+ All Categories
Home > Data & Analytics > Final presentation - Group10(ADS)

Final presentation - Group10(ADS)

Date post: 15-Apr-2017
Category:
Upload: varsha-holennavar
View: 21 times
Download: 4 times
Share this document with a friend
15
Assignment- 3 Group-10 1. Pooja Goyal 2. Shashwat Mehra 3. Varsha Holennavar
Transcript
Page 1: Final presentation - Group10(ADS)

Assignment-3Group-101. Pooja Goyal2. Shashwat Mehra3. Varsha Holennavar

Page 2: Final presentation - Group10(ADS)

Lending Club Data AnalysisLending 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.

Page 3: Final presentation - Group10(ADS)

Project ObjectivePredict 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

Page 4: Final presentation - Group10(ADS)

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.

Page 5: Final presentation - Group10(ADS)

Data Pre-ProcessingWe 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

Page 6: Final presentation - Group10(ADS)

Models:Lo

an st

atus • Logistic

Regression• Neural Network• Random Forest Lo

an

Appr

oval• Logistic

• Neural Network• Random Forest

Inte

rest

Rat

e • Linear Regression

• Neural Network• Boosted

Decision Tree

Page 7: Final presentation - Group10(ADS)

Ex: Loan Status Model

Page 8: Final presentation - Group10(ADS)

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 0.9006Precision 0.914629 0.935 0.9006Recall 0.914629 0.957 0.9006

Page 9: Final presentation - Group10(ADS)

Model Evaluation for Interest Rate

Model Name / Features

Neural Network Linear Regression Boosted Decision Tree

RMSE 1.50 1.79 1.20Co-efficient of Determination

0.83 0.76 0.89

• We have compared over all RMSE and Co-efficient of Determination.

Page 10: Final presentation - Group10(ADS)

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 0.80Precision 0.8194 0.8410 0.780Recall 0.8194 0.8410 0.822

Page 11: Final presentation - Group10(ADS)

Approach for Deployment

Tableau

Page 12: Final presentation - Group10(ADS)

Sentiment AnalysisWe 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

Page 13: Final presentation - Group10(ADS)

Demo

Page 14: Final presentation - Group10(ADS)

Team AssessmentContribution

Pooja Goyal Shashwat Mehra Varsha Holennavar

Page 15: Final presentation - Group10(ADS)

Thank You


Recommended