Data_quality_issues.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5a421147",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "c450c812",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'C:\\cloudxlab\\freelance\\Banktransaction_data_5.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "1296eff4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    M\n",
      "dtype: object\n",
      "0    MUMBAI\n",
      "dtype: object\n",
      "17003.84\n"
     ]
    }
   ],
   "source": [
    "# Printing mode and median values for missing column values\n",
    "print(df.CustGender.mode())\n",
    "print(df.CustLocation.mode())\n",
    "print(df.CustAccountBalance.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "fc9da137",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting customer dob into identical format\n",
    "df['CustomerDOBS'] = pd.to_datetime(df['CustomerDOB'])\n",
    "df['year'], df['month'] ,df['day']= df['CustomerDOBS'].dt.year, df['CustomerDOBS'].dt.month,df['CustomerDOBS'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "a123f1e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "dtype: int64\n",
      "0    1990\n",
      "dtype: int64\n",
      "15\n"
     ]
    }
   ],
   "source": [
    "# Printing mode and median values for missing column values\n",
    "print(df.month.mode())\n",
    "print(df.year.mode())\n",
    "print(df.day.mode().values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "a0dbd029",
   "metadata": {},
   "outputs": [],
   "source": [
    "#insert day,month and year for nan or empty values \n",
    "df.day.fillna(df.day.mode().values[0],inplace=True)\n",
    "df.month.fillna(df.month.mode().values[0],inplace=True)\n",
    "df.year.fillna(df.year.mode().values[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "259202d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting year,month and day to integar\n",
    "cols = ['year', 'month','day']\n",
    "df[cols] = df[cols].applymap(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "36fc7cb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#joining year,month and day to create respective date\n",
    "cols=[\"year\",\"month\",\"day\"]\n",
    "df['date_sudeep_final'] = df[cols].apply(lambda x: '-'.join(x.values.astype(str)), axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "8504f7fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract age from Customer DOB\n",
    "import datetime\n",
    "ages = []\n",
    "months = [int(i.split(\"-\")[-2]) for i in df.date_sudeep_final.values]\n",
    "years = [int(i.split(\"-\")[-3]) for i in df.date_sudeep_final.values]\n",
    "for i in years:\n",
    "    if(len(str(i))) == 4:\n",
    "        ages.append(2022 - i)\n",
    "    else:\n",
    "        ages.append(2022 - (1900 + i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "cacbc590",
   "metadata": {},
   "outputs": [
    {
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       "  1978,\n",
       "  1980,\n",
       "  1992,\n",
       "  1994,\n",
       "  1980,\n",
       "  1990,\n",
       "  1992,\n",
       "  1987,\n",
       "  1985,\n",
       "  1982,\n",
       "  1987,\n",
       "  1983,\n",
       "  1994,\n",
       "  1991,\n",
       "  1982,\n",
       "  1991,\n",
       "  1996,\n",
       "  1990,\n",
       "  1990,\n",
       "  1988,\n",
       "  1993,\n",
       "  1984,\n",
       "  1991,\n",
       "  1989,\n",
       "  1990,\n",
       "  1976,\n",
       "  1973,\n",
       "  1993,\n",
       "  1992,\n",
       "  1989,\n",
       "  1999,\n",
       "  1985,\n",
       "  1983,\n",
       "  1990,\n",
       "  1976,\n",
       "  1987,\n",
       "  1999,\n",
       "  1982,\n",
       "  1993,\n",
       "  1983,\n",
       "  1971,\n",
       "  1981,\n",
       "  1992,\n",
       "  1970,\n",
       "  1990,\n",
       "  1989,\n",
       "  1970,\n",
       "  1991,\n",
       "  1992,\n",
       "  1977,\n",
       "  1990,\n",
       "  1986,\n",
       "  1956,\n",
       "  1992,\n",
       "  1975,\n",
       "  1990,\n",
       "  1985,\n",
       "  1988,\n",
       "  1992,\n",
       "  1990,\n",
       "  1988,\n",
       "  1992,\n",
       "  1991,\n",
       "  1994,\n",
       "  1982,\n",
       "  1976,\n",
       "  1989,\n",
       "  1992,\n",
       "  1990,\n",
       "  1990,\n",
       "  1986,\n",
       "  1985,\n",
       "  1987,\n",
       "  1992,\n",
       "  1982,\n",
       "  1986,\n",
       "  1998,\n",
       "  1992,\n",
       "  1987,\n",
       "  1987,\n",
       "  1953,\n",
       "  1994,\n",
       "  1990,\n",
       "  1987,\n",
       "  1987,\n",
       "  1990,\n",
       "  1990,\n",
       "  1983,\n",
       "  1947,\n",
       "  1994,\n",
       "  1984,\n",
       "  1977,\n",
       "  1989,\n",
       "  1994,\n",
       "  1989,\n",
       "  1992,\n",
       "  2000,\n",
       "  1985,\n",
       "  1990,\n",
       "  1987,\n",
       "  1986,\n",
       "  1990,\n",
       "  1997,\n",
       "  1987,\n",
       "  1986,\n",
       "  1985,\n",
       "  1977,\n",
       "  1992,\n",
       "  1994,\n",
       "  1997,\n",
       "  1987,\n",
       "  1984,\n",
       "  1991,\n",
       "  1993,\n",
       "  1982,\n",
       "  1993,\n",
       "  1979,\n",
       "  1987,\n",
       "  1989,\n",
       "  1990,\n",
       "  1990,\n",
       "  1992,\n",
       "  1969,\n",
       "  1991,\n",
       "  1987,\n",
       "  1991,\n",
       "  1989,\n",
       "  1973,\n",
       "  1994,\n",
       "  1995,\n",
       "  1983,\n",
       "  1979,\n",
       "  1982,\n",
       "  1992,\n",
       "  1985,\n",
       "  1985,\n",
       "  1996,\n",
       "  1992,\n",
       "  1981,\n",
       "  1989,\n",
       "  1990,\n",
       "  1993,\n",
       "  1994,\n",
       "  1991,\n",
       "  1964,\n",
       "  1984,\n",
       "  1989,\n",
       "  1989,\n",
       "  1983,\n",
       "  1989,\n",
       "  1984,\n",
       "  1960,\n",
       "  1985,\n",
       "  1991,\n",
       "  1989,\n",
       "  1989,\n",
       "  1995,\n",
       "  1992,\n",
       "  1997,\n",
       "  1994,\n",
       "  1984,\n",
       "  1979,\n",
       "  1978,\n",
       "  1983,\n",
       "  1986,\n",
       "  1979,\n",
       "  1982,\n",
       "  1990,\n",
       "  1974,\n",
       "  1979,\n",
       "  1977,\n",
       "  1992,\n",
       "  1987,\n",
       "  1985,\n",
       "  1988,\n",
       "  1995,\n",
       "  1982,\n",
       "  1995,\n",
       "  1991,\n",
       "  1977,\n",
       "  1992,\n",
       "  1987,\n",
       "  1990,\n",
       "  1980,\n",
       "  1994,\n",
       "  1990,\n",
       "  1987,\n",
       "  1982,\n",
       "  1992,\n",
       "  1993,\n",
       "  1987,\n",
       "  1997,\n",
       "  1984,\n",
       "  1985,\n",
       "  1987,\n",
       "  1982,\n",
       "  1989,\n",
       "  1995,\n",
       "  1982,\n",
       "  1989,\n",
       "  1985,\n",
       "  1988,\n",
       "  1983,\n",
       "  1960,\n",
       "  1989,\n",
       "  1987,\n",
       "  1973,\n",
       "  1986,\n",
       "  1992,\n",
       "  1991,\n",
       "  1992,\n",
       "  1986,\n",
       "  1990,\n",
       "  1988,\n",
       "  1991,\n",
       "  1986,\n",
       "  1990,\n",
       "  1993,\n",
       "  1990,\n",
       "  1986,\n",
       "  1988,\n",
       "  1983,\n",
       "  1991,\n",
       "  1980,\n",
       "  1990,\n",
       "  1976,\n",
       "  1982,\n",
       "  1989,\n",
       "  1988,\n",
       "  1988,\n",
       "  1996,\n",
       "  1976,\n",
       "  1976,\n",
       "  1989,\n",
       "  1991,\n",
       "  1993,\n",
       "  1973,\n",
       "  1990,\n",
       "  1991,\n",
       "  1988,\n",
       "  1989,\n",
       "  1989,\n",
       "  1982,\n",
       "  1954,\n",
       "  1991,\n",
       "  1990,\n",
       "  1985,\n",
       "  1992,\n",
       "  1972,\n",
       "  1992,\n",
       "  1989,\n",
       "  1990,\n",
       "  1981,\n",
       "  1988,\n",
       "  1992,\n",
       "  1991,\n",
       "  1990,\n",
       "  1989,\n",
       "  1986,\n",
       "  1990,\n",
       "  1989,\n",
       "  1984,\n",
       "  1994,\n",
       "  1995,\n",
       "  1982,\n",
       "  1988,\n",
       "  1989,\n",
       "  1980,\n",
       "  1991,\n",
       "  1994,\n",
       "  1971,\n",
       "  1983,\n",
       "  1990,\n",
       "  1990,\n",
       "  1996,\n",
       "  1987,\n",
       "  1990,\n",
       "  1971,\n",
       "  1988,\n",
       "  1991,\n",
       "  1993,\n",
       "  1984,\n",
       "  1987,\n",
       "  1991,\n",
       "  1982,\n",
       "  1988,\n",
       "  1990,\n",
       "  1988,\n",
       "  1989,\n",
       "  1987,\n",
       "  1979,\n",
       "  1989,\n",
       "  1968,\n",
       "  1987,\n",
       "  1978,\n",
       "  1985,\n",
       "  1984,\n",
       "  1974,\n",
       "  1992,\n",
       "  1948,\n",
       "  1979,\n",
       "  1983,\n",
       "  1990,\n",
       "  1983,\n",
       "  1984,\n",
       "  1987,\n",
       "  1978,\n",
       "  1990,\n",
       "  1986,\n",
       "  1987,\n",
       "  1974,\n",
       "  1992,\n",
       "  1987,\n",
       "  1987,\n",
       "  1978,\n",
       "  1982,\n",
       "  1995,\n",
       "  1986,\n",
       "  1970,\n",
       "  1990,\n",
       "  1990,\n",
       "  1977,\n",
       "  1992,\n",
       "  1990,\n",
       "  1994,\n",
       "  1976,\n",
       "  1989,\n",
       "  1987,\n",
       "  1986,\n",
       "  1981,\n",
       "  1982,\n",
       "  1981,\n",
       "  1991,\n",
       "  1990,\n",
       "  1977,\n",
       "  1986,\n",
       "  1991,\n",
       "  1983,\n",
       "  1980,\n",
       "  1987,\n",
       "  1980,\n",
       "  1979,\n",
       "  1990,\n",
       "  1992,\n",
       "  1990,\n",
       "  1990,\n",
       "  1993,\n",
       "  1979,\n",
       "  1989,\n",
       "  1994,\n",
       "  1990,\n",
       "  1980,\n",
       "  1983,\n",
       "  1991,\n",
       "  1968,\n",
       "  1984,\n",
       "  1988,\n",
       "  1989,\n",
       "  1990,\n",
       "  1994,\n",
       "  1995,\n",
       "  1988,\n",
       "  ...])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months,years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "a59c06ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create new column customer age\n",
    "df['CustomerAge'] = ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "c5879896",
   "metadata": {},
   "outputs": [],
   "source": [
    "#inserting custgender,location and accountbalance for nan or empty values\n",
    "df.CustGender.fillna(df.CustGender.mode().values[0],inplace=True)\n",
    "df.CustLocation.fillna(df.CustLocation.mode().values[0],inplace=True)\n",
    "df.CustAccountBalance.fillna(df.CustAccountBalance.median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "eda5974b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract year,month and day from transaction date\n",
    "df['TransactionDate'] = pd.to_datetime(df['TransactionDate'])\n",
    "df['yeart'], df['montht'] ,df['dayt']= df['TransactionDate'].dt.year, df['TransactionDate'].dt.month,df['TransactionDate'].dt.day\n",
    "cols = ['yeart', 'montht','dayt']\n",
    "df[cols] = df[cols].applymap(np.int64)\n",
    "colst=[\"yeart\",\"montht\",\"dayt\"]\n",
    "df['transaction_date_sudeep'] = df[colst].apply(lambda x: '-'.join(x.values.astype(str)), axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "634c540c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting transactionid into numeric by len function\n",
    "df['len_TransactionID'] = df['TransactionID'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "00a8b442",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting customerid into numeric by len function\n",
    "\n",
    "df['len_CustomerID'] = df['CustomerID'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "6f814992",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 666550 entries, 0 to 666549\n",
      "Data columns (total 21 columns):\n",
      " #   Column                   Non-Null Count   Dtype         \n",
      "---  ------                   --------------   -----         \n",
      " 0   TransactionID            666550 non-null  object        \n",
      " 1   CustomerID               666550 non-null  object        \n",
      " 2   CustomerDOB              627108 non-null  object        \n",
      " 3   CustGender               666550 non-null  object        \n",
      " 4   CustLocation             666550 non-null  object        \n",
      " 5   CustAccountBalance       666550 non-null  float64       \n",
      " 6   TransactionDate          666550 non-null  datetime64[ns]\n",
      " 7   TransactionAmount (INR)  666550 non-null  float64       \n",
      " 8   DQ Issue                 666550 non-null  object        \n",
      " 9   CustomerDOBS             627108 non-null  datetime64[ns]\n",
      " 10  year                     666550 non-null  int64         \n",
      " 11  month                    666550 non-null  int64         \n",
      " 12  day                      666550 non-null  int64         \n",
      " 13  date_sudeep_final        666550 non-null  object        \n",
      " 14  CustomerAge              666550 non-null  int64         \n",
      " 15  yeart                    666550 non-null  int64         \n",
      " 16  montht                   666550 non-null  int64         \n",
      " 17  dayt                     666550 non-null  int64         \n",
      " 18  transaction_date_sudeep  666550 non-null  object        \n",
      " 19  len_TransactionID        666550 non-null  int64         \n",
      " 20  len_CustomerID           666550 non-null  int64         \n",
      "dtypes: datetime64[ns](2), float64(2), int64(9), object(8)\n",
      "memory usage: 106.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "e4dd5ba9",
   "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",
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       "    .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>TransactionID</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>CustomerDOB</th>\n",
       "      <th>CustGender</th>\n",
       "      <th>CustLocation</th>\n",
       "      <th>CustAccountBalance</th>\n",
       "      <th>TransactionDate</th>\n",
       "      <th>TransactionAmount (INR)</th>\n",
       "      <th>DQ Issue</th>\n",
       "      <th>CustomerDOBS</th>\n",
       "      <th>...</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>date_sudeep_final</th>\n",
       "      <th>CustomerAge</th>\n",
       "      <th>yeart</th>\n",
       "      <th>montht</th>\n",
       "      <th>dayt</th>\n",
       "      <th>transaction_date_sudeep</th>\n",
       "      <th>len_TransactionID</th>\n",
       "      <th>len_CustomerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>T20208001</td>\n",
       "      <td>C5841053</td>\n",
       "      <td>10-01-1994</td>\n",
       "      <td>F</td>\n",
       "      <td>JAMSHEDPUR</td>\n",
       "      <td>17819.05</td>\n",
       "      <td>2016-02-08</td>\n",
       "      <td>25.00</td>\n",
       "      <td>N</td>\n",
       "      <td>1994-10-01</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1994-10-1</td>\n",
       "      <td>28</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2016-2-8</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>T20208002</td>\n",
       "      <td>C2142763</td>\n",
       "      <td>04-04-1957</td>\n",
       "      <td>M</td>\n",
       "      <td>JHAJJAR</td>\n",
       "      <td>2270.69</td>\n",
       "      <td>2016-02-08</td>\n",
       "      <td>27999.00</td>\n",
       "      <td>N</td>\n",
       "      <td>1957-04-04</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1957-4-4</td>\n",
       "      <td>65</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2016-2-8</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T20208003</td>\n",
       "      <td>C4417068</td>\n",
       "      <td>26-11-1996</td>\n",
       "      <td>F</td>\n",
       "      <td>MUMBAI</td>\n",
       "      <td>17874.44</td>\n",
       "      <td>2016-02-08</td>\n",
       "      <td>459.00</td>\n",
       "      <td>N</td>\n",
       "      <td>1996-11-26</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>26</td>\n",
       "      <td>1996-11-26</td>\n",
       "      <td>26</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2016-2-8</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>T20208004</td>\n",
       "      <td>C5342380</td>\n",
       "      <td>14-09-1973</td>\n",
       "      <td>F</td>\n",
       "      <td>MUMBAI</td>\n",
       "      <td>866503.21</td>\n",
       "      <td>2016-02-08</td>\n",
       "      <td>2060.00</td>\n",
       "      <td>N</td>\n",
       "      <td>1973-09-14</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>1973-9-14</td>\n",
       "      <td>49</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2016-2-8</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>T20208005</td>\n",
       "      <td>C9031234</td>\n",
       "      <td>24-03-1988</td>\n",
       "      <td>F</td>\n",
       "      <td>NAVI MUMBAI</td>\n",
       "      <td>6714.43</td>\n",
       "      <td>2016-02-08</td>\n",
       "      <td>1762.50</td>\n",
       "      <td>N</td>\n",
       "      <td>1988-03-24</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>1988-3-24</td>\n",
       "      <td>34</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2016-2-8</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666545</th>\n",
       "      <td>T20874546</td>\n",
       "      <td>C6532244</td>\n",
       "      <td>09-09-1982</td>\n",
       "      <td>F</td>\n",
       "      <td>MUMBAI</td>\n",
       "      <td>5315.45</td>\n",
       "      <td>2016-08-31</td>\n",
       "      <td>16.00</td>\n",
       "      <td>N</td>\n",
       "      <td>1982-09-09</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1982-9-9</td>\n",
       "      <td>40</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>2016-8-31</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666546</th>\n",
       "      <td>T20874547</td>\n",
       "      <td>C2425686</td>\n",
       "      <td>22-02-1990</td>\n",
       "      <td>M</td>\n",
       "      <td>NEW DELHI</td>\n",
       "      <td>143945.30</td>\n",
       "      <td>2016-08-31</td>\n",
       "      <td>2659.14</td>\n",
       "      <td>N</td>\n",
       "      <td>1990-02-22</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>1990-2-22</td>\n",
       "      <td>32</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>2016-8-31</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666547</th>\n",
       "      <td>T20874548</td>\n",
       "      <td>C5029245</td>\n",
       "      <td>12-01-1977</td>\n",
       "      <td>M</td>\n",
       "      <td>RAJKOT</td>\n",
       "      <td>12921.53</td>\n",
       "      <td>2016-08-31</td>\n",
       "      <td>524.00</td>\n",
       "      <td>N</td>\n",
       "      <td>1977-12-01</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1977-12-1</td>\n",
       "      <td>45</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>2016-8-31</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666548</th>\n",
       "      <td>T20874549</td>\n",
       "      <td>C8338828</td>\n",
       "      <td>26-01-1981</td>\n",
       "      <td>F</td>\n",
       "      <td>SOLAN</td>\n",
       "      <td>4762.88</td>\n",
       "      <td>2016-08-31</td>\n",
       "      <td>100.00</td>\n",
       "      <td>N</td>\n",
       "      <td>1981-01-26</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>1981-1-26</td>\n",
       "      <td>41</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>2016-8-31</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666549</th>\n",
       "      <td>T20874550</td>\n",
       "      <td>C1142721</td>\n",
       "      <td>19-05-1988</td>\n",
       "      <td>M</td>\n",
       "      <td>PALAMPUR</td>\n",
       "      <td>8936.65</td>\n",
       "      <td>2016-08-31</td>\n",
       "      <td>500.00</td>\n",
       "      <td>N</td>\n",
       "      <td>1988-05-19</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>1988-5-19</td>\n",
       "      <td>34</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>2016-8-31</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>666550 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       TransactionID CustomerID CustomerDOB CustGender CustLocation  \\\n",
       "0          T20208001   C5841053  10-01-1994          F   JAMSHEDPUR   \n",
       "1          T20208002   C2142763  04-04-1957          M      JHAJJAR   \n",
       "2          T20208003   C4417068  26-11-1996          F       MUMBAI   \n",
       "3          T20208004   C5342380  14-09-1973          F       MUMBAI   \n",
       "4          T20208005   C9031234  24-03-1988          F  NAVI MUMBAI   \n",
       "...              ...        ...         ...        ...          ...   \n",
       "666545     T20874546   C6532244  09-09-1982          F       MUMBAI   \n",
       "666546     T20874547   C2425686  22-02-1990          M    NEW DELHI   \n",
       "666547     T20874548   C5029245  12-01-1977          M       RAJKOT   \n",
       "666548     T20874549   C8338828  26-01-1981          F        SOLAN   \n",
       "666549     T20874550   C1142721  19-05-1988          M     PALAMPUR   \n",
       "\n",
       "        CustAccountBalance TransactionDate  TransactionAmount (INR) DQ Issue  \\\n",
       "0                 17819.05      2016-02-08                    25.00        N   \n",
       "1                  2270.69      2016-02-08                 27999.00        N   \n",
       "2                 17874.44      2016-02-08                   459.00        N   \n",
       "3                866503.21      2016-02-08                  2060.00        N   \n",
       "4                  6714.43      2016-02-08                  1762.50        N   \n",
       "...                    ...             ...                      ...      ...   \n",
       "666545             5315.45      2016-08-31                    16.00        N   \n",
       "666546           143945.30      2016-08-31                  2659.14        N   \n",
       "666547            12921.53      2016-08-31                   524.00        N   \n",
       "666548             4762.88      2016-08-31                   100.00        N   \n",
       "666549             8936.65      2016-08-31                   500.00        N   \n",
       "\n",
       "       CustomerDOBS  ...  month  day  date_sudeep_final CustomerAge  yeart  \\\n",
       "0        1994-10-01  ...     10    1          1994-10-1          28   2016   \n",
       "1        1957-04-04  ...      4    4           1957-4-4          65   2016   \n",
       "2        1996-11-26  ...     11   26         1996-11-26          26   2016   \n",
       "3        1973-09-14  ...      9   14          1973-9-14          49   2016   \n",
       "4        1988-03-24  ...      3   24          1988-3-24          34   2016   \n",
       "...             ...  ...    ...  ...                ...         ...    ...   \n",
       "666545   1982-09-09  ...      9    9           1982-9-9          40   2016   \n",
       "666546   1990-02-22  ...      2   22          1990-2-22          32   2016   \n",
       "666547   1977-12-01  ...     12    1          1977-12-1          45   2016   \n",
       "666548   1981-01-26  ...      1   26          1981-1-26          41   2016   \n",
       "666549   1988-05-19  ...      5   19          1988-5-19          34   2016   \n",
       "\n",
       "        montht  dayt  transaction_date_sudeep len_TransactionID  \\\n",
       "0            2     8                 2016-2-8                 9   \n",
       "1            2     8                 2016-2-8                 9   \n",
       "2            2     8                 2016-2-8                 9   \n",
       "3            2     8                 2016-2-8                 9   \n",
       "4            2     8                 2016-2-8                 9   \n",
       "...        ...   ...                      ...               ...   \n",
       "666545       8    31                2016-8-31                 9   \n",
       "666546       8    31                2016-8-31                 9   \n",
       "666547       8    31                2016-8-31                 9   \n",
       "666548       8    31                2016-8-31                 9   \n",
       "666549       8    31                2016-8-31                 9   \n",
       "\n",
       "        len_CustomerID  \n",
       "0                    8  \n",
       "1                    8  \n",
       "2                    8  \n",
       "3                    8  \n",
       "4                    8  \n",
       "...                ...  \n",
       "666545               8  \n",
       "666546               8  \n",
       "666547               8  \n",
       "666548               8  \n",
       "666549               8  \n",
       "\n",
       "[666550 rows x 21 columns]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3376b8ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create dataframe by extract all required columns\n",
    "df_main_s = df[['len_TransactionID','len_CustomerID','CustGender','CustLocation','CustAccountBalance','TransactionAmount (INR)','CustomerAge','dayt','montht','yeart','DQ Issue']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "314fdba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save newly created df into csv\n",
    "df_main_s.to_csv(r'C:\\cloudxlab\\freelance\\sudeep.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6dc96110",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading csv file\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "df_sudeep = pd.read_csv(r'C:\\cloudxlab\\freelance\\sudeep.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1660983f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quality not isses:  94.03 % of the dataset\n",
      "Quality issues:  5.97 % of the dataset\n"
     ]
    }
   ],
   "source": [
    "print('Quality not isses: ', round(df_sudeep['DQ Issue'].value_counts()[0]/len(df_sudeep) * 100,2), '% of the dataset')\n",
    "\n",
    "print('Quality issues: ', round(df_sudeep['DQ Issue'].value_counts()[1]/len(df_sudeep) * 100,2), '% of the dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5527f129",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c8700cbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bac79da2",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_amount = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "57e703b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>len_TransactionID</th>\n",
       "      <th>len_CustomerID</th>\n",
       "      <th>CustGender</th>\n",
       "      <th>CustLocation</th>\n",
       "      <th>CustAccountBalance</th>\n",
       "      <th>TransactionAmount (INR)</th>\n",
       "      <th>CustomerAge</th>\n",
       "      <th>dayt</th>\n",
       "      <th>montht</th>\n",
       "      <th>yeart</th>\n",
       "      <th>DQ Issue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>JAMSHEDPUR</td>\n",
       "      <td>17819.05</td>\n",
       "      <td>25.00</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>M</td>\n",
       "      <td>JHAJJAR</td>\n",
       "      <td>2270.69</td>\n",
       "      <td>27999.00</td>\n",
       "      <td>65</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>MUMBAI</td>\n",
       "      <td>17874.44</td>\n",
       "      <td>459.00</td>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>MUMBAI</td>\n",
       "      <td>866503.21</td>\n",
       "      <td>2060.00</td>\n",
       "      <td>49</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>NAVI MUMBAI</td>\n",
       "      <td>6714.43</td>\n",
       "      <td>1762.50</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666545</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>MUMBAI</td>\n",
       "      <td>5315.45</td>\n",
       "      <td>16.00</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666546</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>M</td>\n",
       "      <td>NEW DELHI</td>\n",
       "      <td>143945.30</td>\n",
       "      <td>2659.14</td>\n",
       "      <td>32</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666547</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>M</td>\n",
       "      <td>RAJKOT</td>\n",
       "      <td>12921.53</td>\n",
       "      <td>524.00</td>\n",
       "      <td>45</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666548</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>SOLAN</td>\n",
       "      <td>4762.88</td>\n",
       "      <td>100.00</td>\n",
       "      <td>41</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
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       "    <tr>\n",
       "      <th>666549</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>M</td>\n",
       "      <td>PALAMPUR</td>\n",
       "      <td>8936.65</td>\n",
       "      <td>500.00</td>\n",
       "      <td>34</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>666550 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        len_TransactionID  len_CustomerID CustGender CustLocation  \\\n",
       "0                       9               8          F   JAMSHEDPUR   \n",
       "1                       9               8          M      JHAJJAR   \n",
       "2                       9               8          F       MUMBAI   \n",
       "3                       9               8          F       MUMBAI   \n",
       "4                       9               8          F  NAVI MUMBAI   \n",
       "...                   ...             ...        ...          ...   \n",
       "666545                  9               8          F       MUMBAI   \n",
       "666546                  9               8          M    NEW DELHI   \n",
       "666547                  9               8          M       RAJKOT   \n",
       "666548                  9               8          F        SOLAN   \n",
       "666549                  9               8          M     PALAMPUR   \n",
       "\n",
       "        CustAccountBalance  TransactionAmount (INR)  CustomerAge  dayt  \\\n",
       "0                 17819.05                    25.00           28     8   \n",
       "1                  2270.69                 27999.00           65     8   \n",
       "2                 17874.44                   459.00           26     8   \n",
       "3                866503.21                  2060.00           49     8   \n",
       "4                  6714.43                  1762.50           34     8   \n",
       "...                    ...                      ...          ...   ...   \n",
       "666545             5315.45                    16.00           40    31   \n",
       "666546           143945.30                  2659.14           32    31   \n",
       "666547            12921.53                   524.00           45    31   \n",
       "666548             4762.88                   100.00           41    31   \n",
       "666549             8936.65                   500.00           34    31   \n",
       "\n",
       "        montht  yeart DQ Issue  \n",
       "0            2   2016        N  \n",
       "1            2   2016        N  \n",
       "2            2   2016        N  \n",
       "3            2   2016        N  \n",
       "4            2   2016        N  \n",
       "...        ...    ...      ...  \n",
       "666545       8   2016        N  \n",
       "666546       8   2016        N  \n",
       "666547       8   2016        N  \n",
       "666548       8   2016        N  \n",
       "666549       8   2016        N  \n",
       "\n",
       "[666550 rows x 11 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sudeep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "39d8dcdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#applying Standard Scaler to all numeric columns values for normalization\n",
    "df_sudeep['normlen_TransactionID'] = scaler_amount.fit_transform(df_sudeep['len_TransactionID'].values.reshape(-1, 1))\n",
    "df_sudeep['normlen_CustomerID'] = scaler_amount.fit_transform(df_sudeep['len_CustomerID'].values.reshape(-1, 1))\n",
    "df_sudeep['normCustAccountBalance'] = scaler_amount.fit_transform(df_sudeep['CustAccountBalance'].values.reshape(-1, 1))\n",
    "df_sudeep['normTransactionAmount (INR)'] = scaler_amount.fit_transform(df_sudeep['TransactionAmount (INR)'].values.reshape(-1, 1))\n",
    "df_sudeep['normCustomerAge'] = scaler_amount.fit_transform(df_sudeep['CustomerAge'].values.reshape(-1, 1))\n",
    "df_sudeep['normdayt'] = scaler_amount.fit_transform(df_sudeep['dayt'].values.reshape(-1, 1))\n",
    "df_sudeep['normmontht'] = scaler_amount.fit_transform(df_sudeep['montht'].values.reshape(-1, 1))\n",
    "df_sudeep['normyeart'] = scaler_amount.fit_transform(df_sudeep['yeart'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c7fe22b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>len_TransactionID</th>\n",
       "      <th>len_CustomerID</th>\n",
       "      <th>CustGender</th>\n",
       "      <th>CustLocation</th>\n",
       "      <th>CustAccountBalance</th>\n",
       "      <th>TransactionAmount (INR)</th>\n",
       "      <th>CustomerAge</th>\n",
       "      <th>dayt</th>\n",
       "      <th>montht</th>\n",
       "      <th>yeart</th>\n",
       "      <th>DQ Issue</th>\n",
       "      <th>normlen_TransactionID</th>\n",
       "      <th>normlen_CustomerID</th>\n",
       "      <th>normCustAccountBalance</th>\n",
       "      <th>normTransactionAmount (INR)</th>\n",
       "      <th>normCustomerAge</th>\n",
       "      <th>normdayt</th>\n",
       "      <th>normmontht</th>\n",
       "      <th>normyeart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>JAMSHEDPUR</td>\n",
       "      <td>17819.05</td>\n",
       "      <td>25.00</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.128643</td>\n",
       "      <td>-0.231550</td>\n",
       "      <td>-1.017684</td>\n",
       "      <td>-1.023564</td>\n",
       "      <td>-2.321762</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>M</td>\n",
       "      <td>JHAJJAR</td>\n",
       "      <td>2270.69</td>\n",
       "      <td>27999.00</td>\n",
       "      <td>65</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.149247</td>\n",
       "      <td>3.909165</td>\n",
       "      <td>3.270609</td>\n",
       "      <td>-1.023564</td>\n",
       "      <td>-2.321762</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>MUMBAI</td>\n",
       "      <td>17874.44</td>\n",
       "      <td>459.00</td>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.128570</td>\n",
       "      <td>-0.167309</td>\n",
       "      <td>-1.249484</td>\n",
       "      <td>-1.023564</td>\n",
       "      <td>-2.321762</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>MUMBAI</td>\n",
       "      <td>866503.21</td>\n",
       "      <td>2060.00</td>\n",
       "      <td>49</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>0.995949</td>\n",
       "      <td>0.069671</td>\n",
       "      <td>1.416212</td>\n",
       "      <td>-1.023564</td>\n",
       "      <td>-2.321762</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>NAVI MUMBAI</td>\n",
       "      <td>6714.43</td>\n",
       "      <td>1762.50</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.143358</td>\n",
       "      <td>0.025635</td>\n",
       "      <td>-0.322285</td>\n",
       "      <td>-1.023564</td>\n",
       "      <td>-2.321762</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666545</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>MUMBAI</td>\n",
       "      <td>5315.45</td>\n",
       "      <td>16.00</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.145212</td>\n",
       "      <td>-0.232882</td>\n",
       "      <td>0.373114</td>\n",
       "      <td>2.046984</td>\n",
       "      <td>0.240029</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666546</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>M</td>\n",
       "      <td>NEW DELHI</td>\n",
       "      <td>143945.30</td>\n",
       "      <td>2659.14</td>\n",
       "      <td>32</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>0.038487</td>\n",
       "      <td>0.158356</td>\n",
       "      <td>-0.554085</td>\n",
       "      <td>2.046984</td>\n",
       "      <td>0.240029</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666547</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>M</td>\n",
       "      <td>RAJKOT</td>\n",
       "      <td>12921.53</td>\n",
       "      <td>524.00</td>\n",
       "      <td>45</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.135133</td>\n",
       "      <td>-0.157688</td>\n",
       "      <td>0.952613</td>\n",
       "      <td>2.046984</td>\n",
       "      <td>0.240029</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666548</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>SOLAN</td>\n",
       "      <td>4762.88</td>\n",
       "      <td>100.00</td>\n",
       "      <td>41</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.145944</td>\n",
       "      <td>-0.220449</td>\n",
       "      <td>0.489014</td>\n",
       "      <td>2.046984</td>\n",
       "      <td>0.240029</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666549</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>M</td>\n",
       "      <td>PALAMPUR</td>\n",
       "      <td>8936.65</td>\n",
       "      <td>500.00</td>\n",
       "      <td>34</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>N</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.140413</td>\n",
       "      <td>-0.161240</td>\n",
       "      <td>-0.322285</td>\n",
       "      <td>2.046984</td>\n",
       "      <td>0.240029</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>666550 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        len_TransactionID  len_CustomerID CustGender CustLocation  \\\n",
       "0                       9               8          F   JAMSHEDPUR   \n",
       "1                       9               8          M      JHAJJAR   \n",
       "2                       9               8          F       MUMBAI   \n",
       "3                       9               8          F       MUMBAI   \n",
       "4                       9               8          F  NAVI MUMBAI   \n",
       "...                   ...             ...        ...          ...   \n",
       "666545                  9               8          F       MUMBAI   \n",
       "666546                  9               8          M    NEW DELHI   \n",
       "666547                  9               8          M       RAJKOT   \n",
       "666548                  9               8          F        SOLAN   \n",
       "666549                  9               8          M     PALAMPUR   \n",
       "\n",
       "        CustAccountBalance  TransactionAmount (INR)  CustomerAge  dayt  \\\n",
       "0                 17819.05                    25.00           28     8   \n",
       "1                  2270.69                 27999.00           65     8   \n",
       "2                 17874.44                   459.00           26     8   \n",
       "3                866503.21                  2060.00           49     8   \n",
       "4                  6714.43                  1762.50           34     8   \n",
       "...                    ...                      ...          ...   ...   \n",
       "666545             5315.45                    16.00           40    31   \n",
       "666546           143945.30                  2659.14           32    31   \n",
       "666547            12921.53                   524.00           45    31   \n",
       "666548             4762.88                   100.00           41    31   \n",
       "666549             8936.65                   500.00           34    31   \n",
       "\n",
       "        montht  yeart DQ Issue  normlen_TransactionID  normlen_CustomerID  \\\n",
       "0            2   2016        N               0.003099            0.012311   \n",
       "1            2   2016        N               0.003099            0.012311   \n",
       "2            2   2016        N               0.003099            0.012311   \n",
       "3            2   2016        N               0.003099            0.012311   \n",
       "4            2   2016        N               0.003099            0.012311   \n",
       "...        ...    ...      ...                    ...                 ...   \n",
       "666545       8   2016        N               0.003099            0.012311   \n",
       "666546       8   2016        N               0.003099            0.012311   \n",
       "666547       8   2016        N               0.003099            0.012311   \n",
       "666548       8   2016        N               0.003099            0.012311   \n",
       "666549       8   2016        N               0.003099            0.012311   \n",
       "\n",
       "        normCustAccountBalance  normTransactionAmount (INR)  normCustomerAge  \\\n",
       "0                    -0.128643                    -0.231550        -1.017684   \n",
       "1                    -0.149247                     3.909165         3.270609   \n",
       "2                    -0.128570                    -0.167309        -1.249484   \n",
       "3                     0.995949                     0.069671         1.416212   \n",
       "4                    -0.143358                     0.025635        -0.322285   \n",
       "...                        ...                          ...              ...   \n",
       "666545               -0.145212                    -0.232882         0.373114   \n",
       "666546                0.038487                     0.158356        -0.554085   \n",
       "666547               -0.135133                    -0.157688         0.952613   \n",
       "666548               -0.145944                    -0.220449         0.489014   \n",
       "666549               -0.140413                    -0.161240        -0.322285   \n",
       "\n",
       "        normdayt  normmontht  normyeart  \n",
       "0      -1.023564   -2.321762        0.0  \n",
       "1      -1.023564   -2.321762        0.0  \n",
       "2      -1.023564   -2.321762        0.0  \n",
       "3      -1.023564   -2.321762        0.0  \n",
       "4      -1.023564   -2.321762        0.0  \n",
       "...          ...         ...        ...  \n",
       "666545  2.046984    0.240029        0.0  \n",
       "666546  2.046984    0.240029        0.0  \n",
       "666547  2.046984    0.240029        0.0  \n",
       "666548  2.046984    0.240029        0.0  \n",
       "666549  2.046984    0.240029        0.0  \n",
       "\n",
       "[666550 rows x 19 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sudeep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "07c4e40f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 666550 entries, 0 to 666549\n",
      "Data columns (total 19 columns):\n",
      " #   Column                       Non-Null Count   Dtype  \n",
      "---  ------                       --------------   -----  \n",
      " 0   len_TransactionID            666550 non-null  int64  \n",
      " 1   len_CustomerID               666550 non-null  int64  \n",
      " 2   CustGender                   666550 non-null  object \n",
      " 3   CustLocation                 666550 non-null  object \n",
      " 4   CustAccountBalance           666550 non-null  float64\n",
      " 5   TransactionAmount (INR)      666550 non-null  float64\n",
      " 6   CustomerAge                  666550 non-null  int64  \n",
      " 7   dayt                         666550 non-null  int64  \n",
      " 8   montht                       666550 non-null  int64  \n",
      " 9   yeart                        666550 non-null  int64  \n",
      " 10  DQ Issue                     666550 non-null  object \n",
      " 11  normlen_TransactionID        666550 non-null  float64\n",
      " 12  normlen_CustomerID           666550 non-null  float64\n",
      " 13  normCustAccountBalance       666550 non-null  float64\n",
      " 14  normTransactionAmount (INR)  666550 non-null  float64\n",
      " 15  normCustomerAge              666550 non-null  float64\n",
      " 16  normdayt                     666550 non-null  float64\n",
      " 17  normmontht                   666550 non-null  float64\n",
      " 18  normyeart                    666550 non-null  float64\n",
      "dtypes: float64(10), int64(6), object(3)\n",
      "memory usage: 96.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df_sudeep.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4763da57",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting DQ issue column into 0 and 1\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "lb = LabelEncoder() \n",
    "df_sudeep['DQ Issue'] = lb.fit_transform(df_sudeep['DQ Issue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8062f299",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>len_TransactionID</th>\n",
       "      <th>len_CustomerID</th>\n",
       "      <th>CustGender</th>\n",
       "      <th>CustLocation</th>\n",
       "      <th>CustAccountBalance</th>\n",
       "      <th>TransactionAmount (INR)</th>\n",
       "      <th>CustomerAge</th>\n",
       "      <th>dayt</th>\n",
       "      <th>montht</th>\n",
       "      <th>yeart</th>\n",
       "      <th>DQ Issue</th>\n",
       "      <th>normlen_TransactionID</th>\n",
       "      <th>normlen_CustomerID</th>\n",
       "      <th>normCustAccountBalance</th>\n",
       "      <th>normTransactionAmount (INR)</th>\n",
       "      <th>normCustomerAge</th>\n",
       "      <th>normdayt</th>\n",
       "      <th>normmontht</th>\n",
       "      <th>normyeart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>JAMSHEDPUR</td>\n",
       "      <td>17819.05</td>\n",
       "      <td>25.00</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.128643</td>\n",
       "      <td>-0.231550</td>\n",
       "      <td>-1.017684</td>\n",
       "      <td>-1.023564</td>\n",
       "      <td>-2.321762</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>M</td>\n",
       "      <td>JHAJJAR</td>\n",
       "      <td>2270.69</td>\n",
       "      <td>27999.00</td>\n",
       "      <td>65</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.149247</td>\n",
       "      <td>3.909165</td>\n",
       "      <td>3.270609</td>\n",
       "      <td>-1.023564</td>\n",
       "      <td>-2.321762</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>MUMBAI</td>\n",
       "      <td>17874.44</td>\n",
       "      <td>459.00</td>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.128570</td>\n",
       "      <td>-0.167309</td>\n",
       "      <td>-1.249484</td>\n",
       "      <td>-1.023564</td>\n",
       "      <td>-2.321762</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>MUMBAI</td>\n",
       "      <td>866503.21</td>\n",
       "      <td>2060.00</td>\n",
       "      <td>49</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>0.995949</td>\n",
       "      <td>0.069671</td>\n",
       "      <td>1.416212</td>\n",
       "      <td>-1.023564</td>\n",
       "      <td>-2.321762</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>NAVI MUMBAI</td>\n",
       "      <td>6714.43</td>\n",
       "      <td>1762.50</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.143358</td>\n",
       "      <td>0.025635</td>\n",
       "      <td>-0.322285</td>\n",
       "      <td>-1.023564</td>\n",
       "      <td>-2.321762</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666545</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>MUMBAI</td>\n",
       "      <td>5315.45</td>\n",
       "      <td>16.00</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.145212</td>\n",
       "      <td>-0.232882</td>\n",
       "      <td>0.373114</td>\n",
       "      <td>2.046984</td>\n",
       "      <td>0.240029</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666546</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>M</td>\n",
       "      <td>NEW DELHI</td>\n",
       "      <td>143945.30</td>\n",
       "      <td>2659.14</td>\n",
       "      <td>32</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>0.038487</td>\n",
       "      <td>0.158356</td>\n",
       "      <td>-0.554085</td>\n",
       "      <td>2.046984</td>\n",
       "      <td>0.240029</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666547</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>M</td>\n",
       "      <td>RAJKOT</td>\n",
       "      <td>12921.53</td>\n",
       "      <td>524.00</td>\n",
       "      <td>45</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.135133</td>\n",
       "      <td>-0.157688</td>\n",
       "      <td>0.952613</td>\n",
       "      <td>2.046984</td>\n",
       "      <td>0.240029</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666548</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>SOLAN</td>\n",
       "      <td>4762.88</td>\n",
       "      <td>100.00</td>\n",
       "      <td>41</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.145944</td>\n",
       "      <td>-0.220449</td>\n",
       "      <td>0.489014</td>\n",
       "      <td>2.046984</td>\n",
       "      <td>0.240029</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666549</th>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>M</td>\n",
       "      <td>PALAMPUR</td>\n",
       "      <td>8936.65</td>\n",
       "      <td>500.00</td>\n",
       "      <td>34</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.140413</td>\n",
       "      <td>-0.161240</td>\n",
       "      <td>-0.322285</td>\n",
       "      <td>2.046984</td>\n",
       "      <td>0.240029</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>666550 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        len_TransactionID  len_CustomerID CustGender CustLocation  \\\n",
       "0                       9               8          F   JAMSHEDPUR   \n",
       "1                       9               8          M      JHAJJAR   \n",
       "2                       9               8          F       MUMBAI   \n",
       "3                       9               8          F       MUMBAI   \n",
       "4                       9               8          F  NAVI MUMBAI   \n",
       "...                   ...             ...        ...          ...   \n",
       "666545                  9               8          F       MUMBAI   \n",
       "666546                  9               8          M    NEW DELHI   \n",
       "666547                  9               8          M       RAJKOT   \n",
       "666548                  9               8          F        SOLAN   \n",
       "666549                  9               8          M     PALAMPUR   \n",
       "\n",
       "        CustAccountBalance  TransactionAmount (INR)  CustomerAge  dayt  \\\n",
       "0                 17819.05                    25.00           28     8   \n",
       "1                  2270.69                 27999.00           65     8   \n",
       "2                 17874.44                   459.00           26     8   \n",
       "3                866503.21                  2060.00           49     8   \n",
       "4                  6714.43                  1762.50           34     8   \n",
       "...                    ...                      ...          ...   ...   \n",
       "666545             5315.45                    16.00           40    31   \n",
       "666546           143945.30                  2659.14           32    31   \n",
       "666547            12921.53                   524.00           45    31   \n",
       "666548             4762.88                   100.00           41    31   \n",
       "666549             8936.65                   500.00           34    31   \n",
       "\n",
       "        montht  yeart  DQ Issue  normlen_TransactionID  normlen_CustomerID  \\\n",
       "0            2   2016         0               0.003099            0.012311   \n",
       "1            2   2016         0               0.003099            0.012311   \n",
       "2            2   2016         0               0.003099            0.012311   \n",
       "3            2   2016         0               0.003099            0.012311   \n",
       "4            2   2016         0               0.003099            0.012311   \n",
       "...        ...    ...       ...                    ...                 ...   \n",
       "666545       8   2016         0               0.003099            0.012311   \n",
       "666546       8   2016         0               0.003099            0.012311   \n",
       "666547       8   2016         0               0.003099            0.012311   \n",
       "666548       8   2016         0               0.003099            0.012311   \n",
       "666549       8   2016         0               0.003099            0.012311   \n",
       "\n",
       "        normCustAccountBalance  normTransactionAmount (INR)  normCustomerAge  \\\n",
       "0                    -0.128643                    -0.231550        -1.017684   \n",
       "1                    -0.149247                     3.909165         3.270609   \n",
       "2                    -0.128570                    -0.167309        -1.249484   \n",
       "3                     0.995949                     0.069671         1.416212   \n",
       "4                    -0.143358                     0.025635        -0.322285   \n",
       "...                        ...                          ...              ...   \n",
       "666545               -0.145212                    -0.232882         0.373114   \n",
       "666546                0.038487                     0.158356        -0.554085   \n",
       "666547               -0.135133                    -0.157688         0.952613   \n",
       "666548               -0.145944                    -0.220449         0.489014   \n",
       "666549               -0.140413                    -0.161240        -0.322285   \n",
       "\n",
       "        normdayt  normmontht  normyeart  \n",
       "0      -1.023564   -2.321762        0.0  \n",
       "1      -1.023564   -2.321762        0.0  \n",
       "2      -1.023564   -2.321762        0.0  \n",
       "3      -1.023564   -2.321762        0.0  \n",
       "4      -1.023564   -2.321762        0.0  \n",
       "...          ...         ...        ...  \n",
       "666545  2.046984    0.240029        0.0  \n",
       "666546  2.046984    0.240029        0.0  \n",
       "666547  2.046984    0.240029        0.0  \n",
       "666548  2.046984    0.240029        0.0  \n",
       "666549  2.046984    0.240029        0.0  \n",
       "\n",
       "[666550 rows x 19 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sudeep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f4e59887",
   "metadata": {},
   "outputs": [],
   "source": [
    "#created new DF with normalization values and dropped custlocation column\n",
    "df_main = df_sudeep[['normlen_TransactionID','normlen_CustomerID','normCustAccountBalance','normTransactionAmount (INR)','normCustomerAge','normdayt','normmontht','normyeart','DQ Issue','CustGender']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c8f75db2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>normlen_TransactionID</th>\n",
       "      <th>normlen_CustomerID</th>\n",
       "      <th>normCustAccountBalance</th>\n",
       "      <th>normTransactionAmount (INR)</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>0.995949</td>\n",
       "      <td>0.069671</td>\n",
       "      <td>1.416212</td>\n",
       "      <td>-1.023564</td>\n",
       "      <td>-2.321762</td>\n",
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       "      <th>666545</th>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.145212</td>\n",
       "      <td>-0.232882</td>\n",
       "      <td>0.373114</td>\n",
       "      <td>2.046984</td>\n",
       "      <td>0.240029</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>666546</th>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>0.038487</td>\n",
       "      <td>0.158356</td>\n",
       "      <td>-0.554085</td>\n",
       "      <td>2.046984</td>\n",
       "      <td>0.240029</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>666547</th>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.135133</td>\n",
       "      <td>-0.157688</td>\n",
       "      <td>0.952613</td>\n",
       "      <td>2.046984</td>\n",
       "      <td>0.240029</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666548</th>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.145944</td>\n",
       "      <td>-0.220449</td>\n",
       "      <td>0.489014</td>\n",
       "      <td>2.046984</td>\n",
       "      <td>0.240029</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>0.003099</td>\n",
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       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>666550 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        normlen_TransactionID  normlen_CustomerID  normCustAccountBalance  \\\n",
       "0                    0.003099            0.012311               -0.128643   \n",
       "1                    0.003099            0.012311               -0.149247   \n",
       "2                    0.003099            0.012311               -0.128570   \n",
       "3                    0.003099            0.012311                0.995949   \n",
       "4                    0.003099            0.012311               -0.143358   \n",
       "...                       ...                 ...                     ...   \n",
       "666545               0.003099            0.012311               -0.145212   \n",
       "666546               0.003099            0.012311                0.038487   \n",
       "666547               0.003099            0.012311               -0.135133   \n",
       "666548               0.003099            0.012311               -0.145944   \n",
       "666549               0.003099            0.012311               -0.140413   \n",
       "\n",
       "        normTransactionAmount (INR)  normCustomerAge  normdayt  normmontht  \\\n",
       "0                         -0.231550        -1.017684 -1.023564   -2.321762   \n",
       "1                          3.909165         3.270609 -1.023564   -2.321762   \n",
       "2                         -0.167309        -1.249484 -1.023564   -2.321762   \n",
       "3                          0.069671         1.416212 -1.023564   -2.321762   \n",
       "4                          0.025635        -0.322285 -1.023564   -2.321762   \n",
       "...                             ...              ...       ...         ...   \n",
       "666545                    -0.232882         0.373114  2.046984    0.240029   \n",
       "666546                     0.158356        -0.554085  2.046984    0.240029   \n",
       "666547                    -0.157688         0.952613  2.046984    0.240029   \n",
       "666548                    -0.220449         0.489014  2.046984    0.240029   \n",
       "666549                    -0.161240        -0.322285  2.046984    0.240029   \n",
       "\n",
       "        normyeart  DQ Issue CustGender  \n",
       "0             0.0         0          F  \n",
       "1             0.0         0          M  \n",
       "2             0.0         0          F  \n",
       "3             0.0         0          F  \n",
       "4             0.0         0          F  \n",
       "...           ...       ...        ...  \n",
       "666545        0.0         0          F  \n",
       "666546        0.0         0          M  \n",
       "666547        0.0         0          M  \n",
       "666548        0.0         0          F  \n",
       "666549        0.0         0          M  \n",
       "\n",
       "[666550 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "67427a0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Applying one hot encoding for Custgender column\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "encoder = OneHotEncoder(categories = 'auto')\n",
    "X = encoder.fit_transform(\n",
    "    df_main['CustGender'].values.reshape(-1,1)).toarray()\n",
    "dfonehot = pd.DataFrame(X)\n",
    "df_main_sudeep = pd.concat([df_main, dfonehot], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8f4026ba",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        normlen_TransactionID  normlen_CustomerID  normCustAccountBalance  \\\n",
       "0                    0.003099            0.012311               -0.128643   \n",
       "1                    0.003099            0.012311               -0.149247   \n",
       "2                    0.003099            0.012311               -0.128570   \n",
       "3                    0.003099            0.012311                0.995949   \n",
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       "...                       ...                 ...                     ...   \n",
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       "666549               0.003099            0.012311               -0.140413   \n",
       "\n",
       "        normTransactionAmount (INR)  normCustomerAge  normdayt  normmontht  \\\n",
       "0                         -0.231550        -1.017684 -1.023564   -2.321762   \n",
       "1                          3.909165         3.270609 -1.023564   -2.321762   \n",
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       "...                             ...              ...       ...         ...   \n",
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       "666549                    -0.161240        -0.322285  2.046984    0.240029   \n",
       "\n",
       "        normyeart  DQ Issue CustGender    0    1  \n",
       "0             0.0         0          F  1.0  0.0  \n",
       "1             0.0         0          M  0.0  1.0  \n",
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       "3             0.0         0          F  1.0  0.0  \n",
       "4             0.0         0          F  1.0  0.0  \n",
       "...           ...       ...        ...  ...  ...  \n",
       "666545        0.0         0          F  1.0  0.0  \n",
       "666546        0.0         0          M  0.0  1.0  \n",
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       "666548        0.0         0          F  1.0  0.0  \n",
       "666549        0.0         0          M  0.0  1.0  \n",
       "\n",
       "[666550 rows x 12 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main_sudeep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0f3dbf50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['F', 'M'], dtype=object)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f6a0e8d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['x0_F', 'x0_M'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = encoder.get_feature_names_out()\n",
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "60891339",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_main_sudeep.columns = ['normlen_TransactionID','normlen_CustomerID','normCustAccountBalance', 'normTransactionAmount (INR)','normCustomerAge','normdayt','normmontht','normyeart','DQ Issue','CustGender','Female','Male']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7a1489b4",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "        normlen_TransactionID  normlen_CustomerID  normCustAccountBalance  \\\n",
       "0                    0.003099            0.012311               -0.128643   \n",
       "1                    0.003099            0.012311               -0.149247   \n",
       "2                    0.003099            0.012311               -0.128570   \n",
       "3                    0.003099            0.012311                0.995949   \n",
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       "...                       ...                 ...                     ...   \n",
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       "666549               0.003099            0.012311               -0.140413   \n",
       "\n",
       "        normTransactionAmount (INR)  normCustomerAge  normdayt  normmontht  \\\n",
       "0                         -0.231550        -1.017684 -1.023564   -2.321762   \n",
       "1                          3.909165         3.270609 -1.023564   -2.321762   \n",
       "2                         -0.167309        -1.249484 -1.023564   -2.321762   \n",
       "3                          0.069671         1.416212 -1.023564   -2.321762   \n",
       "4                          0.025635        -0.322285 -1.023564   -2.321762   \n",
       "...                             ...              ...       ...         ...   \n",
       "666545                    -0.232882         0.373114  2.046984    0.240029   \n",
       "666546                     0.158356        -0.554085  2.046984    0.240029   \n",
       "666547                    -0.157688         0.952613  2.046984    0.240029   \n",
       "666548                    -0.220449         0.489014  2.046984    0.240029   \n",
       "666549                    -0.161240        -0.322285  2.046984    0.240029   \n",
       "\n",
       "        normyeart  DQ Issue CustGender  Female  Male  \n",
       "0             0.0         0          F     1.0   0.0  \n",
       "1             0.0         0          M     0.0   1.0  \n",
       "2             0.0         0          F     1.0   0.0  \n",
       "3             0.0         0          F     1.0   0.0  \n",
       "4             0.0         0          F     1.0   0.0  \n",
       "...           ...       ...        ...     ...   ...  \n",
       "666545        0.0         0          F     1.0   0.0  \n",
       "666546        0.0         0          M     0.0   1.0  \n",
       "666547        0.0         0          M     0.0   1.0  \n",
       "666548        0.0         0          F     1.0   0.0  \n",
       "666549        0.0         0          M     0.0   1.0  \n",
       "\n",
       "[666550 rows x 12 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main_sudeep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "af8bd9ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sudeep = df_main_sudeep[['normlen_TransactionID','normlen_CustomerID','normCustAccountBalance', 'normTransactionAmount (INR)','normCustomerAge','normdayt','normmontht','normyeart','Female','Male']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c811e79e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>normlen_TransactionID</th>\n",
       "      <th>normlen_CustomerID</th>\n",
       "      <th>normCustAccountBalance</th>\n",
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       "      <td>0.012311</td>\n",
       "      <td>-0.145212</td>\n",
       "      <td>-0.232882</td>\n",
       "      <td>0.373114</td>\n",
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       "      <td>-0.135133</td>\n",
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       "      <td>0.003099</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>-0.145944</td>\n",
       "      <td>-0.220449</td>\n",
       "      <td>0.489014</td>\n",
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       "<p>666550 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        normlen_TransactionID  normlen_CustomerID  normCustAccountBalance  \\\n",
       "0                    0.003099            0.012311               -0.128643   \n",
       "1                    0.003099            0.012311               -0.149247   \n",
       "2                    0.003099            0.012311               -0.128570   \n",
       "3                    0.003099            0.012311                0.995949   \n",
       "4                    0.003099            0.012311               -0.143358   \n",
       "...                       ...                 ...                     ...   \n",
       "666545               0.003099            0.012311               -0.145212   \n",
       "666546               0.003099            0.012311                0.038487   \n",
       "666547               0.003099            0.012311               -0.135133   \n",
       "666548               0.003099            0.012311               -0.145944   \n",
       "666549               0.003099            0.012311               -0.140413   \n",
       "\n",
       "        normTransactionAmount (INR)  normCustomerAge  normdayt  normmontht  \\\n",
       "0                         -0.231550        -1.017684 -1.023564   -2.321762   \n",
       "1                          3.909165         3.270609 -1.023564   -2.321762   \n",
       "2                         -0.167309        -1.249484 -1.023564   -2.321762   \n",
       "3                          0.069671         1.416212 -1.023564   -2.321762   \n",
       "4                          0.025635        -0.322285 -1.023564   -2.321762   \n",
       "...                             ...              ...       ...         ...   \n",
       "666545                    -0.232882         0.373114  2.046984    0.240029   \n",
       "666546                     0.158356        -0.554085  2.046984    0.240029   \n",
       "666547                    -0.157688         0.952613  2.046984    0.240029   \n",
       "666548                    -0.220449         0.489014  2.046984    0.240029   \n",
       "666549                    -0.161240        -0.322285  2.046984    0.240029   \n",
       "\n",
       "        normyeart  Female  Male  \n",
       "0             0.0     1.0   0.0  \n",
       "1             0.0     0.0   1.0  \n",
       "2             0.0     1.0   0.0  \n",
       "3             0.0     1.0   0.0  \n",
       "4             0.0     1.0   0.0  \n",
       "...           ...     ...   ...  \n",
       "666545        0.0     1.0   0.0  \n",
       "666546        0.0     0.0   1.0  \n",
       "666547        0.0     0.0   1.0  \n",
       "666548        0.0     1.0   0.0  \n",
       "666549        0.0     0.0   1.0  \n",
       "\n",
       "[666550 rows x 10 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sudeep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b48d0984",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_sudeep = df_main_sudeep[['DQ Issue']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1bca0f51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DQ Issue\n",
       "0           626775\n",
       "1            39775\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_sudeep.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ebb296c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test =  train_test_split(X_sudeep, y_sudeep, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "05d1eb7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((533240, 10), (533240, 1), (133310, 10), (133310, 1))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,y_train.shape,X_test.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "94c99049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DQ Issue\n",
       "0           125403\n",
       "1             7907\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "82068651",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DQ Issue\n",
       "0           501372\n",
       "1            31868\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f148243b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a48ed916",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before over-sampling\n",
      ": 0    501372\n",
      "1     31868\n",
      "Name: DQ Issue, dtype: int64\n",
      "After over-sampling:\n",
      " 0    501372\n",
      "1    501372\n",
      "Name: DQ Issue, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "print(\"Before over-sampling\\n:\", y_train['DQ Issue'].value_counts())\n",
    "\n",
    "sm = SMOTE()\n",
    "\n",
    "X_train_res, y_train_res = sm.fit_resample(X_train, y_train['DQ Issue'])\n",
    "\n",
    "print(\"After over-sampling:\\n\", y_train_res.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "fb894e12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 14 candidates, totalling 70 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sudeep_kumar\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:372: FitFailedWarning: \n",
      "35 fits failed out of a total of 70.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "35 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\sudeep_kumar\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 680, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\sudeep_kumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1461, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"C:\\Users\\sudeep_kumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 447, in _check_solver\n",
      "    raise ValueError(\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "C:\\Users\\sudeep_kumar\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:969: UserWarning: One or more of the test scores are non-finite: [       nan 0.76872861        nan 0.76969296        nan 0.76981164\n",
      "        nan 0.76982261        nan 0.76982261        nan 0.76982261\n",
      "        nan 0.76982261]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 1, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix, auc, roc_curve\n",
    "\n",
    "parameters = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "\n",
    "lr = LogisticRegression()\n",
    "\n",
    "clf = GridSearchCV(lr, parameters, cv=5, verbose=5, n_jobs=3)\n",
    "\n",
    "k = clf.fit(X_train_res, y_train_res)\n",
    "\n",
    "print(k.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "3022295d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 ... 1 0 1]\n",
      "Recall metric in the test dataset: 0.8856709244972809\n",
      "Recall metric in the train dataset: 0.8859968247129876\n"
     ]
    }
   ],
   "source": [
    "lr_gridcv_best = clf.best_estimator_\n",
    "\n",
    "y_test_pre = lr_gridcv_best.predict(X_test)\n",
    "\n",
    "print(y_test_pre)\n",
    "\n",
    "cnf_matrix_test = confusion_matrix(y_test, y_test_pre)\n",
    "\n",
    "print(\"Recall metric in the test dataset:\", (cnf_matrix_test[1,1]/(cnf_matrix_test[1,0]+cnf_matrix_test[1,1] )))\n",
    "\n",
    "\n",
    "y_train_pre = lr_gridcv_best.predict(X_train_res)\n",
    "\n",
    "cnf_matrix_train = confusion_matrix(y_train_res, y_train_pre)\n",
    "\n",
    "print(\"Recall metric in the train dataset:\", (cnf_matrix_train[1,1]/(cnf_matrix_train[1,0]+cnf_matrix_train[1,1] )))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "016626ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.65      0.99      0.79     82683\n",
      "           1       0.89      0.14      0.24     50627\n",
      "\n",
      "    accuracy                           0.67    133310\n",
      "   macro avg       0.77      0.56      0.51    133310\n",
      "weighted avg       0.74      0.67      0.58    133310\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test_pre,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "233d9f7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sudeep_kumar\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sudeep_kumar\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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WQk2zzdCVKyHfoNDgxMbYhS1KbvP6ZvTkxvC4kbArW8EB9WFJPhq+Zvt2ywtC3v9pFp4/txH9axPkAH0aYZe0DOmfb0N3rYVviqCOsPs7QP2dGKQzQ/etg6l50EDYNa3hoPoA6Lxl0LhO+DmTAzZqrx3fTzVx/dlvctwhX7J2UyPOu/unpdb/7MRZ9O85H4CcOkV0bb+OAcMHk5dflwcueZ76dYvIqVPE5Nn78+DrRwPw53MnsW+7dQA0bbSVTVsacMHIc2jeOI/bzn+NQzuv5OWZB3PHCydUwhEYv/nhuxx78GLy8usy/OmT+GxZOwCe+/2jfLO1PkVForCoDhfe+z+VsL9dr5JPSd0KnGxmmyTVA96R9Epcd5eZ3ZG8saTuwEDgMGBv4HVJB5lZITAKuBh4H/g3MAB4BRgCrDWzAyUNBG4HfiqpNXADcFQ8rJmSXjCztWVVdpcFBUmFwGygHlAAjAPuNrOiuP544E6gOSBgpJndVwn7bQmcZ2b3l7H+P2Z27M7up9qqB/a3dtCoDhQY+vVK6N0QujfYvk3Huthd7aFZHZi6Bd25FruvA3Sph42OX7KFhn66DI5vFJ5/mIf+swV7cK/whb+2cPt2t+Ziw1qHwLK+MASNLOm8Zdjje5dMnJYHSwuwR/aCT7ahv8f6Rfa3dtCi5lxt+tLMg3n6Pz244SeT065/dEpPHp3SE4DjD13Eucd/zIYtDQFj6IM/Ysu2euTUKWT0pc/z3mf7MGdJB65/4nvF+a/4/n/YnBeC6rb8HP7x2tHsv1cuB3TIrXBdn/v9o5x1+89KpB178GK6tF3P2XecS48uK7nmzLcZcv+Pi9f/cvQPWf9Nowrvq2pVXveRmRmwKT6tFxfLkOUM4Ekz2woslLQA6C1pEdDczN4DkPQIcCYhKJwB3BjzPwPcG1sR/YFJZpYb80wiBJInytr5ruw+2mJmPc3sMOB7wOmEiIWkvYDHgUvN7BDgOOAiSWdVwn5bAr8sa2WNDggAUggIAAUWltQfPIc1CAEBQrBYVVi6nA+3wt51oUP43aAXN2EDm29vAbSKX8oz8mD/eiEgQPiyzlHxOl2+Al3yNbppNWwpyu4Q3t2C9WscjqV7A9hUBGvS1LGGmLVwbzZsaVD+hkC/by/gtVkHxmdiy7Z6ANTNKaJuTlGabxrj1G99XpwnL78eH33ZkW0FpYNqn25LeOiy5xj3q2e45bzXaFQ/P6s6ndh9Ea98cBAg5izpQLNGW2nTbHNWeauzonif5vIWoK2kGUnLxallScqRNAtYSfiSnhpXXS7pY0ljJLWKaZ2AJUnZl8a0TvFxanqJPGZWAKwH2mQoq0y7ZUzBzFYSmjyXx+g1FBhrZh/E9auBa4CrU/PGfrcxkt6S9IWkK5LW/UbSnLhcGZNvAw6IfXR/TVPepvi3o6Qpcbs5kk6Ib9zY+Hy2pKvitm9JOio+bhsjduKN/quk6fGNvaSyXrOdUmjo4q/R/yyDXg3h0AxfOK9sCi2JFHrzG+zkxtsTlhag2VvR0BXoqpXw6dbidAT6/Sp0ydfw5IaQvr4QPboB+2s77B97YQfVh6c3Zlf/1YXQLqkR2y4npEHY1zWr0KVfw0ub0uevoRrUy+eYg5bw5pz9i9PqqIjxVzzNq9ePY9r8zsxd0qFEnp77LSd3U2OWrGmZsewWjbfw85M/4PKHfsDge87mk6/acd4JH2VVr3bNN7NiXdPi5yvXN6Vd8xgUTIwc8jLjLn+GM3vPy+5Aq4Fw9lFOVguw2syOSlpGly7PCs2sJ9CZ8Ku/B6Er6ACgJ7Ac+FvcPF2/lWVI39E8ae22MQUz+0JSHaA9oa9sXMomM4DuZWQ/BDgJaAZ8JmkUcDjwc6AP4cCnSvo/4FqgR3wDMjkPmGhmIyTlAI0Jb04nM+sBxV1RmQwB1pvZ0ZIaAO9Kes3MFiZvFH85XAzQoEOzcoqsBDkK3UCbitCfVsPCbbBf/dLbfZiHXtmM3d2+ZHq+wX+2wJCksYhCg01F2L3t4bNtaPga7NGOIX3O1jCO0EDod6tCANhq8GV+6L5KlNm9QfiU/n0tzI1BZU0huvhrAOy7jeH85umPKX607e8doG0OrC1E16zC9qkLh5cOajXRCYd+ycdf7hW7joIiq8MFI8+hacOt/OWCiezfIZcvVrQuXt/v2wt47aMD0xVXQo99VrJf+7U8eNm/AKiXU8TsxSHAXH3G2xy+b3iP2jX7hvFXPA3AG7P3Z+ybvVDarveQ+P9GncnqjU1o1WQL9/ziJRatasmshXuny1Ct7KqL18xsnaS3gAHJYwmSHgReik+XAl2SsnUGlsX0zmnSk/MslVQXaAHkxvS+KXneylTH3T3QrKS/GaNVipdj/9pWSSuBDsDxwHNmthlA0rPACcALWZY5HRgTB37+ZWazJH0B7C/pHuBl4LVyyugHHC7p7Pi8BdANKBEU4i+H0QDND+5QkePeOU3rYD0bwPS80kHh823ob7nYrWn656flQbd60DopvV1d7PhGoUvnkAbhHVxfBG3rwuENisuwPg1h/jboUg96NcSub1OqWvbrVsWPdd6y7eMYCW1zYFUBEFs4qwqhTc72dRC6r45vBJ9uqzVB4Xsluo5K2pTXgJlf7M13DlpcHBRy6hRx0mELGXxP+YO7kjFtfmf++OSppdb99fntg9HP/f5RLhh5Ton1K9c3oUPLTfBleN6+xSZWbQitzNUbmwCwdnMj3prblcM6r9wjggKQ6BraaZLaAfkxIDQCTgVul9TRzJbHzc4C5sTHLwCPS7qTMNDcDZhmZoWSNko6BpgKDALuScozGHgPOBuYbGYmaSJwS1LXVD9gWKb67rZTUiXtDxQS+tTmEkbDk/UitBbS2Zr0uJAQzHbqHTOzKcCJwFfAeEmD4oj8twmRdCjwUNy8gO2vVfI3kIBfxbGTnma2n5mVF0h2rXWFoQ8eYGsRmpkXvqCTrShAN67BhrUpvQ7Q5M0lu44AO64R+jC+DUvywyvSog4c3RC+yIe8otBt9fFW2LcedK8fWgNfxX7pvKKQLwt2bCP02jehDT9vKzSpE4LClqJwhhOExzPyoGvp+tdETRps5Yj9ljNlXtfitJZNttC0YXhPGtQtoPeBS1m0anvAPfrApSxa1ZKVG5qmFlfKnMUdOLzr13Rusz6UVy+fLm3XZVW3t+d15bQj/wsYPbqsYFNefdZsbELDevk0rr8NgIb18unTbSmfJ7ViqrPE2UfZLFnoCLwp6WPCj9FJZvYS8JfYTf0xoSfkKgAzmwtMAOYBrwJD45lHAJcRvpcWAJ8TBpkBHgbaxEHp3xB6TIgDzMPjfqcDNycGncuyW1oKMVI+ANwbo9d9hO6eZ+Mv9DbACOKBZGkKMFbSbYQv57OAC4CNhG6m8uq0L/CVmT0oqQlwpKR/A9vM7J+SPgfGxs0XEYLWNEIUTpgIXCZpspnlSzoolll1o2xrCtFfckPoNAtdMt9pBC/G/vcfNkXjN8CGwtCNAyVP7cwrgplb4aqUf94BTeCvuWjIcqgr7PetQ6uhmbCzm6FfrgjvQu9GcEw408SuaY1GrIHwvYBd1CJtECqlT0OYmocuWA4N62BXx7qsLUI3rA6PCw07pUnY3x5u+MDXOXL/ZbRskseLw8YzetJR1M0Jwe+5qYcB0LfHQqbN70xe/vbXr22zb/jTTyZTR0YdGW/MPoB3P923eP33yug6eu73j9KkQT71cgr57mGLuOLh77NwZWtufvokhg98nXp1w/fPP17rzZLVLcut/7uf7cOxhyzmn1c/EU9J7QtA62Zb+MsFE4HQapk460De/+8+O/QaVYVKPPvoY+CINOkXZMgzgvCdmJo+A+iRJj0POCc1Pa4bA4zJtr4KZ0tVvjSnpI4H7kw6JfVE4A5Cl0tX4EIzK3WalKQbgU2J/jdJc4AfmNkiSb8BLoqbPmRmd8dtHieMObxiZlenlLfJzJrGiziuBvIJp4sNIpwe+79sbxUMM7NXJB1CiNybgMnAz8ysaxwj+TPwQ8JX4irgTDNbX9br0vzgDnb0A+dn8Qq66mLDuM7lb+SqlRnjfjvTzFJ7I7LW6pD2dvKYs8vfEHj2uFE7ta/qZpe1FMwy37Yodt/0BpA0FLhO0qupF1WY2Y0pz3skPb6TcK1DatnnZdhv0/h3HKUHuwGOTJPnU0KQSbg+phcB18XFOVeD1NZZUqvFNBdmdp+ZfSvTVXbOObe7VPKYwh7Fp7lwzrk0auIXfjY8KDjnXAq/yY5zzrkSKus6hT2NBwXnnEthBgV+kx3nnHMJ3n3knHMO8DEF55xzKcyDgnPOuQQfaHbOOQeEgWbvPnLOOReFe0rXRh4UnHMuDR9TcM45B2yf+6g28qDgnHOpLIwr1EYeFJxzLg0/+8g55xwQLl7zgWbnnHPFamv3Ue0Mhc45Vw4zZbWUR1JDSdMkfSRprqSbYnprSZMkzY9/WyXlGSZpgaTPJPVPSu8laXZcN1KSYnoDSU/F9KmSuiblGRz3MT/ehjgjDwrOOZfCrPKCArAVONnMvg30BAZIOga4FnjDzLoBb8TnSOoODAQOAwYA90tK3N54FHAx0C0uA2L6EGCtmR0I3AXcHstqDdwA9CHc/viG5OCTjgcF55xLo7Jux2nBpvi0XlwMOIPt94kfB5wZH58BPGlmW81sIbAA6C2pI9DczN4zMwMeScmTKOsZ4JTYiugPTDKz3Hi740lsDyRpeVBwzrk0zLJbgLaSZiQtF6eWJSlH0ixgJeFLeirQwcyWh33ZcqB93LwTsCQp+9KY1ik+Tk0vkcfMCoD1QJsMZZXJB5qdcy6FIYqyP/totZkdlbE8s0Kgp6SWwHOSemTYPF3zwzKk72ietLyl4JxzaViWS4XKNFsHvEXowlkRu4SIf1fGzZYCXZKydQaWxfTOadJL5JFUF2gB5GYoq0weFJxzLlUlDjRLahdbCEhqBJwKfAq8ACTOBhoMPB8fvwAMjGcU7UcYUJ4Wu5g2SjomjhcMSsmTKOtsYHIcd5gI9JPUKg4w94tpZfLuI+ecS6fyrlPoCIyLZxDVASaY2UuS3gMmSBoCLAbOATCzuZImAPOAAmBo7H4CuAwYCzQCXokLwMPAeEkLCC2EgbGsXEnDgelxu5vNLDdTZT0oOOdcGpU1S6qZfQwckSZ9DXBKGXlGACPSpM8ASo1HmFkeMaikWTcGGJNtfcsMCpLuIUOsNLMrst2Jc87tSQwoKvK5j1LN2G21cM656sQAnzq7JDMbl/xcUhMz27zrq+Scc1XP5z4qg6TvSJoHfBKff1vS/bu8Zs45V5V2xTmpe4BsTkm9m3Cp9BoAM/sIOHEX1sk556pYdqej1sRbdmZ19pGZLYmT8SUUlrWtc87VCDWwFZCNbILCEknHAiapPnAFsSvJOedqJAOrpWcfZdN9dCkwlDCJ0leEqV+H7sI6OedcNaAsl5ql3JaCma0Gzt8NdXHOueqjlnYfZXP20f6SXpS0StJKSc9L2n93VM4556qMn31UpseBCYT5O/YGngae2JWVcs65KpW4eC2bpYbJJijIzMabWUFcHqVGxkfnnNuuAjfZqVEyzX3UOj58U9K1wJOEYPBT4OXdUDfnnKs6tfTso0wDzTMpeeeeS5LWGTB8V1XKOeeqmmpgKyAbmeY+2m93VsQ556qNGjqInI2srmiO9xPtDjRMpJnZI7uqUs45V7Vq5iByNsoNCpJuAPoSgsK/gdOAdwAPCs65mquWthSyOfvobMLdgb42s58D3wYa7NJaOedcVSvKcqlhsuk+2mJmRZIKJDUHVgJ+8ZpzruaqxTfZyaalMENSS+BBwhlJHwDTdmWlnHOuqsmyW8otR+oi6U1Jn0iaK+nXMf1GSV9JmhWX05PyDJO0QNJnkvonpfeSNDuuG6k4fbWkBpKeiulTJXVNyjNY0vy4DC6vvtnMffTL+PABSa8CzeONqJ1zruaqvDGFAuC3ZvaBpGbATEmT4rq7zOyO5I0ldQcGAocRZpF4XdJBZlYIjAIuBt4njPEOAF4BhgBrzexASQOB24GfxuvNbgCOikc0U9ILZra2rMqW2VKQdGTqArQG6sbHzjnnymFmy83sg/h4I+HWA50yZDkDeNLMtprZQmAB0FtSR8KP8vfMzAgn+5yZlCdxC+VngFNiK6I/MMnMcmMgmEQIJGXK1FL4W6bjBE7OVLArw3/zqXPKkqquhauAqcterOoquArKGVf+NuWpwMVrbSXNSHo+2sxGpy0zdOscAUwFjgMulzQImEFoTawlBIz3k7ItjWn58XFqOvHvEgAzK5C0HmiTnJ4mT1qZLl47KVNG55yrsYyKTHOx2syOKm8jSU2BfwJXmtkGSaMIM0MkZoj4G3AR6W/SYBnS2cE8aWUz0Oycc7VPJU6dLakeISA8ZmbPApjZCjMrNLMiwok8vePmS4EuSdk7A8tieuc06SXySKoLtAByM5RVJg8KzjmXRiWefSTgYeATM7szKb1j0mZnAXPi4xeAgfGMov2AbsA0M1sObJR0TCxzEPB8Up7EmUVnA5PjuMNEoJ+kVpJaAf1iWpmymubCOedqnco7++g44AJgtqRZMe064FxJPeOeFhEnHTWzuZImAPMIZy4NjWceAVwGjAUaEc46eiWmPwyMl7SA0EIYGMvKlTQcmB63u9nMcjNVNptpLkS4Hef+ZnazpH2AvczMr1VwztVclRQUzOwd0vft/ztDnhHAiDTpM4AeadLzgHPKKGsMMCbb+mbTfXQ/8B3g3Ph8I3Bftjtwzrk9TbZdRzVxeu1suo/6mNmRkj4EMLO1kurv4no551zV8pvslClfUg6xMSWpHTVyGijnnNuuJrYCspFN99FI4DmgvaQRhGmzb9mltXLOuapWiaek7kmymfvoMUkzCdNnCzjTzD7Z5TVzzrmqUkPHC7KRzdlH+wDfAC8mp5nZ4l1ZMeecq1IeFMr0Mtsvl24I7Ad8RpjBzznnaiTV0pHTbLqPvpX8PM6Qeskuq5FzzrkqU+ErmuOc4Efviso451y14d1H6Un6TdLTOsCRwKpdViPnnKtqPtCcUbOkxwWEMYZ/7prqOOdcNeFBobR40VpTM7t6N9XHOeeqBw8KJUmqG+/g47fedM7VKsLPPkpnGmH8YJakF4Cngc2JlYkbRTjnXI3jYwoZtQbWEO7JnLhewQAPCs65msuDQint45lHcyh9r89a+nI552qNWvotlyko5ABN2YEbPzvn3J7Ou49KW25mN++2mjjnXHXiQaGU2nmHCeecs9p79lGm+ymcsttq4Zxz1U0l3U9BUhdJb0r6RNJcSb+O6a0lTZI0P/5tlZRnmKQFkj6T1D8pvZek2XHdSEmK6Q0kPRXTp0rqmpRncNzHfEmDy6tvmUHBzHLLP1znnKuZKvEezQXAb83sUOAYYKik7sC1wBtm1g14Iz4nrhtImIl6AHB/vJAYYBRwMdAtLgNi+hBgrZkdCNwF3B7Lag3cAPQBegM3JAefdLK585pzztU+ldRSMLPlZvZBfLwR+AToBJwBjIubjQPOjI/PAJ40s61mthBYAPSW1BFobmbvmZkBj6TkSZT1DHBKbEX0ByaZWa6ZrQUmsT2QpOVBwTnnUmUbECo4GB27dY4ApgIdzGw5hMABtI+bdQKWJGVbGtM6xcep6SXymFkBsB5ok6GsMlV46mznnKvpRIVOSW0raUbS89FmNrpUmVJTwmSiV5rZhjgcUNbuU6VeK5acvqN50vKg4JxzaVQgKKw2s6MyliXVIwSEx5KmCFohqaOZLY9dQytj+lKgS1L2zsCymN45TXpynqWS6gItgNyY3jclz1uZ6urdR845l07lnX0k4GHgEzO7M2nVC0DibKDBwPNJ6QPjGUX7EQaUp8Uupo2SjollDkrJkyjrbGByHHeYCPST1CoOMPeLaWXyloJzzqVTeRevHQdcAMyWNCumXQfcBkyQNARYDJwDYGZzJU0A5hHOXBpqZoUx32XAWKAR8EpcIASd8ZIWEFoIA2NZuZKGA9PjdjeXd2apBwXnnEtVibOkmtk7lH0xcNrrwcxsBDAiTfoMoEea9DxiUEmzbgwwJtv6elBwzrl0fJoL55xzCbV1mgsPCs45l4bPkuqccy7YgQvTagoPCs45l44HBeecc1DhK5prFA8KzjmXhopqZ1TwoOCcc6l8TME551wy7z5yzjm3nQcF55xzCd5ScM45t50HBeecc0CYEM+nuXDOOQd+nYJzzrlUVjujggcF55xLw1sKzgHjps5jy6YcioqgsED86rSDuO6BRXQ+YCsATZoXsnlDDr/83sE0a1XAH0cv4qCeW5g0oRX3/WH77WP/8swCWncoYFteuLfIsIH7s35NPdp12sbVdy+mSYtC6tSBMbd0ZPrk5lVyrNVJYSH8asBBtOmYz/BHFqbd5rNZjbjyB+H9OOEH6zPmffDmvXl/UnPq1Tc67ruV3961hKYtCtmQm8Pwi7vy31mN+d5Pcrn8lq92uu5mMOqPnZg2uTkNGxXx27sW0+3wLQAM6t2dRk3De51T17j31f/u9P52C794rfqTVAjMBuoRblE3DrjbzIri+uOBO4HmhC7BkWZ2X5pyfgR0N7Pbdlfd9zTXnHMAG3K3fzRuubRr8eOL/7SMzRvDrb235Ylxf92Lrgfn0fWQvFLl3D50H+Z/3LhE2nm/XsGUF1vy0iNt2adbHsMf/YLBfbrvmgPZg/zroXZ06baVbzalv216YSE8PGJvevXdmFXeI0/cyEXXLSOnLjz05448eU97fnH9cuo3NAZf/TWLPmvIok8bVrieg3p355Fp80qkTZ/cjK8WNuB/3/2ETz9ozD3DOjPy5fnF6//y9AJatClMLaraq60Dzek/gdXTFjPraWaHAd8DTgduAJC0F/A4cKmZHUK4J+pFks5KLcTMXvCAsKOME3+0jjf/1QqArVtymDutKdu2Zv8xMoPGzcJ/W5PmheSuqLdLaronWbWsHtPeaM5p560pc5vnx7Tj+NPX07JtQVZ5e/XdSE6M64f2+obVy8Pr3LBxET36bKZ+g9I/g2e+1Ywrf9iNof0O4s8Xd2XL5uze1/cmtuDUs3ORwr42r89hzYo95vdmmVSU3VLT7ElBoZiZrQQuBi6XJGAoMNbMPojrVwPXAFen5pV0oaR74+NzJM2R9JGkKTHtMEnTJM2S9LGkbpK6SpqTVMbvJN0YHx8g6VVJMyW9LemQXXz4u5aJW574gntf/S+nnV/yi6ZHn82sXVWXZQsbZFXUb+9awv2TPuO8K1eQaIs/+re9OPnHa3l0xjyGj1/IfX/oVNlHsMd54IZO/OL6ZaiM/8bVy+vxn1da8P1BqyucF2DiE605+uTSLYxk69fk8PjfO3DbU59z32v/pdvh3/DPf7TLqv6rv65Hu73zi5+33TufNV/HYC/junMPYGj/g/j3o22yKq9aMMIvmGyWckgaI2llynfIjZK+it8zsySdnrRumKQFkj6T1D8pvZek2XHdyPjdh6QGkp6K6VMldU3KM1jS/LgMzubQ99hwbmZfSKoDtAcOI3QnJZsBlNcv8Segv5l9JallTLsU+LuZPSapPpADdMhQxmhCC2W+pD7A/cDJyRtIupgQxGhI49IlVCNXnXEguSvq0aJNPrc9+QVLFjRgztSmAJx05jre+lfLrMq5/fJ9WfN1PRo1KeSPDy3i1LPr8fozrel75jomTWjFP//RnkN7beaaexZzyUkHY1bWfc1rtvcnNadl2wK6Hb6Fj/7TNO02D9zQiSF/WEZOTsXzPv73DuTUNU7+8dqM9fjkgyYs/m9DrvpRNwAK8sWhvTYDcO+wTsydHspfs6Iul516MAAn/HAd5/16Rdq+d8W3867n59NmrwLWra7LtQMPoMuBeXzrmM0Z61JdVOJA81jgXuCRlPS7zOyOEvuUugMDCd9pewOvSzrIzAqBUYTvkfeBfwMDgFeAIcBaMztQ0kDgduCnkloTelOOIrxLMyW9YGYZPwx7bFCIlPR3R97Cd4GxkiYAz8a094A/SOoMPBu/7NPvXGoKHAs8nbRNqZ/RZjaaEDxortbVevgq0Z2zfk093n21BYcc8Q1zpjalTo5x3OnruXxAt6zKSfxS3LI5hzefa8XBR3zD68+0ZsC5a/jD+fsD8MnMJtRvYDRvXcD6NbWzG2ne9Ca8/1pzpr/RnW1bxTcbc7j98n34/b2Li7f570eNuPWyrgCsz81h2hvNyMmBTz9snDHvpAmtmPZ6c257agFlfIS3szAOMWzUl6VWXX7r9sHoQb27M+r1z0qsb9sxn1XLtr9/q5fVo3WH0HJos1fo7mrZtoDjBqzn0w8b7zFBobIGms1sSvKv93KcATxpZluBhZIWAL0lLQKam9l7AJIeAc4kBIUzgBtj/meAe2Mroj8wycxyY55JhEDyRKYK7JHdRwCS9gcKgZXAXEI0TNaL0Fook5ldClwPdAFmSWpjZo8DPwK2ABMlnUwY2E5+rRIjdHWAdXGsI7EcupOHVmUaNCqkUZPC4se9vruxeDDyyBM2smRBA1Yvr19uOXVywhc9hDNO+py6obiclV/Vp+fxmwDocmAe9RsUsX7Nnv7bZMdddN1yHps5j0emzWPYqC/59vEbSwQEgEemfsIj08I2J/xgPb+6dSnHnrY+Y97pbzZjwn0duHHsFzRsXP632yG9NjN3ehO+Whje37xvxNLPs+smPKbfBl5/pjVm8MnMxjRuXkibDgXkfVOnePA775s6zPy/ZmlPSKiOEhevZbPshMtjF/UYSa1iWidgSdI2S2Nap/g4Nb1EHjMrANYDbTKUldEe+d8oqR3wAHCvmZmk+4Cpkp41s1mS2gAjgGvLKecAM5sa8/4Q6CKpBfCFmY2Mgedw4G2gfSx3E/AD4FUz2yBpoaRzzOzpGJ0PN7OPdtnB70Kt2hVww8OLgPBl/uZzrZjxVjhd9LtnpO86Gjd1Hk2aFlG3vvGd/hu47tz9WbG0Hrc8/gU5dY2cHOODt5vxymOhP3n0TXtz5R1L+PH/W4UBd1y1D9sbfC7hpUfC6/WDQWUPPmdy3x86k79VDPvpgUD40v/17eE7ZVDv7mzeVIeCbeK9iS245YnP2fegrfzu7sXc9suu5G8L78fga5YXn4qcSe9TNjD9jWb8/NhDaRBPSQVYu6ouNw3ZD4DCAjjprHUcfVLmsY1qw6wiN9lpKyn5B+jo2DuQyShgOKE9Mhz4G3AR6f8ZLEM6O5inTLI95Kq9NKekjgfuTDol9UTgDqAF0BW40MxKNZMkXQgcZWaXS3oW6EZ48d4AriQEkp8B+cDXwHlmlivpCuAKYCHwFbDIzG6UtB/hDe4Y6/akmd1c1nE0V2vro1N27sVwu9XEZbOqugqugnI6LphpZqm9B1lr1rKzHXHir7Pa9u0Xryl3X7H76CUz65FpnaRhAGZ2a1w3kdA1tAh4M55diaRzgb5mdkliGzN7T1JdwvdWO8LYRF8zuyTm+QfwVrrvxWR7TEvBzHLKWT8F6A0gaShwnaRXUwdVzGwsYeAHM/txmqJujUtq+SOBkWnSFxL66ZxzNciuvKJZUkczWx6fngUkzkx6AXhc0p2EgeZuwDQzK5S0UdIxwFRgEHBPUp7BhPHQs4HJsQdlInBLUtdUP2BYeXXbY4JCRcSL1kpduOacc1kxoJLu0SzpCaAvoZtpKeGMoL6SesY9LQIuATCzufHEl3mEHpGh8cwjgMsIP2gbEQaYX4npDwPj46B0LqGFQOzhGA5Mj9vdnBh0zqRGBgXnnNtplXf20blpkh/OsP0IwphoavoMoFT3k5nlAeeUUdYYYEzWlcWDgnPOpeUT4jnnnCtWgbOPahQPCs45l8pnSXXOOZcQLl6rnVHBg4JzzqVTA2dAzYYHBeecS8NbCs455wIfU3DOObddheY+qlE8KDjnXDrefeSccw4Aq5m32syGBwXnnEvHWwrOOeeK1c6Y4EHBOefSUVHt7D/yoOCcc6kMv3jNOedcIMwvXnPOOZfEg4JzzrliHhScc84BPqbgnHOuJD/7yDnnXGS1tvuoTlVXwDnnqh0jBIVslnJIGiNppaQ5SWmtJU2SND/+bZW0bpikBZI+k9Q/Kb2XpNlx3UhJiukNJD0V06dK6pqUZ3Dcx3xJg7M5dA8KzjmXTlGWS/nGAgNS0q4F3jCzbsAb8TmSugMDgcNinvsl5cQ8o4CLgW5xSZQ5BFhrZgcCdwG3x7JaAzcAfYDewA3JwacsHhSccy4NmWW1lMfMpgC5KclnAOPi43HAmUnpT5rZVjNbCCwAekvqCDQ3s/fMzIBHUvIkynoGOCW2IvoDk8ws18zWApMoHZxK8TEF55xLJ/sxhbaSZiQ9H21mo8vJ08HMlofd2HJJ7WN6J+D9pO2WxrT8+Dg1PZFnSSyrQNJ6oE1yepo8ZfKg4JxzqcygMOuzj1ab2VGVtGelq02G9B3NUybvPnLOuXQqaaC5DCtilxDx78qYvhTokrRdZ2BZTO+cJr1EHkl1gRaE7qqyysrIg4JzzqWza4PCC0DibKDBwPNJ6QPjGUX7EQaUp8Wupo2SjonjBYNS8iTKOhuYHMcdJgL9JLWKA8z9YlpG3n3knHOpDKikezRLegLoSxh7WEo4I+g2YIKkIcBi4BwAM5sraQIwDygAhppZYSzqMsKZTI2AV+IC8DAwXtICQgthYCwrV9JwYHrc7mYzSx3wLsWDgnPOlWJglXNFs5mdW8aqU8rYfgQwIk36DKBHmvQ8YlBJs24MMCbryuJBwTnnSjMqMtBco3hQcM65dGrpNBceFJxzLh0PCs4554LaOyGeBwXnnEtlgE+d7Zxzrpi3FJxzzgUVmuaiRvGg4JxzqQyskq5T2NN4UHDOuXQq6YrmPY0HBeecS8fHFJxzzgEhIPjZR84554p5S8E551xgWGFh+ZvVQB4UnHMuVSVOnb2n8aDgnHPp+CmpzjnnIDQUzFsKzjnngHirTW8pOOeci2rrQLOslp52VVUkrQK+rOp67AJtgdVVXQlXITX5PdvXzNrtaGZJrxJen2ysNrMBO7qv6saDgqsUkmaY2VFVXQ+XPX/PXDp1qroCzjnnqg8PCs4554p5UHCVZXRVV8BVmL9nrhQfU3DOOVfMWwrOOeeKeVBwzjlXzINCDSKpUNIsSXMlfSTpN5LqJK0/XtI0SZ9K+kzS0Erab0tJv8yw/j+VsZ/arLLeW0k/knTt7qu529P4mEINImmTmTWNj9sDjwPvmtkNkvYCpgFnmtkHktoCE4E/m9lzO7nfrsBLZtZj547AlaWq3ltX+3hLoYYys5XAxcDlkgQMBcaa2Qdx/WrgGuDq1LySbpQ0RtJbkr6QdEXSut9ImhOXK2PybcAB8ZfsX9OUtyn+7ShpStxujqQTJOVIGhufz5Z0Vdz2LUlHxcdtJS2Kj3Mk/VXSdEkfS7qksl6zPcVOvrcXSro3Pj4nvu4fSZoS0w6LLY5Z8fXtJqmrpDlJZfxO0o3x8QGSXpU0U9Lbkg7ZxYfvdjGf+6gGM7MvYhdDe+AwYFzKJjOA7mVkPwQ4CWgGfCZpFHA48HOgDyBgqqT/A64FephZz3KqdB4w0cxGSMoBGgM9gU6JVoakluWUMQRYb2ZHS2oAvCvpNTNbWE6+GmUn39uEPwH9zeyrpNf9UuDvZvaYpPpADtAhQxmjgUvNbL6kPsD9wMkVOxpXnXhQqPmU9LcifYUvm9lWYKuklYQvhuOB58xsM4CkZ4ETgBeyLHM6MEZSPeBfZjZL0hfA/pLuAV4GXiunjH7A4ZLOjs9bAN2AWhUUoh19bxPeBcZKmgA8G9PeA/4gqTPwbPyyT79zqSlwLPB00jYNdqAerhrx7qMaTNL+QCGwEpgLpM5z04vwizKdrUmPCwk/INJ/O2TJzKYAJwJfAeMlDTKztcC3gbcI3SAPxc0L2P75bJhUjIBfmVnPuOxnZuUFkhpnJ99bAMzsUuB6oAswS1IbM3sc+BGwBZgo6WRKvhew/f2oA6xLei96mtmhO3lorop5UKihJLUDHgDutXA2wX3AhZJ6xvVtgBHA8AoUOwU4U1JjSU2As4C3gY2Ebqby6rQvsNLMHgQeBo6Mg6J1zOyfwB+BI+PmiwhfbABnJxUzEbgstjaQdFCsS61RWe+tpAPMbKqZ/YkwW2qXGGy+MLORhBbg4cAKoL2kNrHL7gcAZrYBWCjpnFieJH278o/Y7U7efVSzNJI0C6hH+HU3HrgTwMyWS/oZMFpSC6ArcKGZ/V+2hcczW8YSznQBeMjMPgSQ9G4cjHzFzEoNcEZ9gasl5QObgEFAJ+B/tf30ymHx7x3ABEkXAJOTyngo1v2DOMi6Cjgz22PYg+2K9/avkroRWl9vAB8Rxod+Ft+jr4GbzSxf0s3AVEI33adJZZwPjJJ0fazbk7Ect4fyU1JrKYXz2C8FToxdOK6G8PfW7QwPCs4554r5mIJzzrliHhScc84V86DgnHOumAcF55xzxTwouGpH22cEnSPpaUmNd6KssYmrnyU9JKnMqR8k9ZV07A7sY1G83iKr9JRtNlVwXzdK+l1F6+hctjwouOpoS7w6tgewjXB6ZbE4b1KFmdkvzGxehk36EqZtcK7W8qDgqru3gQPjr/g3JT0OzC5rttR4Ve29kuZJepkwYRxxXfLMqwMkfRBnCH1DYfrvS4GrYivlBEntJP0z7mO6pONi3jaSXpP0oaR/kMX0H5L+FWcSnSvp4pR1f4t1eSNereyzj7oq41c0u2pLUl3gNODVmNSbMBvrwvjFWmq2VOAI4GDgW4RJ/OYBY1LKbQc8SLi4a6Gk1maWK+kBYJOZ3RG3exy4y8zekbQPYYqNQ4EbgHfM7GZJ3ydMY12ei+I+GgHTJf3TzNYATYAPzOy3kv4Uy74cn33UVREPCq46SkzpAKGl8DChW2da0hTZZc2WeiLwhJkVAsskJU+RkXAMMCVRlpnlllGPU4Hu2j4DaHNJzeI+fhzzviwpm6uGr5B0VnzcJdZ1DVAEPBXTHwWelc8+6qqQBwVXHW1JvTdD/HLcnJxEmC11Ysp2p1P+NNLZTjVdB/iOmW1JU5espwKQ1JcQYL5jZt9IeouSM78mM5JmH812H85VFh9TcHuqsmZLnQIMjGMOHQk3Ckr1HvBdSfvFvK1jeupsr68RunKI2/WMD6cQJoJD0mlAq3Lq2gJYGwPCIYSWSkIdts8Cex6hW8pnH3VVxoOC21M9RBgv+CDOzvoPQsv3OWA+MBsYBZSaKdTMVhHGAZ6V9BHbu29eBM5KDDQDVwBHxYHseWw/C+om4ERJHxC6sRaXU9dXgbqSPiZMZ/1+0rrNwGGSZhLGDG6O6ecDQ2L95gJnZPGaOLfTfEI855xzxbyl4JxzrpgHBeecc8U8KDjnnCvmQcE551wxDwrOOeeKeVBwzjlXzIOCc865Yv8f9VmdfJF8hd0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "class_names = ['DQ not issue', 'DQ issue']\n",
    "\n",
    "plot_confusion_matrix(k, X_test, y_test,  values_format = '.5g', display_labels=class_names) \n",
    "plt.title(\"Test data Confusion Matrix\")\n",
    "plt.show()\n",
    "\n",
    "plot_confusion_matrix(k, X_train_res, y_train_res,  values_format = '.5g', display_labels=class_names)\n",
    "plt.title(\"Oversampled Train data Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "a042b6c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC-AUC: 0.7767100010328053\n"
     ]
    },
    {
     "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": [
    "y_k =  k.decision_function(X_test)\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_k)\n",
    "\n",
    "roc_auc = auc(fpr,tpr)\n",
    "\n",
    "print(\"ROC-AUC:\", roc_auc)\n",
    "\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b',label='AUC = %0.3f'% roc_auc)\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.xlim([-0.1,1.0])\n",
    "plt.ylim([-0.1,1.01])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd472b23",
   "metadata": {},
   "outputs": [],
   "source": [
    "auc(roc(rand_preds, labels))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "11213bcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DQ Issue\n",
       "0           501372\n",
       "1            31868\n",
       "dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "51e002ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Decision tree with out SMOTE\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier = DecisionTreeClassifier()\n",
    "classifier = classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "138a38d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.9678793788913059\n"
     ]
    }
   ],
   "source": [
    "# decision tree prediction\n",
    "y_pred = classifier.predict(X_test)#Accuracy\n",
    "from sklearn import metrics\n",
    "print('Accuracy Score:', metrics.accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "da7c6e83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.98      0.98    125227\n",
      "           1       0.74      0.72      0.73      8083\n",
      "\n",
      "    accuracy                           0.97    133310\n",
      "   macro avg       0.86      0.85      0.86    133310\n",
      "weighted avg       0.97      0.97      0.97    133310\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "f6255d8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[123174,   2229],\n",
       "       [  2053,   5854]], dtype=int64)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "5ca5cf6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-136-c16bff4b9048>:8: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  clf_normal = clf.fit(X_train,y_train)\n"
     ]
    }
   ],
   "source": [
    "#randam forest with out smote\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "#Create a Gaussian Classifier\n",
    "clf=RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "#Train the model using the training sets y_pred=clf.predict(X_test)\n",
    "clf_normal = clf.fit(X_train,y_train)\n",
    "\n",
    "y_pred=clf_normal.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "51ea8aed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9670992423674143\n"
     ]
    }
   ],
   "source": [
    "#Random Forest\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "3e2d71ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6659815467706849\n"
     ]
    }
   ],
   "source": [
    "#Logistic Regression\n",
    "print(metrics.accuracy_score(y_test,y_test_pre))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "9b99ad0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8612876038581171\n",
      "ROC-AUC: 0.8612876038581171\n"
     ]
    },
    {
     "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": [
    "#Decision tree with out SMOTE\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "y_decision =  classifier.predict_proba(X_test)\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_decision[:,1])\n",
    "\n",
    "print(roc_auc_score(y_test, y_decision[:,1]))\n",
    "\n",
    "roc_auc = auc(fpr,tpr)\n",
    "\n",
    "print(\"ROC-AUC:\", roc_auc)\n",
    "\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b',label='AUC = %0.3f'% roc_auc)\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.xlim([-0.1,1.0])\n",
    "plt.ylim([-0.1,1.01])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "b08ac5c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9860615214413911\n",
      "ROC-AUC: 0.9860615214413911\n"
     ]
    },
    {
     "data": {
      "image/png": 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p7tkShTHFyKpV0KWLa6hu3BjmzoWbbgo7qrizRBEj8+a5E4wTTww7EmNMzIwf77pbeOEF1zV4ip4J2uWxMTJ3rjvhsCuejElyCxe6u2fPOcf18nrNNcXqnojCsBJFjMydC23ahB2FMabQ9u2Dhx5y3W/ceadrmyhVKuWTBFiiiInt22HlSmjVKuxIjDGF8s030LYtDB4MV14J06alVGN1fgJNFCLSWUSWishyERmYy/LKIvKBiMwTkYUi0jvIeIKycKF7tvYJY5LQ99+7Dtp27ICPPoIxY1wX0OY3gSUKESkJvAx0AZoDV4tIzpagW4FFqtoa6Ag8LSKlg4opKNlXPLVsGW4cxpgCWLPGPbdtC0OHujO+rl3DjSlBBVmiaAcsV9WfVHU/MB7olmMdBSqJiAAVgV+AzABjCkR6umvErlcv7EiMMfnavNndONe06aFO/Pr1g0qVwo4sYQWZKGoBayKmM7x5kV4CmgFrgQXAnaqalXNDItJXROaIyJxNmzYFFW+hpae70kQx6E3YmOJLFcaOPXTj3IABcNxxYUeVFIL8acutJUhzTF8IzAWOA9oAL4nI77pfVNXhqpqmqmk1ErDucMECq3YyJqFlZsIll8C118IJJ8APP8DDD0OZMmFHlhSCTBQZQOR1ZbVxJYdIvYF/q7McWAE0DTCmmNu40XUBY4nCmASk3rnpEUe4qqZnn3Vdg9s/bIEEmSi+AxqJSAOvgbon8H6OdVYD5wKIyDFAE+CnAGOKOWvINiZBLVoEZ58Ns2a56SFDoH9/KFky1LCSUWCJQlUzgduAKcBi4G1VXSgi/USkn7faI8DpIrIA+Ay4V1U3BxVTECxRGJNg9u9390OcdJJLFhs3hh1R0gu0Cw9V/Rj4OMe8YRGv1wIXBBlD0H780Q1qdcwxYUdijOHbb92Ic+np0LMnPP88HH102FElPevrqYjWrHGXxdpNnMYkgOnTYds2+OADuOiisKMpNuyCziJavdq6gjEmVFOnwsdexcXdd7sb5yxJxJQliiJavRrq1g07CmNS0JYt0KuXG4Z0yBA3r1QpVxdsYsoSRRHs3u3+Vi1RGBNHqvDWW25siDffhPvug0mTwo6qWLM2iiLI7irGEoUxcfT5566hOi0NPvnEum2OAytRFMHq1e7Z2iiMCVhW1qGxqjt0gAkTXNfgliTiwhJFEWQnCitRGBOgJUtccjjtNMjIcJcYdu/u7rY2cWGJoghWr3Z/s7VydnVojCm6AwfgscegdWt3JdPQofbPFhJLyUWwejXUrOkutDDGxNDevdC+vRtjuEcPeOEFOPbYsKNKWVaiKII1a6zayZiYyvSGoylbFrp1g/fec12CW5IIlSWKIsjIsJKwMTHz2WdurIhvvnHTgwa5ZGFCZ4miCNats0RhTJFt3Qo33ADnnWd94SQoSxSFtHMnbN9uA2QZUyTvvutKEaNGwcCBMG+ea5swCcUaswtp3Tr3bInCmCJYtswVyydNct2Cm4RkJYpCWuuN1VezZrhxGJNUsrLglVdcIzW4Tvy+/daSRIKzRFFIVqIwpoCWLYNzzoF+/dyVTOBumrMb5xKeJYpCyi5RWKIwJh8HDsATT7juNubOhREjXGd+JmlYKi+ktWuhXDmoXDnsSIxJcFOmwN/+BpdfDi+9ZPW1SchKFIW0dq0rTdjVfMbkYvdu+OIL9/oPf4Avv4R33rEkkaR8JwoRqRBkIMlm3Tr7mzcmV9Onu2qmLl1g82Z3NnXmmWFHZYog30QhIqeLyCJgsTfdWkSGBh5Zgtu40cZsN+Yw27bBTTe5Bmtw41ZXrx5qSCY2/JQongUuBLYAqOo84Owgg0oGmzZBjRphR2FMgti+HVq0gH/9C/76Vzd2RHbCMEnPV2O2qq6RwyvjDwYTTnI4eNANgWolCpPydu6EihXdONV33QWdOkHbtmFHZWLMT4lijYicDqiIlBaRAXjVUKnql1/cfUNWojApS9Vd5lqv3qFO/O6+25JEMeUnUfQDbgVqARlAG+CWAGNKeJs2uWdLFCYlLV8O557r2iNatrR2iBTgp+qpiapeGzlDRM4Avg4mpMSXnSis6smknBdegHvvhdKlXVccN94IJewq++LOzzf8os95KWPjRvdsJQqTcvbtgwsvhEWLoG9fSxIpIs8ShYi0B04HaojIXRGLjgRKBh1YIrOqJ5My9uyBwYPh5JOhe3fXDjFggN1pmmKinQ6UBirikkmliMd2oHvwoSWu7ERhVbOmWPv8c2jd2vXTNHu2m1eihCWJFJRniUJVPwc+F5HXVXVVHGNKeBs3QtWq1umlKaZ+/dW1Q7zyChx/PHz6qWu8NinLz0/dbhEZArQAymbPVNWUvZvGbrYzxdqnn8Krr7oqpocfhvLlw47IhMxPS9SbwBKgAfAwsBL4zs/GRaSziCwVkeUiMjCPdTqKyFwRWSgin/uMO1SWKEyxs36963IDXC+vixfDkCGWJAzgL1FUU9XXgAOq+rmq3gCclt+bRKQk8DLQBWgOXC0izXOsUwUYClyiqi2AHgWMPxTWz5MpNlRdtxvNm8P117s7rUWgceOwIzMJxE+iOOA9rxORP4jISUBtH+9rByxX1Z9UdT8wHuiWY51rgH+r6moAVd3oM+5QWYnCFAs//QTnnw833OD6afrmG9cdhzE5+GmjeFREKgN34+6fOBLo7+N9tYA1EdMZwKk51mkMlBKRGbgrqp5X1VE5NyQifYG+AHXr1vWx6+BkZbl+nixRmKS2caPrCrxECRg6FG6+2e6JMHnKN1Go6ofey1+BTvDbndn5ye0aOs1l/ycD5wLlgG9EZJaqLssRw3BgOEBaWlrObcRVdj9PVvVkktL69XDsse4P+Pnn3c1ztf1UEJhUlucphIiUFJGrRWSAiLT05l0kIjOBl3xsOwOoEzFdG1ibyzqTVXWXqm4GvgBaF+gI4szuyjZJae9euP9+14nfzJluXp8+liSML9FKFK/hfuhnAy+IyCqgPTBQVd/zse3vgEYi0gD4GeiJa5OI9B/gJRE5AneD36m48S8Slt2VbZLOl1+6DvyWLnUN1k2ahB2RSTLREkUa0EpVs0SkLLAZaKiq6/1sWFUzReQ2YAquy4+RqrpQRPp5y4ep6mIRmQzMB7KAEaqaXpQDCpolCpNUBgyAp5+G+vVhyhS44IKwIzJJKFqi2K+qWQCquldElvlNEtlU9WPg4xzzhuWYHgIMKch2w5Rd9WRtFCYp1KoFf/kLPPIIVLBh703hREsUTUVkvvdagBO8aQFUVVsFHl0C2rLFPVetGm4cxuRq40a44w5309yVV7okYUwRRUsUzeIWRRL59VcoV851x29MwlCF0aNdYti5E04/PeyITDESrVNA6wgwF7/+CpUrhx2FMRFWrnT3QUyd6hLEiBHQzM7zTOzYHTYFZInCJJzZs90lry+95K5wsiRhYsw6yi4gSxQmISxY4B7XXAM9ekDHjnaFhQmMrxKFiJQTEbv4GksUJmT79sGDD0LbtjBwoJsWsSRhApVvohCRi4G5wGRvuo2IvB9wXAnLEoUJzddfQ5s27lLXnj3hhx+gTJmwozIpwE+JYhCuJ9htAKo6F6gfVECJzhKFCcWqVdChA+zeDZMmuSucbCxeEyd+EkWmqv4aeCRJwhKFiatFi9xzvXrw9tuwcCF07hxuTCbl+EkU6SJyDVBSRBqJyIvAzIDjSkgHDrgTOksUJnCbNsG110LLlvDtt27e5ZfbeBEmFH4Sxe248bL3AWNx3Y33DzCmhLV9u3u2RGECowpjxrhLXCdMcA3XbdqEHZVJcX4uj22iqvcB9wUdTKL71auAs0RhAqHqut2YOBFOO83dONeiRdhRGeMrUTwjIjWBCcB4VV0YcEwJyxKFCURWlrvEVcTdD3HWWXDrrVCyZNiRGQP4qHpS1U5AR2ATMFxEFojI/UEHlogsUZiYW7gQzjgD3nrLTd96q+vUz5KESSC+brhT1fWq+gLQD3dPxYNBBpWoduxwz5UqhRuHKQb27YNBg+Ckk+DHH+EI6yTBJK58/zpFpBlwFdAd2AKMB+4OOK6EtHevey5XLtw4TJL79lu44QZ36es118Bzz9lIWCah+TmN+RcwDrhAVXOOeZ1S9uxxz5YoTJGsXu2Kpx9+CH/4Q9jRGJOvfBOFqp4Wj0CSgSUKU2hTpsCaNXDjjdC9u0sQ5cuHHZUxvuTZRiEib3vPC0RkfsRjQcTIdynFEoUpsM2b4brr3N3UQ4fCwYPu6iZLEiaJRCtR3Ok9XxSPQJJBdqIoWzbcOEwSUIXx4+HOO2HrVrj/frjvPruaySSlPEsUqrrOe3mLqq6KfAC3xCe8xGKJwvi2eLHrgqN+ffj+e9fjq/3hmCTl5/LY83OZ1yXWgSSDPXvc/7pI2JGYhJSVBZ9/7l43bw7TpsE330CrVuHGZUwRRWuj+LOILACa5GijWAGkbBuFtU+YXC1ZAmef7e6snjfPzevY0aqaTLEQrY1iLDAJeBwYGDF/h6r+EmhUCWrvXksUJof9++Gpp1zVUsWK8MYbVoIwxU60RKGqulJEbs25QESqpmKysBKFOUxWlhtMaNYsuOoqeP55OOaYsKMyJubyK1FcBHwPKBBZM6/A8QHGlZAsURjgUGNViRJw003wf/8HF18cdlTGBCbaVU8Xec8NVPV47zn7kXJJAixRGOCTT1zX3+PHu+kbbrAkYYq9fK96EpEzRKSC9/qPIvKMiNQNPrTEk30iaVLQL79Ar15wwQVQqhTUqRN2RMbEjZ/LY/8J7BaR1sBfgVXA6ECjSlBWokhR77/vRpwbM8ZVM82bB2eeGXZUxsSNn04BM1VVRaQb8LyqviYi1wcdWCLaswdq1gw7ChN3+/e7EsTUqdC6ddjRGBN3fkoUO0Tkb8CfgI9EpCRQys/GRaSziCwVkeUiMjDKeqeIyEER6e4v7HDY5bEpIisLhg2DF19001dc4boGtyRhUpSfRHEVsA+4QVXXA7WAIfm9yUsoL+Pu4m4OXC0izfNY70lgSgHiDoVVPaWApUuhUyf4859dCULV3YpvN86ZFOZnKNT1wJtAZRG5CNirqqN8bLsdsFxVf1LV/bgBj7rlst7twDvARv9hh8MSRTF24AD8/e+u1DB/Powc6domrL8WY3xd9XQlMBvoAVwJfOuziqgWsCZiOsObF7ntWsBlwLB8YugrInNEZM6mTZt87DoYliiKsQULXA+vF1/sOvTr3duShDEeP43Z9wGnqOpGABGpAXwKTMznfbn9l2mO6eeAe1X1oET5p1TV4cBwgLS0tJzbiAtVSxTFzu7dbkChyy6Dtm1dSaJly7CjMibh+EkUJbKThGcL/to2MoDIi81rAzmHUk0DxntJojrQVUQyVfU9H9uPq/37XbKw+yiKic8+g759YcUKWLYMGja0JGFMHvz84E8WkSki0ktEegEfAR/7eN93QCMRaSAipYGewPuRK3h3eddX1fq4EsotiZgkwEa3Kza2boU+feC881wXHNOmuSRhjMmTnzGz7xGRy4EzcdVJw1X1XR/vyxSR23BXM5UERqrqQhHp5y2P2i6RaPbudc+WKJLYgQNwyimwciXcey889JB9ocb4kGeiEJFGwD+AE4AFwABV/bkgG1fVj8lR+sgrQahqr4JsO96sRJHENm+GatVc1xuPPAJNmrg2CWOML9GqnkYCHwJX4HqQfTEuESUoSxRJKCsLXn3VVS2NG+fmXX21JQljCiha1VMlVX3Ve71URH6IR0CJyhJFkvnxR9dYPWOGG2nulFPCjsiYpBUtUZQVkZM4dJlruchpVU2pxGGJIom88gr07w9lyrgSRZ8+dk+EMUUQLVGsA56JmF4fMa3AOUEFlYgsUSSRGjWgSxd46SU47riwozEm6eWZKFS1UzwDSXTZicLuo0hAe/bAww9D9eowYABcfrl7GGNiws99FAYrUSSsGTOgVSt48kl32asxJuYsUfhk91EkmG3bXGN1p07u6qbPPnNVTcaYmLNE4ZOVKBLMsmXw+utwzz2uQ79zUqrJzJi48tN7rHhjZT/oTdcVkXbBh5ZYLFEkgPXr4bXX3Ot27VxV01NPQfnyoYZlTHHnp0QxFGgPXO1N78ANSJRSLFGESNUliGbN4PbbYa3Xt6Rd0WRMXPhJFKeq6q3AXgBV3QqUDjSqBJTdRlE65Y48ZP/7n+vA78YbXaP13LmWIIyJMz/djB/whitV+G08iqxAo0pA+/e7JGH3bcXR7t1w2mnuwx82DG66yfX4aoyJKz+J4gXgXeBoEXkM6A7cH2hUCSgzE47w82mZolu+HE44wbU9/OtfcNJJUKtW/u8zxgTCz5jZbwJ/BR7H3a19qapOCDqwRJOZ6TofNQHauxfuu8+1Rbz1lpt30UWWJIwJWb7nyCJSF9gNfBA5T1VXBxlYojlwwEoUgfriC1e1tGyZG6/6ggvCjsgY4/Hz0/cRrn1CgLJAA2Ap0CLAuBKOlSgC9OCDbpyIBg3gk09c47UxJmH4GeHuxMhpEWkL3BxYRAnKShQBUHVXB7RtC3fdBYMHQ4UKYUdljMmhwJeQeN2Lp1zn/taYHUMbNsBVV8ETT7jpSy+Fp5+2JGFMgvLTRnFXxGQJoC2wKbCIEpRVPcWAKrzxhis97NoFaWlhR2SM8cHPOXKliNeZuDaLd4IJJ3FZ1VMRrVzpOvH75BM480w3oFDTpmFHZYzxIepPn3ejXUVVvSdO8SQsK1EU0caN8N13MHQo3Hyz3ThnTBLJM1GIyBGqmuk1Xqc8K1EUwvz5MHWqG0yoXTtYvRoqVcr/fcaYhBLttG629zxXRN4XkT+JyOXZj3gEl0isRFEAe/fCAw/AySfDkCGwdaubb0nCmKTk5xy5KrAFN0Z29v0UCvw7wLgSjl315NNXX7kb55Ysgeuug2eegaOOCjsqY0wRRPvpO9q74imdQwkimwYaVQKyqicftm2Drl2halWYPBkuvDDsiIwxMRDtp68kUJHDE0S2lEsUmZk2Pk6evvnG9fJapQp88IGrcqpYMeyojDExEi1RrFPVwXGLJMFZiSIXmzbBnXfCuHHwzjtw+eXQoUPYURljYixaY7aNvBDB2igiqMLo0a6X14kT4eGHXS+vxphiKdpP37lxiyIJHDwIJUuGHUWC6NPHjRPRvj2MGAHNm4cdkTEmQHkmClX9JZ6BJLqUTxQHD7qSxBFHwGWXucGEbrklxT8UY1JDoLfHikhnEVkqIstFZGAuy68VkfneY6aItA4ynqJI6aqn9HQ44wx48kk3ffHFcPvtliSMSRGBJQqv+4+XgS5Ac+BqEclZR7EC6KCqrYBHgOFBxVNUKVmi2LcPHnrIdQOePTypMSblBHmO3A5Yrqo/AYjIeKAbsCh7BVWdGbH+LKB2gPEUScolijlz3A1zixfDtdfCs89CjRphR2WMCUGQiaIWsCZiOgM4Ncr6fYBJuS0Qkb5AX4C6devGKr4CSblEoeq64vj4Y+jSJexojDEhCrKNwveNeiLSCZco7s1tuaoOV9U0VU2rEdJZ7cGDKdBGMXmyq2oCOOUUN361JQljUl6QiSIDqBMxXRtYm3MlEWkFjAC6qeqWAOMpkszMYlyi2LwZ/vhHlxQmTnSDCkEKZEZjjB9BJorvgEYi0kBESgM9gfcjVxCRurjOBf+kqssCjKXIimXVkyqMHetunHv7bXjwQfjhBxuS1BhzmMBOGb2xLG4DpuD6jRqpqgtFpJ+3fBjwIFANGCoiAJmqmpDjYxbLRLFhgxt1rmVLd+Ncy5ZhR2SMSUCB1i2o6sfAxznmDYt4fSNwY5AxxEqxSRRZWfCf/8Cll8Kxx7puwU88sZgcnDEmCDYepU/FojF70SI3XvXll7uR5wDatLEkYYyJyhKFT0ndmL1/Pwwe7LrdWLoURo2CCy4IOypjTJJI9nPkuEnqqqdLLoEpU+Dqq+G55+Doo8OOyBiTRKxE4VPSJYpdu1xJAqB/fzeg0NixliSMMQVmicKHrCx3JWnSJIqpU90VTE895aY7d7bxIowxhWaJwoeDB91zwjdmb9kC11/vxqouUwY6dgw7ImNMMWCJwoesLPdcIpE/rcmT3Y1zY8fCfffB3LnuCidjjCmiRD9HTghJkSiqV3fdgL/yCrRqFXY0xphixBKFD+p1ZSiJNIp4VpZLCsuXw9NPQ1oazJyZYEEaY4qDRD5HTjgJ8xu8ZAl06OCGIl2wAA4ccPMTJkBjTHFiicIHzbVz9BAcOACPPQatW8PChfCvf7n7I0qVCjsyY0wxZlVPPiRM1dP69fD449CtG7z4IhxzTMgBGWNSgZUofAg1Ueza5doiVKFOHVeSePttSxLGmLixROFDaIni009dz679+sGsWW5evXpxDsIYk+osUfgQ90SxdSvccAOcf767y2/GDGjfPk47N8aYw1kbRQHEJVGougQxdy787W/wwANQrlwcdmyMMbmzROFDXK56WrvW3TRXujQMGQJHHeXGijDGmJBZ1ZMPgVY9Zd8416zZoU78OnWyJGGMSRiWKHwILFEsWwbnnOMaq08+GXr2jPEOjDGm6CxR+BBIohg1yvXJNHcujBgBn30GDRvGcAfGGBMb1kbhQ0wTharbUIsWbuS555+HmjVjsGFjjAmGJYoCKFKi2L0bBg2CHTvgn/90VU1vvx2r0IwxJjBW9eRDka96mj7dVTMNGeJGQcrut9wYY5KAJQofCl31tG0b3HSTa7AGmDYNhg9P8IEtjDHmcPaL5UOhE8X27TBxIvz1r6478E6dYh6bMcYEzdoofChQoli7FkaOdMOR1q0LK1ZAlSpBhmeMMYGyEoUPvhKFqrvMtXlzN2bEokVuviUJY0ySs0ThQ76JYvlyOPdc1x7Rpg3Mn+8ufzXGmGLAqp6K6uBB6NoVNmxwDdV9+lhjtTGmWLFE4UOuJYoFC6BJE9eJ36hRrj3iuONCic8YY4IUaKIQkc7A80BJYISqPpFjuXjLuwK7gV6q+kOQMRXGYYlizx4YPNjdE/HYY3DvvXDaaaHGZ0zYDhw4QEZGBnv37g07lJRXtmxZateuTalSpWK2zcAShYiUBF4GzgcygO9E5H1VXRSxWhegkfc4Ffin95xQshNFzWWfQ+ub4Mcf3cBCffuGG5gxCSIjI4NKlSpRv359JPTB5VOXqrJlyxYyMjJo0KBBzLYbZGV6O2C5qv6kqvuB8UC3HOt0A0apMwuoIiIJ1/GRKtzDU1z0j46uTeLTT+G119yYEcYY9u7dS7Vq1SxJhExEqFatWsxLdkEmilrAmojpDG9eQddBRPqKyBwRmbNp06aYB5ofEfiqzHks7Hy3a5s499y4x2BMorMkkRiC+B6CbKPILdqcvSb5WQdVHQ4MB0hLS4vHeHOHqVMHZu5tC7SN966NMSZ0QZYoMoA6EdO1gbWFWMcYY3x59913ERGWLFny27wZM2Zw0UUXHbZer169mDhxIuAa4gcOHEijRo1o2bIl7dq1Y9KkSUWO5fHHH6dhw4Y0adKEKVOm5LrOvHnzaN++PSeeeCIXX3wx27dv/y2m66+/nhNPPJFmzZrx+OOP//ae/fv307dvXxo3bkzTpk155513ihxrfoJMFN8BjUSkgYiUBnoC7+dY533gOnFOA35V1XUBxmSMKcbGjRvHmWeeyfjx432/54EHHmDdunWkp6eTnp7OBx98wI4dO4oUx6JFixg/fjwLFy5k8uTJ3HLLLRw8ePB3691444088cQTLFiwgMsuu4whQ4YAMGHCBPbt28eCBQv4/vvveeWVV1i5ciUAjz32GEcffTTLli1j0aJFdOjQoUix+hFY1ZOqZorIbcAU3OWxI1V1oYj085YPAz7GXRq7HHd5bO+g4jHGxEf//m7gxlhq0waeey76Ojt37uTrr79m+vTpXHLJJQwaNCjf7e7evZtXX32VFStWUKZMGQCOOeYYrrzyyiLF+5///IeePXtSpkwZGjRoQMOGDZk9ezbt27c/bL2lS5dy9tlnA3D++edz4YUX8sgjjyAi7Nq1i8zMTPbs2UPp0qU58sgjARg5cuRvJaYSJUpQvXr1IsXqR6C3EKvqx6raWFVPUNXHvHnDvCSBd7XTrd7yE1V1TpDxGGOKr/fee4/OnTvTuHFjqlatyg8/5H9L1vLly6lbt+5vP8LR/OUvf6FNmza/ezzxxBO/W/fnn3+mTp1Dteq1a9fm559//t16LVu25P33XUXLhAkTWLPGXdvTvXt3KlSoQM2aNalbty4DBgygatWqbNu2DXCloLZt29KjRw82bNiQb+xFZXdmG2NiKr8z/6CMGzeO/v37A9CzZ0/GjRtH27Zt87wKqKBXBz377LO+19VcRjvLbX8jR47kjjvuYPDgwVxyySWULl0agNmzZ1OyZEnWrl3L1q1bOeusszjvvPM48sgjycjI4IwzzuCZZ57hmWeeYcCAAYwePbpAx1JQliiMMUlvy5YtTJs2jfT0dESEgwcPIiI89dRTVKtWja1btx62/i+//EL16tVp2LAhq1evZseOHVSqVCnqPv7yl78wffr0383v2bMnAwcOPGxe7dq1fysdgLsh8bhcuvhp2rQpU6dOBWDZsmV89NFHAIwdO5bOnTtTqlQpjj76aM444wzmzJlDjx49KF++PJdddhkAPXr04LXXXvPxCRWN9V5njEl6EydO5LrrrmPVqlWsXLmSNWvW0KBBA7766isaNWrE2rVrWbx4MQCrVq1i3rx5tGnThvLly9OnTx/uuOMO9u/fD8C6desYM2bM7/bx7LPPMnfu3N89ciYJgEsuuYTx48ezb98+VqxYwY8//ki7du1+t97GjRsByMrK4tFHH6Vfv34A1K1bl2nTpqGq7Nq1i1mzZtG0aVNEhIsvvpgZM2YA8Nlnn9G8efOYfIbRWKIwxiS9cePG/XaWne2KK65g7NixlClThjFjxtC7d2/atGlD9+7dGTFiBJUrVwbg0UcfpUaNGjRv3pyWLVty6aWXUqNGjSLF06JFC6688kqaN29O586defnllylZsiTgrnSaM2fOb3FnX+Z63HHH0bu3u57n1ltvZefOnbRs2ZJTTjmF3r1706pVKwCefPJJBg0aRKtWrRg9ejRPP/10kWL1Q3KrS0tkaWlpmv0hG2MSw+LFi2nWrFnYYRhPbt+HiHyvqmmF2Z6VKIwxxkRlicIYY0xUliiMMTGRbNXYxVUQ34MlCmNMkZUtW5YtW7ZYsghZ9ngUZcuWjel27T4KY0yR1a5dm4yMDMIYBsAcLnuEu1iyRGGMKbJSpUrFdEQ1k1is6skYY0xUliiMMcZEZYnCGGNMVEl3Z7aIbAJWhbT76sDmkPYdllQ75lQ7XrBjThVNVDV6z4d5SLrGbFUtWicsRSAicwp7C3yySrVjTrXjBTvmVCEihe77yKqejDHGRGWJwhhjTFSWKApmeNgBhCDVjjnVjhfsmFNFoY856RqzjTHGxJeVKIwxxkRlicIYY0xUlihyEJHOIrJURJaLyO8GwxXnBW/5fBFpG0acseTjmK/1jnW+iMwUkdZhxBlL+R1zxHqniMhBEekez/iC4OeYRaSjiMwVkYUi8nm8Y4w1H3/blUXkAxGZ5x1z7zDijBURGSkiG0UkPY/lhfv9UlV7eA+gJPA/4HigNDAPaJ5jna7AJECA04Bvw447Dsd8OnCU97pLKhxzxHrTgI+B7mHHHYfvuQqwCKjrTR8ddtxxOOb/A570XtcAfgFKhx17EY75bKAtkJ7H8kL9flmJ4nDtgOWq+pOq7gfGA91yrNMNGKXOLKCKiNSMd6AxlO8xq+pMVd3qTc4CYtuHcfz5+Z4BbgfeATbGM7iA+Dnma4B/q+pqAFVN9uP2c8wKVBIRASriEkVmfMOMHVX9AncMeSnU75clisPVAtZETGd48wq6TjIp6PH0wZ2RJLN8j1lEagGXAcPiGFeQ/HzPjYGjRGSGiHwvItfFLbpg+Dnml4BmwFpgAXCnqmbFJ7xQFOr3K+m68AiY5DIv5/XDftZJJr6PR0Q64RLFmYFGFDw/x/wccK+qHnQnm0nPzzEfAZwMnAuUA74RkVmquizo4ALi55gvBOYC5wAnAJ+IyJequj3g2MJSqN8vSxSHywDqREzXxp1pFHSdZOLreESkFTAC6KKqW+IUW1D8HHMaMN5LEtWBriKSqarvxSXC2PP7t71ZVXcBu0TkC6A1kKyJws8x9waeUFeBv1xEVgBNgdnxCTHuCvX7ZVVPh/sOaCQiDUSkNNATeD/HOu8D13lXD5wG/Kqq6+IdaAzle8wiUhf4N/CnJD67jJTvMatqA1Wtr6r1gYnALUmcJMDf3/Z/gLNE5AgRKQ+cCiyOc5yx5OeYV+NKUIjIMUAT4Ke4Rhlfhfr9shJFBFXNFJHbgCm4KyZGqupCEennLR+GuwKmK7Ac2I07I0laPo/5QaAaMNQ7w87UJO550+cxFyt+jllVF4vIZGA+kAWMUNVcL7NMBj6/50eA10VkAa5a5l5VTdrux0VkHNARqC4iGcBDQCko2u+XdeFhjDEmKqt6MsYYE5UlCmOMMVFZojDGGBOVJQpjjDFRWaIwxhgTlSUKk5C8HlvnRjzqR1l3Zwz297qIrPD29YOItC/ENkaISHPv9f/lWDazqDF628n+XNK9Xk+r5LN+GxHpGot9m9Rll8eahCQiO1W1YqzXjbKN14EPVXWiiFwA/ENVWxVhe0WOKb/tisgbwDJVfSzK+r2ANFW9LdaxmNRhJQqTFESkooh85p3tLxCR3/X2KiI1ReSLiDPus7z5F4jIN957J4hIfj/gXwANvffe5W0rXUT6e/MqiMhH3hgG6SJylTd/hoikicgTQDkvjje9ZTu957ciz/C9kswVIlJSRIaIyHfixgm42cfH8g1eh24i0k7cWCH/9Z6beHcjDwau8mK5yot9pLef/+b2ORrzO2H3n24Pe+T2AA7iOmubC7yL60XgSG9Zddydpdkl4p3e893Afd7rkkAlb90vgAre/HuBB3PZ3+t4Y04APYBvcR3kLQAq4LqgXgicBFwBvBrx3sre8wzc2ftvMUWskx3jZcAb3uvSuJ48ywF9gfu9+WWAOUCDXOLcGXF8E4DO3vSRwBHe6/OAd7zXvYCXIt7/d+CP3usquH6cKoT9fdsjsR/WhYdJVHtUtU32hIiUAv4uImfjupeoBRwDrI94z3fASG/d91R1roh0AJoDX3vdj5TGnYnnZoiI3A9swvWSey7wrrpO8hCRfwNnAZOBf4jIk7jqqi8LcFyTgBdEpAzQGfhCVfd41V2t5NBIepWBRsCKHO8vJyJzgfrA98AnEeu/ISKNcL2Blspj/xcAl4jIAG+6LFCX5O7TyQTMEoVJFtfiRiA7WVUPiMhK3I/cb1T1Cy+R/AEYLSJDgK3AJ6p6tY993KOqE7MnROS83FZS1WUicjKuz5zHRWSqqg72cxCquldEZuC6t74KGJe9O+B2VZ2Szyb2qGobEakMfAjcCryA67Nouqpe5jX8z8jj/QJcoapL/cRrDFgbhUkelYGNXpLoBNTLuYKI1PPWeRV4DTck5CzgDBHJbnMoLyKNfe7zC+BS7z0VcNVGX4rIccBuVR0D/MPbT04HvJJNbsbjOmM7C9dhHd7zn7PfIyKNvX3mSlV/Be4ABnjvqQz87C3uFbHqDlwVXLYpwO3iFa9E5KS89mFMNksUJlm8CaSJyBxc6WJJLut0BOaKyH9x7QjPq+om3A/nOBGZj0scTf3sUFV/wLVdzMa1WYxQ1f8CJwKzvSqg+4BHc3n7cGB+dmN2DlNxYxt/qm6ITnBjfSwCfhCRdOAV8inxe7HMw3Wf/RSudPM1rv0i23SgeXZjNq7kUcqLLd2bNiYquzzWGGNMVFaiMMYYE5UlCmOMMVFZojDGGBOVJQpjjDFRWaIwxhgTlSUKY4wxUVmiMMYYE9X/AypJEAm/B0jIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#random Forest with out smote\n",
    "\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "y_random =  clf_normal.predict_proba(X_test)\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_random[:,1])\n",
    "\n",
    "roc_auc = auc(fpr,tpr)\n",
    "print(roc_auc_score(y_test, y_random[:,1]))\n",
    "\n",
    "print(\"ROC-AUC:\", roc_auc)\n",
    "\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b',label='AUC = %0.3f'% roc_auc)\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.xlim([-0.1,1.0])\n",
    "plt.ylim([-0.1,1.01])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "3563e717",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Decision Tree with SMOTE\n",
    "classifier_smote = classifier.fit(X_train_res, y_train_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "2044aa74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.9565523966694172\n"
     ]
    }
   ],
   "source": [
    "# decision tree prediction\n",
    "y_pred = classifier_smote.predict(X_test)#Accuracy\n",
    "from sklearn import metrics\n",
    "print('Accuracy Score:', metrics.accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "c2507bc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.98    121733\n",
      "           1       0.87      0.59      0.70     11577\n",
      "\n",
      "    accuracy                           0.96    133310\n",
      "   macro avg       0.91      0.79      0.84    133310\n",
      "weighted avg       0.95      0.96      0.95    133310\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Decision Tree Classifier report\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "f592c489",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9526742179881479\n"
     ]
    }
   ],
   "source": [
    "#random\n",
    "clf=RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "clf_smote = clf.fit(X_train_res, y_train_res)\n",
    "\n",
    "y_pred_random=clf_smote.predict(X_test)\n",
    "\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred_random))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "41709f95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.97    120526\n",
      "           1       0.91      0.56      0.70     12784\n",
      "\n",
      "    accuracy                           0.95    133310\n",
      "   macro avg       0.93      0.78      0.83    133310\n",
      "weighted avg       0.95      0.95      0.95    133310\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#RandomForest Classifier report\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_pred_random,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "d70e0463",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9141033605115059\n",
      "ROC-AUC: 0.9141033605115059\n"
     ]
    },
    {
     "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": [
    "#Decision tree SMOTE\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "y_decision =  classifier_smote.predict_proba(X_test)\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_decision[:,1])\n",
    "\n",
    "print(roc_auc_score(y_test, y_decision[:,1]))\n",
    "\n",
    "roc_auc = auc(fpr,tpr)\n",
    "\n",
    "print(\"ROC-AUC:\", roc_auc)\n",
    "\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b',label='AUC = %0.3f'% roc_auc)\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.xlim([-0.1,1.0])\n",
    "plt.ylim([-0.1,1.01])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "36d8bbc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9840007773960401\n",
      "ROC-AUC: 0.9840007773960401\n"
     ]
    },
    {
     "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": [
    "#random Forest\n",
    "\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "y_random =  clf_smote.predict_proba(X_test)\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_random[:,1])\n",
    "\n",
    "roc_auc = auc(fpr,tpr)\n",
    "print(roc_auc_score(y_test, y_random[:,1]))\n",
    "\n",
    "print(\"ROC-AUC:\", roc_auc)\n",
    "\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b',label='AUC = %0.3f'% roc_auc)\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.xlim([-0.1,1.0])\n",
    "plt.ylim([-0.1,1.01])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bb96ad2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sudeep_kumar\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "#SVM with out smote\n",
    "from sklearn.svm import SVC\n",
    "svclassifier = SVC(kernel='linear',C=1)\n",
    "svclassifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f017eeb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sudeep_kumar\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\sudeep_kumar\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVC(random_state=0, tol=1e-05)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "svclassifier = LinearSVC(random_state=0, tol=1e-5)\n",
    "svclassifier.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fad65c29",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_linear = svclassifier.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "11983ceb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[125344     59]\n",
      " [  7838     69]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      1.00      0.97    125403\n",
      "           1       0.54      0.01      0.02      7907\n",
      "\n",
      "    accuracy                           0.94    133310\n",
      "   macro avg       0.74      0.50      0.49    133310\n",
      "weighted avg       0.92      0.94      0.91    133310\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(y_test, y_pred_linear))\n",
    "print(classification_report(y_test, y_pred_linear))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9741f419",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.9407621333733404\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print('Accuracy Score:', metrics.accuracy_score(y_test,y_pred_linear))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3a65b532",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC-AUC: 0.504127980879968\n"
     ]
    },
    {
     "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": [
    "#random Forest\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "from sklearn.metrics import confusion_matrix, auc, roc_curve\n",
    "from matplotlib import pyplot as plt\n",
    "y_random =  svclassifier.decision_function(X_test)\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_linear)\n",
    "\n",
    "\n",
    "#fpr, tpr, thresholds = roc_curve(y_test, y_random[:,1])\n",
    "\n",
    "roc_auc = auc(fpr,tpr)\n",
    "#print(roc_auc_score(y_test, y_random[:,1]))\n",
    "\n",
    "print(\"ROC-AUC:\", roc_auc)\n",
    "\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b',label='AUC = %0.3f'% roc_auc)\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.xlim([-0.1,1.0])\n",
    "plt.ylim([-0.1,1.01])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "449365d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sudeep_kumar\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "#SVM with out smote\n",
    "from sklearn.svm import SVC\n",
    "svclassifier_rbf = SVC(kernel='rbf')\n",
    "svclassifier_rbf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b9685f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import IsolationForest\n",
    "\n",
    "random_state = np.random.RandomState(42)\n",
    "\n",
    "model=IsolationForest(n_estimators=100,max_samples='auto',contamination=float(0.2),random_state=random_state)\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}