This data set is a loan data records from prosper Loan in the United States, it contains 113,937 loans with 81 variables on each loan, including loan amount, interest rate, current loan status, borrower income, and many others. The report in this part would be structured to provide summary of simple univariate relationships to multivariate relationships, this research provides answers to various questions like whether the monthly loan payment has a correlation or any relationship between loan original,amount, what is the spread of lterm of loan in loan status, identifying the frequency of the categorical variables; Term of loan, borrower's employment status, year of loan, and loan status, are there differences between loans depending on how the loan term large the original loan amount was. Key insights would be generated from this to be able to make a presentation with it. In spite of the fact that the dataframe has 81 features, this study is only interested in few of the features, it would be appropriate to shrink the dataframe to the useful columns for the purpose of this study. The data set consist of 113,937 rows and 81 columns, implying 113,937 recorded observations with 81 features. The main features of interest to this study include but not limited to the following; loan status, loan term, employment Status, is borrower a homeowner or not, borrower state, income verifiable or not and occupation. To get a better understanding of how this features of interest would be investigated a number of features would support this study which include the following features original loan amount, loan origination date, monthly loan payment, loan current days of delinquency, stated monthly income, investors and recommendations. In total 11 features were pulled together and form into a new dataframe to be reference for exploration and analysis
