Prelim3_ProperEncoding+ScalingNumVars_Correlationcheck&drop.ipynb

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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",
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       "      <th>Browser</th>\n",
       "      <th>Region</th>\n",
       "      <th>TrafficType</th>\n",
       "      <th>VisitorType</th>\n",
       "      <th>Weekend</th>\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  "
      ]
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   ],
   "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",
    "pd.set_option('display.max_columns', 60)\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",
    "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/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21d7fb53e08>"
      ]
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     "execution_count": 7,
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     "output_type": "execute_result"
    },
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#In all feature selection procedures, it is good practice to select the features by examining only the training set. \n",
    "#And this is to avoid overfit.\n",
    "\n",
    "f, ax = plt.subplots(figsize=(15,15))\n",
    "corr=x_baseTrain.corr()\n",
    "sns.heatmap(corr,mask=np.zeros_like(corr,dtype=np.bool),\n",
    "            cmap=sns.diverging_palette(220,10,as_cmap=True),square=True,ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Brute force approach below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with the following function we can select highly correlated features\n",
    "# it will remove the first feature that is correlated with anything else\n",
    "# without any other insight.\n",
    "\n",
    "def correlation(dataset, threshold):\n",
    "    col_corr = set()  # Set of all the names of correlated columns\n",
    "    corr_matrix = dataset.corr()\n",
    "    for i in range(len(corr_matrix.columns)):\n",
    "        for j in range(i):\n",
    "            if abs(corr_matrix.iloc[i, j]) > threshold: # we are interested in absolute coeff value\n",
    "                colname = corr_matrix.columns[i]  # getting the name of column\n",
    "                col_corr.add(colname)\n",
    "    return col_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Browser_1',\n",
       " 'Browser_11',\n",
       " 'ExitRates',\n",
       " 'ProductRelated_Duration',\n",
       " 'VisitorType_Returning_Visitor',\n",
       " 'Weekend_True'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_features = correlation(x_baseTrain, 0.8)\n",
    "#len(set(corr_features))\n",
    "corr_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that 6 features are highly correlated with other features in the training set. Very likely, by removing these correlated features, the performance of your machine learning models will drop very little, if at all. We can go ahead and drop the features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second approach looks to identify groups of highly correlated features. And then, we can make further investigation within these groups to decide which feature we keep and which one we remove."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>feature1</th>\n",
       "      <th>feature2</th>\n",
       "      <th>corr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>VisitorType_Returning_Visitor</td>\n",
       "      <td>VisitorType_New_Visitor</td>\n",
       "      <td>0.971133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>VisitorType_New_Visitor</td>\n",
       "      <td>VisitorType_Returning_Visitor</td>\n",
       "      <td>0.971133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ExitRates</td>\n",
       "      <td>BounceRates</td>\n",
       "      <td>0.912278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>BounceRates</td>\n",
       "      <td>ExitRates</td>\n",
       "      <td>0.912278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ProductRelated</td>\n",
       "      <td>ProductRelated_Duration</td>\n",
       "      <td>0.876104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>ProductRelated_Duration</td>\n",
       "      <td>ProductRelated</td>\n",
       "      <td>0.876104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Browser_1</td>\n",
       "      <td>OperatingSystems_1</td>\n",
       "      <td>0.824153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>OperatingSystems_1</td>\n",
       "      <td>Browser_1</td>\n",
       "      <td>0.824153</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        feature1                       feature2      corr\n",
       "0  VisitorType_Returning_Visitor        VisitorType_New_Visitor  0.971133\n",
       "1        VisitorType_New_Visitor  VisitorType_Returning_Visitor  0.971133\n",
       "2                      ExitRates                    BounceRates  0.912278\n",
       "3                    BounceRates                      ExitRates  0.912278\n",
       "4                 ProductRelated        ProductRelated_Duration  0.876104\n",
       "5        ProductRelated_Duration                 ProductRelated  0.876104\n",
       "6                      Browser_1             OperatingSystems_1  0.824153\n",
       "7             OperatingSystems_1                      Browser_1  0.824153"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrmat = x_baseTrain.corr()\n",
    "corrmat = corrmat.abs().unstack() # absolute value of corr coef\n",
    "corrmat = corrmat.sort_values(ascending=False)\n",
    "corrmat = corrmat[corrmat >= 0.8]\n",
    "corrmat = corrmat[corrmat < 1]\n",
    "corrmat = pd.DataFrame(corrmat).reset_index()\n",
    "corrmat.columns = ['feature1', 'feature2', 'corr']\n",
    "corrmat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the 2 approaches I take a decision here to drop Browser_1,ExitRates,ProductRelated_Duration and VisitorType_Returning_Visitor. Lets check correlation again after dropping these columns again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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/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>feature1</th>\n",
       "      <th>feature2</th>\n",
       "      <th>corr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [feature1, feature2, corr]\n",
       "Index: []"
      ]
     },
     "execution_count": 11,
     "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",
    "#previously I had deleted ProductRelated_Duration and Exit Rates but found out both had more info gain(check Prelim 4)\n",
    "\n",
    "x_baseTrain.shape, x_baseTest.shape\n",
    "\n",
    "\n",
    "##Checking correlation once again\n",
    "corrmat = x_baseTrain.corr()\n",
    "corrmat = corrmat.abs().unstack() # absolute value of corr coef\n",
    "corrmat = corrmat.sort_values(ascending=False)\n",
    "corrmat = corrmat[corrmat >= 0.8]\n",
    "corrmat = corrmat[corrmat < 1]\n",
    "corrmat = pd.DataFrame(corrmat).reset_index()\n",
    "corrmat.columns = ['feature1', 'feature2', 'corr']\n",
    "corrmat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hey Hey Hey !!!!!!!!!!what about PCA further"
   ]
  },
  {
   "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": []
  },
  {
   "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": [
    "##ignore below please\n",
    "\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "\n",
    "#feature selection being done on non scaled data\n",
    "#apply SelectKBest class to extract top best features\n",
    "bestfeatures = SelectKBest(score_func=chi2, k='all')\n",
    "bf = bestfeatures.fit(x,y)\n",
    "dfscores = pd.DataFrame(bf.scores_)\n",
    "dfcolumns = pd.DataFrame(x.columns)\n",
    "\n",
    "featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n",
    "featureScores.columns = ['Specs','Score'] \n",
    "#print(featureScores)\n",
    "print(featureScores.nlargest(24,'Score'))\n",
    "\n",
    "\n",
    "#x_scaled=x_baseTest.append(x_baseTrain, ignore_index = True)\n",
    "#y_scaled=y_baseTest.append(y_baseTrain, ignore_index = True)\n",
    "#feature selection being done on scaled data\n",
    "#apply SelectKBest class to extract top best features\n",
    "#bestfeatures = SelectKBest(score_func=chi2, k='all')\n",
    "#bf = bestfeatures.fit(x_scaled,y_scaled)\n",
    "#dfscores = pd.DataFrame(bf.scores_)\n",
    "#dfcolumns = pd.DataFrame(x_scaled.columns)\n",
    "#learning chi2 doesn't work with -ve values(by Standard scaler), use minmax scaler if forced"
   ]
  }
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