The task is to determine what influences and drives the car prices given its technical specs. Specifically, which car(s) I believe to be most under/overpriced and why.
This was a take-home assignment for a Graduate Data Analyst Consultant position at a Machine Learning Consultancy
Within the repo you will find the following:
- car_data_cleaned.csv
(Raw data .csv file sent by the Machine Learning Consultancy)
- code.(pdf/html)
PDF (.pdf) & Web (.html) versions of my notebook, just in case there’s technical difficulties with original Jupyter notebook)
- code.ipynb
(Jupyter Notebook containing all analysis, code, plots, etc)
- method_results_conclusion.pptx
Three slide presentation outlining method, results, and conclusion)
- Exploratory Data Analysis
- Clean Data
- XGBoost Regressor with 5-Fold Cross-Validation
- Under/Over-priced Car Ranking per Fold
- XGBoost Regressor on Top/Bottom 50 in Ranking
- SHapley Additive exPlanations (SHAP)
- 2018 Mercedes-Benz G Class Specs: G550 4x4 Squared
True MSRP = 227 300 Predicted MSRP = 120 390.77 Ratio (True/Predicted) = 1.888
- 2019 Jaguar XE SV Specs: Project 8 AWD
True MSRP = 187 500 Predicted MSRP = 119 680.59 Ratio (True/Predicted) = 1.566
- 2018 Porsche 911 Specs: GT2 RS Coupe
True MSRP = 293 200 Predicted MSRP = 193 417.28 Ratio (True/Predicted) = 1.515
- 2018 Jeep Grand Cherokee Specs: Altitude 4x4
True MSRP = 33 195 Predicted MSRP = 47 957.96 Ratio (True/Predicted) = 0.692
- 2018 Jeep Grand Cherokee Specs: Upland 4x4
True MSRP = 33 195 Predicted MSRP = 47 957.96 Ratio (True/Predicted) = 0.692
- 2019 Dodge Challenger Specs: R/T Scat Pack Widebody RWD
True MSRP = 38 995 Predicted MSRP = 56 335.17 Ratio (True/Predicted) = 0.692
