This repository contains a framework for training and evaluating neural network regression models on a simple dataset. The framework is designed to be simple to use, requiring only plug-and-play components.

The NN_regression.py script provides a complete pipeline for:

• Loading and normalizing regression data
• Training neural network models with customizable architectures
• Evaluating model performance on training, validation, and test sets
• Visualizing regression results and error metrics

• Python 3.6+
• PyTorch
• NumPy
• Pandas
• Matplotlib

You can install the required packages using:

pip install torch numpy pandas matplotlib

To run the framework, use the following command:

python src/NN_regression.py

This will train a neural network on the dataset and save the results to the results directory.

You are encouraged to modify the src/NN_regression.py script to experiment with different neural network architectures and datasets. This is easily done by adding a new architecture to the ARCHITECTURES list using the default format.