Optimizing Car Recommendations: Power Analysis of Machine Learning Algorithms

Title

Optimizing Car Recommendations: Power Analysis of Machine Learning Algorithms

Creator

Nishad, V. Muhammed; Naveen, J.; Hashim, Aaqil Faheem; Kailas, T.S.

Description

The growing demand for efficient automobile recommendation systems has called for the need of algorithms that can proficiently assess and predict user preferences. This research focuses on the assessment of various machine learning algorithms, K-Nearest Neighbors (KNN), Decision Trees, Linear Regression, Weighted Scoring, and Content-Based Filtering. One of the main concerns of this study is to identify which recommendation algorithm is best suited for vehicle suggestions from an application perspective based on cost, mileage, engine size, fuel category, and user reviews. A dataset of 100 records was utilized to perform preliminary analyses so that algorithms were tested. Preprocessing procedures involved missing data handling, normalization of numerical features, and categorical variables encoding so that full precision predictions were obtained. Performances of algorithms were tested in terms of accuracy, scalability, and computational efficiency. Based on results, the highest accuracy was realized by Decision Trees with 85%, followed by Weighted Scoring at 82% and Linear Regression at 78%. Although KNN has an excellent accuracy of 74%, it is less scalable for very large datasets that are needed for an automobile recommendation system. The experimental results of this paper add to the evolving knowledge on the application of machine learning in the automobile world, again reinforcing the adequacy of Decision Trees as a valid technique for car recommendation systems. Recommendations for future studies include enhancing the database and exploring contemporary approaches to improve the accuracy of recommendations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Source

Lecture Notes in Networks and Systems;Volume;1355 LNNS;pp.179-191

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Content based filtering; Decision tree; KNN; Linear regression; Recommendation system; Weighted scoring

Coverage

Nishad V.M., Christ (Deemed to be University), Bengaluru, India; Naveen J., Christ (Deemed to be University), Bengaluru, India; Hashim A.F., Christ (Deemed to be University), Bengaluru, India; Kailas T.S., Christ (Deemed to be University), Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981964882-5;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-4883-2_15

https://www.scopus.com/pages/publications/105010652985?origin=resultslist