LendingClub is a peer-to-peer lending company, headquartered in San Francisco, California.Investors are able to search and browse the loan listings on LendingClub website and select loans that they wanted to invest in based on the information supplied about the borrower, amount of loan, loan grade, and loan purpose. Investors made money from the interest on these loans. LendingClub made money by charging borrowers an origination fee and investors a service fee.
- Fixing Rows and Columns
- Data Preparation and Standardization
- Dealing with Missing Values
- Removing Outliers
- Univariate Analysis on Categorical Variables
- Univariate Analysis on Numerical Variables
- Segmented Univariate Analysis (Using concept of Binning)
- Correlation Metrics and Heat Map for all the variables
- Bivariate Analysis
- Recommendations on the basis of Univariate and Bivariate Analysis
- The data given contains information about past loan applicants and whether they ‘defaulted’ or not.
- The aim is to identify patterns which indicate if a person is likely to default, which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc. through Exploratory Data Analysis (EDA) . Thus, we have to understand how consumer attributes and loan attributes influence the tendency of default.
- When a person applies for a loan, there are two types of decisions that could be taken by the company:
- Loan accepted: If the company approves the loan, there are 3 possible scenarios described below:
- Fully paid: Applicant has fully paid the loan (the principal and the interest rate)
- Current: Applicant is in the process of paying the instalments, i.e. the tenure of the loan is not yet completed. These candidates are not labelled as 'defaulted'.
- Charged-off: Applicant has not paid the instalments in due time for a long period of time, i.e. he/she has defaulted on the loan
- Numpy
- Pandas
- Matplotlib
- Seaborn
- FilterWarnings
- Datetime
- Aarushi Gupta
- Ankit Sharma