Statistical Learning

Exploratory Data Analysis

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

region: the beneficiary's residential area in the US, northeast, southeast, southwest, northwest.

charges: Individual medical costs billed by health insurance

Outcomes

Exploratory Data Analysis

Statistics using Python

Hypothesis testing

Objective

We will dive deep into this data to find some valuable insights

Steps and tasks

1. Import the necessary libraries

2. Read the data as a data frame

3. Perform basic EDA which will include the following:

   a. Shape of the data

   b. Data type of each attribute

   c. Checking the presence of missing values

   d. 5 point summary of numerical attributes

   e. Distribution of ‘bmi’, ‘age’ and ‘charges’ columns.

   f. Measure of skewness of ‘bmi’, ‘age’ and ‘charges’ columns

   g. Checking the presence of outliers in ‘bmi’, ‘age’ and ‘charges' columns

   h. Distribution of categorical columns (include children)

   i. Pair plot that includes all the columns of the data frame

4. Investigate the following hypothesis with statistical evidence

   a. Do charges of people who smoke differ significantly from the people who don't?

   b. Does bmi of males differ significantly from that of females?

   c. Is the proportion of smokers significantly different in different genders?

   d. Is the distribution of bmi across women with no children, one child and two children, the same?