A COMPREHENSIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR PREDICTING CAR PURCHASE BASED ON CUSTOMERS DEMAND

Abstract

The rapid growth of the automotive industry and increasing availability of customer data have made predictive analytics an essential tool for understanding consumer purchasing behavior. This study presents a comprehensive analysis of machine learning algorithms for predicting car purchases based on customer demand and preferences. The proposed approach focuses on analyzing various influencing factors such as customer demographics, income level, lifestyle, vehicle features, price sensitivity, and market trends. Machine learning techniques enable the identification of hidden patterns and relationships within large datasets, which are difficult to capture using traditional statistical methods. The methodology involves data collection from automotive datasets, followed by preprocessing steps such as data cleaning, feature selection, and normalization to improve model performance. Multiple machine learning algorithms, including Linear Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), and Artificial Neural Networks (ANN), are implemented and compared to evaluate their effectiveness in predicting customer purchase decisions. Studies show that ensemble methods and deep learning models often provide higher accuracy due to their ability to capture complex nonlinear relationships . The system also considers key parameters such as vehicle specifications, brand value, mileage, and pricing, which significantly influence purchasing behavior . Experimental results indicate that advanced models like Random Forest and Neural Networks outperform traditional methods in terms of prediction accuracy and reliability. However, challenges such as data quality, model interpretability, and computational complexity remain. Overall, this study highlights the effectiveness of machine learning in predicting car purchase behavior and provides valuable insights for automotive companies, dealers, and customers to make informed decisions.