/Health-Insurance

This project used Hypothesis Testing and Visualization to leverage customer's health information like smoking habits, bmi, age, and gender for checking statistical evidence to make valuable decisions of insurance business like charges for health insurance.

Primary LanguageJupyter Notebook

Applied-Statistics

Project link: https://nbviewer.jupyter.org/github/shoaib555/Applied-Statistics/blob/main/Applied%20statistics.ipynb

Data Description:

The data at hand contains medical costs of people characterized by certain attributes.

Domain:

Healthcare

Context:

Leveraging customer information is paramount for most businesses. In the case of an insurance company, attributes of customers like the ones mentioned below can be crucial in making business decisions. Hence, knowing to explore and generate value out of such data can be an invaluable skill to have.

Attribute Information:

age: age of primary beneficiary. sex: insurance contractor gender, female, male. bmi: Body mass index, providing an understanding of body, weights that are relatively high or low relative to height, objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9. children: Number of children covered by health insurance / Number of dependents. smoker: Smoking(yes,no). region: the beneficiary's residential area in the US, (northeast, southeast, southwest, northwest). charges: Individual medical costs billed by health insurance.