Fraud Transaction Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "55a38245",
   "metadata": {},
   "source": [
    "## Credit Cards fraud on the rise!!\n",
    "\n",
    "Here is my attempt at building a ML solution to predict it effectively. I have used random Forest classifier in this project\n",
    "\n",
    "### Downloading necassary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bfe5a09f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib import gridspec\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, accuracy_score \n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "from sklearn.metrics import f1_score, matthews_corrcoef\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "017bc5bd",
   "metadata": {},
   "source": [
    "### Retrieving raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bcd09978",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"creditcard.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "991f348e",
   "metadata": {},
   "source": [
    "### Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ca171ead",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <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>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   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",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "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]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2500681a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d4999020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2412a0df",
   "metadata": {},
   "source": [
    "### Determining the amount of fraud cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "494dcaf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Valid Transactions: 284315'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud = data[data['Class'] == 1]\n",
    "valid = data[data['Class'] == 0]\n",
    "outlierFraction = len(fraud)/float(len(valid))\n",
    "\n",
    "outlierFraction\n",
    "'Fraud Cases: {}'.format(len(data[data['Class'] == 1]))\n",
    "'Valid Transactions: {}'.format(len(data[data['Class'] == 0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "233397b0",
   "metadata": {},
   "source": [
    "### Amount details of bad transactions (illegal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e2b1b3dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     492.000000\n",
       "mean      122.211321\n",
       "std       256.683288\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         9.250000\n",
       "75%       105.890000\n",
       "max      2125.870000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud.Amount.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57b1a847",
   "metadata": {},
   "source": [
    "### Amount details forr Normal Joe Transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6776cae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    284315.000000\n",
       "mean         88.291022\n",
       "std         250.105092\n",
       "min           0.000000\n",
       "25%           5.650000\n",
       "50%          22.000000\n",
       "75%          77.050000\n",
       "max       25691.160000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid.Amount.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c0b303c",
   "metadata": {},
   "source": [
    "### Plot Correlation Matrix\n",
    "\n",
    "having a graphical display provides us a better idea of how features and colummns in this case correlate with each other and pinpoint which ones are relevant for us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "27d914b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = data.corr()\n",
    "fig = plt.figure(figsize = (12, 9))\n",
    "sns.heatmap(corrmat, vmax = .8, square = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dc1b977",
   "metadata": {},
   "source": [
    "### Splitting the Data\n",
    "\n",
    "Segrating data into X AND Y values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b504ce94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284807, 30)\n",
      "(284807,)\n"
     ]
    }
   ],
   "source": [
    "X = data.drop(['Class'], axis = 1)\n",
    "Y = data[\"Class\"]\n",
    "\n",
    "print(X.shape)\n",
    "print(Y.shape)\n",
    "\n",
    "xData = X.values\n",
    "yData = Y.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c919fc83",
   "metadata": {},
   "outputs": [],
   "source": [
    "xTrain, xTest, yTrain, yTest = train_test_split(\n",
    "        xData, yData, test_size = 0.1, random_state = 22)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f9a61a9",
   "metadata": {},
   "source": [
    "### Building the Random Forest Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1a2bdd09",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8269e4a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = RandomForestClassifier(n_estimators=10,criterion='entropy', random_state=0,max_depth=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4ed70c18",
   "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(criterion=&#x27;entropy&#x27;, max_depth=5, n_estimators=10,\n",
       "                       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-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(criterion=&#x27;entropy&#x27;, max_depth=5, n_estimators=10,\n",
       "                       random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', max_depth=5, n_estimators=10,\n",
       "                       random_state=0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(xTrain,yTrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04465591",
   "metadata": {},
   "source": [
    "### Predicting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1cd50a22",
   "metadata": {},
   "outputs": [],
   "source": [
    "yPred = classifier.predict(xTest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d5101ed",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5851d207",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_outliers = len(fraud)\n",
    "n_errors = (yPred != yTest).sum()\n",
    "  \n",
    "acc = accuracy_score(yTest, yPred)  \n",
    "prec = precision_score(yTest, yPred)  \n",
    "rec = recall_score(yTest, yPred)\n",
    "f1 = f1_score(yTest, yPred)  \n",
    "MCC = matthews_corrcoef(yTest, yPred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3693b8f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy is 0.9996488887328394\n",
      "The precision is 0.918918918918919\n",
      "The recall is 0.8292682926829268\n",
      "The F1-Score is 0.8717948717948718\n",
      "The Matthews correlation coefficient is 0.8727710671519275\n"
     ]
    }
   ],
   "source": [
    "print(\"The accuracy is {}\".format(acc))\n",
    "print(\"The precision is {}\".format(prec))\n",
    "print(\"The recall is {}\".format(rec))\n",
    "print(\"The F1-Score is {}\".format(f1))\n",
    "print(\"The Matthews correlation coefficient is {}\".format(MCC))"
   ]
  }
 ],
 "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}