Predicting Car Acceleration

A Random Forest Prediction Exercise

for the Johns Hopkins Bloomberg School of Public Health

Data Science Specialization -- Developing Data Products

via Coursera.org

May 2015

What does it do?

This Shiny App uses the Auto dataset in the ISLR package. It sets up a Random Forest prediction model to determine a car's acceleration based on several attributes.

Data Exploration - Auto dataset

1. 392 observations (vs. 32--mtcars), 9 variables (vs. 11--mtcars).
2. 2 numeric variables (year, origin) that should be ordinal
3. 3 variables have relatively higher correlation to acceleration
   

The prediction model

Feature Selection and Cross Validation Fine-Tuning

• horsepower
  
• displacement
  
• weight
  
• cylinders
   
  Fine-tune with 10-fold cross validation

Sample Error Rate

Random Forest has lower RMSE and explains more of the data variability.