NaiveBayes_DecisionTrees_SVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "gIMK4HlPJkgG"
   },
   "outputs": [],
   "source": [
    "#imported all the libraries used\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GVR3Iw3eJogE",
    "outputId": "4857f456-863b-46c0-a8c7-65495faf0d79"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Info on transactions\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 273328 entries, 0 to 273327\n",
      "Data columns (total 16 columns):\n",
      "Unnamed: 0        273328 non-null int64\n",
      "User              273328 non-null int64\n",
      "Card              273328 non-null int64\n",
      "Year              273328 non-null int64\n",
      "Month             273328 non-null int64\n",
      "Day               273328 non-null int64\n",
      "Time              273328 non-null object\n",
      "Amount            273328 non-null object\n",
      "Use Chip          273328 non-null object\n",
      "Merchant Name     273328 non-null int64\n",
      "Merchant City     273328 non-null object\n",
      "Merchant State    228151 non-null object\n",
      "Zip               220082 non-null float64\n",
      "MCC               273328 non-null int64\n",
      "Errors?           5072 non-null object\n",
      "Is Fraud?         273328 non-null object\n",
      "dtypes: float64(1), int64(8), object(7)\n",
      "memory usage: 33.4+ MB\n",
      "None\n",
      "\n",
      "Info on cards\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6146 entries, 0 to 6145\n",
      "Data columns (total 13 columns):\n",
      "User                     6146 non-null int64\n",
      "CARD INDEX               6146 non-null int64\n",
      "Card Brand               6146 non-null object\n",
      "Card Type                6146 non-null object\n",
      "Card Number              6146 non-null int64\n",
      "Expires                  6146 non-null object\n",
      "CVV                      6146 non-null int64\n",
      "Has Chip                 6146 non-null object\n",
      "Cards Issued             6146 non-null int64\n",
      "Credit Limit             6146 non-null object\n",
      "Acct Open Date           6146 non-null object\n",
      "Year PIN last Changed    6146 non-null int64\n",
      "Card on Dark Web         6146 non-null object\n",
      "dtypes: int64(6), object(7)\n",
      "memory usage: 624.3+ KB\n",
      "None\n",
      "\n",
      "Info on users\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 18 columns):\n",
      "Person                         2000 non-null object\n",
      "Current Age                    2000 non-null int64\n",
      "Retirement Age                 2000 non-null int64\n",
      "Birth Year                     2000 non-null int64\n",
      "Birth Month                    2000 non-null int64\n",
      "Gender                         2000 non-null object\n",
      "Address                        2000 non-null object\n",
      "Apartment                      528 non-null float64\n",
      "City                           2000 non-null object\n",
      "State                          2000 non-null object\n",
      "Zipcode                        2000 non-null int64\n",
      "Latitude                       2000 non-null float64\n",
      "Longitude                      2000 non-null float64\n",
      "Per Capita Income - Zipcode    2000 non-null object\n",
      "Yearly Income - Person         2000 non-null object\n",
      "Total Debt                     2000 non-null object\n",
      "FICO Score                     2000 non-null int64\n",
      "Num Credit Cards               2000 non-null int64\n",
      "dtypes: float64(3), int64(7), object(8)\n",
      "memory usage: 281.4+ KB\n",
      "None\n",
      "\n",
      "Transactions Statistics\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>User</th>\n",
       "      <th>Card</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Merchant Name</th>\n",
       "      <th>Zip</th>\n",
       "      <th>MCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>2.733280e+05</td>\n",
       "      <td>273328.000000</td>\n",
       "      <td>273328.000000</td>\n",
       "      <td>273328.000000</td>\n",
       "      <td>273328.000000</td>\n",
       "      <td>273328.000000</td>\n",
       "      <td>2.733280e+05</td>\n",
       "      <td>220082.000000</td>\n",
       "      <td>273328.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>1.222290e+07</td>\n",
       "      <td>1003.363318</td>\n",
       "      <td>1.375757</td>\n",
       "      <td>2011.963301</td>\n",
       "      <td>6.546673</td>\n",
       "      <td>15.702548</td>\n",
       "      <td>-4.155375e+17</td>\n",
       "      <td>50874.909329</td>\n",
       "      <td>5526.551224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>7.027960e+06</td>\n",
       "      <td>568.436459</td>\n",
       "      <td>1.420734</td>\n",
       "      <td>5.054697</td>\n",
       "      <td>3.469510</td>\n",
       "      <td>8.815986</td>\n",
       "      <td>4.782507e+18</td>\n",
       "      <td>29421.721774</td>\n",
       "      <td>898.962999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>4.310000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1991.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-9.222899e+18</td>\n",
       "      <td>501.000000</td>\n",
       "      <td>1711.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>6.131172e+06</td>\n",
       "      <td>513.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>-4.500543e+18</td>\n",
       "      <td>28351.000000</td>\n",
       "      <td>5300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>1.225985e+07</td>\n",
       "      <td>1010.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>-7.276121e+17</td>\n",
       "      <td>46562.000000</td>\n",
       "      <td>5499.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1.831159e+07</td>\n",
       "      <td>1478.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>3.397453e+18</td>\n",
       "      <td>77564.000000</td>\n",
       "      <td>5812.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>2.438689e+07</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>9.222877e+18</td>\n",
       "      <td>99829.000000</td>\n",
       "      <td>9402.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0           User           Card           Year  \\\n",
       "count  2.733280e+05  273328.000000  273328.000000  273328.000000   \n",
       "mean   1.222290e+07    1003.363318       1.375757    2011.963301   \n",
       "std    7.027960e+06     568.436459       1.420734       5.054697   \n",
       "min    4.310000e+02       0.000000       0.000000    1991.000000   \n",
       "25%    6.131172e+06     513.000000       0.000000    2008.000000   \n",
       "50%    1.225985e+07    1010.000000       1.000000    2013.000000   \n",
       "75%    1.831159e+07    1478.000000       2.000000    2016.000000   \n",
       "max    2.438689e+07    1999.000000       8.000000    2020.000000   \n",
       "\n",
       "               Month            Day  Merchant Name            Zip  \\\n",
       "count  273328.000000  273328.000000   2.733280e+05  220082.000000   \n",
       "mean        6.546673      15.702548  -4.155375e+17   50874.909329   \n",
       "std         3.469510       8.815986   4.782507e+18   29421.721774   \n",
       "min         1.000000       1.000000  -9.222899e+18     501.000000   \n",
       "25%         4.000000       8.000000  -4.500543e+18   28351.000000   \n",
       "50%         7.000000      16.000000  -7.276121e+17   46562.000000   \n",
       "75%        10.000000      23.000000   3.397453e+18   77564.000000   \n",
       "max        12.000000      31.000000   9.222877e+18   99829.000000   \n",
       "\n",
       "                 MCC  \n",
       "count  273328.000000  \n",
       "mean     5526.551224  \n",
       "std       898.962999  \n",
       "min      1711.000000  \n",
       "25%      5300.000000  \n",
       "50%      5499.000000  \n",
       "75%      5812.000000  \n",
       "max      9402.000000  "
      ]
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     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cards Statistics\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>CARD INDEX</th>\n",
       "      <th>Card Number</th>\n",
       "      <th>CVV</th>\n",
       "      <th>Cards Issued</th>\n",
       "      <th>Year PIN last Changed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>6146.000000</td>\n",
       "      <td>6146.000000</td>\n",
       "      <td>6.146000e+03</td>\n",
       "      <td>6146.000000</td>\n",
       "      <td>6146.000000</td>\n",
       "      <td>6146.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>1003.477058</td>\n",
       "      <td>1.472502</td>\n",
       "      <td>4.820426e+15</td>\n",
       "      <td>506.220794</td>\n",
       "      <td>1.503091</td>\n",
       "      <td>2013.436707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>571.724745</td>\n",
       "      <td>1.463294</td>\n",
       "      <td>1.328582e+15</td>\n",
       "      <td>289.431123</td>\n",
       "      <td>0.519191</td>\n",
       "      <td>4.270699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.001055e+14</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>508.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.486365e+15</td>\n",
       "      <td>257.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>1004.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.108957e+15</td>\n",
       "      <td>516.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1493.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.585237e+15</td>\n",
       "      <td>756.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.997197e+15</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              User   CARD INDEX   Card Number          CVV  Cards Issued  \\\n",
       "count  6146.000000  6146.000000  6.146000e+03  6146.000000   6146.000000   \n",
       "mean   1003.477058     1.472502  4.820426e+15   506.220794      1.503091   \n",
       "std     571.724745     1.463294  1.328582e+15   289.431123      0.519191   \n",
       "min       0.000000     0.000000  3.001055e+14     0.000000      1.000000   \n",
       "25%     508.250000     0.000000  4.486365e+15   257.000000      1.000000   \n",
       "50%    1004.000000     1.000000  5.108957e+15   516.500000      1.000000   \n",
       "75%    1493.750000     2.000000  5.585237e+15   756.000000      2.000000   \n",
       "max    1999.000000     8.000000  6.997197e+15   999.000000      3.000000   \n",
       "\n",
       "       Year PIN last Changed  \n",
       "count            6146.000000  \n",
       "mean             2013.436707  \n",
       "std                 4.270699  \n",
       "min              2002.000000  \n",
       "25%              2010.000000  \n",
       "50%              2013.000000  \n",
       "75%              2017.000000  \n",
       "max              2020.000000  "
      ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "User Statistics\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Current Age</th>\n",
       "      <th>Retirement Age</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Birth Month</th>\n",
       "      <th>Apartment</th>\n",
       "      <th>Zipcode</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>FICO Score</th>\n",
       "      <th>Num Credit Cards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>528.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>45.391500</td>\n",
       "      <td>66.237500</td>\n",
       "      <td>1973.803000</td>\n",
       "      <td>6.439000</td>\n",
       "      <td>693.547348</td>\n",
       "      <td>50535.412000</td>\n",
       "      <td>37.389225</td>\n",
       "      <td>-91.554765</td>\n",
       "      <td>709.734500</td>\n",
       "      <td>3.073000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>18.414092</td>\n",
       "      <td>3.628867</td>\n",
       "      <td>18.421234</td>\n",
       "      <td>3.565338</td>\n",
       "      <td>1897.157861</td>\n",
       "      <td>29359.754742</td>\n",
       "      <td>5.114324</td>\n",
       "      <td>16.283293</td>\n",
       "      <td>67.221949</td>\n",
       "      <td>1.637379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1918.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1060.000000</td>\n",
       "      <td>20.880000</td>\n",
       "      <td>-159.410000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>1961.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>28214.250000</td>\n",
       "      <td>33.837500</td>\n",
       "      <td>-97.395000</td>\n",
       "      <td>681.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>46305.500000</td>\n",
       "      <td>38.250000</td>\n",
       "      <td>-86.440000</td>\n",
       "      <td>711.500000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>1989.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>95.250000</td>\n",
       "      <td>77086.250000</td>\n",
       "      <td>41.200000</td>\n",
       "      <td>-80.130000</td>\n",
       "      <td>753.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>9940.000000</td>\n",
       "      <td>99508.000000</td>\n",
       "      <td>61.200000</td>\n",
       "      <td>-68.670000</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Current Age  Retirement Age   Birth Year  Birth Month    Apartment  \\\n",
       "count  2000.000000     2000.000000  2000.000000  2000.000000   528.000000   \n",
       "mean     45.391500       66.237500  1973.803000     6.439000   693.547348   \n",
       "std      18.414092        3.628867    18.421234     3.565338  1897.157861   \n",
       "min      18.000000       50.000000  1918.000000     1.000000     1.000000   \n",
       "25%      30.000000       65.000000  1961.000000     3.000000     5.000000   \n",
       "50%      44.000000       66.000000  1975.000000     7.000000    10.000000   \n",
       "75%      58.000000       68.000000  1989.000000    10.000000    95.250000   \n",
       "max     101.000000       79.000000  2002.000000    12.000000  9940.000000   \n",
       "\n",
       "            Zipcode     Latitude    Longitude   FICO Score  Num Credit Cards  \n",
       "count   2000.000000  2000.000000  2000.000000  2000.000000       2000.000000  \n",
       "mean   50535.412000    37.389225   -91.554765   709.734500          3.073000  \n",
       "std    29359.754742     5.114324    16.283293    67.221949          1.637379  \n",
       "min     1060.000000    20.880000  -159.410000   480.000000          1.000000  \n",
       "25%    28214.250000    33.837500   -97.395000   681.000000          2.000000  \n",
       "50%    46305.500000    38.250000   -86.440000   711.500000          3.000000  \n",
       "75%    77086.250000    41.200000   -80.130000   753.000000          4.000000  \n",
       "max    99508.000000    61.200000   -68.670000   850.000000          9.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#path defined from which csv file is read\n",
    "path=\"small_data.csv\"\n",
    "data = pd.read_csv(path)\n",
    "\n",
    "path_cards=\"sd254_cards.csv\"\n",
    "data_cards = pd.read_csv(path_cards)\n",
    "\n",
    "path_users=\"sd254_users.csv\"\n",
    "data_users = pd.read_csv(path_users)\n",
    "#One-hot encoding for categorical feature 'Sex'\n",
    "#data = pd.get_dummies(data, columns = ['Sex'])\n",
    "\n",
    "#Displayed datatypes and null/non-null values\n",
    "print(\"Info on transactions\")\n",
    "print(data.info())\n",
    "\n",
    "print()\n",
    "print(\"Info on cards\")\n",
    "print(data_cards.info())\n",
    "\n",
    "print()\n",
    "print(\"Info on users\")\n",
    "print(data_users.info())\n",
    "#Displayed feature statistics\n",
    "print()\n",
    "print(\"Transactions Statistics\")\n",
    "display(data.describe())\n",
    "\n",
    "print()\n",
    "print(\"Cards Statistics\")\n",
    "display(data_cards.describe())\n",
    "\n",
    "print()\n",
    "print(\"User Statistics\")\n",
    "display(data_users.describe())\n",
    "\n",
    "\n",
    "#Labeled all features and target\n",
    "#data = data[['Sex_F', 'Sex_I', 'Sex_M', 'Length', 'Diameter', 'Height', 'Whole Weight', 'Shucked Weight', 'Viscera Weight', 'Shell Weight', 'Rings']]\n",
    "\n",
    "#Scaled data - mean = 0, var = 1\n",
    "#scaler = StandardScaler()\n",
    "#data[['Length', 'Diameter', 'Height', 'Whole Weight', 'Shucked Weight', 'Viscera Weight', 'Shell Weight']] = scaler.fit_transform(data[['Length', 'Diameter', 'Height', 'Whole Weight', 'Shucked Weight', 'Viscera Weight', 'Shell Weight']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "El-8TcX4W4_T",
    "outputId": "c007e132-b57d-4cdf-9a3f-9d5869365113"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>User</th>\n",
       "      <th>Card</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Use Chip</th>\n",
       "      <th>Merchant Name</th>\n",
       "      <th>Merchant City</th>\n",
       "      <th>Merchant State</th>\n",
       "      <th>Zip</th>\n",
       "      <th>MCC</th>\n",
       "      <th>Errors?</th>\n",
       "      <th>Is Fraud?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>431</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>20:24</td>\n",
       "      <td>$57.75</td>\n",
       "      <td>Swipe Transaction</td>\n",
       "      <td>-7091578704632084081</td>\n",
       "      <td>Fontanelle</td>\n",
       "      <td>IA</td>\n",
       "      <td>50846.0</td>\n",
       "      <td>5812</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>639</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>07:05</td>\n",
       "      <td>$30.89</td>\n",
       "      <td>Swipe Transaction</td>\n",
       "      <td>-727612092139916043</td>\n",
       "      <td>Monterey Park</td>\n",
       "      <td>CA</td>\n",
       "      <td>91754.0</td>\n",
       "      <td>5411</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>656</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>20:10</td>\n",
       "      <td>$71.35</td>\n",
       "      <td>Swipe Transaction</td>\n",
       "      <td>-5023497618971072366</td>\n",
       "      <td>Mira Loma</td>\n",
       "      <td>CA</td>\n",
       "      <td>91752.0</td>\n",
       "      <td>5812</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>720</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>20:16</td>\n",
       "      <td>$76.22</td>\n",
       "      <td>Swipe Transaction</td>\n",
       "      <td>-4500542936415012428</td>\n",
       "      <td>La Verne</td>\n",
       "      <td>CA</td>\n",
       "      <td>91750.0</td>\n",
       "      <td>5814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>870</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>06:22</td>\n",
       "      <td>$144.61</td>\n",
       "      <td>Swipe Transaction</td>\n",
       "      <td>-727612092139916043</td>\n",
       "      <td>Monterey Park</td>\n",
       "      <td>CA</td>\n",
       "      <td>91754.0</td>\n",
       "      <td>5411</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>273323</td>\n",
       "      <td>24386349</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>09:51</td>\n",
       "      <td>$182.00</td>\n",
       "      <td>Chip Transaction</td>\n",
       "      <td>1270471339275628505</td>\n",
       "      <td>Waianae</td>\n",
       "      <td>HI</td>\n",
       "      <td>96792.0</td>\n",
       "      <td>3730</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>273324</td>\n",
       "      <td>24386576</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>07:42</td>\n",
       "      <td>$26.94</td>\n",
       "      <td>Online Transaction</td>\n",
       "      <td>-6160036380778658394</td>\n",
       "      <td>ONLINE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4121</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>273325</td>\n",
       "      <td>24386697</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>11:50</td>\n",
       "      <td>$82.74</td>\n",
       "      <td>Chip Transaction</td>\n",
       "      <td>2058452562437950274</td>\n",
       "      <td>Russellville</td>\n",
       "      <td>AL</td>\n",
       "      <td>35653.0</td>\n",
       "      <td>7538</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>273326</td>\n",
       "      <td>24386863</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>15:06</td>\n",
       "      <td>$7.19</td>\n",
       "      <td>Chip Transaction</td>\n",
       "      <td>1641456720718814353</td>\n",
       "      <td>Merrimack</td>\n",
       "      <td>NH</td>\n",
       "      <td>3054.0</td>\n",
       "      <td>5411</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>273327</td>\n",
       "      <td>24386894</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>22:18</td>\n",
       "      <td>$63.43</td>\n",
       "      <td>Chip Transaction</td>\n",
       "      <td>-5162038175624867091</td>\n",
       "      <td>Merrimack</td>\n",
       "      <td>NH</td>\n",
       "      <td>3054.0</td>\n",
       "      <td>5541</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>273328 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  User  Card  Year  Month  Day   Time   Amount  \\\n",
       "0              431     0     0  2003      3   10  20:24   $57.75   \n",
       "1              639     0     0  2003      8   18  07:05   $30.89   \n",
       "2              656     0     0  2003      9    5  20:10   $71.35   \n",
       "3              720     0     0  2003     12   15  20:16   $76.22   \n",
       "4              870     0     0  2004      6   15  06:22  $144.61   \n",
       "...            ...   ...   ...   ...    ...  ...    ...      ...   \n",
       "273323    24386349  1999     1  2019     10    8  09:51  $182.00   \n",
       "273324    24386576  1999     1  2019     12    2  07:42   $26.94   \n",
       "273325    24386697  1999     1  2019     12   24  11:50   $82.74   \n",
       "273326    24386863  1999     1  2020      2   21  15:06    $7.19   \n",
       "273327    24386894  1999     1  2020      2   27  22:18   $63.43   \n",
       "\n",
       "                  Use Chip        Merchant Name  Merchant City Merchant State  \\\n",
       "0        Swipe Transaction -7091578704632084081     Fontanelle             IA   \n",
       "1        Swipe Transaction  -727612092139916043  Monterey Park             CA   \n",
       "2        Swipe Transaction -5023497618971072366      Mira Loma             CA   \n",
       "3        Swipe Transaction -4500542936415012428       La Verne             CA   \n",
       "4        Swipe Transaction  -727612092139916043  Monterey Park             CA   \n",
       "...                    ...                  ...            ...            ...   \n",
       "273323    Chip Transaction  1270471339275628505        Waianae             HI   \n",
       "273324  Online Transaction -6160036380778658394         ONLINE            NaN   \n",
       "273325    Chip Transaction  2058452562437950274   Russellville             AL   \n",
       "273326    Chip Transaction  1641456720718814353      Merrimack             NH   \n",
       "273327    Chip Transaction -5162038175624867091      Merrimack             NH   \n",
       "\n",
       "            Zip   MCC Errors? Is Fraud?  \n",
       "0       50846.0  5812     NaN        No  \n",
       "1       91754.0  5411     NaN        No  \n",
       "2       91752.0  5812     NaN        No  \n",
       "3       91750.0  5814     NaN        No  \n",
       "4       91754.0  5411     NaN        No  \n",
       "...         ...   ...     ...       ...  \n",
       "273323  96792.0  3730     NaN        No  \n",
       "273324      NaN  4121     NaN        No  \n",
       "273325  35653.0  7538     NaN        No  \n",
       "273326   3054.0  5411     NaN        No  \n",
       "273327   3054.0  5541     NaN        No  \n",
       "\n",
       "[273328 rows x 16 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "data"
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  },
  {
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>CARD INDEX</th>\n",
       "      <th>Card Brand</th>\n",
       "      <th>Card Type</th>\n",
       "      <th>Card Number</th>\n",
       "      <th>Expires</th>\n",
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       "      <th>Credit Limit</th>\n",
       "      <th>Acct Open Date</th>\n",
       "      <th>Year PIN last Changed</th>\n",
       "      <th>Card on Dark Web</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Visa</td>\n",
       "      <td>Debit</td>\n",
       "      <td>4344676511950444</td>\n",
       "      <td>12/2022</td>\n",
       "      <td>623</td>\n",
       "      <td>YES</td>\n",
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       "      <td>$24295</td>\n",
       "      <td>09/2002</td>\n",
       "      <td>2008</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Visa</td>\n",
       "      <td>Debit</td>\n",
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       "      <td>12/2020</td>\n",
       "      <td>393</td>\n",
       "      <td>YES</td>\n",
       "      <td>2</td>\n",
       "      <td>$21968</td>\n",
       "      <td>04/2014</td>\n",
       "      <td>2014</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Visa</td>\n",
       "      <td>Debit</td>\n",
       "      <td>4582313478255491</td>\n",
       "      <td>02/2024</td>\n",
       "      <td>719</td>\n",
       "      <td>YES</td>\n",
       "      <td>2</td>\n",
       "      <td>$46414</td>\n",
       "      <td>07/2003</td>\n",
       "      <td>2004</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Visa</td>\n",
       "      <td>Credit</td>\n",
       "      <td>4879494103069057</td>\n",
       "      <td>08/2024</td>\n",
       "      <td>693</td>\n",
       "      <td>NO</td>\n",
       "      <td>1</td>\n",
       "      <td>$12400</td>\n",
       "      <td>01/2003</td>\n",
       "      <td>2012</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Mastercard</td>\n",
       "      <td>Debit (Prepaid)</td>\n",
       "      <td>5722874738736011</td>\n",
       "      <td>03/2009</td>\n",
       "      <td>75</td>\n",
       "      <td>YES</td>\n",
       "      <td>1</td>\n",
       "      <td>$28</td>\n",
       "      <td>09/2008</td>\n",
       "      <td>2009</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>6141</td>\n",
       "      <td>1997</td>\n",
       "      <td>1</td>\n",
       "      <td>Amex</td>\n",
       "      <td>Credit</td>\n",
       "      <td>300609782832003</td>\n",
       "      <td>01/2024</td>\n",
       "      <td>663</td>\n",
       "      <td>YES</td>\n",
       "      <td>1</td>\n",
       "      <td>$6900</td>\n",
       "      <td>11/2000</td>\n",
       "      <td>2013</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6142</td>\n",
       "      <td>1997</td>\n",
       "      <td>2</td>\n",
       "      <td>Visa</td>\n",
       "      <td>Credit</td>\n",
       "      <td>4718517475996018</td>\n",
       "      <td>01/2021</td>\n",
       "      <td>492</td>\n",
       "      <td>YES</td>\n",
       "      <td>2</td>\n",
       "      <td>$5700</td>\n",
       "      <td>04/2012</td>\n",
       "      <td>2012</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6143</td>\n",
       "      <td>1998</td>\n",
       "      <td>0</td>\n",
       "      <td>Mastercard</td>\n",
       "      <td>Credit</td>\n",
       "      <td>5929512204765914</td>\n",
       "      <td>08/2020</td>\n",
       "      <td>237</td>\n",
       "      <td>NO</td>\n",
       "      <td>2</td>\n",
       "      <td>$9200</td>\n",
       "      <td>02/2012</td>\n",
       "      <td>2012</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6144</td>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "      <td>Mastercard</td>\n",
       "      <td>Debit</td>\n",
       "      <td>5589768928167462</td>\n",
       "      <td>01/2020</td>\n",
       "      <td>630</td>\n",
       "      <td>YES</td>\n",
       "      <td>1</td>\n",
       "      <td>$28074</td>\n",
       "      <td>01/2020</td>\n",
       "      <td>2020</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6145</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>Visa</td>\n",
       "      <td>Credit</td>\n",
       "      <td>4994011318343994</td>\n",
       "      <td>12/2020</td>\n",
       "      <td>120</td>\n",
       "      <td>YES</td>\n",
       "      <td>1</td>\n",
       "      <td>$14400</td>\n",
       "      <td>05/2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6146 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      User  CARD INDEX  Card Brand        Card Type       Card Number  \\\n",
       "0        0           0        Visa            Debit  4344676511950444   \n",
       "1        0           1        Visa            Debit  4956965974959986   \n",
       "2        0           2        Visa            Debit  4582313478255491   \n",
       "3        0           3        Visa           Credit  4879494103069057   \n",
       "4        0           4  Mastercard  Debit (Prepaid)  5722874738736011   \n",
       "...    ...         ...         ...              ...               ...   \n",
       "6141  1997           1        Amex           Credit   300609782832003   \n",
       "6142  1997           2        Visa           Credit  4718517475996018   \n",
       "6143  1998           0  Mastercard           Credit  5929512204765914   \n",
       "6144  1999           0  Mastercard            Debit  5589768928167462   \n",
       "6145  1999           1        Visa           Credit  4994011318343994   \n",
       "\n",
       "      Expires  CVV Has Chip  Cards Issued Credit Limit Acct Open Date  \\\n",
       "0     12/2022  623      YES             2       $24295        09/2002   \n",
       "1     12/2020  393      YES             2       $21968        04/2014   \n",
       "2     02/2024  719      YES             2       $46414        07/2003   \n",
       "3     08/2024  693       NO             1       $12400        01/2003   \n",
       "4     03/2009   75      YES             1          $28        09/2008   \n",
       "...       ...  ...      ...           ...          ...            ...   \n",
       "6141  01/2024  663      YES             1        $6900        11/2000   \n",
       "6142  01/2021  492      YES             2        $5700        04/2012   \n",
       "6143  08/2020  237       NO             2        $9200        02/2012   \n",
       "6144  01/2020  630      YES             1       $28074        01/2020   \n",
       "6145  12/2020  120      YES             1       $14400        05/2017   \n",
       "\n",
       "      Year PIN last Changed Card on Dark Web  \n",
       "0                      2008               No  \n",
       "1                      2014               No  \n",
       "2                      2004               No  \n",
       "3                      2012               No  \n",
       "4                      2009               No  \n",
       "...                     ...              ...  \n",
       "6141                   2013               No  \n",
       "6142                   2012               No  \n",
       "6143                   2012               No  \n",
       "6144                   2020               No  \n",
       "6145                   2017               No  \n",
       "\n",
       "[6146 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 538
    },
    "id": "BxHvsDI4W8PA",
    "outputId": "e40221a7-6288-4e4c-f994-971fccaedd48"
   },
   "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>Person</th>\n",
       "      <th>Current Age</th>\n",
       "      <th>Retirement Age</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Birth Month</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Address</th>\n",
       "      <th>Apartment</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Zipcode</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Per Capita Income - Zipcode</th>\n",
       "      <th>Yearly Income - Person</th>\n",
       "      <th>Total Debt</th>\n",
       "      <th>FICO Score</th>\n",
       "      <th>Num Credit Cards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Hazel Robinson</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>1966</td>\n",
       "      <td>11</td>\n",
       "      <td>Female</td>\n",
       "      <td>462 Rose Lane</td>\n",
       "      <td>NaN</td>\n",
       "      <td>La Verne</td>\n",
       "      <td>CA</td>\n",
       "      <td>91750</td>\n",
       "      <td>34.15</td>\n",
       "      <td>-117.76</td>\n",
       "      <td>$29278</td>\n",
       "      <td>$59696</td>\n",
       "      <td>$127613</td>\n",
       "      <td>787</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Sasha Sadr</td>\n",
       "      <td>53</td>\n",
       "      <td>68</td>\n",
       "      <td>1966</td>\n",
       "      <td>12</td>\n",
       "      <td>Female</td>\n",
       "      <td>3606 Federal Boulevard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Little Neck</td>\n",
       "      <td>NY</td>\n",
       "      <td>11363</td>\n",
       "      <td>40.76</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>$37891</td>\n",
       "      <td>$77254</td>\n",
       "      <td>$191349</td>\n",
       "      <td>701</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Saanvi Lee</td>\n",
       "      <td>81</td>\n",
       "      <td>67</td>\n",
       "      <td>1938</td>\n",
       "      <td>11</td>\n",
       "      <td>Female</td>\n",
       "      <td>766 Third Drive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>West Covina</td>\n",
       "      <td>CA</td>\n",
       "      <td>91792</td>\n",
       "      <td>34.02</td>\n",
       "      <td>-117.89</td>\n",
       "      <td>$22681</td>\n",
       "      <td>$33483</td>\n",
       "      <td>$196</td>\n",
       "      <td>698</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Everlee Clark</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>1957</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>3 Madison Street</td>\n",
       "      <td>NaN</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>10069</td>\n",
       "      <td>40.71</td>\n",
       "      <td>-73.99</td>\n",
       "      <td>$163145</td>\n",
       "      <td>$249925</td>\n",
       "      <td>$202328</td>\n",
       "      <td>722</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Kyle Peterson</td>\n",
       "      <td>43</td>\n",
       "      <td>70</td>\n",
       "      <td>1976</td>\n",
       "      <td>9</td>\n",
       "      <td>Male</td>\n",
       "      <td>9620 Valley Stream Drive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>94117</td>\n",
       "      <td>37.76</td>\n",
       "      <td>-122.44</td>\n",
       "      <td>$53797</td>\n",
       "      <td>$109687</td>\n",
       "      <td>$183855</td>\n",
       "      <td>675</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>1995</td>\n",
       "      <td>Jose Faraday</td>\n",
       "      <td>32</td>\n",
       "      <td>70</td>\n",
       "      <td>1987</td>\n",
       "      <td>7</td>\n",
       "      <td>Male</td>\n",
       "      <td>6577 Lexington Lane</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Freeport</td>\n",
       "      <td>NY</td>\n",
       "      <td>11520</td>\n",
       "      <td>40.65</td>\n",
       "      <td>-73.58</td>\n",
       "      <td>$23550</td>\n",
       "      <td>$48010</td>\n",
       "      <td>$87837</td>\n",
       "      <td>703</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1996</td>\n",
       "      <td>Ximena Richardson</td>\n",
       "      <td>62</td>\n",
       "      <td>65</td>\n",
       "      <td>1957</td>\n",
       "      <td>11</td>\n",
       "      <td>Female</td>\n",
       "      <td>2 Elm Drive</td>\n",
       "      <td>955.0</td>\n",
       "      <td>Independence</td>\n",
       "      <td>KY</td>\n",
       "      <td>41051</td>\n",
       "      <td>38.95</td>\n",
       "      <td>-84.54</td>\n",
       "      <td>$24218</td>\n",
       "      <td>$49378</td>\n",
       "      <td>$104480</td>\n",
       "      <td>740</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997</td>\n",
       "      <td>Annika Russell</td>\n",
       "      <td>47</td>\n",
       "      <td>67</td>\n",
       "      <td>1973</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>276 Fifth Boulevard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>NJ</td>\n",
       "      <td>7201</td>\n",
       "      <td>40.66</td>\n",
       "      <td>-74.19</td>\n",
       "      <td>$15175</td>\n",
       "      <td>$30942</td>\n",
       "      <td>$71066</td>\n",
       "      <td>779</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1998</td>\n",
       "      <td>Juelz Roman</td>\n",
       "      <td>66</td>\n",
       "      <td>60</td>\n",
       "      <td>1954</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>259 Valley Boulevard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Camp Hill</td>\n",
       "      <td>PA</td>\n",
       "      <td>17011</td>\n",
       "      <td>40.24</td>\n",
       "      <td>-76.92</td>\n",
       "      <td>$25336</td>\n",
       "      <td>$54654</td>\n",
       "      <td>$27241</td>\n",
       "      <td>618</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1999</td>\n",
       "      <td>Kenia Harris</td>\n",
       "      <td>21</td>\n",
       "      <td>60</td>\n",
       "      <td>1998</td>\n",
       "      <td>11</td>\n",
       "      <td>Female</td>\n",
       "      <td>472 Ocean View Street</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Merrimack</td>\n",
       "      <td>NH</td>\n",
       "      <td>3054</td>\n",
       "      <td>42.86</td>\n",
       "      <td>-71.48</td>\n",
       "      <td>$32325</td>\n",
       "      <td>$65909</td>\n",
       "      <td>$181261</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Person  Current Age  Retirement Age  Birth Year  Birth Month  \\\n",
       "0        Hazel Robinson           53              66        1966           11   \n",
       "1            Sasha Sadr           53              68        1966           12   \n",
       "2            Saanvi Lee           81              67        1938           11   \n",
       "3         Everlee Clark           63              63        1957            1   \n",
       "4         Kyle Peterson           43              70        1976            9   \n",
       "...                 ...          ...             ...         ...          ...   \n",
       "1995       Jose Faraday           32              70        1987            7   \n",
       "1996  Ximena Richardson           62              65        1957           11   \n",
       "1997     Annika Russell           47              67        1973            1   \n",
       "1998        Juelz Roman           66              60        1954            2   \n",
       "1999       Kenia Harris           21              60        1998           11   \n",
       "\n",
       "      Gender                   Address  Apartment           City State  \\\n",
       "0     Female             462 Rose Lane        NaN       La Verne    CA   \n",
       "1     Female    3606 Federal Boulevard        NaN    Little Neck    NY   \n",
       "2     Female           766 Third Drive        NaN    West Covina    CA   \n",
       "3     Female          3 Madison Street        NaN       New York    NY   \n",
       "4       Male  9620 Valley Stream Drive        NaN  San Francisco    CA   \n",
       "...      ...                       ...        ...            ...   ...   \n",
       "1995    Male       6577 Lexington Lane        9.0       Freeport    NY   \n",
       "1996  Female               2 Elm Drive      955.0   Independence    KY   \n",
       "1997  Female       276 Fifth Boulevard        NaN      Elizabeth    NJ   \n",
       "1998    Male      259 Valley Boulevard        NaN      Camp Hill    PA   \n",
       "1999  Female     472 Ocean View Street        NaN      Merrimack    NH   \n",
       "\n",
       "      Zipcode  Latitude  Longitude Per Capita Income - Zipcode  \\\n",
       "0       91750     34.15    -117.76                      $29278   \n",
       "1       11363     40.76     -73.74                      $37891   \n",
       "2       91792     34.02    -117.89                      $22681   \n",
       "3       10069     40.71     -73.99                     $163145   \n",
       "4       94117     37.76    -122.44                      $53797   \n",
       "...       ...       ...        ...                         ...   \n",
       "1995    11520     40.65     -73.58                      $23550   \n",
       "1996    41051     38.95     -84.54                      $24218   \n",
       "1997     7201     40.66     -74.19                      $15175   \n",
       "1998    17011     40.24     -76.92                      $25336   \n",
       "1999     3054     42.86     -71.48                      $32325   \n",
       "\n",
       "     Yearly Income - Person Total Debt  FICO Score  Num Credit Cards  \n",
       "0                    $59696    $127613         787                 5  \n",
       "1                    $77254    $191349         701                 5  \n",
       "2                    $33483       $196         698                 5  \n",
       "3                   $249925    $202328         722                 4  \n",
       "4                   $109687    $183855         675                 1  \n",
       "...                     ...        ...         ...               ...  \n",
       "1995                 $48010     $87837         703                 3  \n",
       "1996                 $49378    $104480         740                 4  \n",
       "1997                 $30942     $71066         779                 3  \n",
       "1998                 $54654     $27241         618                 1  \n",
       "1999                 $65909    $181261         673                 2  \n",
       "\n",
       "[2000 rows x 18 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
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    },
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    "outputId": "985b6a2b-0093-455b-bdf8-73be11337c9b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No     243571\n",
      "Yes     29757\n",
      "Name: Is Fraud?, dtype: int64\n",
      "\n",
      "Insufficient Balance                     2767\n",
      "Bad PIN                                   876\n",
      "Technical Glitch                          544\n",
      "Bad CVV                                   396\n",
      "Bad Card Number                           223\n",
      "Bad Expiration                            209\n",
      "Bad Zipcode                                21\n",
      "Bad PIN,Insufficient Balance               11\n",
      "Bad CVV,Insufficient Balance                4\n",
      "Insufficient Balance,Technical Glitch       4\n",
      "Bad PIN,Technical Glitch                    4\n",
      "Bad Expiration,Insufficient Balance         3\n",
      "Bad Card Number,Insufficient Balance        3\n",
      "Bad Expiration,Technical Glitch             3\n",
      "Bad Expiration,Bad CVV                      2\n",
      "Bad CVV,Technical Glitch                    2\n",
      "Name: Errors?, dtype: int64\n",
      "\n",
      "Mastercard    3209\n",
      "Visa          2326\n",
      "Amex           402\n",
      "Discover       209\n",
      "Name: Card Brand, dtype: int64\n",
      "\n",
      "Debit              3511\n",
      "Credit             2057\n",
      "Debit (Prepaid)     578\n",
      "Name: Card Type, dtype: int64\n",
      "\n",
      "No    6146\n",
      "Name: Card on Dark Web, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data['Is Fraud?'].value_counts())\n",
    "print()\n",
    "print(data[\"Errors?\"].value_counts())\n",
    "print()\n",
    "\n",
    "print(data_cards[\"Card Brand\"].value_counts())\n",
    "print()\n",
    "print(data_cards[\"Card Type\"].value_counts())\n",
    "print()\n",
    "print(data_cards[\"Card on Dark Web\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "qaSdJ6Th8xhe"
   },
   "outputs": [],
   "source": [
    "##Dropping features whihc do not contribute much\n",
    "data = data.drop(['Unnamed: 0', 'Month', 'Day', 'Time', 'Merchant Name', 'Merchant City', 'Merchant State', 'Zip'], axis = 1)\n",
    "data_cards = data_cards.drop(['Card Number', 'Expires', 'CVV', 'Has Chip', 'Cards Issued', 'Acct Open Date', 'Card on Dark Web'], axis = 1)\n",
    "data_users = data_users.drop(['Person', 'Retirement Age', 'Birth Month', 'Birth Year',  'Address', 'Apartment', 'City', 'State','Zipcode' ,'Latitude', 'Longitude', 'Per Capita Income - Zipcode', 'Num Credit Cards'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "UopcjKXmwgBE"
   },
   "outputs": [],
   "source": [
    "data['Amount'] = data['Amount'].apply(lambda x: x[1:])\n",
    "data['Amount'] = pd.to_numeric(data['Amount'])\n",
    "\n",
    "data_cards['Credit Limit'] = data_cards['Credit Limit'].apply(lambda x: x[1:])\n",
    "data_cards['Credit Limit'] = pd.to_numeric(data_cards['Credit Limit'])\n",
    "\n",
    "data_users['Yearly Income - Person'] = data_users['Yearly Income - Person'].apply(lambda x: x[1:])\n",
    "data_users['Yearly Income - Person'] = pd.to_numeric(data_users['Yearly Income - Person'])\n",
    "data_users['Total Debt'] = data_users['Total Debt'].apply(lambda x: x[1:])\n",
    "data_users['Total Debt'] = pd.to_numeric(data_users['Total Debt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "E5yVKCQkwla0"
   },
   "outputs": [],
   "source": [
    "\n",
    "#Label encoding string values\n",
    "encoder = LabelEncoder()\n",
    "\n",
    "data = data.fillna('')\n",
    "data['Errors?']= encoder.fit_transform(data['Errors?'])\n",
    "data['Use Chip']= encoder.fit_transform(data['Use Chip'])\n",
    "data['Is Fraud?']= encoder.fit_transform(data['Is Fraud?'])\n",
    "# Filling NaN values\n",
    "data_cards = data_cards.fillna('')\n",
    "data_cards['Card Brand']= encoder.fit_transform(data_cards['Card Brand'])\n",
    "data_cards['Card Type']= encoder.fit_transform(data_cards['Card Type'])\n",
    "\n",
    "data_users = data_users.fillna('')\n",
    "data_users['Gender']= encoder.fit_transform(data_users['Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "nGtwiB6RZJoM"
   },
   "outputs": [],
   "source": [
    "data_users['User'] = data_users.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "SdparDVjYs4a",
    "outputId": "7dd95b65-4a0e-461d-c3e3-598a7997ce42"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
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       "      <td>63.43</td>\n",
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       "</table>\n",
       "<p>273328 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        User  Card  Year  Amount  Use Chip   MCC  Errors?  Is Fraud?  \\\n",
       "0          0     0  2003   57.75         2  5812        0          0   \n",
       "1          0     0  2003   30.89         2  5411        0          0   \n",
       "2          0     0  2003   71.35         2  5812        0          0   \n",
       "3          0     0  2003   76.22         2  5814        0          0   \n",
       "4          0     0  2004  144.61         2  5411        0          0   \n",
       "...      ...   ...   ...     ...       ...   ...      ...        ...   \n",
       "273323  1999     1  2019  182.00         0  3730        0          0   \n",
       "273324  1999     1  2019   26.94         1  4121        0          0   \n",
       "273325  1999     1  2019   82.74         0  7538        0          0   \n",
       "273326  1999     1  2020    7.19         0  5411        0          0   \n",
       "273327  1999     1  2020   63.43         0  5541        0          0   \n",
       "\n",
       "        Card Brand  Card Type  Credit Limit  Year PIN last Changed  \\\n",
       "0                3          1         24295                   2008   \n",
       "1                3          1         24295                   2008   \n",
       "2                3          1         24295                   2008   \n",
       "3                3          1         24295                   2008   \n",
       "4                3          1         24295                   2008   \n",
       "...            ...        ...           ...                    ...   \n",
       "273323           3          0         14400                   2017   \n",
       "273324           3          0         14400                   2017   \n",
       "273325           3          0         14400                   2017   \n",
       "273326           3          0         14400                   2017   \n",
       "273327           3          0         14400                   2017   \n",
       "\n",
       "        Current Age  Gender  Yearly Income - Person  Total Debt  FICO Score  \n",
       "0                53       0                   59696      127613         787  \n",
       "1                53       0                   59696      127613         787  \n",
       "2                53       0                   59696      127613         787  \n",
       "3                53       0                   59696      127613         787  \n",
       "4                53       0                   59696      127613         787  \n",
       "...             ...     ...                     ...         ...         ...  \n",
       "273323           21       0                   65909      181261         673  \n",
       "273324           21       0                   65909      181261         673  \n",
       "273325           21       0                   65909      181261         673  \n",
       "273326           21       0                   65909      181261         673  \n",
       "273327           21       0                   65909      181261         673  \n",
       "\n",
       "[273328 rows x 17 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.join(data_cards.set_index(['User', 'CARD INDEX']), on=['User', 'Card'])\n",
    "data = data.join(data_users.set_index('User'), on='User')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "8niUnhHFZpY7"
   },
   "outputs": [],
   "source": [
    "data = data.sample(frac=1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "Dz-HIpuEaeip",
    "outputId": "677b04cc-01c9-451a-b333-5c7fe6ff6658"
   },
   "outputs": [
    {
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       "      <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",
       "      <td>273323</td>\n",
       "      <td>1191</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>21.37</td>\n",
       "      <td>2</td>\n",
       "      <td>5813</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6800</td>\n",
       "      <td>2002</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>36319</td>\n",
       "      <td>44737</td>\n",
       "      <td>730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>273324</td>\n",
       "      <td>1791</td>\n",
       "      <td>3</td>\n",
       "      <td>2003</td>\n",
       "      <td>-80.00</td>\n",
       "      <td>2</td>\n",
       "      <td>5499</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8021</td>\n",
       "      <td>2018</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>21397</td>\n",
       "      <td>54771</td>\n",
       "      <td>812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>273325</td>\n",
       "      <td>610</td>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>3.01</td>\n",
       "      <td>2</td>\n",
       "      <td>5541</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>12708</td>\n",
       "      <td>2008</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>31369</td>\n",
       "      <td>0</td>\n",
       "      <td>707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>273326</td>\n",
       "      <td>1888</td>\n",
       "      <td>0</td>\n",
       "      <td>2002</td>\n",
       "      <td>119.28</td>\n",
       "      <td>1</td>\n",
       "      <td>4900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25422</td>\n",
       "      <td>2008</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>109673</td>\n",
       "      <td>242379</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>273327</td>\n",
       "      <td>1092</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2</td>\n",
       "      <td>5499</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>19171</td>\n",
       "      <td>2013</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>44344</td>\n",
       "      <td>82822</td>\n",
       "      <td>744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>273328 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        User  Card  Year  Amount  Use Chip   MCC  Errors?  Is Fraud?  \\\n",
       "0        780     3  2016   71.00         0  5499        0          0   \n",
       "1        580     1  2014    9.96         2  5411        0          0   \n",
       "2       1919     2  2009   66.26         2  5541       14          0   \n",
       "3        777     5  1998   75.00         2  5499        0          0   \n",
       "4       1227     3  2012  -72.00         2  5499        0          0   \n",
       "...      ...   ...   ...     ...       ...   ...      ...        ...   \n",
       "273323  1191     1  2014   21.37         2  5813        0          0   \n",
       "273324  1791     3  2003  -80.00         2  5499        0          0   \n",
       "273325   610     2  2012    3.01         2  5541        0          0   \n",
       "273326  1888     0  2002  119.28         1  4900        0          0   \n",
       "273327  1092     2  2016    1.73         2  5499        0          0   \n",
       "\n",
       "        Card Brand  Card Type  Credit Limit  Year PIN last Changed  \\\n",
       "0                2          1         39606                   2008   \n",
       "1                3          0         11400                   2009   \n",
       "2                0          0          6100                   2019   \n",
       "3                3          1          3134                   2011   \n",
       "4                2          1          4130                   2015   \n",
       "...            ...        ...           ...                    ...   \n",
       "273323           0          0          6800                   2002   \n",
       "273324           2          1          8021                   2018   \n",
       "273325           2          1         12708                   2008   \n",
       "273326           2          1         25422                   2008   \n",
       "273327           3          1         19171                   2013   \n",
       "\n",
       "        Current Age  Gender  Yearly Income - Person  Total Debt  FICO Score  \n",
       "0                54       1                   53986       58381         748  \n",
       "1                67       0                   34954        5942         729  \n",
       "2                56       1                   26510       53023         696  \n",
       "3                72       1                   65055        1676         736  \n",
       "4                44       0                   84365      113529         796  \n",
       "...             ...     ...                     ...         ...         ...  \n",
       "273323           47       1                   36319       44737         730  \n",
       "273324           61       1                   21397       54771         812  \n",
       "273325           53       1                   31369           0         707  \n",
       "273326           51       1                  109673      242379         505  \n",
       "273327           39       0                   44344       82822         744  \n",
       "\n",
       "[273328 rows x 17 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "ab-szNB-N0oZ",
    "outputId": "cc5beac2-5f79-4ca2-ecd3-70bcd9f2e276"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2880x2880 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Box plots and distribution curves of all features\n",
    "\n",
    "fig, ax = plt.subplots(16, 2, figsize = (40, 40))\n",
    "sns.boxplot(x= data[\"User\"], ax = ax[0,0])\n",
    "sns.distplot(data['User'], ax = ax[0,1])\n",
    "sns.boxplot(x= data[\"Card\"], ax = ax[1,0])\n",
    "sns.distplot(data['Card'], ax = ax[1,1])\n",
    "sns.boxplot(x= data[\"Year\"], ax = ax[2,0])\n",
    "sns.distplot(data['Year'], ax = ax[2,1])\n",
    "sns.boxplot(x= data[\"Amount\"], ax = ax[3,0])\n",
    "sns.distplot(data['Amount'], ax = ax[3,1])\n",
    "sns.boxplot(x= data[\"Use Chip\"], ax = ax[4,0])\n",
    "sns.distplot(data['Use Chip'], ax = ax[4,1])\n",
    "sns.boxplot(x= data[\"MCC\"], ax = ax[5,0])\n",
    "sns.distplot(data['MCC'], ax = ax[5,1])\n",
    "sns.boxplot(x= data[\"Errors?\"], ax = ax[6,0])\n",
    "sns.distplot(data['Errors?'], ax = ax[6,1])\n",
    "sns.boxplot(x= data[\"Card Brand\"], ax = ax[7,0])\n",
    "sns.distplot(data['Card Brand'], ax = ax[7,1])\n",
    "sns.boxplot(x= data[\"Card Type\"], ax = ax[8,0])\n",
    "sns.distplot(data['Card Type'], ax = ax[8,1])\n",
    "sns.boxplot(x= data[\"Credit Limit\"], ax = ax[9,0])\n",
    "sns.distplot(data['Credit Limit'], ax = ax[9,1])\n",
    "sns.boxplot(x= data[\"Year PIN last Changed\"], ax = ax[10,0])\n",
    "sns.distplot(data['Year PIN last Changed'], ax = ax[10,1])\n",
    "sns.boxplot(x= data[\"Current Age\"], ax = ax[11,0])\n",
    "sns.distplot(data['Current Age'], ax = ax[11,1])\n",
    "sns.boxplot(x= data[\"Gender\"], ax = ax[12,0])\n",
    "sns.distplot(data['Gender'], ax = ax[12,1])\n",
    "sns.boxplot(x= data[\"Yearly Income - Person\"], ax = ax[13,0])\n",
    "sns.distplot(data['Yearly Income - Person'], ax = ax[13,1])\n",
    "sns.boxplot(x= data[\"Total Debt\"], ax = ax[14,0])\n",
    "sns.distplot(data['Total Debt'], ax = ax[14,1])\n",
    "sns.boxplot(x= data[\"FICO Score\"], ax = ax[15,0])\n",
    "sns.distplot(data['FICO Score'], ax = ax[15,1])\n",
    "\n",
    "plt.savefig('save_as_a_png.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "bvoixc-ReLIq",
    "outputId": "f408ddf5-7e2a-40e6-ebee-c0594d3902e8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Is Fraud?', ylabel='Density'>"
      ]
     },
     "execution_count": 16,
     "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": [
    "#Distribution curves of target\n",
    "\n",
    "sns.distplot(data['Is Fraud?'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 372
    },
    "id": "rMePifNDXtAA",
    "outputId": "3d3f276f-fa9a-43a4-fe5d-d54559449844"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heat map of features\n",
    "fig= plt.figure(figsize = (20, 10))\n",
    "corr = data.corr()\n",
    "dataplot = sb.heatmap(corr, cmap=\"YlGnBu\")\n",
    "data = data.iloc[:,:]\n",
    "plt.savefig('heat.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "gPZ5s1hmeelW",
    "outputId": "99464241-1b15-4660-cf21-b16fd71bdce9"
   },
   "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>User</th>\n",
       "      <th>Card</th>\n",
       "      <th>Year</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Use Chip</th>\n",
       "      <th>MCC</th>\n",
       "      <th>Errors?</th>\n",
       "      <th>Card Brand</th>\n",
       "      <th>Card Type</th>\n",
       "      <th>Credit Limit</th>\n",
       "      <th>Year PIN last Changed</th>\n",
       "      <th>Current Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Yearly Income - Person</th>\n",
       "      <th>Total Debt</th>\n",
       "      <th>FICO Score</th>\n",
       "      <th>Is Fraud?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>780</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>71.00</td>\n",
       "      <td>0</td>\n",
       "      <td>5499</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>39606</td>\n",
       "      <td>2008</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>53986</td>\n",
       "      <td>58381</td>\n",
       "      <td>748</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>580</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>9.96</td>\n",
       "      <td>2</td>\n",
       "      <td>5411</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11400</td>\n",
       "      <td>2009</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>34954</td>\n",
       "      <td>5942</td>\n",
       "      <td>729</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1919</td>\n",
       "      <td>2</td>\n",
       "      <td>2009</td>\n",
       "      <td>66.26</td>\n",
       "      <td>2</td>\n",
       "      <td>5541</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6100</td>\n",
       "      <td>2019</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>26510</td>\n",
       "      <td>53023</td>\n",
       "      <td>696</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>777</td>\n",
       "      <td>5</td>\n",
       "      <td>1998</td>\n",
       "      <td>75.00</td>\n",
       "      <td>2</td>\n",
       "      <td>5499</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3134</td>\n",
       "      <td>2011</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>65055</td>\n",
       "      <td>1676</td>\n",
       "      <td>736</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1227</td>\n",
       "      <td>3</td>\n",
       "      <td>2012</td>\n",
       "      <td>-72.00</td>\n",
       "      <td>2</td>\n",
       "      <td>5499</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4130</td>\n",
       "      <td>2015</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>84365</td>\n",
       "      <td>113529</td>\n",
       "      <td>796</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>273323</td>\n",
       "      <td>1191</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>21.37</td>\n",
       "      <td>2</td>\n",
       "      <td>5813</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6800</td>\n",
       "      <td>2002</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>36319</td>\n",
       "      <td>44737</td>\n",
       "      <td>730</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>273324</td>\n",
       "      <td>1791</td>\n",
       "      <td>3</td>\n",
       "      <td>2003</td>\n",
       "      <td>-80.00</td>\n",
       "      <td>2</td>\n",
       "      <td>5499</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8021</td>\n",
       "      <td>2018</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>21397</td>\n",
       "      <td>54771</td>\n",
       "      <td>812</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>273325</td>\n",
       "      <td>610</td>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>3.01</td>\n",
       "      <td>2</td>\n",
       "      <td>5541</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>12708</td>\n",
       "      <td>2008</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>31369</td>\n",
       "      <td>0</td>\n",
       "      <td>707</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>273326</td>\n",
       "      <td>1888</td>\n",
       "      <td>0</td>\n",
       "      <td>2002</td>\n",
       "      <td>119.28</td>\n",
       "      <td>1</td>\n",
       "      <td>4900</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>25422</td>\n",
       "      <td>2008</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>109673</td>\n",
       "      <td>242379</td>\n",
       "      <td>505</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>273327</td>\n",
       "      <td>1092</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2</td>\n",
       "      <td>5499</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>19171</td>\n",
       "      <td>2013</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>44344</td>\n",
       "      <td>82822</td>\n",
       "      <td>744</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>273328 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        User  Card  Year  Amount  Use Chip   MCC  Errors?  Card Brand  \\\n",
       "0        780     3  2016   71.00         0  5499        0           2   \n",
       "1        580     1  2014    9.96         2  5411        0           3   \n",
       "2       1919     2  2009   66.26         2  5541       14           0   \n",
       "3        777     5  1998   75.00         2  5499        0           3   \n",
       "4       1227     3  2012  -72.00         2  5499        0           2   \n",
       "...      ...   ...   ...     ...       ...   ...      ...         ...   \n",
       "273323  1191     1  2014   21.37         2  5813        0           0   \n",
       "273324  1791     3  2003  -80.00         2  5499        0           2   \n",
       "273325   610     2  2012    3.01         2  5541        0           2   \n",
       "273326  1888     0  2002  119.28         1  4900        0           2   \n",
       "273327  1092     2  2016    1.73         2  5499        0           3   \n",
       "\n",
       "        Card Type  Credit Limit  Year PIN last Changed  Current Age  Gender  \\\n",
       "0               1         39606                   2008           54       1   \n",
       "1               0         11400                   2009           67       0   \n",
       "2               0          6100                   2019           56       1   \n",
       "3               1          3134                   2011           72       1   \n",
       "4               1          4130                   2015           44       0   \n",
       "...           ...           ...                    ...          ...     ...   \n",
       "273323          0          6800                   2002           47       1   \n",
       "273324          1          8021                   2018           61       1   \n",
       "273325          1         12708                   2008           53       1   \n",
       "273326          1         25422                   2008           51       1   \n",
       "273327          1         19171                   2013           39       0   \n",
       "\n",
       "        Yearly Income - Person  Total Debt  FICO Score  Is Fraud?  \n",
       "0                        53986       58381         748          0  \n",
       "1                        34954        5942         729          0  \n",
       "2                        26510       53023         696          0  \n",
       "3                        65055        1676         736          0  \n",
       "4                        84365      113529         796          0  \n",
       "...                        ...         ...         ...        ...  \n",
       "273323                   36319       44737         730          0  \n",
       "273324                   21397       54771         812          0  \n",
       "273325                   31369           0         707          0  \n",
       "273326                  109673      242379         505          0  \n",
       "273327                   44344       82822         744          0  \n",
       "\n",
       "[273328 rows x 17 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[['User', 'Card', 'Year',\t'Amount',\t'Use Chip',\t'MCC',\t'Errors?',\t'Card Brand',\t'Card Type',\t'Credit Limit',\t'Year PIN last Changed',\t'Current Age',\t'Gender',\t'Yearly Income - Person',\t'Total Debt',\t'FICO Score', \t'Is Fraud?']]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "XUrWZPaQFjQ4"
   },
   "outputs": [],
   "source": [
    "#Splitting the dataset to train and test\n",
    "np_data = np.array(data)\n",
    "X = np_data\n",
    "Y = np_data\n",
    "X = X[:,0: (X.shape[1] - 1)]\n",
    "Y = Y[:, X.shape[1]]\n",
    "\n",
    "X = np.c_[np.ones(X.shape[0]), X]\n",
    "\n",
    "X_train = X[0: int(0.8*X.shape[0]), :]\n",
    "X_test = X[int(0.8*X.shape[0]):, :]\n",
    "Y = Y.reshape(Y.shape[0], 1)\n",
    "Y_train = Y[0:int(0.8*Y.shape[0]), :]\n",
    "Y_test = Y[int(0.8*Y.shape[0]):, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BgCeUSiQuwoA"
   },
   "source": [
    "# **First cell below** - \n",
    "Standardize features by removing the mean and scaling to unit variance.\n",
    "\n",
    "The standard score of a sample x is calculated as:\n",
    "\n",
    "z = (x - u) / s\n",
    "\n",
    "where u is the mean of the training samples or zero if with_mean=False, and s is the standard deviation of the training samples or one if with_std=False.\n",
    "\n",
    "# **Second cell below** - \n",
    "Transform features by scaling each feature to a given range.\n",
    "\n",
    "This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one.\n",
    "\n",
    "The transformation is given by:\n",
    "\n",
    "X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))\n",
    "X_scaled = X_std * (max - min) + min\n",
    "where min, max = feature_range.\n",
    "\n",
    "# **Third cell below** - \n",
    "Scale features using statistics that are robust to outliers.\n",
    "\n",
    "This Scaler removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile).\n",
    "\n",
    "Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Median and interquartile range are then stored to be used on later data using the transform method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "PUKI2hNDcoRA"
   },
   "outputs": [],
   "source": [
    "#Standrard scaling\n",
    "np_data = np.array(data)\n",
    "X_mm = np_data\n",
    "Y_mm = np_data\n",
    "X_mm = X_mm[:,0: (X_mm.shape[1] - 1)]\n",
    "Y_mm = Y_mm[:, X_mm.shape[1]]\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X_mm = scaler.fit_transform(X_mm)\n",
    "\n",
    "X_mm = np.c_[np.ones(X_mm.shape[0]), X_mm]\n",
    "\n",
    "X_train_mm = X_mm[0: int(0.8*X_mm.shape[0]), :]\n",
    "X_test_mm = X_mm[int(0.8*X_mm.shape[0]):, :]\n",
    "Y_mm = Y_mm.reshape(Y_mm.shape[0], 1)\n",
    "Y_train_mm = Y_mm[0:int(0.8*Y_mm.shape[0]), :]\n",
    "Y_test_mm = Y_mm[int(0.8*Y_mm.shape[0]):, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "c48paEXzco_u"
   },
   "outputs": [],
   "source": [
    "#Min-Max Scaling\n",
    "np_data = np.array(data)\n",
    "X_ss = np_data\n",
    "Y_ss = np_data\n",
    "X_ss = X_ss[:,0: (X_ss.shape[1] - 1)]\n",
    "Y_ss = Y_ss[:, X_ss.shape[1]]\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_ss = scaler.fit_transform(X_ss)\n",
    "\n",
    "X_ss = np.c_[np.ones(X_ss.shape[0]), X_ss]\n",
    "\n",
    "X_train_ss = X_ss[0: int(0.8*X_ss.shape[0]), :]\n",
    "X_test_ss = X_ss[int(0.8*X_ss.shape[0]):, :]\n",
    "Y_ss = Y_ss.reshape(Y_ss.shape[0], 1)\n",
    "Y_train_ss = Y_ss[0:int(0.8*Y_ss.shape[0]), :]\n",
    "Y_test_ss = Y_ss[int(0.8*Y_ss.shape[0]):, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "gDkwfZNBcpTN"
   },
   "outputs": [],
   "source": [
    "#Robust Scaling\n",
    "np_data = np.array(data)\n",
    "X_r = np_data\n",
    "Y_r= np_data\n",
    "X_r = X_r[:,0: (X_r.shape[1] - 1)]\n",
    "Y_r = Y_r[:, X_r.shape[1]]\n",
    "\n",
    "scaler = RobustScaler()\n",
    "X_r = scaler.fit_transform(X_r)\n",
    "\n",
    "X_r = np.c_[np.ones(X_r.shape[0]), X_r]\n",
    "\n",
    "X_train_r = X_r[0: int(0.8*X_r.shape[0]), :]\n",
    "X_test_r = X_r[int(0.8*X_r.shape[0]):, :]\n",
    "Y_r = Y_r.reshape(Y_r.shape[0], 1)\n",
    "Y_train_r = Y_r[0:int(0.8*Y_r.shape[0]), :]\n",
    "Y_test_r = Y_r[int(0.8*Y_r.shape[0]):, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lyyLqMckfxkD",
    "outputId": "041530dc-5b5d-4558-9d85-318ffa5e48f5"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6jPv0Xpn3Uy7",
    "outputId": "6c1ada01-36ef-48a0-b37a-a15acf93c5d3"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-DJ48t64eY1k"
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2v8U3h-2eZAX",
    "outputId": "65fbea9b-cf8a-4c7e-ca82-2d9d5f9eed0d"
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sD0lVbRDhGvp"
   },
   "source": [
    "## Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "dt=tree.DecisionTreeClassifier()\n",
    "dt_=dt.fit(X_train,Y_train) #Raw Data\n",
    "dt_mm=dt.fit(X_train_mm,Y_train_mm) #Min-max Scaled data\n",
    "dt_ss=dt.fit(X_train_ss,Y_train_ss)#Data with Standard scaling\n",
    "dt_r=dt.fit(X_train_r,Y_train_r)#Data with Robust scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score For training - \n",
      "0.19711701164354117 Raw Data\n",
      "0.39794294390429064 Min Max\n",
      "0.7657480495010565 Standard Scaling\n",
      "0.9999954267316681 Robust Scaling\n",
      "\n",
      "Accuracy Score For testing - \n",
      "0.19683166867888632 Raw Data\n",
      "0.3963158087293748 Min Max\n",
      "0.7632532104050049 Standard Scaling\n",
      "0.9435115062378809 Robust Scaling\n",
      "\n",
      "Precision Score For training - \n",
      "0.1102719421281668 Raw Data\n",
      "0.10210394212313598 Min Max\n",
      "0.15316388332784267 Standard Scaling\n",
      "1.0 Robust Scaling\n",
      "\n",
      "Precision Score For testing - \n",
      "0.10917057516662552 Raw Data\n",
      "0.09986708019494905 Min Max\n",
      "0.14532069382815652 Standard Scaling\n",
      "0.7320605661619487 Robust Scaling\n",
      "\n",
      "0.7528774542992552 Robust Scaling\n",
      "0.7423230974632844 Robust Scaling\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "print(\"Accuracy Score For training - \")\n",
    "print(accuracy_score(Y_train,dt_.predict(X_train)),\"Raw Data\")\n",
    "print(accuracy_score(Y_train_mm,dt_mm.predict(X_train_mm)),\"Min Max\")\n",
    "print(accuracy_score(Y_train_ss,dt_ss.predict(X_train_ss)),\"Standard Scaling\")\n",
    "print(accuracy_score(Y_train_r,dt_r.predict(X_train_r)), \"Robust Scaling\")\n",
    "print()\n",
    "print(\"Accuracy Score For testing - \")\n",
    "print(accuracy_score(Y_test,dt_.predict(X_test)),\"Raw Data\")\n",
    "print(accuracy_score(Y_test_mm,dt_mm.predict(X_test_mm)),\"Min Max\")\n",
    "print(accuracy_score(Y_test_ss,dt_ss.predict(X_test_ss)),\"Standard Scaling\")\n",
    "print(accuracy_score(Y_test_r,dt_r.predict(X_test_r)), \"Robust Scaling\")\n",
    "print()\n",
    "print(\"Precision Score For training - \")\n",
    "print(precision_score(Y_train,dt_.predict(X_train)),\"Raw Data\")\n",
    "print(precision_score(Y_train_mm,dt_mm.predict(X_train_mm)),\"Min Max\")\n",
    "print(precision_score(Y_train_ss,dt_ss.predict(X_train_ss)),\"Standard Scaling\")\n",
    "print(precision_score(Y_train_r,dt_r.predict(X_train_r)), \"Robust Scaling\")\n",
    "print()\n",
    "print(\"Precision Score For testing - \")\n",
    "print(precision_score(Y_test,dt_.predict(X_test)),\"Raw Data\")\n",
    "print(precision_score(Y_test_mm,dt_mm.predict(X_test_mm)),\"Min Max\")\n",
    "print(precision_score(Y_test_ss,dt_ss.predict(X_test_ss)),\"Standard Scaling\")\n",
    "print(precision_score(Y_test_r,dt_r.predict(X_test_r)), \"Robust Scaling\")\n",
    "print()\n",
    "print(recall_score(Y_test_r,dt_r.predict(X_test_r)),\"Robust Scaling\")\n",
    "print(f1_score(Y_test_r,dt_r.predict(X_test_r)),\"Robust Scaling\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37 is the Tree Depth\n"
     ]
    }
   ],
   "source": [
    "print(dt_r.tree_.max_depth,\"is the Tree Depth\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb = GaussianNB()\n",
    "nb_= nb.fit(X=X_train,y=Y_train.ravel())#Raw Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb_mm= nb.fit(X_train_mm,Y_train_mm.ravel())#Min Max scaled data\n",
    "nb_ss= nb.fit(X_train_ss,Y_train_ss.ravel())#Data with robust scaling\n",
    "nb_r = nb.fit(X_train_r,Y_train_r.ravel())#data with robust scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score For training - \n",
      "0.8906714472564963 Raw data\n",
      "0.8909321235514173 Min-Max Scaled\n",
      "0.8884534121155024 Standard Scaling\n",
      "0.8821011424024293 Robust scaling\n",
      "\n",
      "Accuracy Score For testing - \n",
      "0.8917425822266125 Raw data\n",
      "0.891925511286723 Min-Max Scaled\n",
      "0.8889986463249552 Standard Scaling\n",
      "0.8828522299052427 Robust scaling\n",
      "\n",
      "Precision Score For training - \n",
      "0.1566265060240964 Raw data\n",
      "0.0 Min-Max Scaled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.43302026693030155 Standard Scaling\n",
      "0.4112510341023991 Robust scaling\n",
      "\n",
      "Precision Score For testing - \n",
      "0.25 Raw data\n",
      "0.0 Min-Max Scaled\n",
      "0.4170124481327801 Standard Scaling\n",
      "0.4062027231467474 Robust scaling\n",
      "\n",
      "0.18178740690589032 Robust scaling\n",
      "0.25116931711880264 Robust scaling\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Score For training - \")\n",
    "print(accuracy_score(Y_train,nb_.predict(X_train)),\"Raw data\")\n",
    "print(accuracy_score(Y_train_mm,nb_mm.predict(X_train_mm)),\"Min-Max Scaled\")\n",
    "print(accuracy_score(Y_train_ss,nb_ss.predict(X_train_ss)),\"Standard Scaling\")\n",
    "print(accuracy_score(Y_train_r,nb_r.predict(X_train_r)),\"Robust scaling\")\n",
    "print()\n",
    "print(\"Accuracy Score For testing - \")\n",
    "print(accuracy_score(Y_test,nb_.predict(X_test)),\"Raw data\")\n",
    "print(accuracy_score(Y_test_mm,nb_mm.predict(X_test_mm)),\"Min-Max Scaled\")\n",
    "print(accuracy_score(Y_test_ss,nb_ss.predict(X_test_ss)),\"Standard Scaling\")\n",
    "print(accuracy_score(Y_test_r,nb_r.predict(X_test_r)),\"Robust scaling\")\n",
    "print()\n",
    "print(\"Precision Score For training - \")\n",
    "print(precision_score(Y_train,nb_.predict(X_train)),\"Raw data\")\n",
    "print(precision_score(Y_train_mm,nb_mm.predict(X_train_mm)),\"Min-Max Scaled\")\n",
    "print(precision_score(Y_train_ss,nb_ss.predict(X_train_ss)),\"Standard Scaling\")\n",
    "print(precision_score(Y_train_r,nb_r.predict(X_train_r)),\"Robust scaling\")\n",
    "print()\n",
    "print(\"Precision Score For testing - \")\n",
    "print(precision_score(Y_test,nb_.predict(X_test)),\"Raw data\")\n",
    "print(precision_score(Y_test_mm,nb_mm.predict(X_test_mm)),\"Min-Max Scaled\")\n",
    "print(precision_score(Y_test_ss,nb_ss.predict(X_test_ss)),\"Standard Scaling\")\n",
    "print(precision_score(Y_test_r,nb_r.predict(X_test_r)),\"Robust scaling\")\n",
    "print()\n",
    "print(recall_score(Y_test_r,nb_r.predict(X_test_r)),\"Robust scaling\")\n",
    "print(f1_score(Y_test_r,nb_r.predict(X_test_r)),\"Robust scaling\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion Matrix for Naive bayes\n",
    "import seaborn as sn\n",
    "from sklearn.metrics import confusion_matrix\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "ax.set_ylim([0,2])\n",
    "arr=confusion_matrix(Y_test_r,nb_r.predict(X_test_r))\n",
    "\n",
    "sn.heatmap(arr,annot=True)\n",
    "plt.savefig(\"conmat_naive_bayes.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#decision tree confusion matrix\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "ax.set_ylim([0,2])\n",
    "arr=confusion_matrix(Y_test_r,dt_r.predict(X_test_r))\n",
    "\n",
    "sn.heatmap(arr,annot=True)\n",
    "plt.savefig(\"conmat_decision_tree.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_nf = data[data[\"Is Fraud?\"] == 0]\n",
    "data_f = data[data[\"Is Fraud?\"] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        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>User</th>\n",
       "      <th>Card</th>\n",
       "      <th>Year</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Use Chip</th>\n",
       "      <th>MCC</th>\n",
       "      <th>Errors?</th>\n",
       "      <th>Card Brand</th>\n",
       "      <th>Card Type</th>\n",
       "      <th>Credit Limit</th>\n",
       "      <th>Year PIN last Changed</th>\n",
       "      <th>Current Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Yearly Income - Person</th>\n",
       "      <th>Total Debt</th>\n",
       "      <th>FICO Score</th>\n",
       "      <th>Is Fraud?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>780</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>71.00</td>\n",
       "      <td>0</td>\n",
       "      <td>5499</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>39606</td>\n",
       "      <td>2008</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>53986</td>\n",
       "      <td>58381</td>\n",
       "      <td>748</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>580</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>9.96</td>\n",
       "      <td>2</td>\n",
       "      <td>5411</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11400</td>\n",
       "      <td>2009</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>34954</td>\n",
       "      <td>5942</td>\n",
       "      <td>729</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1919</td>\n",
       "      <td>2</td>\n",
       "      <td>2009</td>\n",
       "      <td>66.26</td>\n",
       "      <td>2</td>\n",
       "      <td>5541</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6100</td>\n",
       "      <td>2019</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>26510</td>\n",
       "      <td>53023</td>\n",
       "      <td>696</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>777</td>\n",
       "      <td>5</td>\n",
       "      <td>1998</td>\n",
       "      <td>75.00</td>\n",
       "      <td>2</td>\n",
       "      <td>5499</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3134</td>\n",
       "      <td>2011</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>65055</td>\n",
       "      <td>1676</td>\n",
       "      <td>736</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1227</td>\n",
       "      <td>3</td>\n",
       "      <td>2012</td>\n",
       "      <td>-72.00</td>\n",
       "      <td>2</td>\n",
       "      <td>5499</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4130</td>\n",
       "      <td>2015</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>84365</td>\n",
       "      <td>113529</td>\n",
       "      <td>796</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User  Card  Year  Amount  Use Chip   MCC  Errors?  Card Brand  Card Type  \\\n",
       "0   780     3  2016   71.00         0  5499        0           2          1   \n",
       "1   580     1  2014    9.96         2  5411        0           3          0   \n",
       "2  1919     2  2009   66.26         2  5541       14           0          0   \n",
       "3   777     5  1998   75.00         2  5499        0           3          1   \n",
       "4  1227     3  2012  -72.00         2  5499        0           2          1   \n",
       "\n",
       "   Credit Limit  Year PIN last Changed  Current Age  Gender  \\\n",
       "0         39606                   2008           54       1   \n",
       "1         11400                   2009           67       0   \n",
       "2          6100                   2019           56       1   \n",
       "3          3134                   2011           72       1   \n",
       "4          4130                   2015           44       0   \n",
       "\n",
       "   Yearly Income - Person  Total Debt  FICO Score  Is Fraud?  \n",
       "0                   53986       58381         748          0  \n",
       "1                   34954        5942         729          0  \n",
       "2                   26510       53023         696          0  \n",
       "3                   65055        1676         736          0  \n",
       "4                   84365      113529         796          0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_nf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "\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>User</th>\n",
       "      <th>Card</th>\n",
       "      <th>Year</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Use Chip</th>\n",
       "      <th>MCC</th>\n",
       "      <th>Errors?</th>\n",
       "      <th>Card Brand</th>\n",
       "      <th>Card Type</th>\n",
       "      <th>Credit Limit</th>\n",
       "      <th>Year PIN last Changed</th>\n",
       "      <th>Current Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Yearly Income - Person</th>\n",
       "      <th>Total Debt</th>\n",
       "      <th>FICO Score</th>\n",
       "      <th>Is Fraud?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1294</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>37.66</td>\n",
       "      <td>1</td>\n",
       "      <td>5814</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>31535</td>\n",
       "      <td>2009</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>41473</td>\n",
       "      <td>98151</td>\n",
       "      <td>724</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>124</td>\n",
       "      <td>2</td>\n",
       "      <td>2009</td>\n",
       "      <td>74.56</td>\n",
       "      <td>1</td>\n",
       "      <td>5311</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13469</td>\n",
       "      <td>2009</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>25723</td>\n",
       "      <td>34007</td>\n",
       "      <td>746</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1452</td>\n",
       "      <td>0</td>\n",
       "      <td>2010</td>\n",
       "      <td>121.95</td>\n",
       "      <td>2</td>\n",
       "      <td>4121</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1129</td>\n",
       "      <td>2012</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>22583</td>\n",
       "      <td>62461</td>\n",
       "      <td>683</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1085</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>117.94</td>\n",
       "      <td>1</td>\n",
       "      <td>5977</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>18171</td>\n",
       "      <td>2014</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>46763</td>\n",
       "      <td>65106</td>\n",
       "      <td>696</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>161</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>133.83</td>\n",
       "      <td>1</td>\n",
       "      <td>4829</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>52609</td>\n",
       "      <td>2010</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>82295</td>\n",
       "      <td>1555</td>\n",
       "      <td>736</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    User  Card  Year  Amount  Use Chip   MCC  Errors?  Card Brand  Card Type  \\\n",
       "14  1294     1  2015   37.66         1  5814        0           3          1   \n",
       "29   124     2  2009   74.56         1  5311        0           2          1   \n",
       "37  1452     0  2010  121.95         2  4121        0           2          1   \n",
       "39  1085     0  2016  117.94         1  5977        0           2          1   \n",
       "40   161     2  2008  133.83         1  4829        0           3          1   \n",
       "\n",
       "    Credit Limit  Year PIN last Changed  Current Age  Gender  \\\n",
       "14         31535                   2009           68       1   \n",
       "29         13469                   2009           45       0   \n",
       "37          1129                   2012           49       0   \n",
       "39         18171                   2014           47       1   \n",
       "40         52609                   2010           48       1   \n",
       "\n",
       "    Yearly Income - Person  Total Debt  FICO Score  Is Fraud?  \n",
       "14                   41473       98151         724          1  \n",
       "29                   25723       34007         746          1  \n",
       "37                   22583       62461         683          1  \n",
       "39                   46763       65106         696          1  \n",
       "40                   82295        1555         736          1  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.scatter(data_nf[\"Card Type\"],data_nf[\"Errors?\"] ,color = \"green\")\n",
    "plt.scatter(data_f[\"Card Type\"],data_f[\"Errors?\"] , color = \"tomato\")\n",
    "plt.xlabel(\"Card Type\")\n",
    "plt.ylabel(\"Errors?\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "x1 = plt.scatter(data_nf[\"Yearly Income - Person\"],data_nf[\"Total Debt\"] ,color = \"green\")\n",
    "x2 = plt.scatter(data_f[\"Yearly Income - Person\"],data_f[\"Total Debt\"] ,color = \"blue\")\n",
    "plt.xlabel(\"Yearly Income - Person\")\n",
    "plt.ylabel(\"Total Debt\")\n",
    "plt.legend([x1,x2],['Not Fraud','Fraud'])\n",
    "plt.savefig(\"incomeVdebt.jpg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.scatter(data_nf[\"Yearly Income - Person\"],data_nf[\"Amount\"] ,color = \"green\")\n",
    "plt.scatter(data_f[\"Yearly Income - Person\"],data_f[\"Amount\"] ,color = \"blue\")\n",
    "plt.xlabel(\"Yearly Income - Person\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([x1,x2],['Not Fraud','Fraud'])\n",
    "plt.savefig(\"incomeVamount.jpg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.scatter(data_nf[\"FICO Score\"],data_nf[\"Total Debt\"] ,color = \"green\")\n",
    "plt.scatter(data_f[\"FICO Score\"],data_f[\"Total Debt\"] ,color = \"blue\")\n",
    "plt.xlabel(\"FICO Score\")\n",
    "plt.ylabel(\"Total Debt\")\n",
    "plt.legend([x1,x2],['Not Fraud','Fraud'])\n",
    "plt.savefig(\"ficoVdebt.jpg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.scatter(data_nf[\"Credit Limit\"],data_nf[\"Total Debt\"] ,color = \"green\")\n",
    "plt.scatter(data_f[\"Credit Limit\"],data_f[\"Total Debt\"] ,color = \"blue\")\n",
    "plt.xlabel(\"Credit Limit\")\n",
    "plt.ylabel(\"Total Debt\")\n",
    "plt.legend([x1,x2],['Not Fraud','Fraud'])\n",
    "plt.savefig(\"debtVcreditLimit.jpg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.scatter(data_nf[\"Credit Limit\"],data_nf[\"Yearly Income - Person\"] ,color = \"green\")\n",
    "plt.scatter(data_f[\"Credit Limit\"],data_f[\"Yearly Income - Person\"] ,color = \"blue\")\n",
    "plt.xlabel(\"Credit Limit\")\n",
    "plt.ylabel(\"Yearly Income - Person\")\n",
    "plt.legend([x1,x2],['Not Fraud','Fraud'])\n",
    "plt.savefig(\"credit_limitVincome.jpg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separating Features\n",
    "features = data.columns\n",
    "features = features[0:len(features)-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MIn-max scaling the data for PCA to work\n",
    "x = data.loc[:, features].values\n",
    "# Separating out the target\n",
    "y = data.loc[:,['Is Fraud?']].values\n",
    "# Standardizing the features\n",
    "x = MinMaxScaler().fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>User</th>\n",
       "      <th>Card</th>\n",
       "      <th>Year</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Use Chip</th>\n",
       "      <th>MCC</th>\n",
       "      <th>Errors?</th>\n",
       "      <th>Card Brand</th>\n",
       "      <th>Card Type</th>\n",
       "      <th>Credit Limit</th>\n",
       "      <th>Year PIN last Changed</th>\n",
       "      <th>Current Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Yearly Income - Person</th>\n",
       "      <th>Total Debt</th>\n",
       "      <th>FICO Score</th>\n",
       "      <th>Is Fraud?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>780</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>71.00</td>\n",
       "      <td>0</td>\n",
       "      <td>5499</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>39606</td>\n",
       "      <td>2008</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>53986</td>\n",
       "      <td>58381</td>\n",
       "      <td>748</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>580</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>9.96</td>\n",
       "      <td>2</td>\n",
       "      <td>5411</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11400</td>\n",
       "      <td>2009</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>34954</td>\n",
       "      <td>5942</td>\n",
       "      <td>729</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1919</td>\n",
       "      <td>2</td>\n",
       "      <td>2009</td>\n",
       "      <td>66.26</td>\n",
       "      <td>2</td>\n",
       "      <td>5541</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6100</td>\n",
       "      <td>2019</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>26510</td>\n",
       "      <td>53023</td>\n",
       "      <td>696</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>777</td>\n",
       "      <td>5</td>\n",
       "      <td>1998</td>\n",
       "      <td>75.00</td>\n",
       "      <td>2</td>\n",
       "      <td>5499</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3134</td>\n",
       "      <td>2011</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>65055</td>\n",
       "      <td>1676</td>\n",
       "      <td>736</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1227</td>\n",
       "      <td>3</td>\n",
       "      <td>2012</td>\n",
       "      <td>-72.00</td>\n",
       "      <td>2</td>\n",
       "      <td>5499</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4130</td>\n",
       "      <td>2015</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>84365</td>\n",
       "      <td>113529</td>\n",
       "      <td>796</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User  Card  Year  Amount  Use Chip   MCC  Errors?  Card Brand  Card Type  \\\n",
       "0   780     3  2016   71.00         0  5499        0           2          1   \n",
       "1   580     1  2014    9.96         2  5411        0           3          0   \n",
       "2  1919     2  2009   66.26         2  5541       14           0          0   \n",
       "3   777     5  1998   75.00         2  5499        0           3          1   \n",
       "4  1227     3  2012  -72.00         2  5499        0           2          1   \n",
       "\n",
       "   Credit Limit  Year PIN last Changed  Current Age  Gender  \\\n",
       "0         39606                   2008           54       1   \n",
       "1         11400                   2009           67       0   \n",
       "2          6100                   2019           56       1   \n",
       "3          3134                   2011           72       1   \n",
       "4          4130                   2015           44       0   \n",
       "\n",
       "   Yearly Income - Person  Total Debt  FICO Score  Is Fraud?  \n",
       "0                   53986       58381         748          0  \n",
       "1                   34954        5942         729          0  \n",
       "2                   26510       53023         696          0  \n",
       "3                   65055        1676         736          0  \n",
       "4                   84365      113529         796          0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting cumulative variance withnumber of components to determine the number of features to reduce\n",
    "pca = PCA().fit(x)\n",
    "\n",
    "# % matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (12,6)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "xi = np.arange(1, 17, step=1)\n",
    "y = np.cumsum(pca.explained_variance_ratio_)\n",
    "\n",
    "plt.ylim(0.0,1.1)\n",
    "plt.plot(xi, y, marker='o', linestyle='--', color='b')\n",
    "\n",
    "plt.xlabel('Number of Components')\n",
    "plt.xticks(np.arange(0, 17, step=1)) #change from 0-based array index to 1-based human-readable label\n",
    "plt.ylabel('Cumulative variance (%)')\n",
    "plt.title('The number of components needed to explain variance')\n",
    "\n",
    "plt.axhline(y=0.95, color='r', linestyle='-')\n",
    "plt.text(0.5, 0.85, '95% cut-off threshold', color = 'red', fontsize=17)\n",
    "\n",
    "ax.grid(axis='x')\n",
    "plt.savefig(\"PCA_ComponentAalyis.jpg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reducing the dimensions from 16->10\n",
    "pca = PCA(n_components=10)\n",
    "principalComponents = pca.fit_transform(x)\n",
    "principalDf = pd.DataFrame(data = principalComponents\n",
    "             , columns = [1,2,3,4,5,6,7,8,9,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.490005</td>\n",
       "      <td>0.687907</td>\n",
       "      <td>-0.056696</td>\n",
       "      <td>0.084829</td>\n",
       "      <td>-0.110653</td>\n",
       "      <td>0.209919</td>\n",
       "      <td>-0.115841</td>\n",
       "      <td>-0.205866</td>\n",
       "      <td>0.125780</td>\n",
       "      <td>0.010576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.466609</td>\n",
       "      <td>-0.330758</td>\n",
       "      <td>0.119156</td>\n",
       "      <td>0.235576</td>\n",
       "      <td>0.435223</td>\n",
       "      <td>0.072171</td>\n",
       "      <td>0.060138</td>\n",
       "      <td>-0.076163</td>\n",
       "      <td>-0.148679</td>\n",
       "      <td>-0.191948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.502890</td>\n",
       "      <td>-0.316691</td>\n",
       "      <td>0.780895</td>\n",
       "      <td>-0.388346</td>\n",
       "      <td>-0.379940</td>\n",
       "      <td>0.029713</td>\n",
       "      <td>0.033631</td>\n",
       "      <td>0.452253</td>\n",
       "      <td>0.071703</td>\n",
       "      <td>0.066145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.535350</td>\n",
       "      <td>-0.440132</td>\n",
       "      <td>-0.255150</td>\n",
       "      <td>0.051377</td>\n",
       "      <td>0.195321</td>\n",
       "      <td>0.473133</td>\n",
       "      <td>-0.037734</td>\n",
       "      <td>0.007445</td>\n",
       "      <td>0.212520</td>\n",
       "      <td>0.325441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.473717</td>\n",
       "      <td>-0.328033</td>\n",
       "      <td>-0.047813</td>\n",
       "      <td>-0.118030</td>\n",
       "      <td>-0.107616</td>\n",
       "      <td>0.004782</td>\n",
       "      <td>-0.335837</td>\n",
       "      <td>0.201950</td>\n",
       "      <td>0.145184</td>\n",
       "      <td>-0.020657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         1         2         3         4         5         6         7   \\\n",
       "0  0.490005  0.687907 -0.056696  0.084829 -0.110653  0.209919 -0.115841   \n",
       "1 -0.466609 -0.330758  0.119156  0.235576  0.435223  0.072171  0.060138   \n",
       "2  0.502890 -0.316691  0.780895 -0.388346 -0.379940  0.029713  0.033631   \n",
       "3  0.535350 -0.440132 -0.255150  0.051377  0.195321  0.473133 -0.037734   \n",
       "4 -0.473717 -0.328033 -0.047813 -0.118030 -0.107616  0.004782 -0.335837   \n",
       "\n",
       "         8         9         10  \n",
       "0 -0.205866  0.125780  0.010576  \n",
       "1 -0.076163 -0.148679 -0.191948  \n",
       "2  0.452253  0.071703  0.066145  \n",
       "3  0.007445  0.212520  0.325441  \n",
       "4  0.201950  0.145184 -0.020657  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principalDf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = principalDf.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = data[\"Is Fraud?\"].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X[0: int(0.8*X.shape[0]), :]\n",
    "X_test = X[int(0.8*X.shape[0]):, :]\n",
    "Y = Y.reshape(Y.shape[0], 1)\n",
    "Y_train = Y[0:int(0.8*Y.shape[0]), :]\n",
    "Y_test = Y[int(0.8*Y.shape[0]):, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using grid searh to find the best pramas\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# defining parameter range\n",
    "param_grid = {'C': [0.1, 1, 10],\n",
    "              'gamma': [1, 0.1, 0.01, 0.001],\n",
    "              'kernel': ['rbf', 'linear']}\n",
    " \n",
    "grid = GridSearchCV(SVC(), param_grid,refit = True , n_jobs=7)##Multithreading to makes things parallel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "model = SVC(C=1)\n",
    "model.fit(X_train,Y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9022427102769546"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_predicted = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_tp = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score For training - \n",
      "0.9018256487181129\n",
      "\n",
      "Accuracy Score For testing - \n",
      "0.9022427102769546\n",
      "\n",
      "Precision Score For training - \n",
      "0.6762759229298426\n",
      "\n",
      "Precision Score For testing - \n",
      "0.6608084358523726\n",
      "\n",
      "0.19141354148990328\n",
      "0.2968421052631579\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Score For training - \")\n",
    "print(accuracy_score(Y_train,Y_tp))\n",
    "\n",
    "print()\n",
    "print(\"Accuracy Score For testing - \")\n",
    "print(accuracy_score(Y_test,Y_predicted))\n",
    "\n",
    "print()\n",
    "print(\"Precision Score For training - \")\n",
    "print(precision_score(Y_train,Y_tp))\n",
    "\n",
    "print()\n",
    "print(\"Precision Score For testing - \")\n",
    "print(precision_score(Y_test,Y_predicted))\n",
    "\n",
    "print()\n",
    "print(recall_score(Y_test,Y_predicted))\n",
    "print(f1_score(Y_test,Y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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6+9c+s6z2GlltLUmS1EhSSoOBwTX0LwS2iYi2wF+Britoastk8ChJklRAbcvO9XqulD6KiPHAzkDbiFg1yy52BmZkw2YAXYDpEbEqsBYwu1r7V6p/Zlnty+SytSRJUgEroNp63SzjSES0BPYFpgDjgcOzYccC92Sv783ek/U/mlJKWftRWTX2xsDmwHPARGDzrHq7BVVFNffWdt1mHiVJkgpYAZnHDYCbsqroVYBRKaX7I+JVYGREnA+8CAzNxg8FhkfEVGAOVcEgKaXJETEKeBWoAAZly+FExMnAGKAZcH1KaXJtk4qqgLThLPjwzXI+u0fScmvZcffGnoKkJqJi/oxo7Dm8333POsc4HcY/3ujXsbzMPEqSJBWRmlzcVy8MHiVJkgpYkQUzKxODR0mSpAJSpZlHSZIk5VTWzKO36pEkSVJuZh4lSZIKSBbMSJIkKa+yLlsbPEqSJBVQ1oIZ9zxKkiQpNzOPkiRJBTTwQ/pWWgaPkiRJBZR12drgUZIkqQCDR0mSJOVW1mVrC2YkSZKUm5lHSZKkAly2liRJUm4+YUaSJEm5+YQZSZIk5VZZ0syjBTOSJEnKzcyjJElSAe55lCRJUm5WW0uSJCk3bxIuSZIk1cLMoyRJUgEuW0uSJCm3st6qx+BRkiSpAKutJUmSlJsFM5IkSVItzDxKkiQV4J5HSZIk5eaeR0mSJOVW1j2PBo+SJEkFlHXZ2oIZSZIk5dbgmcctuh7W0KeQ9A2xSpTzf/GSmib3PEqSJCm3si5bGzxKkiQVUNJ6Gfc8SpIkKT8zj5IkSQW4bC1JkqTcLJiRJElSbpWNPYFGYvAoSZJUQKKcmUcLZiRJkpSbmUdJkqQCKkt6rx6DR0mSpAIqS7psbfAoSZJUQFn3PBo8SpIkFVDWamsLZiRJkpSbmUdJkqQCXLaWJElSbmVdtjZ4lCRJKqCswaN7HiVJkpSbmUdJkqQC3PMoSZKk3CrLGTsaPEqSJBXhE2YkSZKUW0kfbW3BjCRJkvIz8yhJklRAWW/VY/AoSZJUQGW451GSJEk5uedRkiRJuVXWw1GTiOgSEeMj4tWImBwRp2Tta0fEuIh4PfvZLmuPiLgiIqZGxCsRsV217zo2G/96RBxbrX37iPhb9pkrImpPpxo8SpIkrZwqgF+klLYEdgIGRcSWwJnAIymlzYFHsvcA+wObZ8dA4M9QFWwC5wI7At2Ac78KOLMxP672uV61TcrgUZIkqYDKqPtRk5TSeymlSdnrT4EpQCfgYOCmbNhNwCHZ64OBYanKM0DbiNgA2A8Yl1Kak1KaC4wDemV9bVJKz6SUEjCs2nctk3seJUmSCliRNwmPiI2AbYFngQ4ppfeyrplAh+x1J2BatY9Nz9pqap++lPYamXmUJEkqINXDEREDI+L5asfAr58nIloDdwI/Tyl9stgcqjKGK7R2x8yjJElSI0kpDQYGL6s/IppTFTjenFK6K2t+PyI2SCm9ly09z8raZwBdqn28c9Y2A9jra+2PZe2dlzK+RmYeJUmSCmjoPY9Z5fNQYEpK6Y/Vuu4FvqqYPha4p1p7v6zqeifg42x5ewzQMyLaZYUyPYExWd8nEbFTdq5+1b5rmcw8SpIkFbACnjCzK3AM8LeIeClr+xVwETAqIgYAbwNHZn2jgQOAqcDnQH+AlNKciPgdMDEb99uU0pzs9UnAjUBL4MHsqJHBoyRJUgENvdEwpTQBllmVs89Sxidg0DK+63rg+qW0Pw98d3nmZfAoSZJUQG3Lzt9U7nmUJElSbmYeJUmSClgBex5XSgaPkiRJBRg8SpIkKbdU0j2PBo+SJEkFlDXzaMGMJEmScjPzKEmSVEBZM48Gj5IkSQU09E3CV1YGj5IkSQV4k3BJkiSpFmYeJUmSCnDPoyRJknIzeJQkSVJuFsxIkiQpNwtmJEmSpFqYeZQkSSrAPY+SJEnKzT2PkiRJyq2ypOGjex4lSZKUm5lHSZKkAtzzKEmSpNzKuWht8ChJklSImUdJkiTl5k3CJUmSpFqYeZQkSSqgrLfqMXiUJEkqoJyho8GjJElSIRbMSJIkKbeyLltbMCNJkqTczDxKkiQVUM68o8GjJElSIe55lCRJUm7ueZQkSZJqYeZRkiSpgHLmHQ0eJUmSCnHPoyRJknJLJc09GjxKkiQVUNbMowUzkiRJys3MoyRJUgFlvVWPwaMkSVIB5QwdDR4lSZIKKWvm0T2Pym2VVVbhvkdv5bpbLl+ir2On9bn57sHc9+itjH78NvbqsVudz9d5w47cNWYYjz53D1dcdxHNm1f9X+fo4w7nwSdGcf/4kYy6/3o2+/YmdT6XpPoz+NrfM33aS7w46eGl9m+xxaY88fg9fPrJG5x66n/UyzlbtGjBzSOu4dVXJzDhyfv41rc6A/C9723DxOfGMPG5MTw/cSwHH9SrXs4nQVXBTF2PpsjgUbn1/4+jeeP1fy21b9AvTmD0PePovXcffvbjs/jtJWfl/t7DjurNKb9c8h+QM845hev/cjN7dzuYTz76lCP7HgrAvXc8yP57HMmB3Y/i2qtu4uzfnVbsgiQ1iGHDb+fA3n2X2T9nzkeceto5XHbZtcv93d/6VmfGjb19ifb+/Y9i7kcfs+WWu3HFFUO48IJfATB58j/YaecD2KHbfhzYuy9XX30RzZo1W+7zSvp/Bo/KZf0N1qP7vrtx24i/LrU/pUTr1q0AWLNNa96f+QFQla08879+zt3jRjD68dvoc+xhuc+58+478OC9VZmLO0fex7777wXAZ5/NWzRmjTVaksq5aiCttCZMeJa5cz9aZv8HH8zmhRdeZsGCiiX6ju7zA/5nwv1MfG4MV199Eausku+fqd69ezJ8eFVQeeddD9C9e9Xqxxdf/C8LFy4EYPXVVyP5C0P1KNXDn6bIPY/K5TcXnM5F511Oq9ZrLLX/8kuuZdjt19Dvx0exxhotOeawnwBwZN9D+PSTzzhk3760aNGc20ffyJPjn2b6O+/WeL52a7flk48/XfRLf+a779Nhg/UW9R9z/JEcf2JfmrdoTt9D62fZS1Lj6tp1M444ojd77nUIFRUVXHHFBRzd51BG3HxnrZ/t1HF9pk9/D4CFCxfy8Sef0L59O2bPnssOO2zLkMG/Z8MNO9O//ymLfq9IddVUl53rqnDwGBH9U0o3LKNvIDAQoH2rzrRZfZ2ip9FKYO+euzP7wzn8/eUp7Ljr9ksdc9APenHHyPsYes1wtv3e1vzhmvPptdvh7L7XznTdanP2790DqMpKbrTJhnz26TxG3FW1ZNW2XRuaN2/Ovvt3B+AXJ/2aWe9/WOOchl8/iuHXj+Kgw3ox6LQTOP3kc+rxiiU1hu7dd2Pbbf+Np596AICWLVfng1mzAbh91HVstFEXWrRoTpcunZj43BgArrxqKMOGjarxeydOfJFttt2Hrl03Y+h1f+KhMeP58ssvG/ZiVApNNXNYV3XJPJ4HLDV4TCkNBgYDbLLOtuX8m/0G2b7bNuzTa0/26rEbq63WgtZrtuKPfz6f00789aIxR/zoEPofOQiAF59/hdVWa8Ha7dsSEfzXmRfz5Pinl/jeA7sfBVTteey8YUcuv2Tx/U9t1lqTZs2asXDhQtbv2IH335u1xHfcd9cYfnfprzi9Pi9YUqOICEaMuINf/+aiJfqOOPIEoGrP43VDLmPfnkcs1j/j3Zl07rwBM2a8R7NmzVirTRtmz5672Jh//GMqn302j6222oJJk15puAuRvuFq3EwSEa8s4/gb0GEFzVGN7NLzr2TXrXuxx3bf52cDz+TpCRMXCxwB3p0+k1326AbApptvzGqrr8bsD+fyxPin+FH/I1h11ar/p2y86Ya0XGP1XOd9ZsLz7H9QVcbysKN68/CDjwGw0SYbLhrTvefuvPXmtLpeoqSVwPhHJ3DoD77Puuu2B6Bdu7ZsuGGnXJ+9//5xHHNMVUB52A++z2OP/Q8AG23UZVGBzIYbdmKLLTbl7bf9naH6UdZq69oyjx2A/YC5X2sP4KkGmZGajJ+feSJ/e+lVHnnocS48549ceNlvOP4nfUkpLVpGvm34X+ncpSP3PXoLRDBn9lx+cky+6uiLf3s5Vwy5iNPOOolX//Yao26+G4BjBvyQXffckYoFFXz88Sf856DfNNQlSipg+LCr2GOPnVlnnbV5842J/PZ3f1h0q60hQ0bQocO6PP3UaNq0aU1lZSU/PfkE/n2b7kz5x+v817mXMPqBW1hllVVYsGABPzvl17zzzoxaz3nDDSO58YbLefXVCcyd8xF9jzkJgF136cbpp5/EggUVVFZW8rNTzl4iIykVVVnSAqyoqfIsIoYCN6SUJiyl75aU0tG1ncBla0l5Tf/0g8aegqQmYv6X06Ox59D3Wz+oc4wz4u27Gv06lleNmceU0oAa+moNHCVJkr6pfMKMJEmSVAvv8yhJklSAt+qRJElSbk21WrquDB4lSZIKKOueR4NHSZKkAsq6bG3BjCRJknIz8yhJklSAex4lSZKUW00PWvkmc9lakiSpgEpSnY/aRMT1ETErIv5erW3tiBgXEa9nP9tl7RERV0TE1Ih4JSK2q/aZY7Pxr0fEsdXat4+Iv2WfuSIian3ijcGjJEnSyutGoNfX2s4EHkkpbQ48kr0H2B/YPDsGAn+GqmATOBfYEegGnPtVwJmN+XG1z339XEsweJQkSSqgsh6O2qSUngDmfK35YOCm7PVNwCHV2oelKs8AbSNiA2A/YFxKaU5KaS4wDuiV9bVJKT2Tqtbgh1X7rmUyeJQkSSog1cOfiBgYEc9XOwbmOHWHlNJ72euZQIfsdSdgWrVx07O2mtqnL6W9RhbMSJIkFVAfNwlPKQ0GBtfh8ykiVmjljplHSZKkAlJKdT4Kej9bcib7OStrnwF0qTauc9ZWU3vnpbTXyOBRkiSpabkX+Kpi+ljgnmrt/bKq652Aj7Pl7TFAz4holxXK9ATGZH2fRMROWZV1v2rftUwuW0uSJBWwIm4SHhG3AnsB60TEdKqqpi8CRkXEAOBt4Mhs+GjgAGAq8DnQHyClNCcifgdMzMb9NqX0VRHOSVRVdLcEHsyOmufU0De43GSdbct5B01Jy236px809hQkNRHzv5xe6/0IG1rPLr3qHOOMnfZQo1/H8jLzKEmSVEB9FMw0RQaPkiRJBfh4QkmSJKkWZh4lSZIKcNlakiRJuSWDR0mSJOVV6Z5HSZIkqWZmHiVJkgooZ97R4FGSJKkQC2YkSZKUm8GjJEmScvMm4ZIkSVItzDxKkiQV4LK1JEmScvMm4ZIkScqtrHseDR4lSZIKKOuytQUzkiRJys3MoyRJUgEuW0uSJCm3si5bGzxKkiQVUNZqa/c8SpIkKTczj5IkSQVUuudRkiRJeZV12drgUZIkqQAzj5IkScqtrJlHC2YkSZKUm5lHSZKkAly2liRJUm5lXbY2eJQkSSrAzKMkSZJyK2vm0YIZSZIk5WbmUZIkqYCUKht7Co3C4FGSJKmAypIuWxs8SpIkFZBKWjDjnkdJkiTlZuZRkiSpAJetJUmSlFtZl60NHiVJkgrwJuGSJEnKzZuES5IkSbUw8yhJklSAex4lSZKUm9XWkiRJyq2smUf3PEqSJCk3M4+SJEkFeKseSZIk5VbWZWuDR0mSpAIsmJEkSVJuZc08WjAjSZKk3Mw8SpIkFWDBjCRJknIr67OtDR4lSZIKMPMoSZKk3CyYkSRJkmph5lGSJKkA9zxKkiQpt7IuWxs8SpIkFVDW4NE9j5IkScrNzKMkSVIB5cw7QpQ15arGFREDU0qDG3seklZ+/r6QVi4uW6uxDGzsCUhqMvx9Ia1EDB4lSZKUm8GjJEmScjN4VGNx/5KkvPx9Ia1ELJiRJElSbmYeJUmSlJvBo1a4iOgVEa9FxNSIOLOx5yNp5RQR10fErIj4e2PPRdL/M3jUChURzYCrgf2BLYE+EbFl485K0krqRqBXY09C0uIMHrWidQOmppTeTCnNB0YCBzfynCSthFJKTwBzGnsekhZn8KgVrRMwrdr76VmbJElqAgweJUmSlJvBo1a0GUCXau87Z22SJKkJMHjUijYR2DwiNo6IFsBRwL2NPCdJkpSTwaNWqJRSBXAyMAaYAoxKKU1u3FlJWhlFxK3A08AWETE9IgY09pwk+YQZSZIkLQczj5IkScrN4FGSJEm5GTxKkiQpN4NHSZIk5WbwKEmSpNwMHiVJkpSbwaMkSZJyM3iUJElSbv8HjFChCMCTY4MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SVM confusion matrix\n",
    "import seaborn as sn\n",
    "from sklearn import metrics\n",
    "arr=metrics.confusion_matrix(Y_test,Y_predicted)\n",
    "df_cm=pd.DataFrame(arr)\n",
    "print(\"Confusion Matrix\")\n",
    "sn.heatmap(df_cm,annot=True)\n",
    "plt.savefig(\"conmat_SVM.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open(\"SVM_weight\",\"wb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(model,file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "ML_Project_fin_fraud.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}