The goal of this repository is to explain how ipywidgets widgets can be linked to matplotlib charts to make them interactive. When doing exploratory data analysis, its quite common to explore data from various perspectives to understand it better
The insurance.csv file includes 1,338 examples of beneficiaries currently enrolled in the insurance plan, with features indicating characteristics of the patient as well as the total medical expenses charged to the plan for the calendar year.
The features are:
- age: This is an integer indicating the age of the primary beneficiary (excluding those above 64 years, since they are generally covered by the government).
- sex: This is the policy holder's gender, either male or female.
- bmi: This is the body mass index (BMI), which provides a sense of how over or under-weight a person is relative to their height. BMI is equal to weight (in - -kilograms) divided by height (in meters) squared. An ideal BMI is within the range of 18.5 to 24.9.
- children: This is an integer indicating the number of children / dependents covered by the insurance plan.
- smoker: This is yes or no depending on whether the insured regularly smokes tobacco.
- region: This is the beneficiary's place of residence in the U.S., divided into four geographic regions: northeast, southeast, southwest, or northwest.
- charges: Individual medical costs billed by health insurance
We will investigate these variables combining matplotlib charts with ipywidgets widgets to generate interactive charts that update whenever the widget values change.
- Python, pandas, numpy, matplotlib, seaborn, ipywidgets