Presentation for the Project - Loan Prediction System Using Machine Learning

1. Loan Prediction System
Using Machine
Learning
Group – 57
Guide – Dr. Pallavi Sapkale

2. Group Members
Sr. Name Roll No Contact
1) Ritesh Bhoir 19ET5008 9594534746
2) Sanil Nair 19ET5010 7710838920
3) Mamata Gupta 19ET5012 7039748452
4) Akash Kale 19ET5002 7721018792

3. Content
1) Title Page
2) Introduction
3) Literature Review
4) Generation Gap
5) Problem Statement
6) Hardware & Software used
7) Aim and objective of the Research
8) Proposed Work
9) Machine Learning Models
10) User Interface (UI)
11) Result & Working
12) Additional Features
13) Advantages
14) Applications
15) Conclusion & Discussion
16) Contributions

4. INTRODUCTION
Loans are the core business of banks. The main profit
comes directly from the loan’s interest. The loan companies
grant a loan after an intensive process of verification and
validation. However, they still don’t have assurance if the
applicant can repay the loan with no difficulties. In our Final
Year Project, we built a predictive model to predict if an
applicant is eligible for the loan or not. We prepareda model
to predict the target variable. And made a User Interface /
web application.

5. Literature Review
In[1] The author has described a system designed particularly for bank employees to categorize the loan application
based on priority. The paper has shown the output in the form of yes or no based on the different parameters enters in
the given form such as loan history, gender, income, etc. for loan applications.
In[2] the author has taken the customer data from the banks which has approved their loan request. The author has
used the Logistic Regression algorithm of machine learning technology and focused on the pre-processing steps
required to be performed after getting the datasets for the better model development.
In[3] the author has described about the prediction of modernized loan approval system based on machine learning
approach to know the status whether the loan will pass or not. They have also given the details of technology used
such as XG Boot, Random forest and decision tree to classify the data into the appropriate classes and has found the
good accuracy.
In[4] the author has described the system for commercial bank to predict the loan status using classification
methodology. The author has used the K-Nearest Neighbor classifier machine learning method and has explained it’s
detailed working to predict even without building a model. The paper shows the KNN model accuracy as 75.08
In[5] the author has discussed about the existing system problem which consumes a lot of time and efforts of bank
employees and there are chances to occur human error due to manual checking. The author has developed an
automated system using decision tree algorithm to solve the existing system problem.

6. Generation Gap
This is the user interface of the old
system, which has an outdated
user interface. Old systems were
made using java, so they needed
to be installed on the device. And
old system didn’t provide feature of
online backup.
Fig. Old System

7. Problem Statement & Problem Solution
Problem Statement
1) Intensive time Consumption process of verification and validation.
2) Human errors can be introduced during the validationprocess.
3) No cross referencing previous loan records
4) Lot of human resource required.
Problem Solution
1) Our Machine learning model calculates all the parameters given and predictsif
the applicant is eligible for loan or not in very less time.
2) Time required for verification, and validation reduces significantly.

8. Hardware Used
1) Windows Computer
Software/Code Editor Used
1) JUPYTER Notebook
2) VS Code
Libraries Used
1) Pandas
2) NumPy
3) Seaborn
4) Matplotlib
5) Sklearn
6) Pickle
Languages Used
1) Python3 & Python Flask
2) HTML5 with Tailwind CSS
3) JavaScript
Hardware & Software Used

9. Fig. Web App
Aim and objective of the Research
• To make the process of loan approval easy using
fewer resources and human resources.
• To make the web-app which can be accessible
by anyone on the internet without downloading
any desktop software.
• To make the prediction model which will make
the accurate predictions and help banks to make
approving the loans very easy.

10. Proposed Work
1)Data Collection –Two CSV files: train, test are used for this
Project.
2) Analyzing Data – Analyzing what kind of data we are
dealing with.
3)Data Cleaning – Cleaning the Data of any null values,if
present.
4)Model Building- After analyzing and cleaning the data we
can build ML models.
5)Evaluating Performance Metrics of Models- Evaluating
which ML model works best & tuning it.
6)Exporting the model and building the User Interface/Web
app using HTML5 & Tailwind CSS.
7) Writing the backend Server Script Using Python3.
8) Connecting Model, backend & frontend.
Fig. Project Flow chart

11. Models
Random Forest Classifier
Accuracy – 77.922 %
Logistic Regression
Accuracy – 77.272%
Decision Tree Classifier
Accuracy – 68.1818 %
o we got highest accuracy from Random Forest classifier, which is 77.92%.

12. Random Forest Classifier
Random forest is a flexible, easy to use machine
learning algorithm that produces good results, even
without hyper-parameter tuning, a great result most of
the time. It is also one of the most used algorithms,
because of its simplicity and diversity (it can be used
for both classification and regression tasks).
Random Forest classifier Accuracy - 77.92%.
Fig. Random Forest Classifier Flow Chart

13. User Interface (UI)
User interface (UI) is responsive and compatible with all screensizes.
Fig. Web-app on DesktopScreen Fig. Web-app on TabletScreen

14. Result & Working
Step 1
Open the Web-appand
navigate to the
prediction form page
where we fill our data
Fig. Web-app Home page

15. Working
Step 2
Fill all the data in the given
form and press predict
button to get theprediction.
(All the data will then be
send to the backendscript
and then to the prediction
model for processing.)
Fig. Web-app – Predictionform page

16. Working
Step 3
View the predicted
result

17. Additional Features
1) Interest Rate Calculator
2) Global Finance News Section
3) Online Database Backup

18. Interest Rate Calculator
The interest rate is the amount a
lender/bank charges a borrower/loan
applicant and is a percentage of the
principal the amount loaned. We have
added a feature where users can calculate
simple interest easily.
Fig. Web-app – Simple Interest rate Calculator section

19. Global Finance News Section
Financial deals shouldn’t be conducted
without knowing the global market’s
condition. Here we added a section
where users can see the news related to
global financial conditions.
Fig. Web-app – Global finance news section

20. Online Database Backup
Backing up data is very important in every
business, bank should have all the records
of entries, so here we backup of all the data
in the online database and display that data
on the prediction page.
Fig. Web-app – Online Database Backup

21. Advantages
1) There have been number of cases of input glitches, Human errors in content while filling it manually while in
automated prediction system is more secure, reliable.
2) The complexity of filing loan in banks will become much easier as a applicant will knew how much amount of loan
he is viable.
3) Machine learning increase the accuracy compare to old traditional system and results get displayed in polar
format (yes or no).

22. Applications
1) In Banking Sector.
2) Co-operate sectors which provides loans to
their employees.
3) An individual/applicant who wants to know
about his capability of taking loan.

23. Conclusion & Discussion
The system approved or rejects the loan applications.
Recovery of loans is a major contributing parameter in the
financial statements of a bank. It is very difficult to predict the
possibility of payment of loan by the customer. Machine
Learning (ML) techniques are very useful in predicting
outcomes for large amount of data. In our project, three
machine learning algorithms, Logistic Regression (LR),
Decision Tree (DT) and Random Forest (RF) are applied to
predict the loan approval of customers. The experimental
results conclude that the accuracy of Random Forest
machine algorithm is better than compared to Logistic
Regression and decision tree machine learning approaches.

24. Contributions
Ritesh Bhoir – Machine learning Model, Web-app & PPT
Mamata Gupta – Machine learning Model, Project Report, Web app
Sanil Nair – Machine learning Model, Database & Interest Rate Calculator
Akash Kale – Machine learning Model, UI & Finance news Section

25. Thank You