Basketball Point Prediction

Instructions on running notebook and seeing results for linear and ridge regression models:

The notebook for the linear and ridge regression models is called DS4400_Project.ipynb

In order to run the linear and ridge regression models. Simply run all cells in the provided notebook in sequential order.

Instructions to run neural network and see results

1. Install venv if not installed.
2. Create a new virtual environment and install requirements.txt using pip in the environment
3. Run the command below in a terminal window

python neural_network.py

Note, default configuration variables are set in file:

For data parsing

games_to_look_back = 10

For the model architecture

Defining input size, hidden layer size, output size and batch size respectively

n_in, n_h, n_out, hidden_layers, activation_function = 44, 50, 1, 3, 'relu'

For training and evaluation

k_folds = 10 epochs = 150

There are 6 outputs to acknowledge:

1. Average training accuracy over the number of folds
2. Average training loss over the number of folds
3. Average testing accuracy over the number of folds
4. A list of all training accuracies during k-fold validation
5. A list of all training losses during k-fold validation
6. A list of all testing accuracies during k-fold validation