Welcome to the Deep Weather Prediction Models repository. This project aims to harness the power of deep learning to predict weather conditions accurately. It features two models: one for single-step predictions, which forecast the immediate next step, and another for multi-step predictions, which forecast several steps into the future. Additionally, this repository includes scripts for analyzing and visualizing the dataset, which consists of 20 years of comprehensive weather data.
- Single-Step Prediction Model: Predicts the next immediate weather condition using a time series analysis approach.
- Multi-Step Prediction Model: Forecasts weather conditions multiple steps ahead, enabling more long-term planning and analysis.
- Data Analysis Scripts: Tools to examine and understand the dataset through various statistical methods and visualizations.
- Data Visualization Tools: Scripts to generate graphs and charts that illustrate trends and patterns in the weather data over two decades.
- Python 3.8 or higher
- TensorFlow 2.x
- Pandas for data manipulation
- Matplotlib and Seaborn for data visualization
To get a local copy up and running, follow these simple steps.
Ensure you have Python 3.8 or higher installed on your machine, along with pip for installing packages.
Contributions make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/WeatherFeature) - Commit your Changes (
git commit -m 'Add some WeatherFeature') - Push to the Branch (
git push origin feature/WeatherFeature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for more information.
- Hat tip to anyone whose code was used
- Inspiration
- etc