Healthcare Cost Prediction with Machine Learning

This project builds a machine learning model to predict individual healthcare charges using demographic and behavioral data. The model is trained and evaluated using Random Forest Regressor with hyperparameter tuning via GridSearchCV.

🔧 Technologies

• Python (pandas, scikit-learn, seaborn, matplotlib)
• Jupyter Notebook
• Streamlit (for optional dashboard)

📁 Contents

• Healthcare_Cost_Prediction.ipynb: Complete analysis, model training, and evaluation
• data.csv: Sample dataset (or instructions to load it)
• requirements.txt: Python dependencies

📊 Key Results

• Best model RMSE: ~$4,800
• R² Score: ~0.82
• Major cost drivers: Smoking status, BMI, Age

🚀 Run It

Clone the repository and open the notebook with Jupyter:

git clone https://github.com/Mekusgood/healthcare-cost-prediction.git
cd healthcare-cost-prediction
jupyter lab