Comparative Study of Supervised Learning Techniques

Authors

  • Tripti Verma
  • Krutibhash Nayak

DOI:

https://doi.org/10.37591/rtpc.v6i3.2329

Abstract

Abstract

Diabetes is an incessant ailment that happens either when the pancreas does not deliver enough insulin or when the body cannot adequately utilize the insulin it produces. Insulin is a hormone that manages glucose. Hyperglycaemia, or raised glucose, is a typical impact of uncontrolled diabetes and after some time prompts genuine harm to huge numbers of the body's frameworks, particularly the nerves and veins. In 2014, 8.5% of grown-ups matured 18 years and more seasoned had diabetes. In 2016, diabetes was the immediate reason for 1.6 million passing, and in 2012, high blood glucose was the reason for another 2.2 million passing. In this project we are detecting whether the person is diabetic or not using Jupyter notebook on Anaconda software. Various features in the data set are given, and on the basis of those features, we are going to find whether the person is having diabetes or not. Various Supervised Learning Techniques are going to be implemented in this project for finding whether the person is diabetic or not.

Keywords: Support Vector Machine (SVM), machine learning, analysis, detection, techniques

Cite this Article

Tripti Verma, Krutibhash Nayak. Comparative Study of Supervised Learning Techniques. Recent Trends in Parallel Computing. 2019; 6(3): 23–28p.


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Published

2019-11-14

Issue

Vol. 6 No. 3 (2019)

Section

Research Articles

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