This week, Ellis Hughes and I use a TidyTuesday data set that details the number of things various countries have sent into space over the past several decades. In this short video we discuss exploratory data analysis via plots and cover some data wrangling techniques.
To watch our screen cast, CLICK HERE.
To access our code, CLICK HERE.
This week, Ellis Hughes and I have a super fun TidyX Screen Cast that incorporates a number of different important tasks in your programming arsenal: writing custom functions and returning lists.
The functions we write don’t only explain how many Spaghettio’s it takes to write out Lord of the Rings but can actually be applied to various different books and novels (as we show in the episode).
To watch the screen cast, CLICK HERE.
To access our code, CLICK HERE.
In this installment of predicting whether MLB pitchers will make the Hall of Fame, Ellis Hughes and I take the Random Forest model we built in Episode 175 and discuss how you can extract the predictions from each of the individual trees and then build uncertainty intervals so that you can construct an entire distribution of predictions. We then take these predictions and build a custom function for comparing the predicted distribution of two players and whether they will make it to the Hall of Fame.
To watch the screen cast, CLICK HERE.
To access our code, CLICK HERE.
Ellis Hughes and I continue to work with the MLB pitcher data, courtesy of the {Lahman} baseball package.
This week we walk through using a random forest model to calculate the probability a pitcher will make it to the Hall of Fame given several different performance stats.
In this episode we cover:
To watch our screen cast, CLICK HERE.
To access our code, CLICK HERE.
Continuing to build on our modeling strategy from the previous few weeks, this week Ellis Hughes and I show how to set up a neural network prediction model in R using {tensorflow}.
To watch our screen cast, CLICK HERE.
To access our code, CLICK HERE.