Crop recommendation and yield prediction using machine learning algorithms

Agriculture is the foundation of many countries' economies, particularly in India and Tamil Nadu. The young generation who are new to farming may confront challenge not understanding what sow reap benefit from. This a problem that has be addressed, it one we addressing. Predicting proper crop production will aid making better decisions, reducing losses managing risk price fluctuations. existing system deployed, unlike ours, which done by applying classification regression algorithms calculate type recommendations yield predictions. Agricultural industries must use machine learning anticipate from given dataset. supervised technique used analyse dataset order capture information multiple sources, such as variable identification, uni-variate analysis, bi-variate multi-variate missing value treatments, so on. A comparison was conducted identify algorithm more accurate predicting best harvest. results show proposed accuracy when comparing entropy calculation, precision, Recall, F1 Score, Sensitivity, Specificity, Entropy.