Exploratory Data analysis on Employee health Expenditure dataset

Objectives -

• To understand the data using descriptive statistics using pandas, Numpy.
• Derive Insights based on descriptive statistics.
• To visualize data using Matplotlib & Seaborn liabraries.

Data Dictionary

• id : Employee unique identification number

• 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

• dependent : Number of children covered by health insurance / Number of dependents --> descrete values from 0-4

• Alcohol : Alcohol categories --> 'daily', 'weekend', 'rarely', 'party', 'no'

• Smoker : Smoking categories --> 'yes','no'

• Zone : The beneficiary's residential area in the US, northeast, southeast, southwest, northwest.

• Expenditure : Individual medical costs billed by health insurance

Outcomes -

• Overview of the data by slicing across dimensions.
• Hands on practise of Data wrangling using Pandas liabrary.
• Hands on practise of Visualization liabraries like Matplotlib, Seaborn.