utils.py

import pandas as pd
import numpy as np
import pickle

# Define the name of the pickle file containing a pre-trained data preprocessing pipeline.
pipeline_pkl = "full_pipeline.pkl"

# Function to load data from a pickle file.
def load_pickle(filename):
    with open(filename, 'rb') as file:
        data = pickle.load(file)
        return data

# Load the pre-processing pipeline from the pickle file.
preprocessor = load_pickle(pipeline_pkl)

# Function to create new columns in the training data.
def create_new_columns(train_data):
    # Calculate 'Monthly Variations' column as the difference between 'TotalCharges' and the product of 'tenure' and 'MonthlyCharges'.
    train_data['Monthly Variations'] = (train_data.loc[:, 'TotalCharges']) -((train_data.loc[:, 'tenure'] * train_data.loc[:, 'MonthlyCharges']))
    
    # Define labels for 'tenure_group' based on a range of values.
    labels =['{0}-{1}'.format(i, i+2) for i in range(0, 73, 3)]
    
    # Create a 'tenure_group' column by binning 'tenure' values into the specified labels.
    train_data['tenure_group'] = pd.cut(train_data['tenure'], bins=(range(0, 78, 3)), right=False, labels=labels)
    
    # Drop the 'tenure' column from the DataFrame.
    train_data.drop(columns=['tenure'], inplace=True)
    
    return train_data

# Function to create a processed DataFrame from the processed data.
def create_processed_dataframe(processed_data, train_data):
    # Select numerical columns from the training data.
    train_num_cols=train_data.select_dtypes(exclude=['object', 'category']).columns
    
    # Get feature names from the categorical encoder in the preprocessor.
    cat_features = preprocessor.named_transformers_['categorical']['cat_encoder'].get_feature_names()
    
    # Concatenate numerical and categorical feature names.
    labels = np.concatenate([train_num_cols, cat_features])
    
    # Create a DataFrame from the processed data with the specified column labels.
    processed_dataframe = pd.DataFrame(processed_data.toarray(), columns=labels)
    
    return processed_dataframe