Creating PredictHealth's Custom Predictors: Feature Engineering for Better Insurance Cost Models

Welcome back! In the last lesson, you learned how to validate and evaluate predictive models using PredictHealth’s insurance data. You practiced splitting your data into training, validation, and test sets, building preprocessing pipelines, and using metrics and visualizations to assess your models. These are essential skills for building reliable models that generalize well to new data.

In this lesson, we will take your modeling skills to the next level by focusing on feature engineering — the process of creating custom predictors from raw data. While you have already worked with basic features like age, BMI, and categorical variables, real-world data often contains hidden patterns that can be revealed by transforming or combining existing features. Feature engineering helps you capture these patterns, leading to more accurate and insightful models.

By the end of this lesson, you will know how to create new, meaningful features from raw data, visualize their impact, and compare models built with engineered features to those using only the original variables. This will help you understand the true power of custom predictors in predictive modeling.

You have already seen how to use raw features such as age, BMI, and smoker status in your models. However, these raw features do not always capture the full story. Feature engineering allows you to transform these basic variables into new predictors that can better reflect real-world relationships.

For example, instead of using age as a simple number, you might group ages into categories that make sense for insurance risk, such as "Young Adult," "Adult," "Middle-aged," and "Senior." Similarly, BMI can be grouped into standard health categories like "Underweight," "Normal," "Overweight," and "Obese." You can also create new features, such as family size, by combining the number of children with the insured person, or convert categorical variables like smoker status into numeric values for easier modeling.

Let's look at how you can create these custom predictors in code:

import pandas as pd

# Make a copy so we don't change the original data
feature_data = insurance_data.copy()

# Convert age into age groups
bins = [0, 25, 40, 55, 100]
labels = ['Young Adult', 'Adult', 'Middle-aged', 'Senior']
feature_data['age_group'] = pd.cut(feature_data['age'], bins=bins, labels=labels)

# Convert BMI into health categories
bmi_bins = [0, 18.5, 25, 30, 100]
bmi_labels = ['Underweight', 'Normal', 'Overweight', 'Obese']
feature_data['bmi_category'] = pd.cut(feature_data['bmi'], bins=bmi_bins, labels=bmi_labels)

# Create a family size feature
feature_data['family_size'] = feature_data['children'] + 1  # +1 for the insured person

# Convert smoker to a numeric value
feature_data['smoker_numeric'] = feature_data['smoker'].map({'yes': 1, 'no': 0})

Here's how a sample row looks before and after transformation:

Notice how age becomes age_group, bmi becomes bmi_category, children becomes family_size, and smoker becomes smoker_numeric. These transformations make the data more meaningful for modeling.