This project focuses on predicting flow rates using machine learning models. It includes a series of Python functions for data processing, model training, and result visualization.

• Purpose: Loads multiple datasets from CSV files located in a specific path.
• Parameters:
  • path: Path where the CSV files are located.
• Returns: List of DataFrames loaded from the CSV files.

• Purpose: Preprocesses data for modeling, based on the input length and a specific date/time.
• Parameters:
  • input_data: DataFrame with the input data.
  • in_length: Length of the input (number of time steps).
  • date_hour: Specific date and time to start the prediction.
• Returns: A dictionary with preprocessed data for modeling.

• Purpose: Loads pre-trained models from a specific path.
• Parameters:
  • ruta: Path where the models are stored.
  • in_length: Input length for the models.
• Returns: Dictionary of loaded models.

• Purpose: Plots the NSE index for each output length in the training, validation, and test sets.
• Parameters:
  • nse_train: List of NSE indices for the training set.
  • nse_val: List of NSE indices for the validation set.
  • nse_test: List of NSE indices for the test set.
  • out_lengths: List of output lengths used in the predictions.

Contributions are welcome. Please create a pull request to propose improvements or open an issue to discuss what you would like to change.

This project is licensed under the MIT License.