codsoft_5_creditcard_frauddetection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9c9629ef",
   "metadata": {},
   "source": [
    "Here the goal is to build a machine learning model to identify fraudulent credit card transactions.\n",
    "\n",
    "So I have trained a classification algorithm, random forests to classify transactions as fraudulent or genuine."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b9ad315",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dde161ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3032ac22",
   "metadata": {},
   "source": [
    "# Load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c8dcfdad",
   "metadata": {},
   "outputs": [],
   "source": [
    "df5 = pd.read_csv(r\"C:\\Users\\dhara\\Downloads\\archive (10)\\creditcard.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25ba4f05",
   "metadata": {},
   "source": [
    "# Explore the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4c5e267d",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
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       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.792993</td>\n",
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       "      <td>0.061458</td>\n",
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       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
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       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
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       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d0ce5171",
   "metadata": {},
   "outputs": [
    {
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       "      <td>3.031260</td>\n",
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       "      <td>-0.087371</td>\n",
       "      <td>0.004455</td>\n",
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       "            Time         V1         V2        V3        V4        V5  \\\n",
       "284802  172786.0 -11.881118  10.071785 -9.834783 -2.066656 -5.364473   \n",
       "284803  172787.0  -0.732789  -0.055080  2.035030 -0.738589  0.868229   \n",
       "284804  172788.0   1.919565  -0.301254 -3.249640 -0.557828  2.630515   \n",
       "284805  172788.0  -0.240440   0.530483  0.702510  0.689799 -0.377961   \n",
       "284806  172792.0  -0.533413  -0.189733  0.703337 -0.506271 -0.012546   \n",
       "\n",
       "              V6        V7        V8        V9  ...       V21       V22  \\\n",
       "284802 -2.606837 -4.918215  7.305334  1.914428  ...  0.213454  0.111864   \n",
       "284803  1.058415  0.024330  0.294869  0.584800  ...  0.214205  0.924384   \n",
       "284804  3.031260 -0.296827  0.708417  0.432454  ...  0.232045  0.578229   \n",
       "284805  0.623708 -0.686180  0.679145  0.392087  ...  0.265245  0.800049   \n",
       "284806 -0.649617  1.577006 -0.414650  0.486180  ...  0.261057  0.643078   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28  Amount  \\\n",
       "284802  1.014480 -0.509348  1.436807  0.250034  0.943651  0.823731    0.77   \n",
       "284803  0.012463 -1.016226 -0.606624 -0.395255  0.068472 -0.053527   24.79   \n",
       "284804 -0.037501  0.640134  0.265745 -0.087371  0.004455 -0.026561   67.88   \n",
       "284805 -0.163298  0.123205 -0.569159  0.546668  0.108821  0.104533   10.00   \n",
       "284806  0.376777  0.008797 -0.473649 -0.818267 -0.002415  0.013649  217.00   \n",
       "\n",
       "        Class  \n",
       "284802      0  \n",
       "284803      0  \n",
       "284804      0  \n",
       "284805      0  \n",
       "284806      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df5.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "825f7017",
   "metadata": {},
   "outputs": [
    {
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       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>1.168375e-15</td>\n",
       "      <td>3.416908e-16</td>\n",
       "      <td>-1.379537e-15</td>\n",
       "      <td>2.074095e-15</td>\n",
       "      <td>9.604066e-16</td>\n",
       "      <td>1.487313e-15</td>\n",
       "      <td>-5.556467e-16</td>\n",
       "      <td>1.213481e-16</td>\n",
       "      <td>-2.406331e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.654067e-16</td>\n",
       "      <td>-3.568593e-16</td>\n",
       "      <td>2.578648e-16</td>\n",
       "      <td>4.473266e-15</td>\n",
       "      <td>5.340915e-16</td>\n",
       "      <td>1.683437e-15</td>\n",
       "      <td>-3.660091e-16</td>\n",
       "      <td>-1.227390e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b7bdb33d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "df5.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3248235a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      0\n",
       "V1        0\n",
       "V2        0\n",
       "V3        0\n",
       "V4        0\n",
       "V5        0\n",
       "V6        0\n",
       "V7        0\n",
       "V8        0\n",
       "V9        0\n",
       "V10       0\n",
       "V11       0\n",
       "V12       0\n",
       "V13       0\n",
       "V14       0\n",
       "V15       0\n",
       "V16       0\n",
       "V17       0\n",
       "V18       0\n",
       "V19       0\n",
       "V20       0\n",
       "V21       0\n",
       "V22       0\n",
       "V23       0\n",
       "V24       0\n",
       "V25       0\n",
       "V26       0\n",
       "V27       0\n",
       "V28       0\n",
       "Amount    0\n",
       "Class     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "379cfa70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cd9bbdbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
       "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
       "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
       "       'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb080cb",
   "metadata": {},
   "source": [
    "# Data Visualization and Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "abde1f73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.histplot(df5['Amount'], bins=50, kde=True)\n",
    "plt.title('Distribution of Transaction Amounts')\n",
    "plt.xlabel('Transaction Amount')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a89d87c8",
   "metadata": {},
   "source": [
    "Here the histogram shows the Common transaction amounts.\n",
    "\n",
    "Insights into whether transactions are mostly low, high, or evenly distributed.\n",
    "\n",
    "And Unusually high or low transaction amounts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "304d0ada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,10))\n",
    "sns.countplot(x='Class', data=df5)\n",
    "plt.title('Distribution of Fraudulent vs. Genuine Transactions')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xticks([0, 1], ['Genuine', 'Fraudulent'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dd6284af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop the 'Time' column and the target 'Class' for correlation matrix\n",
    "corr_matrix = df5.drop(columns=['Time', 'Class']).corr()\n",
    "plt.figure(figsize=(14, 10))\n",
    "sns.heatmap(corr_matrix, annot=False, cmap='coolwarm')\n",
    "plt.title('Correlation Matrix of Features')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1795f5cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1eed1c0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df5.drop(columns=['Time', 'Class'])\n",
    "y = df5['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "29e5e8a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize the features\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0215817e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a85293a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_df = pd.DataFrame(data=X_pca, columns=['PC1', 'PC2'])\n",
    "pca_df['Class'] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "30adb281",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='PC1', y='PC2', hue='Class', data=pca_df, alpha=0.7)\n",
    "plt.title('PCA of Fraudulent vs. Genuine Transactions')\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23e26a11",
   "metadata": {},
   "source": [
    "Principal Component 1 and 2 are the two main dimensions that capture the most variance in the data.\n",
    "\n",
    "Each point represents a transaction, plotted based on these two components. Points are colored by class (genuine vs. fraudulent).\n",
    "\n",
    "If genuine and fraudulent transactions form distinct clusters, it indicates that the model can differentiate between them.\n",
    "More overlap between classes suggests more difficulty in distinguishing fraudulent from genuine transactions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f94b1b1a",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f5558ce4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize the Amount column\n",
    "scaler = StandardScaler()\n",
    "df5['Amount'] = scaler.fit_transform(df5[['Amount']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2acd4f8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate features and target variable\n",
    "X = df5.drop(columns=['Class'])\n",
    "y = df5['Class']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff989287",
   "metadata": {},
   "source": [
    "# Handling class imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eefe5083",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e6408e55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply SMOTE to balance the dataset\n",
    "smote = SMOTE(random_state=42)\n",
    "X_resampled, y_resampled = smote.fit_resample(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "948376c3",
   "metadata": {},
   "source": [
    "# Split the data into tarin and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4d8ee955",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a037a37d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaa52edc",
   "metadata": {},
   "source": [
    "# RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c2601a56",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a7d6aa2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "122b246f",
   "metadata": {},
   "outputs": [
    {
     "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(random_state=42)</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(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4e671c59",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6ffeda56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, ..., 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b57863ad",
   "metadata": {},
   "source": [
    "# Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "53cac6b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d8936c44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     56750\n",
      "           1       1.00      1.00      1.00     56976\n",
      "\n",
      "    accuracy                           1.00    113726\n",
      "   macro avg       1.00      1.00      1.00    113726\n",
      "weighted avg       1.00      1.00      1.00    113726\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a916c0c",
   "metadata": {},
   "source": [
    "Here the model's accuracy is almost 100% so it is pretty efficient to detect the fraud credit card transaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ee6446e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[56739,    11],\n",
       "       [    0, 56976]], dtype=int64)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3d525357",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, fmt='d', cmap='Blues', xticklabels=['Genuine', 'Fraudulent'], yticklabels=['Genuine', 'Fraudulent'])\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "117b3fe0",
   "metadata": {},
   "source": [
    "Here Higher values of True positive and True negative are better, indicating correct predictions. And Lower values of False positive and False Negative are better, indicating fewer errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "371b9df9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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