analysing credit score by using machine learning

1. CREDIT SCORE ANALYSIS USING MACHINE
LEARNING
By:
MANICHANDANA.D-160117737310
VENKATA SANTHOSH.T -160116737059

2. CONTENTS
• Introduction
• Problem Statement
• Software and Hardware Requirements
• Existing System
• Proposed System
• Expected Outputs
• Conclusion
• References

3. Introduction:
• This project is designed for prediction purpose.
• The banks usually use it to determine who should get
credit, how much credit they should receive, and which
operational strategy can be taken to reduce the credit
risk
• The main aim of this project is to learn and prediction of
system for managing all the activities of credit.

4. Problem Statement:
• It is widely applied in many industries especially in the
banking.
• The banks usually use it to determine who should get
credit, how much credit they should receive, and which
operational strategy can be taken to reduce the credit
risk.
• It is time-consuming to check the entire personal
portfolios and generate a credit report manually in bank
ing sector.

5. Software Requirements:
• Operating System: Windows,Unix.
• Programming Language: R language.
• Machine Algorithms:PCA,MCA

6. Hardware Requirements:
• Processor: 1.6 GHz processor
• RAM: 16 GB
• Operating System: Windows 10
• Disk Space: 1 GB

7. Existing System
• Under the existing system,It is widely applied in many
industries especially in the banking.
• The banks usually use it to determine who should get credit,
how much credit they should receive, and which operational
strategy can be taken to reduce the credit risk. Generally, it
contains two main parts:
1. Building the statistical model.
2. Applying a statistical model to assign a score to a credit
application or an existingcredit account.

8. Proposed System
• Individuals can view their credit ratings without going to the
company for enquiry.
• In proposed system we mainly used Machine Algorithms like
PCA,MCA.
• Principal Component Analysis (PCA) is an unsupervised, non-
parametric statistical technique primarily used for dimensionality
reduction.
• Multiple Correspondence Analysis(MCA) is a factor analysis
approach. It deals with a tabular dataset where a set of
examples are described by a set of categorical variables.

9. OUTPUT SCREENS:

10. Decision Tree:

11. CLUSTER:

12. TREE:

13. Conclusion
• Minimizes the default risk.
• Helpful for both the parties.
• Plays a critical role in Indian Financial System.
• Not much popular.
• Minimizes the load on banks.
• Economic Development of the country.

14. References
• www.cibil.com
• www.onemint.com
• www.bankbazaar.com
• www.allbankingsolutions.com
• www.creditvidya.com
• www.wiki/cibil.com