https://github.com/hitthecodelabs/weatherforecast-lstm
TensorFlow and Python for analyzing and forecasting weather data
https://github.com/hitthecodelabs/weatherforecast-lstm
cnn keras lstm matplotlib numpy pandas python tensorflow weather weather-forecast
Last synced: 2 months ago
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TensorFlow and Python for analyzing and forecasting weather data
- Host: GitHub
- URL: https://github.com/hitthecodelabs/weatherforecast-lstm
- Owner: hitthecodelabs
- License: mit
- Created: 2023-11-12T05:32:10.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-12T05:56:41.000Z (over 2 years ago)
- Last Synced: 2025-02-04T11:43:59.870Z (over 1 year ago)
- Topics: cnn, keras, lstm, matplotlib, numpy, pandas, python, tensorflow, weather, weather-forecast
- Language: Jupyter Notebook
- Homepage:
- Size: 797 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# WeatherForecast-LSTM
This 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.
## Project Structure
- `weather_model.ipynb`: A Jupyter Notebook that contains the main analysis, including data preprocessing, model training, and visualization.
- `utils.py`: A Python module that provides functions used in the notebook for data division, model creation, plotting, preprocessing, and forecasting.
## `weather_model.ipynb`
This Jupyter Notebook contains the core analysis workflow:
1. Importing necessary libraries.
2. Data loading and preprocessing.
3. Data visualization.
4. Building and training the neural network model.
5. Forecasting and visualizing the results.
## `utils.py`
This Python module contains essential functions:
- `dividir`: Splits the dataset into training and validation sets.
- `modelo`: Creates and compiles a TensorFlow Sequential model for time series forecasting.
- `plot_tt` and `plot_tt2`: Functions for plotting time series data. `plot_tt2` also saves the plot as an image.
- `tensorial_preprocessing`: Prepares the data for training in TensorFlow format.
- `to_forecast`: Generates forecasts using the trained TensorFlow model.
## Usage
To 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.
## Requirements
This project requires the following libraries:
- TensorFlow
- NumPy
- Pandas
- Matplotlib
Install these libraries using pip:
```bash
pip install tensorflow numpy pandas matplotlib
```
## Contributing
Contributions to this project are welcome. Please fork the repository and open a pull request with your changes or suggestions.
## License
This project is open-sourced under the MIT License. See the LICENSE file for more details.