An Efficient Suicide based Dataset using Machine Learning Algorithms
Sidharth Raj. R.S1, A. John Paul Praveen2, S. Balaji3
1Sidharthraj. R.S, Department of Electronics and Communication Engineering, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
2A. John Paul Praveen, Department of Electronics and Communication Engineering, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
3S. Balaji, Department of Electronics and Communication Engineering, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
Manuscript received on 15 October 2019 | Revised Manuscript received on 29 October 2019 | Manuscript Published on 26 December 2019 | PP: 1267-1271 | Volume-8 Issue-12S October 2019 | Retrieval Number: K134110812S19/2019©BEIESP | DOI: 10.35940/ijitee.K1341.10812S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A study is presented on analyzing the major factors that affect the number of suicides in different parts of India from year 2000 to 2012 and using them to predict the number of suicides in the future. By analyzing the data and predicting the major causes of suicides it can help government to know which part of population is most affected, so that the government can provide required steps to avoid suicides. The Indian government records the database of each suicide occurs in India. Along with the age-group, cause of death, state of victim, this data was made public by crime branch bureau of the data analytics purpose. Relationship will be made between the different features of suicide so that a linear relationship can be formed with the help of linear regression and other machine learning algorithms will be used to develop a model for the prediction of number of suicides in the future. It has been found that the results obtained by machine learning algorithms are more accurate when compared with the traditional algorithms.
Keywords: Suicide, Analysis, Linear Regression, Machine Learning Algorithms.
Scope of the Article: Machine Learning