Our aim from the project is to make use of pandas, matplotlib, & seaborn libraries from python to extract insights from the data & scikit-learn libraries for machine learning. Secondly, to learn how to hypertune the parameters using random search cross validation machine learning model. And in the end, to predict whether the loan applicant can replay the loan or not using voting ensembling techniques of combining the predictions from multiple machine learning algorithms.
Loan id, Gender, Married, Dependents, Education, Self Employed, Applicant income, Coapplicant income, Loan Amount,Credit History, Property_Area, Loan_Status
1.Applicants who are male and married tends to have more applicant income whereas applicant who are female and married have least applicant income
2.Applicants who are male and are graduated have more applicant income over the applicants who have not graduated.
3.Again the applicants who are married and graduated have the more applicant income.
4.Applicants who are not self employed have more applicant income than the applicants who are self employed.
5.Applicants who have more dependents have least applicant income whereas applicants which have no dependents have maximum applicant income.
6.Applicants who have property in urban and have credit history have maximum applicant income
7.Loan Amount is linearly dependent on Applicant income
8.Male applicants are more than female applicants.
9.No of applicants who are married are more than no of applicants who are not married.
10.Applicants with graduation are more than applicants whith no graduation.