This presentation summarizes the student's internship working on a machine learning project to predict loan approvals. The internship tasks included working with Python libraries and machine learning algorithms to build and develop a model using loan dataset. The project involved classifying loan applications as approved or rejected. Key skills gained included Python, machine learning algorithms, software engineering, and soft skills like communication, time management and teamwork.

1. Internship: Final Presentation
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2. INTERNSHIP DOMAIN: MACHINE LEARNING
Internship Work
on
“Your Project Name"
c
Student Name
College Name
Branch
USN / Reg No
Internship
Duration
4 Weeks ( 1st September 2021to 30th
September 2021)
Company
Internship Guide
Your Guide Name , Designation
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3. Internship Tasks
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.
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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
1. Working with python libraries
2. Working with ML Algorithms
3. Activities on Model Building
4. Project Development with datasets
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6. Introduction to Project Work / Roles
• Loan Approval Prediction.
In this article, we are going to solve the Loan Approval Prediction.
• the loan will be approved or not.
This is a classification problem in which we need to classify whether the loan will be
approved or not.
Predicting modeling modeling
Classification refers to a predictive modeling problem where a class label is predicted
for a given example of input data.
personal information
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 sanctioned or not.
• Loan Prediction is very helpful for employee of banks as well as for the applicant also.
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7. Software / Hardware Tools Details
Software:
• Python 3.6/3.7
• Anaconda Navigator
• Jupyter notebook
Hardware :
• Minimum PC Requirements
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8. Implementation Details
8
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• We have used multiple algorithms for Loan Prediction like Decision Tree, K-Nearest Neighbor(KNN)
Algorithm, Logistic Regression, ...
• A decision tree is a non-parametric supervised learning algorithm, which is utilized for both
classification and regression tasks
• Machine learning is a field of computer science that involves the learning of pattern identification and
computational learning theory in artificial intelligence
• 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.
• System will accept loan application form as an input.
• 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. after a system has been processed by using the
training set, it makes the prediction against the test set
• Data cleaning and processing: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 coarse data. I

9. Skills Utilized
1. Software Engineering Skills
2. Algorithms and Model Building
3. Administrative and collaboration tools
using Github.
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10. Skills Utilized
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11. What I learnt ?
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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
management,
● time management
● and more

12. Internship Outcomes
● Now I am able to design applications
● Working with SDLC Approach
● Growth : Critical Thinking and analysis
● Lead the team andprojects
● (Add if any)
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13. Project Demo / Screenshots

14. 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 level and
prepare me for futurecareer.
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15. Thank You
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