| import pandas as pd | |
| import numpy as np | |
| import pickle | |
| def load_pickle(filename): | |
| with open(filename, 'rb') as file: | |
| data = pickle.load(file) | |
| return data | |
| preprocessor = load_pickle('full_pipeline.pkl') | |
| def create_new_columns(train_data): | |
| train_data['Monthly Variations'] = (train_data.loc[:, 'TotalCharges']) -((train_data.loc[:, 'tenure'] * train_data.loc[:, 'MonthlyCharges'])) | |
| labels =['{0}-{1}'.format(i, i+2) for i in range(0, 73, 3)] | |
| train_data['tenure_group'] = pd.cut(train_data['tenure'], bins=(range(0, 78, 3)), right=False, labels=labels) | |
| train_data.drop(columns=['tenure'], inplace=True) | |
| def create_processed_dataframe(processed_data, train_data): | |
| train_num_cols=train_data.select_dtypes(exclude=['object', 'category']).columns | |
| cat_features = preprocessor.named_transformers_['categorical']['cat_encoder'].get_feature_names() | |
| labels = np.concatenate([train_num_cols, cat_features]) | |
| processed_dataframe = pd.DataFrame(processed_data.toarray(), columns=labels) | |
| return processed_dataframe |