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Old Car Price Prediction using Machine Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 7 |
| Year of Publication: 2023 |
| Authors: Aditya Sirohi, Akshatra Balyan, Sunil Kumar |
10.5120/ijca2023922725
|
Aditya Sirohi, Akshatra Balyan, Sunil Kumar . Old Car Price Prediction using Machine Learning. International Journal of Computer Applications. 185, 7 ( May 2023), 28-33. DOI=10.5120/ijca2023922725
Abstract
Guessing the value of old cars is a topic of intense interest since it calls for distinctive effort from a subject-matter specialist. The manufacturer in the industry determines the cost of a new automobile, plus any additional taxes that the government must pay. Customers who purchase a new automobile are therefore certain that their financial commitment will be beneficial. However, old car sales are rising internationally as a outcome of new car price increases and consumers' financial inability to purchase them. Almost the previous ten years, the number of automobiles manufactured has constantly increased; in 2022, there will be over 80 million passenger cars produced. With the use of machine learning techniques like Extra Trees Regressor, Random Forest Regressor, and Regression Trees. we'll try to expend the model that predicts the cost of a old vehicle using past customer data and a specified set of characteristics. Regression algorithms are employed because they give clients continuous results as opposed to categorical final results. As a result, it will be feasible to forecast the exact cost of an automobile rather than just its price range. The user interface, which requests input from any user and shows a car's cost in response to that input, was likewise built using React js. The main aim of this exploration is to produce machine learning models that can directly predict an old car's cost based on its parameters so that customer or user may make best decisions.
References
- Enis gegic, Becir Isakovic, Dino Keco, Zerina Masetic, Jasmin Kevric, “Car Price Prediction Using Machine Learning Technique”, (TEM Journal 2019)
- Nitis Monburinon, Prajak Chertchom, Thongchai Kaewkiriya, Suwat Rungpheung, Sabir Buya, Pitchayakit Boonpou, “Prediction of Prices for Old Car by using Regression Models” (ICBIR 2018)
- Used cars database. (n.d.) Retrieved from: https://www.kaggle.com/orgesleka/used-carsdatabase. [accessed: June 04, 2018].
- Pattabiraman Venkatasubbu, Mukkesh Ganesh, “Used Cars Price Prediction using Supervised Learning Techniques”, International Journal of Engineering and Advanced Technology (IJEAT), Vol. 9, Issue 1S3, Dec. 2019.
- Sameerchand Pudaruth, “Predicting the Price of Used Cars using Machine Learning Techniques”;(IJICT 2014)
- Listiani M. 2009. Support Vector Regression Analysis for Price Prediction in a Car Leasing Application. Master Thesis. Hamburg University of Technology
- Chen, Tianqi, and Carlos Guestrin. "Xgboost: A scalable tree boosting system." Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. ACM, 2016.
- Ke, Guolin, et al. "Lightgbm: A highly efficient gradient boosting decision tree." Advances in Neural Information Processing Systems. 2017.
- Fisher, Walter D. "On grouping for maximum homogeneity." Journal of the American statistical Association 53.284 (1958): 789-798.
- https://scikitlearn.org/stable/modules/classes.html: Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011.
- Frost,J. 2013. Multiple Regression Analysis: Use Adjusted R-squared and Predicted R-squared to Include the Correct Number of Variables. Available online from: http://blog.minitab.com/blog/adventures-instatistics/multiple-regession-analysis-use-adjusted-rsquared-and-predicted-r-squared-to-include-the-correctnumber-of-variables (Last accessed: 29-11-2016).
- Aizerman, M. A. (1964). Theoretical foundations of the potential function method in pattern recognition learning. Automation and remote control, 25, 821- 837.
- Ashish Chandak, Prajwal Ganorkar, Shyam Sharma, Ayushi Bagmar, Soumya Tiwari, Car Price Prediction Using Machine Learning,
- Ramesh, Shashank Markapuram. “Price Optimization with Machine Learning: What Every Retailer Should Know.” 7Learnings, 23 Aug. 2022, https://7learnings.com/blog/price-optimization-with-machine-learning-what-every-retailer-should-know/.
- Reese, Austin. “Used Cars Dataset.” Kaggle, 6 May 2021, https://www.kaggle.com/datasets/austinreese/craigslist-carstrucks-data.
- Gokce, Enes. “Predicting Used Car Prices with Machine Learning Techniques.” Medium, Towards Data Science, 10 Jan. 2020, https://towardsdatascience.com/predicting-used-car-prices-with-machine-learning-techniques-8a9d8313952.
- Kumbar, Kshitij, et al. “CS 229 Project Report: Predicting Used Car Prices - Stanford University.” CS 229 Project Report, 2019, https://cs229.stanford.edu/proj2019aut/data/assignment_308832_raw/26612934.pdf.
- Ghori, Mohammed. “Data Cleaning + EDA + Used Cars Prediction (86%).” Kaggle, Kaggle, 1 June 2020, https://www.kaggle.com/code/msagmj/data-cleaning-eda-used-cars-prediction-86.
- Weiran, Sun. “Hyper Parameter Tuning with Randomised Grid Search.” Medium, Towards Data Science, 4 Sept. 2019, https://towardsdatascience.com/hyper-parameter-tuning-with-randomised-grid-search-54f865d27926.
- N. Monburinon, P. Chertchom, T. Kaewkiriya, S. Rungpheung, S. Buya and P. Boonpou, "Prediction of prices for used car by using regression models," 2018 5th International Conference on Business and Industrial Research (ICBIR), Bangkok, 2018, pp. 115-119.
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