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India is an agricultural country and its economy is largely based upon crop
productivity and rainfall. For analyzing the crop productivity, rainfall prediction is
require and necessary to all farmers. Rainfall Prediction is the application of
science and technology to predict the state of the atmosphere. It is important to
exactly determine the rainfall for effective use of water resources, crop
productivity and pre planning of water structures. Using different data mining
techniques it can predict rainfall.Data mining techniques are used to estimate the
rainfall numerically. This paper focuses some of the popular data mining
algorithms for rainfall prediction. Naive Bayes, K-Nearest Neighbour algorithm,
Decision Tree are some of the algorithms compared in this paper. From that
comparison, it can analyze which method gives better accuracy for rainfall
prediction. Prediction of crops may be accurately through with the help of data
mining techniques and considering the environmental parameters. During this
work, the classifiers used area unit support vector machine and data processing.

Rainfall Prediction is one of the most challenging tasks. Though already many
algorithms have being proposed but still accurate prediction of rainfall is very
difficult. In an agricultural country like India, the success or failure of the crops
and water scarcity in any year is always viewed with greatest concern. A small
fluctuation in the seasonal rainfall can have devastating impacts on agriculture
sector. Accurate rainfall prediction has a potential benefit of preventing causalities
and damages caused by natural disasters. Under certain circumstances such as
flood and drought, highly accurate rainfall prediction is useful for agriculture
management and disaster prevention. In this paper, various algorithms have been
analyzed. Data mining techniques are efficiently used in rainfall prediction

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