return X_train, X_test, y_train, y_test except Exception as e: print(f"An error occurred: {e}")
import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler tinymodel ginger #63.zip
# 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] return X_train, X_test, y_train, y_test except Exception as
# 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) :-1] y = data.iloc[:
# Feature scaling scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test)
