Publication Type : Journal Article
Publisher : Indian Journal of Science and Technology
Source : Indian Journal of Science and Technology, Volume 9, Number 45 (2016)
Url : http://126.96.36.199/index.php/indjst/article/view/106484
Keywords : Data analysis, prediction, Random forest, Text Analytics, XGBoost.
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Center for Computational Engineering and Networking (CEN), Computer Science
Year : 2016
Abstract : This paper aims at predicting the cuisine based on the ingredients using tree boosting algorithm. Methods/ Analysis: Text mining is important tool for data mining in Ecommerce websites. Ecommerce business is growing with significant rate both in Business-to-Business (B2B) and Business to Customer (B2C) categories. The machine learning based models and prediction method are used in real world ecommerce data to increase the revenue and study customer behavior. Many online cooking and recipe sharing websites have ardent to evolution of recipe recommendation system. In this paper, we describe a scalable end to end tree boosting system algorithms to predict cuisine based on the ingredients and also explored different data analysis and explained about the dataset types and their performances. Novelty/ Improvement: An accuracy of about 80% is obtained for cuisine prediction using XG-Boosting algorithm.
Cite this Research Publication : R. M. Kumar, Dr. M. Anand Kumar, Dr. Soman K. P., and Venkatesh, R., “Cuisine Prediction based on Ingredients using Tree Boosting Algorithms”, Indian Journal of Science and Technology, vol. 9, 2016.