Heart Disease Neural Networks
Build neural networks on heart disease data with PyTorch.
What you'll do
- Prepare a tabular dataset for a PyTorch model (encode, scale, split)
- Define, train, and evaluate a small feed-forward neural network
- Read accuracy and loss curves to judge fit
Dataset: Heart Disease (UCI-style) · One row per patient · Clinical measurements with a binary heart-disease target
Steps
- Load heart.csv and split into train/validation sets
- Encode categorical fields and scale numeric features
- Define a small nn.Module and train it with an optimizer and loss
- Evaluate on the validation set and inspect the confusion matrix
Requirements: see
requirements.txt in the Resources folder.
Install with pip install -r requirements.txt after
creating your virtual environment.