Mini Machine Learning Project

The purpose of this project is to incorporate critical features of project structure and organization into a demo.

Running the model

run.py contains code for building the entire model - loading data, generating features, building a model, and making new predictions. If you want to run the model and make predictions on the small training data set, just run:

python3 run.py

If you have new data stored in new_data.csv, then run the following:

python3 run.py --path_to_new_data new_data.csv

Data Loading

data_loading.py contains code for loading data from a locally saved csv file into a pandas DataFrame.

Model Building

classifier.py contains code for building a keras/tensorflow deep learning binary classifier model.

Feature Engineering, Data Cleaning, etc.

This project was built using a very small Kaggle data set that had processed/numerical features already. Feature generation and data cleaning are not within the scope of this mini-project.

Model Performance

This project was set up with keras tuner as a starting point for hyperparameter tuning. However, the model has not been tuned to improve bias or variance significantly. Variance improvements are also restricted here by the small size of the training data set.

Data Source

Data was obtained from Kaggle: https://www.kaggle.com/ronitf/heart-disease-uci/data

Original source: https://archive.ics.uci.edu/ml/datasets/Heart+Disease