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Project overview
In this project, you’ll assume the role of a data analyst for a hospital administration working to predict patient medical insurance costs. Using a dataset of patient demographic and health information, you’ll apply your skills in data exploration, linear regression modeling, model evaluation, and results interpretation to develop a predictive model.
This hands-on project allows you to showcase your ability to leverage Python and linear regression to solve a real-world challenge. You’ll work through the entire data science pipeline from data loading and exploratory analysis to model fitting, diagnostics, and extracting insights. The final model could help hospitals forecast costs and allocate resources more effectively.
Objective: Develop a linear regression model to predict patient medical insurance costs based on demographic and health data, and interpret the model results to provide actionable insights.
Key skill required
To complete this project, it's recommended to build these foundational skills in Python
- Investigating and preparing data for analysis using Python and pandas
- Defining the components of a linear regression model
- Fitting and interpreting a linear regression model
- Evaluating the fit and assumptions of a linear regression model
Projects steps
Step 1: Introduction
Step 2: Exploring The Dataset
Step 3: Dividing The Data
Step 4: Build The Model
Step 5: Residual Diagnostics
Step 6: Interpreting The Model
Step 7: Final Model Evaluation
Step 8: Drawing Conclusions
Step 9: Next Steps
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