Improvising Purchase Prediction using hybrid approach utilizing Apache Mahout with C45. and Naive Bayes classifier
E-commerce Purchase Prediction; Clicking Behavior Data; Probability Statistics
Ecommerce has grown rapidly over the years. Many people are using popular channels to buy goods and services on the internet. It therefore becomes very important for shopping sites to accurately predict what customers want to buy to increase sales or increase customer satisfaction. Traditional algorithms like joint filtering are extremely popular in predicting user settings in the book, guide, or music area. But they face issues where rating data is sparse or not available in the shopping domain. Compared to the number of ratings on an ecommerce shopping site, the volume of many user click data is sufficient for the user's liking. So in this article, we propose a prediction model based on probability statistics that use user click behavior data.
"Improvising Purchase Prediction using hybrid approach utilizing Apache Mahout with C45. and Naive Bayes classifier ", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.3, Issue 6, page no.33 - 39, June-2018, Available :https://ijsdr.org/papers/IJSDR1806007.pdf
Volume 3
Issue 6,
June-2018
Pages : 33 - 39
Paper Reg. ID: IJSDR_180365
Published Paper Id: IJSDR1806007
Downloads: 000347207
Research Area: Engineering
Country: Aurangabad, Maharashtra, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
