Credit Card Fraud Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credit-Card-Fraud-Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# By-Anmol Srivastava\n",
    "# Data Science and Analysis Project "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Information\n",
    "This project is based on an investigation to detect number of fraud transations as compared to normal or valid transations so that the bank companies can improve the payment transcation platform.\n",
    "Here we have use a small static data set from Kaggle\n",
    "# STEP 1\n",
    "# Importing all necessary libaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading dataset\n",
    "data=pd.read_csv(\"creditcard.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "\n",
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       "            Time         V1         V2        V3        V4        V5  \\\n",
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       "\n",
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       "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]"
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#finding for fraud==1 && normal==0 ,then differentating it up.\n",
    "fraud=data.loc[data['Class']==1]\n",
    "normal=data.loc[data['Class']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <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>541</th>\n",
       "      <td>406.0</td>\n",
       "      <td>-2.312227</td>\n",
       "      <td>1.951992</td>\n",
       "      <td>-1.609851</td>\n",
       "      <td>3.997906</td>\n",
       "      <td>-0.522188</td>\n",
       "      <td>-1.426545</td>\n",
       "      <td>-2.537387</td>\n",
       "      <td>1.391657</td>\n",
       "      <td>-2.770089</td>\n",
       "      <td>...</td>\n",
       "      <td>0.517232</td>\n",
       "      <td>-0.035049</td>\n",
       "      <td>-0.465211</td>\n",
       "      <td>0.320198</td>\n",
       "      <td>0.044519</td>\n",
       "      <td>0.177840</td>\n",
       "      <td>0.261145</td>\n",
       "      <td>-0.143276</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>472.0</td>\n",
       "      <td>-3.043541</td>\n",
       "      <td>-3.157307</td>\n",
       "      <td>1.088463</td>\n",
       "      <td>2.288644</td>\n",
       "      <td>1.359805</td>\n",
       "      <td>-1.064823</td>\n",
       "      <td>0.325574</td>\n",
       "      <td>-0.067794</td>\n",
       "      <td>-0.270953</td>\n",
       "      <td>...</td>\n",
       "      <td>0.661696</td>\n",
       "      <td>0.435477</td>\n",
       "      <td>1.375966</td>\n",
       "      <td>-0.293803</td>\n",
       "      <td>0.279798</td>\n",
       "      <td>-0.145362</td>\n",
       "      <td>-0.252773</td>\n",
       "      <td>0.035764</td>\n",
       "      <td>529.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4920</th>\n",
       "      <td>4462.0</td>\n",
       "      <td>-2.303350</td>\n",
       "      <td>1.759247</td>\n",
       "      <td>-0.359745</td>\n",
       "      <td>2.330243</td>\n",
       "      <td>-0.821628</td>\n",
       "      <td>-0.075788</td>\n",
       "      <td>0.562320</td>\n",
       "      <td>-0.399147</td>\n",
       "      <td>-0.238253</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.294166</td>\n",
       "      <td>-0.932391</td>\n",
       "      <td>0.172726</td>\n",
       "      <td>-0.087330</td>\n",
       "      <td>-0.156114</td>\n",
       "      <td>-0.542628</td>\n",
       "      <td>0.039566</td>\n",
       "      <td>-0.153029</td>\n",
       "      <td>239.93</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6108</th>\n",
       "      <td>6986.0</td>\n",
       "      <td>-4.397974</td>\n",
       "      <td>1.358367</td>\n",
       "      <td>-2.592844</td>\n",
       "      <td>2.679787</td>\n",
       "      <td>-1.128131</td>\n",
       "      <td>-1.706536</td>\n",
       "      <td>-3.496197</td>\n",
       "      <td>-0.248778</td>\n",
       "      <td>-0.247768</td>\n",
       "      <td>...</td>\n",
       "      <td>0.573574</td>\n",
       "      <td>0.176968</td>\n",
       "      <td>-0.436207</td>\n",
       "      <td>-0.053502</td>\n",
       "      <td>0.252405</td>\n",
       "      <td>-0.657488</td>\n",
       "      <td>-0.827136</td>\n",
       "      <td>0.849573</td>\n",
       "      <td>59.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6329</th>\n",
       "      <td>7519.0</td>\n",
       "      <td>1.234235</td>\n",
       "      <td>3.019740</td>\n",
       "      <td>-4.304597</td>\n",
       "      <td>4.732795</td>\n",
       "      <td>3.624201</td>\n",
       "      <td>-1.357746</td>\n",
       "      <td>1.713445</td>\n",
       "      <td>-0.496358</td>\n",
       "      <td>-1.282858</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.379068</td>\n",
       "      <td>-0.704181</td>\n",
       "      <td>-0.656805</td>\n",
       "      <td>-1.632653</td>\n",
       "      <td>1.488901</td>\n",
       "      <td>0.566797</td>\n",
       "      <td>-0.010016</td>\n",
       "      <td>0.146793</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279863</th>\n",
       "      <td>169142.0</td>\n",
       "      <td>-1.927883</td>\n",
       "      <td>1.125653</td>\n",
       "      <td>-4.518331</td>\n",
       "      <td>1.749293</td>\n",
       "      <td>-1.566487</td>\n",
       "      <td>-2.010494</td>\n",
       "      <td>-0.882850</td>\n",
       "      <td>0.697211</td>\n",
       "      <td>-2.064945</td>\n",
       "      <td>...</td>\n",
       "      <td>0.778584</td>\n",
       "      <td>-0.319189</td>\n",
       "      <td>0.639419</td>\n",
       "      <td>-0.294885</td>\n",
       "      <td>0.537503</td>\n",
       "      <td>0.788395</td>\n",
       "      <td>0.292680</td>\n",
       "      <td>0.147968</td>\n",
       "      <td>390.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280143</th>\n",
       "      <td>169347.0</td>\n",
       "      <td>1.378559</td>\n",
       "      <td>1.289381</td>\n",
       "      <td>-5.004247</td>\n",
       "      <td>1.411850</td>\n",
       "      <td>0.442581</td>\n",
       "      <td>-1.326536</td>\n",
       "      <td>-1.413170</td>\n",
       "      <td>0.248525</td>\n",
       "      <td>-1.127396</td>\n",
       "      <td>...</td>\n",
       "      <td>0.370612</td>\n",
       "      <td>0.028234</td>\n",
       "      <td>-0.145640</td>\n",
       "      <td>-0.081049</td>\n",
       "      <td>0.521875</td>\n",
       "      <td>0.739467</td>\n",
       "      <td>0.389152</td>\n",
       "      <td>0.186637</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280149</th>\n",
       "      <td>169351.0</td>\n",
       "      <td>-0.676143</td>\n",
       "      <td>1.126366</td>\n",
       "      <td>-2.213700</td>\n",
       "      <td>0.468308</td>\n",
       "      <td>-1.120541</td>\n",
       "      <td>-0.003346</td>\n",
       "      <td>-2.234739</td>\n",
       "      <td>1.210158</td>\n",
       "      <td>-0.652250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.751826</td>\n",
       "      <td>0.834108</td>\n",
       "      <td>0.190944</td>\n",
       "      <td>0.032070</td>\n",
       "      <td>-0.739695</td>\n",
       "      <td>0.471111</td>\n",
       "      <td>0.385107</td>\n",
       "      <td>0.194361</td>\n",
       "      <td>77.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281144</th>\n",
       "      <td>169966.0</td>\n",
       "      <td>-3.113832</td>\n",
       "      <td>0.585864</td>\n",
       "      <td>-5.399730</td>\n",
       "      <td>1.817092</td>\n",
       "      <td>-0.840618</td>\n",
       "      <td>-2.943548</td>\n",
       "      <td>-2.208002</td>\n",
       "      <td>1.058733</td>\n",
       "      <td>-1.632333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.583276</td>\n",
       "      <td>-0.269209</td>\n",
       "      <td>-0.456108</td>\n",
       "      <td>-0.183659</td>\n",
       "      <td>-0.328168</td>\n",
       "      <td>0.606116</td>\n",
       "      <td>0.884876</td>\n",
       "      <td>-0.253700</td>\n",
       "      <td>245.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281674</th>\n",
       "      <td>170348.0</td>\n",
       "      <td>1.991976</td>\n",
       "      <td>0.158476</td>\n",
       "      <td>-2.583441</td>\n",
       "      <td>0.408670</td>\n",
       "      <td>1.151147</td>\n",
       "      <td>-0.096695</td>\n",
       "      <td>0.223050</td>\n",
       "      <td>-0.068384</td>\n",
       "      <td>0.577829</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.164350</td>\n",
       "      <td>-0.295135</td>\n",
       "      <td>-0.072173</td>\n",
       "      <td>-0.450261</td>\n",
       "      <td>0.313267</td>\n",
       "      <td>-0.289617</td>\n",
       "      <td>0.002988</td>\n",
       "      <td>-0.015309</td>\n",
       "      <td>42.53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>492 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time        V1        V2        V3        V4        V5        V6  \\\n",
       "541        406.0 -2.312227  1.951992 -1.609851  3.997906 -0.522188 -1.426545   \n",
       "623        472.0 -3.043541 -3.157307  1.088463  2.288644  1.359805 -1.064823   \n",
       "4920      4462.0 -2.303350  1.759247 -0.359745  2.330243 -0.821628 -0.075788   \n",
       "6108      6986.0 -4.397974  1.358367 -2.592844  2.679787 -1.128131 -1.706536   \n",
       "6329      7519.0  1.234235  3.019740 -4.304597  4.732795  3.624201 -1.357746   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "279863  169142.0 -1.927883  1.125653 -4.518331  1.749293 -1.566487 -2.010494   \n",
       "280143  169347.0  1.378559  1.289381 -5.004247  1.411850  0.442581 -1.326536   \n",
       "280149  169351.0 -0.676143  1.126366 -2.213700  0.468308 -1.120541 -0.003346   \n",
       "281144  169966.0 -3.113832  0.585864 -5.399730  1.817092 -0.840618 -2.943548   \n",
       "281674  170348.0  1.991976  0.158476 -2.583441  0.408670  1.151147 -0.096695   \n",
       "\n",
       "              V7        V8        V9  ...       V21       V22       V23  \\\n",
       "541    -2.537387  1.391657 -2.770089  ...  0.517232 -0.035049 -0.465211   \n",
       "623     0.325574 -0.067794 -0.270953  ...  0.661696  0.435477  1.375966   \n",
       "4920    0.562320 -0.399147 -0.238253  ... -0.294166 -0.932391  0.172726   \n",
       "6108   -3.496197 -0.248778 -0.247768  ...  0.573574  0.176968 -0.436207   \n",
       "6329    1.713445 -0.496358 -1.282858  ... -0.379068 -0.704181 -0.656805   \n",
       "...          ...       ...       ...  ...       ...       ...       ...   \n",
       "279863 -0.882850  0.697211 -2.064945  ...  0.778584 -0.319189  0.639419   \n",
       "280143 -1.413170  0.248525 -1.127396  ...  0.370612  0.028234 -0.145640   \n",
       "280149 -2.234739  1.210158 -0.652250  ...  0.751826  0.834108  0.190944   \n",
       "281144 -2.208002  1.058733 -1.632333  ...  0.583276 -0.269209 -0.456108   \n",
       "281674  0.223050 -0.068384  0.577829  ... -0.164350 -0.295135 -0.072173   \n",
       "\n",
       "             V24       V25       V26       V27       V28  Amount  Class  \n",
       "541     0.320198  0.044519  0.177840  0.261145 -0.143276    0.00      1  \n",
       "623    -0.293803  0.279798 -0.145362 -0.252773  0.035764  529.00      1  \n",
       "4920   -0.087330 -0.156114 -0.542628  0.039566 -0.153029  239.93      1  \n",
       "6108   -0.053502  0.252405 -0.657488 -0.827136  0.849573   59.00      1  \n",
       "6329   -1.632653  1.488901  0.566797 -0.010016  0.146793    1.00      1  \n",
       "...          ...       ...       ...       ...       ...     ...    ...  \n",
       "279863 -0.294885  0.537503  0.788395  0.292680  0.147968  390.00      1  \n",
       "280143 -0.081049  0.521875  0.739467  0.389152  0.186637    0.76      1  \n",
       "280149  0.032070 -0.739695  0.471111  0.385107  0.194361   77.89      1  \n",
       "281144 -0.183659 -0.328168  0.606116  0.884876 -0.253700  245.00      1  \n",
       "281674 -0.450261  0.313267 -0.289617  0.002988 -0.015309   42.53      1  \n",
       "\n",
       "[492 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      3.972743e+07\n",
       "V1       -2.347799e+03\n",
       "V2        1.782899e+03\n",
       "V3       -3.460374e+03\n",
       "V4        2.234678e+03\n",
       "V5       -1.550403e+03\n",
       "V6       -6.876865e+02\n",
       "V7       -2.739816e+03\n",
       "V8        2.807529e+02\n",
       "V9       -1.269912e+03\n",
       "V10      -2.793026e+03\n",
       "V11       1.869685e+03\n",
       "V12      -3.079621e+03\n",
       "V13      -5.379224e+01\n",
       "V14      -3.430088e+03\n",
       "V15      -4.572094e+01\n",
       "V16      -2.036853e+03\n",
       "V17      -3.279592e+03\n",
       "V18      -1.105184e+03\n",
       "V19       3.348844e+02\n",
       "V20       1.831811e+02\n",
       "V21       3.510855e+02\n",
       "V22       6.912050e+00\n",
       "V23      -1.983152e+01\n",
       "V24      -5.172411e+01\n",
       "V25       2.039285e+01\n",
       "V26       2.541088e+01\n",
       "V27       8.392280e+01\n",
       "V28       3.722831e+01\n",
       "Amount    6.012797e+04\n",
       "Class     4.920000e+02\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "492"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fraud)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "284315"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(normal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
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       "      <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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
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       "      <td>-0.338321</td>\n",
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       "      <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",
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       "    <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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284802</th>\n",
       "      <td>172786.0</td>\n",
       "      <td>-11.881118</td>\n",
       "      <td>10.071785</td>\n",
       "      <td>-9.834783</td>\n",
       "      <td>-2.066656</td>\n",
       "      <td>-5.364473</td>\n",
       "      <td>-2.606837</td>\n",
       "      <td>-4.918215</td>\n",
       "      <td>7.305334</td>\n",
       "      <td>1.914428</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213454</td>\n",
       "      <td>0.111864</td>\n",
       "      <td>1.014480</td>\n",
       "      <td>-0.509348</td>\n",
       "      <td>1.436807</td>\n",
       "      <td>0.250034</td>\n",
       "      <td>0.943651</td>\n",
       "      <td>0.823731</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284803</th>\n",
       "      <td>172787.0</td>\n",
       "      <td>-0.732789</td>\n",
       "      <td>-0.055080</td>\n",
       "      <td>2.035030</td>\n",
       "      <td>-0.738589</td>\n",
       "      <td>0.868229</td>\n",
       "      <td>1.058415</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>0.294869</td>\n",
       "      <td>0.584800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214205</td>\n",
       "      <td>0.924384</td>\n",
       "      <td>0.012463</td>\n",
       "      <td>-1.016226</td>\n",
       "      <td>-0.606624</td>\n",
       "      <td>-0.395255</td>\n",
       "      <td>0.068472</td>\n",
       "      <td>-0.053527</td>\n",
       "      <td>24.79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284804</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>1.919565</td>\n",
       "      <td>-0.301254</td>\n",
       "      <td>-3.249640</td>\n",
       "      <td>-0.557828</td>\n",
       "      <td>2.630515</td>\n",
       "      <td>3.031260</td>\n",
       "      <td>-0.296827</td>\n",
       "      <td>0.708417</td>\n",
       "      <td>0.432454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.232045</td>\n",
       "      <td>0.578229</td>\n",
       "      <td>-0.037501</td>\n",
       "      <td>0.640134</td>\n",
       "      <td>0.265745</td>\n",
       "      <td>-0.087371</td>\n",
       "      <td>0.004455</td>\n",
       "      <td>-0.026561</td>\n",
       "      <td>67.88</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284805</th>\n",
       "      <td>172788.0</td>\n",
       "      <td>-0.240440</td>\n",
       "      <td>0.530483</td>\n",
       "      <td>0.702510</td>\n",
       "      <td>0.689799</td>\n",
       "      <td>-0.377961</td>\n",
       "      <td>0.623708</td>\n",
       "      <td>-0.686180</td>\n",
       "      <td>0.679145</td>\n",
       "      <td>0.392087</td>\n",
       "      <td>...</td>\n",
       "      <td>0.265245</td>\n",
       "      <td>0.800049</td>\n",
       "      <td>-0.163298</td>\n",
       "      <td>0.123205</td>\n",
       "      <td>-0.569159</td>\n",
       "      <td>0.546668</td>\n",
       "      <td>0.108821</td>\n",
       "      <td>0.104533</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284806</th>\n",
       "      <td>172792.0</td>\n",
       "      <td>-0.533413</td>\n",
       "      <td>-0.189733</td>\n",
       "      <td>0.703337</td>\n",
       "      <td>-0.506271</td>\n",
       "      <td>-0.012546</td>\n",
       "      <td>-0.649617</td>\n",
       "      <td>1.577006</td>\n",
       "      <td>-0.414650</td>\n",
       "      <td>0.486180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261057</td>\n",
       "      <td>0.643078</td>\n",
       "      <td>0.376777</td>\n",
       "      <td>0.008797</td>\n",
       "      <td>-0.473649</td>\n",
       "      <td>-0.818267</td>\n",
       "      <td>-0.002415</td>\n",
       "      <td>0.013649</td>\n",
       "      <td>217.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284315 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time         V1         V2        V3        V4        V5  \\\n",
       "0            0.0  -1.359807  -0.072781  2.536347  1.378155 -0.338321   \n",
       "1            0.0   1.191857   0.266151  0.166480  0.448154  0.060018   \n",
       "2            1.0  -1.358354  -1.340163  1.773209  0.379780 -0.503198   \n",
       "3            1.0  -0.966272  -0.185226  1.792993 -0.863291 -0.010309   \n",
       "4            2.0  -1.158233   0.877737  1.548718  0.403034 -0.407193   \n",
       "...          ...        ...        ...       ...       ...       ...   \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",
       "0       0.462388  0.239599  0.098698  0.363787  ... -0.018307  0.277838   \n",
       "1      -0.082361 -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672   \n",
       "2       1.800499  0.791461  0.247676 -1.514654  ...  0.247998  0.771679   \n",
       "3       1.247203  0.237609  0.377436 -1.387024  ... -0.108300  0.005274   \n",
       "4       0.095921  0.592941 -0.270533  0.817739  ... -0.009431  0.798278   \n",
       "...          ...       ...       ...       ...  ...       ...       ...   \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",
       "0      -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62   \n",
       "1       0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69   \n",
       "2       0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66   \n",
       "3      -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50   \n",
       "4      -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99   \n",
       "...          ...       ...       ...       ...       ...       ...     ...   \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",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "...       ...  \n",
       "284802      0  \n",
       "284803      0  \n",
       "284804      0  \n",
       "284805      0  \n",
       "284806      0  \n",
       "\n",
       "[284315 rows x 31 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f2ba0c70ef0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 422.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x='Amount',y='Time',hue='Class', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28481, 31)\n",
      "                Time            V1            V2            V3            V4  \\\n",
      "count   28481.000000  28481.000000  28481.000000  28481.000000  28481.000000   \n",
      "mean    94705.035216     -0.001143     -0.018290      0.000795      0.000350   \n",
      "std     47584.727034      1.994661      1.709050      1.522313      1.420003   \n",
      "min         0.000000    -40.470142    -63.344698    -31.813586     -5.266509   \n",
      "25%     53924.000000     -0.908809     -0.610322     -0.892884     -0.847370   \n",
      "50%     84551.000000      0.031139      0.051775      0.178943     -0.017692   \n",
      "75%    139392.000000      1.320048      0.792685      1.035197      0.737312   \n",
      "max    172784.000000      2.411499     17.418649      4.069865     16.715537   \n",
      "\n",
      "                 V5            V6            V7            V8            V9  \\\n",
      "count  28481.000000  28481.000000  28481.000000  28481.000000  28481.000000   \n",
      "mean      -0.015666      0.003634     -0.008523     -0.003040      0.014536   \n",
      "std        1.395552      1.334985      1.237249      1.204102      1.098006   \n",
      "min      -42.147898    -19.996349    -22.291962    -33.785407     -8.739670   \n",
      "25%       -0.703986     -0.765807     -0.562033     -0.208445     -0.632488   \n",
      "50%       -0.068037     -0.269071      0.028378      0.024696     -0.037100   \n",
      "75%        0.603574      0.398839      0.559428      0.326057      0.621093   \n",
      "max       28.762671     22.529298     36.677268     19.587773      8.141560   \n",
      "\n",
      "       ...           V21           V22           V23           V24  \\\n",
      "count  ...  28481.000000  28481.000000  28481.000000  28481.000000   \n",
      "mean   ...      0.004740      0.006719     -0.000494     -0.002626   \n",
      "std    ...      0.744743      0.728209      0.645945      0.603968   \n",
      "min    ...    -16.640785    -10.933144    -30.269720     -2.752263   \n",
      "25%    ...     -0.224842     -0.535877     -0.163047     -0.360582   \n",
      "50%    ...     -0.029075      0.014337     -0.012678      0.038383   \n",
      "75%    ...      0.189068      0.533936      0.148065      0.434851   \n",
      "max    ...     22.588989      6.090514     15.626067      3.944520   \n",
      "\n",
      "                V25           V26           V27           V28        Amount  \\\n",
      "count  28481.000000  28481.000000  28481.000000  28481.000000  28481.000000   \n",
      "mean      -0.000917      0.004762     -0.001689     -0.004154     89.957884   \n",
      "std        0.520679      0.488171      0.418304      0.321646    270.894630   \n",
      "min       -7.025783     -2.534330     -8.260909     -9.617915      0.000000   \n",
      "25%       -0.319611     -0.328476     -0.071712     -0.053379      5.980000   \n",
      "50%        0.015231     -0.049750      0.000914      0.010753     22.350000   \n",
      "75%        0.351466      0.253580      0.090329      0.076267     78.930000   \n",
      "max        5.541598      3.118588     11.135740     15.373170  19656.530000   \n",
      "\n",
      "              Class  \n",
      "count  28481.000000  \n",
      "mean       0.001720  \n",
      "std        0.041443  \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]\n"
     ]
    }
   ],
   "source": [
    "data1= data.sample(frac=0.1, random_state = 1)\n",
    "print(data1.shape)\n",
    "print(data1.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histograms of each parameter \n",
    "data.hist(figsize = (20,20))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2bfcdcae10>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdcaf60>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdad0f0>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdad240>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdad390>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdad4e0>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdad630>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdad780>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdad8d0>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdada20>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfced4ba8>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdadc88>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdaddd8>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdadf28>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdb30b8>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdb3208>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdb3358>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdb34a8>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdb35f8>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdb3748>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdb3898>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdb39e8>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdb3b38>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdb3c88>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdb3dd8>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdb3f28>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdb70b8>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdb7208>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdb7358>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdb74a8>,\n",
       " <matplotlib.lines.Line2D at 0x7f2bfcdb75f8>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the dataset\n",
    "plt.plot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Logistic Regressing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn.model_selection import train_test_split \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data.iloc[:,:-1]\n",
    "Y=data['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test, Y_train, Y_test=train_test_split(X,Y, test_size=0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf= linear_model.LogisticRegression(C=1e5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=100000.0, class_weight=None, dual=False,\n",
       "          fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
       "          multi_class='warn', n_jobs=None, penalty='l2', random_state=None,\n",
       "          solver='warn', tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#making model\n",
    "y_pred=np.array(clf.predict(X_test))\n",
    "y=np.array(Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix , classification_report ,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[93804    18]\n",
      " [   64   101]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(Y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9991275389149563\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(Y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     93822\n",
      "           1       0.85      0.61      0.71       165\n",
      "\n",
      "   micro avg       1.00      1.00      1.00     93987\n",
      "   macro avg       0.92      0.81      0.86     93987\n",
      "weighted avg       1.00      1.00      1.00     93987\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#printing report \n",
    "print(classification_report(Y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now using only y to predict report to be more precise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[93804    18]\n",
      " [   64   101]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9991275389149563\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     93822\n",
      "           1       0.85      0.61      0.71       165\n",
      "\n",
      "   micro avg       1.00      1.00      1.00     93987\n",
      "   macro avg       0.92      0.81      0.86     93987\n",
      "weighted avg       1.00      1.00      1.00     93987\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#printing report \n",
    "print(classification_report(y,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#as we can see there is no change in report as well as confusion matrix also same.So throught this we can colclude that our model is working "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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