Using Linear Regression For Used Car Price Prediction

Abstract


 
 Recently, there have been studies on the use of machine learning algorithms for price prediction in many different areas such as stock market, rent a house and used car sales. Studies give information about which algorithm is more successful in price prediction using different machine learning methods. The most commonly used method for price prediction is the linear regression model. In our study, we examined the effectiveness of the linear regression model for used car price prediction. In our study, we applied the linear regression model on a data set that includes the features and price information of vehicles in Turkey for the year 2020. As a result, when we selected 1/3 of the data set as the test data, we observed that the R2 score for the prediction success of our model was 73%. More successful results can be obtained with different data sets or a more detailed data preprocessing.

Revolutionizing the used car market: Predicting prices with XGBoost

“…In contemporary dynamic and ever-evolving automobile industry landscape, the ability to make informed decisions has become a critical factor in an organization's success. In fact, in the selling process of a vehicle, the most important issue is to determine the most affordable price without giving a price below the value of the car [1]. Neither too high nor too slow is a good situation.…”