This was a weekly lab done in Harvard's S-14A course. This was my first experience with building machine learning models with sklearn and XGBoost. The final product was built with a Flask backend. When run, the application will show the evaluation for a random property in the csv file and compare the predicted value to the actual value. Different model types explored in this project were XGBoost and different types of Forest models.
BakerWJ/Machine-Learning-Lab
A website built on Flask that displays predicted values of properties in Massachusetts based on XGBoost model predictions
Python