Diamond Price Prediction Using Data Mining Techniques

Kalpa Nigam Acharya () and Manas Ranjan Senapati
Additional contact information
Kalpa Nigam Acharya: Veer Surendra Sai University of Technology
Manas Ranjan Senapati: Veer Surendra Sai University of Technology

A chapter in Proceedings of the 4th International Conference on Research in Management and Technovation, 2024, pp 71-85 from Springer

Abstract: Abstract Data mining is the technique of uncovering different patterns and finding relationships and anomalies in the large datasets. It is used for the extraction of valuable information from the available data present in the form of a dataset. Prediction has been proposed as one of the useful techniques of data mining which can be used for predicting about future trends. Generally, regression is preferred for prediction. In a regression problem, the final output is calculated as the mean of all outputs, which is also called Aggregation. In this paper, a diamonds dataset was downloaded from Kaggle. The dataset consists of 53,940 instances and 10 features. The following dimensions were used for predicting the prices of a diamond: carat, cut, color, clarity, depth, table, price, x, y and z. Based on the above, three regression algorithms, i.e., Random Forest Regressor, K-Neighbors Regressor and Decision Tree Regressor and one optimization technique, i.e., Particle Swarm Optimization had been applied and the performance of the four algorithms were compared based on different performance and validation metrics such as testing accuracy (R2-score), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The testing accuracy of Random Forest Regressor was found to be 0.9822175620286142, Particle Swarm Optimization 0.9809447466912986, K-Neighbors Regressor 0.9683118493918951 and Decision Tree Regressor 0.9675318135094464. It was observed that the testing accuracy of Random Forest Regressor was the best as compared to the rest others.

Keywords: Data mining; Machine learning; Diamond price; Regression; Random forest regressor; K-neighbors regressor; Decision tree regressor; Particle swarm optimization (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-99-8472-5_8

Ordering information: This item can be ordered from
http://www.springer.com/9789819984725

DOI: 10.1007/978-981-99-8472-5_8

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().