# Predicting Car Mileage Using Machine Learning

**Audience**

Data Scientists

**Purpose**

This article explores if car year of manufacture and weight are good predictors of mileage of an automobile.

**Purpose of ML Model**Predict the car mileage per gallon based on features like weight and year of manufacture. KNN (K-Nearest Neighbor) regression model is being used.

**About the Dataset**

Auto dataset available in R, ISLR package was used for this analysis. 392 observations of cars with 9 attributes as follows

mpg : miles per gallon

cylinders : number of cylinders between 4 to 8

displacement : in cubic inches

horsepower : engine horsepower

weight : in pounds

acceleration : time to accelerate 0 to 60 miles in seconds

year : model year

origin: origin of car as American (1), European (2) or Japanese (3)

name : vehicle name

**ML Modeling **

- Divide dataset into train and test sets, 65% and 35% approximately.

- Scale weight and year columns of the test set, as standard deviations are different.

- Standardize the test set columns based on the original mean and standard deviation of the training set.

- Ran KNN regression with k-value of 1. This resulted in a MSE of 15.25

- Applied 10-fold cross validation, KNN regression for 50 k-values and computed mean squared error.

**Analysis**A k-value of 17 was chosen since it has the lowest MSE of 9.05. Larger k-value would mean low variance model.