Annual Rainfall Prediction of Various States in India using Linear Regression
Deepa.G1, Dinesh.B2, Naveen Kumar.K3
1Dr. G. DEEPA, Assistant Professor, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Tamil Nadu, India.
2Dinesh.B, Student, M.Sc Data Science, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Tamil Nadu, India.
3Naveen Kumar.K, Student, M.Sc Data Science, Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Tamil Nadu, India.
Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 951-954 | Volume-9 Issue-2, July 2020. | Retrieval Number: B4786079220/2020©BEIESP | DOI: 10.35940/ijrte.B4786.079220
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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: Rainfall prediction is a significant part in agriculture, so prediction of rainfall is essential for the best financial development of our nation. In this paper, we represent the linear regression method to predict the yearly rainfall in different states of India. To predict the estimate of yearly rainfall, the linear regression is implemented on the data set and the coefficients are used to predict the yearly rainfall based on the corresponding parameter values. Finally an estimate value of what the rainfall might be at a given values and places can be establish easily. In this paper, we demonstrate how to predict the yearly rainfall in all the states from the year 1901 to 2015 by using simple multi linear regression concepts. Then we train the model using train _test_ split and analyze various performance measures like Mean squared error, Root mean squared error, R^2 and we visualize the data using scatter plots, box plots, expected and predicted values.
Keywords: Rainfall Prediction, Linear Regression, Learning Process, Machine Learning.