Prelim2_ProperEncoding+ScalingNumVars_AllBasemodels(lg,rf,dt,NB,KNN,LinearSVC,AdaBoost,GradientBoosting,xgboost).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import make_scorer, accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import cross_val_score, cross_validate\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "myData=pd.read_csv('imputedData.csv')\n",
    "myData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12330 entries, 0 to 12329\n",
      "Data columns (total 57 columns):\n",
      "Administrative                   12330 non-null int64\n",
      "Administrative_Duration          12330 non-null float64\n",
      "Informational                    12330 non-null int64\n",
      "Informational_Duration           12330 non-null float64\n",
      "ProductRelated                   12330 non-null int64\n",
      "ProductRelated_Duration          12330 non-null float64\n",
      "BounceRates                      12330 non-null float64\n",
      "ExitRates                        12330 non-null float64\n",
      "PageValues                       12330 non-null float64\n",
      "TrafficType                      12330 non-null int64\n",
      "SpecialDay                       12330 non-null float64\n",
      "Month_Aug                        12330 non-null uint8\n",
      "Month_Dec                        12330 non-null uint8\n",
      "Month_Feb                        12330 non-null uint8\n",
      "Month_Jul                        12330 non-null uint8\n",
      "Month_June                       12330 non-null uint8\n",
      "Month_Mar                        12330 non-null uint8\n",
      "Month_May                        12330 non-null uint8\n",
      "Month_Nov                        12330 non-null uint8\n",
      "Month_Oct                        12330 non-null uint8\n",
      "Month_Sep                        12330 non-null uint8\n",
      "OperatingSystems_1               12330 non-null uint8\n",
      "OperatingSystems_2               12330 non-null uint8\n",
      "OperatingSystems_3               12330 non-null uint8\n",
      "OperatingSystems_4               12330 non-null uint8\n",
      "OperatingSystems_5               12330 non-null uint8\n",
      "OperatingSystems_6               12330 non-null uint8\n",
      "OperatingSystems_7               12330 non-null uint8\n",
      "OperatingSystems_8               12330 non-null uint8\n",
      "Browser_1                        12330 non-null uint8\n",
      "Browser_2                        12330 non-null uint8\n",
      "Browser_3                        12330 non-null uint8\n",
      "Browser_4                        12330 non-null uint8\n",
      "Browser_5                        12330 non-null uint8\n",
      "Browser_6                        12330 non-null uint8\n",
      "Browser_7                        12330 non-null uint8\n",
      "Browser_8                        12330 non-null uint8\n",
      "Browser_9                        12330 non-null uint8\n",
      "Browser_10                       12330 non-null uint8\n",
      "Browser_11                       12330 non-null uint8\n",
      "Browser_12                       12330 non-null uint8\n",
      "Browser_13                       12330 non-null uint8\n",
      "Region_1                         12330 non-null uint8\n",
      "Region_2                         12330 non-null uint8\n",
      "Region_3                         12330 non-null uint8\n",
      "Region_4                         12330 non-null uint8\n",
      "Region_5                         12330 non-null uint8\n",
      "Region_6                         12330 non-null uint8\n",
      "Region_7                         12330 non-null uint8\n",
      "Region_8                         12330 non-null uint8\n",
      "Region_9                         12330 non-null uint8\n",
      "VisitorType_New_Visitor          12330 non-null uint8\n",
      "VisitorType_Other                12330 non-null uint8\n",
      "VisitorType_Returning_Visitor    12330 non-null uint8\n",
      "Weekend_False                    12330 non-null uint8\n",
      "Weekend_True                     12330 non-null uint8\n",
      "Revenue                          12330 non-null bool\n",
      "dtypes: bool(1), float64(7), int64(4), uint8(45)\n",
      "memory usage: 1.6 MB\n"
     ]
    }
   ],
   "source": [
    "#One hot encoding of categorical variables\n",
    "\n",
    "#Create list with features to be dummified cols.\n",
    "nonum_feats_names = ['Month','OperatingSystems','Browser','Region','VisitorType','Weekend']\n",
    "\n",
    "\n",
    "#Boolean to dummify logic\n",
    "#Weekend_map={False:0,True:1}\n",
    "#pd.get_dummies(myData['Weekend'].map(Weekend_map).astype('category')\n",
    "\n",
    "\n",
    "dataModel = pd.concat([myData[['Administrative', 'Administrative_Duration', 'Informational','Informational_Duration', \n",
    "                              'ProductRelated','ProductRelated_Duration','BounceRates','ExitRates','PageValues',\n",
    "                               'TrafficType','SpecialDay']],\n",
    "                       pd.get_dummies(myData[nonum_feats_names].astype('category')),myData['Revenue']],axis=1)\n",
    "                      \n",
    "dataModel.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: Revenue, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Label Encoding of revenue\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "myData['Revenue'] = le.fit_transform(myData['Revenue'])\n",
    "myData['Revenue'].value_counts()\n",
    "\n",
    "myData['Revenue'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x: (12330, 56)\n",
      "Shape of y: (12330,)\n"
     ]
    }
   ],
   "source": [
    "# getting dependent and independent variables\n",
    "\n",
    "x = dataModel\n",
    "# removing the target column revenue from x\n",
    "x = x.drop(['Revenue'], axis = 1)\n",
    "\n",
    "y = myData['Revenue']\n",
    "\n",
    "# checking the shapes\n",
    "print(\"Shape of x:\", x.shape)\n",
    "print(\"Shape of y:\", y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x_train : (8631, 56)\n",
      "Shape of y_train : (8631,)\n",
      "Shape of x_test : (3699, 56)\n",
      "Shape of y_test : (3699,)\n"
     ]
    }
   ],
   "source": [
    "# splitting the data\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_baseTrain, x_baseTest, y_baseTrain, y_baseTest = train_test_split(x, y, test_size = 0.3, random_state = 42)\n",
    "\n",
    "# checking the shapes\n",
    "\n",
    "print(\"Shape of x_train :\", x_baseTrain.shape)\n",
    "print(\"Shape of y_train :\", y_baseTrain.shape)\n",
    "print(\"Shape of x_test :\", x_baseTest.shape)\n",
    "print(\"Shape of y_test :\", y_baseTest.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:25: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    }
   ],
   "source": [
    "#SCALING WITH STANDARD Z SCORE SCALER\n",
    "#Scaling the data first fitting it and transforming the training set\n",
    "#to later apply the fit to transform the test set.\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_cols_names = ['Administrative', 'Administrative_Duration', 'Informational','Informational_Duration', \n",
    "                              'ProductRelated','ProductRelated_Duration','BounceRates','ExitRates','PageValues',\n",
    "                               'TrafficType','SpecialDay']\n",
    "\n",
    "#Instantiate Satandard Scaler\n",
    "scaler = StandardScaler()\n",
    "#Fit transform the numerical features in the training dataset to a new dataframe\n",
    "scaled_numfeats_train = pd.DataFrame(scaler.fit_transform(x_baseTrain[num_cols_names]), \n",
    "                                     columns=num_cols_names, index= x_baseTrain.index)\n",
    "#Integrate scaled values to the training set\n",
    "for col in num_cols_names:\n",
    "    x_baseTrain[col] = scaled_numfeats_train[col]\n",
    "    \n",
    "    \n",
    "#Transform the numerical features inthe training dataset to a new dataframe\n",
    "scaled_numfeats_test = pd.DataFrame(scaler.transform(x_baseTest[num_cols_names]),\n",
    "                                    columns=num_cols_names, index= x_baseTest.index)\n",
    "#Integrate scaled values to the test set\n",
    "for col in num_cols_names:\n",
    "    x_baseTest[col] = scaled_numfeats_test[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>TrafficType</th>\n",
       "      <th>SpecialDay</th>\n",
       "      <th>Month_Aug</th>\n",
       "      <th>Month_Dec</th>\n",
       "      <th>Month_Feb</th>\n",
       "      <th>Month_Jul</th>\n",
       "      <th>Month_June</th>\n",
       "      <th>Month_Mar</th>\n",
       "      <th>Month_May</th>\n",
       "      <th>Month_Nov</th>\n",
       "      <th>Month_Oct</th>\n",
       "      <th>Month_Sep</th>\n",
       "      <th>OperatingSystems_1</th>\n",
       "      <th>OperatingSystems_2</th>\n",
       "      <th>OperatingSystems_3</th>\n",
       "      <th>OperatingSystems_4</th>\n",
       "      <th>OperatingSystems_5</th>\n",
       "      <th>OperatingSystems_6</th>\n",
       "      <th>OperatingSystems_7</th>\n",
       "      <th>OperatingSystems_8</th>\n",
       "      <th>Browser_1</th>\n",
       "      <th>Browser_2</th>\n",
       "      <th>Browser_3</th>\n",
       "      <th>Browser_4</th>\n",
       "      <th>Browser_5</th>\n",
       "      <th>Browser_6</th>\n",
       "      <th>Browser_7</th>\n",
       "      <th>Browser_8</th>\n",
       "      <th>Browser_9</th>\n",
       "      <th>Browser_10</th>\n",
       "      <th>Browser_11</th>\n",
       "      <th>Browser_12</th>\n",
       "      <th>Browser_13</th>\n",
       "      <th>Region_1</th>\n",
       "      <th>Region_2</th>\n",
       "      <th>Region_3</th>\n",
       "      <th>Region_4</th>\n",
       "      <th>Region_5</th>\n",
       "      <th>Region_6</th>\n",
       "      <th>Region_7</th>\n",
       "      <th>Region_8</th>\n",
       "      <th>Region_9</th>\n",
       "      <th>VisitorType_New_Visitor</th>\n",
       "      <th>VisitorType_Other</th>\n",
       "      <th>VisitorType_Returning_Visitor</th>\n",
       "      <th>Weekend_False</th>\n",
       "      <th>Weekend_True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>8916</td>\n",
       "      <td>0.211602</td>\n",
       "      <td>0.359573</td>\n",
       "      <td>-0.397052</td>\n",
       "      <td>-0.252949</td>\n",
       "      <td>0.366979</td>\n",
       "      <td>-0.079307</td>\n",
       "      <td>-0.361858</td>\n",
       "      <td>-0.615653</td>\n",
       "      <td>-0.314838</td>\n",
       "      <td>1.721701</td>\n",
       "      <td>-0.3047</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>772</td>\n",
       "      <td>1.123404</td>\n",
       "      <td>2.069214</td>\n",
       "      <td>1.164560</td>\n",
       "      <td>1.483566</td>\n",
       "      <td>1.155859</td>\n",
       "      <td>0.744399</td>\n",
       "      <td>-0.407457</td>\n",
       "      <td>-0.786715</td>\n",
       "      <td>-0.204095</td>\n",
       "      <td>-0.515315</td>\n",
       "      <td>-0.3047</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12250</td>\n",
       "      <td>-0.396266</td>\n",
       "      <td>-0.228151</td>\n",
       "      <td>-0.397052</td>\n",
       "      <td>-0.252949</td>\n",
       "      <td>2.125054</td>\n",
       "      <td>1.769261</td>\n",
       "      <td>-0.439481</td>\n",
       "      <td>-0.620297</td>\n",
       "      <td>-0.131644</td>\n",
       "      <td>-0.515315</td>\n",
       "      <td>-0.3047</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7793</td>\n",
       "      <td>-0.092332</td>\n",
       "      <td>0.350877</td>\n",
       "      <td>-0.397052</td>\n",
       "      <td>-0.252949</td>\n",
       "      <td>-0.489519</td>\n",
       "      <td>-0.332151</td>\n",
       "      <td>-0.277332</td>\n",
       "      <td>-0.334763</td>\n",
       "      <td>1.631839</td>\n",
       "      <td>-0.018200</td>\n",
       "      <td>-0.3047</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6601</td>\n",
       "      <td>4.770613</td>\n",
       "      <td>3.059134</td>\n",
       "      <td>4.287783</td>\n",
       "      <td>5.156750</td>\n",
       "      <td>3.071710</td>\n",
       "      <td>2.131509</td>\n",
       "      <td>-0.313420</td>\n",
       "      <td>-0.605466</td>\n",
       "      <td>0.223989</td>\n",
       "      <td>-0.763872</td>\n",
       "      <td>-0.3047</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Administrative  Administrative_Duration  Informational  \\\n",
       "8916         0.211602                 0.359573      -0.397052   \n",
       "772          1.123404                 2.069214       1.164560   \n",
       "12250       -0.396266                -0.228151      -0.397052   \n",
       "7793        -0.092332                 0.350877      -0.397052   \n",
       "6601         4.770613                 3.059134       4.287783   \n",
       "\n",
       "       Informational_Duration  ProductRelated  ProductRelated_Duration  \\\n",
       "8916                -0.252949        0.366979                -0.079307   \n",
       "772                  1.483566        1.155859                 0.744399   \n",
       "12250               -0.252949        2.125054                 1.769261   \n",
       "7793                -0.252949       -0.489519                -0.332151   \n",
       "6601                 5.156750        3.071710                 2.131509   \n",
       "\n",
       "       BounceRates  ExitRates  PageValues  TrafficType  SpecialDay  Month_Aug  \\\n",
       "8916     -0.361858  -0.615653   -0.314838     1.721701     -0.3047          0   \n",
       "772      -0.407457  -0.786715   -0.204095    -0.515315     -0.3047          0   \n",
       "12250    -0.439481  -0.620297   -0.131644    -0.515315     -0.3047          0   \n",
       "7793     -0.277332  -0.334763    1.631839    -0.018200     -0.3047          1   \n",
       "6601     -0.313420  -0.605466    0.223989    -0.763872     -0.3047          1   \n",
       "\n",
       "       Month_Dec  Month_Feb  Month_Jul  Month_June  Month_Mar  Month_May  \\\n",
       "8916           0          0          0           0          0          0   \n",
       "772            0          0          0           0          1          0   \n",
       "12250          0          0          0           0          0          0   \n",
       "7793           0          0          0           0          0          0   \n",
       "6601           0          0          0           0          0          0   \n",
       "\n",
       "       Month_Nov  Month_Oct  Month_Sep  OperatingSystems_1  \\\n",
       "8916           1          0          0                   1   \n",
       "772            0          0          0                   0   \n",
       "12250          1          0          0                   0   \n",
       "7793           0          0          0                   0   \n",
       "6601           0          0          0                   0   \n",
       "\n",
       "       OperatingSystems_2  OperatingSystems_3  OperatingSystems_4  \\\n",
       "8916                    0                   0                   0   \n",
       "772                     1                   0                   0   \n",
       "12250                   1                   0                   0   \n",
       "7793                    1                   0                   0   \n",
       "6601                    1                   0                   0   \n",
       "\n",
       "       OperatingSystems_5  OperatingSystems_6  OperatingSystems_7  \\\n",
       "8916                    0                   0                   0   \n",
       "772                     0                   0                   0   \n",
       "12250                   0                   0                   0   \n",
       "7793                    0                   0                   0   \n",
       "6601                    0                   0                   0   \n",
       "\n",
       "       OperatingSystems_8  Browser_1  Browser_2  Browser_3  Browser_4  \\\n",
       "8916                    0          0          0          0          0   \n",
       "772                     0          0          1          0          0   \n",
       "12250                   0          0          1          0          0   \n",
       "7793                    0          0          0          0          0   \n",
       "6601                    0          0          1          0          0   \n",
       "\n",
       "       Browser_5  Browser_6  Browser_7  Browser_8  Browser_9  Browser_10  \\\n",
       "8916           0          0          0          1          0           0   \n",
       "772            0          0          0          0          0           0   \n",
       "12250          0          0          0          0          0           0   \n",
       "7793           1          0          0          0          0           0   \n",
       "6601           0          0          0          0          0           0   \n",
       "\n",
       "       Browser_11  Browser_12  Browser_13  Region_1  Region_2  Region_3  \\\n",
       "8916            0           0           0         0         0         0   \n",
       "772             0           0           0         0         0         1   \n",
       "12250           0           0           0         0         1         0   \n",
       "7793            0           0           0         0         0         0   \n",
       "6601            0           0           0         0         0         1   \n",
       "\n",
       "       Region_4  Region_5  Region_6  Region_7  Region_8  Region_9  \\\n",
       "8916          0         0         1         0         0         0   \n",
       "772           0         0         0         0         0         0   \n",
       "12250         0         0         0         0         0         0   \n",
       "7793          0         0         0         1         0         0   \n",
       "6601          0         0         0         0         0         0   \n",
       "\n",
       "       VisitorType_New_Visitor  VisitorType_Other  \\\n",
       "8916                         0                  0   \n",
       "772                          0                  0   \n",
       "12250                        0                  0   \n",
       "7793                         0                  0   \n",
       "6601                         0                  0   \n",
       "\n",
       "       VisitorType_Returning_Visitor  Weekend_False  Weekend_True  \n",
       "8916                               1              1             0  \n",
       "772                                1              1             0  \n",
       "12250                              1              1             0  \n",
       "7793                               1              1             0  \n",
       "6601                               1              0             1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', 60)\n",
    "x_baseTest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy : 0.8851813231375275\n",
      "Testing Accuracy : 0.8821303054879697\n",
      "ROC AUC Score : 0.6669879752825253\n",
      "[[3059   65]\n",
      " [ 371  204]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.98      0.93      3124\n",
      "           1       0.76      0.35      0.48       575\n",
      "\n",
      "    accuracy                           0.88      3699\n",
      "   macro avg       0.83      0.67      0.71      3699\n",
      "weighted avg       0.87      0.88      0.86      3699\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'fit_time': array([0.0508647 , 0.04986811, 0.05086279, 0.04687428, 0.0498693 ,\n",
      "       0.04886866, 0.04687405, 0.04587746, 0.04986715, 0.04787302]), 'score_time': array([0.00598288, 0.00797749, 0.00698161, 0.0069809 , 0.00598192,\n",
      "       0.00698209, 0.00598431, 0.00797844, 0.00598431, 0.00598335]), 'test_accuracy': array([0.87962963, 0.86805556, 0.88310185, 0.88876014, 0.89455388,\n",
      "       0.88991889, 0.87369641, 0.89339513, 0.88515081, 0.88283063]), 'test_precision': array([0.734375  , 0.67857143, 0.76190476, 0.76056338, 0.78378378,\n",
      "       0.75675676, 0.66216216, 0.75308642, 0.77419355, 0.72222222]), 'test_recall': array([0.35074627, 0.28358209, 0.35820896, 0.40601504, 0.43609023,\n",
      "       0.42105263, 0.36842105, 0.45864662, 0.36090226, 0.39097744]), 'test_f1_score': array([0.47474747, 0.4       , 0.48730964, 0.52941176, 0.56038647,\n",
      "       0.5410628 , 0.47342995, 0.57009346, 0.49230769, 0.50731707])}\n",
      "Mean Accuracy : 0.8839092925146556\n",
      "Mean Standard Deviation : 0.008001567066819103\n",
      "Mean precision score : 0.7387619463822989\n",
      "Mean Standard Deviation precision score : 0.03821713435428709\n",
      "Mean recall score : 0.38346425765907305\n",
      "Mean Standard Deviation recall score : 0.047770113713051156\n",
      "Mean f1 score : 0.5036066334598898\n",
      "Mean Standard Deviation f1 score : 0.047537522391217404\n"
     ]
    }
   ],
   "source": [
    "# MODELLING ____ LOGISTIC REGRESSION\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(x_baseTrain, y_baseTrain)\n",
    "\n",
    "y_basePred = model.predict(x_baseTest)\n",
    "\n",
    "# evaluating the model\n",
    "print(\"Training Accuracy :\", model.score(x_baseTrain, y_baseTrain))\n",
    "print(\"Testing Accuracy :\", model.score(x_baseTest, y_baseTest))\n",
    "print(\"ROC AUC Score :\", roc_auc_score(y_baseTest, y_basePred))\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_baseTest, y_basePred)\n",
    "print(cm)\n",
    "#plt.rcParams['figure.figsize'] = (6, 6)\n",
    "#sns.heatmap(cm ,annot = True)\n",
    "\n",
    "# classification report\n",
    "cr = classification_report(y_baseTest, y_basePred)\n",
    "print(cr)\n",
    "\n",
    "\n",
    "# cross validation\n",
    "scoring = {'accuracy' : make_scorer(accuracy_score), \n",
    "           'precision' : make_scorer(precision_score),\n",
    "           'recall' : make_scorer(recall_score), \n",
    "           'f1_score' : make_scorer(f1_score)}\n",
    "cvs = cross_validate(estimator = model, X = x_baseTrain, y = y_baseTrain, cv = 10,scoring=scoring)\n",
    "print(cvs)\n",
    "print(\"Mean Accuracy :\", np.mean(cvs['test_accuracy']))\n",
    "print(\"Mean Standard Deviation :\", np.std(cvs['test_accuracy']))\n",
    "print(\"Mean precision score :\", np.mean(cvs['test_precision']))\n",
    "print(\"Mean Standard Deviation precision score :\", np.std(cvs['test_precision']))\n",
    "print(\"Mean recall score :\", np.mean(cvs['test_recall']))\n",
    "print(\"Mean Standard Deviation recall score :\", np.std(cvs['test_recall']))\n",
    "print(\"Mean f1 score :\", np.mean(cvs['test_f1_score']))\n",
    "print(\"Mean Standard Deviation f1 score :\", np.std(cvs['test_f1_score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy : 0.9932800370756575\n",
      "Testing Accuracy : 0.8972695323060287\n",
      "ROC AUC Score : 0.7305834214774815\n",
      "[[3038   86]\n",
      " [ 294  281]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.97      0.94      3124\n",
      "           1       0.77      0.49      0.60       575\n",
      "\n",
      "    accuracy                           0.90      3699\n",
      "   macro avg       0.84      0.73      0.77      3699\n",
      "weighted avg       0.89      0.90      0.89      3699\n",
      "\n",
      "{'fit_time': array([0.08781338, 0.07878113, 0.07480049, 0.07580185, 0.08375573,\n",
      "       0.09374881, 0.08981419, 0.07773852, 0.07978129, 0.07180548]), 'score_time': array([0.01595712, 0.01396489, 0.01396179, 0.01396132, 0.0189395 ,\n",
      "       0.0189147 , 0.0159564 , 0.01600409, 0.01396394, 0.01396394]), 'test_accuracy': array([0.89351852, 0.87615741, 0.90277778, 0.89918888, 0.88760139,\n",
      "       0.89918888, 0.88876014, 0.89571263, 0.89327146, 0.89443155]), 'test_precision': array([0.73863636, 0.65517241, 0.79761905, 0.76136364, 0.69565217,\n",
      "       0.7875    , 0.71264368, 0.73626374, 0.78082192, 0.73863636]), 'test_recall': array([0.48507463, 0.42537313, 0.5       , 0.5037594 , 0.48120301,\n",
      "       0.47368421, 0.46616541, 0.5037594 , 0.42857143, 0.4887218 ]), 'test_f1_score': array([0.58558559, 0.5158371 , 0.6146789 , 0.60633484, 0.56888889,\n",
      "       0.5915493 , 0.56363636, 0.59821429, 0.55339806, 0.58823529])}\n",
      "Mean Accuracy : 0.8930608631880113\n",
      "Mean Standard Deviation : 0.007160425008579453\n",
      "Mean precision score : 0.7404309331194432\n",
      "Mean Standard Deviation precision score : 0.04198294609370879\n",
      "Mean recall score : 0.47563124228481646\n",
      "Mean Standard Deviation recall score : 0.027021665242121146\n",
      "Mean f1 score : 0.5786358616753773\n",
      "Mean Standard Deviation f1 score : 0.027672138276155334\n"
     ]
    }
   ],
   "source": [
    "# MODELLING ____RANDOM FOREST\n",
    "\n",
    "model = RandomForestClassifier()  #can try estimators=200,mex depth=30\n",
    "model.fit(x_baseTrain, y_baseTrain)\n",
    "\n",
    "y_basePred = model.predict(x_baseTest)\n",
    "\n",
    "# evaluating the model\n",
    "print(\"Training Accuracy :\", model.score(x_baseTrain, y_baseTrain))\n",
    "print(\"Testing Accuracy :\", model.score(x_baseTest, y_baseTest))\n",
    "print(\"ROC AUC Score :\", roc_auc_score(y_baseTest, y_basePred))\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_baseTest, y_basePred)\n",
    "print(cm)\n",
    "\n",
    "# classification report\n",
    "cr = classification_report(y_baseTest, y_basePred)\n",
    "print(cr)\n",
    "\n",
    "\n",
    "# cross validation\n",
    "scoring = {'accuracy' : make_scorer(accuracy_score), \n",
    "           'precision' : make_scorer(precision_score),\n",
    "           'recall' : make_scorer(recall_score), \n",
    "           'f1_score' : make_scorer(f1_score)}\n",
    "cvs = cross_validate(estimator = model, X = x_baseTrain, y = y_baseTrain, cv = 10,scoring=scoring)\n",
    "print(cvs)\n",
    "print(\"Mean Accuracy :\", np.mean(cvs['test_accuracy']))\n",
    "print(\"Mean Standard Deviation :\", np.std(cvs['test_accuracy']))\n",
    "print(\"Mean precision score :\", np.mean(cvs['test_precision']))\n",
    "print(\"Mean Standard Deviation precision score :\", np.std(cvs['test_precision']))\n",
    "print(\"Mean recall score :\", np.mean(cvs['test_recall']))\n",
    "print(\"Mean Standard Deviation recall score :\", np.std(cvs['test_recall']))\n",
    "print(\"Mean f1 score :\", np.mean(cvs['test_f1_score']))\n",
    "print(\"Mean Standard Deviation f1 score :\", np.std(cvs['test_f1_score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy : 1.0\n",
      "Testing Accuracy : 0.8637469586374696\n",
      "ROC AUC Score : 0.7348605466792852\n",
      "[[2880  244]\n",
      " [ 260  315]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.92      0.92      3124\n",
      "           1       0.56      0.55      0.56       575\n",
      "\n",
      "    accuracy                           0.86      3699\n",
      "   macro avg       0.74      0.73      0.74      3699\n",
      "weighted avg       0.86      0.86      0.86      3699\n",
      "\n",
      "{'fit_time': array([0.0548532 , 0.05983949, 0.05886984, 0.05684805, 0.05587554,\n",
      "       0.05884266, 0.05684829, 0.05983973, 0.05984044, 0.05984116]), 'score_time': array([0.00598478, 0.00698137, 0.00698066, 0.00598383, 0.00595856,\n",
      "       0.00598383, 0.00598359, 0.00598383, 0.00598335, 0.00598836]), 'test_accuracy': array([0.86921296, 0.85069444, 0.86458333, 0.87369641, 0.86442642,\n",
      "       0.86558517, 0.85515643, 0.82502897, 0.86426914, 0.85266821]), 'test_precision': array([0.57777778, 0.51824818, 0.56589147, 0.59090909, 0.55797101,\n",
      "       0.56296296, 0.52739726, 0.44303797, 0.56666667, 0.52054795]), 'test_recall': array([0.58208955, 0.52985075, 0.54477612, 0.58646617, 0.57894737,\n",
      "       0.57142857, 0.57894737, 0.52631579, 0.5112782 , 0.57142857]), 'test_f1_score': array([0.57992565, 0.52398524, 0.55513308, 0.58867925, 0.56826568,\n",
      "       0.56716418, 0.55197133, 0.48109966, 0.53754941, 0.54480287])}\n",
      "Mean Accuracy : 0.8585321490862396\n",
      "Mean Standard Deviation : 0.013144298629899276\n",
      "Mean precision score : 0.5431410341022948\n",
      "Mean Standard Deviation precision score : 0.0406988418849055\n",
      "Mean recall score : 0.5581528447985635\n",
      "Mean Standard Deviation recall score : 0.02604361870207963\n",
      "Mean f1 score : 0.549857633432265\n",
      "Mean Standard Deviation f1 score : 0.029421008289360895\n"
     ]
    }
   ],
   "source": [
    "# MODELLING ____DECISION TREE\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(x_baseTrain, y_baseTrain)\n",
    "\n",
    "y_basePred = model.predict(x_baseTest)\n",
    "\n",
    "# evaluating the model\n",
    "print(\"Training Accuracy :\", model.score(x_baseTrain, y_baseTrain))\n",
    "print(\"Testing Accuracy :\", model.score(x_baseTest, y_baseTest))\n",
    "print(\"ROC AUC Score :\", roc_auc_score(y_baseTest, y_basePred))\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_baseTest, y_basePred)\n",
    "print(cm)\n",
    "\n",
    "# classification report\n",
    "cr = classification_report(y_baseTest, y_basePred)\n",
    "print(cr)\n",
    "\n",
    "\n",
    "# cross validation\n",
    "scoring = {'accuracy' : make_scorer(accuracy_score), \n",
    "           'precision' : make_scorer(precision_score),\n",
    "           'recall' : make_scorer(recall_score), \n",
    "           'f1_score' : make_scorer(f1_score)}\n",
    "cvs = cross_validate(estimator = model, X = x_baseTrain, y = y_baseTrain, cv = 10,scoring=scoring)\n",
    "print(cvs)\n",
    "print(\"Mean Accuracy :\", np.mean(cvs['test_accuracy']))\n",
    "print(\"Mean Standard Deviation :\", np.std(cvs['test_accuracy']))\n",
    "print(\"Mean precision score :\", np.mean(cvs['test_precision']))\n",
    "print(\"Mean Standard Deviation precision score :\", np.std(cvs['test_precision']))\n",
    "print(\"Mean recall score :\", np.mean(cvs['test_recall']))\n",
    "print(\"Mean Standard Deviation recall score :\", np.std(cvs['test_recall']))\n",
    "print(\"Mean f1 score :\", np.mean(cvs['test_f1_score']))\n",
    "print(\"Mean Standard Deviation f1 score :\", np.std(cvs['test_f1_score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy : 0.35650561927934193\n",
      "Testing Accuracy : 0.3646931603135983\n",
      "ROC AUC Score : 0.6040132494572177\n",
      "[[ 802 2322]\n",
      " [  28  547]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.26      0.41      3124\n",
      "           1       0.19      0.95      0.32       575\n",
      "\n",
      "    accuracy                           0.36      3699\n",
      "   macro avg       0.58      0.60      0.36      3699\n",
      "weighted avg       0.85      0.36      0.39      3699\n",
      "\n",
      "{'fit_time': array([0.01495981, 0.01595759, 0.01595759, 0.01495934, 0.01595759,\n",
      "       0.01595712, 0.01496005, 0.01695395, 0.01495981, 0.01595712]), 'score_time': array([0.00797868, 0.00797868, 0.00698233, 0.00797963, 0.00698137,\n",
      "       0.00698113, 0.00797939, 0.00698185, 0.00797844, 0.00797892]), 'test_accuracy': array([0.36342593, 0.30787037, 0.37847222, 0.4032445 , 0.35341831,\n",
      "       0.35225956, 0.34762457, 0.33835458, 0.28422274, 0.36542923]), 'test_precision': array([0.19047619, 0.17597765, 0.19236641, 0.19391026, 0.18978102,\n",
      "       0.18859649, 0.19109195, 0.18803419, 0.17385445, 0.19196429]), 'test_recall': array([0.95522388, 0.94029851, 0.94029851, 0.90977444, 0.97744361,\n",
      "       0.96992481, 1.        , 0.9924812 , 0.96992481, 0.96992481]), 'test_f1_score': array([0.31761787, 0.29647059, 0.31939163, 0.31968296, 0.31784841,\n",
      "       0.31578947, 0.32086852, 0.31616766, 0.29485714, 0.32049689])}\n",
      "Mean Accuracy : 0.34943219970494077\n",
      "Mean Standard Deviation : 0.03212528520137055\n",
      "Mean precision score : 0.1876052901068168\n",
      "Mean Standard Deviation precision score : 0.006572735098110674\n",
      "Mean recall score : 0.9625294579732915\n",
      "Mean Standard Deviation recall score : 0.025519695046492992\n",
      "Mean f1 score : 0.31391911509347\n",
      "Mean Standard Deviation f1 score : 0.00927478270880931\n"
     ]
    }
   ],
   "source": [
    "# MODELLING ____NAIVE BAYES\n",
    "\n",
    "model = GaussianNB()\n",
    "model.fit(x_baseTrain, y_baseTrain)\n",
    "\n",
    "y_basePred = model.predict(x_baseTest)\n",
    "\n",
    "# evaluating the model\n",
    "print(\"Training Accuracy :\", model.score(x_baseTrain, y_baseTrain))\n",
    "print(\"Testing Accuracy :\", model.score(x_baseTest, y_baseTest))\n",
    "print(\"ROC AUC Score :\", roc_auc_score(y_baseTest, y_basePred))\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_baseTest, y_basePred)\n",
    "print(cm)\n",
    "\n",
    "# classification report\n",
    "cr = classification_report(y_baseTest, y_basePred)\n",
    "print(cr)\n",
    "\n",
    "\n",
    "# cross validation\n",
    "scoring = {'accuracy' : make_scorer(accuracy_score), \n",
    "           'precision' : make_scorer(precision_score),\n",
    "           'recall' : make_scorer(recall_score), \n",
    "           'f1_score' : make_scorer(f1_score)}\n",
    "cvs = cross_validate(estimator = model, X = x_baseTrain, y = y_baseTrain, cv = 10,scoring=scoring)\n",
    "print(cvs)\n",
    "print(\"Mean Accuracy :\", np.mean(cvs['test_accuracy']))\n",
    "print(\"Mean Standard Deviation :\", np.std(cvs['test_accuracy']))\n",
    "print(\"Mean precision score :\", np.mean(cvs['test_precision']))\n",
    "print(\"Mean Standard Deviation precision score :\", np.std(cvs['test_precision']))\n",
    "print(\"Mean recall score :\", np.mean(cvs['test_recall']))\n",
    "print(\"Mean Standard Deviation recall score :\", np.std(cvs['test_recall']))\n",
    "print(\"Mean f1 score :\", np.mean(cvs['test_f1_score']))\n",
    "print(\"Mean Standard Deviation f1 score :\", np.std(cvs['test_f1_score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy : 0.9027922604564941\n",
      "Testing Accuracy : 0.8694241686942417\n",
      "ROC AUC Score : 0.6644321661192452\n",
      "[[3005  119]\n",
      " [ 364  211]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.96      0.93      3124\n",
      "           1       0.64      0.37      0.47       575\n",
      "\n",
      "    accuracy                           0.87      3699\n",
      "   macro avg       0.77      0.66      0.70      3699\n",
      "weighted avg       0.85      0.87      0.85      3699\n",
      "\n",
      "{'fit_time': array([0.10471988, 0.0679183 , 0.06983972, 0.09081531, 0.07878876,\n",
      "       0.09275293, 0.07083797, 0.10173941, 0.08579707, 0.08580589]), 'score_time': array([2.2500124 , 2.17783475, 2.05049086, 2.10078144, 1.85304427,\n",
      "       2.28884196, 1.75929356, 2.21606183, 2.22007656, 2.06843305]), 'test_accuracy': array([0.87152778, 0.86689815, 0.875     , 0.88760139, 0.8783314 ,\n",
      "       0.89339513, 0.85979143, 0.8829664 , 0.87935035, 0.8712297 ]), 'test_precision': array([0.66666667, 0.63768116, 0.68055556, 0.73684211, 0.67073171,\n",
      "       0.75308642, 0.57692308, 0.70512821, 0.70422535, 0.64473684]), 'test_recall': array([0.34328358, 0.32835821, 0.36567164, 0.42105263, 0.41353383,\n",
      "       0.45864662, 0.33834586, 0.41353383, 0.37593985, 0.36842105]), 'test_f1_score': array([0.45320197, 0.43349754, 0.47572816, 0.53588517, 0.51162791,\n",
      "       0.57009346, 0.42654028, 0.52132701, 0.49019608, 0.46889952])}\n",
      "Mean Accuracy : 0.8766091719722455\n",
      "Mean Standard Deviation : 0.009423274982004775\n",
      "Mean precision score : 0.6776577090245051\n",
      "Mean Standard Deviation precision score : 0.0486408214400386\n",
      "Mean recall score : 0.3826787117046347\n",
      "Mean Standard Deviation recall score : 0.04019177232047772\n",
      "Mean f1 score : 0.4886997093654424\n",
      "Mean Standard Deviation f1 score : 0.04378968397829791\n"
     ]
    }
   ],
   "source": [
    "# MODELLING ____KNN\n",
    "\n",
    "model = KNeighborsClassifier()\n",
    "model.fit(x_baseTrain, y_baseTrain)\n",
    "\n",
    "y_basePred = model.predict(x_baseTest)\n",
    "\n",
    "# evaluating the model\n",
    "print(\"Training Accuracy :\", model.score(x_baseTrain, y_baseTrain))\n",
    "print(\"Testing Accuracy :\", model.score(x_baseTest, y_baseTest))\n",
    "print(\"ROC AUC Score :\", roc_auc_score(y_baseTest, y_basePred))\n",
    "\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_baseTest, y_basePred)\n",
    "print(cm)\n",
    "\n",
    "# classification report\n",
    "cr = classification_report(y_baseTest, y_basePred)\n",
    "print(cr)\n",
    "\n",
    "\n",
    "# cross validation\n",
    "scoring = {'accuracy' : make_scorer(accuracy_score), \n",
    "           'precision' : make_scorer(precision_score),\n",
    "           'recall' : make_scorer(recall_score), \n",
    "           'f1_score' : make_scorer(f1_score)}\n",
    "cvs = cross_validate(estimator = model, X = x_baseTrain, y = y_baseTrain, cv = 10,scoring=scoring)\n",
    "print(cvs)\n",
    "print(\"Mean Accuracy :\", np.mean(cvs['test_accuracy']))\n",
    "print(\"Mean Standard Deviation :\", np.std(cvs['test_accuracy']))\n",
    "print(\"Mean precision score :\", np.mean(cvs['test_precision']))\n",
    "print(\"Mean Standard Deviation precision score :\", np.std(cvs['test_precision']))\n",
    "print(\"Mean recall score :\", np.mean(cvs['test_recall']))\n",
    "print(\"Mean Standard Deviation recall score :\", np.std(cvs['test_recall']))\n",
    "print(\"Mean f1 score :\", np.mean(cvs['test_f1_score']))\n",
    "print(\"Mean Standard Deviation f1 score :\", np.std(cvs['test_f1_score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy : 0.8820530645348164\n",
      "Testing Accuracy : 0.8788861854555285\n",
      "ROC AUC Score : 0.6473295106608027\n",
      "[[3072   52]\n",
      " [ 396  179]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.98      0.93      3124\n",
      "           1       0.77      0.31      0.44       575\n",
      "\n",
      "    accuracy                           0.88      3699\n",
      "   macro avg       0.83      0.65      0.69      3699\n",
      "weighted avg       0.87      0.88      0.86      3699\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'fit_time': array([0.66422391, 0.63928986, 0.63755202, 0.65570617, 0.8178134 ,\n",
      "       0.67322683, 0.65823889, 0.65524769, 0.64628434, 0.65526295]), 'score_time': array([0.00698161, 0.00698161, 0.        , 0.0069809 , 0.00698018,\n",
      "       0.00695467, 0.00797844, 0.00698137, 0.00597191, 0.00696588]), 'test_accuracy': array([0.87847222, 0.86689815, 0.875     , 0.88412514, 0.89107764,\n",
      "       0.88412514, 0.87137891, 0.89223638, 0.88399072, 0.887471  ]), 'test_precision': array([0.76363636, 0.71111111, 0.74074074, 0.81132075, 0.81967213,\n",
      "       0.7704918 , 0.66666667, 0.77777778, 0.81132075, 0.8       ]), 'test_recall': array([0.31343284, 0.23880597, 0.29850746, 0.32330827, 0.37593985,\n",
      "       0.35338346, 0.33082707, 0.42105263, 0.32330827, 0.36090226]), 'test_f1_score': array([0.44444444, 0.3575419 , 0.42553191, 0.46236559, 0.51546392,\n",
      "       0.48453608, 0.44221106, 0.54634146, 0.46236559, 0.49740933])}\n",
      "Mean Accuracy : 0.8814775308628698\n",
      "Mean Standard Deviation : 0.007935384374056097\n",
      "Mean precision score : 0.7672738103792851\n",
      "Mean Standard Deviation precision score : 0.0467020597887927\n",
      "Mean recall score : 0.33394680731679943\n",
      "Mean Standard Deviation recall score : 0.04628159310377283\n",
      "Mean f1 score : 0.46382112866909875\n",
      "Mean Standard Deviation f1 score : 0.0496405947032337\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "# MODELLING ____Linear SVC\n",
    "\n",
    "model = LinearSVC()\n",
    "model.fit(x_baseTrain, y_baseTrain)\n",
    "\n",
    "y_basePred = model.predict(x_baseTest)\n",
    "\n",
    "# evaluating the model\n",
    "print(\"Training Accuracy :\", model.score(x_baseTrain, y_baseTrain))\n",
    "print(\"Testing Accuracy :\", model.score(x_baseTest, y_baseTest))\n",
    "print(\"ROC AUC Score :\", roc_auc_score(y_baseTest, y_basePred))\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_baseTest, y_basePred)\n",
    "print(cm)\n",
    "\n",
    "# classification report\n",
    "cr = classification_report(y_baseTest, y_basePred)\n",
    "print(cr)\n",
    "\n",
    "\n",
    "# cross validation\n",
    "scoring = {'accuracy' : make_scorer(accuracy_score), \n",
    "           'precision' : make_scorer(precision_score),\n",
    "           'recall' : make_scorer(recall_score), \n",
    "           'f1_score' : make_scorer(f1_score)}\n",
    "cvs = cross_validate(estimator = model, X = x_baseTrain, y = y_baseTrain, cv = 10,scoring=scoring)\n",
    "print(cvs)\n",
    "print(\"Mean Accuracy :\", np.mean(cvs['test_accuracy']))\n",
    "print(\"Mean Standard Deviation :\", np.std(cvs['test_accuracy']))\n",
    "print(\"Mean precision score :\", np.mean(cvs['test_precision']))\n",
    "print(\"Mean Standard Deviation precision score :\", np.std(cvs['test_precision']))\n",
    "print(\"Mean recall score :\", np.mean(cvs['test_recall']))\n",
    "print(\"Mean Standard Deviation recall score :\", np.std(cvs['test_recall']))\n",
    "print(\"Mean f1 score :\", np.mean(cvs['test_f1_score']))\n",
    "print(\"Mean Standard Deviation f1 score :\", np.std(cvs['test_f1_score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy : 0.8983895261267524\n",
      "Testing Accuracy : 0.8888888888888888\n",
      "ROC AUC Score : 0.750454823804487\n",
      "[[2972  152]\n",
      " [ 259  316]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.95      0.94      3124\n",
      "           1       0.68      0.55      0.61       575\n",
      "\n",
      "    accuracy                           0.89      3699\n",
      "   macro avg       0.80      0.75      0.77      3699\n",
      "weighted avg       0.88      0.89      0.88      3699\n",
      "\n",
      "{'fit_time': array([0.52459669, 0.49662209, 0.50365257, 0.4886694 , 0.49165893,\n",
      "       0.48473763, 0.49667144, 0.48766875, 0.49171066, 0.48966432]), 'score_time': array([0.05789495, 0.05186176, 0.05388212, 0.0538795 , 0.05285907,\n",
      "       0.05384898, 0.05385542, 0.05186176, 0.05385685, 0.05289793]), 'test_accuracy': array([0.90046296, 0.87384259, 0.88657407, 0.90382387, 0.91772885,\n",
      "       0.89455388, 0.8783314 , 0.88412514, 0.90023202, 0.88863109]), 'test_precision': array([0.73076923, 0.62626263, 0.66363636, 0.73148148, 0.76271186,\n",
      "       0.68421053, 0.6147541 , 0.62222222, 0.72380952, 0.66666667]), 'test_recall': array([0.56716418, 0.46268657, 0.54477612, 0.59398496, 0.67669173,\n",
      "       0.58646617, 0.56390977, 0.63157895, 0.57142857, 0.55639098]), 'test_f1_score': array([0.63865546, 0.53218884, 0.59836066, 0.65560166, 0.71713147,\n",
      "       0.63157895, 0.58823529, 0.62686567, 0.63865546, 0.60655738])}\n",
      "Mean Accuracy : 0.8928305890474414\n",
      "Mean Standard Deviation : 0.012490455386508803\n",
      "Mean precision score : 0.682652460393134\n",
      "Mean Standard Deviation precision score : 0.04986412406094516\n",
      "Mean recall score : 0.5755077993491191\n",
      "Mean Standard Deviation recall score : 0.053012208416138885\n",
      "Mean f1 score : 0.6233830845340833\n",
      "Mean Standard Deviation f1 score : 0.04564380825262331\n"
     ]
    }
   ],
   "source": [
    "# MODELLING ____Ada Boost Classifier\n",
    "\n",
    "model = AdaBoostClassifier()\n",
    "model.fit(x_baseTrain, y_baseTrain)\n",
    "\n",
    "y_basePred = model.predict(x_baseTest)\n",
    "\n",
    "# evaluating the model\n",
    "print(\"Training Accuracy :\", model.score(x_baseTrain, y_baseTrain))\n",
    "print(\"Testing Accuracy :\", model.score(x_baseTest, y_baseTest))\n",
    "print(\"ROC AUC Score :\", roc_auc_score(y_baseTest, y_basePred))\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_baseTest, y_basePred)\n",
    "print(cm)\n",
    "\n",
    "# classification report\n",
    "cr = classification_report(y_baseTest, y_basePred)\n",
    "print(cr)\n",
    "\n",
    "\n",
    "# cross validation\n",
    "scoring = {'accuracy' : make_scorer(accuracy_score), \n",
    "           'precision' : make_scorer(precision_score),\n",
    "           'recall' : make_scorer(recall_score), \n",
    "           'f1_score' : make_scorer(f1_score)}\n",
    "cvs = cross_validate(estimator = model, X = x_baseTrain, y = y_baseTrain, cv = 10,scoring=scoring)\n",
    "print(cvs)\n",
    "print(\"Mean Accuracy :\", np.mean(cvs['test_accuracy']))\n",
    "print(\"Mean Standard Deviation :\", np.std(cvs['test_accuracy']))\n",
    "print(\"Mean precision score :\", np.mean(cvs['test_precision']))\n",
    "print(\"Mean Standard Deviation precision score :\", np.std(cvs['test_precision']))\n",
    "print(\"Mean recall score :\", np.mean(cvs['test_recall']))\n",
    "print(\"Mean Standard Deviation recall score :\", np.std(cvs['test_recall']))\n",
    "print(\"Mean f1 score :\", np.mean(cvs['test_f1_score']))\n",
    "print(\"Mean Standard Deviation f1 score :\", np.std(cvs['test_f1_score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy : 0.9210983663538408\n",
      "Testing Accuracy : 0.8991619356582861\n",
      "ROC AUC Score : 0.7785317040583422\n",
      "[[2979  145]\n",
      " [ 228  347]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.95      0.94      3124\n",
      "           1       0.71      0.60      0.65       575\n",
      "\n",
      "    accuracy                           0.90      3699\n",
      "   macro avg       0.82      0.78      0.80      3699\n",
      "weighted avg       0.89      0.90      0.90      3699\n",
      "\n",
      "{'fit_time': array([0.82878375, 0.8188417 , 0.80584502, 0.82681513, 0.82878351,\n",
      "       0.82080436, 0.81182957, 0.81684232, 0.81285262, 0.81781292]), 'score_time': array([0.01396298, 0.01395726, 0.01296496, 0.01396251, 0.01396298,\n",
      "       0.01396298, 0.01396251, 0.01496005, 0.01596117, 0.01396298]), 'test_accuracy': array([0.91203704, 0.8900463 , 0.90972222, 0.92468134, 0.9212051 ,\n",
      "       0.91425261, 0.89918888, 0.91193511, 0.90603248, 0.90487239]), 'test_precision': array([0.75892857, 0.68224299, 0.75      , 0.80357143, 0.78761062,\n",
      "       0.76576577, 0.69827586, 0.7394958 , 0.76530612, 0.73394495]), 'test_recall': array([0.63432836, 0.54477612, 0.62686567, 0.67669173, 0.66917293,\n",
      "       0.63909774, 0.60902256, 0.66165414, 0.56390977, 0.60150376]), 'test_f1_score': array([0.69105691, 0.60580913, 0.68292683, 0.73469388, 0.72357724,\n",
      "       0.69672131, 0.65060241, 0.6984127 , 0.64935065, 0.66115702])}\n",
      "Mean Accuracy : 0.9093973463866553\n",
      "Mean Standard Deviation : 0.00959470926016993\n",
      "Mean precision score : 0.7485142112854711\n",
      "Mean Standard Deviation precision score : 0.035297930007899186\n",
      "Mean recall score : 0.6227022780832678\n",
      "Mean Standard Deviation recall score : 0.04144695080010724\n",
      "Mean f1 score : 0.6794308075462181\n",
      "Mean Standard Deviation f1 score : 0.036468549961964865\n"
     ]
    }
   ],
   "source": [
    "# MODELLING ____XGBOOST classifier\n",
    "\n",
    "model = XGBClassifier()\n",
    "model.fit(x_baseTrain, y_baseTrain)\n",
    "\n",
    "y_basePred = model.predict(x_baseTest)\n",
    "\n",
    "# evaluating the model\n",
    "print(\"Training Accuracy :\", model.score(x_baseTrain, y_baseTrain))\n",
    "print(\"Testing Accuracy :\", model.score(x_baseTest, y_baseTest))\n",
    "print(\"ROC AUC Score :\", roc_auc_score(y_baseTest, y_basePred))\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_baseTest, y_basePred)\n",
    "print(cm)\n",
    "\n",
    "# classification report\n",
    "cr = classification_report(y_baseTest, y_basePred)\n",
    "print(cr)\n",
    "\n",
    "\n",
    "# cross validation\n",
    "scoring = {'accuracy' : make_scorer(accuracy_score), \n",
    "           'precision' : make_scorer(precision_score),\n",
    "           'recall' : make_scorer(recall_score), \n",
    "           'f1_score' : make_scorer(f1_score)}\n",
    "cvs = cross_validate(estimator = model, X = x_baseTrain, y = y_baseTrain, cv = 10,scoring=scoring)\n",
    "print(cvs)\n",
    "print(\"Mean Accuracy :\", np.mean(cvs['test_accuracy']))\n",
    "print(\"Mean Standard Deviation :\", np.std(cvs['test_accuracy']))\n",
    "print(\"Mean precision score :\", np.mean(cvs['test_precision']))\n",
    "print(\"Mean Standard Deviation precision score :\", np.std(cvs['test_precision']))\n",
    "print(\"Mean recall score :\", np.mean(cvs['test_recall']))\n",
    "print(\"Mean Standard Deviation recall score :\", np.std(cvs['test_recall']))\n",
    "print(\"Mean f1 score :\", np.mean(cvs['test_f1_score']))\n",
    "print(\"Mean Standard Deviation f1 score :\", np.std(cvs['test_f1_score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy : 0.9230680106592515\n",
      "Testing Accuracy : 0.894566098945661\n",
      "ROC AUC Score : 0.7694252073707064\n",
      "[[2971  153]\n",
      " [ 237  338]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.95      0.94      3124\n",
      "           1       0.69      0.59      0.63       575\n",
      "\n",
      "    accuracy                           0.89      3699\n",
      "   macro avg       0.81      0.77      0.79      3699\n",
      "weighted avg       0.89      0.89      0.89      3699\n",
      "\n",
      "{'fit_time': array([1.16888905, 1.09604263, 1.135988  , 1.05318379, 1.23768973,\n",
      "       1.10102868, 1.10113382, 1.22969556, 1.09910202, 1.08110523]), 'score_time': array([0.0109551 , 0.00997305, 0.01097035, 0.01097035, 0.01097083,\n",
      "       0.01099777, 0.01196623, 0.0109601 , 0.01097012, 0.01097751]), 'test_accuracy': array([0.90856481, 0.88194444, 0.90625   , 0.9188876 , 0.91425261,\n",
      "       0.90845886, 0.89571263, 0.90961761, 0.90603248, 0.89791183]), 'test_precision': array([0.74774775, 0.64545455, 0.74311927, 0.79439252, 0.76576577,\n",
      "       0.73684211, 0.68376068, 0.72357724, 0.75      , 0.70642202]), 'test_recall': array([0.61940299, 0.52985075, 0.60447761, 0.63909774, 0.63909774,\n",
      "       0.63157895, 0.60150376, 0.66917293, 0.58646617, 0.57894737]), 'test_f1_score': array([0.67755102, 0.58196721, 0.66666667, 0.70833333, 0.69672131,\n",
      "       0.68016194, 0.64      , 0.6953125 , 0.65822785, 0.63636364])}\n",
      "Mean Accuracy : 0.9047632891142856\n",
      "Mean Standard Deviation : 0.009972935367105156\n",
      "Mean precision score : 0.7297081891532413\n",
      "Mean Standard Deviation precision score : 0.040301152584487014\n",
      "Mean recall score : 0.6099596004937717\n",
      "Mean Standard Deviation recall score : 0.03713965158332534\n",
      "Mean f1 score : 0.6641305472783068\n",
      "Mean Standard Deviation f1 score : 0.0354553875077925\n"
     ]
    }
   ],
   "source": [
    "# MODELLING ____gradient boosting Classifier\n",
    "\n",
    "model = GradientBoostingClassifier()\n",
    "model.fit(x_baseTrain, y_baseTrain)\n",
    "\n",
    "y_basePred = model.predict(x_baseTest)\n",
    "\n",
    "# evaluating the model\n",
    "print(\"Training Accuracy :\", model.score(x_baseTrain, y_baseTrain))\n",
    "print(\"Testing Accuracy :\", model.score(x_baseTest, y_baseTest))\n",
    "print(\"ROC AUC Score :\", roc_auc_score(y_baseTest, y_basePred))\n",
    "\n",
    "# confusion matrix\n",
    "cm = confusion_matrix(y_baseTest, y_basePred)\n",
    "print(cm)\n",
    "\n",
    "# classification report\n",
    "cr = classification_report(y_baseTest, y_basePred)\n",
    "print(cr)\n",
    "\n",
    "\n",
    "# cross validation\n",
    "scoring = {'accuracy' : make_scorer(accuracy_score), \n",
    "           'precision' : make_scorer(precision_score),\n",
    "           'recall' : make_scorer(recall_score), \n",
    "           'f1_score' : make_scorer(f1_score)}\n",
    "cvs = cross_validate(estimator = model, X = x_baseTrain, y = y_baseTrain, cv = 10,scoring=scoring)\n",
    "print(cvs)\n",
    "print(\"Mean Accuracy :\", np.mean(cvs['test_accuracy']))\n",
    "print(\"Mean Standard Deviation :\", np.std(cvs['test_accuracy']))\n",
    "print(\"Mean precision score :\", np.mean(cvs['test_precision']))\n",
    "print(\"Mean Standard Deviation precision score :\", np.std(cvs['test_precision']))\n",
    "print(\"Mean recall score :\", np.mean(cvs['test_recall']))\n",
    "print(\"Mean Standard Deviation recall score :\", np.std(cvs['test_recall']))\n",
    "print(\"Mean f1 score :\", np.mean(cvs['test_f1_score']))\n",
    "print(\"Mean Standard Deviation f1 score :\", np.std(cvs['test_f1_score']))"
   ]
  }
 ],
 "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}