preprocessing.py

import pandas as pd
from sklearn.preprocessing import MinMaxScaler


def preprocess(df, option):

    # Defining the map function
    def binary_map(feature):
        return feature.map({'Yes': 1, 'No': 0})

    # Encode binary categorical features
    binary_list = ['SeniorCitizen', 'Dependents', 'PhoneService', 'PaperlessBilling']
    df[binary_list] = df[binary_list].apply(binary_map)

    # Drop values based on operational options
    if (option == "Online"):
        columns = ['SeniorCitizen', 'Dependents', 'tenure', 'PhoneService', 'PaperlessBilling', 'MonthlyCharges',
                   'TotalCharges', 'MultipleLines_No_phone_service', 'MultipleLines_Yes', 'InternetService_Fiber_optic',
                   'InternetService_No', 'OnlineSecurity_No_internet_service', 'OnlineSecurity_Yes',
                   'OnlineBackup_No_internet_service', 'TechSupport_No_internet_service', 'TechSupport_Yes',
                   'StreamingTV_No_internet_service', 'StreamingTV_Yes', 'StreamingMovies_No_internet_service',
                   'StreamingMovies_Yes', 'Contract_One_year', 'Contract_Two_year', 'PaymentMethod_Electronic_check']
        # Encoding the other categorical categoric features with more than two categories
        df = pd.get_dummies(df).reindex(columns=columns, fill_value=0)
    elif (option == "Batch"):
        pass
        df = df[['SeniorCitizen', 'Dependents', 'tenure', 'PhoneService', 'MultipleLines', 'InternetService',
                 'OnlineSecurity',
                 'OnlineBackup', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',
                 'PaymentMethod',
                 'MonthlyCharges', 'TotalCharges']]
        columns = ['SeniorCitizen', 'Dependents', 'tenure', 'PhoneService', 'PaperlessBilling', 'MonthlyCharges',
                   'TotalCharges', 'MultipleLines_No_phone_service', 'MultipleLines_Yes', 'InternetService_Fiber_optic',
                   'InternetService_No', 'OnlineSecurity_No_internet_service', 'OnlineSecurity_Yes',
                   'OnlineBackup_No_internet_service', 'TechSupport_No_internet_service', 'TechSupport_Yes',
                   'StreamingTV_No_internet_service', 'StreamingTV_Yes', 'StreamingMovies_No_internet_service',
                   'StreamingMovies_Yes', 'Contract_One_year', 'Contract_Two_year', 'PaymentMethod_Electronic_check']
        # Encoding the other categorical categoric features with more than two categories
        df = pd.get_dummies(df).reindex(columns=columns, fill_value=0)
    else:
        print("Incorrect operational options")

    # feature scaling
    sc = MinMaxScaler()
    df['tenure'] = sc.fit_transform(df[['tenure']])
    df['MonthlyCharges'] = sc.fit_transform(df[['MonthlyCharges']])
    df['TotalCharges'] = sc.fit_transform(df[['TotalCharges']])
    return df