Estimating Automobile Prices Utilizing Machine Learning Algorithms

This study delves further into machine learning algorithms for estimating automotive prices, using Python-based frameworks. The study includes critical processes such as data collection, preprocessing, feature selection, model evaluation, and implementation. Our technique attempts to examine the efficacy of different machine learning models in predicting automobile prices by applying multiple regression-based algorithms such as Linear Regression, Random Forest, Gradient Boosting, Support Vector Regression, and K-Nearest Neighbors. The paper also tackles critical process issues such as data quality, feature selection, and model interpretability. The findings contribute to a data-driven methodology that can help buyers, sellers, and automotive experts make better decisions by giving accurate real-time price projections.