Portfolio-Project-US-Medical-Insurance-Costs

This is a portfolio project for the Codecademy Data Science Career Path. The project aims to analyze a dataset of US medical insurance costs.

The project can be viewed on Kaggle here.

Tools and Techniques

This project was completed using Python and the following libraries:

• Pandas
• Matplotlib

Sections:

Importing and exploring the insurance dataset

The first section of the project focuses on importing the dataset, exploring the data types, and finding the basic statistics for each column in the dataset. The code uses a try-except block to handle the different working directories in Kaggle and GitHub.

Analysis of Insurance Dataset

The second section of the project analyzes the dataset using various visualizations to explore the relationships between the variables. This includes histograms, scatterplots, and bar charts.

Studying Correlations

The third section of the project studies the correlation between variables using the Pearson correlation coefficient. This section aims to find out which variables have the strongest correlation with each other.

Potential Areas of Bias in the dataset

The final section of the project looks at potential areas of bias in the dataset. This includes looking at the distribution of individuals in different regions and the impact of smoking on insurance costs.

Feel free to check out the project on Kaggle and provide any feedback or suggestions!