2.Feature_Selection.ipynb

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Feature Selection"],"metadata":{"id":"OT06BCeO7qSW"}},{"cell_type":"markdown","source":["## 2.1 Import"],"metadata":{"id":"3OGn53Z7If9l"}},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","import math\n","import time\n","\n","from matplotlib.ticker import MultipleLocator, LinearLocator, AutoMinorLocator\n","from sklearn.linear_model import LinearRegression, Lasso, Ridge, ElasticNet\n","from sklearn.model_selection import KFold, RepeatedKFold, GridSearchCV, train_test_split, cross_val_score\n","from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error, mean_absolute_percentage_error\n","from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler\n","from sklearn.feature_selection import RFE\n","from sklearn.decomposition import PCA\n","from sklearn.ensemble import RandomForestRegressor"],"metadata":{"id":"gTPlGSRjhdn7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["data = pd.read_csv(\"/data/data.csv\")\n","data_dropped_outlier = pd.read_csv(\"/data/data_dropped_outlier.csv\")\n","data_tf_outlier = pd.read_csv(\"/data/data_tf_outlier.csv\")\n","data_enc = pd.read_csv(\"/data/data_enc.csv\")\n","data_drop_outlier_enc = pd.read_csv(\"/data/data_drop_outlier_enc.csv\")\n","data_tf_outlier_enc = pd.read_csv(\"/data/data_tf_outlier_enc.csv\")"],"metadata":{"id":"6p1sOrFxIfVR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":488},"id":"BWr1ZiZiUMHZ","executionInfo":{"status":"ok","timestamp":1658802594221,"user_tz":-420,"elapsed":67,"user":{"displayName":"Muhammad Rizqiansyah","userId":"00610466379705292754"}},"outputId":"707eadcb-a082-44f9-d2ef-ec883570db6b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Store  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price         CPI  \\\n","0         1    1643690.90             0        42.31       2.572  211.096358   \n","1         1    1641957.44             1        38.51       2.548  211.242170   \n","2         1    1611968.17             0        39.93       2.514  211.289143   \n","3         1    1409727.59             0        46.63       2.561  211.319643   \n","4         1    1554806.68             0        46.50       2.625  211.350143   \n","...     ...           ...           ...          ...         ...         ...   \n","6430     45     713173.95             0        64.88       3.997  192.013558   \n","6431     45     733455.07             0        64.89       3.985  192.170412   \n","6432     45     734464.36             0        54.47       4.000  192.327265   \n","6433     45     718125.53             0        56.47       3.969  192.330854   \n","6434     45     760281.43             0        58.85       3.882  192.308899   \n","\n","      Unemployment  Day  Month  Year  ...  Super_Bowl  Labor_Day  \\\n","0            8.106    5      2  2010  ...           0          0   \n","1            8.106   12      2  2010  ...           1          0   \n","2            8.106   19      2  2010  ...           0          0   \n","3            8.106   26      2  2010  ...           0          0   \n","4            8.106    5      3  2010  ...           0          0   \n","...            ...  ...    ...   ...  ...         ...        ...   \n","6430         8.684   28      9  2012  ...           0          0   \n","6431         8.667    5     10  2012  ...           0          0   \n","6432         8.667   12     10  2012  ...           0          0   \n","6433         8.667   19     10  2012  ...           0          0   \n","6434         8.667   26     10  2012  ...           0          0   \n","\n","      Thanksgiving  Christmas  Before_Super_Bowl  Before_Labor_Day  \\\n","0                0          0                  1                 0   \n","1                0       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    0                   3          1  \n","3                       0                 0                   3          1  \n","4                       0                 0                   3          1  \n","...                   ...               ...                 ...        ...  \n","6430                    0                 0                   3          1  \n","6431                    0                 0                   3          1  \n","6432                    0                 0                   3          1  \n","6433                    0                 0                   3          1  \n","6434                    0                 0                   3          1  \n","\n","[6435 rows x 21 columns]"],"text/html":["\n","  <div id=\"df-1b9ed0d2-db30-4eff-9e6a-dd698f9a91c3\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr 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<td>1643690.90</td>\n","      <td>0</td>\n","      <td>42.31</td>\n","      <td>2.572</td>\n","      <td>211.096358</td>\n","      <td>8.106</td>\n","      <td>5</td>\n","      <td>2</td>\n","      <td>2010</td>\n","      <td>...</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>3</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>1641957.44</td>\n","      <td>1</td>\n","      <td>38.51</td>\n","      <td>2.548</td>\n","      <td>211.242170</td>\n","      <td>8.106</td>\n","      <td>12</td>\n","      <td>2</td>\n","      <td>2010</td>\n","      <td>...</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>3</td>\n","      <td>1</td>\n","    </tr>\n","    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These features are too much to process and not all of them are relevant so we need to do a feature selection first."],"metadata":{"id":"eXuBX4WI8YoV"}},{"cell_type":"markdown","source":["One way to look at *feature improtance* is to use the coefficients generated by the regressor (source: [feature importance for linear models -AnalyticsVidhya](https://medium.com/analytics-vidhya/feature-importance-explained-bfc8d874bcf#:~:text=Source-,What%20is%20Feature%20importance%20%3F,both%20classification%20and%20regression%20problem)). Therefore, we will use [RFE (Recursive Feature Elimination)](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html), one of the most popular *feature selection* methods. By using *estimator* which can produce coefficient values (which in this case we use coefficients from the model), RFE will eliminate features recursively based on *feature importance* of each feature."],"metadata":{"id":"XJgaiI2C8fSB"}},{"cell_type":"code","source":["# Set features and target values to seperated variables\n","\n","# Data with outliers\n","y = data_enc[target]\n","X = data_enc[features] \n","\n","# Data with outliers dropped\n","y_2 = data_drop_outlier_enc[target]\n","X_2 = data_drop_outlier_enc[features]\n","\n","# Data with outliers floored and capped\n","y_3 = data_tf_outlier_enc[target]\n","X_3 = data_tf_outlier_enc[features]"],"metadata":{"id":"5wCs-PEK7sm0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Split train and test dataset\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state = 42)\n","\n","X_train_2, X_test_2, y_train_2, y_test_2 = train_test_split(X_2, y_2, test_size = 0.4, random_state = 42)\n","\n","X_train_3, X_test_3, y_train_3, y_test_3 = train_test_split(X_3, y_3, test_size = 0.4, random_state = 42)"],"metadata":{"id":"BOPmTLKQ7skL"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Dataset with outliers\n","print(\"Original dataset :\", X.shape, y.shape)\n","print(\"Training dataset :\", X_train.shape, y_train.shape)\n","print(\"Testing dataset  :\", X_test.shape,\"\", y_test.shape)\n","print(\"\")\n","\n","#Dataset with outliers removed\n","print(\"Original dataset 2 :\", X_2.shape, y_2.shape)\n","print(\"Training dataset 2 :\", X_train_2.shape, y_train_2.shape)\n","print(\"Testing dataset  2 :\", X_test_2.shape,\"\", y_test_2.shape)\n","print(\"\")\n","\n","#Dataset with outliers floored and capped\n","print(\"Original dataset 3 :\", X_3.shape, y_3.shape)\n","print(\"Training dataset 3 :\", X_train_3.shape, y_train_3.shape)\n","print(\"Testing dataset  3 :\", X_test_3.shape,\"\", y_test_3.shape)"],"metadata":{"id":"3a_0niP77shJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1658802594226,"user_tz":-420,"elapsed":21,"user":{"displayName":"Muhammad Rizqiansyah","userId":"00610466379705292754"}},"outputId":"d693d7b0-4d6d-4380-babc-64e260dd3269"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Original dataset : (6435, 158) (6435,)\n","Training dataset : (3861, 158) (3861,)\n","Testing dataset  : (2574, 158)  (2574,)\n","\n","Original dataset 2 : (6057, 158) (6057,)\n","Training dataset 2 : (3634, 158) (3634,)\n","Testing dataset  2 : (2423, 158)  (2423,)\n","\n","Original dataset 3 : (6435, 158) (6435,)\n","Training dataset 3 : (3861, 158) (3861,)\n","Testing dataset  3 : (2574, 158)  (2574,)\n"]}]},{"cell_type":"markdown","source":["## 2.3 Feature Ranking using RFE (Recursive Feature Elimination)"],"metadata":{"id":"7dghPufL9z1N"}},{"cell_type":"markdown","source":["To use RFE, we must fill in the parameter n_features_to_select with the number of features we want to select. The problem is, we don't know how many features produce the best *performance*. Therefore, we will loop with all possible number of features, then we will choose the number of features that have the best *performance*."],"metadata":{"id":"5OJtfgo499tR"}},{"cell_type":"code","source":["# Feature Selection with RFE\n","\n","rfe_train = []; rfe_test = []\n","# Looping through all the features\n","for i in range (len(features)):\n","    # Perform RFE\n","    rfe           = RFE(estimator = LinearRegression(), n_features_to_select = X_train.shape[1]-i)\n","    rfe           = rfe.fit(X_train, y_train)\n","    # Perform regression (Linear Regression here since it's the simplest one) with the selected features\n","    regressor     = LinearRegression()\n","    regressor.fit(X_train.loc[:,rfe.support_], y_train)\n","    # Predict the target values for the both training and testing sets\n","    y_pred_train  = regressor.predict(X_train.loc[:,rfe.support_])\n","    y_pred_test   = regressor.predict(X_test.loc[:,rfe.support_])\n","    # Calculate the r2 score\n","    r2_train      = r2_score(y_train, y_pred_train)\n","    r2_test       = r2_score(y_test, y_pred_test)\n","    # Store the evaluation results\n","    rfe_train.append(r2_train)\n","    rfe_test.append(r2_test)\n","\n","fig, ax = plt.subplots(figsize=(15, 8))\n","plt.plot(rfe_test, color = palette[0], label = \"Test Dataset\", marker = 'o', markevery = 5)\n","plt.plot(rfe_train, color = palette[5], label = \"Train Dataset\", marker = 'o', markevery = 5)\n","\n","# Change major ticks to show every 20.\n","ax.xaxis.set_major_locator(MultipleLocator(20))\n","# Change minor ticks to show every 5. (20/4 = 5)\n","ax.xaxis.set_minor_locator(AutoMinorLocator(4))\n","ax.yaxis.set_minor_locator(AutoMinorLocator(4))\n","# Turn grid on for both major and minor ticks.\n","ax.grid(which = \"major\", linestyle = \"--\")\n","ax.grid(which = \"minor\", linestyle = \":\")\n","plt.xlabel(\"Number of Dropped Features\")\n","plt.ylabel(\"R2 Score\")\n","plt.title (\"R2 Score for Recursive Feature Elimination (RFE)\", fontsize = 20)\n","plt.legend()\n","plt.show()"],"metadata":{"id":"1XVzqm4D7ser","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1658803008627,"user_tz":-420,"elapsed":414419,"user":{"displayName":"Muhammad Rizqiansyah","userId":"00610466379705292754"}},"outputId":"1c9bd00a-28e5-44ab-c9a0-f8a5610ad1b2"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["Based on the RFE graph above, the R2 Score starts to drop drastically when the number of features dropped is >75, which means there are around 76 (151-75) features that have a higher *importance* than other features."],"metadata":{"id":"3Wxis-hD-CVk"}},{"cell_type":"markdown","source":["## 2.4 Feature Decomposition using PCA (Principal Component Analysis)"],"metadata":{"id":"babZNc4L-KRI"}},{"cell_type":"markdown","source":["Unlike RFE which will eliminate the feature with the lowest coefficient value, PCA will combine n-component features according to the *variance* value of each feature, so that each feature still has a role in the modeling according to its respective contribution."],"metadata":{"id":"VDA724rI-Quv"}},{"cell_type":"code","source":["# Feature Selection with PCA\n","\n","pca_train = []; pca_test = []\n","# Looping through all the features\n","for i in range (len(features)):\n","    # Perform PCA\n","    pca           = PCA(n_components = X_train.shape[1]-i)\n","    X_train_pca   = pca.fit_transform(X_train)\n","    X_test_pca    = pca.fit_transform(X_test)\n","    # Predict the target values for the both training and testing sets\n","    regressor     = LinearRegression()\n","    regressor.fit(X_train_pca, y_train)\n","    y_pred_train  = regressor.predict(X_train_pca)\n","    y_pred_test   = regressor.predict(X_test_pca)\n","    # Calculate the r2 score\n","    r2_train      = r2_score(y_train, y_pred_train)\n","    r2_test       = r2_score(y_test, y_pred_test)\n","    # Store the evaluation results\n","    pca_train.append(r2_train)\n","    pca_test.append(r2_test)\n","\n","fig, ax = plt.subplots(figsize=(15, 8))\n","plt.plot(pca_test, color = palette[0], label = \"Test Dataset\", marker = 'o', markevery = 5)\n","plt.plot(pca_train, color = palette[5], label = \"Train Dataset\", marker = 'o', markevery = 5)\n","\n","# Change major ticks to show every 20.\n","ax.xaxis.set_major_locator(MultipleLocator(20))\n","# Change minor ticks to show every 5. (20/4 = 5)\n","ax.xaxis.set_minor_locator(AutoMinorLocator(4))\n","ax.yaxis.set_minor_locator(AutoMinorLocator(4))\n","# Turn grid on for both major and minor ticks.\n","ax.grid(which = \"major\", linestyle = \"--\")\n","ax.grid(which = \"minor\", linestyle = \":\")\n","plt.xlabel(\"Number of Dropped N-Components\")\n","plt.ylabel(\"R2 Score\")\n","plt.title (\"R2 Score for Principal Component Analysis (PCA)\", fontsize = 20)\n","plt.legend()\n","plt.show()"],"metadata":{"id":"Thpg1oKB7sby","colab":{"base_uri":"https://localhost:8080/","height":518},"executionInfo":{"status":"ok","timestamp":1658803046300,"user_tz":-420,"elapsed":37715,"user":{"displayName":"Muhammad Rizqiansyah","userId":"00610466379705292754"}},"outputId":"7faa6f90-b514-497f-dead-f1aee5f54bb1"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["It turns out that PCA has a *performance* that tends to be unstable compared to RFE because the R2 Score on the testing dataset fluctuates drastically and irregularly along with the increasing number of N-Components that are dropped so it seems safer to use RFE."],"metadata":{"id":"9P29HIJv-nNk"}},{"cell_type":"code","source":["rfe_final   = RFE(estimator = LinearRegression(), n_features_to_select = X_train.shape[1]-75) # Train with choosen number of features\n","rfe_final   = rfe_final.fit(X_train, y_train)\n","rfe_result  = pd.DataFrame(data = rfe_final.feature_names_in_, columns = [\"Features\"]) # Feature names\n","rfe_result[\"Selected\"] = rfe_final.support_ # RFE selected value of each feature (True/False)\n","rfe_result[\"Ranking\"]  = rfe_final.ranking_ # RFE ranking of each feature\n","\n","pd.set_option(\"display.max_rows\", 500) # Show all rows\n","rfe_result.sort_values(by = \"Ranking\", ignore_index = True) # Sort feature order by ranking"],"metadata":{"id":"sMLzn9jj7sZb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1658803048143,"user_tz":-420,"elapsed":1847,"user":{"displayName":"Muhammad Rizqiansyah","userId":"00610466379705292754"}},"outputId":"b6bc6ec8-c029-4cd3-b46c-40dd4cda324e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                Features  Selected  Ranking\n","0           Holiday_Flag      True        1\n","1               Month_12      True        1\n","2               Month_11      True        1\n","3               Month_10      True        1\n","4                Month_9      True        1\n","5                Month_8      True        1\n","6                Month_7      True        1\n","7                Month_6      True        1\n","8                Month_5      True        1\n","9                Month_4      True        1\n","10          WeekOfYear_5      True        1\n","11               Month_3      True        1\n","12               Month_1      True        1\n","13         WeekOfYear_51      True        1\n","14              Store_45      True        1\n","15              Store_44      True        1\n","16              Store_43      True        1\n","17              Store_42      True        1\n","18              Store_41      True        1\n","19              Store_39      True        1\n","20              Store_38      True        1\n","21               Month_2      True        1\n","22              Store_37      True        1\n","23          WeekOfYear_6      True        1\n","24          WeekOfYear_8      True        1\n","25         WeekOfYear_50      True        1\n","26         WeekOfYear_49      True        1\n","27         WeekOfYear_48      True        1\n","28         WeekOfYear_47      True        1\n","29         WeekOfYear_46      True        1\n","30         WeekOfYear_45      True        1\n","31         WeekOfYear_44      True        1\n","32         WeekOfYear_43      True        1\n","33         WeekOfYear_42      True        1\n","34          WeekOfYear_7      True        1\n","35         WeekOfYear_41      True        1\n","36         WeekOfYear_39      True        1\n","37         WeekOfYear_38      True        1\n","38         WeekOfYear_37      True        1\n","39         WeekOfYear_36      True        1\n","40         WeekOfYear_35      True        1\n","41         WeekOfYear_34      True        1\n","42         WeekOfYear_33      True        1\n","43         WeekOfYear_32      True        1\n","44         WeekOfYear_31      True        1\n","45         WeekOfYear_40      True        1\n","46              Store_36      True        1\n","47         WeekOfYear_52      True        1\n","48              Store_11      True        1\n","49   Before_Thanksgiving      True        1\n","50      Before_Christmas      True        1\n","51               Store_1      True        1\n","52               Store_2      True        1\n","53               Store_3      True        1\n","54               Store_4      True        1\n","55               Store_5      True        1\n","56               Store_9      True        1\n","57      Before_Labor_Day      True        1\n","58               Store_6      True        1\n","59              Store_20      True        1\n","60              Store_19      True        1\n","61              Store_16      True        1\n","62              Store_15      True        1\n","63              Store_14      True        1\n","64              Store_13      True        1\n","65              Store_33      True        1\n","66               Store_7      True        1\n","67              Store_21      True        1\n","68     Before_Super_Bowl      True        1\n","69              Store_23      True        1\n","70          Thanksgiving      True        1\n","71              Store_32      True        1\n","72              Store_31      True        1\n","73              Store_30      True        1\n","74              Store_29      True        1\n","75              Store_28      True        1\n","76              Store_27      True        1\n","77             Christmas      True        1\n","78              Store_25      True        1\n","79              Store_10      True        1\n","80            Super_Bowl      True        1\n","81              Store_24      True        1\n","82             Labor_Day      True        1\n","83              Store_18     False        2\n","84                Day_31     False        3\n","85                 Day_6     False        4\n","86              Store_17     False        5\n","87              Store_35     False        6\n","88               Store_8     False        7\n","89         WeekOfYear_17     False        8\n","90                 Day_7     False        9\n","91          WeekOfYear_9     False       10\n","92         WeekOfYear_22     False       11\n","93         WeekOfYear_23     False       12\n","94         WeekOfYear_24     False       13\n","95         WeekOfYear_26     False       14\n","96         WeekOfYear_25     False       15\n","97                 Day_1     False       16\n","98              Store_40     False       17\n","99         WeekOfYear_15     False       18\n","100        WeekOfYear_16     False       19\n","101        WeekOfYear_20     False       20\n","102        WeekOfYear_10     False       21\n","103        WeekOfYear_11     False       22\n","104             Store_12     False       23\n","105             Store_26     False       24\n","106         Unemployment     False       25\n","107            Year_2010     False       26\n","108           Fuel_Price     False       27\n","109        WeekOfYear_27     False       28\n","110             Store_34     False       29\n","111             Store_22     False       30\n","112            Year_2012     False       31\n","113            Year_2011     False       32\n","114        WeekOfYear_30     False       33\n","115         WeekOfYear_4     False       34\n","116               Day_28     False       35\n","117                Day_3     False       36\n","118                Day_2     False       37\n","119        WeekOfYear_14     False       38\n","120               Day_30     False       39\n","121               Day_18     False       40\n","122               Day_21     False       41\n","123               Day_16     False       42\n","124        WeekOfYear_12     False       43\n","125               Day_29     False       44\n","126               Day_26     False       45\n","127               Day_24     False       46\n","128               Day_22     False       47\n","129               Day_25     False       48\n","130        WeekOfYear_29     False       49\n","131   Unemployment_class     False       50\n","132               Day_27     False       51\n","133         WeekOfYear_1     False       52\n","134        WeekOfYear_18     False       53\n","135                Day_8     False       54\n","136               Day_19     False       55\n","137               Day_12     False       56\n","138                Day_9     False       57\n","139               Day_10     False       58\n","140               Day_11     False       59\n","141               Day_13     False       60\n","142                Day_5     False       61\n","143         WeekOfYear_3     False       62\n","144                  CPI     False       63\n","145               Day_14     False       64\n","146               Day_15     False       65\n","147        WeekOfYear_19     False       66\n","148         WeekOfYear_2     False       67\n","149        WeekOfYear_13     False       68\n","150        WeekOfYear_21     False       69\n","151               Day_23     False       70\n","152               Day_20     False       71\n","153               Day_17     False       72\n","154                Day_4     False       73\n","155          Temperature     False       74\n","156        WeekOfYear_28     False       75\n","157            CPI_class     False       76"],"text/html":["\n","  <div id=\"df-600f89de-609e-44ef-8c92-bb68cfab0aef\">\n","    <div class=\"colab-df-container\">\n","      <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>Features</th>\n","      <th>Selected</th>\n","      <th>Ranking</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Holiday_Flag</td>\n","      <td>True</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Month_12</td>\n","      <td>True</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      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<td>False</td>\n","      <td>56</td>\n","    </tr>\n","    <tr>\n","      <th>138</th>\n","      <td>Day_9</td>\n","      <td>False</td>\n","      <td>57</td>\n","    </tr>\n","    <tr>\n","      <th>139</th>\n","      <td>Day_10</td>\n","      <td>False</td>\n","      <td>58</td>\n","    </tr>\n","    <tr>\n","      <th>140</th>\n","      <td>Day_11</td>\n","      <td>False</td>\n","      <td>59</td>\n","    </tr>\n","    <tr>\n","      <th>141</th>\n","      <td>Day_13</td>\n","      <td>False</td>\n","      <td>60</td>\n","    </tr>\n","    <tr>\n","      <th>142</th>\n","      <td>Day_5</td>\n","      <td>False</td>\n","      <td>61</td>\n","    </tr>\n","    <tr>\n","      <th>143</th>\n","      <td>WeekOfYear_3</td>\n","      <td>False</td>\n","      <td>62</td>\n","    </tr>\n","    <tr>\n","      <th>144</th>\n","      <td>CPI</td>\n","      <td>False</td>\n","      <td>63</td>\n","    </tr>\n","    <tr>\n","      <th>145</th>\n","      <td>Day_14</td>\n","      <td>False</td>\n","      <td>64</td>\n","    </tr>\n","    <tr>\n","      <th>146</th>\n","      <td>Day_15</td>\n","      <td>False</td>\n","      <td>65</td>\n","    </tr>\n","    <tr>\n","      <th>147</th>\n","      <td>WeekOfYear_19</td>\n","      <td>False</td>\n","      <td>66</td>\n","    </tr>\n","    <tr>\n","      <th>148</th>\n","      <td>WeekOfYear_2</td>\n","      <td>False</td>\n","      <td>67</td>\n","    </tr>\n","    <tr>\n","      <th>149</th>\n","      <td>WeekOfYear_13</td>\n","      <td>False</td>\n","      <td>68</td>\n","    </tr>\n","    <tr>\n","      <th>150</th>\n","      <td>WeekOfYear_21</td>\n","      <td>False</td>\n","      <td>69</td>\n","    </tr>\n","    <tr>\n","      <th>151</th>\n","      <td>Day_23</td>\n","      <td>False</td>\n","      <td>70</td>\n","    </tr>\n","    <tr>\n","      <th>152</th>\n","      <td>Day_20</td>\n","      <td>False</td>\n","      <td>71</td>\n","    </tr>\n","    <tr>\n","      <th>153</th>\n","      <td>Day_17</td>\n","      <td>False</td>\n","      <td>72</td>\n","    </tr>\n","    <tr>\n","      <th>154</th>\n","      <td>Day_4</td>\n","      <td>False</td>\n","      <td>73</td>\n","    </tr>\n","    <tr>\n","      <th>155</th>\n","      <td>Temperature</td>\n","      <td>False</td>\n","      <td>74</td>\n","    </tr>\n","    <tr>\n","      <th>156</th>\n","      <td>WeekOfYear_28</td>\n","      <td>False</td>\n","      <td>75</td>\n","    </tr>\n","    <tr>\n","      <th>157</th>\n","      <td>CPI_class</td>\n","      <td>False</td>\n","      <td>76</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-600f89de-609e-44ef-8c92-bb68cfab0aef')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path 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However, it doesn't make sense to just remove some features from the overall categorical feature, specifically the \"WeekOfYear\" feature (such as removing \"WeekOfYear_19\" and \"WeekOfYear_10\" but not the others) because it makes the distribution of features uneven and still there are several WeekOfYear features with high *ranking*. Therefore, we will select only numeric features and categorical features whose overall encoded features have a low *rank*."],"metadata":{"id":"jEFsDSdS-zet"}},{"cell_type":"code","source":["cols_to_drop = [\"Temperature\", \"CPI\", \"Fuel_Price\", \"Unemployment\", \"Day_1\", \"Day_2\", \"Day_3\", \"Day_4\", \"Day_5\", \n","                \"Day_6\", \"Day_7\", \"Day_8\", \"Day_9\", \"Day_10\", \"Day_11\", \"Day_12\", \"Day_13\", \"Day_14\", \"Day_15\", \n","                \"Day_16\", \"Day_17\", \"Day_18\", \"Day_19\", \"Day_20\", \"Day_21\", \"Day_22\", \"Day_23\", \"Day_24\", \"Day_25\",\n","                \"Day_26\", \"Day_27\", \"Day_28\", \"Day_29\", \"Day_30\", \"Day_31\"]\n","\n","# Drop selected column (with lowest importance feature)\n","data_enc = data_enc.drop(cols_to_drop, axis = 1)\n","data_drop_outlier_enc = data_drop_outlier_enc.drop(cols_to_drop, axis = 1)\n","data_tf_outlier_enc = data_tf_outlier_enc.drop(cols_to_drop, axis = 1)\n","new_features     = [i for i in data_enc.columns if i not in [target]] #store all features name"],"metadata":{"id":"8b1enOV17sW4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Set features and target values to seperated variables\n","\n","# Data with outliers\n","y = data_enc[target]\n","X = data_enc[new_features]\n","\n","# Data with outliers dropped\n","y_2 = data_drop_outlier_enc[target]\n","X_2 = data_drop_outlier_enc[new_features]\n","\n","# Data with outliers floored and capped\n","y_3 = data_tf_outlier_enc[target]\n","X_3 = data_tf_outlier_enc[new_features]"],"metadata":{"id":"N7O_9FUr7sUQ"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## 2.5 Exporting Data"],"metadata":{"id":"CnPhOEvIcVDP"}},{"cell_type":"code","source":["X.to_csv(\"/data/X.csv\", index=False)\n","y.to_csv(\"/data/y.csv\", index=False)\n","X_2.to_csv(\"/data/X_2.csv\", index=False)\n","y_2.to_csv(\"/data/y_2.csv\", index=False)\n","X_3.to_csv(\"/data/X_3.csv\", index=False)\n","y_3.to_csv(\"/data/y_3.csv\", index=False)"],"metadata":{"id":"S8_kiEMQ7sPu"},"execution_count":null,"outputs":[]}]}