Deep Learning, Stock Prediction, Python, Financial Analysis, Programming.
(data['Close'].shift(-target_horizon) > 0).astype(int) # Drop the last 'target_horizon' rows with NaN target values data = data.iloc[:-target_horizon] # Separate features and target variable X = data.drop(columns=['Target']) y = data['Target'] # Replace infinities with NaNs, then fill NaNs with