RNN Models for Temperature Prediction

This repository contains Python code for predicting the temperature 1 hour into the future using two different Recurrent Neural Network (RNN) models: one with simple vanilla RNN cells and the other with Gated Recurrent Unit (GRU) cells. The models are trained and evaluated using the "jena_climate_2009_2016" dataset.

Dataset

The temperature prediction models are trained and evaluated using the "jena_climate_2009_2016" dataset. This dataset contains weather measurements recorded at the Weather Station in Jena, Germany, from 2009 to 2016.

To use this dataset, follow these steps:

1. Download the dataset from the following source link: jena_climate_2009_2016.csv.zip.

2. Extract the contents of the zip file.

3. The dataset file should be named "jena_climate_2009_2016.csv".

4. Place the "jena_climate_2009_2016.csv" file in the project's root directory.

Requirements

Make sure you have the following dependencies installed:

• NumPy
• Matplotlib
• scikit-learn
• TensorFlow
• Pandas
• Seaborn
• pandas-profiling

You can install the dependencies using the following command:

pip install numpy matplotlib scikit-learn tensorflow pandas seaborn pandas-profiling

Usage

1. Clone the repository to your local machine:

   git clone https://github.com/Roon311/RNN-Models.git

2. Navigate to the project directory:

   cd RNN-Models

3. Run the Jupyter Notebook Temperature_Prediction.ipynb to train and evaluate the RNN models for temperature prediction.

4. Follow the instructions and code provided in the notebook to understand and execute the temperature prediction process using both vanilla RNN and GRU models.

5. Experiment with different hyperparameters, architectures, or preprocessing techniques to improve the model's performance.

Contact

For any questions or inquiries, please contact [[email protected]].

Good luck with your temperature prediction models!