# Split data into training and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

return X_train, X_test, y_train, y_test except Exception as e: print(f"An error occurred: {e}")

# Assume `data.csv` was extracted and you're working with it def prepare_features(data_path): try: data = pd.read_csv(data_path) # Assume the last column is the target variable X = data.iloc[:, :-1] y = data.iloc[:, -1]

import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler