click here to download project abstract
– new applicant granted the loan or not using machine learning models trained on
the historical data set.
CONTENT
Chapter
List of content
Page no.
I
Abstract
1
Introduction
2
Literature Survey
3 Existing System
3.1 Disadvantages of
Existing System
3.2 Proposed System
3.3 Advantages of
Proposed System
3.4 System Architecture
3.5 System Requirements
3.6 Hardware
Requirements
3.7 Software
Requirements
4 Software Environment
Python
4.1
History of Python
4.2 Python Features
4.3 Interactive Mode
Programming
4.4 Script Mode
Programming
4.5 Flask Frame Work
5
Modules
5.1 Module Description
5.2 Dataset
5.3
Data Preparation
6
Model Selection
6.1 Analyze and
Prediction
6.2 Requirement Analysis
7
Functional
Requirements
7.1 Non Functional
Requirements
8 System Design and
Testing Plan
8.1 System Design and
Output Design
8.2 System Study
8.3 Feasibility Study
8.4 Economical
Feasability
8.5 Social Feasability
8.6 Data Flow Diagram
9 UML Diagrams
10
System testing
10.1 Unit Testing
10.2 Integration testing
10.3 Functional testing
10.4 White Box Testing
10.5 Black Box testing
10.6 Acceptance testing
11
Conclusion
12
References
1 INTRODUCTION
As the data are increasing daily due to digitization in the banking sector, people
want to apply for loans through the internet. Artificial intelligence (AI), as a
typical method for information investigation, has gotten more consideration
increasingly. Individuals of various businesses are utilizing AI calculations to
take care of the issues dependent on their industry information. Banks are facing
a significant problem in the approval of the loan. Daily there are so many
applications that are challenging to manage by the bank employees, and also the
chances of some mistakes are high. Most banks earn profit from the loan, but it
is risky to choose deserving customers from the number of applications. One
mistake can make a massive loss to a bank. Loan distribution is the primary
business of almost every bank. This project aims to provide a loan to a deserving
applicant out of all applicants. An efficient and non-biased system that reduces
the bank’s time employs checking every applicant on a priority basis. The bank
authorities complete all other customer’s other formalities on time, which
positively impacts the customers. The best part is that it is efficient for both banks
and applicants. This system allows jumping on particular applications that
deserve to be approved on a priority basis. There are some features for the
prediction like- ‘Gender’, ‘Married’, ‘Dependents’, ‘Education’, ‘Self_
Employed’,
‘ApplicantIncome’,
‘CoapplicantIncome’,
‘LoanAmount’,
‘Loan_Amount_Term’, ‘Credit_History’, ‘Property_Area’, ‘Loan_Status’.
