Authors: Peter Eusebio, Lola Johnston, Yannik Kumar
Date: June 2020
GitHub repository for a final project in a Machine Learning course at the University of Chicago: https://github.com/Pete-Best/predicting_intoxication
Accelerometer data was used to predict TAC levels of 13 research subjects. The data was provided by the UCI Machine Learning Database. The project is based on Killian, J.A., Passino, K.M., Nandi, A., Madden, D.R. and Clapp, J., Learning to Detect Heavy Drinking Episodes Using Smartphone Accelerometer Data. In Proceedings of the 4th International Workshop on Knowledge Discovery in Healthcare Data co-located with the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019) (pp. 35-42).
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eda.ipynb explores the accelerometer and TAC data.
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join_together.ipynb joins the accelerometer and TAC data from the UCI Machine Learning Database. Its output is fed into feature_extraction.ipynb.
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feature_extraction.ipynb extracts features from the accelerometer data to be used for predicting TAC.
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feedforward_networks.ipynb builds feedforward neural networks to predict TAC using the explanatory data.
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recurrent_and_convolutional_networks.ipynb builds RNNs and CNNs to predict TAC using the explanatory data.
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modeling_sklearn.ipynb uses a variety of classifiers to predict TAC using the explanatory data.