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@article{155556,
        author = {Anurima Majumdar and Romik Banerjee and Rounak Ghosh and Sagar Ghosh and Rudradip Bhattacharjee and Antara Ghosal and Palasri Dhar and Nabaneeta Banerjee, Sayak Saha, Subhrajit Pallob},
        title = {Loan Prediction by using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {1},
        pages = {1277-1282},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155556},
        abstract = {In the basic environment of a banking system, all the banks have a range of products to sell but the most prominent source of income of most of the banks is mainly dependent on the credit line. So, the earnings come from the interest of that particular loans which are being credited. The profit or loss of a bank primarily depends to a great extent on loans that is whether customers of the bank are paying the loan back or defaulting. By estimating the loan delinquent, the Non-Performing Assets are reduced by the bank. Thus, it makes the study very important for this phenomenon. Various researches done in this era shows that there are so various methods for studying the problem in order to control the loan default. Since the right predictions are very important for the products to be maximized, it is very important to study the nature and structure of the various methods and their comparison. A very important advance in predictive analytics is used to understand the problems of identifying the loan defaulters (i)Data Collection, (ii) Cleaning of Data and (iii)Evaluation of Performance. The test and experiments found that the Naïve Bayes model has a more better performance than other models in terms of loan forecasting.},
        keywords = {Machine learning, Decision Tree, prediction,   
  Python.},
        month = {},
        }