Used Car Price Prediction Model: A Machine Learning Approach

Daniel Aprillio Budiono; Kevin Sander Utomo; Kenny Jinhiro Wibowo; Marcell Jeremy Wiradinata

doi:10.29040/ijcis.v5i1.147

Used Car Price Prediction Model: A Machine Learning Approach

Daniel Aprillio Budiono, Kevin Sander Utomo, Kenny Jinhiro Wibowo, Marcell Jeremy Wiradinata

Abstract

The impact of the Covid-19 pandemic over the past two years has slowed down the economy, including the market of used cars. However, the recent decline in the number of cases infected with Covid-19 has reignited interest in the used car market. One of many persisting issues found in the used car market is that sellers want the highest price possible; but, buyers and used car dealers bid the lowest price due to economic stability uncertainty. To accelerate the recovery of the used car industry, various innovations are required. This study proposes the use of the K-Nearest Neighbors (KNN) regression model to predict used car prices to address this issue. The proposed KNN model is a machine learning algorithm which is capable of handling multi-dimensional data and its robustness to noisy data, making it suitable for predicting used car prices based on multiple factors. By analyzing collected data on used car prices, a machine learning-based regression model can be developed to predict used car prices based on factors commonly used in the used car industry, such as year of production, car type, car condition, and others. This study makes use of 504 used car data collected through web scraping as a secondary data collection method. With a relatively small error rate of 8.3% and an R² value of 98.8%, the results of this analysis can provide insight for used car buyers and sellers, to better gauge the price of used cars in the market.

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DOI: https://doi.org/10.29040/ijcis.v5i1.147