Improving the Automobile Purchasing Behavior of Customer: Classification Techniques
S.Kavitha1, S.Manikandan2

1S.Kavitha, Research Scholar, Research & Development Centre, Bharathiar University, Coimbatore, TamilNadu, India.
2S.Manikandan, 2Research Supervisor, Prof & Head, Department of CSE,Sriram Engineering College, Chennai, TamilNadu, India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 2219-2223 | Volume-9 Issue-2, December, 2019. | Retrieval Number:  B2924129219/2019©BEIESP | DOI: 10.35940/ijeat.B2924.129219
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Abstract: Data mining (DM) is the automate detection of relevant pattern from the database. E-Commerce is a very famous as well as frequently used new technique in the real world applications. DM is an automate detection of relevant patterns from large amount of information repositories. E-Commerce is a Killer-domain for data mining. DM is often a complex process and may require a variety of steps before some results are obtained. To predict behaviors and future trends many tools are available in DM, also allowing the businesses to make proactive pathways for the customer. In this research work, it is taken online shoppers purchasing vehicle data set and find accuracy in terms of its purchasing behavior using some of the classification algorithms. The classification algorithms namely Bayes Net and NavieBayse are utilized for the analysis and a comparative study of both the algorithms are carried out. Finally, the performance of the chosen algorithm is suggested for analyzing the vehicle data set based on the purchasing behavior of the customer and predicts some accuracy.
Keywords: Classification Algorithms, Bayes Net, Naïve Bayes Algorithms.