Payment-Fraud-Prediction-Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0bc6dbf5-a3b8-4681-8ef0-b9a7aaeeadb7",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2f66eaf3-32c8-4ffd-8891-98839e698596",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bf380e5-4459-443c-93a6-fbdd5ab3f1d6",
   "metadata": {},
   "source": [
    "## Reading CSV and making it into a dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "be2787fa-8c41-46ba-9224-3e6748fc0d52",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('DATATHON_EVENT_DATASET.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1546e72-2340-46f5-94d9-bfa83d33ec55",
   "metadata": {},
   "source": [
    "## Checking the types of data in the frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b841ce10-f635-4e6a-995b-002a673a85b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time                             int64\n",
       "Transaction_Type                object\n",
       "Amount                         float64\n",
       "Origin_ID                       object\n",
       "Initial_Origin_Balance         float64\n",
       "Final_Origin_Balance           float64\n",
       "Destination_ID                  object\n",
       "Initial_Destination_Balance    float64\n",
       "Final_Destination_Balance      float64\n",
       "Fraud                           object\n",
       "Expected_Fraud                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "084c219f-c808-4173-b4ab-40a6352f3602",
   "metadata": {},
   "source": [
    "## Checking the Expected Fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "03e05b1b-9bda-45de-b15e-4ab4298234ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     53500\n",
       "unique        2\n",
       "top          No\n",
       "freq      53484\n",
       "Name: Expected_Fraud, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Expected_Fraud\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f55b0a8-4173-4398-9b25-4227cf6fb3a5",
   "metadata": {},
   "source": [
    "## Checking the Time - Bound must be between 1 and 744"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1178b7e2-51db-4e70-8e6f-5f2ec6d7fb74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    58150.000000\n",
       "mean       260.806569\n",
       "std        161.090714\n",
       "min          1.000000\n",
       "25%        157.000000\n",
       "50%        253.000000\n",
       "75%        354.000000\n",
       "max        743.000000\n",
       "Name: Time, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Time\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f763e24a-c922-4e2e-aae0-18da15ad90f8",
   "metadata": {},
   "source": [
    "## Checking the Unique items in the Transaction Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "309259a9-cdde-4e9b-a912-823f2a3cccfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CASH_OUT', 'PAYMENT', 'CASH_IN', 'TRANSFER', 'DEBIT',\n",
       "       'CASH_OUUUT', 'Trans_fer'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Transaction_Type\"].unique() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57185171-825d-4858-b5ee-907079b67327",
   "metadata": {},
   "source": [
    "## Checking Minimum and Maximum Amount of Transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7c56d14d-be4b-4931-9a29-468abab576e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5.815000e+04\n",
       "mean     3.613641e+05\n",
       "std      1.163212e+06\n",
       "min      0.000000e+00\n",
       "25%      1.625524e+04\n",
       "50%      9.645477e+04\n",
       "75%      2.558266e+05\n",
       "max      6.988673e+07\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Amount\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c32bbac-7ed1-4f10-adef-101142d9378b",
   "metadata": {},
   "source": [
    "## Converting the ID's of Origin and Destination\n",
    " 1. If the id starts with \"C\" then it is marked as 0\n",
    " 2. If the id starts with \"D\" then it is marked as 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "111500a5-7d9e-4f63-bd3c-4d9d9e274ffd",
   "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>Time</th>\n",
       "      <th>Transaction_Type</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Origin_ID</th>\n",
       "      <th>Initial_Origin_Balance</th>\n",
       "      <th>Final_Origin_Balance</th>\n",
       "      <th>Destination_ID</th>\n",
       "      <th>Initial_Destination_Balance</th>\n",
       "      <th>Final_Destination_Balance</th>\n",
       "      <th>Fraud</th>\n",
       "      <th>Expected_Fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>183806.32</td>\n",
       "      <td>0</td>\n",
       "      <td>19391.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>382572.19</td>\n",
       "      <td>566378.51</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>137</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>521.37</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>179</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>3478.18</td>\n",
       "      <td>0</td>\n",
       "      <td>19853.00</td>\n",
       "      <td>16374.82</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>355</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>1716.05</td>\n",
       "      <td>0</td>\n",
       "      <td>5769.17</td>\n",
       "      <td>4053.13</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>354</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>253129.93</td>\n",
       "      <td>0</td>\n",
       "      <td>1328499.49</td>\n",
       "      <td>1581629.42</td>\n",
       "      <td>0</td>\n",
       "      <td>2713220.48</td>\n",
       "      <td>2460090.55</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58145</th>\n",
       "      <td>743</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>0</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58146</th>\n",
       "      <td>743</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>0</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58147</th>\n",
       "      <td>743</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>0</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>68488.84</td>\n",
       "      <td>6379898.11</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58148</th>\n",
       "      <td>743</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>0</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58149</th>\n",
       "      <td>743</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>0</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>6510099.11</td>\n",
       "      <td>7360101.63</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58150 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Time Transaction_Type      Amount Origin_ID  Initial_Origin_Balance  \\\n",
       "0       162         CASH_OUT   183806.32         0                19391.00   \n",
       "1       137          PAYMENT      521.37         0                    0.00   \n",
       "2       179          PAYMENT     3478.18         0                19853.00   \n",
       "3       355          PAYMENT     1716.05         0                 5769.17   \n",
       "4       354          CASH_IN   253129.93         0              1328499.49   \n",
       "...     ...              ...         ...       ...                     ...   \n",
       "58145   743         CASH_OUT   339682.13         0               339682.13   \n",
       "58146   743         TRANSFER  6311409.28         0              6311409.28   \n",
       "58147   743         CASH_OUT  6311409.28         0              6311409.28   \n",
       "58148   743         TRANSFER   850002.52         0               850002.52   \n",
       "58149   743         CASH_OUT   850002.52         0               850002.52   \n",
       "\n",
       "       Final_Origin_Balance Destination_ID  Initial_Destination_Balance  \\\n",
       "0                      0.00              0                    382572.19   \n",
       "1                      0.00              1                         0.00   \n",
       "2                  16374.82              1                         0.00   \n",
       "3                   4053.13              1                         0.00   \n",
       "4                1581629.42              0                   2713220.48   \n",
       "...                     ...            ...                          ...   \n",
       "58145                  0.00              0                         0.00   \n",
       "58146                  0.00              0                         0.00   \n",
       "58147                  0.00              0                     68488.84   \n",
       "58148                  0.00              0                         0.00   \n",
       "58149                  0.00              0                   6510099.11   \n",
       "\n",
       "       Final_Destination_Balance Fraud Expected_Fraud  \n",
       "0                      566378.51    No             No  \n",
       "1                           0.00    No             No  \n",
       "2                           0.00    No             No  \n",
       "3                           0.00    No             No  \n",
       "4                     2460090.55    No             No  \n",
       "...                          ...   ...            ...  \n",
       "58145                  339682.13   Yes             No  \n",
       "58146                       0.00   Yes             No  \n",
       "58147                 6379898.11   Yes             No  \n",
       "58148                       0.00   Yes             No  \n",
       "58149                 7360101.63   Yes             No  \n",
       "\n",
       "[58150 rows x 11 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Origin_ID'] = data['Origin_ID'].astype(str)\n",
    "data['Destination_ID'] = data['Destination_ID'].astype(str)\n",
    "for index, i in enumerate(data[\"Origin_ID\"]):\n",
    "    if i[0].lower() == 'c':\n",
    "        data.at[index, 'Origin_ID'] = 0\n",
    "    elif i[0].lower() == 'm':\n",
    "        data.at[index, 'Origin_ID'] = 1\n",
    "\n",
    "for index, i in enumerate(data[\"Destination_ID\"]):\n",
    "    if i[0].lower() == 'c':\n",
    "        data.at[index, 'Destination_ID'] = 0\n",
    "    elif i[0].lower() == 'm':\n",
    "        data.at[index, 'Destination_ID'] = 1\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8ed5e00-6997-46c2-8ac4-466ca421628c",
   "metadata": {},
   "source": [
    "## Checking the Intial and Final Balances of the Origin and Destination \n",
    "1. If the Id is Merchant then the final and initial need not be checked\n",
    "2. If the Id is Customer then we remove the rows with the inital and final balance as ZERO since it doesn't make any sense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1daef5cf-7bcc-47ca-804a-247363ce1d2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Transaction_Type</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Origin_ID</th>\n",
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       "      <th>Final_Origin_Balance</th>\n",
       "      <th>Destination_ID</th>\n",
       "      <th>Initial_Destination_Balance</th>\n",
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       "      <th>Fraud</th>\n",
       "      <th>Expected_Fraud</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>183806.32</td>\n",
       "      <td>0</td>\n",
       "      <td>19391.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>382572.19</td>\n",
       "      <td>566378.51</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>179</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>3478.18</td>\n",
       "      <td>0</td>\n",
       "      <td>19853.00</td>\n",
       "      <td>16374.82</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>355</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>1716.05</td>\n",
       "      <td>0</td>\n",
       "      <td>5769.17</td>\n",
       "      <td>4053.13</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>354</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>253129.93</td>\n",
       "      <td>0</td>\n",
       "      <td>1328499.49</td>\n",
       "      <td>1581629.42</td>\n",
       "      <td>0</td>\n",
       "      <td>2713220.48</td>\n",
       "      <td>2460090.55</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>321</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>24768.57</td>\n",
       "      <td>0</td>\n",
       "      <td>21573.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58141</th>\n",
       "      <td>742</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>63416.99</td>\n",
       "      <td>0</td>\n",
       "      <td>63416.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>276433.18</td>\n",
       "      <td>339850.17</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58143</th>\n",
       "      <td>743</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>1258818.82</td>\n",
       "      <td>0</td>\n",
       "      <td>1258818.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>503464.50</td>\n",
       "      <td>1762283.33</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58145</th>\n",
       "      <td>743</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>0</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>339682.13</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58147</th>\n",
       "      <td>743</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>0</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>68488.84</td>\n",
       "      <td>6379898.11</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58149</th>\n",
       "      <td>743</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>0</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>6510099.11</td>\n",
       "      <td>7360101.63</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36261 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Time Transaction_Type      Amount Origin_ID  Initial_Origin_Balance  \\\n",
       "0       162         CASH_OUT   183806.32         0                19391.00   \n",
       "2       179          PAYMENT     3478.18         0                19853.00   \n",
       "3       355          PAYMENT     1716.05         0                 5769.17   \n",
       "4       354          CASH_IN   253129.93         0              1328499.49   \n",
       "5       321          PAYMENT    24768.57         0                21573.00   \n",
       "...     ...              ...         ...       ...                     ...   \n",
       "58141   742         CASH_OUT    63416.99         0                63416.99   \n",
       "58143   743         CASH_OUT  1258818.82         0              1258818.82   \n",
       "58145   743         CASH_OUT   339682.13         0               339682.13   \n",
       "58147   743         CASH_OUT  6311409.28         0              6311409.28   \n",
       "58149   743         CASH_OUT   850002.52         0               850002.52   \n",
       "\n",
       "       Final_Origin_Balance Destination_ID  Initial_Destination_Balance  \\\n",
       "0                      0.00              0                    382572.19   \n",
       "2                  16374.82              1                         0.00   \n",
       "3                   4053.13              1                         0.00   \n",
       "4                1581629.42              0                   2713220.48   \n",
       "5                      0.00              1                         0.00   \n",
       "...                     ...            ...                          ...   \n",
       "58141                  0.00              0                    276433.18   \n",
       "58143                  0.00              0                    503464.50   \n",
       "58145                  0.00              0                         0.00   \n",
       "58147                  0.00              0                     68488.84   \n",
       "58149                  0.00              0                   6510099.11   \n",
       "\n",
       "       Final_Destination_Balance Fraud Expected_Fraud  \n",
       "0                      566378.51    No             No  \n",
       "2                           0.00    No             No  \n",
       "3                           0.00    No             No  \n",
       "4                     2460090.55    No             No  \n",
       "5                           0.00    No             No  \n",
       "...                          ...   ...            ...  \n",
       "58141                  339850.17   Yes             No  \n",
       "58143                 1762283.33   Yes            NaN  \n",
       "58145                  339682.13   Yes             No  \n",
       "58147                 6379898.11   Yes             No  \n",
       "58149                 7360101.63   Yes             No  \n",
       "\n",
       "[36261 rows x 11 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[~((data['Origin_ID'] == 0) & (data['Initial_Origin_Balance'] == 0) & (data['Final_Origin_Balance'] == 0))]\n",
    "data = data[~((data['Destination_ID'] == 0) & (data['Initial_Destination_Balance'] == 0) & (data['Final_Destination_Balance'] == 0))]\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d1fe758-cd2a-45a7-9d45-6d37ba9d629c",
   "metadata": {},
   "source": [
    "## Checking the intial and final balances\n",
    "1. If the CASH IN then I remove if the Final Origin Balance and Initial Destination Balance is ZERO\n",
    "2. If the CASH OUT then I remove if the Inital Origin Balance and Final Destination Balance is ZERO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e2615eab-4c32-4d11-9c1a-96148d4da2b2",
   "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>Time</th>\n",
       "      <th>Transaction_Type</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Origin_ID</th>\n",
       "      <th>Initial_Origin_Balance</th>\n",
       "      <th>Final_Origin_Balance</th>\n",
       "      <th>Destination_ID</th>\n",
       "      <th>Initial_Destination_Balance</th>\n",
       "      <th>Final_Destination_Balance</th>\n",
       "      <th>Fraud</th>\n",
       "      <th>Expected_Fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>183806.32</td>\n",
       "      <td>0</td>\n",
       "      <td>19391.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>382572.19</td>\n",
       "      <td>566378.51</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>354</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>253129.93</td>\n",
       "      <td>0</td>\n",
       "      <td>1328499.49</td>\n",
       "      <td>1581629.42</td>\n",
       "      <td>0</td>\n",
       "      <td>2713220.48</td>\n",
       "      <td>2460090.55</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>347</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>266999.97</td>\n",
       "      <td>0</td>\n",
       "      <td>7411271.89</td>\n",
       "      <td>7678271.86</td>\n",
       "      <td>0</td>\n",
       "      <td>386812.88</td>\n",
       "      <td>119812.91</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15</td>\n",
       "      <td>CASH_IN</td>\n",
       "      <td>53230.73</td>\n",
       "      <td>0</td>\n",
       "      <td>895081.62</td>\n",
       "      <td>948312.35</td>\n",
       "      <td>0</td>\n",
       "      <td>7015146.49</td>\n",
       "      <td>6961915.77</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>41</td>\n",
       "      <td>DEBIT</td>\n",
       "      <td>819.35</td>\n",
       "      <td>0</td>\n",
       "      <td>274494.00</td>\n",
       "      <td>273674.65</td>\n",
       "      <td>0</td>\n",
       "      <td>923509.57</td>\n",
       "      <td>924328.92</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58139</th>\n",
       "      <td>742</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>258355.42</td>\n",
       "      <td>0</td>\n",
       "      <td>258355.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>25176.67</td>\n",
       "      <td>283532.09</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58141</th>\n",
       "      <td>742</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>63416.99</td>\n",
       "      <td>0</td>\n",
       "      <td>63416.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>276433.18</td>\n",
       "      <td>339850.17</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58143</th>\n",
       "      <td>743</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>1258818.82</td>\n",
       "      <td>0</td>\n",
       "      <td>1258818.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>503464.50</td>\n",
       "      <td>1762283.33</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58147</th>\n",
       "      <td>743</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>0</td>\n",
       "      <td>6311409.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>68488.84</td>\n",
       "      <td>6379898.11</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58149</th>\n",
       "      <td>743</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>0</td>\n",
       "      <td>850002.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>6510099.11</td>\n",
       "      <td>7360101.63</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20914 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Time Transaction_Type      Amount Origin_ID  Initial_Origin_Balance  \\\n",
       "0       162         CASH_OUT   183806.32         0                19391.00   \n",
       "4       354          CASH_IN   253129.93         0              1328499.49   \n",
       "9       347          CASH_IN   266999.97         0              7411271.89   \n",
       "10       15          CASH_IN    53230.73         0               895081.62   \n",
       "14       41            DEBIT      819.35         0               274494.00   \n",
       "...     ...              ...         ...       ...                     ...   \n",
       "58139   742         CASH_OUT   258355.42         0               258355.42   \n",
       "58141   742         CASH_OUT    63416.99         0                63416.99   \n",
       "58143   743         CASH_OUT  1258818.82         0              1258818.82   \n",
       "58147   743         CASH_OUT  6311409.28         0              6311409.28   \n",
       "58149   743         CASH_OUT   850002.52         0               850002.52   \n",
       "\n",
       "       Final_Origin_Balance Destination_ID  Initial_Destination_Balance  \\\n",
       "0                      0.00              0                    382572.19   \n",
       "4                1581629.42              0                   2713220.48   \n",
       "9                7678271.86              0                    386812.88   \n",
       "10                948312.35              0                   7015146.49   \n",
       "14                273674.65              0                    923509.57   \n",
       "...                     ...            ...                          ...   \n",
       "58139                  0.00              0                     25176.67   \n",
       "58141                  0.00              0                    276433.18   \n",
       "58143                  0.00              0                    503464.50   \n",
       "58147                  0.00              0                     68488.84   \n",
       "58149                  0.00              0                   6510099.11   \n",
       "\n",
       "       Final_Destination_Balance Fraud Expected_Fraud  \n",
       "0                      566378.51    No             No  \n",
       "4                     2460090.55    No             No  \n",
       "9                      119812.91    No             No  \n",
       "10                    6961915.77    No             No  \n",
       "14                     924328.92    No            NaN  \n",
       "...                          ...   ...            ...  \n",
       "58139                  283532.09   Yes             No  \n",
       "58141                  339850.17   Yes             No  \n",
       "58143                 1762283.33   Yes            NaN  \n",
       "58147                 6379898.11   Yes             No  \n",
       "58149                 7360101.63   Yes             No  \n",
       "\n",
       "[20914 rows x 11 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[\n",
    "    ~(\n",
    "        # For CASH_IN transactions\n",
    "        ((data[\"Transaction_Type\"] == 'CASH_IN') & \n",
    "         (data['Final_Origin_Balance'] == 0) |\n",
    "         (data['Initial_Destination_Balance'] == 0)) |\n",
    "        # For CASH_OUT transactions\n",
    "        ((data['Transaction_Type'] == 'CASH_OUT') & \n",
    "         (data['Final_Destination_Balance'] == 0) | \n",
    "         (data['Initial_Origin_Balance'] == 0))\n",
    "    )\n",
    "]\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87bcac41-650b-4770-98cd-77d9429abd42",
   "metadata": {},
   "source": [
    "## Encoding data for the model\n",
    "1. Variables used\n",
    "   - Transaction_Type\n",
    "   - Origin_ID\n",
    "   - Destination_ID\n",
    "   - Fraud\n",
    "   \n",
    "- Removing the Expecetd_Fraud column since it doesnt make any sense- all the values are NO "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9131c9d8-99b0-4f00-b0da-f2c8aa442fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the 'Expected_Fraud' column as it contains many missing values and isn't critical\n",
    "data_cleaned = data.drop(columns=['Expected_Fraud'])\n",
    "\n",
    "# Encode categorical variables ('Transaction_Type', 'Origin_ID', 'Destination_ID', 'Fraud')\n",
    "label = LabelEncoder()\n",
    "\n",
    "data_cleaned['Transaction_Type'] = label.fit_transform(data_cleaned['Transaction_Type'])\n",
    "data_cleaned['Origin_ID'] = label.fit_transform(data_cleaned['Origin_ID'])\n",
    "data_cleaned['Destination_ID'] = label.fit_transform(data_cleaned['Destination_ID'])\n",
    "data_cleaned['Fraud'] = label.fit_transform(data_cleaned['Fraud'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3d8d89b-36c7-4c74-a016-32b326640419",
   "metadata": {},
   "source": [
    "## Model Fitting and Scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5bd1b83a-350c-4ef3-919c-e69b037b0f5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9906765479321061"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the features (X) and target (y)\n",
    "x = data_cleaned.drop('Fraud',axis=1)\n",
    "y = data_cleaned['Fraud']\n",
    "\n",
    "# Split the data into training and testing sets (80% training, 20% testing)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
    "model = RandomForestClassifier(n_estimators = 100)\n",
    "model.fit(x_train,y_train)\n",
    "model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d7c8fcb-d62f-40d0-8322-f56b9c47c34d",
   "metadata": {},
   "source": [
    "## Performance Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6b87ccf9-d13e-46ea-9396-02257866dc42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Scores: [0.96666667 0.96666667 0.93333333 0.96666667 0.9       ]\n",
      "Mean Accuracy Score: 0.9466666666666667\n",
      "Precision Scores: [0.96969697 0.96969697 0.94444444 0.96969697 0.9023569 ]\n",
      "Mean Precision Score: 0.9511784511784512\n",
      "Recall Scores: [0.96666667 0.96666667 0.93333333 0.96666667 0.9       ]\n",
      "Mean Recall Score: 0.9466666666666667\n",
      "F1 Scores: [0.96658312 0.96658312 0.93265993 0.96658312 0.89974937]\n",
      "Mean F1 Score: 0.9464317359054201\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.metrics import make_scorer, accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "# Load a sample dataset\n",
    "# Define your model\n",
    "model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "\n",
    "# Perform 5-Fold Cross-Validation\n",
    "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "# Cross-Validation with Accuracy\n",
    "accuracy_scores = cross_val_score(model, X, y, cv=cv, scoring='accuracy')\n",
    "print(\"Accuracy Scores:\", accuracy_scores)\n",
    "print(\"Mean Accuracy Score:\", accuracy_scores.mean())\n",
    "\n",
    "# Cross-Validation with Precision (weighted)\n",
    "precision_scorer = make_scorer(precision_score, average='weighted')\n",
    "precision_scores = cross_val_score(model, X, y, cv=cv, scoring=precision_scorer)\n",
    "print(\"Precision Scores:\", precision_scores)\n",
    "print(\"Mean Precision Score:\", precision_scores.mean())\n",
    "\n",
    "\n",
    "# Cross-Validation with Recall (weighted)\n",
    "recall_scorer = make_scorer(recall_score, average='weighted')\n",
    "recall_scores = cross_val_score(model, X, y, cv=cv, scoring=recall_scorer)\n",
    "print(\"Recall Scores:\", recall_scores)\n",
    "print(\"Mean Recall Score:\", recall_scores.mean())\n",
    "\n",
    "# Cross-Validation with F1 Score (weighted)\n",
    "f1_scorer = make_scorer(f1_score, average='weighted')\n",
    "f1_scores = cross_val_score(model, X, y, cv=cv, scoring=f1_scorer)\n",
    "print(\"F1 Scores:\", f1_scores)\n",
    "print(\"Mean F1 Score:\", f1_scores.mean())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e937b29-8cb7-47d7-9188-cdf8bf6aece0",
   "metadata": {},
   "source": [
    "## Predicting Fraud for test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7601a88-477c-4165-b801-87ab303e8273",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Assuming you have already trained your model and split the data\n",
    "# Generate predictions\n",
    "model.fit(x_train,y_train)\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "# Convert predictions to a pandas Series\n",
    "y_pred_series = pd.Series(y_pred, name='Predictions')\n",
    "\n",
    "# Display the Series\n",
    "print(y_pred_series)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b329a97-28ba-4247-9b50-4f68d65487e2",
   "metadata": {},
   "source": [
    "## ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e72c4ecb-f54b-4a1a-a74f-b06e0915bbdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get probabilities for positive class\n",
    "y_proba = model.predict_proba(x_test)[:, 1]\n",
    "roc_auc = roc_auc_score(y_test, y_proba)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_proba)\n",
    "\n",
    "print(\"AUC-ROC:\", roc_auc)\n",
    "\n",
    "# Plot ROC Curve\n",
    "plt.plot(fpr, tpr, label=f'ROC curve (area = {roc_auc:.2f})')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9af2775b-d444-45eb-a8f4-b871aeb0ea06",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8af741e-9fc7-44e2-93bc-5ba28dfdc39d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Assuming you have already trained your model and split the data\n",
    "# Get the predictions from your model\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "# Generate the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Print the confusion matrix\n",
    "print(\"Confusion Matrix:\")\n",
    "print(cm)\n",
    "\n",
    "# Optional: Visualize the confusion matrix using a heatmap\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Not Fraud', 'Fraud'], yticklabels=['Not Fraud', 'Fraud'])\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41704f6b-e2ee-4990-add4-0ce2a8047c9d",
   "metadata": {},
   "source": [
    "## Exporting the Model as .pkl(pickle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b0555fe8-36d3-4d5d-b7f8-28c7d03eafe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Model.pkl','wb') as file:\n",
    "    pickle.dump(model,file)"
   ]
  }
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