This project proposes a viable and user-friendly yield prediction system for the farmers. The user provides the soil type, temperature, humidity and rainfall as input. Machine learning algorithms allow choosing the most profitable crop list or predicting the crop yield for a user-selected crop. To predict the crop yield, selected Machine Learning algorithms such as Support Vector Machine (SVM), Guassian Naïve Bayes (GNB), Logistic Regression, and K-Nearest Neighbour (KNN) are used. Among them, the Guassian Naïve Bayes (GNB) showed the best results with 95% accuracy. This project can be further designed such that the crop disease can be detected along with the possible prediction of the yield.