Home pricing is a popular and well discussed problem, not only among home buyers, but also among investors, real-estate agencies, online listing platforms, etc. A traditional way to predict home prices is by analyzing home characteristics, such as location, square feet, building year, building materials, etc. However, with the increasing data availabilities, more complex and accurate models utilizing unconventional datasets or modeling techniques are emerging. This project aims to explore the relationship between home prices and various weather observations to better predict home prices.

Exploratory data analysis practice using boxplot, histogram, bivariate analysis, binning, etc.

This exercise conducted analysis on a bank classification data. Utilizing logistic regression and GBM models, I went through the basic GLM modeling techniques and steps, and compared the results with GBM.