Fraud prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "554cde8b",
   "metadata": {},
   "source": [
    "# Fraud Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f88b1ba5",
   "metadata": {},
   "source": [
    "### importing Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "29f16d32",
   "metadata": {},
   "outputs": [],
   "source": [
    "def warn(*args,**kwargs):\n",
    "    pass\n",
    "import warnings\n",
    "warnings.warn = warn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e33ea111",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eaa99e1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading dataset\n",
    "credit_card_data = pd.read_csv(r'Desktop\\fraudTrain.csv',encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "758bba8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>trans_date_trans_time</th>\n",
       "      <th>cc_num</th>\n",
       "      <th>merchant</th>\n",
       "      <th>category</th>\n",
       "      <th>amt</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>gender</th>\n",
       "      <th>street</th>\n",
       "      <th>...</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>city_pop</th>\n",
       "      <th>job</th>\n",
       "      <th>dob</th>\n",
       "      <th>trans_num</th>\n",
       "      <th>unix_time</th>\n",
       "      <th>merch_lat</th>\n",
       "      <th>merch_long</th>\n",
       "      <th>is_fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2019-01-01 00:00:18</td>\n",
       "      <td>2703186189652095</td>\n",
       "      <td>fraud_Rippin, Kub and Mann</td>\n",
       "      <td>misc_net</td>\n",
       "      <td>4.97</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td>Banks</td>\n",
       "      <td>F</td>\n",
       "      <td>561 Perry Cove</td>\n",
       "      <td>...</td>\n",
       "      <td>36.0788</td>\n",
       "      <td>-81.1781</td>\n",
       "      <td>3495</td>\n",
       "      <td>Psychologist, counselling</td>\n",
       "      <td>1988-03-09</td>\n",
       "      <td>0b242abb623afc578575680df30655b9</td>\n",
       "      <td>1325376018</td>\n",
       "      <td>36.011293</td>\n",
       "      <td>-82.048315</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01 00:00:44</td>\n",
       "      <td>630423337322</td>\n",
       "      <td>fraud_Heller, Gutmann and Zieme</td>\n",
       "      <td>grocery_pos</td>\n",
       "      <td>107.23</td>\n",
       "      <td>Stephanie</td>\n",
       "      <td>Gill</td>\n",
       "      <td>F</td>\n",
       "      <td>43039 Riley Greens Suite 393</td>\n",
       "      <td>...</td>\n",
       "      <td>48.8878</td>\n",
       "      <td>-118.2105</td>\n",
       "      <td>149</td>\n",
       "      <td>Special educational needs teacher</td>\n",
       "      <td>1978-06-21</td>\n",
       "      <td>1f76529f8574734946361c461b024d99</td>\n",
       "      <td>1325376044</td>\n",
       "      <td>49.159047</td>\n",
       "      <td>-118.186462</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2019-01-01 00:00:51</td>\n",
       "      <td>38859492057661</td>\n",
       "      <td>fraud_Lind-Buckridge</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>220.11</td>\n",
       "      <td>Edward</td>\n",
       "      <td>Sanchez</td>\n",
       "      <td>M</td>\n",
       "      <td>594 White Dale Suite 530</td>\n",
       "      <td>...</td>\n",
       "      <td>42.1808</td>\n",
       "      <td>-112.2620</td>\n",
       "      <td>4154</td>\n",
       "      <td>Nature conservation officer</td>\n",
       "      <td>1962-01-19</td>\n",
       "      <td>a1a22d70485983eac12b5b88dad1cf95</td>\n",
       "      <td>1325376051</td>\n",
       "      <td>43.150704</td>\n",
       "      <td>-112.154481</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-01-01 00:01:16</td>\n",
       "      <td>3534093764340240</td>\n",
       "      <td>fraud_Kutch, Hermiston and Farrell</td>\n",
       "      <td>gas_transport</td>\n",
       "      <td>45.00</td>\n",
       "      <td>Jeremy</td>\n",
       "      <td>White</td>\n",
       "      <td>M</td>\n",
       "      <td>9443 Cynthia Court Apt. 038</td>\n",
       "      <td>...</td>\n",
       "      <td>46.2306</td>\n",
       "      <td>-112.1138</td>\n",
       "      <td>1939</td>\n",
       "      <td>Patent attorney</td>\n",
       "      <td>1967-01-12</td>\n",
       "      <td>6b849c168bdad6f867558c3793159a81</td>\n",
       "      <td>1325376076</td>\n",
       "      <td>47.034331</td>\n",
       "      <td>-112.561071</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2019-01-01 00:03:06</td>\n",
       "      <td>375534208663984</td>\n",
       "      <td>fraud_Keeling-Crist</td>\n",
       "      <td>misc_pos</td>\n",
       "      <td>41.96</td>\n",
       "      <td>Tyler</td>\n",
       "      <td>Garcia</td>\n",
       "      <td>M</td>\n",
       "      <td>408 Bradley Rest</td>\n",
       "      <td>...</td>\n",
       "      <td>38.4207</td>\n",
       "      <td>-79.4629</td>\n",
       "      <td>99</td>\n",
       "      <td>Dance movement psychotherapist</td>\n",
       "      <td>1986-03-28</td>\n",
       "      <td>a41d7549acf90789359a9aa5346dcb46</td>\n",
       "      <td>1325376186</td>\n",
       "      <td>38.674999</td>\n",
       "      <td>-78.632459</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296670</th>\n",
       "      <td>1296670</td>\n",
       "      <td>2020-06-21 12:12:08</td>\n",
       "      <td>30263540414123</td>\n",
       "      <td>fraud_Reichel Inc</td>\n",
       "      <td>entertainment</td>\n",
       "      <td>15.56</td>\n",
       "      <td>Erik</td>\n",
       "      <td>Patterson</td>\n",
       "      <td>M</td>\n",
       "      <td>162 Jessica Row Apt. 072</td>\n",
       "      <td>...</td>\n",
       "      <td>37.7175</td>\n",
       "      <td>-112.4777</td>\n",
       "      <td>258</td>\n",
       "      <td>Geoscientist</td>\n",
       "      <td>1961-11-24</td>\n",
       "      <td>440b587732da4dc1a6395aba5fb41669</td>\n",
       "      <td>1371816728</td>\n",
       "      <td>36.841266</td>\n",
       "      <td>-111.690765</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296671</th>\n",
       "      <td>1296671</td>\n",
       "      <td>2020-06-21 12:12:19</td>\n",
       "      <td>6011149206456997</td>\n",
       "      <td>fraud_Abernathy and Sons</td>\n",
       "      <td>food_dining</td>\n",
       "      <td>51.70</td>\n",
       "      <td>Jeffrey</td>\n",
       "      <td>White</td>\n",
       "      <td>M</td>\n",
       "      <td>8617 Holmes Terrace Suite 651</td>\n",
       "      <td>...</td>\n",
       "      <td>39.2667</td>\n",
       "      <td>-77.5101</td>\n",
       "      <td>100</td>\n",
       "      <td>Production assistant, television</td>\n",
       "      <td>1979-12-11</td>\n",
       "      <td>278000d2e0d2277d1de2f890067dcc0a</td>\n",
       "      <td>1371816739</td>\n",
       "      <td>38.906881</td>\n",
       "      <td>-78.246528</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296672</th>\n",
       "      <td>1296672</td>\n",
       "      <td>2020-06-21 12:12:32</td>\n",
       "      <td>3514865930894695</td>\n",
       "      <td>fraud_Stiedemann Ltd</td>\n",
       "      <td>food_dining</td>\n",
       "      <td>105.93</td>\n",
       "      <td>Christopher</td>\n",
       "      <td>Castaneda</td>\n",
       "      <td>M</td>\n",
       "      <td>1632 Cohen Drive Suite 639</td>\n",
       "      <td>...</td>\n",
       "      <td>32.9396</td>\n",
       "      <td>-105.8189</td>\n",
       "      <td>899</td>\n",
       "      <td>Naval architect</td>\n",
       "      <td>1967-08-30</td>\n",
       "      <td>483f52fe67fabef353d552c1e662974c</td>\n",
       "      <td>1371816752</td>\n",
       "      <td>33.619513</td>\n",
       "      <td>-105.130529</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296673</th>\n",
       "      <td>1296673</td>\n",
       "      <td>2020-06-21 12:13:36</td>\n",
       "      <td>2720012583106919</td>\n",
       "      <td>fraud_Reinger, Weissnat and Strosin</td>\n",
       "      <td>food_dining</td>\n",
       "      <td>74.90</td>\n",
       "      <td>Joseph</td>\n",
       "      <td>Murray</td>\n",
       "      <td>M</td>\n",
       "      <td>42933 Ryan Underpass</td>\n",
       "      <td>...</td>\n",
       "      <td>43.3526</td>\n",
       "      <td>-102.5411</td>\n",
       "      <td>1126</td>\n",
       "      <td>Volunteer coordinator</td>\n",
       "      <td>1980-08-18</td>\n",
       "      <td>d667cdcbadaaed3da3f4020e83591c83</td>\n",
       "      <td>1371816816</td>\n",
       "      <td>42.788940</td>\n",
       "      <td>-103.241160</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296674</th>\n",
       "      <td>1296674</td>\n",
       "      <td>2020-06-21 12:13:37</td>\n",
       "      <td>4292902571056973207</td>\n",
       "      <td>fraud_Langosh, Wintheiser and Hyatt</td>\n",
       "      <td>food_dining</td>\n",
       "      <td>4.30</td>\n",
       "      <td>Jeffrey</td>\n",
       "      <td>Smith</td>\n",
       "      <td>M</td>\n",
       "      <td>135 Joseph Mountains</td>\n",
       "      <td>...</td>\n",
       "      <td>45.8433</td>\n",
       "      <td>-113.8748</td>\n",
       "      <td>218</td>\n",
       "      <td>Therapist, horticultural</td>\n",
       "      <td>1995-08-16</td>\n",
       "      <td>8f7c8e4ab7f25875d753b422917c98c9</td>\n",
       "      <td>1371816817</td>\n",
       "      <td>46.565983</td>\n",
       "      <td>-114.186110</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1296675 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0 trans_date_trans_time               cc_num  \\\n",
       "0                 0   2019-01-01 00:00:18     2703186189652095   \n",
       "1                 1   2019-01-01 00:00:44         630423337322   \n",
       "2                 2   2019-01-01 00:00:51       38859492057661   \n",
       "3                 3   2019-01-01 00:01:16     3534093764340240   \n",
       "4                 4   2019-01-01 00:03:06      375534208663984   \n",
       "...             ...                   ...                  ...   \n",
       "1296670     1296670   2020-06-21 12:12:08       30263540414123   \n",
       "1296671     1296671   2020-06-21 12:12:19     6011149206456997   \n",
       "1296672     1296672   2020-06-21 12:12:32     3514865930894695   \n",
       "1296673     1296673   2020-06-21 12:13:36     2720012583106919   \n",
       "1296674     1296674   2020-06-21 12:13:37  4292902571056973207   \n",
       "\n",
       "                                    merchant       category     amt  \\\n",
       "0                 fraud_Rippin, Kub and Mann       misc_net    4.97   \n",
       "1            fraud_Heller, Gutmann and Zieme    grocery_pos  107.23   \n",
       "2                       fraud_Lind-Buckridge  entertainment  220.11   \n",
       "3         fraud_Kutch, Hermiston and Farrell  gas_transport   45.00   \n",
       "4                        fraud_Keeling-Crist       misc_pos   41.96   \n",
       "...                                      ...            ...     ...   \n",
       "1296670                    fraud_Reichel Inc  entertainment   15.56   \n",
       "1296671             fraud_Abernathy and Sons    food_dining   51.70   \n",
       "1296672                 fraud_Stiedemann Ltd    food_dining  105.93   \n",
       "1296673  fraud_Reinger, Weissnat and Strosin    food_dining   74.90   \n",
       "1296674  fraud_Langosh, Wintheiser and Hyatt    food_dining    4.30   \n",
       "\n",
       "               first       last gender                         street  ...  \\\n",
       "0           Jennifer      Banks      F                 561 Perry Cove  ...   \n",
       "1          Stephanie       Gill      F   43039 Riley Greens Suite 393  ...   \n",
       "2             Edward    Sanchez      M       594 White Dale Suite 530  ...   \n",
       "3             Jeremy      White      M    9443 Cynthia Court Apt. 038  ...   \n",
       "4              Tyler     Garcia      M               408 Bradley Rest  ...   \n",
       "...              ...        ...    ...                            ...  ...   \n",
       "1296670         Erik  Patterson      M       162 Jessica Row Apt. 072  ...   \n",
       "1296671      Jeffrey      White      M  8617 Holmes Terrace Suite 651  ...   \n",
       "1296672  Christopher  Castaneda      M     1632 Cohen Drive Suite 639  ...   \n",
       "1296673       Joseph     Murray      M           42933 Ryan Underpass  ...   \n",
       "1296674      Jeffrey      Smith      M           135 Joseph Mountains  ...   \n",
       "\n",
       "             lat      long  city_pop                                job  \\\n",
       "0        36.0788  -81.1781      3495          Psychologist, counselling   \n",
       "1        48.8878 -118.2105       149  Special educational needs teacher   \n",
       "2        42.1808 -112.2620      4154        Nature conservation officer   \n",
       "3        46.2306 -112.1138      1939                    Patent attorney   \n",
       "4        38.4207  -79.4629        99     Dance movement psychotherapist   \n",
       "...          ...       ...       ...                                ...   \n",
       "1296670  37.7175 -112.4777       258                       Geoscientist   \n",
       "1296671  39.2667  -77.5101       100   Production assistant, television   \n",
       "1296672  32.9396 -105.8189       899                    Naval architect   \n",
       "1296673  43.3526 -102.5411      1126              Volunteer coordinator   \n",
       "1296674  45.8433 -113.8748       218           Therapist, horticultural   \n",
       "\n",
       "                dob                         trans_num   unix_time  merch_lat  \\\n",
       "0        1988-03-09  0b242abb623afc578575680df30655b9  1325376018  36.011293   \n",
       "1        1978-06-21  1f76529f8574734946361c461b024d99  1325376044  49.159047   \n",
       "2        1962-01-19  a1a22d70485983eac12b5b88dad1cf95  1325376051  43.150704   \n",
       "3        1967-01-12  6b849c168bdad6f867558c3793159a81  1325376076  47.034331   \n",
       "4        1986-03-28  a41d7549acf90789359a9aa5346dcb46  1325376186  38.674999   \n",
       "...             ...                               ...         ...        ...   \n",
       "1296670  1961-11-24  440b587732da4dc1a6395aba5fb41669  1371816728  36.841266   \n",
       "1296671  1979-12-11  278000d2e0d2277d1de2f890067dcc0a  1371816739  38.906881   \n",
       "1296672  1967-08-30  483f52fe67fabef353d552c1e662974c  1371816752  33.619513   \n",
       "1296673  1980-08-18  d667cdcbadaaed3da3f4020e83591c83  1371816816  42.788940   \n",
       "1296674  1995-08-16  8f7c8e4ab7f25875d753b422917c98c9  1371816817  46.565983   \n",
       "\n",
       "         merch_long  is_fraud  \n",
       "0        -82.048315         0  \n",
       "1       -118.186462         0  \n",
       "2       -112.154481         0  \n",
       "3       -112.561071         0  \n",
       "4        -78.632459         0  \n",
       "...             ...       ...  \n",
       "1296670 -111.690765         0  \n",
       "1296671  -78.246528         0  \n",
       "1296672 -105.130529         0  \n",
       "1296673 -103.241160         0  \n",
       "1296674 -114.186110         0  \n",
       "\n",
       "[1296675 rows x 23 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first 5 transactions\n",
    "credit_card_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "72fdee7e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1296675 entries, 0 to 1296674\n",
      "Data columns (total 23 columns):\n",
      " #   Column                 Non-Null Count    Dtype  \n",
      "---  ------                 --------------    -----  \n",
      " 0   Unnamed: 0             1296675 non-null  int64  \n",
      " 1   trans_date_trans_time  1296675 non-null  object \n",
      " 2   cc_num                 1296675 non-null  int64  \n",
      " 3   merchant               1296675 non-null  object \n",
      " 4   category               1296675 non-null  object \n",
      " 5   amt                    1296675 non-null  float64\n",
      " 6   first                  1296675 non-null  object \n",
      " 7   last                   1296675 non-null  object \n",
      " 8   gender                 1296675 non-null  object \n",
      " 9   street                 1296675 non-null  object \n",
      " 10  city                   1296675 non-null  object \n",
      " 11  state                  1296675 non-null  object \n",
      " 12  zip                    1296675 non-null  int64  \n",
      " 13  lat                    1296675 non-null  float64\n",
      " 14  long                   1296675 non-null  float64\n",
      " 15  city_pop               1296675 non-null  int64  \n",
      " 16  job                    1296675 non-null  object \n",
      " 17  dob                    1296675 non-null  object \n",
      " 18  trans_num              1296675 non-null  object \n",
      " 19  unix_time              1296675 non-null  int64  \n",
      " 20  merch_lat              1296675 non-null  float64\n",
      " 21  merch_long             1296675 non-null  float64\n",
      " 22  is_fraud               1296675 non-null  int64  \n",
      "dtypes: float64(5), int64(6), object(12)\n",
      "memory usage: 227.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# first 5 transactions\n",
    "credit_card_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a916c81a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>cc_num</th>\n",
       "      <th>amt</th>\n",
       "      <th>zip</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>city_pop</th>\n",
       "      <th>unix_time</th>\n",
       "      <th>merch_lat</th>\n",
       "      <th>merch_long</th>\n",
       "      <th>is_fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.296675e+06</td>\n",
       "      <td>1.296675e+06</td>\n",
       "      <td>1.296675e+06</td>\n",
       "      <td>1.296675e+06</td>\n",
       "      <td>1.296675e+06</td>\n",
       "      <td>1.296675e+06</td>\n",
       "      <td>1.296675e+06</td>\n",
       "      <td>1.296675e+06</td>\n",
       "      <td>1.296675e+06</td>\n",
       "      <td>1.296675e+06</td>\n",
       "      <td>1.296675e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.483370e+05</td>\n",
       "      <td>4.171920e+17</td>\n",
       "      <td>7.035104e+01</td>\n",
       "      <td>4.880067e+04</td>\n",
       "      <td>3.853762e+01</td>\n",
       "      <td>-9.022634e+01</td>\n",
       "      <td>8.882444e+04</td>\n",
       "      <td>1.349244e+09</td>\n",
       "      <td>3.853734e+01</td>\n",
       "      <td>-9.022646e+01</td>\n",
       "      <td>5.788652e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.743180e+05</td>\n",
       "      <td>1.308806e+18</td>\n",
       "      <td>1.603160e+02</td>\n",
       "      <td>2.689322e+04</td>\n",
       "      <td>5.075808e+00</td>\n",
       "      <td>1.375908e+01</td>\n",
       "      <td>3.019564e+05</td>\n",
       "      <td>1.284128e+07</td>\n",
       "      <td>5.109788e+00</td>\n",
       "      <td>1.377109e+01</td>\n",
       "      <td>7.586269e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.041621e+10</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.257000e+03</td>\n",
       "      <td>2.002710e+01</td>\n",
       "      <td>-1.656723e+02</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>1.325376e+09</td>\n",
       "      <td>1.902779e+01</td>\n",
       "      <td>-1.666712e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.241685e+05</td>\n",
       "      <td>1.800429e+14</td>\n",
       "      <td>9.650000e+00</td>\n",
       "      <td>2.623700e+04</td>\n",
       "      <td>3.462050e+01</td>\n",
       "      <td>-9.679800e+01</td>\n",
       "      <td>7.430000e+02</td>\n",
       "      <td>1.338751e+09</td>\n",
       "      <td>3.473357e+01</td>\n",
       "      <td>-9.689728e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.483370e+05</td>\n",
       "      <td>3.521417e+15</td>\n",
       "      <td>4.752000e+01</td>\n",
       "      <td>4.817400e+04</td>\n",
       "      <td>3.935430e+01</td>\n",
       "      <td>-8.747690e+01</td>\n",
       "      <td>2.456000e+03</td>\n",
       "      <td>1.349250e+09</td>\n",
       "      <td>3.936568e+01</td>\n",
       "      <td>-8.743839e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.725055e+05</td>\n",
       "      <td>4.642255e+15</td>\n",
       "      <td>8.314000e+01</td>\n",
       "      <td>7.204200e+04</td>\n",
       "      <td>4.194040e+01</td>\n",
       "      <td>-8.015800e+01</td>\n",
       "      <td>2.032800e+04</td>\n",
       "      <td>1.359385e+09</td>\n",
       "      <td>4.195716e+01</td>\n",
       "      <td>-8.023680e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.296674e+06</td>\n",
       "      <td>4.992346e+18</td>\n",
       "      <td>2.894890e+04</td>\n",
       "      <td>9.978300e+04</td>\n",
       "      <td>6.669330e+01</td>\n",
       "      <td>-6.795030e+01</td>\n",
       "      <td>2.906700e+06</td>\n",
       "      <td>1.371817e+09</td>\n",
       "      <td>6.751027e+01</td>\n",
       "      <td>-6.695090e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0        cc_num           amt           zip           lat  \\\n",
       "count  1.296675e+06  1.296675e+06  1.296675e+06  1.296675e+06  1.296675e+06   \n",
       "mean   6.483370e+05  4.171920e+17  7.035104e+01  4.880067e+04  3.853762e+01   \n",
       "std    3.743180e+05  1.308806e+18  1.603160e+02  2.689322e+04  5.075808e+00   \n",
       "min    0.000000e+00  6.041621e+10  1.000000e+00  1.257000e+03  2.002710e+01   \n",
       "25%    3.241685e+05  1.800429e+14  9.650000e+00  2.623700e+04  3.462050e+01   \n",
       "50%    6.483370e+05  3.521417e+15  4.752000e+01  4.817400e+04  3.935430e+01   \n",
       "75%    9.725055e+05  4.642255e+15  8.314000e+01  7.204200e+04  4.194040e+01   \n",
       "max    1.296674e+06  4.992346e+18  2.894890e+04  9.978300e+04  6.669330e+01   \n",
       "\n",
       "               long      city_pop     unix_time     merch_lat    merch_long  \\\n",
       "count  1.296675e+06  1.296675e+06  1.296675e+06  1.296675e+06  1.296675e+06   \n",
       "mean  -9.022634e+01  8.882444e+04  1.349244e+09  3.853734e+01 -9.022646e+01   \n",
       "std    1.375908e+01  3.019564e+05  1.284128e+07  5.109788e+00  1.377109e+01   \n",
       "min   -1.656723e+02  2.300000e+01  1.325376e+09  1.902779e+01 -1.666712e+02   \n",
       "25%   -9.679800e+01  7.430000e+02  1.338751e+09  3.473357e+01 -9.689728e+01   \n",
       "50%   -8.747690e+01  2.456000e+03  1.349250e+09  3.936568e+01 -8.743839e+01   \n",
       "75%   -8.015800e+01  2.032800e+04  1.359385e+09  4.195716e+01 -8.023680e+01   \n",
       "max   -6.795030e+01  2.906700e+06  1.371817e+09  6.751027e+01 -6.695090e+01   \n",
       "\n",
       "           is_fraud  \n",
       "count  1.296675e+06  \n",
       "mean   5.788652e-03  \n",
       "std    7.586269e-02  \n",
       "min    0.000000e+00  \n",
       "25%    0.000000e+00  \n",
       "50%    0.000000e+00  \n",
       "75%    0.000000e+00  \n",
       "max    1.000000e+00  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# last 5 rows\n",
    "credit_card_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bfe0767e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>gender</th>\n",
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       "      <th>lat</th>\n",
       "      <th>long</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2703186189652095</td>\n",
       "      <td>4.97</td>\n",
       "      <td>F</td>\n",
       "      <td>28654</td>\n",
       "      <td>36.0788</td>\n",
       "      <td>-81.1781</td>\n",
       "      <td>3495</td>\n",
       "      <td>1325376018</td>\n",
       "      <td>36.011293</td>\n",
       "      <td>-82.048315</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>107.23</td>\n",
       "      <td>F</td>\n",
       "      <td>99160</td>\n",
       "      <td>48.8878</td>\n",
       "      <td>-118.2105</td>\n",
       "      <td>149</td>\n",
       "      <td>1325376044</td>\n",
       "      <td>49.159047</td>\n",
       "      <td>-118.186462</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>38859492057661</td>\n",
       "      <td>220.11</td>\n",
       "      <td>M</td>\n",
       "      <td>83252</td>\n",
       "      <td>42.1808</td>\n",
       "      <td>-112.2620</td>\n",
       "      <td>4154</td>\n",
       "      <td>1325376051</td>\n",
       "      <td>43.150704</td>\n",
       "      <td>-112.154481</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3534093764340240</td>\n",
       "      <td>45.00</td>\n",
       "      <td>M</td>\n",
       "      <td>59632</td>\n",
       "      <td>46.2306</td>\n",
       "      <td>-112.1138</td>\n",
       "      <td>1939</td>\n",
       "      <td>1325376076</td>\n",
       "      <td>47.034331</td>\n",
       "      <td>-112.561071</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>375534208663984</td>\n",
       "      <td>41.96</td>\n",
       "      <td>M</td>\n",
       "      <td>24433</td>\n",
       "      <td>38.4207</td>\n",
       "      <td>-79.4629</td>\n",
       "      <td>99</td>\n",
       "      <td>1325376186</td>\n",
       "      <td>38.674999</td>\n",
       "      <td>-78.632459</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0            cc_num     amt gender    zip      lat      long  \\\n",
       "0           0  2703186189652095    4.97      F  28654  36.0788  -81.1781   \n",
       "1           1      630423337322  107.23      F  99160  48.8878 -118.2105   \n",
       "2           2    38859492057661  220.11      M  83252  42.1808 -112.2620   \n",
       "3           3  3534093764340240   45.00      M  59632  46.2306 -112.1138   \n",
       "4           4   375534208663984   41.96      M  24433  38.4207  -79.4629   \n",
       "\n",
       "   city_pop   unix_time  merch_lat  merch_long  is_fraud  \n",
       "0      3495  1325376018  36.011293  -82.048315         0  \n",
       "1       149  1325376044  49.159047 -118.186462         0  \n",
       "2      4154  1325376051  43.150704 -112.154481         0  \n",
       "3      1939  1325376076  47.034331 -112.561071         0  \n",
       "4        99  1325376186  38.674999  -78.632459         0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_data = credit_card_data.drop(['merchant','street','city','state','trans_date_trans_time','dob','first','last','trans_num','category','job'],axis=1)\n",
    "credit_card_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ec6a57e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_card_data['gender'] = pd.get_dummies(credit_card_data['gender'], drop_first =  True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d099a2ce",
   "metadata": {},
   "outputs": [
    {
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       "      <td>36.011293</td>\n",
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       "      <td>99160</td>\n",
       "      <td>48.8878</td>\n",
       "      <td>-118.2105</td>\n",
       "      <td>149</td>\n",
       "      <td>1325376044</td>\n",
       "      <td>49.159047</td>\n",
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       "      <td>1</td>\n",
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       "      <td>42.1808</td>\n",
       "      <td>-112.2620</td>\n",
       "      <td>4154</td>\n",
       "      <td>1325376051</td>\n",
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       "      <td>-112.154481</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "   Unnamed: 0            cc_num     amt  gender    zip      lat      long  \\\n",
       "0           0  2703186189652095    4.97       0  28654  36.0788  -81.1781   \n",
       "1           1      630423337322  107.23       0  99160  48.8878 -118.2105   \n",
       "2           2    38859492057661  220.11       1  83252  42.1808 -112.2620   \n",
       "3           3  3534093764340240   45.00       1  59632  46.2306 -112.1138   \n",
       "4           4   375534208663984   41.96       1  24433  38.4207  -79.4629   \n",
       "\n",
       "   city_pop   unix_time  merch_lat  merch_long  is_fraud  \n",
       "0      3495  1325376018  36.011293  -82.048315         0  \n",
       "1       149  1325376044  49.159047 -118.186462         0  \n",
       "2      4154  1325376051  43.150704 -112.154481         0  \n",
       "3      1939  1325376076  47.034331 -112.561071         0  \n",
       "4        99  1325376186  38.674999  -78.632459         0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_card_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "55a1ef36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1296675 entries, 0 to 1296674\n",
      "Data columns (total 12 columns):\n",
      " #   Column      Non-Null Count    Dtype  \n",
      "---  ------      --------------    -----  \n",
      " 0   Unnamed: 0  1296675 non-null  int64  \n",
      " 1   cc_num      1296675 non-null  int64  \n",
      " 2   amt         1296675 non-null  float64\n",
      " 3   gender      1296675 non-null  uint8  \n",
      " 4   zip         1296675 non-null  int64  \n",
      " 5   lat         1296675 non-null  float64\n",
      " 6   long        1296675 non-null  float64\n",
      " 7   city_pop    1296675 non-null  int64  \n",
      " 8   unix_time   1296675 non-null  int64  \n",
      " 9   merch_lat   1296675 non-null  float64\n",
      " 10  merch_long  1296675 non-null  float64\n",
      " 11  is_fraud    1296675 non-null  int64  \n",
      "dtypes: float64(5), int64(6), uint8(1)\n",
      "memory usage: 110.1 MB\n"
     ]
    }
   ],
   "source": [
    "credit_card_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "16c1c28c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = credit_card_data['is_fraud']\n",
    "x = credit_card_data.drop(['is_fraud'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "352d7ebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>cc_num</th>\n",
       "      <th>amt</th>\n",
       "      <th>gender</th>\n",
       "      <th>zip</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>city_pop</th>\n",
       "      <th>unix_time</th>\n",
       "      <th>merch_lat</th>\n",
       "      <th>merch_long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2703186189652095</td>\n",
       "      <td>4.97</td>\n",
       "      <td>0</td>\n",
       "      <td>28654</td>\n",
       "      <td>36.0788</td>\n",
       "      <td>-81.1781</td>\n",
       "      <td>3495</td>\n",
       "      <td>1325376018</td>\n",
       "      <td>36.011293</td>\n",
       "      <td>-82.048315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>630423337322</td>\n",
       "      <td>107.23</td>\n",
       "      <td>0</td>\n",
       "      <td>99160</td>\n",
       "      <td>48.8878</td>\n",
       "      <td>-118.2105</td>\n",
       "      <td>149</td>\n",
       "      <td>1325376044</td>\n",
       "      <td>49.159047</td>\n",
       "      <td>-118.186462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>38859492057661</td>\n",
       "      <td>220.11</td>\n",
       "      <td>1</td>\n",
       "      <td>83252</td>\n",
       "      <td>42.1808</td>\n",
       "      <td>-112.2620</td>\n",
       "      <td>4154</td>\n",
       "      <td>1325376051</td>\n",
       "      <td>43.150704</td>\n",
       "      <td>-112.154481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3534093764340240</td>\n",
       "      <td>45.00</td>\n",
       "      <td>1</td>\n",
       "      <td>59632</td>\n",
       "      <td>46.2306</td>\n",
       "      <td>-112.1138</td>\n",
       "      <td>1939</td>\n",
       "      <td>1325376076</td>\n",
       "      <td>47.034331</td>\n",
       "      <td>-112.561071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>375534208663984</td>\n",
       "      <td>41.96</td>\n",
       "      <td>1</td>\n",
       "      <td>24433</td>\n",
       "      <td>38.4207</td>\n",
       "      <td>-79.4629</td>\n",
       "      <td>99</td>\n",
       "      <td>1325376186</td>\n",
       "      <td>38.674999</td>\n",
       "      <td>-78.632459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0            cc_num     amt  gender    zip      lat      long  \\\n",
       "0           0  2703186189652095    4.97       0  28654  36.0788  -81.1781   \n",
       "1           1      630423337322  107.23       0  99160  48.8878 -118.2105   \n",
       "2           2    38859492057661  220.11       1  83252  42.1808 -112.2620   \n",
       "3           3  3534093764340240   45.00       1  59632  46.2306 -112.1138   \n",
       "4           4   375534208663984   41.96       1  24433  38.4207  -79.4629   \n",
       "\n",
       "   city_pop   unix_time  merch_lat  merch_long  \n",
       "0      3495  1325376018  36.011293  -82.048315  \n",
       "1       149  1325376044  49.159047 -118.186462  \n",
       "2      4154  1325376051  43.150704 -112.154481  \n",
       "3      1939  1325376076  47.034331 -112.561071  \n",
       "4        99  1325376186  38.674999  -78.632459  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0920ccd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: is_fraud, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6c149950",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train , X_test , Y_train , Y_test = train_test_split(x , y , random_state = 42 , test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "64ec2cf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7506"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e70f066b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5986"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ba2dddd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1520"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "74789ed3",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b4776a13",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c8e034f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "74664cc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.84915931, -0.31864488, -0.40300154, ..., -0.79742859,\n",
       "        -1.4380815 , -0.08744271],\n",
       "       [ 0.4018383 , -0.31593235, -0.3167202 , ...,  0.41484723,\n",
       "         1.72920458, -2.36236846],\n",
       "       [ 1.63557956,  2.80668465, -0.21767984, ...,  1.67081746,\n",
       "        -1.28135485, -0.37403537],\n",
       "       ...,\n",
       "       [-1.37878785, -0.31590968,  0.3029137 , ..., -1.36729131,\n",
       "        -0.72182044, -1.16220896],\n",
       "       [ 0.0616183 , -0.31862539, -0.40874942, ...,  0.06894856,\n",
       "         1.81416974, -0.52861191],\n",
       "       [-1.40543102, -0.31861897,  0.44477891, ..., -1.39519741,\n",
       "        -0.69443167,  0.53619566]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "531144cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.06082005, -0.31864798,  0.7844722 , ...,  1.05948609,\n",
       "         0.3684825 ,  0.82516323],\n",
       "       [-0.26894775, -0.31862521, -0.11364956, ..., -0.27910408,\n",
       "         0.82610165, -2.42755471],\n",
       "       [-1.43699466, -0.3150879 , -0.40287521, ..., -1.43350211,\n",
       "         0.37918829,  0.82275279],\n",
       "       ...,\n",
       "       [-1.58351073, -0.3149499 ,  0.16092216, ..., -1.63640912,\n",
       "         0.57807903, -0.08400875],\n",
       "       [ 1.34179679, -0.31405408, -0.40849676, ...,  1.37090587,\n",
       "        -0.45745302,  0.77553274],\n",
       "       [ 0.02687853, -0.31864804, -0.31804663, ...,  0.02779027,\n",
       "         0.64740272, -2.41474758]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c46692e8",
   "metadata": {},
   "source": [
    "# Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "232d587f",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4627c5b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training the logistic regression model with traiing data\n",
    "log_reg.fit(X_train_scaled, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "900bdcb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.predict(X_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "14de1bff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.993661673125494"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(X_train_scaled,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9b1bf566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9935758767617174"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(X_test_scaled,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "63a4cb72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.99\n"
     ]
    }
   ],
   "source": [
    "accuracy = log_reg.score(X_test, Y_test)\n",
    "print(f\"Accuracy: {accuracy:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f85b3dde",
   "metadata": {},
   "source": [
    "# Desicion Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "838c27dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=4)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D_tree = DecisionTreeClassifier(criterion = 'entropy', max_depth = 4)\n",
    "D_tree.fit(X_train_scaled,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0f18275b",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = D_tree.predict(X_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "286e8600",
   "metadata": {},
   "outputs": [
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     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1e0e003f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9946584533518422"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D_tree.score(X_train_scaled,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "36473767",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9944897526365511"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D_tree.score(X_test_scaled,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bf6fd3ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.96\n"
     ]
    }
   ],
   "source": [
    "accuracy = D_tree.score(X_test, Y_test)\n",
    "print(f\"Accuracy: {accuracy:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "919fea33",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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