Project 2

Clustering:

The K-Means clustering identified 3 clusters in the dataset, visualized using PCA.

Clusters represent different classes of cars based on features like MSRP, horsepower, and weight.

Prediction:

The model performed well, with an accuracy score of around

X% (depending on data), demonstrating that the features used are effective in classifying car types.

Feature importance analysis revealed that features like MSRP, Horsepower, and Weight had the most significant impact on predictions.