1. The document summarizes an internship project on loan prediction using machine learning algorithms. It discusses the tasks completed, tools and algorithms used, and outcomes. 2. As part of the internship, the student worked on building a model to predict loan approvals using a decision tree classification algorithm and Python libraries. 3. Key skills gained included experience with machine learning, algorithms, software engineering practices, and project management. The internship helped boost the student's technical and professional skills.

1. 1
Internship: Final Presentation
GOVERNMENT ENGINEERING COLLEGE
RAICHUR-584135
Welcome to Internship Seminar presentation
On
“Loan Prediction”
Presented by
ALTAF
3GU20CS400
Under the guidance of
PROF. SUSHMA T SHEDOLE
Asst. Professor,
Department of Computer Science &
Engineering

2. INTERNSHIP DOMAIN: MACHINE LEARNING
Internship Work
on
“Loan Prediction”
Details
2
Student Name Altaf
College Name Gov’t Engineering College Raichur
Branch Computer Science and Engineering
USN / Reg No 3GU20CS400
Internship Duration 4 Weeks ( 25th August 2022 to 23rd September
2022)
Company Internship Guide Vinit Kumar , Design Engineering

3. Content
3
1. About Company
2. Internship Task
3. Introduction on Project work / Roles
4. Software / Hardware Tools Details
5. Implementation Details
6. Skills Utilized
7. What I Learnt ?
8. Internship Outcomes
9. Project Demo / Screenshots
10.Conclusion

4. About Company
InfiData Technologies An ISO 9001:2015 Certified IT
Company, Accreditated by An International
Accreditation Service (IAS). Head quartered in "silicon
valley" of India Bengaluru, started in the year 2015. We
are highly specialized in the design and development of
websites, software application development, mobile app
development,E-Commerce solutions and more.
Our team of expert professionals works on the latest
software tools and technologies to give the best and
promising services to our customers.
4
OUR SERVICES
Development | Training
|Consulting
Address:
#1421, 1st Floor,16th B Cross, Sri
Radha Building, Opp.To Dr. Agarwal
Eye Hospital, Yelahanka New Town,
Bengaluru-64
www.infidata.in | info@infidata.in

5. Internship Tasks
5
5
1. Working with python libraries
2. Working with ML Algorithms
3. Activities on Model Building
4. Project Development with
datasets

6. Introduction to Project Work / Roles
• In this article, we are going to solve the Loan
Approval Prediction. This is a classification
problem in which we need to classify whether
the loan will be approved or not.
• Classification refers to a predictive modeling
problem where a class label is predicted for a
given example of input data
• Applicants provides the system about their
personal information and according to their
information system gives his status of
availability of loan
• A time limit can be set for the applicant to
check whether his/her loan can be
6

7. Software / Hardware Tools Details
Software:
• Python 3.6/3.7
• VS Code
• PyCharm
Hardware :
• Minimum PC Requirements
7

8. 8
Decision Tree Classification Algorithm
• Decision Tree is a Supervised learning technique
that can be used for both classification and
Regression problems, but mostly it is preferred for
solving Classification problems.
• It is a tree-structured classifier, where internal
nodes represent the features of a dataset, branches
represent the decision rules and each leaf node
represents the outcome.
• It is a graphical representation for getting all the
possible solutions to a problem/decision based on
given conditions
• In order to build a tree, we use the CART algorithm,
which stands for Classification and Regression Tree
algorithm.

9. 9
Decision Tree Terminologies
• Root Node: Root node is from where
the decision tree starts.
• Leaf Node: Leaf nodes are the final
output node
• Splitting: Splitting is the process of
dividing the decision node/root node
• Branch/Sub Tree: A tree formed by
splitting the tree
• Pruning: Removing the unwanted
branches from the tree.
• Parent/Child node: The root node , sub

10. Implementation Details
10
10
• Loan Dataset is very useful in our system for
prediction of more accurate result. Using the loan
Dataset the system will automatically predict
which costumer’s loan it should approve and
which to reject.
• Typically, here the system separate a dataset into
a training set and testing set ,most of the data
use for training, and a smaller portions of data is
use for testing.
• In Data cleaning the system detect and correct
corrupt or inaccurate records from database and
refers to identifying incomplete, incorrect,
inaccurate or irrelevant parts of the data and then
replacing , modifying or detecting the dirty or

11. 11
This dataset is named Loan Prediction Dataset data set. The
dataset contains a set of 613 records under 13 attributes:

12. Skills Utilized
12
1. Software Engineering Skills
2. Algorithms and Model Building
3. Administrative and collaboration
tools using Github.

13. What I havelearnt?
13
Technical skills:
● Python
● ML Algorithms
● Working with
Libraries and
datasets
● Frameworks
Soft skills:
● Teamwork in an
international
working
environment
● Communication
skills
● design
Working
culture
Management
skills:
● project
managemen
t,
● time
management
● and more

14. Internship Outcomes
14
● Now I am able to design Applications
Working with SDLC Approach
● Growth : Critical Thinking and
analysis
● Lead the team and projects
● Gain confidence , knowledge and
skills

15. Project Screenshots

16. Project screen shots
16

17. Conclusion
 This internship has been an
excellent and rewarding
experience.
 It was a great opportunity to
improve personal and
professional skills.
 These valuable skills have
boosted my professional skills to
a higher leveland prepare me
for futurecareer.
17

18. References
 https://www.kaggle.com/datasets/prathamtripathi/drug-classification
 https://www.javatpoint.com/machine-learning-decision-tree-classifier
 https://www.turing.com/kb/an-introduction-to-decision-tree-algorithm-for-
beginners
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