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| import pandas as pd | |
| import numpy as np | |
| from sklearn.preprocessing import StandardScaler, OneHotEncoder | |
| from sklearn.compose import ColumnTransformer | |
| from sklearn.pipeline import Pipeline | |
| from sklearn.impute import SimpleImputer | |
| class LoanDataProcessor: | |
| """ | |
| Class for preprocessing loan data for machine learning models. | |
| """ | |
| def __init__(self): | |
| """Initialize the data processor.""" | |
| self.preprocessor = None | |
| self.categorical_features = ['gender', 'employment_status', 'payment_history'] | |
| self.numerical_features = ['age', 'annual_income', 'credit_score', 'loan_amount', | |
| 'interest_rate', 'loan_term', 'days_past_due', | |
| 'previous_defaults', 'monthly_payment', 'debt_to_income'] | |
| def fit(self, X): | |
| """ | |
| Fit the preprocessor on the training data. | |
| Parameters: | |
| ----------- | |
| X : pandas.DataFrame | |
| The training data | |
| Returns: | |
| -------- | |
| self : LoanDataProcessor | |
| The fitted processor | |
| """ | |
| # Define preprocessing for numerical features | |
| numerical_transformer = Pipeline(steps=[ | |
| ('imputer', SimpleImputer(strategy='median')), | |
| ('scaler', StandardScaler()) | |
| ]) | |
| # Define preprocessing for categorical features | |
| categorical_transformer = Pipeline(steps=[ | |
| ('imputer', SimpleImputer(strategy='most_frequent')), | |
| ('onehot', OneHotEncoder(handle_unknown='ignore')) | |
| ]) | |
| # Combine preprocessing steps | |
| self.preprocessor = ColumnTransformer( | |
| transformers=[ | |
| ('num', numerical_transformer, self.numerical_features), | |
| ('cat', categorical_transformer, self.categorical_features) | |
| ]) | |
| # Fit the preprocessor | |
| self.preprocessor.fit(X) | |
| return self | |
| def transform(self, X): | |
| """ | |
| Transform the data using the fitted preprocessor. | |
| Parameters: | |
| ----------- | |
| X : pandas.DataFrame | |
| The data to transform | |
| Returns: | |
| -------- | |
| numpy.ndarray | |
| The transformed data | |
| """ | |
| if self.preprocessor is None: | |
| raise ValueError("Preprocessor has not been fitted. Call fit() first.") | |
| return self.preprocessor.transform(X) | |
| def fit_transform(self, X): | |
| """ | |
| Fit the preprocessor and transform the data. | |
| Parameters: | |
| ----------- | |
| X : pandas.DataFrame | |
| The data to fit and transform | |
| Returns: | |
| -------- | |
| numpy.ndarray | |
| The transformed data | |
| """ | |
| return self.fit(X).transform(X) | |
| def get_feature_names(self): | |
| """ | |
| Get the names of the transformed features. | |
| Returns: | |
| -------- | |
| list | |
| List of feature names after transformation | |
| """ | |
| if self.preprocessor is None: | |
| raise ValueError("Preprocessor has not been fitted. Call fit() first.") | |
| # Get feature names from the column transformer | |
| feature_names = [] | |
| # Get numerical feature names (these stay the same) | |
| feature_names.extend(self.numerical_features) | |
| # Get categorical feature names (these are expanded by one-hot encoding) | |
| categorical_features = self.preprocessor.transformers_[1][1].named_steps['onehot'].get_feature_names_out( | |
| self.categorical_features) | |
| feature_names.extend(categorical_features) | |
| return feature_names | |
| def prepare_data(self, data, target_column='recovery_status'): | |
| """ | |
| Prepare data for model training or prediction. | |
| Parameters: | |
| ----------- | |
| data : pandas.DataFrame | |
| The data to prepare | |
| target_column : str, optional | |
| The name of the target column, by default 'recovery_status' | |
| Returns: | |
| -------- | |
| tuple | |
| (X, y) if target_column is in data, otherwise just X | |
| """ | |
| # Drop customer_id as it's not a feature | |
| if 'customer_id' in data.columns: | |
| data = data.drop('customer_id', axis=1) | |
| if target_column in data.columns: | |
| X = data.drop(target_column, axis=1) | |
| y = data[target_column] | |
| return X, y | |
| else: | |
| return data | |