'data' folder contains data for classification problem.
'ml_models.py' contains required ML models: heuristic (kNN), 2 baseline models (Decision Tree, Logistic Regression) and a neural network model.
'rest_api.py' contains REST API in Flask, user chooses one of four models, inputs parameters and gets accuracy score, confusion matrix and downloadable csv file with predicted labels.
'templates' folder contains all necessary html templates for REST API.
'models_evaluations.py' contains a one class that defines a method for each model to plot accuracy metric based on given range of parameters for three models, and one method for neural network to evaluate and pick best hyperparameters.
'requiremets.txt' contains required libraries to run the REST API.