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

Authors: Mohd. Samee Uddin, Rabab Fatima Hussain, Asfiya Samreen, Saleha Butool

DOI: https://doi.org/10.37082/IJIRMPS.v11.i1.230312

Short DOI: https://doi.org/gspr3n

Country: India

Full-text Research PDF File:   View   |   Download

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.

Keywords: Supervised Machine Learning, Naive Bayes, Random Forest Tree, Support Vector Machine, KNN, Cosine Similarity

Paper Id: 230312

Published On: 2023-01-02

Published In: Volume 11, Issue 1, January-February 2023

Share this