Predicting Pre-Owned Car Prices Using Machine Learning
Keywords:
Predicting Pre-Owned, Car Prices, Machine Learning, Linear Regression, Decision Tree, Random Forest
Abstract
The steady increase in annual car manufacturing over the past decade is reflected in 2016's record high of more than 90 million passenger vehicles. As a result, there is now a booming industry dedicated to pre-owned automobiles. Both buyers and sellers can now more easily access information on the factors that determine a used car's current market value thanks to the proliferation of internet marketplaces. Using Machine Learning Algorithms like Lasso Regression, Multiple Regression, and Regression Trees, we'll attempt to build a statistical model that can predict the price of a used car based on historical client data and a number of characteristics. Predicting the future value of a car is essential for both consumers and sellers in the auto market. The ability of machine learning algorithms to reliably estimate car pricing based on factors like make, model, mileage, year, and more has been demonstrated. In this research, we offer a model for predicting the future cost of a car using machine learning. In this research, we offer a machine learning-based method for predicting future auto prices. By using feature engineering, data normalisation, and missing value handling, among other pre-processing approaches, we examine a sizable collection of historical automobile sales data. Then, we use machine learning algorithms like linear regression, decision trees, random forests, and support vector machines to train and assess the performance of our model.
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Published
2023-07-31
How to Cite
M, M. A., P. M. H, M. I., R, S., & Priya, B. (2023). Predicting Pre-Owned Car Prices Using Machine Learning. Central Asian Journal of Medical and Natural Science, 4(4), 187-203. Retrieved from https://cajmns.centralasianstudies.org/index.php/CAJMNS/article/view/1686
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