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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
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