Table 5.

Imported libraries and their purposes.

Library/module	Purpose
os	Operating system utilities (e.g., file path handling)
random	Python random number generation, for reproducibility
numpy	Numerical computing, array manipulation, reshaping sequences
pandas	Reading Excel files and handling tabular data (DataFrames)
matplotlib.pyplot	Visualization of true vs. predicted values and performance plots
sklearn.preprocessing.StandardScaler	Standardizes features and target for better model training. Prevents scale issues
sklearn.model_selection.KFold	K-Fold Cross-Validation: splits data into k folds for model evaluation. Reduces overfitting bias
sklearn.metrics.mean_squared_error	Computes MSE, used for RMSE calculation (performance metric)
sklearn.metrics.r2_score	Computes R² score, measures proportion of variance explained by the model
tensorflow	Deep learning framework used for defining and training neural networks
tensorflow.keras.layers	Provides core neural network layers like Conv1D, LSTM, Dense, Dropout, MaxPooling1D
tensorflow.keras.models	Functions for building, compiling, and managing models (Model, Sequential)
tensorflow.keras.regularizers	Provides L2 regularization to prevent overfitting
tensorflow.keras.callbacks.EarlyStopping	Stops training when validation loss stops improving, prevents overfitting
tensorflow.keras.callbacks.ReduceLROnPlateau	Reduces learning rate if the validation loss plateaus, improving convergence
tensorflow.keras.backend	Provides backend functions like K.clear_session() to reset models and release memory between folds