The document discusses a decision tree-based loan prediction system, focusing on the application of data mining techniques in the banking sector to enhance loan approval processes. It outlines objectives such as identifying relevant attributes and determining the best model, along with methodologies used in data mining, including classification, clustering, and regression. Additionally, it emphasizes the importance of data mining for improving customer credibility and reducing fraud in banking.

1. DEPARTMENT OF COMPUTER ENGINEERING
S.S.V.P.S.’s B.S. DEORE COLLEGE OF ENGINEERING, DHULE
2017-2018

2.  Introduction
 Objectives
 Data Mining
 Decision Tree Induction
 Data Mining In Banking
 Methodology
 Expected Results
 Conclusion
 Bibliography
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3. ◦ Competition in Banking
◦ Struggles of Various Banks
◦ Credit Risk
◦ Loan Prediction
◦ Data Mining Techniques
◦ Using in Banking Sector
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4.  Mainly Two Objectives
 Identification of Relevant Attributes
 Determining the Best Model
 Decision Tree Induction Algorithm
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5. What is Data Mining?
Different Data Mining Techniques
1. Classification
2. Clustering
3. Association Rule Mining
4. Regression ,etc.
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6. Fig. Steps of Knowledge Extraction
Knowledge Extraction
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7. Decision Tree Induction
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8.  Tremendous Growth In Banking Industry
 Adapting Data Mining Techniques
 Helps to Find Credible Customers
 Preventing Frauds
 Certain Areas That Effectively Use Data Mining
1. Marketing
2. Risk Management
3. Customer Relationship Management
Data Mining In Banking
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9. CRISP-DM Framework
Methodology
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10. Proposed Model
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11. Architecture of Proposed Model
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12. Relevant Attributes along With Rank
Decision Tree Based Loan Prediction
Ranking
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13. Finding Eligible Loan Amount
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14. Comparison Among different Loan Schemes
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15. A system called loan credibility prediction system that helps the
organization in making right decision to approve or reject the
loan request of the customers.
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16. [1] Sivasree M. S. ,P. G. Scholar, Rekha Sunny T. “Loan Credibility Prediction
System Based on Decision Tree Algorithm” , IRJET Vol. 4 Issue
09.September 2015.
[2] Dileep B. Desai, Dr. R.V.Kulkarni “A Review: Application of Data Mining
Tools in CRM for Selected Banks”, (IJCSIT) International Journal of
Computer Science and Information Technologies, Vol. 4 (2), 2013, 199 –
201.
[3] Dr. Madan Lal Bhasin, “Data Mining: A Competitive Tool in the Banking
and Retail Industries”, The Chartered Accountant October 2006
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17. Presented By
Swapnil Jawahar Patil
Bhushan Sanjay Patil
Kalyani Ramkrishna Deore
Mayuri Bhalchandra Talware
Akshay Bandu Jadhav
DEPARTMENT OF COMPUTER ENGINEERING
S.S.V.P.S.’s B.S. DEORE COLLEGE OF ENGINEERING, DHULE
2017-2018
Guided By
Manisha S. Patil

18. 23-Sep-17 18