Data mining a field at the intersection of computer science and statistics is the process that attempts to discover patterns in huge data sets

Data mining a field at the intersection of computer science and statistics is the process that attempts to discover patterns in huge data sets. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.
information from tremendous amount of accumulated data sets. In present era, Data Mining is becoming popular in banking field because there is a call for efficient analytical methodology for detecting unknown and useful information in banks data. Skills and knowledge are important requirement for achieving Data Mining task
Data Mining can help by contributing in solving banks problems by finding patterns, associations and correlations which are hidden in the banks information stored in the data bases 3. By using data mining to analyses patterns and trends, bank executives can predict, with increased accuracy, how customers will react to adjustments in interest rates, which customers will be at a higher risk for defaulting on a loan
By using data mining Bank managers need to know if the customers they are dealing with are reliable or not for example, loans can be risky decisions for banks if they do not know anything about their customers 5. Banks provide loan to its customers by verifying the various details relating to the loan such as amount of loan, lending rate, repayment period, demography, income and credit history of the borrower. Customers with bank for longer periods, with high income groups are likely to get loans very easily. Even though, banks are wary while providing loan, there are chances for loan defaults by customers. Data mining technique helps to distinguish borrowers who repay loans promptly from those who don’t 8.
measuring loan default risk is very important for our economy. Earlier Assessing of credit was usually done using statistical and mathematical methods by analysts. Nowadays Data mining techniques have gained popularity over the years because of their ability in discovering practical knowledge from the database and transforming them into useful information. Without apply these techniques
banks will face huge losses and lending becomes very tough for the banks
Data mining techniques like classification and prediction can be applied to overcome this to a great extent in these study we apply classification techniques, decision tree, naïve bayes and random forest.
2.2.1 Classification
Classification of data is very typical task in data mining, there are large number of classifiers that are used to classify the data such as bayes, function, rule based and tree the goal of classification is to correctly predict the value of designated discrete class variable given attributes . These approach frequently employs decision tree or neural networks based classification algorithm. The data classification process involves learning and classification; in learning the training data are analyzed by classification algorithm, in classification test data are used to estimate the accuracy of the classification rules.
In other word, Classification is the most commonly applied data mining technique, which employs a set of pre-classified examples to develop a model that can classify the population of records at large 4. credit risk applications are particularly well suited to classification technique; the goal of classification is to accurately predict the target class for each case in the data. For example, a classification model could be used to identify loan applicants as safe, or risk.
The dataset is partition in two parts training and testing to avoid the problem of overfitting; a training set is used to build the model as the classifier which can classify the data items into its appropriate classes. A test set is used to validate the model.

2.2.1.1 Decision Tree (DT)

Decision tree is one of the most common approaches for classification and predictions. It is the predictive machine-learning model that cl