A Consumer Behavior Prediction Method for E-Commerce Application
Kareena1, Raj Kumar2
1Kareena, Department of CSE, Institute of Engineering, Chandigarh University, Gharuan, Mohali (Punjab), India.
2Er. Raj Kumar, Department of CSE, Institute of Engineering, Chandigarh University, Gharuan, Mohali (Punjab), India.
Manuscript received on 24 August 2019 | Revised Manuscript received on 05 September 2019 | Manuscript Published on 16 September 2019 | PP: 983-988 | Volume-8 Issue-2S6 July 2019 | Retrieval Number: B11710782S619/2019©BEIESP | DOI: 10.35940/ijrte.B1171.0782S619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The consumer behavior analysis is the technique which is applied to analyze consumer behavior. The customer behavior analysis has the three steps which are pre-processing, feature extraction and classification for prediction. In the previous work, Naïve Bayes was applied for the consumer behavior analysis. In this work, hybrid classifier is designed for the customer behavior analysis using Decision Tree and KNN. The proposed method is implemented in anaconda python and results are compared with the previously used Naïve Bayes method, for this analysis consumer reviews from Amazon website are used.
Keywords: Consumer Behavior, Decision Tree, KNN, Naïve Bayes.
Scope of the Article: Regression and Prediction