Final presentation - Group10(ADS)
- 1. Assignment-3 Group-10 1. Pooja Goyal 2. Shashwat Mehra 3. Varsha Holennavar
- 2. Lending Club Data Analysis Lending Club (LC) data, LC is a peer-to- peer online lending platform. It is the world’s largest marketplace connecting borrowers and investors, where consumers and small business owners lower the cost of their credit and enjoy a better experience than traditional bank lending, and investors earn attractive risk-adjusted returns.
- 3. Project Objective Predict if lenders can make default payment for the borrowed loan Predict Interest Rate to be charged on the loan amount Predict if the loan will be approved for an interest rate of 10% or below End Users : Borrowers And Lenders
- 4. Data Exploration For each loan, over 100 characteristics are recorded in the table. We have explored Data Dictionary from the Lending Club website, which gives us the information about the features in the dataset. We explored the dataset using r and Tableau to understand and find correlations between different features.
- 5. Data Pre-Processing We are selecting 31 columns from 115 columns available based on the data exploration and feature co-relation methods. Removing NA’s Removing Wildcards Removing Outliers Creating Calculated Fields •Fico Mean •Indicator •Monthly Income
- 6. Models:Loanstatus • Logistic Regression • Neural Network • Random Forest LoanApproval • Logistic • Neural Network • Random Forest InterestRate • Linear Regression • Neural Network • Boosted Decision Tree
- 7. Ex: Loan Status Model
- 8. Model Evaluation for Loan Status • We have compared over all accuracy, recall, precision, ROC curve and confusion matrix • If this model is to help lenders avoid bad loans, the true positive rate must be much more robust Neural Network Logistic Regression Random forest Accuracy 0.914629 0.910 0.9006 Precision 0.914629 0.935 0.9006 Recall 0.914629 0.957 0.9006
- 9. Model Evaluation for Interest Rate Model Name / Features Neural Network Linear Regression Boosted Decision Tree RMSE 1.50 1.79 1.20 Co-efficient of Determination 0.83 0.76 0.89 • We have compared over all RMSE and Co-efficient of Determination.
- 10. Model Evaluation for Loan Approval • We have compared over all accuracy, recall, precision, ROC curve and confusion matrix Neural Network Logistic Regression Random forest Accuracy 0.8194 0.8410 0.80 Precision 0.8194 0.8410 0.780 Recall 0.8194 0.8410 0.822
- 12. Sentiment Analysis We collected tweets for lending club from Twitter Incorporated our Research project to detect Sentiments of Tweets Used Tableau for visualization of Results Incorporated the visualizations on front End
- 13. Demo
- 14. Team Assessment Contribution Pooja Goyal Shashwat Mehra Varsha Holennavar
- 15. Thank You