Predict Insurance Costs with Linear Regression in Python [Machine Learning Tutorial]
In this video, we 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.
Recommended Prerequisites:
Python Basics for Data Analysis → https://www.dataquest.io/path/python-...
Access the Project: https://www.dataquest.io/projects/gui...
Video Chapters:
00:00:00 - Intro
00:06:06 - Project Brief
00:07:27 - Loading and Inspecting the Data
00:09:52 - Exploratory Data Analysis (EDA)
00:24:28 - Building the Linear Regression Model
00:30:16 - Evaluating Model Performance
00:33:28 - Analyzing and Interpreting Residuals
00:39:43 - Refining the Model
00:49:26 - Audience Q&A
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