model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%matplotlib_inline` not found.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import nan as NA\n",
    "%matplotlib_inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('online_shoppers_intention.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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>Administrative</th>\n",
       "      <th>Administrative_Duration</th>\n",
       "      <th>Informational</th>\n",
       "      <th>Informational_Duration</th>\n",
       "      <th>ProductRelated</th>\n",
       "      <th>ProductRelated_Duration</th>\n",
       "      <th>BounceRates</th>\n",
       "      <th>ExitRates</th>\n",
       "      <th>PageValues</th>\n",
       "      <th>SpecialDay</th>\n",
       "      <th>Month</th>\n",
       "      <th>OperatingSystems</th>\n",
       "      <th>Browser</th>\n",
       "      <th>Region</th>\n",
       "      <th>TrafficType</th>\n",
       "      <th>VisitorType</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>627.500000</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Returning_Visitor</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Administrative  Administrative_Duration  Informational  \\\n",
       "0               0                      0.0              0   \n",
       "1               0                      0.0              0   \n",
       "2               0                      0.0              0   \n",
       "3               0                      0.0              0   \n",
       "4               0                      0.0              0   \n",
       "\n",
       "   Informational_Duration  ProductRelated  ProductRelated_Duration  \\\n",
       "0                     0.0               1                 0.000000   \n",
       "1                     0.0               2                64.000000   \n",
       "2                     0.0               1                 0.000000   \n",
       "3                     0.0               2                 2.666667   \n",
       "4                     0.0              10               627.500000   \n",
       "\n",
       "   BounceRates  ExitRates  PageValues  SpecialDay Month  OperatingSystems  \\\n",
       "0         0.20       0.20         0.0         0.0   Feb                 1   \n",
       "1         0.00       0.10         0.0         0.0   Feb                 2   \n",
       "2         0.20       0.20         0.0         0.0   Feb                 4   \n",
       "3         0.05       0.14         0.0         0.0   Feb                 3   \n",
       "4         0.02       0.05         0.0         0.0   Feb                 3   \n",
       "\n",
       "   Browser  Region  TrafficType        VisitorType  Weekend  Revenue  \n",
       "0        1       1            1  Returning_Visitor    False    False  \n",
       "1        2       1            2  Returning_Visitor    False    False  \n",
       "2        1       9            3  Returning_Visitor    False    False  \n",
       "3        2       2            4  Returning_Visitor    False    False  \n",
       "4        3       1            4  Returning_Visitor     True    False  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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>Administrative</th>\n",
       "      <th>Administrative_Duration</th>\n",
       "      <th>Informational</th>\n",
       "      <th>Informational_Duration</th>\n",
       "      <th>ProductRelated</th>\n",
       "      <th>ProductRelated_Duration</th>\n",
       "      <th>BounceRates</th>\n",
       "      <th>ExitRates</th>\n",
       "      <th>PageValues</th>\n",
       "      <th>SpecialDay</th>\n",
       "      <th>OperatingSystems</th>\n",
       "      <th>Browser</th>\n",
       "      <th>Region</th>\n",
       "      <th>TrafficType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "      <td>12330.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.315166</td>\n",
       "      <td>80.818611</td>\n",
       "      <td>0.503569</td>\n",
       "      <td>34.472398</td>\n",
       "      <td>31.731468</td>\n",
       "      <td>1194.746220</td>\n",
       "      <td>0.022191</td>\n",
       "      <td>0.043073</td>\n",
       "      <td>5.889258</td>\n",
       "      <td>0.061427</td>\n",
       "      <td>2.124006</td>\n",
       "      <td>2.357097</td>\n",
       "      <td>3.147364</td>\n",
       "      <td>4.069586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.321784</td>\n",
       "      <td>176.779107</td>\n",
       "      <td>1.270156</td>\n",
       "      <td>140.749294</td>\n",
       "      <td>44.475503</td>\n",
       "      <td>1913.669288</td>\n",
       "      <td>0.048488</td>\n",
       "      <td>0.048597</td>\n",
       "      <td>18.568437</td>\n",
       "      <td>0.198917</td>\n",
       "      <td>0.911325</td>\n",
       "      <td>1.717277</td>\n",
       "      <td>2.401591</td>\n",
       "      <td>4.025169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>184.137500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014286</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>598.936905</td>\n",
       "      <td>0.003112</td>\n",
       "      <td>0.025156</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>93.256250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1464.157214</td>\n",
       "      <td>0.016813</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>3398.750000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2549.375000</td>\n",
       "      <td>705.000000</td>\n",
       "      <td>63973.522230</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>361.763742</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Administrative  Administrative_Duration  Informational  \\\n",
       "count    12330.000000             12330.000000   12330.000000   \n",
       "mean         2.315166                80.818611       0.503569   \n",
       "std          3.321784               176.779107       1.270156   \n",
       "min          0.000000                 0.000000       0.000000   \n",
       "25%          0.000000                 0.000000       0.000000   \n",
       "50%          1.000000                 7.500000       0.000000   \n",
       "75%          4.000000                93.256250       0.000000   \n",
       "max         27.000000              3398.750000      24.000000   \n",
       "\n",
       "       Informational_Duration  ProductRelated  ProductRelated_Duration  \\\n",
       "count            12330.000000    12330.000000             12330.000000   \n",
       "mean                34.472398       31.731468              1194.746220   \n",
       "std                140.749294       44.475503              1913.669288   \n",
       "min                  0.000000        0.000000                 0.000000   \n",
       "25%                  0.000000        7.000000               184.137500   \n",
       "50%                  0.000000       18.000000               598.936905   \n",
       "75%                  0.000000       38.000000              1464.157214   \n",
       "max               2549.375000      705.000000             63973.522230   \n",
       "\n",
       "        BounceRates     ExitRates    PageValues    SpecialDay  \\\n",
       "count  12330.000000  12330.000000  12330.000000  12330.000000   \n",
       "mean       0.022191      0.043073      5.889258      0.061427   \n",
       "std        0.048488      0.048597     18.568437      0.198917   \n",
       "min        0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.014286      0.000000      0.000000   \n",
       "50%        0.003112      0.025156      0.000000      0.000000   \n",
       "75%        0.016813      0.050000      0.000000      0.000000   \n",
       "max        0.200000      0.200000    361.763742      1.000000   \n",
       "\n",
       "       OperatingSystems       Browser        Region   TrafficType  \n",
       "count      12330.000000  12330.000000  12330.000000  12330.000000  \n",
       "mean           2.124006      2.357097      3.147364      4.069586  \n",
       "std            0.911325      1.717277      2.401591      4.025169  \n",
       "min            1.000000      1.000000      1.000000      1.000000  \n",
       "25%            2.000000      2.000000      1.000000      2.000000  \n",
       "50%            2.000000      2.000000      3.000000      2.000000  \n",
       "75%            3.000000      2.000000      4.000000      4.000000  \n",
       "max            8.000000     13.000000      9.000000     20.000000  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Administrative             0\n",
       "Administrative_Duration    0\n",
       "Informational              0\n",
       "Informational_Duration     0\n",
       "ProductRelated             0\n",
       "ProductRelated_Duration    0\n",
       "BounceRates                0\n",
       "ExitRates                  0\n",
       "PageValues                 0\n",
       "SpecialDay                 0\n",
       "Month                      0\n",
       "OperatingSystems           0\n",
       "Browser                    0\n",
       "Region                     0\n",
       "TrafficType                0\n",
       "VisitorType                0\n",
       "Weekend                    0\n",
       "Revenue                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CHECK NULL\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12330 entries, 0 to 12329\n",
      "Data columns (total 18 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Administrative           12330 non-null  int64  \n",
      " 1   Administrative_Duration  12330 non-null  float64\n",
      " 2   Informational            12330 non-null  int64  \n",
      " 3   Informational_Duration   12330 non-null  float64\n",
      " 4   ProductRelated           12330 non-null  int64  \n",
      " 5   ProductRelated_Duration  12330 non-null  float64\n",
      " 6   BounceRates              12330 non-null  float64\n",
      " 7   ExitRates                12330 non-null  float64\n",
      " 8   PageValues               12330 non-null  float64\n",
      " 9   SpecialDay               12330 non-null  float64\n",
      " 10  Month                    12330 non-null  object \n",
      " 11  OperatingSystems         12330 non-null  int64  \n",
      " 12  Browser                  12330 non-null  int64  \n",
      " 13  Region                   12330 non-null  int64  \n",
      " 14  TrafficType              12330 non-null  int64  \n",
      " 15  VisitorType              12330 non-null  object \n",
      " 16  Weekend                  12330 non-null  bool   \n",
      " 17  Revenue                  12330 non-null  bool   \n",
      "dtypes: bool(2), float64(7), int64(7), object(2)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Administrative             0.0\n",
      "Administrative_Duration    0.0\n",
      "Informational              0.0\n",
      "Informational_Duration     0.0\n",
      "ProductRelated             0.0\n",
      "ProductRelated_Duration    0.0\n",
      "BounceRates                0.0\n",
      "ExitRates                  0.0\n",
      "PageValues                 0.0\n",
      "SpecialDay                 0.0\n",
      "Month                      0.0\n",
      "OperatingSystems           0.0\n",
      "Browser                    0.0\n",
      "Region                     0.0\n",
      "TrafficType                0.0\n",
      "VisitorType                0.0\n",
      "Weekend                    0.0\n",
      "Revenue                    0.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "missing = (df.isnull().sum())/df.shape[0]\n",
    "print(missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows :  12330\n",
      "Number of columns :  18\n",
      "Independent Variables : \n",
      " ['Administrative', 'Administrative_Duration', 'Informational', 'Informational_Duration', 'ProductRelated', 'ProductRelated_Duration', 'BounceRates', 'ExitRates', 'PageValues', 'SpecialDay', 'Month', 'OperatingSystems', 'Browser', 'Region', 'TrafficType', 'VisitorType', 'Weekend']\n",
      "Target Column :  Revenue\n",
      "Shape of the dataset :  (12330, 18)\n"
     ]
    }
   ],
   "source": [
    "# TARGET IS REVENUE\n",
    "print(\"Number of rows : \",df.shape[0])\n",
    "print(\"Number of columns : \",df.shape[1])\n",
    "print(\"Independent Variables : \\n\",list(df.columns[:-1]))\n",
    "print(\"Target Column : \",df.columns[-1])\n",
    "print(\"Shape of the dataset : \",df.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Categorical Data Columns :  4\n",
      "Number of Numerical Data Columns :  14\n",
      "Numerical Columns : \n",
      " ['Administrative', 'Administrative_Duration', 'Informational', 'Informational_Duration', 'ProductRelated', 'ProductRelated_Duration', 'BounceRates', 'ExitRates', 'PageValues', 'SpecialDay', 'OperatingSystems', 'Browser', 'Region', 'TrafficType']\n",
      "Categorical Columns : \n",
      " ['Month', 'VisitorType', 'Weekend', 'Revenue']\n"
     ]
    }
   ],
   "source": [
    "# Categorical columns and Numerical Values\n",
    "numerical_vals = [x for x in df.select_dtypes(include=np.number)]\n",
    "categorical_vals = [x for x in df.select_dtypes(exclude=np.number)]\n",
    "\n",
    "print(\"Number of Categorical Data Columns : \", len(categorical_vals))\n",
    "print(\"Number of Numerical Data Columns : \", len(numerical_vals))\n",
    "print(\"Numerical Columns : \\n\", numerical_vals)\n",
    "print(\"Categorical Columns : \\n\", categorical_vals) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    10422\n",
       "True      1908\n",
       "Name: Revenue, dtype: int64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Revenue'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Month'>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Monthly Revenue plot\n",
    "df.groupby('Month')['Revenue'].value_counts().unstack('Revenue').plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Weekend'>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Weekend Revenue plot\n",
    "df.groupby('Weekend')['Revenue'].value_counts().unstack('Revenue').plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: ylabel='VisitorType'>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visitor type Revenue plot\n",
    "df['VisitorType'].value_counts().plot.pie(y='VisitorType')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pvt=df[['Administrative_Duration','Informational_Duration','ProductRelated_Duration','VisitorType']]\n",
    "pd.pivot_table(df_pvt, values=['Administrative_Duration','Informational_Duration','ProductRelated_Duration'],columns=['VisitorType'], aggfunc='mean').plot(kind='bar', figsize=(10, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Turning Categorical df to Numerical\n",
    "Month={'Feb':2, 'Mar':3, 'May':5, 'Oct':10, 'June':6, 'Jul':7, 'Aug':8, 'Nov':11, 'Sep':9,'Dec':12}\n",
    "df['Month']=df['Month'].map(Month)\n",
    "\n",
    "VisitorType={'Returning_Visitor':3, 'New_Visitor':2, 'Other':1}\n",
    "df['VisitorType']=df['VisitorType'].map(VisitorType)\n",
    "d={True:1,False:0}\n",
    "df['Weekend']=df['Weekend'].map(d)\n",
    "df['Revenue']=df['Revenue'].map(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "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>Administrative</th>\n",
       "      <th>Administrative_Duration</th>\n",
       "      <th>Informational</th>\n",
       "      <th>Informational_Duration</th>\n",
       "      <th>ProductRelated</th>\n",
       "      <th>ProductRelated_Duration</th>\n",
       "      <th>BounceRates</th>\n",
       "      <th>ExitRates</th>\n",
       "      <th>PageValues</th>\n",
       "      <th>SpecialDay</th>\n",
       "      <th>Month</th>\n",
       "      <th>OperatingSystems</th>\n",
       "      <th>Browser</th>\n",
       "      <th>Region</th>\n",
       "      <th>TrafficType</th>\n",
       "      <th>VisitorType</th>\n",
       "      <th>Weekend</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>627.500000</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Administrative  Administrative_Duration  Informational  \\\n",
       "0               0                      0.0              0   \n",
       "1               0                      0.0              0   \n",
       "2               0                      0.0              0   \n",
       "3               0                      0.0              0   \n",
       "4               0                      0.0              0   \n",
       "\n",
       "   Informational_Duration  ProductRelated  ProductRelated_Duration  \\\n",
       "0                     0.0               1                 0.000000   \n",
       "1                     0.0               2                64.000000   \n",
       "2                     0.0               1                 0.000000   \n",
       "3                     0.0               2                 2.666667   \n",
       "4                     0.0              10               627.500000   \n",
       "\n",
       "   BounceRates  ExitRates  PageValues  SpecialDay  Month  OperatingSystems  \\\n",
       "0         0.20       0.20         0.0         0.0      2                 1   \n",
       "1         0.00       0.10         0.0         0.0      2                 2   \n",
       "2         0.20       0.20         0.0         0.0      2                 4   \n",
       "3         0.05       0.14         0.0         0.0      2                 3   \n",
       "4         0.02       0.05         0.0         0.0      2                 3   \n",
       "\n",
       "   Browser  Region  TrafficType  VisitorType  Weekend  Revenue  \n",
       "0        1       1            1            3        0        0  \n",
       "1        2       1            2            3        0        0  \n",
       "2        1       9            3            3        0        0  \n",
       "3        2       2            4            3        0        0  \n",
       "4        3       1            4            3        1        0  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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>Administrative</th>\n",
       "      <th>Administrative_Duration</th>\n",
       "      <th>Informational</th>\n",
       "      <th>Informational_Duration</th>\n",
       "      <th>ProductRelated</th>\n",
       "      <th>ProductRelated_Duration</th>\n",
       "      <th>BounceRates</th>\n",
       "      <th>ExitRates</th>\n",
       "      <th>PageValues</th>\n",
       "      <th>SpecialDay</th>\n",
       "      <th>Month</th>\n",
       "      <th>OperatingSystems</th>\n",
       "      <th>Browser</th>\n",
       "      <th>Region</th>\n",
       "      <th>TrafficType</th>\n",
       "      <th>VisitorType</th>\n",
       "      <th>Weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.696993</td>\n",
       "      <td>-0.457191</td>\n",
       "      <td>-0.396478</td>\n",
       "      <td>-0.244931</td>\n",
       "      <td>-0.691003</td>\n",
       "      <td>-0.624348</td>\n",
       "      <td>3.667189</td>\n",
       "      <td>3.229316</td>\n",
       "      <td>-0.317178</td>\n",
       "      <td>-0.308821</td>\n",
       "      <td>-1.665924</td>\n",
       "      <td>-1.233426</td>\n",
       "      <td>-0.790293</td>\n",
       "      <td>-0.894178</td>\n",
       "      <td>-0.762629</td>\n",
       "      <td>0.401025</td>\n",
       "      <td>-0.550552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.696993</td>\n",
       "      <td>-0.457191</td>\n",
       "      <td>-0.396478</td>\n",
       "      <td>-0.244931</td>\n",
       "      <td>-0.668518</td>\n",
       "      <td>-0.590903</td>\n",
       "      <td>-0.457683</td>\n",
       "      <td>1.171473</td>\n",
       "      <td>-0.317178</td>\n",
       "      <td>-0.308821</td>\n",
       "      <td>-1.665924</td>\n",
       "      <td>-0.136078</td>\n",
       "      <td>-0.207952</td>\n",
       "      <td>-0.894178</td>\n",
       "      <td>-0.514182</td>\n",
       "      <td>0.401025</td>\n",
       "      <td>-0.550552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.696993</td>\n",
       "      <td>-0.457191</td>\n",
       "      <td>-0.396478</td>\n",
       "      <td>-0.244931</td>\n",
       "      <td>-0.691003</td>\n",
       "      <td>-0.624348</td>\n",
       "      <td>3.667189</td>\n",
       "      <td>3.229316</td>\n",
       "      <td>-0.317178</td>\n",
       "      <td>-0.308821</td>\n",
       "      <td>-1.665924</td>\n",
       "      <td>2.058618</td>\n",
       "      <td>-0.790293</td>\n",
       "      <td>2.437081</td>\n",
       "      <td>-0.265735</td>\n",
       "      <td>0.401025</td>\n",
       "      <td>-0.550552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.696993</td>\n",
       "      <td>-0.457191</td>\n",
       "      <td>-0.396478</td>\n",
       "      <td>-0.244931</td>\n",
       "      <td>-0.668518</td>\n",
       "      <td>-0.622954</td>\n",
       "      <td>0.573535</td>\n",
       "      <td>1.994610</td>\n",
       "      <td>-0.317178</td>\n",
       "      <td>-0.308821</td>\n",
       "      <td>-1.665924</td>\n",
       "      <td>0.961270</td>\n",
       "      <td>-0.207952</td>\n",
       "      <td>-0.477771</td>\n",
       "      <td>-0.017289</td>\n",
       "      <td>0.401025</td>\n",
       "      <td>-0.550552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.696993</td>\n",
       "      <td>-0.457191</td>\n",
       "      <td>-0.396478</td>\n",
       "      <td>-0.244931</td>\n",
       "      <td>-0.488636</td>\n",
       "      <td>-0.296430</td>\n",
       "      <td>-0.045196</td>\n",
       "      <td>0.142551</td>\n",
       "      <td>-0.317178</td>\n",
       "      <td>-0.308821</td>\n",
       "      <td>-1.665924</td>\n",
       "      <td>0.961270</td>\n",
       "      <td>0.374389</td>\n",
       "      <td>-0.894178</td>\n",
       "      <td>-0.017289</td>\n",
       "      <td>0.401025</td>\n",
       "      <td>1.816360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Administrative  Administrative_Duration  Informational  \\\n",
       "0       -0.696993                -0.457191      -0.396478   \n",
       "1       -0.696993                -0.457191      -0.396478   \n",
       "2       -0.696993                -0.457191      -0.396478   \n",
       "3       -0.696993                -0.457191      -0.396478   \n",
       "4       -0.696993                -0.457191      -0.396478   \n",
       "\n",
       "   Informational_Duration  ProductRelated  ProductRelated_Duration  \\\n",
       "0               -0.244931       -0.691003                -0.624348   \n",
       "1               -0.244931       -0.668518                -0.590903   \n",
       "2               -0.244931       -0.691003                -0.624348   \n",
       "3               -0.244931       -0.668518                -0.622954   \n",
       "4               -0.244931       -0.488636                -0.296430   \n",
       "\n",
       "   BounceRates  ExitRates  PageValues  SpecialDay     Month  OperatingSystems  \\\n",
       "0     3.667189   3.229316   -0.317178   -0.308821 -1.665924         -1.233426   \n",
       "1    -0.457683   1.171473   -0.317178   -0.308821 -1.665924         -0.136078   \n",
       "2     3.667189   3.229316   -0.317178   -0.308821 -1.665924          2.058618   \n",
       "3     0.573535   1.994610   -0.317178   -0.308821 -1.665924          0.961270   \n",
       "4    -0.045196   0.142551   -0.317178   -0.308821 -1.665924          0.961270   \n",
       "\n",
       "    Browser    Region  TrafficType  VisitorType   Weekend  \n",
       "0 -0.790293 -0.894178    -0.762629     0.401025 -0.550552  \n",
       "1 -0.207952 -0.894178    -0.514182     0.401025 -0.550552  \n",
       "2 -0.790293  2.437081    -0.265735     0.401025 -0.550552  \n",
       "3 -0.207952 -0.477771    -0.017289     0.401025 -0.550552  \n",
       "4  0.374389 -0.894178    -0.017289     0.401025  1.816360  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler \n",
    "  \n",
    "scaler = StandardScaler() \n",
    "  \n",
    "scaler.fit(df.drop('Revenue', axis = 1)) \n",
    "scaled_features = scaler.transform(df.drop('Revenue', axis = 1)) \n",
    "  \n",
    "data_feat = pd.DataFrame(scaled_features, columns = df.columns[:-1]) \n",
    "data_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SPLIT DATA\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaled_features, df['Revenue'], test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []\n",
    "models.append(('LogisticRegression: ', LogisticRegression()))\n",
    "models.append(('Naive Byes: ', GaussianNB()))\n",
    "models.append(('K-Nearest Neighbour: ', KNeighborsClassifier(n_neighbors=15)))\n",
    "models.append(('Decision Tree: ', DecisionTreeClassifier(criterion = 'entropy')))\n",
    "models.append(('Support Vector Machine: ', SVC(kernel='linear')))\n",
    "models.append(('Kernel - SVM: ', SVC(kernel='rbf')))\n",
    "models.append(('Random Forest: ', RandomForestClassifier(n_estimators=500, criterion='entropy')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = []\n",
    "train_accuracy = []\n",
    "test_accuracy = []\n",
    "for name, model in models :\n",
    "    cross_validation_result = cross_val_score(model, X_train,y_train, cv = 10, scoring='accuracy')\n",
    "    model.fit(X_train,y_train)\n",
    "    test_accuracy.append(model.score(X_test,y_test))\n",
    "    names.append(name)\n",
    "    train_accuracy.append(cross_validation_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model name | Training accuracy | Testing accuracy\n",
      "LogisticRegression:  0.89 , 0.87\n",
      "Naive Byes:  0.81 , 0.79\n",
      "K-Nearest Neighbour:  0.89 , 0.87\n",
      "Decision Tree:  0.87 , 0.86\n",
      "Support Vector Machine:  0.89 , 0.87\n",
      "Kernel - SVM:  0.9 , 0.88\n",
      "Random Forest:  0.9 , 0.89\n"
     ]
    }
   ],
   "source": [
    "print(\"Model name |\", \"Training accuracy |\", \"Testing accuracy\")\n",
    "for i in range(len(names)):\n",
    "    print(names[i], round(train_accuracy[i].mean(), 2), \",\", round(test_accuracy[i], 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Model: Random Forest: \n",
      "Training Accuracy: 0.9\n",
      "Testing Accuracy: 0.89\n"
     ]
    }
   ],
   "source": [
    "best_model_index = np.argmax(test_accuracy)  # Find the index of the model with the highest testing accuracy\n",
    "best_model_name = names[best_model_index]\n",
    "best_train_accuracy = round(train_accuracy[best_model_index].mean(), 2)\n",
    "best_test_accuracy = round(test_accuracy[best_model_index], 2)\n",
    "\n",
    "print(\"Best Model:\", best_model_name)\n",
    "print(\"Training Accuracy:\", best_train_accuracy)\n",
    "print(\"Testing Accuracy:\", best_test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = RandomForestClassifier(n_estimators=500, criterion='entropy')\n",
    "classifier.fit(X_train,y_train)\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2957  120]\n",
      " [ 266  356]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.895647472289808"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.96      0.94      3077\n",
      "           1       0.75      0.57      0.65       622\n",
      "\n",
      "    accuracy                           0.90      3699\n",
      "   macro avg       0.83      0.77      0.79      3699\n",
      "weighted avg       0.89      0.90      0.89      3699\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}