Hybrid Model for Predicting Used Car Prices: Integrating Natural Language Processing with Random Forest Regressor

The automotive sector has a notable upswing in the sales of pre-owned vehicles, hence necessitating the development of precise pricing models to enable knowledgeable decision-making for both purchasers and vendors. In order to predict used automobile prices, we provide a novel hybrid methodology in this work that combines Natural Language Processing (NLP) methods with the Random Forest regressor. Our method leverages structured and unstructured data sources to extract valuable contextual information that is hidden in textual listings for automobiles. Preprocessing a heterogeneous dataset with unstructured textual descriptions and structured attributes (make, model, year, mileage) is part of the methodology. We leverage natural language processing (NLP) approaches to extract informative features from textual representations, improving the model's comprehension of subtleties in the conditions and attributes linked to individual car listings.