This project focuses on predicting temperature in Melbourne using real-life time series data. The prediction will be achieved by leveraging the power of Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks in combination. These deep learning techniques will be employed to effectively process sequential data and capture patterns in the temperature time series. The model will be trained on historical temperature records, and its performance will be evaluated using appropriate evaluation metrics.
- Clone this repository.
- Install required dependencies, including TensorFlow:
pip install tensorflow. - Obtain the real-life time series temperature data for Melbourne and place it in the appropriate data directory.
Run the notebook in you IDE
The model's predictions will be evaluated using relevant metrics, and the results will be visualized for analysis.
This project is part of a Lab assignment from deeplearning.ai course "Time Series Data with Tensorflow" from coursera
Contributions to this project are highly appreciated. If you discover any issues or have ideas for enhancements, please feel free to open an issue or submit a pull request.