Prediction of Car Purchase based on User Demands using Supervised Machine Learning

Abstract

One of the key sectors of the national economy is the auto industry. Cars are becoming more and more common as a form of private transportation. When a buyer wants to purchase the ideal vehicle, particularly a car, an evaluation is necessary. Because it is an expensive vehicle, there are a lot of conditions and elements to consider before buying a new one, including price, headlamp, cylinder volume, and spare parts. Therefore, it is crucial for the consumer to choose a purchase that can meet all of the criteria before making any other decisions. In our research, we therefore suggest various well-known methods to improve accuracy for a car purchase. These algorithms were used on our dataset, which consists of 50 data. With a prediction accuracy of 86.7%, Support Vector Machine (SVM) produces the best result of the bunch. In this study, we also present comparison findings for all data samples using various methods for precision, recall, and F1 score.