An Explainable Machine Learning Method is a B.Tech project topic for Electrical Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
An Explainable Machine Learning Method Project Details
| Abstract |
This project presents an explainable machine learning framework designed to predict the market valuation of used electric vehicles within emerging regional markets, specifically focusing on the Iraqi automotive sector. Traditional internal combustion engine valuation models often fail to capture the unique pricing determinants of electric vehicles, such as battery capacity, state of health, and driving range. To address this limitation, a dataset comprising regional electric vehicle advertisements is analyzed using tree-based regression algorithms, including CatBoost, XGBoost, and Random Forest. To ensure model transparency and interpretability, SHapley Additive exPlanations are integrated into the pipeline, allowing for a detailed feature-importance analysis of how specific parameters influence the final price prediction. The implementation
demonstrates that the CatBoost model achieves superior predictive accuracy, yielding an R-squared value of approximately 0.94. This framework provides a structured methodology for stakeholders, financial institutions, and consumers to accurately assess electric vehicle depreciation and market value, bridging the gap between complex machine learning predictions and human-understandable decision-making in emerging electric vehicle markets.
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| Reference Paper |
An Explainable Machine Learning Method for Predicting Electric Vehicle Prices in the Iraqi Market |
| Domain |
Electrical Engineering |
| Sub-Domain |
Electric Vehicle Projects |
| PDF Download |
Download / View PDF |
| Get Help |
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