https://github.com/hitthecodelabs/flowpredictor_modeling
Predicting flow rates using machine learning models
https://github.com/hitthecodelabs/flowpredictor_modeling
keras lstm python tensorflow
Last synced: 27 days ago
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Predicting flow rates using machine learning models
- Host: GitHub
- URL: https://github.com/hitthecodelabs/flowpredictor_modeling
- Owner: hitthecodelabs
- License: mit
- Created: 2023-11-23T06:14:56.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-23T06:33:36.000Z (over 2 years ago)
- Last Synced: 2025-10-06T19:37:35.226Z (8 months ago)
- Topics: keras, lstm, python, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 1.72 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# FlowPredictor_Modeling
## Overview
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.
## Functions
### `cargar_datos(path='./')`
- **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.
### `preprocesar_datos(input_data, in_length, date_hour, out_lengths)`
- **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.
### `cargar_modelos(ruta, in_length)`
- **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.
### `graficar_indice_nse(nse_train, nse_val, nse_test, out_lengths)`
- **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
Contributions are welcome. Please create a pull request to propose improvements or open an issue to discuss what you would like to change.
## License
This project is licensed under the [MIT License](https://opensource.org/licenses/MIT).