This paper is published in Volume-5, Issue-5, 2019
Area
Machine Learning,Linear Regression
Author
Aniket Dixit
Org/Univ
Jaipur Engineering College and Research Center, Jaipur, Rajasthan, India
Pub. Date
19 September, 2019
Paper ID
V5I5-1168
Publisher
Keywords
Linear regression, Machine learning, Predictive modeling

Citationsacebook

IEEE
Aniket Dixit. Rainfall prediction using modified linear regression, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Aniket Dixit (2019). Rainfall prediction using modified linear regression. International Journal of Advance Research, Ideas and Innovations in Technology, 5(5) www.IJARIIT.com.

MLA
Aniket Dixit. "Rainfall prediction using modified linear regression." International Journal of Advance Research, Ideas and Innovations in Technology 5.5 (2019). www.IJARIIT.com.

Abstract

Analytics usually involves finding out past historical knowledge to analysis potential trends. atmospheric phenomenon is that the state of the atmosphere at a given time in terms of weather variables like precipitation, cloud conditions, temperature, etc., the prevailing models use data processing techniques to predict the precipitation. the most disadvantage of those systems is that it doesn't offer associate estimates of the expected precipitation. The system calculates the average of values and perceives the state of the atmosphere, which doesn't yield estimate results. This paper represents a mathematical methodology referred to as regression to predict the precipitation in varied districts in the southern states of Bharat. The regression methodology is changed to get the foremost optimum error proportion by iterating and adding some proportion of error to the input values. This methodology provides an associate estimate of precipitation exploitation different atmospherical parameters like average temperature and inclementness to predict the precipitation. The regression is applied to the set of knowledge and also the coefficients area unit accustomed predict the precipitation supported the corresponding values of the parameters. the most advantage of this model is that this model estimates the precipitation supported the previous correlation between the various atmospherical parameters. Thus, associate estimate price of what the precipitation may well be at a given period and place will be found simply.
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