Analysis of loan data

The fico score does not reflect this while the LC score seems to partially capture that risk: Fully paying borrowers tend to have slightly older credit history, which is to be expected.

This is the feature that has the highest impact on default rates. We will use Excel style pivot table and cross-tabulation. Now the distribution looks much closer to normal and effect of extreme values has been significantly subsided.

Building a credit model and see if we can predict reliably defaults. Box plot for fare can be plotted by: It is zero in most cases. You can quickly code this to create your first submission on AV Datahacks.

What this means is that there is an equal chance for applicants with different housing types to default. Next, we will look at making predictive models. As can be seen above, average observed interest rates differ by month, year, and geography. The data consists in 4 files updated every quarter on the same day as the quarterly results of the company are released.

The frequency table can be printed by following command: Clearly, both ApplicantIncome and LoanAmount require some amount of data munging. Its density changed from being condensed over a range FICO scores of to to a density that covers the entire FICO range with a mean that is lower than the minimum score they used to require to get an A rating: Scatterplot of Interest Rate and Approved Loan Counts Therefore, it comes as no surprise that a scatter plot of interest rates and number of approved cases for the time period presents a positive relationship, as all else being equal, increasing demand drives up the prices.

Analysis of Loan Data Essay Sample

Aiming at providing lower cost transaction fees than other financial intermediaries, LendingClub hit the highest IPO in the tech sector in They contain information on almost all the loans issued by LC. In this assignment we are trying to find relation between the interest rates and the various factors like amount, loan length, debt to income ratio, monthly income, FICO score etc.

The information available for each loan consists of all the details of the loans at the time of their issuance as well as more information relative to the latest status of loan such as how much principal has been paid so far, how much interest, if the loan was fully paid or defaulted, or if the borrower is late on payments etc.

I hope your love for pandas the animal would have increased by now — given the amount of help, the library can provide you in analyzing datasets. We have two options now: Third look at the data: Random Forest Random forest is another algorithm for solving the classification problem. The Snapshot of the analysis done in R is shown below Conclusion The analysis shows the relation between the interest rates charged and the different factors affecting it.

For instance, let us look at the chances of getting a loan based on credit history. First look at the data, exploratory research: So the mean represents the probability of getting loan. The probability of default increases stepwise as we move down the rating grade of borrowers. As expected, higher revolving utilization mean higher risk of default.

The chances of getting a loan will be higher for: In addition to these problems with numerical fields, we should also look at the non-numerical fields i. Here are the problems, we are already aware of: Data Munging in Python: Fully paying borrower tend to have slightly more accounts but too many accounts may be bad too.

Loan Data Analysis and Visualization using Lending Club Data

Second look at the data:Where can I find peer-to-peer loan data (large dataset to be used in a statistical analysis in academics)? Analysis of Loan Data Essay Sample. As we all know the history of loans as old as the history of money.

Earlier there used to be different mechanism of. Analysis of Lending Club's data. The information available for each loan consists of all the details of the loans at the time of their issuance as well as more information relative to the latest status of loan such as how much principal has been paid so far, how much interest, if the loan was fully paid or defaulted, or if the borrower is.

Loan Data Analysis and Visualization using Lending Club Data. Linlin Cheng. Posted on Jul 23, I. Introduction.

A Complete Tutorial to Learn Data Science with Python from Scratch

LendingClub, Corp LC is the first and largest online Peer-to-Peer (“P2P”) platform to facilitate lending and borrowing of unsecured loans ranging from $1, to $35, Aiming at providing lower cost transaction fees than.

We use cookies on kaggle to deliver our services, analyze web traffic, and improve your experience on the site.

Analysis of Lending Club's data

By using kaggle, you agree to our use of cookies. FHFA economists and policy experts provide reliable research and policy analysis about critical topics impacting the nation’s housing finance sector. Federal Home Loan Bank Member Data.

Federal Home Loan Bank members include thrift institutions, commercial banks, credit unions and insurance companies.

Download
Analysis of loan data
Rated 0/5 based on 51 review