Enhancing Vehicle Price Prediction with Machine Learning Models: An Analytical Approach

Machine learning based vehicle price prediction system is developed to predict accurate price, providing insights for sellers, buyers, and dealers. Leveraging machine learning algorithms, this work explores various analytical techniques to enable smarter data driven decision making system for automotive stakeholders. The work demonstrates analysis on a dataset containing both numerical and categorical features, for example- price as the target feature, brand, engine size, mileage etc. Since the dataset consists of heterogeneous data types, several preprocessing steps are performed including scaling, missing value handling, and categorical variables encoding. Exploratory data analysis is conducted to find the intricate patterns of the data showing necessary visualization. Furthermore, a correlation based feature selections strategy is applied to drop superfluous features from the dataset. Hyperplane based learning algorithm, bootstrap aggregation model, and distance based model are developed for price prediction. Mean squarred error (MSE), mean absolute error (MAE), and coefficient of determination ((R²) are used for evaluation of the model performance. Although all the implemented models perform well, the random forest is selected as the best among the proposed model outperforming the existing works of the literature. The methodology and findings of the work highlights the worthiness of the prediction system. The system is providing a scalable and accurate pricing in vehicle markets. As there is high likelihood of parametric changes in the dataset due to market fluctuations, consumer centric model deployment should be the next focus.