The central focus of this mini project is the classification of the Rice dataset using a variety of machine learning algorithms integrated within the Weka platform. The dataset includes several characteristics related to rice grains, including quantitative parameters like perimeter, area, asymmetry coefficient, and kernel groove length in addition to physical measurements like length and width. The main objective is to use and assess various classification algorithms, including Decision Trees, Support Vector Machines (SVM), k-Nearest Neighbours (k-NN), and Naive Bayes, in order to predict the rice grain categorization labels according to their unique characteristics. The main objective of this study is to determine which machine learning algorithm best classifies this particular dataset, improving the efficiency of rice grain classification processes.