Forecasting, Deep Learning, Arima, Goldman Sachs, Data Science.
more accurate results. from sklearn.preprocessing import MinMaxScaler # Scale the features scaler = MinMaxScaler() train_scaled = scaler.fit_transform(train_df.drop('Close', axis=1)) test_scaled = scaler.transform(test_df.drop('Close', axis=1)) # Convert the scaled data back to a DataFrame