This repository contains a notebook and datset used to build a k-nearest neighbor (KNN) model to predict a car's market price using its attributes.

The KNN algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. In this repository I will solve a regression problem, car price. I will also investigate the effect of varying parameters on the accuracy of a k-nearest neighbors prediction.

To train and test the algorithim I'll be using a dataset that contains information on various cars. For each car I have information about the technical aspects of the vehicle such as the motor's displacement, the weight of the car, the miles per gallon, how fast the car accelerates, and more. You can read more about the data set here and can download it directly here.