Abstract

This paper explores the use of machine learning to predict the sales of luxury cars in the globe, and BMW as a case study of its sales data in the global market between the period 2010 and 2024. The study focuses on three regression algorithms, which include the Linear Regression, K-Nearest Neighbours (KNN) and Support Vector Machines (SVM) and sees their predictive accuracy, generalisation performance and business applicability. In Python with the help of the Google Colab, an end-to-end analytical pipeline was developed entailing data preprocessing, outlier management, feature engineering, and time-sensitive traintest division. RMSE, MAE, MAPE, and R2 were used as metrics of modeling performance.  The results show that although Linear Regression makes predictability that is stable and interpretable, the regression blandishes short-term market unpredictabilities. KNN is realized to have severe overfitting and does not work well on unseen data. Conversely, SVM has a higher predictive accuracy, generalisation in that the highest R 2 and least error measure were recorded in the test dataset. This paper finds that kernel-based models, with strong feature engineering backgrounds, can be useful especially in predicting the sale of luxury cars in unfamiliar complex and dynamic markets around the world.

Publication Date

4-2026

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Ehsan Warriach

Comments

This thesis has been embargoed. The full-text will be available on or around 11/14/2026.

Campus

RIT Dubai

Download

Share

COinS