![]() ![]() Loans$BorrowerState <- tolower(state.names) State.names <- unlist(sapply(loans$BorrowerState, function(x) if(length(state.name) = 0) "District of Columbia" else state.name) ) # Change state abbreviations to full names so we can merge our Listings.match$BorrowerState <- as.character(listings.match$BorrowerState) # contains more detailed information than the Loans file # Obtain the active loans from the Listings file, since it Listings <- read.csv("Listings.CSV", header=TRUE) Loans <- read.csv("Loans.CSV", header=TRUE) # Warning: this is a very large dataset that required ~10 minutes # to read into R on a fast 8-core Xeon server. Let’s jump right into the visualizations by state: This information includes extended credit profiles of users, groups that users belong to, social networks within the user base and even retroscores, or how a loan would be rated by Prosper under a new heuristic given macroeconomic shifts over time. ![]() However, Prosper also provides additional information regarding their user base and loan performance history. ![]() Similar to Lending Club, Prosper provides loan-level data such as interest rate, amount funded/requested, borrower state, borrower debt to income ratio, etc. Java -jar ProsperXMLtoCSV.jar ProsperXMLFileLocation CSVDestinationDirectory jar file run the following command (changing the parameters of course!): If you are going to follow the route I took and download the latest XML file, ProsperDataExport_xml.zip, you will find this utility helpful in converting the XML files to CSVs: Convert Prosper XML to CSV You can access the Prosper Marketplace data via an API or by simply downloading XML files that are updated nightly. Due to the positive feedback received on this post I thought I would re-create the analysis on another peer-to-peer lending dataset, courtesy of. ![]()
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