A project practicing machine learning classification algorithms.
Create a model that predicts whether or not a loan will default using the historical data.
For companies like Lending Club correctly predicting whether or not a loan will be a default is very important. In this project, using the historical data from 2007 to 2015, you have to build a machine learning model to predict the chance of default for future loans. As you will see later this dataset is highly imbalanced and includes a lot of features that make this problem more challenging.
Domain: Finance
credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.\n purpose: The purpose of the loan (takes values "credit_card", "debt_consolidation", "educational", "major_purchase", "small_business", and "all_other").\n int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.\n installment: The monthly installments owed by the borrower if the loan is funded.\n log.annual.inc: The natural log of the self-reported annual income of the borrower. dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income). fico: The FICO credit score of the borrower. days.with.cr.line: The number of days the borrower has had a credit line. revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle). revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available). inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months. delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years. pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).
- Data collection
- Exploratory data analysis
- Balance the imbalanced dataset
- Splitting data
- Feature engineering - Encoding and data scaling
- Balancing the imbalanced dataset
- Made predictions using some ML algorithms
- Used cross-validation and hyperparameter tuning of the best-performing base model
- Serialized the best performing model using pickle and joblib
| Machine learning Classifier | Accuracy % |
|---|---|
| XGBOOST with cross validation (mean score) | 88 |
| XgBoost with hyper parameter tuning | 84 |
| Xg boost | 82 |
| Random forest | 81 |
| Logistic Regression | 61 |
| Deep learning with regularization | 61 |
| Support vector machine | 57 |
Ensemble models performed better on the dataset