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Cuisine Prediction based on Ingredients using Tree Boosting Algorithms

Affiliations

  • Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore - 641112, Tamil Nadu, India

Abstract


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.

Keywords

Data Analysis, Prediction, Random Forest, Text Analytics, XGBoost.

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References


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