click here to download the project abstract
click here to download the project base paper
Loans are no longer considered a last resort to buy a sought-after smartphone or a
dream house. Over the last decade or so, people have become less hesitant in applying
for a loan, whether it’s personal, vehicle, education, business, or home especially when
they don’t have a lump sum at their disposal. Besides, Home and Education Loans
provide tax advantages that reduce tax liability and increase the cash in hand from
salary income.to get loans with minimal paperwork, quick eligibility checks, and
competitive interest rates. They have opened an online channel to apply and submit
documents for the approval process. If you still find the loan application and review
process intimidating, credit history is indicative of your future repayment behaviour,
based on your pattern in settling past loans. It helps the bank to know if you will be
punctual and regular with your payments. Banks weigh your employment history and
current engagement to ensure that your source of income is reliable.
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TABLE OF CONTENTS
CHAPTER
No.
TITLE
PAGE
No
ABSTRACT
i
LIST OF FIGURES
v
1
INTRODUCTION
1
1.1 Objective of the project
1.1.1
Necessity
1.1.2
Software development method
1.1.3
Layout of the document
1.2 Overview of the designed project
1
1
1
1
2
2
LITERATURE SURVEY
2.1 Literature Survey
3
3
3
AIM AND SCOPE OF THE PRESENT INVESTIGATION
8
3.1 Project Proposal
3.1.1 Mission
3.1.2 Goal
3.2 Scope of the Project
3.3 Overview of the project
3.4 Existing system
3.4.1 Disadvantages
3.5 Preparing the dataset
3.6 Proposed system
3.6.1 Exploratory Data Analysis of loan approval
3.6.2 Data Wrangling
3.6.3 Data collection
3.6.4 Building the classification model
3.6.5 Advantages
3.8 Flow chart
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iii
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EXPERIMENTAL OR MATERIALS AND METHODS; ALGORITHMS
USED
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4.1 System Study
4.1.1 System requirement specifications
4.2 System Specifications
4.2.1 Machine Learning Overview
4.2.2 Flask Overview
4.3 Steps to download & install Python
4.3.1 IDE Installation for python
4.3.2 Python File Creation
4.4 Python Libraries needed
4.4.1 Numpy library
4.4.2 Pandas library
4.4.3 Matplotlib library
4.4.4 Seaborn library
4.4.5 Scikit Learn library
4.4.6 Flask
4.5 Modules
4.6 UML diagrams
4.6.1 Use Case Diagram
4.6.2 Class Diagram
4.6.3 Activity Diagram
4.6.4 Sequence Diagram
4.6.5 Entity Relationship Diagram
4.7 Module Details
4.7.1 Data Pre-processing
4.7.2 Data Validation /Cleaning /Preparing Process
4.7.3 Exploration data analysis of visualization
4.7.4 Comparing Algorithm with prediction in the form of best
accuracy result
4.7.5 Algorithm and Techniques
4.7.6 Deployment Using Flask
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