- Lending Club is a US Peer to Peer Loan Company. It enables the borrowers to create unsecured personal Loans between $1000 and $40,000.
- The Loan period is for a standard three years. The investors can select the loans which they want to invest into based on information supplied about the borrower, grade of loan, loan purpose etc.
- Lending club makes money by charging borrowers an origination fee and investors a service Fee.
- People save money in the banks but that offers them with lower interest rates.
- Lending club is helping in transforming the banking sector by making credit more affordable and investing more rewarding.
- Here we need to classify each of the borrower as a defaulter or not using the data collected when the loan has been given.
- To Classify if a borrower will default the loan using the borrower's financial history.
- We need to predict the target variable as 1> paid off and 0 > charged off.
- The metrics we use for the problem:
- AUC – ROC Curve
- Accuracy
- Time Taken
Here is the code for the Lending club data analysis where I have performed the following:
- Preprocessing
- Exploratory Data Analysis
- Model evaluation using different Machine learing models which were as follows :
- Linear Regression
- Logistic Regression
- Support Vector Machine
- K nearest Neighbour
- XG Boosting
- Neural Network
- Comparision of the Models and choose the best one based on three factors.