import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler
# 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] tinymodel ginger #63.zip
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
# Feature scaling scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) :-1] y = data.iloc[: