Deep Learning, Stock Prediction, Python, Financial Analysis, Programming.
zeros X.replace([np.inf, -np.inf], np.nan, inplace=True) X.fillna(0, inplace=True) # Split the dataset 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) # Normalize the features scaler = StandardScaler() X_train_scaled =