Linear Regression is a good starting point for predicting medical insurance costs. The idea is to model charges as a function of features like age, BMI, number of children, smoking habits, and region.

Steps usually include:

1. Prepare the data – encode categorical variables (like sex, smoker, region) into numerical values.

2. Split the data – use train-test split to evaluate the model’s performance.

3. Train the model – fit Linear Regression on training data.

4. Evaluate – use metrics like Mean Squared Error (MSE) and R² score to check accuracy.

5. Predict – use the model to estimate charges for new individuals based on their features.

Keep in mind: Linear Regression works well if the relationship is mostly linear. For more complex patterns, Polynomial Regression or Random Forest can improve predictions.

If you want, I can also share a Python example with dataset and code for better understanding.