Research Article
Multivariate Analysis and Comparison of Machine Learning Algorithms: A Case Study of Cereals of America
@INPROCEEDINGS{10.1007/978-3-031-35081-8_21, author={Rashika Gupta and E. Lavanya and Nonita Sharma and Monika Mangla}, title={Multivariate Analysis and Comparison of Machine Learning Algorithms: A Case Study of Cereals of America}, proceedings={Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part II}, proceedings_a={ICISML PART 2}, year={2023}, month={7}, keywords={Feature selection linear regression KNN random forest logistic regression decision tree}, doi={10.1007/978-3-031-35081-8_21} }- Rashika Gupta
E. Lavanya
Nonita Sharma
Monika Mangla
Year: 2023
Multivariate Analysis and Comparison of Machine Learning Algorithms: A Case Study of Cereals of America
ICISML PART 2
Springer
DOI: 10.1007/978-3-031-35081-8_21
Abstract
This research work aims to analyze the nutritional value of different cereals available in the market through various machine learning models. This analysis is supplemented with the visualization of data also for enhanced understanding. This understanding enables users to devise market strategies as they are competent to evaluate quality of each product and thus its reception in the market. The works starts with statistical analysis through of the data through various plots which provides insight of the data. Further authors perform a comparative analysis of different cereals based on various parameters. This analysis helps to determine the best cereal according to our requirements. The authors have implemented machine learning models on the data to predict the vitamins of any cereal based on their nutritional value. The implementation of various models viz. Linear regression, decision tree, logistic regression, random forest, and KNN advocates the efficacy of various machine learning models to the given problem.
