTsLjlqrMXgVKx9gmymmKOea3btP+Lndk+N2j013c7hlbvbhd+/Z/fFHjHz8YisxnBZjdFq91Sr0bVb5vYwhVuy5Pa26lDrWnl1eNZeb+/bGkg2u0Uu19F79nm93+w92PeKTQSna665Rpdeeqlqa2u1ePFixcbGqqqqSi+//LI+/vhjPfDAA4GOCAAAAADoo2he0KssVos8nuPP8HO73bIyww8AEEAmo12Jjtlqcu1Tk2uv2j01cnvbZJBJVqNTIeZUhVuyZDQceznKnjAaTIq1j5fTPVQNrp1qdVfK5WmSVx6ZDHZZjZEKNacp1DxIhuPsJdrhrf/WObqjxVXWWYIeb3bfN6Jto1XW8o4kr2rbN6q2feMRz9uMsQozp/soqf/Z7RZ1HLPw83buAQj0hilTpuiXv/ylfvnLX+qjjz7qfDwuLk533nmn5s6dG8B0AAAAAIC+jOYFvcpqtXQ5w8/lcslq+/Y3UAEA+DYMBoPCLIO/3tOz5+LsExWnid0ebzVFKcY07qReKyVk4Ukd11PfzO6zGqPlMCd1OdZuilOcfbKqWj/rnNH37+cSFGef0ieX8zxZdrv1mDP8PF8Xfibjscta4GQcOHBAOTk5euKJJ1RXV6etW7fq/vvvV21tbaCjAQAAAAD6MAo/9CrriZb0dDHDDwCAvsbtaZXF6JTF6FSYJb1bx4Sa02QPjVOTa786PA0yyiy7Kf64S4EGM5vDog5Xx1GPfzPDz8iSnugljzzyiH7zm9/owgsvVGZmpiQpOTlZ+/bt07333iuHw6FzzjknwCkBAAAAAH0RzQt6lcVilnT8JT07XC7ZHaH+CwQAAE7IZLQr1l7Y8+MMdjktQ32QqG9xhFjV1lYvl8slo9H49TKeBnk9HhmMBpnNzPBD7/j73/+u7373u7rssss6H0tMTNQtt9yi6OhoPfnkkxR+AAAAAIBjovBDrzKZzF0u4dXa2qqkhDg/JgIAAPh2nJEOfbVtp3bu3iuPxyuv1yODwSCPx6P4uFhZLCxXjt5RXl6u/Pxj76E5YsQI/fa3v/VzIgAAAABAsKDwQ6+yWMyS4fjPNze3KCIqxH+BAAAAvqWo6DCNHT1Sg9JS5XK75Ha55XK75XK5FBnhVIQzPNAR0U+kpaXp448/1qRJk456bvXq1UpM7H9L5gIAAAAAegeFH3qV2WyS4fgreqqluUXhzhAZTUZ53Mff6w8AAKAvMJmNCgm1KyTUrtiY6EDHQT933nnn6Y477pDL5dKcOXMUExOj6upqvfXWW3ryySd10003BToiAAAAAKCPovBDrzKZzV3s4Hd4hp8kOSMcqq1u8k8oAACAkxQRycoE8J/ly5errKxMjz32mB5//PHOx00mky666CJdfPHFAcsGAAAAAOjbKPzQq8wmk9RF5fdN4RcRGULhBwAA+ryIyNBAR8AAc+ONN+qKK67Qhg0bVFtbK6fTqZEjRyoqKirQ0QAAAAAAfRiFH3qV2WxWV5v4tXxT+EWFSqr0TygAAICTxN7DCITw8HAVFRUFOgYAAAAAIIgYAx0A/YvZYpa83Zjhx80zAAAQBA5/SAkAAAAAAKBvo/BDr7JYDu/h5z1O6dfW1i6Xy83NMwAAEBQiokLl8XS1QzEAAAAAAEDgUfihVzkcDpnNZrlcruOOaW5uYT8cAAAQFCKjQuRxewIdAwAAAAAAoEsUfuhVDoddFqtZHR1dF36RLOkJAACCQERUqJjfBwAAAAAA+joKP/SqkFCHLGazOjo6jjumpaWFJT0BAEBQiIgMkclkCHQMAAAAAACALlH4oVc5HA5ZLBa5upjh19rSqshoZvgBAIC+Lzo2XEYjb5kBAAAAAEDfxt0L9CqHwy6r1dLlDL/mZmb4AQCA4BAZzXsWAAAAAADQ91H4oVcZDAaFhYepw3X8GX5Njc2Kjg33YyoAAICTQ+EHAAAAAACCAYUfep0zIlwd7ccv/Orq6uSMCFFIqM2PqQAAAHomNMyu0DB7oGMAAAAAAACcEIUfel1EhFMu1/GX9KytrZckJSZH+ikRAABAzyWmRAY6AgAAAAAAQLdQ+KHXOZ1h8ng8x32+7uvCLyElyl+RAAAAeiyJ9yoAAAAAACBIUPih1zkcDkmG4z7f3Nyi9rYObqIBAIA+LTElSm738T/EBAAAAAAA0FdQ+KHXhYQ4uur7JEk1NXUUfgAAoE9LTImS1+MNdAwAAAAAAIATovBDr3OEOOT1eOT1Hv8GWX19vRIp/AAAQB+WnBotk5m3ywAAAAAAoO/jDgZ6XUiIQyaTqcslsOrq6pWURuEHAAD6rpRBMTIYTrBsAQAAAAAAQB9A4YdeFxYWKqvNqvb29uOOqautV0JiJDfRAABAn2QwGBQb7wx0DAAAAAAAgG6h8EOvi4yMkN1uU2tr63HH1NbWyWqzKComzI/JAAAAuicmLlxmiynQMQAAAAAAALqFwg+9Liw8VCEhDrW1th13TF1tvSQpMSXST6kAAAC6j72GAQAAAABAMKHwQ68zGAyKi49Va1eFX12DJCmJm2kAAKAPSkrlPQoAAAAAAAgeFH7wicTEeLW1HX8Pv46ODjU2NCkxJdqPqQAAALonMTlKrg53oGMAAAAAAAB0C4UffCI6+sSfiq+trefT8wAAoE9KTImSwRDoFAAAAAAAAN1D4QefiIh0yuv1yuv1HndMdXWN0jPj/ZgKAACgezKzE2UymwIdAwAAAAAAoFso/OATERFOmc1muVyu446prKzS4CFxMpr4zxAAAPQdRpNRaemxgY4BAAAAAADQbTQt8InISKfsdptaW9uOO6ayvEpWm0UpaezjBwAA+o60wTEyW5jdBwAAAAAAggeFH3wiIjLixIVf5SFJ0pChCf6KBQAAcEIZvDcBAAAAAABBhsIPPmG32xQR4VRbF4VfS0ur6usalTE00Y/JAAAAupYxNFGuDnegYwAAAAAAAHQbhR98Jj4hVm1txy/8pMP7+A3J5lP0AACg78jKSWSPYQAAAAAAEFS4kwGfiU+IU0dHR5djKisPaUg2M/wAAEDfkZmTJKPREOgYAAAAAAAA3UbhB5+JjIo44ZjKiirFJUQoLNzuh0QAAABdC49wKComLNAxAAAAAAAAeoTCDz4TGxsjg8Egl+v4e+BUVFRJktKzWNYTAAAE3pChvCcBAAAAAADBh8IPPhMXH6PQ0BA1Nzcfd0xNda1cLhfLegIAgD4hY2iiPB5PoGMAAAAAAAD0CIUffCYmJkph4WFqbjp+4efxeFRVWc2n6QEAQJ+QkZUgj8cb6BgAAAAAAAA9QuEHnzGbzUpLS1FTF4WfJFVWHtKQbAo/AAAQeEPzkmU2mwIdAwAAAAAAoEco/OBTaYNS1N7e0eWYyooqpWcmyGg0+CkVAADA0YwmowalxwY6BgAAAAAAQI9R+MGn4uJjZJDk9R5/aayK8krZ7BalZcT5LxgAAMB/GZQRK4vVHOgYAAAAAAAAPUbhB5+Ki4+V1WZVW1vbcceUlpbL7fYov2CQH5MBAAAcKb9gEPv3AQAAAACAoEThB5+Kj4tRaGiImppajjumo8Ol8rJK5Rek+TEZAADAkYaPGiyPxxPoGAAAAAAAAD1G4QefCg0LVUxstJqbmrscd/BgqYaPZoYfAAAInBFjB8tsNgU6BgAAAAAAQI9R+MGnDAaDBqenqbn5+DP8JOlASakSU6IVHRvmp2QAAAD/FhMXrriEiEDHAAAAAAAAOCkUfvC5pOQEeb1dL491oKRUkpTHPn4AACAA8kfxHgQAAAAAAAQvCj/4XFxcrAwGg9xu93HHNDY2qbq6TvkUfgAAIADyRw2Sq+P471UAAAAAAAD6Mgo/+Fx8fIxCQkLU3NT1sp6l7OMHAAACZOSYdJkt7N8HAAAAAACCE4UffC4mNloRUU41NjZ2Oa5k/0Fl5iTJZrf4KRkAAIDkCLEqPSs+0DEAAAAAAABOGoUffM5sNmto1hA1NHRd+B0oKZXJZFTu8BQ/JQMAAJByh6fKaORtMQAAAAAACF7c2YBfDM5Ik9vtltfrPe6YqqpqtTS3Kn/UYD8mAwAAA11ewSC5XOzfBwAAAAAAgheFH/wiNTVJNptNbW3tXY47cLBU+aPS/JQKAABAGjlmMDP8AAAAAABAUOPOBvwiOSVJ4eFhaqhv6HLcwZIy5Y1Mk9Fo8FMyAAAwkBlNRuXy3gMAAAAAAAQ5Cj/4RUiIQ4PT01R/gsKvpOSgQkLtyhia4KdkAABgIMvMTpTdbgl0DAAAAAAAgG+Fwg9+kzU0Q+3tHV2OOXigTG2t7Ro3eaifUgEAgIFs3KQsud2eQMcAAAAAAAD4Vij84DcpqckymYxyuVzHHePxeLR3734VTs7yYzIAADBQTSjKltHAcp4AAAAAACC4UfjBb1JSE7/ex6+xy3F7du9TXsEghYTZ/JQMAAAMRGHhduUMT5GB/fsAAAAAAECQo/CD30RFRSohMU4NDV0Xfrt27pXJZNSY8UP8lAwAAAxEoycMkdHI22H0Xx6PRw888ICKiopUUFCglStXau/evccc++tf/1o5OTnH/HPrrbd2jps1a9ZRz990003+uiQAAAAAwHGYAx0AA4fBYFB2TpaKt+3sclx9fYMqKw5p3OSh+vCdLX5KBwAABprCyUPlcrllNpsCHQXwiQcffFBPP/207rzzTiUkJOjuu+/W5ZdfrldeeUVWq/WIsStXrtS55557xGPPPvusfve73+miiy6SJDU2NurgwYN6+OGHlZ+f3znObrf7/mI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      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pie_bar_plot('is_fraud')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e7b4c8cf-5cb5-4f40-bc75-8995422cb480",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#we discovered the fraud column is imbalanced\n",
    "#so we will fix it later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e468065a-7e9f-473f-b03b-9fb1f4ec87bb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs=plt.subplots(3,2)\n",
    "#month\n",
    "df.loc[df['is_fraud']==1,'month'].value_counts().sort_index().plot(kind='line',ax=axs[0,0],marker='o',fontsize=15)\n",
    "axs[0,0].set_xticks(range(0,12))\n",
    "df.loc[df['is_fraud']==1,'month'].value_counts(ascending=True).plot(kind='bar',ax=axs[0,1],fontsize=15)\n",
    "fig.suptitle('Fraudulent Analysis',fontsize=18,fontweight='bold')\n",
    "#day\n",
    "df.loc[df['is_fraud']==1,'day'].value_counts(ascending=True).plot(kind='line',ax=axs[1,0],marker='o',fontsize=15)\n",
    "df.loc[df['is_fraud']==1,'day'].value_counts(ascending=True).plot(kind='bar',ax=axs[1,1],fontsize=15)\n",
    "#hour\n",
    "df.loc[df['is_fraud']==1,'hour'].value_counts().sort_index().plot(kind='line',ax=axs[2,0],marker='o',fontsize=15)\n",
    "axs[2,0].set_xticks(range(0,24))\n",
    "df.loc[df['is_fraud']==1,'hour'].value_counts(ascending=True).plot(kind='bar',ax=axs[2,1],fontsize=15)\n",
    "fig.suptitle('Fraudlent Analysis',fontsize=18,fontweight='bold')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aff4a7dc-20ec-4262-8f98-c284caf8b128",
   "metadata": {},
   "source": [
    "#### We conclude that most fraudulent activity's occurs:\n",
    "- On March\n",
    "- On Sunday\n",
    "- At 10 PM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d85bf92c-198f-4197-89d5-c5aa4ae28aed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "F         4899\n",
       "M         4752\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['is_fraud']==1,['gender']].value_counts()\n",
    "#male and female exposed to fraud equally [aproximately]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5d9585bd-ffc8-4590-a7fe-7e47c9923376",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-test: t-statistic = 0.443, p-value = 0.66, p-value<0.05? = False\n"
     ]
    }
   ],
   "source": [
    "sns.barplot(data=df,x='is_fraud',y='city_pop',ci=None)\n",
    "plt.title('Average city population for fraud and non-fraud cases',fontsize=15)\n",
    "plt.show()\n",
    "fraud_population=df[df['is_fraud']==1]['city_pop']\n",
    "non_fraud_population=df[df['is_fraud']==0]['city_pop']\n",
    "t_stat,p_value=ttest_ind(fraud_population,non_fraud_population)\n",
    "print(f'T-test: t-statistic = {round(t_stat,3)}, p-value = {round(p_value,2)}, p-value<0.05? = {p_value<0.05}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "08e6ec4a-cb59-4a70-8089-9a3942852c5a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#since we accept the null hypothesis,we conclude that ther is no significant difference between means\n",
    "#so,we conclude also that city_pop does not help us on the target ('is_fraud'),so we wiil drop it"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fd63145-7757-41b8-ad3b-1c59b2915b94",
   "metadata": {},
   "source": [
    "# Further preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "35537690-3937-4ebe-b700-8c976d31f2b4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trans_date_trans_time</th>\n",
       "      <th>cc_num</th>\n",
       "      <th>merchant</th>\n",
       "      <th>category</th>\n",
       "      <th>amt</th>\n",
       "      <th>gender</th>\n",
       "      <th>city</th>\n",
       "      <th>zip</th>\n",
       "      <th>city_pop</th>\n",
       "      <th>job</th>\n",
       "      <th>is_fraud</th>\n",
       "      <th>split</th>\n",
       "      <th>hour</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>age</th>\n",
       "      <th>distance_km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01 00:00:18</td>\n",
       "      <td>2703186189652095</td>\n",
       "      <td>Rippin, Kub and Mann</td>\n",
       "      <td>misc_net</td>\n",
       "      <td>4.97</td>\n",
       "      <td>F</td>\n",
       "      <td>Moravian Falls</td>\n",
       "      <td>28654</td>\n",
       "      <td>3495</td>\n",
       "      <td>Psychologist, counselling</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>78.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-01 00:00:44</td>\n",
       "      <td>630423337322</td>\n",
       "      <td>Heller, Gutmann and Zieme</td>\n",
       "      <td>grocery_pos</td>\n",
       "      <td>107.23</td>\n",
       "      <td>F</td>\n",
       "      <td>Orient</td>\n",
       "      <td>99160</td>\n",
       "      <td>149</td>\n",
       "      <td>Special educational needs teacher</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>30.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-01 00:00:51</td>\n",
       "      <td>38859492057661</td>\n",
       "      <td>Lind-Buckridge</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>220.11</td>\n",
       "      <td>M</td>\n",
       "      <td>Malad City</td>\n",
       "      <td>83252</td>\n",
       "      <td>4154</td>\n",
       "      <td>Nature conservation officer</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>108.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-01 00:01:16</td>\n",
       "      <td>3534093764340240</td>\n",
       "      <td>Kutch, Hermiston and Farrell</td>\n",
       "      <td>gas_transport</td>\n",
       "      <td>45.00</td>\n",
       "      <td>M</td>\n",
       "      <td>Boulder</td>\n",
       "      <td>59632</td>\n",
       "      <td>1939</td>\n",
       "      <td>Patent attorney</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>95.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-01 00:03:06</td>\n",
       "      <td>375534208663984</td>\n",
       "      <td>Keeling-Crist</td>\n",
       "      <td>misc_pos</td>\n",
       "      <td>41.96</td>\n",
       "      <td>M</td>\n",
       "      <td>Doe Hill</td>\n",
       "      <td>24433</td>\n",
       "      <td>99</td>\n",
       "      <td>Dance movement psychotherapist</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>0</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>77.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  trans_date_trans_time            cc_num                      merchant  \\\n",
       "0   2019-01-01 00:00:18  2703186189652095          Rippin, Kub and Mann   \n",
       "1   2019-01-01 00:00:44      630423337322     Heller, Gutmann and Zieme   \n",
       "2   2019-01-01 00:00:51    38859492057661                Lind-Buckridge   \n",
       "3   2019-01-01 00:01:16  3534093764340240  Kutch, Hermiston and Farrell   \n",
       "4   2019-01-01 00:03:06   375534208663984                 Keeling-Crist   \n",
       "\n",
       "        category     amt gender            city    zip  city_pop  \\\n",
       "0       misc_net    4.97      F  Moravian Falls  28654      3495   \n",
       "1    grocery_pos  107.23      F          Orient  99160       149   \n",
       "2  entertainment  220.11      M      Malad City  83252      4154   \n",
       "3  gas_transport   45.00      M         Boulder  59632      1939   \n",
       "4       misc_pos   41.96      M        Doe Hill  24433        99   \n",
       "\n",
       "                                 job  is_fraud  split  hour      day  month  \\\n",
       "0          Psychologist, counselling         0  train     0  Tuesday      1   \n",
       "1  Special educational needs teacher         0  train     0  Tuesday      1   \n",
       "2        Nature conservation officer         0  train     0  Tuesday      1   \n",
       "3                    Patent attorney         0  train     0  Tuesday      1   \n",
       "4     Dance movement psychotherapist         0  train     0  Tuesday      1   \n",
       "\n",
       "   age  distance_km  \n",
       "0   31        78.60  \n",
       "1   41        30.21  \n",
       "2   57       108.21  \n",
       "3   52        95.67  \n",
       "4   33        77.56  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e1234b75-961b-4235-8aee-42fd22d073c6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#convert gender into binary classification\n",
    "df=pd.get_dummies(df,columns=['gender'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3259e3d2-dc4c-4745-9c34-273e09d9f439",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#we will get the time between transactions for each card\n",
    "#Time=0 for every first transaction and time will be represented in hours.\n",
    "df.sort_values(['cc_num','trans_date_trans_time'],inplace=True)\n",
    "df['hours_diff_bet_trans']=((df.groupby('cc_num')[['trans_date_trans_time']].diff())/np.timedelta64(1,'h'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f5e01ecf-68f4-46d5-8324-ff11d85519fc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th>trans_date_trans_time</th>\n",
       "      <th>cc_num</th>\n",
       "      <th>merchant</th>\n",
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       "      <th>hours_diff_bet_trans</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>2019-01-01 12:47:15</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>Jones, Sawayn and Romaguera</td>\n",
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       "      <td>127.61</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2724</th>\n",
       "      <td>2019-01-02 08:44:57</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>Berge LLC</td>\n",
       "      <td>gas_transport</td>\n",
       "      <td>52.94</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>82514</td>\n",
       "      <td>1645</td>\n",
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       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>8</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>110.31</td>\n",
       "      <td>False</td>\n",
       "      <td>19.961667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2726</th>\n",
       "      <td>2019-01-02 08:47:36</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>Luettgen PLC</td>\n",
       "      <td>gas_transport</td>\n",
       "      <td>82.08</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>82514</td>\n",
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       "      <td>Information systems manager</td>\n",
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       "      <td>21.79</td>\n",
       "      <td>False</td>\n",
       "      <td>0.044167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2882</th>\n",
       "      <td>2019-01-02 12:38:14</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>Daugherty LLC</td>\n",
       "      <td>kids_pets</td>\n",
       "      <td>34.79</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>82514</td>\n",
       "      <td>1645</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>12</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>87.20</td>\n",
       "      <td>False</td>\n",
       "      <td>3.843889</td>\n",
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       "    <tr>\n",
       "      <th>2907</th>\n",
       "      <td>2019-01-02 13:10:46</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>Beier and Sons</td>\n",
       "      <td>home</td>\n",
       "      <td>27.18</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>82514</td>\n",
       "      <td>1645</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>13</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>74.21</td>\n",
       "      <td>False</td>\n",
       "      <td>0.542222</td>\n",
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      ],
      "text/plain": [
       "     trans_date_trans_time       cc_num                     merchant  \\\n",
       "1017   2019-01-01 12:47:15  60416207185  Jones, Sawayn and Romaguera   \n",
       "2724   2019-01-02 08:44:57  60416207185                    Berge LLC   \n",
       "2726   2019-01-02 08:47:36  60416207185                 Luettgen PLC   \n",
       "2882   2019-01-02 12:38:14  60416207185                Daugherty LLC   \n",
       "2907   2019-01-02 13:10:46  60416207185               Beier and Sons   \n",
       "\n",
       "           category    amt           city    zip  city_pop  \\\n",
       "1017       misc_net   7.27  Fort Washakie  82514      1645   \n",
       "2724  gas_transport  52.94  Fort Washakie  82514      1645   \n",
       "2726  gas_transport  82.08  Fort Washakie  82514      1645   \n",
       "2882      kids_pets  34.79  Fort Washakie  82514      1645   \n",
       "2907           home  27.18  Fort Washakie  82514      1645   \n",
       "\n",
       "                              job  is_fraud  split  hour        day  month  \\\n",
       "1017  Information systems manager         0  train    12    Tuesday      1   \n",
       "2724  Information systems manager         0  train     8  Wednesday      1   \n",
       "2726  Information systems manager         0  train     8  Wednesday      1   \n",
       "2882  Information systems manager         0  train    12  Wednesday      1   \n",
       "2907  Information systems manager         0  train    13  Wednesday      1   \n",
       "\n",
       "      age  distance_km  gender_M  hours_diff_bet_trans  \n",
       "1017   33       127.61     False                   NaN  \n",
       "2724   33       110.31     False             19.961667  \n",
       "2726   33        21.79     False              0.044167  \n",
       "2882   33        87.20     False              3.843889  \n",
       "2907   33        74.21     False              0.542222  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e9b9d1a3-180b-4cbc-b382-1bda98d853cc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.loc[df['hours_diff_bet_trans'].isna(),'hours_diff_bet_trans']=0\n",
    "df['hours_diff_bet_trans']=df['hours_diff_bet_trans'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a382ddcc-82ce-4f4a-be89-cf5dd7d1d840",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "      <td>False</td>\n",
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       "      <th>2724</th>\n",
       "      <td>2019-01-02 08:44:57</td>\n",
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       "      <td>110.31</td>\n",
       "      <td>False</td>\n",
       "      <td>19</td>\n",
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       "      <th>2726</th>\n",
       "      <td>2019-01-02 08:47:36</td>\n",
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       "    <tr>\n",
       "      <th>2882</th>\n",
       "      <td>2019-01-02 12:38:14</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>Daugherty LLC</td>\n",
       "      <td>kids_pets</td>\n",
       "      <td>34.79</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>82514</td>\n",
       "      <td>1645</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>12</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>87.20</td>\n",
       "      <td>False</td>\n",
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       "      <th>2907</th>\n",
       "      <td>2019-01-02 13:10:46</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>Beier and Sons</td>\n",
       "      <td>home</td>\n",
       "      <td>27.18</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>82514</td>\n",
       "      <td>1645</td>\n",
       "      <td>Information systems manager</td>\n",
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       "      <td>Wednesday</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>74.21</td>\n",
       "      <td>False</td>\n",
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      ],
      "text/plain": [
       "     trans_date_trans_time       cc_num                     merchant  \\\n",
       "1017   2019-01-01 12:47:15  60416207185  Jones, Sawayn and Romaguera   \n",
       "2724   2019-01-02 08:44:57  60416207185                    Berge LLC   \n",
       "2726   2019-01-02 08:47:36  60416207185                 Luettgen PLC   \n",
       "2882   2019-01-02 12:38:14  60416207185                Daugherty LLC   \n",
       "2907   2019-01-02 13:10:46  60416207185               Beier and Sons   \n",
       "\n",
       "           category    amt           city    zip  city_pop  \\\n",
       "1017       misc_net   7.27  Fort Washakie  82514      1645   \n",
       "2724  gas_transport  52.94  Fort Washakie  82514      1645   \n",
       "2726  gas_transport  82.08  Fort Washakie  82514      1645   \n",
       "2882      kids_pets  34.79  Fort Washakie  82514      1645   \n",
       "2907           home  27.18  Fort Washakie  82514      1645   \n",
       "\n",
       "                              job  is_fraud  split  hour        day  month  \\\n",
       "1017  Information systems manager         0  train    12    Tuesday      1   \n",
       "2724  Information systems manager         0  train     8  Wednesday      1   \n",
       "2726  Information systems manager         0  train     8  Wednesday      1   \n",
       "2882  Information systems manager         0  train    12  Wednesday      1   \n",
       "2907  Information systems manager         0  train    13  Wednesday      1   \n",
       "\n",
       "      age  distance_km  gender_M  hours_diff_bet_trans  \n",
       "1017   33       127.61     False                     0  \n",
       "2724   33       110.31     False                    19  \n",
       "2726   33        21.79     False                     0  \n",
       "2882   33        87.20     False                     3  \n",
       "2907   33        74.21     False                     0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f0fe3131-520e-488e-af90-c5846a336179",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                T          p-val\n",
      "T-test  27.266899  7.200860e-158\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pingouin.ttest(df[df['is_fraud']==0]['hours_diff_bet_trans'],df[df['is_fraud']==1]['hours_diff_bet_trans'],alternative='two-sided')[['T','p-val']])\n",
    "sns.barplot(data=df,x='is_fraud',y='hours_diff_bet_trans',ci=None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "039e71f2-2ba0-4f6b-9029-fa4522ace4e0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#since p-value<0.05 , we reject the null hypothesis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1e3a649a-0c02-4734-bf0d-5d28174511dc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#make day feature numerical\n",
    "df['day']=df['trans_date_trans_time'].dt.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2885fa53-2836-4af6-841a-472f2c095cda",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cc_num</th>\n",
       "      <th>cc_freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>60416207185</td>\n",
       "      <td>2196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2724</th>\n",
       "      <td>60416207185</td>\n",
       "      <td>2196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2726</th>\n",
       "      <td>60416207185</td>\n",
       "      <td>2196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2882</th>\n",
       "      <td>60416207185</td>\n",
       "      <td>2196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2907</th>\n",
       "      <td>60416207185</td>\n",
       "      <td>2196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           cc_num  cc_freq\n",
       "1017  60416207185     2196\n",
       "2724  60416207185     2196\n",
       "2726  60416207185     2196\n",
       "2882  60416207185     2196\n",
       "2907  60416207185     2196"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#handling and extracting features from cc_num\n",
    "freq=df.groupby('cc_num').size()\n",
    "df['cc_freq']=df['cc_num'].apply(lambda x:freq[x])\n",
    "df[['cc_num','cc_freq']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0589110c-258d-4ddc-bd1c-4c1750c5ac9d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#we got freq for each cc numbers\n",
    "def his_show(col):\n",
    "    fig,axs=plt.subplots(1,2,sharex=True)\n",
    "    for i in [0,1]:\n",
    "        sns.histplot(df[df['is_fraud']==i][col],bins=6,ax=axs[i])\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "46701bfb-9830-452f-8e39-1e80118283ed",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "his_show('cc_freq')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d59006b6-c770-435e-9043-20fc299b94f0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def class_det(x):\n",
    "    for idx,val in enumerate(list(range(800,5000,800))):\n",
    "        if x<val:\n",
    "            return idx+1\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "27e28f05-40a4-4bf2-94c5-122358bb88fa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 1 2 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "df['cc_freq_class']=df['cc_freq'].apply(class_det)\n",
    "print(df['cc_freq_class'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3bb9e698-9d9c-4368-b34c-cb692f5e4e09",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "his_show('cc_freq_class')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a823dc3-8c7e-4b26-8e5a-b08886815a29",
   "metadata": {},
   "source": [
    "#### Now clearly frauds occurs more in credit cards with less use(new ones) and for genuine transactions,it follows a normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "edb13e68-8e9e-4f2c-9888-05820149d21b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#drop unnecessary column\n",
    "df.drop(columns=['cc_num','trans_date_trans_time','city_pop'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7dd254bf-630b-49fb-a890-a213468ac91a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['merchant', 'category', 'amt', 'city', 'zip', 'job', 'is_fraud',\n",
       "       'split', 'hour', 'day', 'month', 'age', 'distance_km', 'gender_M',\n",
       "       'hours_diff_bet_trans', 'cc_freq', 'cc_freq_class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "efa54250-5032-49f5-b7ca-6bcfb5ae1fef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cc_freq</th>\n",
       "      <th>cc_freq_class</th>\n",
       "      <th>city</th>\n",
       "      <th>job</th>\n",
       "      <th>age</th>\n",
       "      <th>gender_M</th>\n",
       "      <th>merchant</th>\n",
       "      <th>category</th>\n",
       "      <th>distance_km</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>hours_diff_bet_trans</th>\n",
       "      <th>amt</th>\n",
       "      <th>is_fraud</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>2196</td>\n",
       "      <td>3</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>Jones, Sawayn and Romaguera</td>\n",
       "      <td>misc_net</td>\n",
       "      <td>127.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>7.27</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2724</th>\n",
       "      <td>2196</td>\n",
       "      <td>3</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>Berge LLC</td>\n",
       "      <td>gas_transport</td>\n",
       "      <td>110.31</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>52.94</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2726</th>\n",
       "      <td>2196</td>\n",
       "      <td>3</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>Luettgen PLC</td>\n",
       "      <td>gas_transport</td>\n",
       "      <td>21.79</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>82.08</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2882</th>\n",
       "      <td>2196</td>\n",
       "      <td>3</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>Daugherty LLC</td>\n",
       "      <td>kids_pets</td>\n",
       "      <td>87.20</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>34.79</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2907</th>\n",
       "      <td>2196</td>\n",
       "      <td>3</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>Beier and Sons</td>\n",
       "      <td>home</td>\n",
       "      <td>74.21</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>27.18</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      cc_freq  cc_freq_class           city                          job  age  \\\n",
       "1017     2196              3  Fort Washakie  Information systems manager   33   \n",
       "2724     2196              3  Fort Washakie  Information systems manager   33   \n",
       "2726     2196              3  Fort Washakie  Information systems manager   33   \n",
       "2882     2196              3  Fort Washakie  Information systems manager   33   \n",
       "2907     2196              3  Fort Washakie  Information systems manager   33   \n",
       "\n",
       "      gender_M                     merchant       category  distance_km  \\\n",
       "1017     False  Jones, Sawayn and Romaguera       misc_net       127.61   \n",
       "2724     False                    Berge LLC  gas_transport       110.31   \n",
       "2726     False                 Luettgen PLC  gas_transport        21.79   \n",
       "2882     False                Daugherty LLC      kids_pets        87.20   \n",
       "2907     False               Beier and Sons           home        74.21   \n",
       "\n",
       "      month  day  month  hour  hours_diff_bet_trans    amt  is_fraud  split  \n",
       "1017      1    1      1    12                     0   7.27         0  train  \n",
       "2724      1    2      1     8                    19  52.94         0  train  \n",
       "2726      1    2      1     8                     0  82.08         0  train  \n",
       "2882      1    2      1    12                     3  34.79         0  train  \n",
       "2907      1    2      1    13                     0  27.18         0  train  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reorder columns\n",
    "df=df[['cc_freq', 'cc_freq_class','city','job','age','gender_M','merchant','category','distance_km','month','day','month','hour','hours_diff_bet_trans','amt','is_fraud','split']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b153c79e-7d4f-4690-a47a-e51035f9ff04",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# we will enocde ( city,job,merchant,category ) prepare for our model usinf WOEEncoder\n",
    "for col in ['city','job','merchant','category']:\n",
    "    df[col]=WOEEncoder().fit_transform(df[col],df['is_fraud'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8dc495bb-3f08-4414-ae11-666a11a01899",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cc_freq</th>\n",
       "      <th>cc_freq_class</th>\n",
       "      <th>city</th>\n",
       "      <th>job</th>\n",
       "      <th>age</th>\n",
       "      <th>gender_M</th>\n",
       "      <th>merchant</th>\n",
       "      <th>category</th>\n",
       "      <th>distance_km</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "      <th>hours_diff_bet_trans</th>\n",
       "      <th>amt</th>\n",
       "      <th>is_fraud</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>2196</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.193426</td>\n",
       "      <td>0.163804</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>0.472059</td>\n",
       "      <td>0.925860</td>\n",
       "      <td>127.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>7.27</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2724</th>\n",
       "      <td>2196</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.193426</td>\n",
       "      <td>0.163804</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.499771</td>\n",
       "      <td>-0.238221</td>\n",
       "      <td>110.31</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>52.94</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2726</th>\n",
       "      <td>2196</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.193426</td>\n",
       "      <td>0.163804</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.132212</td>\n",
       "      <td>-0.238221</td>\n",
       "      <td>21.79</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>82.08</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2882</th>\n",
       "      <td>2196</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.193426</td>\n",
       "      <td>0.163804</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.620062</td>\n",
       "      <td>-1.019735</td>\n",
       "      <td>87.20</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>34.79</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2907</th>\n",
       "      <td>2196</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.193426</td>\n",
       "      <td>0.163804</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>-1.296190</td>\n",
       "      <td>-1.238421</td>\n",
       "      <td>74.21</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>27.18</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      cc_freq  cc_freq_class      city       job  age  gender_M  merchant  \\\n",
       "1017     2196              3 -0.193426  0.163804   33     False  0.472059   \n",
       "2724     2196              3 -0.193426  0.163804   33     False -0.499771   \n",
       "2726     2196              3 -0.193426  0.163804   33     False -0.132212   \n",
       "2882     2196              3 -0.193426  0.163804   33     False -0.620062   \n",
       "2907     2196              3 -0.193426  0.163804   33     False -1.296190   \n",
       "\n",
       "      category  distance_km  month  day  month  hour  hours_diff_bet_trans  \\\n",
       "1017  0.925860       127.61      1    1      1    12                     0   \n",
       "2724 -0.238221       110.31      1    2      1     8                    19   \n",
       "2726 -0.238221        21.79      1    2      1     8                     0   \n",
       "2882 -1.019735        87.20      1    2      1    12                     3   \n",
       "2907 -1.238421        74.21      1    2      1    13                     0   \n",
       "\n",
       "        amt  is_fraud  split  \n",
       "1017   7.27         0  train  \n",
       "2724  52.94         0  train  \n",
       "2726  82.08         0  train  \n",
       "2882  34.79         0  train  \n",
       "2907  27.18         0  train  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#WOE > 0 : the category is more likely associact with fraud\n",
    "#WOE < 0 : the category is moer likely associated with non-fraud\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f4f39be6-332e-4d2d-8c82-d895fcf051e5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train=df[df['split']=='train'].drop(['split','is_fraud'],axis=1)\n",
    "y_train=df[df['split']=='train']['is_fraud']\n",
    "x_test=df[df['split']=='test'].drop(['split','is_fraud'],axis=1)\n",
    "y_test=df[df['split']=='test']['is_fraud']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "97539383-f071-4b51-bab0-7055decb69e5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3,3))\n",
    "plt.pie([len(x_train),len(x_test)],autopct='%.2f%%',textprops={'color':'white'},explode=[0,0.05],shadow=True)\n",
    "plt.legend(['Train','Test'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5b284a1e-4367-45d3-8210-74344f57bc51",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['cc_freq', 'cc_freq_class', 'city', 'job', 'age', 'merchant',\n",
       "       'category', 'distance_km', 'month', 'day', 'month', 'hour',\n",
       "       'hours_diff_bet_trans', 'amt'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# handling outliers\n",
    "x_train.select_dtypes(include='number').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c564e518-70f5-4714-95cc-6f8a8bd45ebb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#outliers in train data before scaling\n",
    "def count_outliers(l:list):\n",
    "    for col in l:\n",
    "        print(f'Outliers In {col}:',len(detect_outliers(x_train,0,[col])))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "44980e69-4c0d-41f8-9cd7-bfb46bcfe030",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def boxplot_outlier(ds:dict):\n",
    "    plt.figure(figsize=(18,8))\n",
    "    c=1\n",
    "    for _,df_x in ds.items():\n",
    "        for col in ['hours_diff_bet_trans','amt']:\n",
    "            plt.subplot(2,2,c)\n",
    "            df_x[col].plot(kind='box',vert=False)\n",
    "            c+=1\n",
    "    plt.suptitle('Detecting Outliers in Train|Test Data',fontsize=20)\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "47f9229d-3ff7-460b-a97f-f380fcb93678",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers In hours_diff_bet_trans: 86989\n",
      "Outliers In amt: 67290\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datasets={'x_train':x_train,'x_test':x_test}\n",
    "cols=['hours_diff_bet_trans','amt']\n",
    "count_outliers(cols)\n",
    "boxplot_outlier(datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6dabcce7-d4eb-4ef7-9ea9-ca736bd36231",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#aplying log scale\n",
    "for col in cols:\n",
    "    x_train[col]=np.log1p(x_train[col])\n",
    "    x_test[col]=np.log1p(x_test[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ac8add83-2e6c-498c-960c-34f4af5a1467",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers In hours_diff_bet_trans: 222\n",
      "Outliers In amt: 818\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_outliers(cols)\n",
    "boxplot_outlier(datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84e59d0f-e06b-41e2-a8d5-b72fdff87ee5",
   "metadata": {},
   "source": [
    "#### As we can see the log transformation make the distribution more symmetric and reduced the impact of extreme values (we did not removed them).Also we can deal with this data seperately.But fro now,log functions are commonly used to handle right-skewed distributions.after applying our model we can revert data with exp.function to recover the actual data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "23742250-3a25-468c-84c5-eb5e5a69013b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "scaler=StandardScaler().fit(x_train)\n",
    "x_train=scaler.transform(x_train)\n",
    "x_test=scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ec4bd4c-dcc8-49f4-9378-ff1e482434a2",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e474f6b-1d9f-43d1-9401-009a9e857f1c",
   "metadata": {},
   "source": [
    "#### we want to avoid false negative as much as possible.A false negative case means that fraud-postive transaction is assesed to genuine transaction , In this use case false positive (a genuine transaction,in this use as fraud - positive)are not as important as preventing a fraud.so our focus is about(RECALL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "05efd3a8-e8fc-4bae-8907-201e593f4302",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Let's try our model without handling the imbalanced data of fraud detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "5fa40f51-dd54-4626-a2d4-529b31ee9ca0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "evl_models={'Logistic Regression':LogisticRegression(random_state=10),\n",
    "           'Decision Tree':DecisionTreeClassifier(random_state=10),\n",
    "          'Random Forest':RandomForestClassifier(random_state=10)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "86d9c27f-a86b-4b02-8a0e-1cb8e8281068",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def evl_model(xtrain,ytrain,xtest,ytest):\n",
    "    sns.set(rc={'figure.figsize':(18,6)})\n",
    "    i=0\n",
    "    for name,model in evl_models.items():\n",
    "        fig,axs=plt.subplots(1,2)\n",
    "        print('Model:'+name)\n",
    "        print('_'*30)\n",
    "        start=time()\n",
    "        model.fit(xtrain,ytrain)\n",
    "        y_pred=model.predict(xtest)\n",
    "        end=time()\n",
    "        #confusion matrix\n",
    "        cm=confusion_matrix(ytest,y_pred,labels=model.classes_)\n",
    "        cmd=ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=model.classes_)\n",
    "        cmd.plot(colorbar=False,ax=axs[i])\n",
    "        axs[i].grid(False)\n",
    "        #AUC-ROC curve\n",
    "        y_proba=model.predict_proba(xtest)\n",
    "        fpr,tpr,_=roc_curve(ytest,y_proba[:,1])\n",
    "        roc_display=RocCurveDisplay(fpr=fpr,tpr=tpr).plot(ax=axs[i+1])\n",
    "        axs[i+1].plot([0,1],[1,0],color='g')\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "        #we focus on recall = TP/TP+FN\n",
    "        #such that it's score that model predict (non-fraud) while it's fraud\n",
    "        print('Recall Score: {}\\nPrecision: {}\\nF1-Score: {}\\nAccuracy Score: {}\\nAUC Score: {}\\nRunning Time: {} Mins'.format(recall_score(ytest,y_pred),precision_score(ytest,y_pred),f1_score(ytest,y_pred),accuracy_score(ytest,y_pred),roc_auc_score(ytest,y_proba[:,1]),round((end-start)/60.0,2)))\n",
    "        print('*'*30)\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "d4036a96-1288-49da-9676-695ee1575e37",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model:Logistic Regression\n",
      "______________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall Score: 0.1916083916083916\n",
      "Precision: 0.8253012048192772\n",
      "F1-Score: 0.3110102156640182\n",
      "Accuracy Score: 0.9967231640451379\n",
      "AUC Score: 0.8631324813540742\n",
      "Running Time: 0.05 Mins\n",
      "******************************\n",
      "Model:Decision Tree\n",
      "______________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall Score: 0.772960372960373\n",
      "Precision: 0.7284710017574693\n",
      "F1-Score: 0.7500565482922417\n",
      "Accuracy Score: 0.9980115849916954\n",
      "AUC Score: 0.8859219955246865\n",
      "Running Time: 0.22 Mins\n",
      "******************************\n",
      "Model:Random Forest\n",
      "______________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall Score: 0.6662004662004662\n",
      "Precision: 0.972108843537415\n",
      "F1-Score: 0.7905947441217152\n",
      "Accuracy Score: 0.9986378007590166\n",
      "AUC Score: 0.9728142022279753\n",
      "Running Time: 4.71 Mins\n",
      "******************************\n"
     ]
    }
   ],
   "source": [
    "evl_model(x_train,y_train,x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87c856ee-6fe6-4935-b627-120e12e0cf7b",
   "metadata": {},
   "source": [
    "### Conclusion(From Imbalanced Data)\n",
    "- Logistic Regression:Gives a very weak recall accuracy\n",
    "- Decision Tree:Give the Highes recall accuracy but low precision\n",
    "- Random Forest:Gives high recall accuracy and high precision\n",
    "- Note:Our Focus is not on model accuracy(Confusion matrix accuracy) TP+TN/TP+TN+FP+FN,Since we Focus on best accuracy for model to predict the real fraud transaction and don't predict non-fraud and we detect it from (True positive Rate 'Recall')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a8173f2-1709-4cde-b9dd-7af017f9118a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "widgets": {
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 },
 "nbformat": 4,
 "nbformat_minor": 5
}