THE APPLICATION OF MULTIPLE LINEAR REGRESSION ALGORITHM AND PYTHON FOR CROP YIELD PREDICTION IN AGRICULTURE
Abstract
The difference between Linear regression and Multiple Linear regression methods is at number of independent variables (parameters). Not always every result depends on only one thing. Therefore, Multiple Linear regression method is more effective than Linear regression. For example, in automobile industry, each details of a car can be made different technology and company. As well as, each detail can have various quality and material. That is why, every car obviously has different price. And this can bring the issue that calculate the price of a car not easily. Not only in automobile industry but also any manufacturer company has this kind of problem in today`s world. Agriculture also has such kind of problems. One of them is prediction crop yield for next year or next seasons. Especially, it is the most important thing in countries that rely on agriculture. Because in agriculture major there are so many different parameters that impact on crop yield. The weather, rainfall, amount of minerals that is given by farmers can be example for it. Due to the process of calculating is complicated we have clear and real dataset about crop yield of the last years. There are several steps from data collection to prediction. These steps are illustrated in this diagram [2]: