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Cuisine Prediction based on Ingredients using Tree Boosting Algorithms
Objective: 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.
Data Analysis, Prediction, Random Forest, Text Analytics, XGBoost.
- Su H, Lin T-W, Li C-T, Shan M-K, Chang J. Automatic recipe cuisine classification by ingredients. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication (UbiComp ‘14 Adjunct). ACM: New York, NY; 2014.
- Kumar MA, Se S, Soman KP. AMRITA-CEN@FIRE 2015: Extracting entities for social media texts in Indian languages. CEUR Workshop Proceedings. 2015; 1587:85–8.
- Kuo F-F, Li C-T, Shan M-K, Lee S-Y. Intelligent menu planning: recommending set of recipes by ingredients. Proceedings of the ACM multimedia 2012 workshop on Multimedia for Cooking and Eating Activities (CEA ); 2012.
- Agrawal R, Srikant R. Fast algorithms for mining association rules in large databases. Proceedings of the 20th International Conference on Very Large Data Bases(VLDB ‘94). Bocca JB, Jarke M, Zaniolo C, editors, Morgan Kaufmann Publishers Inc.: San Francisco, CA, USA; 1994. p. 487–99.
- Jiang Y, Yu S. Mining e-commerce data to analyze the target customer behavior. Proceedings of the First International Workshop on Knowledge Discovery and Data Mining (WKDD ‘08). IEEE Computer Society: Washington, DC, USA; 2008.
- Hao K, Duo-Lin L, Zhi-Jie L. The research on e-commerce website success mode. 2010 Asia-Pacific Conference on Wearable Computing Systems (APWCS), Shenzhen; 2010. p. 299–302. Chen, Carlos Guestrin. XGBoost: A Scalable Tree Boosting System.
- Anagnostopoulos T, Anagnostopoulos C, Hadjiefthymiades S. Mobility prediction based on machine learning. 2011 IEEE 12th International Conference on Mobile Data Management, Lulea; 2011. p. 27–30.
- Sowjanya K, Singhal A, Choudhary C. MobDBTest: A machine learning based system for predicting diabetes risk using mobile devices. Advance Computing Conference (IACC), 2015 IEEE International, Banglore; 2015. p. 397– 402.
- Porshnev A, Redkin I, Shevchenko A. Machine learning in prediction of stock market indicators based on historical data and data from twitter sentiment analysis. 2013 IEEE 13th International Conference on Data Mining Workshops, Dallas, TX; 2013. p. 440–4.
- Grossmann E. AdaTree: Boosting a weak classifier into a decision tree. Computer Vision and Pattern Recognition Workshop; 2004. p. 105. DOI: 10.1109/CVPR.2004.22.
- Abinaya N, John N, Ganesh HBB, Kumar MA, Soman KP. AMRITA-CEN@FIRE-2014: Named entity recognition for Indian languages using rich features, ACM International Conference Proceeding Series, 2014 Dec 05–07; 2014. p.103–11.
- Sanjay SP, Kumar MA, Soman KP. AMRITA-CEN-NLP@ FIRE 2015:CRF based named entity extraction for twitter microposts, CEUR Workshop Proceedings. 2015; 1587:96–9.
- Abinaya N, Kumar MA, Soman KP. Randomized kernel approach for Named Entity Recognition in Tamil. Indian Journal of Science and Technology. 2015; 8(24).
- Kumar RMRV, Kumar MA, Soman KP. AmritaCEN-NLP@ FIRE 2015: Language identification for Indian languages in social media text. CEUR Workshop Proceedings. 2015; 1587:26–8.
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