Credit card Fraud Predicting Using Decison Tree and logistic regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Transaction ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day of Week</th>\n",
       "      <th>Time</th>\n",
       "      <th>Type of Card</th>\n",
       "      <th>Entry Mode</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Type of Transaction</th>\n",
       "      <th>Merchant Group</th>\n",
       "      <th>Country of Transaction</th>\n",
       "      <th>Shipping Address</th>\n",
       "      <th>Country of Residence</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#3577 209</td>\n",
       "      <td>14-Oct-20</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>19</td>\n",
       "      <td>Visa</td>\n",
       "      <td>Tap</td>\n",
       "      <td>£5</td>\n",
       "      <td>POS</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>M</td>\n",
       "      <td>25.2</td>\n",
       "      <td>RBS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#3039 221</td>\n",
       "      <td>14-Oct-20</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>17</td>\n",
       "      <td>MasterCard</td>\n",
       "      <td>PIN</td>\n",
       "      <td>£288</td>\n",
       "      <td>POS</td>\n",
       "      <td>Services</td>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>F</td>\n",
       "      <td>49.6</td>\n",
       "      <td>Lloyds</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#2694 780</td>\n",
       "      <td>14-Oct-20</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>14</td>\n",
       "      <td>Visa</td>\n",
       "      <td>Tap</td>\n",
       "      <td>£5</td>\n",
       "      <td>POS</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>India</td>\n",
       "      <td>India</td>\n",
       "      <td>India</td>\n",
       "      <td>F</td>\n",
       "      <td>42.2</td>\n",
       "      <td>Barclays</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#2640 960</td>\n",
       "      <td>13-Oct-20</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>14</td>\n",
       "      <td>Visa</td>\n",
       "      <td>Tap</td>\n",
       "      <td>£28</td>\n",
       "      <td>POS</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>India</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>F</td>\n",
       "      <td>51.0</td>\n",
       "      <td>Barclays</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#2771 031</td>\n",
       "      <td>13-Oct-20</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>23</td>\n",
       "      <td>Visa</td>\n",
       "      <td>CVC</td>\n",
       "      <td>£91</td>\n",
       "      <td>Online</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>M</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Halifax</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Transaction ID       Date Day of Week  Time Type of Card Entry Mode Amount  \\\n",
       "0      #3577 209  14-Oct-20   Wednesday    19         Visa        Tap     £5   \n",
       "1      #3039 221  14-Oct-20   Wednesday    17   MasterCard        PIN   £288   \n",
       "2      #2694 780  14-Oct-20   Wednesday    14         Visa        Tap     £5   \n",
       "3      #2640 960  13-Oct-20     Tuesday    14         Visa        Tap    £28   \n",
       "4      #2771 031  13-Oct-20     Tuesday    23         Visa        CVC    £91   \n",
       "\n",
       "  Type of Transaction Merchant Group Country of Transaction Shipping Address  \\\n",
       "0                 POS  Entertainment         United Kingdom   United Kingdom   \n",
       "1                 POS       Services                    USA              USA   \n",
       "2                 POS     Restaurant                  India            India   \n",
       "3                 POS  Entertainment         United Kingdom            India   \n",
       "4              Online    Electronics                    USA              USA   \n",
       "\n",
       "  Country of Residence Gender   Age      Bank  Fraud  \n",
       "0       United Kingdom      M  25.2       RBS      0  \n",
       "1                  USA      F  49.6    Lloyds      0  \n",
       "2                India      F  42.2  Barclays      0  \n",
       "3       United Kingdom      F  51.0  Barclays      0  \n",
       "4       United Kingdom      M  38.0   Halifax      1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading dataset\n",
    "\n",
    "dataset = pd.read_csv(\"CreditCardData.csv\")\n",
    "\n",
    "# Now extracting the data\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 16 columns):\n",
      " #   Column                  Non-Null Count   Dtype  \n",
      "---  ------                  --------------   -----  \n",
      " 0   Transaction ID          100000 non-null  object \n",
      " 1   Date                    100000 non-null  object \n",
      " 2   Day of Week             100000 non-null  object \n",
      " 3   Time                    100000 non-null  int64  \n",
      " 4   Type of Card            100000 non-null  object \n",
      " 5   Entry Mode              100000 non-null  object \n",
      " 6   Amount                  99994 non-null   object \n",
      " 7   Type of Transaction     100000 non-null  object \n",
      " 8   Merchant Group          99990 non-null   object \n",
      " 9   Country of Transaction  100000 non-null  object \n",
      " 10  Shipping Address        99995 non-null   object \n",
      " 11  Country of Residence    100000 non-null  object \n",
      " 12  Gender                  99996 non-null   object \n",
      " 13  Age                     100000 non-null  float64\n",
      " 14  Bank                    100000 non-null  object \n",
      " 15  Fraud                   100000 non-null  int64  \n",
      "dtypes: float64(1), int64(2), object(13)\n",
      "memory usage: 12.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# Now [erforming Exploratory Analysis \n",
    "\n",
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 16)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking into the shape of the dataset\n",
    "\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Transaction ID             0\n",
       "Date                       0\n",
       "Day of Week                0\n",
       "Time                       0\n",
       "Type of Card               0\n",
       "Entry Mode                 0\n",
       "Amount                     6\n",
       "Type of Transaction        0\n",
       "Merchant Group            10\n",
       "Country of Transaction     0\n",
       "Shipping Address           5\n",
       "Country of Residence       0\n",
       "Gender                     4\n",
       "Age                        0\n",
       "Bank                       0\n",
       "Fraud                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let us see if their are any null values\n",
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we will be dropping the rows containing Null Values \n",
    "\n",
    "dataset.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Transaction ID            0\n",
       "Date                      0\n",
       "Day of Week               0\n",
       "Time                      0\n",
       "Type of Card              0\n",
       "Entry Mode                0\n",
       "Amount                    0\n",
       "Type of Transaction       0\n",
       "Merchant Group            0\n",
       "Country of Transaction    0\n",
       "Shipping Address          0\n",
       "Country of Residence      0\n",
       "Gender                    0\n",
       "Age                       0\n",
       "Bank                      0\n",
       "Fraud                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let us see if their are any null values\n",
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Exploratory Data Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\anaconda\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Entry Mode Often Used in Fraud Transactions'}, xlabel='Entry Mode', ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What entry Mode is often Used in Fraud Transactions\n",
    "plt.figure(figsize=(15,6))\n",
    "df1 = dataset[dataset[\"Fraud\"] == 1]\n",
    "\n",
    "plt.title(\"Entry Mode Often Used in Fraud Transactions\")\n",
    "sns.countplot(df1[\"Entry Mode\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\anaconda\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Type of Card Often used in Fraudulent Transactions'}, xlabel='Type of Card', ylabel='count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What Type of Card is often Used in Fraud Transactions\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "df1 = dataset[dataset[\"Fraud\"] == 1]\n",
    "\n",
    "plt.title(\"Type of Card Often used in Fraudulent Transactions\")\n",
    "sns.countplot(df1[\"Type of Card\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Merchant group with Most Fraudulent Transactions'}, xlabel='Merchant Group', ylabel='No of Fraud Transactions'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1656x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Which merchant group has Large Number of Fraud Transactions \n",
    "\n",
    "plt.figure(figsize=(23,6))\n",
    "df1 = dataset[dataset[\"Fraud\"] == 1]\n",
    "\n",
    "plt.title(\"Which Merchant Group has Large number of Fraudulent Transactions\")\n",
    "merchant_group = dict(df1[\"Merchant Group\"].value_counts(),sorted=True)\n",
    "\n",
    "del merchant_group[\"sorted\"]\n",
    "plt.title(\"Merchant group with Most Fraudulent Transactions\")\n",
    "plt.xlabel(\"Merchant Group\")\n",
    "plt.ylabel(\"No of Fraud Transactions\")\n",
    "sns.barplot(x=list(merchant_group.keys()),y=list(merchant_group.values()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\anaconda\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Bank with Most Fraud Transactions'}, xlabel='Bank', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1656x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Which Bank has the Most Fraud Cases\n",
    "\n",
    "plt.figure(figsize=(23,6))\n",
    "df1 = dataset[dataset[\"Fraud\"] == 1]\n",
    "\n",
    "plt.title(\"Bank with Most Fraud Transactions\")\n",
    "sns.countplot(df1[\"Bank\"])\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\anaconda\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':\"Type of Transaction's Mostly used in Fraudulent transactions\"}, xlabel='Type of Transaction', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1656x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Which Type Of Transaction is often Fraudulent\n",
    "\n",
    "plt.figure(figsize=(23,6))\n",
    "df1 = dataset[dataset[\"Fraud\"] == 1]\n",
    "\n",
    "plt.title(\"Type of Transaction's Mostly used in Fraudulent transactions\")\n",
    "sns.countplot(df1[\"Type of Transaction\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Transaction ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day of Week</th>\n",
       "      <th>Time</th>\n",
       "      <th>Type of Card</th>\n",
       "      <th>Entry Mode</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Type of Transaction</th>\n",
       "      <th>Merchant Group</th>\n",
       "      <th>Country of Transaction</th>\n",
       "      <th>Shipping Address</th>\n",
       "      <th>Country of Residence</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#3577 209</td>\n",
       "      <td>14-Oct-20</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>19</td>\n",
       "      <td>Visa</td>\n",
       "      <td>Tap</td>\n",
       "      <td>£5</td>\n",
       "      <td>POS</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>M</td>\n",
       "      <td>25.2</td>\n",
       "      <td>RBS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#3039 221</td>\n",
       "      <td>14-Oct-20</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>17</td>\n",
       "      <td>MasterCard</td>\n",
       "      <td>PIN</td>\n",
       "      <td>£288</td>\n",
       "      <td>POS</td>\n",
       "      <td>Services</td>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>F</td>\n",
       "      <td>49.6</td>\n",
       "      <td>Lloyds</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#2694 780</td>\n",
       "      <td>14-Oct-20</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>14</td>\n",
       "      <td>Visa</td>\n",
       "      <td>Tap</td>\n",
       "      <td>£5</td>\n",
       "      <td>POS</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>India</td>\n",
       "      <td>India</td>\n",
       "      <td>India</td>\n",
       "      <td>F</td>\n",
       "      <td>42.2</td>\n",
       "      <td>Barclays</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#2640 960</td>\n",
       "      <td>13-Oct-20</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>14</td>\n",
       "      <td>Visa</td>\n",
       "      <td>Tap</td>\n",
       "      <td>£28</td>\n",
       "      <td>POS</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>India</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>F</td>\n",
       "      <td>51.0</td>\n",
       "      <td>Barclays</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#2771 031</td>\n",
       "      <td>13-Oct-20</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>23</td>\n",
       "      <td>Visa</td>\n",
       "      <td>CVC</td>\n",
       "      <td>£91</td>\n",
       "      <td>Online</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>M</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Halifax</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Transaction ID       Date Day of Week  Time Type of Card Entry Mode Amount  \\\n",
       "0      #3577 209  14-Oct-20   Wednesday    19         Visa        Tap     £5   \n",
       "1      #3039 221  14-Oct-20   Wednesday    17   MasterCard        PIN   £288   \n",
       "2      #2694 780  14-Oct-20   Wednesday    14         Visa        Tap     £5   \n",
       "3      #2640 960  13-Oct-20     Tuesday    14         Visa        Tap    £28   \n",
       "4      #2771 031  13-Oct-20     Tuesday    23         Visa        CVC    £91   \n",
       "\n",
       "  Type of Transaction Merchant Group Country of Transaction Shipping Address  \\\n",
       "0                 POS  Entertainment         United Kingdom   United Kingdom   \n",
       "1                 POS       Services                    USA              USA   \n",
       "2                 POS     Restaurant                  India            India   \n",
       "3                 POS  Entertainment         United Kingdom            India   \n",
       "4              Online    Electronics                    USA              USA   \n",
       "\n",
       "  Country of Residence Gender   Age      Bank  Fraud  \n",
       "0       United Kingdom      M  25.2       RBS      0  \n",
       "1                  USA      F  49.6    Lloyds      0  \n",
       "2                India      F  42.2  Barclays      0  \n",
       "3       United Kingdom      F  51.0  Barclays      0  \n",
       "4       United Kingdom      M  38.0   Halifax      1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now Creating A Correlation Matrix using pandas and then Visualizing it using HeatMap**\n",
    "\n",
    "But for that we have to Encode the Columns First using Label Encoder from String/Object Data Type to Numeric Data Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Firstly we will drop (Transaction ID\tDate\tDay of Week\tTime) these columns\n",
    "\n",
    "dataset.drop(columns=[\"Transaction ID\",\"Date\",\"Day of Week\",\"Time\"] , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Type of Card</th>\n",
       "      <th>Entry Mode</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Type of Transaction</th>\n",
       "      <th>Merchant Group</th>\n",
       "      <th>Country of Transaction</th>\n",
       "      <th>Shipping Address</th>\n",
       "      <th>Country of Residence</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>25.2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>288</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>49.6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>42.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Type of Card  Entry Mode Amount  Type of Transaction  Merchant Group  \\\n",
       "0             1           2      5                    2               2   \n",
       "1             0           1    288                    2               8   \n",
       "2             1           2      5                    2               7   \n",
       "3             1           2     28                    2               2   \n",
       "4             1           0     91                    1               1   \n",
       "\n",
       "   Country of Transaction  Shipping Address  Country of Residence  Gender  \\\n",
       "0                       4                 4                     4       1   \n",
       "1                       3                 3                     3       0   \n",
       "2                       1                 1                     1       0   \n",
       "3                       4                 1                     4       0   \n",
       "4                       3                 3                     4       1   \n",
       "\n",
       "    Age  Bank  Fraud  \n",
       "0  25.2     7      0  \n",
       "1  49.6     4      0  \n",
       "2  42.2     0      0  \n",
       "3  51.0     0      0  \n",
       "4  38.0     3      1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "encoder_ = LabelEncoder()\n",
    "\n",
    "colum = list(dataset.columns)\n",
    "dataset[colum[0]] = encoder_.fit_transform(dataset[colum[0]])\n",
    "dataset[colum[1]] = encoder_.fit_transform(dataset[colum[1]])\n",
    "dataset[colum[3]] = encoder_.fit_transform(dataset[colum[3]])\n",
    "dataset[colum[4]] = encoder_.fit_transform(dataset[colum[4]])\n",
    "dataset[colum[5]] = encoder_.fit_transform(dataset[colum[5]])\n",
    "dataset[colum[6]] = encoder_.fit_transform(dataset[colum[6]])\n",
    "dataset[colum[7]] = encoder_.fit_transform(dataset[colum[7]])\n",
    "dataset[colum[8]] = encoder_.fit_transform(dataset[colum[8]])\n",
    "dataset[colum[10]] = encoder_.fit_transform(dataset[colum[10]])\n",
    "dataset[\"Amount\"] = dataset[\"Amount\"].str.replace(\"£\",\" \")\n",
    "\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Type of Card</th>\n",
       "      <th>Entry Mode</th>\n",
       "      <th>Type of Transaction</th>\n",
       "      <th>Merchant Group</th>\n",
       "      <th>Country of Transaction</th>\n",
       "      <th>Shipping Address</th>\n",
       "      <th>Country of Residence</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Fraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Type of Card</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005694</td>\n",
       "      <td>-0.003484</td>\n",
       "      <td>-0.000028</td>\n",
       "      <td>0.001301</td>\n",
       "      <td>-0.000712</td>\n",
       "      <td>0.000984</td>\n",
       "      <td>-0.000169</td>\n",
       "      <td>0.001071</td>\n",
       "      <td>-0.003393</td>\n",
       "      <td>0.034850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Entry Mode</th>\n",
       "      <td>0.005694</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.295332</td>\n",
       "      <td>-0.002886</td>\n",
       "      <td>-0.004392</td>\n",
       "      <td>-0.008114</td>\n",
       "      <td>-0.000571</td>\n",
       "      <td>0.002770</td>\n",
       "      <td>0.004307</td>\n",
       "      <td>0.001013</td>\n",
       "      <td>-0.007564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type of Transaction</th>\n",
       "      <td>-0.003484</td>\n",
       "      <td>0.295332</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003133</td>\n",
       "      <td>0.001282</td>\n",
       "      <td>-0.003532</td>\n",
       "      <td>-0.001435</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.001890</td>\n",
       "      <td>0.001025</td>\n",
       "      <td>0.030787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Merchant Group</th>\n",
       "      <td>-0.000028</td>\n",
       "      <td>-0.002886</td>\n",
       "      <td>-0.003133</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003680</td>\n",
       "      <td>-0.009532</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>0.002101</td>\n",
       "      <td>-0.001430</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>-0.069093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country of Transaction</th>\n",
       "      <td>0.001301</td>\n",
       "      <td>-0.004392</td>\n",
       "      <td>0.001282</td>\n",
       "      <td>0.003680</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.774593</td>\n",
       "      <td>0.751047</td>\n",
       "      <td>-0.000123</td>\n",
       "      <td>0.002547</td>\n",
       "      <td>0.000911</td>\n",
       "      <td>-0.307040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shipping Address</th>\n",
       "      <td>-0.000712</td>\n",
       "      <td>-0.008114</td>\n",
       "      <td>-0.003532</td>\n",
       "      <td>-0.009532</td>\n",
       "      <td>0.774593</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.565925</td>\n",
       "      <td>0.000984</td>\n",
       "      <td>0.003952</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>-0.292431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country of Residence</th>\n",
       "      <td>0.000984</td>\n",
       "      <td>-0.000571</td>\n",
       "      <td>-0.001435</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>0.751047</td>\n",
       "      <td>0.565925</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000667</td>\n",
       "      <td>0.000612</td>\n",
       "      <td>-0.002338</td>\n",
       "      <td>0.105816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>-0.000169</td>\n",
       "      <td>0.002770</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.002101</td>\n",
       "      <td>-0.000123</td>\n",
       "      <td>0.000984</td>\n",
       "      <td>0.000667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003907</td>\n",
       "      <td>-0.004252</td>\n",
       "      <td>0.047535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.001071</td>\n",
       "      <td>0.004307</td>\n",
       "      <td>0.001890</td>\n",
       "      <td>-0.001430</td>\n",
       "      <td>0.002547</td>\n",
       "      <td>0.003952</td>\n",
       "      <td>0.000612</td>\n",
       "      <td>-0.003907</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001935</td>\n",
       "      <td>-0.002778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bank</th>\n",
       "      <td>-0.003393</td>\n",
       "      <td>0.001013</td>\n",
       "      <td>0.001025</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>0.000911</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>-0.002338</td>\n",
       "      <td>-0.004252</td>\n",
       "      <td>-0.001935</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.006736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fraud</th>\n",
       "      <td>0.034850</td>\n",
       "      <td>-0.007564</td>\n",
       "      <td>0.030787</td>\n",
       "      <td>-0.069093</td>\n",
       "      <td>-0.307040</td>\n",
       "      <td>-0.292431</td>\n",
       "      <td>0.105816</td>\n",
       "      <td>0.047535</td>\n",
       "      <td>-0.002778</td>\n",
       "      <td>-0.006736</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Type of Card  Entry Mode  Type of Transaction  \\\n",
       "Type of Card                1.000000    0.005694            -0.003484   \n",
       "Entry Mode                  0.005694    1.000000             0.295332   \n",
       "Type of Transaction        -0.003484    0.295332             1.000000   \n",
       "Merchant Group             -0.000028   -0.002886            -0.003133   \n",
       "Country of Transaction      0.001301   -0.004392             0.001282   \n",
       "Shipping Address           -0.000712   -0.008114            -0.003532   \n",
       "Country of Residence        0.000984   -0.000571            -0.001435   \n",
       "Gender                     -0.000169    0.002770             0.000385   \n",
       "Age                         0.001071    0.004307             0.001890   \n",
       "Bank                       -0.003393    0.001013             0.001025   \n",
       "Fraud                       0.034850   -0.007564             0.030787   \n",
       "\n",
       "                        Merchant Group  Country of Transaction  \\\n",
       "Type of Card                 -0.000028                0.001301   \n",
       "Entry Mode                   -0.002886               -0.004392   \n",
       "Type of Transaction          -0.003133                0.001282   \n",
       "Merchant Group                1.000000                0.003680   \n",
       "Country of Transaction        0.003680                1.000000   \n",
       "Shipping Address             -0.009532                0.774593   \n",
       "Country of Residence          0.000479                0.751047   \n",
       "Gender                        0.002101               -0.000123   \n",
       "Age                          -0.001430                0.002547   \n",
       "Bank                          0.000394                0.000911   \n",
       "Fraud                        -0.069093               -0.307040   \n",
       "\n",
       "                        Shipping Address  Country of Residence    Gender  \\\n",
       "Type of Card                   -0.000712              0.000984 -0.000169   \n",
       "Entry Mode                     -0.008114             -0.000571  0.002770   \n",
       "Type of Transaction            -0.003532             -0.001435  0.000385   \n",
       "Merchant Group                 -0.009532              0.000479  0.002101   \n",
       "Country of Transaction          0.774593              0.751047 -0.000123   \n",
       "Shipping Address                1.000000              0.565925  0.000984   \n",
       "Country of Residence            0.565925              1.000000  0.000667   \n",
       "Gender                          0.000984              0.000667  1.000000   \n",
       "Age                             0.003952              0.000612 -0.003907   \n",
       "Bank                            0.001212             -0.002338 -0.004252   \n",
       "Fraud                          -0.292431              0.105816  0.047535   \n",
       "\n",
       "                             Age      Bank     Fraud  \n",
       "Type of Card            0.001071 -0.003393  0.034850  \n",
       "Entry Mode              0.004307  0.001013 -0.007564  \n",
       "Type of Transaction     0.001890  0.001025  0.030787  \n",
       "Merchant Group         -0.001430  0.000394 -0.069093  \n",
       "Country of Transaction  0.002547  0.000911 -0.307040  \n",
       "Shipping Address        0.003952  0.001212 -0.292431  \n",
       "Country of Residence    0.000612 -0.002338  0.105816  \n",
       "Gender                 -0.003907 -0.004252  0.047535  \n",
       "Age                     1.000000 -0.001935 -0.002778  \n",
       "Bank                   -0.001935  1.000000 -0.006736  \n",
       "Fraud                  -0.002778 -0.006736  1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we can see the correlation Matrix\n",
    "\n",
    "dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(24,7))\n",
    "sns.heatmap(dataset.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now, We will implementing Two Models**\n",
    "\n",
    "1. Logistic regression\n",
    "\n",
    "2. Decision Tree Clasifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now our Data is ready !\n"
     ]
    }
   ],
   "source": [
    "# Firstly we will select x and y variable an then split the data for testing and training \n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x = dataset.drop(columns=[\"Fraud\"])\n",
    "y = dataset.Fraud\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,random_state=42,test_size=0.3)\n",
    "\n",
    "print(\"Now our Data is ready !\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# firstly importing all the required libraries\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# for our model performance evaluation\n",
    "\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now Below we will Implement our First Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg = LogisticRegression()\n",
    "\n",
    "log_reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now predicting the tet data \n",
    "\n",
    "predictions_ = log_reg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Model: 94.58\n"
     ]
    }
   ],
   "source": [
    "# Now evaluating our Model \n",
    "\n",
    "print(\"Accuracy of Model: %.2f\"%(accuracy_score(y_test,predictions_)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now Plotting a Confusion Matrix \n",
    "\n",
    "cnf_matrix = confusion_matrix(y_test,predictions_)\n",
    "\n",
    "plt.figure(figsize=(14,6))\n",
    "sns.heatmap(cnf_matrix,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now lets ee using a bar plot how much accurate our model is predicting as Looking at the accuracy the model is working Great!\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Actual\")\n",
    "sns.countplot(x=y_test)\n",
    "plt.subplot(1,2,2)\n",
    "\n",
    "plt.title(\"Predicted\")\n",
    "plt.xlabel(\"Fraud\")\n",
    "sns.countplot(x=predictions_)\n",
    "\n",
    "\n",
    "plt.suptitle(\"Below we are Plotting the Actual vs Predicted Results \\nUsing Logistic Regression\\n\\n\".upper())\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking on Above Analysis and Visualizations we can say that Our Model is Working Extremely well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now Implementing Our second Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_tree = DecisionTreeClassifier()\n",
    "\n",
    "dec_tree.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now predicting the tet data \n",
    "\n",
    "predictions_ = dec_tree.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Model: 96.30\n"
     ]
    }
   ],
   "source": [
    "# Now evaluating our Model \n",
    "\n",
    "print(\"Accuracy of Model: %.2f\"%(accuracy_score(y_test,predictions_)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now Plotting a Confusion Matrix \n",
    "\n",
    "cnf_matrix = confusion_matrix(y_test,predictions_)\n",
    "\n",
    "plt.figure(figsize=(14,6))\n",
    "sns.heatmap(cnf_matrix,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now lets ee using a bar plot how much accurate our model is predicting as Looking at the accuracy the model is working Great!\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Actual\")\n",
    "sns.countplot(x=y_test)\n",
    "plt.subplot(1,2,2)\n",
    "\n",
    "plt.title(\"Predicted\")\n",
    "plt.xlabel(\"Fraud\")\n",
    "sns.countplot(x=predictions_)\n",
    "\n",
    "\n",
    "plt.suptitle(\"Below we are Plotting the Actual vs Predicted Results Using Decision Tree\".upper())\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
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