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Rainfall is one of the most vital components of agriculture and also predicting it is the most challenging task. In general, weather and rainfall are highly non-linear and complex phenomena, which require progressive computer modeling and simulation for their precise prediction. Numerous and diverse machine learning models are used to predict the rainfall which are Multiple Linear Regression, Neural networks, K-means, Naïve Bayes and more. These systems implement one of these applications by extracting, training and testing data sets and finding and predicting the rainfall. This study shows the using of Multiple Linear Regression and Neural networks to predict rainfall and Decision Trees algorithm to recommend crops. Thus, we inferred that we can predict the rainfall and recommend crops with reasonable accuracy. Keywords— Neural networks, Linear Regression, Decision Tree, Rainfall, Crop Recommendation, Machine Learning.

Rainfall Prediction using Linear approach Neural Networks and Crop Recommendation based on Decision Tree