Impact of climatic change on agricultural product yield using K-Means and multiple linear regressions

Adequate information about climate change helps farmers to prepare and helps boost crop yield. Over the years, crops prediction was performed by manually considering farmer's experience on the particular crop in relation to the weather. This method was Inadequate, depends on the farmer's unreliable memory and grossly inaccurate. There is a need to introduce computational means to study and predict optimal climatic factors for improved crop growth and yield. The aim of this research work is to study the impact of climatic changes on the yield production of roots and tubers crops. K-means classification algorithm, Multiple Linear Regression, Python programming language, Flask Framework, Python machine learning packages numpy, matplotlib, Scikit-learn are the methodology used. While the obtained results show that CO2 Emission and Temperature does not really play a key role on how climate impact yield of root and tubers, rainfall plays more role; therefore, the study concludes that the three variables (temperature, rainfall, and CO2 Emission) are not enough to predict agricultural yield. It is therefore recommended that further research should be carried out to determine how other climatic factors such as soil type; humidity, sunlight etc. affect the yield of crops. The objective of this research is to study climatic change using data mining techniques, to design a predictive model using multiple linear regression to find the most optimal temperature and rainfall for effective crop yield and to simulate the multiple linear regression model design that achieve a high accuracy and a high generality in terms of climate change to crop yield.