2.Credit_card_Fraud_Detection_balanced_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "393fd8af-7d9b-4fa3-9176-1d98e711b8ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "raw",
   "id": "18201373-9579-4e97-9530-401320b8530d",
   "metadata": {},
   "source": [
    "#Preprocessed dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2d5ef192-f07c-4d75-85bc-6710d61531ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>Card</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Zip</th>\n",
       "      <th>MCC</th>\n",
       "      <th>Amount</th>\n",
       "      <th>UseChip</th>\n",
       "      <th>MerchantName</th>\n",
       "      <th>MerchantCity</th>\n",
       "      <th>MerchantState</th>\n",
       "      <th>IssFraud?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>48072.000000</td>\n",
       "      <td>5921.0</td>\n",
       "      <td>$16.68</td>\n",
       "      <td>Swipe Transaction</td>\n",
       "      <td>Ross Package Store</td>\n",
       "      <td>Berkley</td>\n",
       "      <td>MI</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>51694.676895</td>\n",
       "      <td>4899.0</td>\n",
       "      <td>$224.70</td>\n",
       "      <td>Online Transaction</td>\n",
       "      <td>Digital Delivery Company 7</td>\n",
       "      <td>ONLINE</td>\n",
       "      <td>CA</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>51694.676895</td>\n",
       "      <td>4722.0</td>\n",
       "      <td>$145.61</td>\n",
       "      <td>Online Transaction</td>\n",
       "      <td>Travel Booking Company 4</td>\n",
       "      <td>ONLINE</td>\n",
       "      <td>CA</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>48111.000000</td>\n",
       "      <td>3405.0</td>\n",
       "      <td>$229.21</td>\n",
       "      <td>Swipe Transaction</td>\n",
       "      <td>Car Rental Company 4</td>\n",
       "      <td>Belleville</td>\n",
       "      <td>MI</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>48075.000000</td>\n",
       "      <td>5411.0</td>\n",
       "      <td>$11.00</td>\n",
       "      <td>Swipe Transaction</td>\n",
       "      <td>Supermarket Chain 3</td>\n",
       "      <td>Southfield</td>\n",
       "      <td>MI</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691915</th>\n",
       "      <td>1999.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11370.000000</td>\n",
       "      <td>7230.0</td>\n",
       "      <td>$52.42</td>\n",
       "      <td>Chip Transaction</td>\n",
       "      <td>Wright Beauticians</td>\n",
       "      <td>East Elmhurst</td>\n",
       "      <td>NY</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691916</th>\n",
       "      <td>1999.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11373.000000</td>\n",
       "      <td>5942.0</td>\n",
       "      <td>$7.57</td>\n",
       "      <td>Chip Transaction</td>\n",
       "      <td>Bookstore Company 1</td>\n",
       "      <td>Elmhurst</td>\n",
       "      <td>NY</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691917</th>\n",
       "      <td>1999.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11373.000000</td>\n",
       "      <td>5411.0</td>\n",
       "      <td>$7.27</td>\n",
       "      <td>Chip Transaction</td>\n",
       "      <td>Supermarket Chain 1</td>\n",
       "      <td>Elmhurst</td>\n",
       "      <td>NY</td>\n",
       "      <td>No</td>\n",
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       "      <th>691918</th>\n",
       "      <td>1999.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11370.000000</td>\n",
       "      <td>5947.0</td>\n",
       "      <td>$5.39</td>\n",
       "      <td>Chip Transaction</td>\n",
       "      <td>Acme Souvenirs</td>\n",
       "      <td>East Elmhurst</td>\n",
       "      <td>NY</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691919</th>\n",
       "      <td>1999.0</td>\n",
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       "      <td>24.0</td>\n",
       "      <td>11370.000000</td>\n",
       "      <td>7542.0</td>\n",
       "      <td>$15.59</td>\n",
       "      <td>Chip Transaction</td>\n",
       "      <td>Fox East Elmhurst Car Cleaners</td>\n",
       "      <td>East Elmhurst</td>\n",
       "      <td>NY</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>691920 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          User  Card    Year  Month   Day           Zip     MCC   Amount  \\\n",
       "0          1.0   1.0  2005.0    9.0   6.0  48072.000000  5921.0   $16.68   \n",
       "1          1.0   1.0  2005.0    9.0   9.0  51694.676895  4899.0  $224.70   \n",
       "2          1.0   1.0  2005.0    9.0   9.0  51694.676895  4722.0  $145.61   \n",
       "3          1.0   1.0  2005.0    9.0   9.0  48111.000000  3405.0  $229.21   \n",
       "4          1.0   1.0  2005.0    9.0   9.0  48075.000000  5411.0   $11.00   \n",
       "...        ...   ...     ...    ...   ...           ...     ...      ...   \n",
       "691915  1999.0   4.0  2019.0    4.0  13.0  11370.000000  7230.0   $52.42   \n",
       "691916  1999.0   4.0  2019.0    4.0  15.0  11373.000000  5942.0    $7.57   \n",
       "691917  1999.0   4.0  2019.0    4.0  15.0  11373.000000  5411.0    $7.27   \n",
       "691918  1999.0   4.0  2019.0    4.0  17.0  11370.000000  5947.0    $5.39   \n",
       "691919  1999.0   4.0  2019.0    4.0  24.0  11370.000000  7542.0   $15.59   \n",
       "\n",
       "                   UseChip                    MerchantName   MerchantCity  \\\n",
       "0        Swipe Transaction              Ross Package Store        Berkley   \n",
       "1       Online Transaction      Digital Delivery Company 7         ONLINE   \n",
       "2       Online Transaction        Travel Booking Company 4         ONLINE   \n",
       "3        Swipe Transaction            Car Rental Company 4     Belleville   \n",
       "4        Swipe Transaction             Supermarket Chain 3     Southfield   \n",
       "...                    ...                             ...            ...   \n",
       "691915    Chip Transaction              Wright Beauticians  East Elmhurst   \n",
       "691916    Chip Transaction             Bookstore Company 1       Elmhurst   \n",
       "691917    Chip Transaction             Supermarket Chain 1       Elmhurst   \n",
       "691918    Chip Transaction                  Acme Souvenirs  East Elmhurst   \n",
       "691919    Chip Transaction  Fox East Elmhurst Car Cleaners  East Elmhurst   \n",
       "\n",
       "       MerchantState IssFraud?  \n",
       "0                 MI        No  \n",
       "1                 CA        No  \n",
       "2                 CA        No  \n",
       "3                 MI        No  \n",
       "4                 MI        No  \n",
       "...              ...       ...  \n",
       "691915            NY        No  \n",
       "691916            NY        No  \n",
       "691917            NY        No  \n",
       "691918            NY        No  \n",
       "691919            NY        No  \n",
       "\n",
       "[691920 rows x 13 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"Preprocessed_credit_card_detection.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f46daf83-3ea2-4a6b-90d2-f577ceb298f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User', 'Card', 'Year', 'Month', 'Day', 'Zip', 'MCC', 'Amount',\n",
       "       'UseChip', 'MerchantName', 'MerchantCity', 'MerchantState',\n",
       "       'IssFraud?'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2443654a-5226-4874-9b92-bbd38dd79e8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IssFraud?\n",
       "No     691048\n",
       "Yes       872\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"IssFraud?\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8d423eb2-8340-457a-9f95-531bca61b538",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User             0\n",
       "Card             0\n",
       "Year             0\n",
       "Month            0\n",
       "Day              0\n",
       "Zip              0\n",
       "MCC              0\n",
       "Amount           0\n",
       "UseChip          0\n",
       "MerchantName     0\n",
       "MerchantCity     0\n",
       "MerchantState    0\n",
       "IssFraud?        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7fb4e0de-7f84-4f9b-9557-108658831f03",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>Card</th>\n",
       "      <th>Year</th>\n",
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       "      <th>Day</th>\n",
       "      <th>Amount</th>\n",
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       "      <th>MerchantName</th>\n",
       "      <th>MerchantCity</th>\n",
       "      <th>MerchantState</th>\n",
       "      <th>Zip</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>$16.68</td>\n",
       "      <td>Swipe Transaction</td>\n",
       "      <td>Ross Package Store</td>\n",
       "      <td>Berkley</td>\n",
       "      <td>MI</td>\n",
       "      <td>48072.000000</td>\n",
       "      <td>5921.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>$224.70</td>\n",
       "      <td>Online Transaction</td>\n",
       "      <td>Digital Delivery Company 7</td>\n",
       "      <td>ONLINE</td>\n",
       "      <td>CA</td>\n",
       "      <td>51694.676895</td>\n",
       "      <td>4899.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>$145.61</td>\n",
       "      <td>Online Transaction</td>\n",
       "      <td>Travel Booking Company 4</td>\n",
       "      <td>ONLINE</td>\n",
       "      <td>CA</td>\n",
       "      <td>51694.676895</td>\n",
       "      <td>4722.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>$229.21</td>\n",
       "      <td>Swipe Transaction</td>\n",
       "      <td>Car Rental Company 4</td>\n",
       "      <td>Belleville</td>\n",
       "      <td>MI</td>\n",
       "      <td>48111.000000</td>\n",
       "      <td>3405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>$11.00</td>\n",
       "      <td>Swipe Transaction</td>\n",
       "      <td>Supermarket Chain 3</td>\n",
       "      <td>Southfield</td>\n",
       "      <td>MI</td>\n",
       "      <td>48075.000000</td>\n",
       "      <td>5411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>691915</th>\n",
       "      <td>1999.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>$52.42</td>\n",
       "      <td>Chip Transaction</td>\n",
       "      <td>Wright Beauticians</td>\n",
       "      <td>East Elmhurst</td>\n",
       "      <td>NY</td>\n",
       "      <td>11370.000000</td>\n",
       "      <td>7230.0</td>\n",
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       "      <th>691916</th>\n",
       "      <td>1999.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>$7.57</td>\n",
       "      <td>Chip Transaction</td>\n",
       "      <td>Bookstore Company 1</td>\n",
       "      <td>Elmhurst</td>\n",
       "      <td>NY</td>\n",
       "      <td>11373.000000</td>\n",
       "      <td>5942.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691917</th>\n",
       "      <td>1999.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>$7.27</td>\n",
       "      <td>Chip Transaction</td>\n",
       "      <td>Supermarket Chain 1</td>\n",
       "      <td>Elmhurst</td>\n",
       "      <td>NY</td>\n",
       "      <td>11373.000000</td>\n",
       "      <td>5411.0</td>\n",
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       "      <th>691918</th>\n",
       "      <td>1999.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>$5.39</td>\n",
       "      <td>Chip Transaction</td>\n",
       "      <td>Acme Souvenirs</td>\n",
       "      <td>East Elmhurst</td>\n",
       "      <td>NY</td>\n",
       "      <td>11370.000000</td>\n",
       "      <td>5947.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691919</th>\n",
       "      <td>1999.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>$15.59</td>\n",
       "      <td>Chip Transaction</td>\n",
       "      <td>Fox East Elmhurst Car Cleaners</td>\n",
       "      <td>East Elmhurst</td>\n",
       "      <td>NY</td>\n",
       "      <td>11370.000000</td>\n",
       "      <td>7542.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>691920 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          User  Card    Year  Month   Day   Amount             UseChip  \\\n",
       "0          1.0   1.0  2005.0    9.0   6.0   $16.68   Swipe Transaction   \n",
       "1          1.0   1.0  2005.0    9.0   9.0  $224.70  Online Transaction   \n",
       "2          1.0   1.0  2005.0    9.0   9.0  $145.61  Online Transaction   \n",
       "3          1.0   1.0  2005.0    9.0   9.0  $229.21   Swipe Transaction   \n",
       "4          1.0   1.0  2005.0    9.0   9.0   $11.00   Swipe Transaction   \n",
       "...        ...   ...     ...    ...   ...      ...                 ...   \n",
       "691915  1999.0   4.0  2019.0    4.0  13.0   $52.42    Chip Transaction   \n",
       "691916  1999.0   4.0  2019.0    4.0  15.0    $7.57    Chip Transaction   \n",
       "691917  1999.0   4.0  2019.0    4.0  15.0    $7.27    Chip Transaction   \n",
       "691918  1999.0   4.0  2019.0    4.0  17.0    $5.39    Chip Transaction   \n",
       "691919  1999.0   4.0  2019.0    4.0  24.0   $15.59    Chip Transaction   \n",
       "\n",
       "                          MerchantName   MerchantCity MerchantState  \\\n",
       "0                   Ross Package Store        Berkley            MI   \n",
       "1           Digital Delivery Company 7         ONLINE            CA   \n",
       "2             Travel Booking Company 4         ONLINE            CA   \n",
       "3                 Car Rental Company 4     Belleville            MI   \n",
       "4                  Supermarket Chain 3     Southfield            MI   \n",
       "...                                ...            ...           ...   \n",
       "691915              Wright Beauticians  East Elmhurst            NY   \n",
       "691916             Bookstore Company 1       Elmhurst            NY   \n",
       "691917             Supermarket Chain 1       Elmhurst            NY   \n",
       "691918                  Acme Souvenirs  East Elmhurst            NY   \n",
       "691919  Fox East Elmhurst Car Cleaners  East Elmhurst            NY   \n",
       "\n",
       "                 Zip     MCC  \n",
       "0       48072.000000  5921.0  \n",
       "1       51694.676895  4899.0  \n",
       "2       51694.676895  4722.0  \n",
       "3       48111.000000  3405.0  \n",
       "4       48075.000000  5411.0  \n",
       "...              ...     ...  \n",
       "691915  11370.000000  7230.0  \n",
       "691916  11373.000000  5942.0  \n",
       "691917  11373.000000  5411.0  \n",
       "691918  11370.000000  5947.0  \n",
       "691919  11370.000000  7542.0  \n",
       "\n",
       "[691920 rows x 12 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "independent=df[['User', 'Card', 'Year', 'Month', 'Day', 'Amount', 'UseChip',\n",
    "       'MerchantName', 'MerchantCity', 'MerchantState', 'Zip', 'MCC']]\n",
    "independent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c0f5ab12-5461-43fe-ab8a-ea94fea6a8c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IssFraud?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691915</th>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691916</th>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691917</th>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691918</th>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691919</th>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>691920 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       IssFraud?\n",
       "0             No\n",
       "1             No\n",
       "2             No\n",
       "3             No\n",
       "4             No\n",
       "...          ...\n",
       "691915        No\n",
       "691916        No\n",
       "691917        No\n",
       "691918        No\n",
       "691919        No\n",
       "\n",
       "[691920 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dependent=df[['IssFraud?']]\n",
    "dependent"
   ]
  },
  {
   "cell_type": "raw",
   "id": "5abff6c0-a119-4501-9c2c-9ad1db640359",
   "metadata": {},
   "source": [
    "#Using undersampling balance the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5e06e1ee-e1b3-4a75-8c9d-68f836cc21cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "ros=RandomUnderSampler(random_state=42)\n",
    "x_ros,y_ros=ros.fit_resample(independent,dependent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ac2d539d-2ec5-4f6c-9963-1107cd5b95d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1744, 12)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_ros.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "75cd91a1-b583-4d52-bef9-c169d25f5f60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IssFraud?\n",
       "No           872\n",
       "Yes          872\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_ros.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9c3fe42d-0928-48a8-b6b4-023b5525e80d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>Card</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Amount</th>\n",
       "      <th>UseChip</th>\n",
       "      <th>MerchantName</th>\n",
       "      <th>MerchantCity</th>\n",
       "      <th>MerchantState</th>\n",
       "      <th>Zip</th>\n",
       "      <th>MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>609890</th>\n",
       "      <td>1750.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>$21.42</td>\n",
       "      <td>Swipe Transaction</td>\n",
       "      <td>Lukass Theaters</td>\n",
       "      <td>Tiffin</td>\n",
       "      <td>OH</td>\n",
       "      <td>44883.000000</td>\n",
       "      <td>7832.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677647</th>\n",
       "      <td>1959.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>$76.99</td>\n",
       "      <td>Chip Transaction</td>\n",
       "      <td>Jadens Wholesale</td>\n",
       "      <td>Lancaster</td>\n",
       "      <td>CA</td>\n",
       "      <td>93535.000000</td>\n",
       "      <td>5300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59562</th>\n",
       "      <td>182.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>$2.19</td>\n",
       "      <td>Swipe Transaction</td>\n",
       "      <td>Supermarket Chain 3</td>\n",
       "      <td>Houston</td>\n",
       "      <td>TX</td>\n",
       "      <td>77096.000000</td>\n",
       "      <td>5411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155077</th>\n",
       "      <td>458.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>$45.73</td>\n",
       "      <td>Chip Transaction</td>\n",
       "      <td>Supermarket Chain 3</td>\n",
       "      <td>Flint</td>\n",
       "      <td>MI</td>\n",
       "      <td>48532.000000</td>\n",
       "      <td>5411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674259</th>\n",
       "      <td>1949.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$1.25</td>\n",
       "      <td>Chip Transaction</td>\n",
       "      <td>Convenience Store Chain 1</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>NY</td>\n",
       "      <td>11213.000000</td>\n",
       "      <td>5499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691161</th>\n",
       "      <td>1998.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>$193.24</td>\n",
       "      <td>Swipe Transaction</td>\n",
       "      <td>Neufelder Tegucigalpa Wine and Liquor</td>\n",
       "      <td>Tegucigalpa</td>\n",
       "      <td>Honduras</td>\n",
       "      <td>51694.676895</td>\n",
       "      <td>5921.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691871</th>\n",
       "      <td>1999.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>$221.96</td>\n",
       "      <td>Swipe Transaction</td>\n",
       "      <td>Abrils Wholesale</td>\n",
       "      <td>Saint Louis</td>\n",
       "      <td>MO</td>\n",
       "      <td>63146.000000</td>\n",
       "      <td>5300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691872</th>\n",
       "      <td>1999.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>$26.69</td>\n",
       "      <td>Swipe Transaction</td>\n",
       "      <td>Abrils Wholesale</td>\n",
       "      <td>Saint Louis</td>\n",
       "      <td>MO</td>\n",
       "      <td>63146.000000</td>\n",
       "      <td>5300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691873</th>\n",
       "      <td>1999.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>$103.95</td>\n",
       "      <td>Chip Transaction</td>\n",
       "      <td>Cox Saint Louis Restaurant</td>\n",
       "      <td>Saint Louis</td>\n",
       "      <td>MO</td>\n",
       "      <td>63146.000000</td>\n",
       "      <td>5812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691874</th>\n",
       "      <td>1999.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>$0.24</td>\n",
       "      <td>Online Transaction</td>\n",
       "      <td>Digital Content Company 2</td>\n",
       "      <td>ONLINE</td>\n",
       "      <td>CA</td>\n",
       "      <td>51694.676895</td>\n",
       "      <td>5815.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1744 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          User  Card    Year  Month   Day   Amount             UseChip  \\\n",
       "609890  1750.0   0.0  2015.0    7.0  16.0   $21.42   Swipe Transaction   \n",
       "677647  1959.0   1.0  2016.0    5.0   5.0   $76.99    Chip Transaction   \n",
       "59562    182.0   2.0  2012.0   11.0  23.0    $2.19   Swipe Transaction   \n",
       "155077   458.0   2.0  2019.0    5.0  15.0   $45.73    Chip Transaction   \n",
       "674259  1949.0   0.0  2018.0    1.0   4.0    $1.25    Chip Transaction   \n",
       "...        ...   ...     ...    ...   ...      ...                 ...   \n",
       "691161  1998.0   2.0  2013.0    1.0  26.0  $193.24   Swipe Transaction   \n",
       "691871  1999.0   3.0  2020.0    1.0  26.0  $221.96   Swipe Transaction   \n",
       "691872  1999.0   3.0  2020.0    1.0  26.0   $26.69   Swipe Transaction   \n",
       "691873  1999.0   3.0  2020.0    1.0  26.0  $103.95    Chip Transaction   \n",
       "691874  1999.0   3.0  2020.0    1.0  26.0    $0.24  Online Transaction   \n",
       "\n",
       "                                 MerchantName MerchantCity MerchantState  \\\n",
       "609890                        Lukass Theaters       Tiffin            OH   \n",
       "677647                       Jadens Wholesale    Lancaster            CA   \n",
       "59562                     Supermarket Chain 3      Houston            TX   \n",
       "155077                    Supermarket Chain 3        Flint            MI   \n",
       "674259              Convenience Store Chain 1     Brooklyn            NY   \n",
       "...                                       ...          ...           ...   \n",
       "691161  Neufelder Tegucigalpa Wine and Liquor  Tegucigalpa      Honduras   \n",
       "691871                       Abrils Wholesale  Saint Louis            MO   \n",
       "691872                       Abrils Wholesale  Saint Louis            MO   \n",
       "691873             Cox Saint Louis Restaurant  Saint Louis            MO   \n",
       "691874              Digital Content Company 2       ONLINE            CA   \n",
       "\n",
       "                 Zip     MCC  \n",
       "609890  44883.000000  7832.0  \n",
       "677647  93535.000000  5300.0  \n",
       "59562   77096.000000  5411.0  \n",
       "155077  48532.000000  5411.0  \n",
       "674259  11213.000000  5499.0  \n",
       "...              ...     ...  \n",
       "691161  51694.676895  5921.0  \n",
       "691871  63146.000000  5300.0  \n",
       "691872  63146.000000  5300.0  \n",
       "691873  63146.000000  5812.0  \n",
       "691874  51694.676895  5815.0  \n",
       "\n",
       "[1744 rows x 12 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_ros"
   ]
  },
  {
   "cell_type": "raw",
   "id": "1ce30a3a-875a-4e13-aa00-e262c0132789",
   "metadata": {},
   "source": [
    "#Now save the balanced data in a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "38383ac4-b647-4cc9-99ae-b8608a9bbcb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Undersampled data has been saved to undersampled_data.csv.\n"
     ]
    }
   ],
   "source": [
    "# Convert the undersampled arrays to a Pandas DataFrame\n",
    "undersampled_data = pd.DataFrame(data=x_ros, columns=independent.columns)\n",
    "\n",
    "# Add the 'dependent' variable as a new column to the DataFrame\n",
    "undersampled_data['target'] = y_ros\n",
    "\n",
    "# Replace 'undersampled_data.csv' with the desired filename\n",
    "output_file = 'undersampled_data.csv'\n",
    "\n",
    "# Save the DataFrame to a new CSV file\n",
    "undersampled_data.to_csv(output_file, index=False)\n",
    "\n",
    "print(f\"Undersampled data has been saved to {output_file}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2807d155-202d-4eea-8776-ccc905c4eea5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "END HERE \n"
     ]
    }
   ],
   "source": [
    "print(\"END HERE \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "739f128a-e312-4c7d-8128-f208f971b0fc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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