基于基本机器学习算法的自杀和降雨数据集的基础研究

U. Harita, V. U. Kumar, Dorababu Sudarsa, G. R. Krishna, C. Z. Basha, B. S. S. P. Kumar
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引用次数: 6

摘要

印度的自杀率一直在以惊人的速度增长。由于环境的变化,印度的降雨量是不可预测的。由于这种不可预测的降雨,农民面临着巨大的损失。这导致了生命的损失,并导致农民进入自杀状态。对降雨量和自杀率的相关分析将帮助农民避免降雨造成的损失,并可以避免进一步的自杀。印度利用线性回归、逻辑回归、支持向量机和随机森林等机器学习算法实现了降雨量和自杀率的预测。相关分析提供了更好的预测结果,极大地支持了农民,避免了损失。
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A Fundamental Study on Suicides and Rainfall Datasets Using basic Machine Learning Algorithms
Suicides in India have been registering at an alarming rate. The rainfall rate in India is unpredictable due to changes in environment. Farmers faces huge losses due to this unpredictable rains. This leads into loss of lives and results the farmers into suicide state. Relative analysis of rainfall and suicide rate will support farmers to avoid losses due to rainfall and further suicides can be avoided. Prediction of rainfall and suicide rate is achieved in India using machine learning algorithms such as Linear regression, Logistic regression, Support Vector Machine and Random Forest. Relative analysis provides better prediction results which greatly supports the farmers and avoid losses.
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