Guided Project: Predicting Insurance Costs
- Last updated on May 9, 2025 at 6:07 PM
About this Webinar
In this hands-on Project Lab, Dataquest’s Senior Content Developer, Anna Strahl, will guide you through how to develop a linear regression model in Python to predict patient medical insurance costs.
You’ll step into the role of a data analyst at a hospital administration, using real-world patient data, including demographic and health information. By the end of the session, you'll have a complete understanding of how to build, evaluate, and interpret predictive models to support strategic decision-making.
This project is ideal for learners comfortable with Python, pandas, NumPy, Matplotlib, Seaborn, and intermediate-level data science concepts.
What You'll Learn:
- How to clean, explore, and prepare healthcare data for analysis.
- Techniques for building and interpreting linear regression models.
- Methods to assess model performance using diagnostic techniques.
- Ways to draw actionable insights from your predictive model results.
- Practical Python techniques to apply to real-world healthcare projects.
Key Skills Covered in This Project:
- Data preparation and exploratory analysis using pandas and NumPy.
- Data visualization with Matplotlib and Seaborn to identify patterns.
- Building and fine-tuning linear regression models with scikit-learn.
- Evaluating model assumptions and performance metrics.
- Interpreting and communicating model findings effectively.
- Leveraging Python for healthcare data analysis.
📌 Note: This is a premium project that has been opened up for free to all webinar participants from May 2- 9.
New to Python? Begin with our Python Basics for Data Analysis course to build the foundational skills needed for this project.
Before You Start: Pre-Instruction
To make the most of this project walkthrough, follow these preparatory steps:
1. Review the Project
Access the project and familiarize yourself with the goals and structure:
- Start the project here
2. Access the Solution Notebook:
You can view and download it here to see what we’ll be covering:
Helpful Tips
New to Markdown? We recommend learning the basics to format headers and add context to your Jupyter notebook: Markdown Guide.
For file sharing and project uploads, it is important that you create a GitHub account ahead of the webinar: Sign Up on GitHub.
Want to work offline?
1. Set Up Your Workspace
We'll work with a basics.ipynb file, which can be rendered in the following tools:
- Jupyter Notebook (local installation required)
- Google Colab (browser-based, no installation needed)
2. Download the Resource Files
To follow along with the webinar, you'll need the insurance.csv dataset, which contains information on individual medical insurance bills. Each bill is associated with some demographic and personal characteristics of the person who received it.
