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.model_selection import train_test_split

def load_data(filepath):
    """Load data from CSV file"""
    return pd.read_csv(filepath)
    
def preprocess_data(df, target_column='loan_approved'):
    """Preprocess data for model training"""
    # Split features and target
    X = df.drop(columns=[target_column])
    y = df[target_column]
    
    # Identify numeric and categorical columns
    numeric_features = X.select_dtypes(include=['int64', 'float64']).columns
    categorical_features = X.select_dtypes(include=['object']).columns
    
    # Create preprocessing pipelines
    numeric_transformer = Pipeline(steps=[
        ('scaler', StandardScaler())
    ])
    
    categorical_transformer = Pipeline(steps=[
        ('onehot', OneHotEncoder(handle_unknown='ignore'))
    ])
    
    # Combine preprocessing steps
    preprocessor = ColumnTransformer(
        transformers=[
            ('num', numeric_transformer, numeric_features),
            ('cat', categorical_transformer, categorical_features)
        ])
    
    # Split data
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    
    return preprocessor, X_train, X_test, y_train, y_test