this project is used to predict m edical insurance cost based on linear regression

1. National EducationSociety®
Jawaharlal Nehru National College of Engineering
Department of Information Science &Engineering
An Internship seminar presentation on
“Medical Insurance Cost Prediction”
Internal Guide:
Mr. Vishwas C.G.M B.E,M.Tech
Assistant Professor,
Dept. of IS&E JNNCE,
Shimoga
External Guide:
Mr.Abhishekh G
Lead Application Engineer
Take It Smart (OPC) Pvt.Ltd,
Bangalore
Presented By:
Vidya G.S
USN:4JN17IS107

2. ABSTRACT
Insurance is a policy that eliminates or decreases loss costs occurred by
various risks. Various factors influence the cost of insurance, These
considerations contribute to the insurance policy formulation.
 Machine learning (ML) for the insurance industry sector can make the
wording of insurance policies more efficient. This study demonstrates how
linear regression can forecast insurance costs. .
In this work we will use Machine Learning Classification and ensemble
techniques on a dataset to predict medical cost. Which are K-Nearest Neighbor
(KNN), Logistic Regression (LR), Decision Tree (DT), Support Vector
Machine (SVM) and Random Forest (RF).
 The Project work gives the accurate or higher accuracy model shows that
the model is capable of predicting effectively.

3. CONTENTS
 Organization Profile
 Tasks performed
 Reflection notes
 Conclusion
 Reference

4. ORGANIZATION PROFILE
Founded by a team of young, dynamic and experienced professionals, TAKE IT
SMART (OPC) PVT.LTD. is a locally own company with the people having
collective experience for more than decades.
TAKE IT SMART (OPC) PVT.LTD is a leading solution provider for e-business
Enabling, Solution Consultancy, Turn-key Solution and Post Implementation and
Customized Support and also providing integration of enterprise solution to open
platform standards e business solution.

5. TASK PERFORMED
This internship gave an opportunity to learn various algorithms of machine
learning.
Made use of standard modules of machine learning.
Python was the language used to implement the above mentioned
concepts.
 Worked on Jupyter Notebook– that provide a wide range of libraries to
develop a python application.
Developed a mini project using the algorithms learnt.

6. Machine Learning algorithm
Logistic regression: Logistic regression is a process of modeling the
probability of a discrete outcome given an input variable. The most
common logistic regression models a binary outcome; something that can
take two values such as true/false, yes/no, and so on.
Decision Tree: Decision tree is the most powerful and popular tool for
classification and prediction. A Decision tree is a flowchart like tree
structure, where each internal node denotes a test on an attribute, each
branch represents an outcome of the test, and each leaf node (terminal
node) holds a class label.

7. Medical insurance Cost Prediction
Model can correctly anticipate insurance policy costs. this decreases human effort
and resources and improves the company's profitability. thus, the accuracies can
be improved . our objective is to forecast insurance charges in this article. the
value of insurance fees is based on different variables. as a result, insurance fees
are continuous values. the regression is the best choice available to fulfil our
needs.

8. Medical Insurance Cost Prediction
Objective
The objective of the study is to predictive the insurance cost based on age, BMI,
child number, the region of the person living, gender, and whether a client is
smoking or not. These features contribute to our target variable prediction of
insurance costs. For the measurement of the cost of insurance, several regression
models are applied in this study.

9. Workflow Diagram

10. Data Set

11. OUTPUT
Figure 1: Accuracy result

12. REFLECTION NOTES
Got hand on work on projects related to machine learning.This
internship provided an opportunity to develop a mini project “SONAR
Mine vs Rock Prediction” which helped to understand the learnt concept
in depths.
Technical outcomes:
Was introduced to many machine learning algorithms that has been in
recent use.
Linear Regression
SVMAlgorithm
Decision TreeAlgorithm
 Random Forest

13. Non technical outcome:
Communication
Time management
 Critical thinking
Experience:
 Resume weight age
 Exposure
 Hand on experience on real world project

14. CONCLUSION
This internship has been an excellent and rewarding experience. I can
conclude that there have been a lot I’ve learnt from my work at TAKE IT
SMART (OPC) PVT.LTD. Needless to say, the technical aspects of the work
I’ve done are not flawless and could be improved provided enough time.
The internship project “Medical Insurance Cost Prediction” is effort to
implement concepts learnt in the internship period which helps to find
effective method to predict Medical Insurance cost.

15. REFERENCES
[1]. Gupta, S., & Tripathi P. “An emerging trend of big data analytics with health
insurance in India”. In 2016 International Conference on Innovation and
Challenges in Cyber Security (ICICCS-INBUSH) pp. 64-69. 2016.
[2]. Singh, R., Ayyar, M. P., Pavan, T. S., Gosain, S., & Shah, R. R. “ Automating
Car Insurance Claims Using Deep Learning Techniques”. IEEE Fifth
International Conference on Multimedia Big Data (BigMM) pp. 199-200.2019
[3].Pesantez-Narvaez, J., Guillen, M., & Alcañiz, M. “Predicting motor insurance
claims using telematics data—XGBoost versus logistic regression” 2019.
[4].Kowshalya,G.,&Nandhini, M.“Predicting fraudulent claims in automobile
insurance”. In Second International Conference on Inventive Communication
and Computational Technologies (ICICCT) pp. 1338-1343. 2018.