{"id":24986995,"url":"https://github.com/hitthecodelabs/weatherforecast-lstm","last_synced_at":"2026-04-11T12:02:44.130Z","repository":{"id":206764304,"uuid":"717650100","full_name":"hitthecodelabs/WeatherForecast-LSTM","owner":"hitthecodelabs","description":"TensorFlow and Python for analyzing and forecasting weather data","archived":false,"fork":false,"pushed_at":"2023-11-12T05:56:41.000Z","size":816,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-04T11:43:59.870Z","etag":null,"topics":["cnn","keras","lstm","matplotlib","numpy","pandas","python","tensorflow","weather","weather-forecast"],"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":"2023-11-12T05:32:10.000Z","updated_at":"2023-12-02T02:08:53.000Z","dependencies_parsed_at":null,"dependency_job_id":"c51c064c-a9ff-4d24-95c9-4deb28b6ba61","html_url":"https://github.com/hitthecodelabs/WeatherForecast-LSTM","commit_stats":null,"previous_names":["hitthecodelabs/weatherforecast-lstm"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hitthecodelabs%2FWeatherForecast-LSTM","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hitthecodelabs%2FWeatherForecast-LSTM/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hitthecodelabs%2FWeatherForecast-LSTM/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hitthecodelabs%2FWeatherForecast-LSTM/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hitthecodelabs","download_url":"https://codeload.github.com/hitthecodelabs/WeatherForecast-LSTM/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246174530,"owners_count":20735413,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["cnn","keras","lstm","matplotlib","numpy","pandas","python","tensorflow","weather","weather-forecast"],"created_at":"2025-02-04T11:35:13.552Z","updated_at":"2025-10-28T03:01:57.873Z","avatar_url":"https://github.com/hitthecodelabs.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# WeatherForecast-LSTM\n\nThis project demonstrates the use of TensorFlow and Python for analyzing and forecasting weather data. It consists of a Jupyter Notebook that performs data analysis, model training, and prediction, along with a Python module that provides essential functions for data processing and model building.\n\n## Project Structure\n\n- `weather_model.ipynb`: A Jupyter Notebook that contains the main analysis, including data preprocessing, model training, and visualization.\n- `utils.py`: A Python module that provides functions used in the notebook for data division, model creation, plotting, preprocessing, and forecasting.\n\n## `weather_model.ipynb`\n\nThis Jupyter Notebook contains the core analysis workflow:\n1. Importing necessary libraries.\n2. Data loading and preprocessing.\n3. Data visualization.\n4. Building and training the neural network model.\n5. Forecasting and visualizing the results.\n\n## `utils.py`\n\nThis Python module contains essential functions:\n- `dividir`: Splits the dataset into training and validation sets.\n- `modelo`: Creates and compiles a TensorFlow Sequential model for time series forecasting.\n- `plot_tt` and `plot_tt2`: Functions for plotting time series data. `plot_tt2` also saves the plot as an image.\n- `tensorial_preprocessing`: Prepares the data for training in TensorFlow format.\n- `to_forecast`: Generates forecasts using the trained TensorFlow model.\n\n## Usage\n\nTo use this project, clone the repository and run the Jupyter Notebook `weather_model.ipynb`. Ensure that `utils.py` is in the same directory as the notebook, as it imports functions from this module.\n\n## Requirements\n\nThis project requires the following libraries:\n- TensorFlow\n- NumPy\n- Pandas\n- Matplotlib\n\nInstall these libraries using pip:\n\n```bash\npip install tensorflow numpy pandas matplotlib\n```\n\n## Contributing\nContributions to this project are welcome. Please fork the repository and open a pull request with your changes or suggestions.\n\n## License\nThis project is open-sourced under the MIT License. See the LICENSE file for more details. \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhitthecodelabs%2Fweatherforecast-lstm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhitthecodelabs%2Fweatherforecast-lstm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhitthecodelabs%2Fweatherforecast-lstm/lists"}