Design and Implementation of Various Regression Models for Yield Prediction
Vishal Jain1, Vaidhehi V2
1Vishal Jain*, Computer Science Department, Christ (Deemed to be University), Bangalore, India.
2Vaidhehi V., Computer Science Department, Christ (Deemed to be University), Bangalore, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 21, 2020. | Manuscript published on March 10, 2020. | PP: 1280-1284 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2766039520/2020©BEIESP | DOI: 10.35940/ijitee.E2766.039520
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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: Agriculture is the backbone of India. In order to support farmers in India, this research is focused on the design of various predictive models that are used to predict the yield value for a specific crop in Indian states. This research work considers Rice, Wheat, and Bajra crops in Tamil-Nadu, Rajasthan, Uttar Pradesh states respectively. The various regression models such as Linear, Multiple, C4.5 and Random Forest are considered in this work. R squared value is used to evaluate the performance of the regression models. The result of this work shows that Random Forest model is better in performance.
Keywords: Predictive Analysis, Regression Models, Linear Regression, Multiple Regression, Random forest and C4.5 Algorithm.
Scope of the Article: Probabilistic Models and Methods