Introduction This is post three of a multi-part analysis of college basketball game outcomes. Part one shows how the data was aquired and loaded, and part two is a brief exploratory data analysis exploring a few teams for the 2005 season. Links to those can be found below.
Post 1 Post 2
This post will focus on some data munging and feature engineering. The data set is currently not set up very well for modeling.
Introduction This post is part two of a multi-part analysis of college basketball outcomes. Part one showed a couple cool features of R markdown code chunks, and how to use the Kaggle API to download data. If you didn’t see that one, you can find it here.
In this post, I am going to dig into the data set a bit, to understand some of the fields and their relationships with one another.
Introduction Well hello there world. This is my first blog post. Yay.
I am going to be starting a series of posts revolving around a Kaggle competition a couble of years back that used NCAA basketball regular season and tournament data.
The data used in this analysis comes from the kaggle competition ’ March Machine Learning Mania 2016’. This tutorial will walk through a few things to get us ready for an analysis.