Exercises 3.1 and 3.2 from Kuhn and Johnson’s Applied Predictive Modeling. Analysis of the Glass and Soybean datasets, including predictor distributions, relationships, outliers, suggested transformations, near-zero variance, and missing data.

My planned approach for calculating each chess player’s expected score and identifying the tournament’s greatest over performers and under performers.

My planned approach for recreating, tidying, transforming, and analyzing airline arrival data in R.

R code for extracting chess tournament player information and calculating each player’s average opponent pre-rating.

My planned approach for cleaning chess tournament results and calculating each player’s average opponent pre-rating in R.

Exercises 3.1–3.5 and 3.7–3.9

My planned approach for calculating year-to-date and six-day moving averages for multiple stock price series in R.

My planned approach for using movie-rating data to create Global Baseline Estimate recommendations in R.

An analysis of classification performance at probability thresholds of 0.2, 0.5, and 0.8.

My planned approach for loading and transforming the Titanic dataset in R