by Nicholas Archambault
This project predicts car prices based on a variety of technical characteristics, including engine size, fuel efficiency, and horsepower. Using the k-nearest neighbors algorithm with varying k-values and feature combinations allows for visualization and understanding of what makes an effective and accurate KNN model.
- Prepare dataset for machine learning applications with rigorous cleaning process.
- Initiate and gauge the comparative results of univariate and multivariate k-nearest neighbors models in order to understand which features most accurately predict car price.
- Modify k-values and tune hyperparameters to achieve more accurate predictions.
- Plot results to visualize the benefits and compromises of k-nearest neighbors testing.
Analysis of the success of univariate, multivariate, and hyperparameter-tuned k-nearest neighbors models and how mean squared error changes with different model attributes.