{"id":24986991,"url":"https://github.com/hitthecodelabs/flowpredictor_modeling","last_synced_at":"2026-05-08T07:32:33.979Z","repository":{"id":208765248,"uuid":"722432277","full_name":"hitthecodelabs/FlowPredictor_Modeling","owner":"hitthecodelabs","description":"Predicting flow rates using machine learning models","archived":false,"fork":false,"pushed_at":"2023-11-23T06:33:36.000Z","size":1801,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-06T19:37:35.226Z","etag":null,"topics":["keras","lstm","python","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/hitthecodelabs.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-11-23T06:14:56.000Z","updated_at":"2024-11-13T09:42:23.000Z","dependencies_parsed_at":null,"dependency_job_id":"7d6249a2-9869-41b9-8b54-c0d038dc2457","html_url":"https://github.com/hitthecodelabs/FlowPredictor_Modeling","commit_stats":null,"previous_names":["hitthecodelabs/flowpredictor_modeling"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/hitthecodelabs/FlowPredictor_Modeling","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hitthecodelabs%2FFlowPredictor_Modeling","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hitthecodelabs%2FFlowPredictor_Modeling/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hitthecodelabs%2FFlowPredictor_Modeling/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hitthecodelabs%2FFlowPredictor_Modeling/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hitthecodelabs","download_url":"https://codeload.github.com/hitthecodelabs/FlowPredictor_Modeling/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hitthecodelabs%2FFlowPredictor_Modeling/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32771007,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-08T02:36:36.067Z","status":"ssl_error","status_checked_at":"2026-05-08T02:36:07.210Z","response_time":54,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["keras","lstm","python","tensorflow"],"created_at":"2025-02-04T11:35:12.700Z","updated_at":"2026-05-08T07:32:33.954Z","avatar_url":"https://github.com/hitthecodelabs.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# FlowPredictor_Modeling\n\n## Overview\nThis 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.\n\n## Functions\n\n### `cargar_datos(path='./')`\n- **Purpose**: Loads multiple datasets from CSV files located in a specific path.\n- **Parameters**:\n  - `path`: Path where the CSV files are located.\n- **Returns**: List of DataFrames loaded from the CSV files.\n\n### `preprocesar_datos(input_data, in_length, date_hour, out_lengths)`\n- **Purpose**: Preprocesses data for modeling, based on the input length and a specific date/time.\n- **Parameters**:\n  - `input_data`: DataFrame with the input data.\n  - `in_length`: Length of the input (number of time steps).\n  - `date_hour`: Specific date and time to start the prediction.\n- **Returns**: A dictionary with preprocessed data for modeling.\n\n### `cargar_modelos(ruta, in_length)`\n- **Purpose**: Loads pre-trained models from a specific path.\n- **Parameters**:\n  - `ruta`: Path where the models are stored.\n  - `in_length`: Input length for the models.\n- **Returns**: Dictionary of loaded models.\n\n### `graficar_indice_nse(nse_train, nse_val, nse_test, out_lengths)`\n- **Purpose**: Plots the NSE index for each output length in the training, validation, and test sets.\n- **Parameters**:\n  - `nse_train`: List of NSE indices for the training set.\n  - `nse_val`: List of NSE indices for the validation set.\n  - `nse_test`: List of NSE indices for the test set.\n  - `out_lengths`: List of output lengths used in the predictions.\n\n## Contributions\nContributions are welcome. Please create a pull request to propose improvements or open an issue to discuss what you would like to change.\n\n## License\nThis project is licensed under the [MIT License](https://opensource.org/licenses/MIT).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhitthecodelabs%2Fflowpredictor_modeling","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhitthecodelabs%2Fflowpredictor_modeling","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhitthecodelabs%2Fflowpredictor_modeling/lists"}