Yashasvi _Padam_FraudCode.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "5066b98d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix, ConfusionMatrixDisplay,f1_score,roc_auc_score\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "from imblearn.under_sampling import NearMiss\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e34d612e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>9839.64</td>\n",
       "      <td>C1231006815</td>\n",
       "      <td>170136.0</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>M1979787155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>C1666544295</td>\n",
       "      <td>21249.0</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>M2044282225</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C1305486145</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C553264065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C840083671</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>C38997010</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>C2048537720</td>\n",
       "      <td>41554.0</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>M1230701703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
       "0     1   PAYMENT   9839.64  C1231006815       170136.0       160296.36   \n",
       "1     1   PAYMENT   1864.28  C1666544295        21249.0        19384.72   \n",
       "2     1  TRANSFER    181.00  C1305486145          181.0            0.00   \n",
       "3     1  CASH_OUT    181.00   C840083671          181.0            0.00   \n",
       "4     1   PAYMENT  11668.14  C2048537720        41554.0        29885.86   \n",
       "\n",
       "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
       "0  M1979787155             0.0             0.0        0               0  \n",
       "1  M2044282225             0.0             0.0        0               0  \n",
       "2   C553264065             0.0             0.0        1               0  \n",
       "3    C38997010         21182.0             0.0        1               0  \n",
       "4  M1230701703             0.0             0.0        0               0  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('Fraud.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "94ca5a85",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>9839.64</td>\n",
       "      <td>170136.0</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>21249.0</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>41554.0</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   step      type    amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
       "0     1   PAYMENT   9839.64       170136.0       160296.36             0.0   \n",
       "1     1   PAYMENT   1864.28        21249.0        19384.72             0.0   \n",
       "2     1  TRANSFER    181.00          181.0            0.00             0.0   \n",
       "3     1  CASH_OUT    181.00          181.0            0.00         21182.0   \n",
       "4     1   PAYMENT  11668.14        41554.0        29885.86             0.0   \n",
       "\n",
       "   newbalanceDest  isFraud  isFlaggedFraud  \n",
       "0             0.0        0               0  \n",
       "1             0.0        0               0  \n",
       "2             0.0        1               0  \n",
       "3             0.0        1               0  \n",
       "4             0.0        0               0  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.drop(\"nameOrig\",axis=1)\n",
    "data=data.drop(\"nameDest\",axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "672ad9ce",
   "metadata": {},
   "source": [
    "Checking if all 'isFlaggedFraud' = 1 are Fraud or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ba18090f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2736446</th>\n",
       "      <td>212</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>5996407</th>\n",
       "      <td>425</td>\n",
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       "      <th>6168499</th>\n",
       "      <td>554</td>\n",
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       "      <td>3576297.10</td>\n",
       "      <td>3576297.10</td>\n",
       "      <td>3576297.10</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>10399045.08</td>\n",
       "      <td>10399045.08</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>3441041.46</td>\n",
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       "      <td>3171085.59</td>\n",
       "      <td>3171085.59</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>6362460</th>\n",
       "      <td>730</td>\n",
       "      <td>TRANSFER</td>\n",
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       "      <td>17316255.05</td>\n",
       "      <td>17316255.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6362462</th>\n",
       "      <td>730</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>7316255.05</td>\n",
       "      <td>17316255.05</td>\n",
       "      <td>17316255.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6362584</th>\n",
       "      <td>741</td>\n",
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       "      <td>5674547.89</td>\n",
       "      <td>5674547.89</td>\n",
       "      <td>5674547.89</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "         step      type       amount  oldbalanceOrg  newbalanceOrig  \\\n",
       "2736446   212  TRANSFER   4953893.08     4953893.08      4953893.08   \n",
       "3247297   250  TRANSFER   1343002.08     1343002.08      1343002.08   \n",
       "3760288   279  TRANSFER    536624.41      536624.41       536624.41   \n",
       "5563713   387  TRANSFER   4892193.09     4892193.09      4892193.09   \n",
       "5996407   425  TRANSFER  10000000.00    19585040.37     19585040.37   \n",
       "5996409   425  TRANSFER   9585040.37    19585040.37     19585040.37   \n",
       "6168499   554  TRANSFER   3576297.10     3576297.10      3576297.10   \n",
       "6205439   586  TRANSFER    353874.22      353874.22       353874.22   \n",
       "6266413   617  TRANSFER   2542664.27     2542664.27      2542664.27   \n",
       "6281482   646  TRANSFER  10000000.00    10399045.08     10399045.08   \n",
       "6281484   646  TRANSFER    399045.08    10399045.08     10399045.08   \n",
       "6296014   671  TRANSFER   3441041.46     3441041.46      3441041.46   \n",
       "6351225   702  TRANSFER   3171085.59     3171085.59      3171085.59   \n",
       "6362460   730  TRANSFER  10000000.00    17316255.05     17316255.05   \n",
       "6362462   730  TRANSFER   7316255.05    17316255.05     17316255.05   \n",
       "6362584   741  TRANSFER   5674547.89     5674547.89      5674547.89   \n",
       "\n",
       "         oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
       "2736446             0.0             0.0        1               1  \n",
       "3247297             0.0             0.0        1               1  \n",
       "3760288             0.0             0.0        1               1  \n",
       "5563713             0.0             0.0        1               1  \n",
       "5996407             0.0             0.0        1               1  \n",
       "5996409             0.0             0.0        1               1  \n",
       "6168499             0.0             0.0        1               1  \n",
       "6205439             0.0             0.0        1               1  \n",
       "6266413             0.0             0.0        1               1  \n",
       "6281482             0.0             0.0        1               1  \n",
       "6281484             0.0             0.0        1               1  \n",
       "6296014             0.0             0.0        1               1  \n",
       "6351225             0.0             0.0        1               1  \n",
       "6362460             0.0             0.0        1               1  \n",
       "6362462             0.0             0.0        1               1  \n",
       "6362584             0.0             0.0        1               1  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data['isFlaggedFraud'] == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5247f738",
   "metadata": {},
   "source": [
    "Since all the targeted rows are fraud so 'isFlaggedFraud' can be dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "4d86a084",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>9839.64</td>\n",
       "      <td>170136.0</td>\n",
       "      <td>160296.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>21249.0</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>41554.0</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step      type    amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
       "0     1   PAYMENT   9839.64       170136.0       160296.36             0.0   \n",
       "1     1   PAYMENT   1864.28        21249.0        19384.72             0.0   \n",
       "2     1  TRANSFER    181.00          181.0            0.00             0.0   \n",
       "3     1  CASH_OUT    181.00          181.0            0.00         21182.0   \n",
       "4     1   PAYMENT  11668.14        41554.0        29885.86             0.0   \n",
       "\n",
       "   newbalanceDest  isFraud  \n",
       "0             0.0        0  \n",
       "1             0.0        0  \n",
       "2             0.0        1  \n",
       "3             0.0        1  \n",
       "4             0.0        0  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.drop(\"isFlaggedFraud\",axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3656a21f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "step              0\n",
       "type              0\n",
       "amount            0\n",
       "oldbalanceOrg     0\n",
       "newbalanceOrig    0\n",
       "oldbalanceDest    0\n",
       "newbalanceDest    0\n",
       "isFraud           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_null = data.isnull().sum()\n",
    "is_null"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e106dc6",
   "metadata": {},
   "source": [
    "The data contains no null values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87f50b58",
   "metadata": {},
   "source": [
    "Encoding the categorical data in 'type'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "307b281d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9839.64</td>\n",
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       "      <td>160296.36</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>21249.0</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>181.00</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21182.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>41554.0</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step  type    amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
       "0     1     3   9839.64       170136.0       160296.36             0.0   \n",
       "1     1     3   1864.28        21249.0        19384.72             0.0   \n",
       "2     1     4    181.00          181.0            0.00             0.0   \n",
       "3     1     1    181.00          181.0            0.00         21182.0   \n",
       "4     1     3  11668.14        41554.0        29885.86             0.0   \n",
       "\n",
       "   newbalanceDest  isFraud  \n",
       "0             0.0        0  \n",
       "1             0.0        0  \n",
       "2             0.0        1  \n",
       "3             0.0        1  \n",
       "4             0.0        0  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lt = LabelEncoder()\n",
    "data['type'] = lt.fit_transform(data['type'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b5baa03",
   "metadata": {},
   "source": [
    "Scaling the data using a min-max scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "bf559e1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "def scaleColumns(df, cols_to_scale):\n",
    "    for col in cols_to_scale:\n",
    "        df[col] = pd.DataFrame(scaler.fit_transform(pd.DataFrame(data[col])),columns=[col])\n",
    "    return df\n",
    "\n",
    "\n",
    "data = scaleColumns(data,['amount','oldbalanceOrg','newbalanceOrig','oldbalanceDest','newbalanceDest','step'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b23aff65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,12))\n",
    "sns.heatmap(data.corr(), cmap='coolwarm', square=True, linewidths=0.5, vmin=-1, vmax=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "42f73d74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isFraud           1.000000\n",
       "amount            0.076688\n",
       "step              0.031578\n",
       "type              0.020833\n",
       "oldbalanceOrg     0.010154\n",
       "newbalanceDest    0.000535\n",
       "oldbalanceDest   -0.005885\n",
       "newbalanceOrig   -0.008148\n",
       "Name: isFraud, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()['isFraud'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "6f7fe2fe",
   "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>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>3</td>\n",
       "      <td>0.000106</td>\n",
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       "      <td>0.003233</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.000059</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.000697</td>\n",
       "      <td>0.000603</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step  type    amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
       "0   0.0     3  0.000106       0.002855        0.003233        0.000000   \n",
       "1   0.0     3  0.000020       0.000357        0.000391        0.000000   \n",
       "2   0.0     4  0.000002       0.000003        0.000000        0.000000   \n",
       "3   0.0     1  0.000002       0.000003        0.000000        0.000059   \n",
       "4   0.0     3  0.000126       0.000697        0.000603        0.000000   \n",
       "\n",
       "   newbalanceDest  \n",
       "0             0.0  \n",
       "1             0.0  \n",
       "2             0.0  \n",
       "3             0.0  \n",
       "4             0.0  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.drop('isFraud',axis=1)\n",
    "y= data['isFraud']\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "97890437",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test,y_train, y_test = train_test_split(X,y ,random_state=104,test_size=0.3, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f099b780",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train['isFraud'] = y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b16ba9c5",
   "metadata": {},
   "source": [
    "Checking the number of legit and fraud cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "cfee0a5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4448131\n",
       "1       5703\n",
       "Name: isFraud, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['isFraud'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c8a6aae",
   "metadata": {},
   "source": [
    "As the legit cases are way more than that of fraud cases. So we have to reduce the number of legit cases. Using the Near Miss under sampler for reducing the number of majority class. According to Near Miss algorithm, if two points belonging to different classes are very close to each other in the distribution, then the algorithm eliminates the datapoint of the larger class thereby trying to balance the distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "95fc105b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape Counter({0: 4448131, 1: 5703})\n",
      "Resampled dataset shape Counter({0: 5703, 1: 5703})\n"
     ]
    }
   ],
   "source": [
    "print('Original dataset shape %s' % Counter(y_train))\n",
    "nm = NearMiss()\n",
    "X_res, y_res = nm.fit_resample(X_train, y_train)\n",
    "print('Resampled dataset shape %s' % Counter(y_res))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59f14742",
   "metadata": {},
   "source": [
    "Now the number of legit and fraud cases are equal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d8229c87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5703\n",
       "1    5703\n",
       "Name: isFraud, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_res['isFraud'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3614186a",
   "metadata": {},
   "source": [
    "The target label is stored in 'y' and the variables are stored in 'X'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "576b53eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "      <th>newbalanceDest</th>\n",
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       "  </thead>\n",
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       "      <td>282</td>\n",
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       "      <td>0.000052</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step  type    amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
       "0     9     4  0.000151       0.000078             0.0        0.000000   \n",
       "1   282     4  0.000304       0.000168             0.0        0.000008   \n",
       "2     9     4  0.000067       0.000091             0.0        0.000008   \n",
       "3   282     4  0.000229       0.000060             0.0        0.000000   \n",
       "4     9     4  0.000200       0.000093             0.0        0.000000   \n",
       "\n",
       "   newbalanceDest  \n",
       "0        0.000000  \n",
       "1        0.000087  \n",
       "2        0.000025  \n",
       "3        0.000060  \n",
       "4        0.000052  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_res=X_res.drop(\"isFraud\",axis=1)\n",
    "X_res.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f71fda46",
   "metadata": {},
   "source": [
    "Using SVM on the undersampled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f0423f28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(random_state=0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = SVC(kernel = 'rbf', random_state = 0)\n",
    "classifier.fit(X_res,y_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "69614c35",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "631dc9e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1_Score is  0.004616610453289019\n",
      "Accuracy is  58.703228125101504\n",
      "ROC AUC Score is  0.657566571736509\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, predictions, labels=classifier.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=classifier.classes_)\n",
    "disp.plot()\n",
    "print('F1_Score is ',f1_score(y_test,predictions))\n",
    "print('Accuracy is ',accuracy_score(y_test,predictions)*100)\n",
    "print('ROC AUC Score is ',roc_auc_score(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3dc5771",
   "metadata": {},
   "source": [
    "Using Logistic Regression on the undersampled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9695e709",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression(random_state=0,max_iter =10000000,verbose = 1).fit(X_res, y_res)\n",
    "clf_prediction = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "91c2f05b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1_Score is  0.004202483684567255\n",
      "Accuracy is  50.04652171589691\n",
      "ROC AUC Score is  0.6508311729360913\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, clf_prediction, labels=clf.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=clf.classes_)\n",
    "disp.plot()\n",
    "print('F1_Score is ',f1_score(y_test,clf_prediction))\n",
    "print('Accuracy is ',accuracy_score(y_test,clf_prediction)*100)\n",
    "print('ROC AUC Score is ',roc_auc_score(y_test,clf_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a288a1bc",
   "metadata": {},
   "source": [
    "Using Random Forest Classifier on the undersampled data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9e5d7f37",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.6s finished\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, random_state=0, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=10, random_state=0, verbose=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=10, random_state=0, verbose=1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFC = RandomForestClassifier(max_depth=10, random_state=0,verbose =1)\n",
    "RFC.fit(X_res,y_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cdcd09cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    9.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1_Score is  0.003440185223404248\n",
      "Accuracy is  25.879223757927818\n",
      "ROC AUC Score is  0.6153801625060351\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RFC_prediction = RFC.predict(X_test)\n",
    "cm = confusion_matrix(y_test, RFC_prediction, labels=RFC.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=RFC.classes_)\n",
    "disp.plot()\n",
    "print('F1_Score is ',f1_score(y_test,RFC_prediction))\n",
    "print('Accuracy is ',accuracy_score(y_test,RFC_prediction)*100)\n",
    "print('ROC AUC Score is ',roc_auc_score(y_test,RFC_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3929b16",
   "metadata": {},
   "source": [
    "Now trying the SMOTE oversampling technique. SMOTE first starts by choosing random data from the minority class, then k-nearest neighbors from the data are set. Synthetic data would then be made between the random data and the randomly selected k-nearest neighbor. This process is repeated until the distribution is balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "864dbc89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape Counter({0: 4448131, 1: 5703})\n",
      "Resample dataset shape Counter({0: 4448131, 1: 4448131})\n"
     ]
    }
   ],
   "source": [
    "smote = SMOTE()\n",
    "x_smote, y_smote = smote.fit_resample(X_train, y_train)\n",
    "print('Original dataset shape', Counter(y_train))\n",
    "print('Resample dataset shape', Counter(y_smote))\n",
    "x_smote=x_smote.drop(\"isFraud\",axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7265497",
   "metadata": {},
   "source": [
    "Using SVM on oversampled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a0cc2c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = SVC(kernel = 'rbf', random_state = 0,max_iter = 1000)\n",
    "classifier.fit(x_smote,y_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bb58a754",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6a8207bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1_Score is  0.016113569321533926\n",
      "Accuracy is  91.61257469407256\n",
      "ROC AUC Score is  0.7194775207151979\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, predictions, labels=classifier.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=classifier.classes_)\n",
    "disp.plot()\n",
    "print('F1_Score is ',f1_score(y_test,predictions))\n",
    "print('Accuracy is ',accuracy_score(y_test,predictions)*100)\n",
    "print('ROC AUC Score is ',roc_auc_score(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1bb5067",
   "metadata": {},
   "source": [
    "Using Logsitic Regression on oversampled data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0433c153",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "C:\\Users\\asus\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   31.1s finished\n"
     ]
    }
   ],
   "source": [
    "clf = LogisticRegression(random_state=0,verbose = 1).fit(x_smote, y_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0b0df435",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1_Score is  0.03126277607188525\n",
      "Accuracy is  94.16555863255493\n",
      "ROC AUC Score is  0.8289445233529685\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf_prediction = clf.predict(X_test)\n",
    "cm = confusion_matrix(y_test, clf_prediction, labels=clf.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=clf.classes_)\n",
    "disp.plot()\n",
    "print('F1_Score is ',f1_score(y_test,clf_prediction))\n",
    "print('Accuracy is ',accuracy_score(y_test,clf_prediction)*100)\n",
    "print('ROC AUC Score is ',roc_auc_score(y_test,clf_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a56f1080",
   "metadata": {},
   "source": [
    "Random Forest Classifier with over sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e97e4802",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "building tree 1 of 100\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   13.4s remaining:    0.0s\n",
      "building tree 2 of 100\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   27.1s remaining:    0.0s\n",
      "building tree 3 of 100\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   39.0s remaining:    0.0s\n",
      "building tree 4 of 100\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:   48.3s remaining:    0.0s\n",
      "building tree 5 of 100\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:  1.0min remaining:    0.0s\n",
      "building tree 6 of 100\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:  1.2min remaining:    0.0s\n",
      "building tree 7 of 100\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:  1.4min remaining:    0.0s\n",
      "building tree 8 of 100\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:  1.7min remaining:    0.0s\n",
      "building tree 9 of 100\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:  1.9min remaining:    0.0s\n",
      "building tree 10 of 100\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:  2.1min remaining:    0.0s\n",
      "building tree 11 of 100\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:  2.3min remaining:    0.0s\n",
      "building tree 12 of 100\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:  2.6min remaining:    0.0s\n",
      "building tree 13 of 100\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:  2.7min remaining:    0.0s\n",
      "building tree 14 of 100\n",
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      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed: 21.6min finished\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, random_state=0, verbose=50)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=10, random_state=0, verbose=50)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=10, random_state=0, verbose=50)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFC = RandomForestClassifier(max_depth=10, random_state=0,verbose =50)\n",
    "RFC.fit(x_smote,y_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "39650096",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    9.2s finished\n",
      "F1_Score is  0.17592661093437895\n",
      "Accuracy is  98.83287073564036\n",
      "ROC AUC Score is  0.9678964716245861\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RFC_prediction = RFC.predict(X_test)\n",
    "cm = confusion_matrix(y_test, RFC_prediction, labels=RFC.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=RFC.classes_)\n",
    "disp.plot()\n",
    "print('F1_Score is ',f1_score(y_test,RFC_prediction))\n",
    "print('Accuracy is ',accuracy_score(y_test,RFC_prediction)*100)\n",
    "print('ROC AUC Score is ',roc_auc_score(y_test,RFC_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63de704c",
   "metadata": {},
   "source": [
    "Using DNN (The best working model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "ca916af8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "305ef587",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(12, input_shape=(7,), activation='relu'))\n",
    "model.add(Dense(8, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "4851d50a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "82d8fc0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "8897/8897 [==============================] - 7s 712us/step - loss: 0.3452 - accuracy: 0.8391\n",
      "Epoch 2/50\n",
      "8897/8897 [==============================] - 6s 712us/step - loss: 0.1433 - accuracy: 0.9495\n",
      "Epoch 3/50\n",
      "8897/8897 [==============================] - 6s 711us/step - loss: 0.0744 - accuracy: 0.9791\n",
      "Epoch 4/50\n",
      "8897/8897 [==============================] - 6s 717us/step - loss: 0.0588 - accuracy: 0.9841\n",
      "Epoch 5/50\n",
      "8897/8897 [==============================] - 6s 712us/step - loss: 0.0524 - accuracy: 0.9861\n",
      "Epoch 6/50\n",
      "8897/8897 [==============================] - 6s 713us/step - loss: 0.0483 - accuracy: 0.9873\n",
      "Epoch 7/50\n",
      "8897/8897 [==============================] - 6s 718us/step - loss: 0.0450 - accuracy: 0.9881\n",
      "Epoch 8/50\n",
      "8897/8897 [==============================] - 6s 723us/step - loss: 0.0419 - accuracy: 0.9888\n",
      "Epoch 9/50\n",
      "8897/8897 [==============================] - 6s 720us/step - loss: 0.0381 - accuracy: 0.9895\n",
      "Epoch 10/50\n",
      "8897/8897 [==============================] - 6s 713us/step - loss: 0.0352 - accuracy: 0.9903\n",
      "Epoch 11/50\n",
      "8897/8897 [==============================] - 6s 711us/step - loss: 0.0328 - accuracy: 0.9910\n",
      "Epoch 12/50\n",
      "8897/8897 [==============================] - 6s 707us/step - loss: 0.0309 - accuracy: 0.9914\n",
      "Epoch 13/50\n",
      "8897/8897 [==============================] - 6s 712us/step - loss: 0.0292 - accuracy: 0.9918\n",
      "Epoch 14/50\n",
      "8897/8897 [==============================] - 6s 710us/step - loss: 0.0281 - accuracy: 0.9921\n",
      "Epoch 15/50\n",
      "8897/8897 [==============================] - 6s 706us/step - loss: 0.0270 - accuracy: 0.9923\n",
      "Epoch 16/50\n",
      "8897/8897 [==============================] - 6s 710us/step - loss: 0.0261 - accuracy: 0.9926\n",
      "Epoch 17/50\n",
      "8897/8897 [==============================] - 6s 700us/step - loss: 0.0254 - accuracy: 0.9927\n",
      "Epoch 18/50\n",
      "8897/8897 [==============================] - 6s 713us/step - loss: 0.0244 - accuracy: 0.9930\n",
      "Epoch 19/50\n",
      "8897/8897 [==============================] - 6s 711us/step - loss: 0.0237 - accuracy: 0.9932\n",
      "Epoch 20/50\n",
      "8897/8897 [==============================] - 6s 712us/step - loss: 0.0232 - accuracy: 0.9933\n",
      "Epoch 21/50\n",
      "8897/8897 [==============================] - 6s 712us/step - loss: 0.0229 - accuracy: 0.9934\n",
      "Epoch 22/50\n",
      "8897/8897 [==============================] - 6s 715us/step - loss: 0.0224 - accuracy: 0.9935\n",
      "Epoch 23/50\n",
      "8897/8897 [==============================] - 6s 715us/step - loss: 0.0221 - accuracy: 0.9937\n",
      "Epoch 24/50\n",
      "8897/8897 [==============================] - 6s 716us/step - loss: 0.0218 - accuracy: 0.9938\n",
      "Epoch 25/50\n",
      "8897/8897 [==============================] - 6s 715us/step - loss: 0.0214 - accuracy: 0.9939\n",
      "Epoch 26/50\n",
      "8897/8897 [==============================] - 6s 719us/step - loss: 0.0211 - accuracy: 0.9939\n",
      "Epoch 27/50\n",
      "8897/8897 [==============================] - 6s 721us/step - loss: 0.0210 - accuracy: 0.9940\n",
      "Epoch 28/50\n",
      "8897/8897 [==============================] - 6s 722us/step - loss: 0.0207 - accuracy: 0.9941\n",
      "Epoch 29/50\n",
      "8897/8897 [==============================] - 6s 714us/step - loss: 0.0205 - accuracy: 0.9942\n",
      "Epoch 30/50\n",
      "8897/8897 [==============================] - 6s 714us/step - loss: 0.0204 - accuracy: 0.9942\n",
      "Epoch 31/50\n",
      "8897/8897 [==============================] - 6s 709us/step - loss: 0.0203 - accuracy: 0.9942\n",
      "Epoch 32/50\n",
      "8897/8897 [==============================] - 6s 716us/step - loss: 0.0199 - accuracy: 0.9943\n",
      "Epoch 33/50\n",
      "8897/8897 [==============================] - 7s 775us/step - loss: 0.0199 - accuracy: 0.9944\n",
      "Epoch 34/50\n",
      "8897/8897 [==============================] - 7s 790us/step - loss: 0.0197 - accuracy: 0.9944\n",
      "Epoch 35/50\n",
      "8897/8897 [==============================] - 7s 775us/step - loss: 0.0197 - accuracy: 0.9944\n",
      "Epoch 36/50\n",
      "8897/8897 [==============================] - 7s 788us/step - loss: 0.0196 - accuracy: 0.9945\n",
      "Epoch 37/50\n",
      "8897/8897 [==============================] - 7s 760us/step - loss: 0.0194 - accuracy: 0.9945\n",
      "Epoch 38/50\n",
      "8897/8897 [==============================] - 7s 778us/step - loss: 0.0195 - accuracy: 0.9945\n",
      "Epoch 39/50\n",
      "8897/8897 [==============================] - 7s 788us/step - loss: 0.0192 - accuracy: 0.9946\n",
      "Epoch 40/50\n",
      "8897/8897 [==============================] - 7s 755us/step - loss: 0.0193 - accuracy: 0.9946\n",
      "Epoch 41/50\n",
      "8897/8897 [==============================] - 7s 763us/step - loss: 0.0191 - accuracy: 0.9947\n",
      "Epoch 42/50\n",
      "8897/8897 [==============================] - 6s 712us/step - loss: 0.0191 - accuracy: 0.9947\n",
      "Epoch 43/50\n",
      "8897/8897 [==============================] - 6s 704us/step - loss: 0.0190 - accuracy: 0.9947\n",
      "Epoch 44/50\n",
      "8897/8897 [==============================] - 6s 721us/step - loss: 0.0190 - accuracy: 0.9947\n",
      "Epoch 45/50\n",
      "8897/8897 [==============================] - 6s 703us/step - loss: 0.0186 - accuracy: 0.9948\n",
      "Epoch 46/50\n",
      "8897/8897 [==============================] - 6s 691us/step - loss: 0.0187 - accuracy: 0.9948\n",
      "Epoch 47/50\n",
      "8897/8897 [==============================] - 6s 693us/step - loss: 0.0188 - accuracy: 0.9948\n",
      "Epoch 48/50\n",
      "8897/8897 [==============================] - 6s 690us/step - loss: 0.0183 - accuracy: 0.9949\n",
      "Epoch 49/50\n",
      "8897/8897 [==============================] - 6s 695us/step - loss: 0.0185 - accuracy: 0.9948\n",
      "Epoch 50/50\n",
      "8897/8897 [==============================] - 6s 689us/step - loss: 0.0184 - accuracy: 0.9949\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x1b1ead0f9d0>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_smote, y_smote, epochs=50, batch_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "f55745a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59650/59650 [==============================] - 26s 434us/step\n"
     ]
    }
   ],
   "source": [
    "predictions = (model.predict(X_test) > 0.5).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "26d04fdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = predictions.reshape(1908786)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "16105807",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1_Score is  0.42595276664677295\n",
      "Accuracy is  99.6472627104348\n",
      "ROC AUC Score is  0.9958467005513573\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, predictions)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()\n",
    "print('F1_Score is ',f1_score(y_test,predictions))\n",
    "print('Accuracy is ',accuracy_score(y_test,predictions)*100)\n",
    "print('ROC AUC Score is ',roc_auc_score(y_test,predictions))"
   ]
  }
 ],
 "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}