Data mining is the process of selecting, exploring and modelling large amounts of data to I’m covered previously and non-patterns of data. Sourced data mining methodology has five stages sampling, exploration, modification, modelling and assessment. The sampling stage is desirable if the data for analysis are too voluminous for responsible processing time or if it is desirable to avoid problems of generalization by dividing the data into different set of modern construction and model validation. Expropriation and modification refer to the review of data to enhance understanding of it and the transformation of data. Next stage is modelling where the actual data analysis happens using traditional statistical methods as well as non-traditional statistical methods such as neural networks and decision three. In the end the It’s meant stage compares the models and the results from the data mining model by using a common yardstick. Data mining has three main tools, the first one is description and visualization which understand the data set and detect its hidden patterns. The second tool is Association and clustering, Association is to determine which variables go together and Clustering is the group objects in such a way that objects belonging to the same cluster at similar and objectives belong to different clusters are dissimilar. The final data mining tool is classification and estimation, Classification predicts target variable that is categorized in nature in contrast with estimation which refers to the production of a target variable that is metric in nature.