Abstract
Currently, owning a car is a necessity, as it plays a significant role in human transportation for different purposes such as going to work and to the hospital. However, with the current economic challenges, buying expensive cars can be a burden. The car market has shifted toward more affordable used cars. Due to the increasing number of used cars being sold, the price of used cars has become a major issue that could affect our sustainable way of living. The objective of this research is to understand the impact of the problem and to find empirical solutions by implementing a variety of machine learning techniques and big data tools on the prices of used cars. Thus, we develop a linear regression model that can estimate used car prices based on various features to answer the following research questions: (R.Q.1) How significantly does an independent feature in the dataset affect the dependent variable (car price)? (R.Q.2) Is a linear regression model effective for prediction of used car prices? (R.Q.3) How does prediction of used car prices support sustainability? Finally, we present our results in the form of answers to these questions, including some limitations and future research.