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-basics-for-data-analysis/ Access the Project: https://www.dataquest.io/projects/guided-project-a-predicting-insurance-costs/ Video Chapters: Project Brief: 6:06 Loading and inspecting the data: 7:27 Exploratory data analysis (EDA): 9:52 Building the linear regression model: 24:28 Evaluating model performance: 30:16 Analyzing and Interpreting Residuals: 33:28 Refining the model: 39:43 Audience Q&A: 49:26 #MachineLearning #pythonprojects #LinearRegression #Python #DataScience #HealthcareAnalytics