Feature Engineering: Boosting Your Data for Better Model Performance

In machine learning, feature engineering is like turning raw ingredients into a gourmet dish—your model can only be as good as the data you feed it. It’s the process of transforming raw data into meaningful features that make your model smarter and more accurate. Good feature engineering helps your model uncover the patterns in the data, so it can make better predictions. In this article, we’ll break down why feature engineering matters, go through some common techniques, and show real examples from a heart disease prediction project using the UCI Heart Disease dataset. You can see the full project at the link below.

Heart-Disease-Machine-Learning-Exploration/src/Heart_Disease_Detection.ipynb at main · abroniewski/Heart-Disease-Machine-Learning-Exploration

What is Feature Engineering?

Think of feature engineering as customizing your data for your model. You’re either:

1. Creating new features from existing data,
2. Transforming features to make them more usable, or
3. Selecting the most important ones to keep things simple.

It’s all about helping the model see the patterns in the data more clearly.

Why Does Feature Engineering Matter?

Good feature engineering is like giving your model a pair of glasses—it makes things clearer and easier to understand. Here’s why it’s so important:

• Boosts accuracy: By making the data more relevant and meaningful.
• Prevents overfitting: By focusing on the key features and cutting out the noise.
• Speeds up training: Fewer features mean less data for the model to process.
• Improves understanding: Clean, well-engineered features are easier to explain to others (or yourself!).

Common Feature Engineering Techniques

Let’s dive into some of the most useful techniques. You can see all of these in practice in the project notebook linked ??

1. Feature Creation

Creating new features from existing data can sometimes reveal patterns that weren’t obvious before. For example, in the UCI Heart Disease dataset, instead of just using cholesterol and age separately, we could combine them into a new feature: the cholesterol-to-age ratio. This might be more helpful for predicting heart disease than either feature on its own.

# Example: Creating a new feature (cholesterol-to-age ratio)

hd_train['cholesterol_age_ratio'] = hd_train['cholesterol'] / hd_train['age']        

2. Feature Transformation

Sometimes raw data needs a bit of tweaking to be more useful. This could mean reducing skewed data, dealing with outliers, or just making everything fit on the same scale. For example, we might apply a transformation to resting blood pressure to smooth out the data, making it easier for the model to digest.

# Example: Applying transformations to skewed data

# Square transformation
hd_train['max_heart_rate_achieved'] = hd_train['max_heart_rate_achieved'] ** 2

# Inverse transformation  
hd_train['resting_blood_pressure'] = 1 / hd_train['resting_blood_pressure']            

3. Handling Categorical Data

Machine learning models don’t understand categories like “male” or “female” out of the box—they need numbers. This is where one-hot encoding or label encoding comes in. For example, we can convert sex and chest pain type into numerical columns.

There's actually a LOT that goes into figuring out how best to represent data as a number... The big easy question is weather or not the order is important, like a best-to-worst list, or if it is random, like a shoe size.

# Example: One-hot encoding categorical variables

hd_train = pd.get_dummies(hd_train, columns=['sex', 'chest_pain_type', 'fasting_blood_sugar'])        

4. Feature Scaling

Some machine learning models, like logistic regression or neural networks, care about the scale of your features. If one feature is measured in thousands and another in decimals, the model might overfocus on the bigger numbers. To fix this, we scale all features so they fit within a similar range.

The scaling needs to be done on the training data, not the full data set, so that we don\t introduce data leakage into the model. We want to make sure that the predictive model can only "see" the data that would exist if we were actually using it in a similar situation.

# Example: Scaling features using standardization
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(hd_train)        

5. Feature Selection

Not every feature is useful—sometimes, extra data just adds noise. Feature selection is about picking the most important features for your model. You can do this manually, using domain knowledge, or rely on automated methods like recursive feature elimination (RFE) or regularization techniques like Lasso.

For the UCI Heart Disease dataset, let’s say we’ve found that features like cholesterol or fasting blood sugar don’t have much predictive power (i.e. they don't correlate very well with heart disease). We’d drop them to keep our model clean and efficient.

# Example: Dropping irrelevant features
hd_train.drop(columns=["fasting_blood_sugar", "cholesterol", "resting_blood_pressure"], inplace=True)        

Watch Out for These Pitfalls

While feature engineering can do wonders, there are some common traps to avoid:

1. Overfitting: Creating too many features can make your model memorize the training data instead of learning from it.
2. Introducing Bias: Creating features based on assumptions that don’t hold up in real-world data can lead to biased predictions.
3. Multicollinearity: Features that are too similar to each other (highly correlated) can mess with your model, especially in linear models.
4. Data Leakage: Using information from the test set when creating features will give your model an unfair advantage—and skew your results.

Feature Engineering in Action: UCI Heart Disease Dataset

Let’s put it all together with the UCI Heart Disease dataset. Here’s what we did:

1. Created new features like the cholesterol-to-age ratio.
2. Transformed features to make skewed data (like heart rate) easier for the model to process.
3. Encoded categorical variables so the model could work with them.
4. Scaled the features to ensure everything was on a level playing field.
5. Selected the most important features and dropped those that didn’t add much value.

# Final feature engineering pipeline

# Imputing missing values
hd_train.loc[hd_train["thalassemia"] == "?", "thalassemia"] = hd_train["thalassemia"].astype(float).mean()
hd_train.loc[hd_train["major_vessels_count"] == "?", "major_vessels_count"] = hd_train["major_vessels_count"].astype(float).mean()

# Transforming features
hd_train['max_heart_rate_achieved'] = hd_train['max_heart_rate_achieved'] ** 2
hd_train['resting_blood_pressure'] = 1 / hd_train['resting_blood_pressure']

# Creating new features
hd_train['cholesterol_age_ratio'] = hd_train['cholesterol'] / hd_train['age']

# One-hot encoding categorical variables
hd_train = pd.get_dummies(hd_train, columns=['sex', 'chest_pain_type', 'fasting_blood_sugar'])

# Scaling features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(hd_train)

# Dropping irrelevant features
hd_train.drop(columns=["fasting_blood_sugar", "cholesterol", "resting_blood_pressure"], inplace=True)        

Conclusion

Feature engineering is like the secret sauce of machine learning. It’s the part of the process where you get to be creative with your data—building, transforming, and selecting features that make your model better. But, like any creative process, it requires balance. Too much feature engineering can lead to overfitting or biases, while too little can leave your model underperforming.

In the case of the UCI Heart Disease dataset, even simple transformations and feature selection steps can make a big difference in model accuracy and clarity. The key is to experiment, trust the data, and stay mindful of the common pitfalls. With a solid feature engineering process, your models will be more accurate, efficient, and ready to tackle real-world data.