Insurance Charges Prediction with Linear Regression: Demographics and Costs Insights

Visit the releases page to download artifacts: https://github.com/mhdee740/insurance-charges-prediction-linear-regression/releases

A practical guide to predicting individual medical insurance charges using linear regression. This project walks through data cleaning, exploratory data analysis (EDA), feature selection, and regression modeling. All code and results live in the analysis notebook.

Emojis help map ideas to steps. 📈🧪🧠💡🧭

This project is built around a clean, repeatable workflow. It emphasizes understanding how demographic and lifestyle factors influence insurance costs. The notebook captures the full journey from raw data to a transparent model that you can reproduce, audit, and extend.

Table of contents

• Overview
• Why this project exists
• What you’ll learn
• Repository structure
• Data and features
• Data cleaning and preprocessing
• Exploratory data analysis
• Feature engineering and selection
• Modeling approach
• Evaluation and interpretation
• Reproducing results
• Visualization and storytelling
• How to run the notebook
• Data privacy and ethics
• Testing and validation
• Roadmap and future work
• Collaboration and contribution
• Release assets
• Licensing and credits

Overview This repository centers on a practical machine learning task: predicting individual medical insurance charges with a linear regression model. It uses a dataset that captures key demographic and lifestyle attributes. The analysis notebook demonstrates how to clean data, inspect distributions, test assumptions, and build a robust predictive model. The end goal is a transparent, interpretable model that helps stakeholders understand which factors drive costs and by how much.

Why this project exists Insurance costs affect individuals and families every month. Linear regression offers a simple, interpretable approach to estimate charges based on observable factors. The notebook approach makes it easy to audit the model. You can follow each step, inspect the data, and see how changes in features shift predictions. The project also serves as a template for similar predictive tasks in healthcare and finance where clarity matters.

What you’ll learn

• How to clean a dataset with demographic and lifestyle features
• How to perform basic and advanced EDA to reveal patterns
• How to engineer meaningful features that improve model performance
• How to select a subset of features without losing interpretability
• How to fit an ordinary least squares (OLS) linear regression model
• How to diagnose model assumptions and assess fit
• How to measure predictive accuracy with RMSE, MAE, and R-squared
• How to document the workflow for reproducibility and peer review
• How to present findings in a clear, data-driven narrative

Repository structure

• notebooks/
  • analysis.ipynb (the core analysis notebook containing data loading, cleaning, EDA, feature engineering, model training, evaluation, and results)
• data/
  • raw/
  • processed/
• src/
  • preprocessing.py
  • feature_engineering.py
  • model.py
  • evaluation.py
• figures/
  • plots/
  • charts/
• requirements.txt
• environment.yml
• README.md (this file)
• LICENSE
• CHANGELOG.md
• CONTRIBUTING.md

Data and features This project uses a synthetic dataset designed to mimic real-world insurance data while preserving privacy. The data model captures common factors that influence charges, such as age, sex, BMI, smoking status, number of children, and geographic region. The target variable is the annual medical insurance charges for an individual.

Core features (typical examples)

• age: Age of the person in years
• sex: Gender category (e.g., male, female)
• bmi: Body mass index
• children: Number of children
• smoker: Smoking status (yes/no)
• region: Geographic region
• charges: Target variable representing annual insurance charges

The notebook demonstrates how to handle categorical features (one-hot encoding or similar encoding strategies), scale numeric features if needed, and manage missing values. The goal is to produce a clean dataset ready for regression modeling, while preserving interpretability.

Data cleaning and preprocessing

• Handle missing values with transparent rules (e.g., imputation for numeric features, mode for categorical features)
• Normalize or standardize numeric features when appropriate to improve numerical stability
• Encode categorical features with simple, interpretable methods (one-hot encoding)
• Detect and address outliers in key features (age, bmi, charges) with robust statistics
• Create a clean, well-documented preprocessing pipeline so the same steps can be reproduced on new data

Exploratory data analysis

• Univariate analysis: distributions for age, bmi, charges, and other features
• Bivariate relationships: charges vs age, charges vs bmi, charges vs smoker status
• Categorical feature inspection: how region and smoker status relate to charges
• Correlation analysis to identify linear relationships and potential multicollinearity
• Visual storytelling: clear plots that reveal patterns while remaining accessible

Feature engineering and selection

• Interaction terms: age × smoker, bmi × smoker for potential combined effects
• Binning continuous variables where meaningful (e.g., age groups) for interpretability
• Polynomial features limited to avoid overfitting in a simple linear model
• Feature selection guided by domain knowledge and statistical criteria (p-values, adjusted R-squared)
• Regularization considerations: explanation of why plain OLS is favored for interpretability in this context
• A transparent rationale for keeping or removing features based on their contribution to the model

Modeling approach

• Model type: Ordinary Least Squares (OLS) linear regression
• Assumptions: linearity, independence, homoscedasticity, normality of residuals
• Training pipeline: split data into train/validation (and optionally test) sets
• Diagnostic checks: residual plots, Q-Q plots, and leverage analysis to identify influential points
• Data leakage prevention: ensure the training data is separated from any test data to avoid optimistic performance estimates
• Interpretability: coefficient estimates provide direct insights into how each feature affects charges
• Reproducibility: a fixed random seed for data splitting and model fitting ensures consistent results

Evaluation and interpretation

• Metrics used: RMSE, MAE, and R-squared
• Baseline vs enhanced models: comparison to show the impact of feature engineering
• Interpretability of coefficients: explain how to read a coefficient (e.g., the effect of being a smoker on charges)
• Model limitations: discuss the extent to which linear assumptions hold and where the model may fall short
• Hold-out evaluation: emphasis on a robust evaluation strategy that mirrors real-world use

Reproducing results This project is designed for reproducibility. The analysis notebook contains all the steps from raw data loading to final evaluation. To reproduce results, follow the setup and run instructions in the notebook. All code and results are consolidated in the analysis notebook for transparency and auditability.

• The notebook is the single source of truth for code and results.
• Steps to reproduce are described in the setup section below, with commands you can copy and run in a terminal or notebook cell.
• If you want to inspect individual components, the src folder contains modular scripts for preprocessing, feature engineering, modeling, and evaluation.

Visualization and storytelling Clear visuals help communicate findings. The notebook includes several plots:

• Scatter plots of charges against key numerical features
• Box plots for charges by categorical categories (e.g., region, smoker)
• Residual diagnostics to assess model fit
• Partial dependence-like visuals showing the marginal effect of selected features
• A summary figure that translates numerical results into actionable insights

The visuals emphasize accessibility. They use concise labels, readable color palettes, and captions that explain what each plot communicates. The goal is to help stakeholders grasp the main drivers of charges without wading through technical details.

How to run the notebook

• Prerequisites:
  • Python 3.8 or newer (a recent Python environment helps with compatibility)
  • Git to manage the repository (optional if you download the archive)
• Setup:
  • Create a virtual environment (recommended)
    • Python: 3.x
    • Use venv, conda, or another environment manager you prefer
  • Install dependencies:
    • pip install -r requirements.txt
• Run the notebook:
  • Navigate to the notebooks folder and open analysis.ipynb with Jupyter:
    • jupyter notebook notebooks/analysis.ipynb
• What you will see:
  • Data loading and cleaning steps
  • EDA visualizations
  • Feature engineering steps
  • Model training and evaluation results
  • Interpretations of the coefficient estimates
  • Final outputs and recommendations
• If you want to reproduce exact results:
  • Use the same random seed used in the notebook
  • Do not modify the preprocessing or feature engineering steps unless you understand the impact

Data sources, privacy, and ethics

• The dataset used in this repository is synthetic. It is designed to resemble real-world patterns without exposing private information.
• When dealing with real data, ensure you follow data governance rules, privacy laws, and ethical guidelines. Respect consent and data minimization principles.
• The notebook demonstrates transparent documentation of feature engineering choices and model limitations, which is essential for responsible deployment.
• Consider the fairness implications of model predictions. Reflect on potential biases in demographics or lifestyle factors and how they could affect decision-making.

Testing and validation

• Unit tests (where applicable) cover preprocessing and simple utility functions.
• Model validation steps are included in the notebook. They demonstrate how to split data, fit the model, and assess performance on held-out data.
• Diagnostic plots help spot issues like heteroscedasticity or nonlinearity that violate OLS assumptions.
• The tutorial style keeps tests lightweight while still illustrating best practices in model validation.

Roadmap and future work

• Improve feature engineering with domain-informed interactions that remain interpretable
• Explore alternative regression approaches (e.g., ridge, lasso) to study regularization effects
• Add cross-validation to better estimate model performance on unseen data
• Extend the dataset to handle additional factors that may influence charges (e.g., chronic conditions, geographic mobility)
• Develop a lightweight API to serve predictions in a privacy-preserving manner
• Create a companion blog post or slide deck to translate technical results into business implications

Collaboration and contribution

• This project welcomes thoughtful contributions. If you want to propose improvements, follow the standard contribution workflow:
  • Fork the repository
  • Create a feature branch
  • Implement changes with clear, small commits
  • Open a pull request with a description of the changes and their impact
• For quick feedback, open issues to discuss design decisions, data handling, or interpretation of results.
• The notebook is the primary artifact for code and results. Ensure any changes to the notebook preserve reproducibility and clarity.

Release assets

• The project publishes release artifacts in the Releases section. These artifacts typically include notebooks, sample data, and a starter environment configuration to help you reproduce results quickly.
• Access the artifacts here: https://github.com/mhdee740/insurance-charges-prediction-linear-regression/releases
• If you want to download assets directly, use the releases page link above. The release bundle is designed to streamline setup and provide a consistent starting point for experiments.

Technical architecture and rationale

• Data handling: The pipeline is designed to be understandable and auditable. It uses a straightforward two-step approach: clean data to a stable form, then train a model on the cleaned data.
• Feature pipeline: Categorical features are encoded to preserve interpretability. Numeric features are kept in their natural scale where possible to support direct interpretation of coefficients.
• Model choice: Linear regression is chosen for its transparency. Stakeholders can observe how each feature influences charges, in dollar terms.
• Diagnostics: The notebook includes practical checks for model adequacy. It shows how to interpret residuals and verify assumptions.

Directory and file highlights

• notebooks/analysis.ipynb
  • The central analysis notebook containing:
    • Data ingestion
    • Data cleaning steps
    • EDA dashboards and plots
    • Feature engineering routines
    • Model fitting with OLS
    • Diagnostic plots and interpretation
    • Final evaluation and narrative insights
• data/raw/
  • The raw synthetic dataset used for the analysis
• data/processed/
  • The cleaned and feature-engineered dataset ready for modeling
• src/preprocessing.py
  • Data cleaning utilities and encoding logic
• src/feature_engineering.py
  • Feature creation and selection routines
• src/model.py
  • Linear regression model wrapper and fit/evaluate methods
• src/evaluation.py
  • Metrics and diagnostic functions
• figures/ and figures/plots/
  • Visual outputs from the notebook
• requirements.txt
  • Dependency list for a reproducible environment
• environment.yml
  • Conda environment specification
• LICENSE
• CHANGELOG.md
• CONTRIBUTING.md
  • How to contribute to the project

Visual identity and branding

• The project uses clean visuals that emphasize clarity over complexity.
• Color palettes favor accessible contrast to ensure readability in plots and dashboards.
• Emoji usage aligns with the topic: charts, health, and data storytelling.

Notes on reproducibility

• The notebook is designed to be executed in a standard Python data science stack. It demonstrates exactly how to load the synthetic data, apply the preprocessing steps, train the model, and inspect the results.
• Reproducibility is aided by:
  • A fixed random seed for any data splits
  • Documented preprocessing steps
  • Clear, minimal, and well-commented code blocks
  • A single source of truth for modeling decisions within the notebook

Troubleshooting common issues

• If dependencies fail to install:
  • Ensure you are using a compatible Python version (3.8+)
  • Use the provided requirements.txt or environment.yml to install dependencies
• If the notebook cannot access data files:
  • Confirm the data paths match the repository structure
  • If using a fresh environment, re-run the data download or generation step from data/raw
• If plots do not render:
  • Verify the notebook kernel has access to plotting libraries
  • Check for missing dependencies and install them via the requirements file

Security and privacy

• The repository uses synthetic data to avoid exposing personal information.
• If you replace the synthetic data with real data, treat it as sensitive and follow your organization’s data handling policies.
• The notebook includes explicit documentation of data handling steps to support auditability and accountability.

Changelog and history

• The project maintains a changelog to track major improvements, fixes, and feature additions.
• Each release entry clarifies what changed and why, helping users understand the evolution of the analysis.

Appendix: Frequently asked questions

• Q: What is the primary target variable in this project? A: The target is annual medical insurance charges for an individual.
• Q: Why choose linear regression? A: It offers transparent interpretability. Stakeholders can see how each feature affects charges in dollars.
• Q: How are categorical features handled? A: They are encoded in a way that preserves interpretability and model compatibility.
• Q: Can I use this notebook with real data? A: Yes, replace the synthetic data with your real dataset, ensuring privacy and governance requirements are met and that you adjust preprocessing to fit the new data shape.

Release assets (duplicate link for convenience)

• Access the same release portal here: https://github.com/mhdee740/insurance-charges-prediction-linear-regression/releases
• The release page contains bundles suitable for quick setup and reproducibility.

Final notes

• This repository presents a disciplined approach to predicting insurance charges with a linear model. The emphasis is on clarity, reproducibility, and interpretability. The notebook serves as both a tutorial and a reference for practitioners who want to understand how to connect data cleaning, feature engineering, and simple regression into a cohesive analytics workflow.
• The content reflects real-world patterns in a compact, readable form. It is designed to be extended by researchers, analysts, and practitioners who value transparent modeling choices and accessible results.

Releases link continued

• For artifacts and the latest updates, revisit the releases page at the same URL: https://github.com/mhdee740/insurance-charges-prediction-linear-regression/releases

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