Predictive data mining automatically create classification model from training dataset and apply such model to automatically predict other classes of unclassified datasets. Predictive data mining deals with learning models to support clinicians in diagnostics, therapeutic, or monitoring tasks. It learns from past experience and applies knowledge gained to future situations, by applying machines learning methods to build multivariate models from different kind of data and subsequently make inferences on unknown data. Machine learning model is related to the exploitation of supervised classification approaches. Prior to applying the learning model, the data is pre processed to remove noise and ensure data mining principle is applied on real data. In Machine Learning and Data Mining fields, classification is usually approached as a supervised learning task. A search algorithm is used to induce a classifier from a set of correctly classified data instances, called the training set. Another set of correctly classified data instances, known as the testing set, is used to measure the quality of the classifier obtained after the learning process. Different kinds of models can be used to represent classifiers, and there are a great variety of algorithms available for inducing classifiers from data. In this paper discussed about previous research related to prediction and classification algorithms used in our work.