‘Predictive modelling and analysis of loan eligibility process’is the project we are working upon which falls under the BFSI domain (Banking Financial services and Insurance sector). Based on customer detail provided while filling online application form,the data set has details like Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. The primary purpose of working on this project is to build the model to automate the initial loan process. 1.2Need of the Study Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers. 1.3 Business or Enterprise under study The data lended by the insurance and banking companies is under the study.The data are collected by the respective companies based on details provided by the company while filling the application 6 1.4 Business Model of Enterprise Selecting the relevant variables from the dataset and arranging their values in order of importance to create a models to predict the probability of loan status of an individual in the future by performing different types of algorithm 1.5 Data Sources The data is collected from various insurance and banking companies. I retrived this data from online platform kaggle Data Set Description: Contains 614 rows and 13 columns The response variable is ‘Loan status’ 1.6 Tools & Techniques Tools: Jupyter Notebook. Techniques: Logistic Regression, Random Forrest Classification, Xg Boosting Classifier, Gradient Boosting Classifier