An Exploring Regression Technique for Predicting the Sell Price of the Fertilizer from MP State Cooperative Marketing Federation MPSCMF Dataset

For the last couple of years, agriculture has become main backbone of an Indian economy and its 70% population predominantly depends on agro industry products like rice, wheat, jwar etc.. In order to meet the food grain requirements of the world, Indian agriculture is highly depended on the needs of chemical fertilizer which is being considered as an essential input to the agriculture. In this direction, predicting the sell quantity of chemical fertilizer is an important issue because most of the farmers highly depend on these fertilizers for their successful crop yield production. In the past, various conventional tools and techniques are applied on agriculture dataset. However, these tools and techniques could not be getting succeeded to achieve their goals. The regression techniques have capability to model the relationship among two or more variables and could be helpful for successful prediction. This study focus on three most prominent regression techniques like liner, multiple and logistic for predicting the sell quantity of fertilizer. Finally, experimental results had shown that logistic regression model outperformed than others in terms of error measures.