Exploring Machine Learning Models to Predict the Diamond Price: A Data Mining Utility Using Weka
- Title
- Exploring Machine Learning Models to Predict the Diamond Price: A Data Mining Utility Using Weka
- Creator
- Amzad Basha M.S.; Martha Sucharitha M.; Devi A.U.; Ashok M.; Oveis P.M.
- Description
- In contrast to gold and platinum, whose values may be fairly determined, determining a diamond's worth involves a far more complex set of considerations. The appropriate rate is based on many factors, not just one of the stones. Diamonds are graded based on their appearance, carat weight, cut quality, and how well they have presented dimensions like a table's surface, depth, and breadth. In order to accurately forecast diamond prices, this study seeks to develop the most effective approaches possible. Different machine learning classifiers are trained on the diamond dataset to forecast diamond prices based on the features. This article shows how to analyze diamond prices using WEKA's data mining software. Diamond data have been utilized for this study. These methods include M5P, Random Forest, Multilayer perceptron, Decision Stump, REP Trees, and M5Rules. For the purpose of estimating the cost of a diamond, different Machine Learning classifiers are compared and contrasted. Performance measures and analysis showed that Random Forest was the best-performing classifier. Experimental findings show, as shown by the coefficient of correlation that Random Forest is better than other classification methods. 2023 IEEE.
- Source
- 2023 9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023, pp. 1870-1877.
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Diamonds; M5P; Machine Learning Classifiers; REP Trees and Data Mining; Weka
- Coverage
- Amzad Basha M.S., Gandhi Institute of Technology and Management (Deemed to be University), GITAM School of Business, Bengaluru, India; Martha Sucharitha M., Deemed to be University, Department of Professional Studies Christ, Bengaluru, India; Devi A.U., SRM Valliammai Engineering College, Department of Management Studies, Chengalpattu, India; Ashok M., Rajalakshmi Institute of Technology, Department of Computer Science and Engineering, Chennai, India; Oveis P.M., Gandhi Institute of Technology and Management (Deemed to be University), GITAM School of Business, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835039737-6
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Amzad Basha M.S.; Martha Sucharitha M.; Devi A.U.; Ashok M.; Oveis P.M., “Exploring Machine Learning Models to Predict the Diamond Price: A Data Mining Utility Using Weka,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19951.