Introduction Healthcare is a crucial aspect of modern life, affecting the well-being and longevity of individuals worldwide. Access to affordable and high-quality healthcare is a major concern for people in every corner of the globe, and the cost of medical treatment can pose a significant financial burden, especially for those who are not covered by insurance. While healthcare costs continue to rise, insurance companies are tasked with finding ways to minimize risk and optimize costs to provide their clients with the best possible coverage. To achieve this goal, insurers need to have a comprehensive understanding of their clients’ health status, lifestyle habits, and other relevant factors that may impact their health. In recent years, the healthcare industry has undergone significant changes and innovations, thanks to the development of new technologies and the application of data-driven insights. These advancements have helped healthcare providers and insurance companies better understand the healthcare needs of their clients, and have enabled them to deliver more targeted and personalized care. By leveraging data analytics, insurers can gain valuable insights into their clients’ health status, which allows them to develop more effective and efficient insurance plans that meet the needs of their clients. One of the key challenges that insurance companies face in the healthcare industry is the need to minimize risk while still providing comprehensive coverage to their clients. This requires a proactive approach to healthcare, where insurers work closely with their clients to understand their health needs and risks. By collecting and analysing data on their clients’ health status, lifestyle habits, and other relevant factors, insurers can identify potential risks and develop strategies to mitigate them. For example, insurers may offer incentives to encourage healthy behaviour, such as discounts on gym memberships or wellness programs. By promoting healthy habits, insurers can help reduce the risk of illness and disease, which ultimately benefits both the insurer and the client. In addition to promoting healthy habits, insurers must also be proactive in managing chronic conditions. Chronic conditions, such as diabetes and heart disease, can be costly to treat and can pose a significant risk to insurers. By providing targeted support and resources to clients with chronic conditions, insurers can help their clients manage their health more effectively and reduce the overall cost of care. This may include providing access to specialized care teams, offering disease management programs, and providing incentives for medication adherence and regular check-ups. Data analytics is a critical tool for insurers looking to optimize their costs and minimize risk in the healthcare industry. By collecting and analysing data on their clients’ health status and healthcare utilization, insurers can gain valuable insights into their clients’ needs and risks. This data can be used to develop more effective insurance plans that meet the specific needs of their clients, while also minimizing risk and reducing costs. For example, insurers can use data analytics to identify high-risk clients and develop targeted interventions to help manage their health more effectively. This may include offering personalized health coaching, providing access to specialized care teams, or offering financial incentives for healthy behaviour. In addition to using data analytics to optimize costs, insurers can also leverage technology to improve the overall quality of care. Telemedicine and other virtual care solutions have become increasingly popular in recent years, as they provide a more convenient and cost-effective way for patients to access healthcare services. By offering virtual care solutions, insurers can help their clients access care more easily and cost-effectively, which can ultimately improve their health outcomes. While technology and data analytics have the potential to transform the healthcare industry, they also pose significant challenges. Data privacy and security are critical concerns, as the collection and use of personal health data must be done in a way that is both ethical and secure. Insurers must also ensure that their virtual care solutions and other technological innovations are accessible to all clients, regardless of their income or technological literacy. In conclusion, healthcare is a crucial aspect of modern life, impacting the well-being and longevity of individuals worldwide. Access to affordable and high-quality healthcare is a major concern BusinessProblem We all know that Health care is very important domain in the market. It is directly linked with the life of the individual; hence we have to be always be proactive in this particular domain. Money plays a major role in this domain, because sometime treatment becomes super costly and if any individual is not covered under the insurance then it will become a pretty tough financial situation for that individual. The companies in the medical insurance also want to reduce their risk by optimizing the insurance cost, because we all know a healthy body is in the hand of the individual only. If individual eat healthy and do proper exercise the chance of getting ill is drastically reduced. Goal&Objective: The objective of this exercise is to build a model, using data that provide the optimum insurance cost for an individual. You have to use the health and habit related parameters for the estimated cost of insurance File:Data.csv Targetvariable:insurance_cost Data Report Missing Value Replacing missing values in BMI column from mean and dropping the Applicant ID and year last admit because in year last admit there is lot of missing values which means that patient never admit before this study so the reason of removal of these column is creating extra dimension to the data frame while converting it into dummy variable so it is better to remove the remove the column to to avoid drastic increase in the data dimension. Now from the above box plot we can see that the variables do not contain much extreme values in the data So we can perform models on the given data and if it not works then we come to remove lawyers from the data based on quartile deviation or standard deviation. From Hebrew pair plot we can see that insurance and weight are perfectly positively correlated while all other variables not seem like perfect correlated so we will perform correlation matrix numerical variable to check weather independent variable are highly correlated with each other or not if they are highly correlated then we will remove that column From the above correlation matrix, we can see that our numerical data is not highly correlated with each other So we will so we will continue with the given data for further investigation From the eBook data description we can see that the age is ranging from 274 add the average cost of insurance is 27,147 and the maximum will visited doctor in last one year is 12 and the average daily steps covered by the patient is 5000. Converting categorical variable into dummy variable and treating first column as a reference variable After the conversion of categorical variable into dummy variable now we will separate the target variable and the dependent variable so that we can perform model on it. Show the given problem is the regression problem so we will use regression models to predict insurance costs half a patient covered by insurance companies should there are many different models we will use some of them are o linear regression o rigid regression o lasso o elastic net o polynomial regression o k nearest neighbour o support vector machine o decision tree regression o random forest regressor below is the given result based on the above models From the table below we can see that R2 score and best parameter of the models and we will select best model from them Best Model selection Random forest is the best performing model among the all model PowerPoint Presentation Capstone Presentation Guidelines to be followed : Presentation should complete in 10 mins 5 minutes will be devoted to Q&A Business Problem Understanding Mention whatever is applicable Modelling Approach Used & Why Subheading Lorem Ipsum is simply dummy text of the printing and typesetting industry. Insights from Analysis   Recommendations Go Back to Capstone Project – PGP-DSBA Course Content Final PPT submission Submission type : File Upload Due Date : Mar 10, 11:59 PM Total Marks : 20 Available from : Mar 03, 8:00 AM Description Dear Learner, You need to submit the Final Presentation Here. Do follow the guidelines mention in the module in regards to how to create PPT. You need to use PPT template. The Presentation is of 20 marks which will include 1. Presentation Submission 2. Presentation of the deck in front of the mentor as per the slots given. 3. Viva in regards to the Project and methodologies applied Regards, Program Office Scoring guide (Rubric) – Some Rubric (2) Evaluated Criteria Ratings Points 1.1 Business Problem Understanding (Explanation) 4 Final PPT submission: Capstone Project – PGP-DSBA – G… https://olympus.mygreatlearning.com/courses/74432/as… 1 of 3 3/10/23, 06:45 https://olympus.mygreatlearning.com/courses/74432