Prelim6_Encoding+Scaling_Ft.select_Univariate+ROC_AUC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import roc_auc_score, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <td>Returning_Visitor</td>\n",
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       "      <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",
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      "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "pd.set_option('display.max_columns', 60)\n",
    "myData=pd.read_csv('imputedData.csv')\n",
    "myData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
    "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": 28,
   "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": 28,
     "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": 29,
   "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": 30,
   "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": 31,
   "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": "markdown",
   "metadata": {},
   "source": [
    "In all feature selection procedures, it is good practice to select the features by examining only the training set. And this is to avoid overfit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loop to build a tree, make predictions and get the roc-auc\n",
    "# for each feature of the train set\n",
    "\n",
    "roc_values = []\n",
    "for feature in x_baseTrain.columns:\n",
    "    clf = DecisionTreeClassifier()\n",
    "    clf.fit(x_baseTrain[feature].to_frame(), y_baseTrain)\n",
    "    y_scored = clf.predict_proba(x_baseTest[feature].to_frame())\n",
    "    roc_values.append(roc_auc_score(y_baseTest, y_scored[:, 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TrafficType                      0.642461\n",
       "Administrative                   0.630249\n",
       "ProductRelated                   0.620175\n",
       "Month_Nov                        0.592558\n",
       "PageValues                       0.584450\n",
       "ExitRates                        0.566526\n",
       "Informational                    0.561337\n",
       "VisitorType_Returning_Visitor    0.552021\n",
       "BounceRates                      0.550649\n",
       "Month_May                        0.550608\n",
       "VisitorType_New_Visitor          0.549685\n",
       "OperatingSystems_2               0.546457\n",
       "SpecialDay                       0.532660\n",
       "OperatingSystems_3               0.532516\n",
       "Weekend_False                    0.524512\n",
       "Weekend_True                     0.524512\n",
       "Month_Mar                        0.520394\n",
       "Month_Dec                        0.517950\n",
       "Month_Feb                        0.508344\n",
       "Month_Oct                        0.507958\n",
       "Browser_1                        0.507698\n",
       "Region_1                         0.507368\n",
       "Browser_4                        0.507094\n",
       "Browser_2                        0.507054\n",
       "Browser_5                        0.506833\n",
       "Region_3                         0.506327\n",
       "Browser_3                        0.504572\n",
       "Browser_10                       0.504353\n",
       "Browser_13                       0.504326\n",
       "OperatingSystems_4               0.504272\n",
       "Region_4                         0.503780\n",
       "Month_June                       0.503698\n",
       "Region_2                         0.502713\n",
       "VisitorType_Other                0.502336\n",
       "Browser_7                        0.501462\n",
       "Month_Aug                        0.501455\n",
       "Browser_6                        0.501185\n",
       "Browser_12                       0.500870\n",
       "Month_Jul                        0.500833\n",
       "Region_7                         0.500233\n",
       "Region_8                         0.500011\n",
       "Browser_9                        0.500000\n",
       "Month_Sep                        0.499855\n",
       "Browser_11                       0.499680\n",
       "OperatingSystems_5               0.499680\n",
       "OperatingSystems_7               0.499611\n",
       "Browser_8                        0.498815\n",
       "OperatingSystems_6               0.498239\n",
       "Region_5                         0.494452\n",
       "OperatingSystems_8               0.494415\n",
       "Informational_Duration           0.492929\n",
       "Region_9                         0.492570\n",
       "Region_6                         0.486827\n",
       "ProductRelated_Duration          0.481084\n",
       "OperatingSystems_1               0.477892\n",
       "Administrative_Duration          0.472960\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's add the variable names and order it for clearer visualisation\n",
    "roc_values = pd.Series(roc_values)\n",
    "roc_values.index = x_baseTrain.columns\n",
    "roc_values.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23ce0726b48>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# and now let's plot\n",
    "roc_values.sort_values(ascending=False).plot.bar(figsize=(20, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41\n",
      "features which should be retained\n",
      "\n",
      " Administrative                   0.630249\n",
      "Informational                    0.561337\n",
      "ProductRelated                   0.620175\n",
      "BounceRates                      0.550649\n",
      "ExitRates                        0.566526\n",
      "PageValues                       0.584450\n",
      "TrafficType                      0.642461\n",
      "SpecialDay                       0.532660\n",
      "Month_Aug                        0.501455\n",
      "Month_Dec                        0.517950\n",
      "Month_Feb                        0.508344\n",
      "Month_Jul                        0.500833\n",
      "Month_June                       0.503698\n",
      "Month_Mar                        0.520394\n",
      "Month_May                        0.550608\n",
      "Month_Nov                        0.592558\n",
      "Month_Oct                        0.507958\n",
      "OperatingSystems_2               0.546457\n",
      "OperatingSystems_3               0.532516\n",
      "OperatingSystems_4               0.504272\n",
      "Browser_1                        0.507698\n",
      "Browser_2                        0.507054\n",
      "Browser_3                        0.504572\n",
      "Browser_4                        0.507094\n",
      "Browser_5                        0.506833\n",
      "Browser_6                        0.501185\n",
      "Browser_7                        0.501462\n",
      "Browser_10                       0.504353\n",
      "Browser_12                       0.500870\n",
      "Browser_13                       0.504326\n",
      "Region_1                         0.507368\n",
      "Region_2                         0.502713\n",
      "Region_3                         0.506327\n",
      "Region_4                         0.503780\n",
      "Region_7                         0.500233\n",
      "Region_8                         0.500011\n",
      "VisitorType_New_Visitor          0.549685\n",
      "VisitorType_Other                0.502336\n",
      "VisitorType_Returning_Visitor    0.552021\n",
      "Weekend_False                    0.524512\n",
      "Weekend_True                     0.524512\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# a roc auc value of 0.5 indicates random decision\n",
    "# let's check how many features show a roc-auc value\n",
    "# higher than random\n",
    "\n",
    "print(len(roc_values[roc_values > 0.5]))\n",
    "print(\"features which should be retained\\n\\n\",roc_values[roc_values > 0.5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "41 of 56 features show a predictive performance higher than 0.5. This means that we could remove 15 features from this dataset using this method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features which can be deleted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features which can be deleted\n",
      "\n",
      " Administrative_Duration    0.472960\n",
      "Informational_Duration     0.492929\n",
      "ProductRelated_Duration    0.481084\n",
      "Month_Sep                  0.499855\n",
      "OperatingSystems_1         0.477892\n",
      "OperatingSystems_5         0.499680\n",
      "OperatingSystems_6         0.498239\n",
      "OperatingSystems_7         0.499611\n",
      "OperatingSystems_8         0.494415\n",
      "Browser_8                  0.498815\n",
      "Browser_9                  0.500000\n",
      "Browser_11                 0.499680\n",
      "Region_5                   0.494452\n",
      "Region_6                   0.486827\n",
      "Region_9                   0.492570\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"features which can be deleted\\n\\n\",roc_values[roc_values <= 0.5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying this feature selection on corr fixed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\neera\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4102: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  errors=errors,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((8631, 52), (3699, 52))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_baseTrain.drop(labels=['Browser_1','BounceRates','ProductRelated','VisitorType_Returning_Visitor'], \n",
    "                 axis=1, inplace=True)\n",
    "x_baseTest.drop(labels=['Browser_1','BounceRates','ProductRelated','VisitorType_Returning_Visitor'], \n",
    "                axis=1, inplace=True)\n",
    "\n",
    "x_baseTrain.shape, x_baseTest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23ce056d9c8>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# loop to build a tree, make predictions and get the roc-auc\n",
    "# for each feature of the train set\n",
    "\n",
    "roc_values = []\n",
    "for feature in x_baseTrain.columns:\n",
    "    clf = DecisionTreeClassifier()\n",
    "    clf.fit(x_baseTrain[feature].to_frame(), y_baseTrain)\n",
    "    y_scored = clf.predict_proba(x_baseTest[feature].to_frame())\n",
    "    roc_values.append(roc_auc_score(y_baseTest, y_scored[:, 1]))\n",
    "    \n",
    "# let's add the variable names and order it for clearer visualisation\n",
    "roc_values = pd.Series(roc_values)\n",
    "roc_values.index = x_baseTrain.columns\n",
    "roc_values.sort_values(ascending=False)\n",
    "\n",
    "# and now let's plot\n",
    "roc_values.sort_values(ascending=False).plot.bar(figsize=(20, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37\n",
      "features which should be retained\n",
      "\n",
      " Administrative             0.630249\n",
      "Informational              0.561337\n",
      "ExitRates                  0.566526\n",
      "PageValues                 0.584450\n",
      "TrafficType                0.642461\n",
      "SpecialDay                 0.532660\n",
      "Month_Aug                  0.501455\n",
      "Month_Dec                  0.517950\n",
      "Month_Feb                  0.508344\n",
      "Month_Jul                  0.500833\n",
      "Month_June                 0.503698\n",
      "Month_Mar                  0.520394\n",
      "Month_May                  0.550608\n",
      "Month_Nov                  0.592558\n",
      "Month_Oct                  0.507958\n",
      "OperatingSystems_2         0.546457\n",
      "OperatingSystems_3         0.532516\n",
      "OperatingSystems_4         0.504272\n",
      "Browser_2                  0.507054\n",
      "Browser_3                  0.504572\n",
      "Browser_4                  0.507094\n",
      "Browser_5                  0.506833\n",
      "Browser_6                  0.501185\n",
      "Browser_7                  0.501462\n",
      "Browser_10                 0.504353\n",
      "Browser_12                 0.500870\n",
      "Browser_13                 0.504326\n",
      "Region_1                   0.507368\n",
      "Region_2                   0.502713\n",
      "Region_3                   0.506327\n",
      "Region_4                   0.503780\n",
      "Region_7                   0.500233\n",
      "Region_8                   0.500011\n",
      "VisitorType_New_Visitor    0.549685\n",
      "VisitorType_Other          0.502336\n",
      "Weekend_False              0.524512\n",
      "Weekend_True               0.524512\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# a roc auc value of 0.5 indicates random decision\n",
    "# let's check how many features show a roc-auc value\n",
    "# higher than random\n",
    "\n",
    "print(len(roc_values[roc_values > 0.5]))\n",
    "print(\"features which should be retained\\n\\n\",roc_values[roc_values > 0.5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "37 of 52 features show a predictive performance higher than 0.5. This means that we could remove 15 features from this dataset using this method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features which can be deleted in corr fixed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features which can be deleted\n",
      "\n",
      " Administrative_Duration    0.472960\n",
      "Informational_Duration     0.492929\n",
      "ProductRelated_Duration    0.481084\n",
      "Month_Sep                  0.499855\n",
      "OperatingSystems_1         0.477892\n",
      "OperatingSystems_5         0.499680\n",
      "OperatingSystems_6         0.498239\n",
      "OperatingSystems_7         0.499611\n",
      "OperatingSystems_8         0.494415\n",
      "Browser_8                  0.498815\n",
      "Browser_9                  0.500000\n",
      "Browser_11                 0.499680\n",
      "Region_5                   0.494452\n",
      "Region_6                   0.486827\n",
      "Region_9                   0.492570\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"features which can be deleted\\n\\n\",roc_values[roc_values <= 0.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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