The document summarizes a presentation on using machine learning to predict loan approvals. It includes: 1) An introduction describing the project goal of using machine learning techniques to analyze past beneficiary data and predict which future applicants will be approved loans. 2) An outline of the presentation covering the introduction, types of machine learning, problem statement, process, model demonstration, and conclusion. 3) Details of the data analysis process including exploratory analysis, feature engineering, model building using logistic regression, decision trees, random forest and XGBoost algorithms, and conclusions. The random forest algorithm achieved the best prediction accuracy of 98%. Key factors for prediction were loan amount figures and proposal status. Future work

1. Accurate Loan Approval Prediction
Final Project Presentation on
Machine Learning With Python

2. Team Members
Malinda Ramadhani
Project implementor
Project Supervisor
Prof. Gaurav Mishra

3. Presentation Outline
•Introduction
•What is machine learning?
•Kinds of machine Learning
•Problem Statement
•Scope and Objective
•Process and Software Specification
•Process Model(Use case Diagram)
•Demonstration
•Conclusion and Future Scope

4. Introduction
•Machine Learning is a sub-area of artificial intelligence, whereby
the term refers to the ability of IT systems to independently find
solutions to problems by recognizing patterns in databases. We
will be using machine learning algorithms along with some data
analysis techniques in our project.
•Our project focus is to use existing beneficiarie’s details and analyze
it further by applying a few machine learning techniques and
predict which future applicant can be approved the loan.

5. What is Machine Learning ?
Historical Data
Analyses
Learns Decisions/Predictions
Output

6. Machine Learning
Supervised Learning
Unsupervised
Learning
Classification Regression Association
Reinforcement
Learning
Clustering

7. Problem Statement
•The Tanzania Education Fund Authority was established since 2003 with the purpose to
suppliment government efforts on soliciting funds and aquisition to educational institutions
•The Authority seeking businesses innovations such as an automatic Grants / loan evalution
systems that is based on machine learning for risk evaluation models,loan evaluation models
aswell as grants evaluation process models. Institutions first apply for Educational loan after
that validates the institutions eligibility for loan or Grant.
What Authority knows?
Beneficiaries details like Institution Name, Education
Level, Requested Amount, Expected output, Priority
Area,Total number of enrollment, Year of establishment
and others.
What Authority wants?
The Authority wants to automate the loan process based
on beneficiaries details provided while filling online
application form.

8. Scope and Objective
• The main objective of this project is predicting Loan Approvals using
Python Machine learning techniques.
• Learn to analyze given data by performing exploratory data analysis.
• Learn to deal with missing data and treat Outliers.
• Understand and perform Feature engineering.
• Go through different Machine Learning algorithms and understand
how they work.
• Finally build a Machine Learning Model to predict loan approval.

9. Process and Software Requirement
•Exploratory Data Analysis
• Univariate Analysis
• Bivariate Analysis
•Missing Value and Outlier Treatment
•Feature Engineering
•Model Building
• Logistic Regression
• Decision Tree
• Random Forest
• XGBoost

10. Software Requirement
•Anaconda with Python 3.8
•Jupyter Notebook
Libraries used:
•Pandas
•SciKit Learn
•Matplotlib
•Numpy
•Seaborn
•XGBoost

11. Build ML Model

12. Univariate analysis of categorical variables

13. Univariate Analysis:
• Around 1146 of the requested loan were approved.
• Around 572 of requested amount were rejected.

14. Bivariate analysis
Each variable will be analysed in correlation to loan status

15. Statistical Summary

16. Bivariate analysis
Each variable will be analysed in correlation to loan status

18. Correlation table for a more detailed analysis

19. Missing Value Imputation
For numerical variables: imputation using mean or median
For categorical variables: imputation using mode

20. Missing Value Imputation
For numerical variables: imputation using mean or median
For categorical variables: imputation using mode

21. Outlier Treatment
NOTE:As we take the log transformation, it
does not affect the smaller values much, but
reduces the larger values. So, we get a
distribution similar to normal distribution.

22. Feature Engineering
•DEFICIT_AMOUNT: during bivariate analysis we will combine the
REQUESTED_AMOUNT and APPROVED_AMOUNT. If the difference is low, the
chances of loan approval might also be high.
•REQUESTED_AMOUNT, RECOMMENDED_AMOUNT,APPROVED_AMOUNT are
numerical features that after being analysed will shows the clear missing
NUMERICAL features values.
•PROPOSAL_STATUS (non-numerical values) are categorical feature which will be
converted numerical binary values ( 1 and 0s ) and will be used to predict the
proposal status (Approved = 1 and Rejected = 0).

23. Model Building
Logistic Regression

24. Model Building
Logistic Regression

25. Model Building
XGBoost Model

26. Model Building
Decision Tree

27. Model Building
Random Forest (RF)

28. Conclusion and Future Scope
•Out of all the classification algorithms used on the dataset, the Random Forest
algorithm gives the best overall prediction accuracy of approximatel 98%.
•REQUESTED_AMOUNT, RECOMMENDED_AMOUNT, APPROVED_AMOUNT,
PROPOSAL_STATUS were the most important factors for predicting the class of the
loan beneficiary.
•We can optimize the hyper parameters of our model and improve the accuracy.
•An app with proper UI can be built, which will take various inputs from the user like
requested amount, Year of establishment, loan duration,objective of loan, expected
results,priority area and give a prediction of whether Education institutional loan
application can be approved by the Tanzania Education Fund Authority.

29. Bibliography
•Machine Learning Courses
• Amity University
• YouTube(SimpliLearn, Edureka)
•Machine Learning Repository
• Kaggle
•Websites
• www.kdnuggets.com
• www.analyticsvidhya.com
• www.machinelearningmastery.com
Prepared by Malinda Ramadhani
PGD Data Science
Amity University

30. Bibliography
You have to prepare a presentation around following important points
Introduction and motivation of the project
Precise problem definition of the project
Propose Machine learning methods
Results
Conclusion
You will have to present this via recording with the help of the studio feature on the LMS