mjrodri / Simple-Weather-Neural-Network

This project focuses on harnessing the capabilities of recurrent neural networks (RNNs) to create a weather prediction system. The primary objective is to forecast future weather conditions based on historical data, enabling users to anticipate changes and make informed decisions.

Simple-Weather-Neural-Network

Weather Prediction with Recurrent Neural Networks

Overview

This project aims to predict future weather conditions using a recurrent neural network (RNN). The system utilizes historical weather data to forecast key variables such as temperature, humidity, and wind speed. The predictions assist users in planning activities and responding to changing weather patterns.

Key Features

• LSTM-based Neural Network: The project employs Long Short-Term Memory networks for effective time-series prediction, capturing temporal dependencies in historical weather data.

• Data Collection: Historical weather data is collected from reliable sources, ensuring diverse and comprehensive information for training the neural network.

• Data Preprocessing: The data undergoes preprocessing to handle missing values, ensure consistency, and extract relevant features. Feature engineering techniques enhance the model's ability to capture patterns.

• Training and Evaluation: The neural network is trained on historical data, with model performance evaluated using metrics such as Mean Absolute Error (MAE). Fine-tuning is performed based on evaluation results.

• Predictions: The trained model is used to make accurate predictions for future time points, providing users with timely and reliable weather forecasts.

Getting Started

1. Data Collection:

   • Gather historical weather data from reliable sources or use your own dataset. Ensure the dataset includes key variables such as temperature, humidity, and wind speed.

2. Installation:

   • Clone this repository to your local machine.
   • Install the required dependencies using:

     pip install -r requirements.txt

3. Data Preprocessing:

   • Run the data preprocessing script to clean and preprocess the dataset:

     python data_preprocessing.py

4. Model Training:

   • Train the neural network by running the training script:

     python train_model.py

5. Prediction:

   • Use the trained model to make predictions by running the prediction script:

     python make_predictions.py

Results

• The model's accuracy can be evaluated using metrics such as MAE, which quantifies the difference between predicted and actual values.

• Visualizations of actual vs. predicted weather conditions can be generated for further analysis.

Contributing

Contributions to enhance the project, optimize the model, or add additional features are welcome. Please fork the repository and submit a pull request.

License

This project is licensed under the MIT License.

Acknowledgments

Special thanks to [Source of Historical Weather Data] for providing the dataset used in this project.