Predicting Used Car Prices with Regression Techniques |
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| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-6 |
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| Year of Publication : 2024 | ||
| Authors : Saurabh Kumar, Avinash Sinha | ||
| DOI : 10.14445/22312803/IJCTT-V72I6P118 | ||
How to Cite?
Saurabh Kumar, Avinash Sinha, "Predicting Used Car Prices with Regression Techniques," International Journal of Computer Trends and Technology, vol. 72, no. 6, pp. 132-141, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I6P118
Abstract
This paper explores the predictive modeling of used car prices using regression techniques, focusing on the Indian automotive market. Utilizing historical data from CarDekho.com, the goal of this paper is to identify key predictors of used car prices and develop a robust multiple linear regression model. The dataset includes various features such as model, year of manufacture, kilometers are driven, fuel type, seller type, transmission type, number of previous owners, mileage, engine size, and maximum power. Data preprocessing involved converting units to numerical values and calculating the car’s age. The exploratory data analysis revealed that car age, brand, and power are significant determinants of price, while the number of seats and engine size had less impact. Multiple models were tested, including transformations and variable selection methods. The final model, employing the Weighted Least Squares (WLS) method, explained 90% of the variation in used car prices. Model validation showed a high correlation between actual and predicted prices, with a mean absolute percentage error (MAPE) of approximately 20%. The results highlight the efficacy of regression techniques in price prediction and provide valuable insights for consumers and sellers in the used car market. This study demonstrates the importance of data-driven approaches in understanding market dynamics and optimizing pricing strategies.
Keywords
Predictive modeling, Artificial intelligence, Used car prices, Regression analysis, Machine learning.
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