The week's pick
Reseach Article
A Case Study on Car Evaluation and Prediction: Comparative Analysis using Data Mining Models
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 172 - Number 9 |
| Year of Publication: 2017 |
| Authors: Pravarti Jain, Santosh Kr Vishwakarma |
10.5120/ijca2017915205
|
Pravarti Jain, Santosh Kr Vishwakarma . A Case Study on Car Evaluation and Prediction: Comparative Analysis using Data Mining Models. International Journal of Computer Applications. 172, 9 ( Aug 2017), 21-25. DOI=10.5120/ijca2017915205
Abstract
At the point when an individual consider of buying a car, there are many aspects that could impact his/her choice on which kind of car he/she is interested in. There are different selection criteria for buying a car such as prize, maintenance, comfort, and safety precautions, etc. In this paper, we applied various data mining classification models to the car evaluation dataset. The model created with the training dataset has been evaluated with the standard metrics such as accuracy, precision and recall. Our experimental results show that decision trees are the most suitable kind of dataset for the car evaluation dataset.
References
- Ronald F., 2004 "Decision Model for Car Evaluation Final Project in Pattern Recognition.".
- Marko B. and Rajkovic V. 1988 "Knowledge acquisition and explanation for multi-attribute decision making." 8th Intl Workshop on Expert Systems and their Applications.
- Eduard A. S. and E. K. Özyirmidokuz 2015 has purposed a system named as “Mining Customer Feedback Documents” international journal of knowledge engineering.
- Minqing H. B. L. Department of Computer Science from University of Illinois at Chicago 851 South Morgan Street Chicago, IL 60607-7053 “Mining and Summarizing Customer Reviews.” American association for artificial intelligence.
- Gamon M. and Aue A., S. Corston-Oliver, and Eric R. Natural Language dispensation Microsoft Research, Redmond, WA 98052, USA “Pulse: Mining Customer Opinions from Free Text”.
- Nicolas C. and Ziegler, Skubacz M. Maximilian Viermetz has work on “Mining and Exploring Unstructured Customer Feedback Data Using Language Models and Tree map Visualizations”.
- Murali K. P. work on “Analysis of Unstructured Data: Applications of Text Analytics and Sentiment Mining”.
- Marcelo D. M. and Renate J. S. work on sentimental analysis named as “Using Sentiment Analysis to Assess Customer Satisfaction in an Online Job Search Company”.
- UCI Machine Learning Group [online] ftp://ftp.ics.uci.edu/bar/machine-learning-databases.
- Josef K., and Fabio R. (Eds.) 2000 “Lecture Notes in Computer Science” 1857 ‘Multiple Classifier Systems’, First International Workshop, MCS 2000 Cagliari, Italy.
- Duda, R. Hart. Peter E., Stork, David G., “Pattern Recognition” 2ndEdition p. cm. “A Wiley-Interscience Publication.” Partial Contents: Part 1. Pattern classification.
- Ganjikunta, R. S., “A Study on Multiple Classifier Systems”, Computer Science and Engineering, MSU Project for CSE802.
- ittler, J., Member, IEEE Computer Society, Mohamad H., Robert P.W. Duin, and Jiri M. “On Combining Classifiers”. Centre for Vision, Speech and Signal Processing, School of Electronic Engineering, Information Technology, and Mathematics, University of Surrey, Guildford GU2 5XH, United Kingdom.
- Tin K. H., Member, 1994 IEEE, Jonathan J. Hull, Member, IEEE, and Sargur N. Shihari, Senior Member IEEE “Decision Combination in Multiple Classifier Systems”. IEEE Transactions on Pattern Analysis and Machine Intelligence.
- Zapan B., Bohance M., Demsar J. and Bratko I. work on “Feature transformation by function decomposition” to appear in IEEE.
- Bohance M., Zapan B., Bratko I. and Cestnik B. work on “A function-decomposition methed for development of hierarchical multi-attribute decision models”.
- Zapan B., Bohance M., Bratko I. and Demsar J. work on “Machine learning by function decomposition” to appear in ICML.
- Bohance M. and Rajkovic V. work on “Knowledge acquisition and explanation for multi-attribute decision making”.
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