STATSML 302: A Concept Course on Feature Engineering Techniques for Machine Learning

Transform raw data into predictive power with essential feature engineering techniques.

Overview

Feature engineering is a vital process that transforms raw data into insights that machine learning models can harness. By selecting, modifying, and creating the right features, you can significantly enhance model performance. In this guide, we’ll dive into essential feature engineering techniques, each illustrated with unique, real-world examples to give you practical insights.

Imputation: Filling Missing Values

In real-world datasets, missing values are commonplace. Imputation replaces these gaps with estimates, ensuring models can make full use of the available data.

• Mean/Median Imputation: Numerical missing values are replaced with the feature’s mean or median. This method is simple yet effective in preventing the model from focusing too heavily on gaps in data.

• Mode Imputation: For categorical data, replacing missing values with the most frequent category (mode) preserves data consistency.

Example: Imputation in Marketing Campaign Data

In a marketing dataset for predicting customer lifetime value (CLV), essential attributes like “monthly spend” or “engagement score” may sometimes be missing, perhaps due to data gaps in recent purchases or website activity tracking. By imputing missing “monthly spend” values with the median spend of similar customers, we avoid data loss and ensure this feature’s predictive power is retained. This imputation helps the model accurately gauge customer value, even when recent spend data is incomplete.

Alternative Imputation with K-Nearest Neighbors (KNN): For a more tailored imputation, KNN can be applied to fill missing values based on customer similarity. For instance, to estimate a missing “engagement score” for a customer, KNN identifies the most similar customers (based on features like demographics or purchase history) and averages their engagement scores. This approach ensures that imputed values reflect patterns in customer behavior, increasing the accuracy of customer segmentation and lifetime value predictions.

Scaling: Aligning Feature Ranges

Data often includes features measured on different scales, which can mislead models. Scaling aligns features to ensure that one feature's magnitude doesn’t dominate another's importance.

• Normalization: Transforms feature values to a 0-1 range, a common choice for algorithms sensitive to input scales, like neural networks.

• Standardization: Centers data around a mean of 0 and variance of 1, useful for algorithms like linear regression, which are sensitive to scale differences.

Example: Standardization in Marketing Spend Optimization

In a marketing campaign model predicting ROI, features like “advertising spend” (in thousands of dollars) and “campaign duration” (in days) may vary widely in scale. Advertising spend is typically much larger numerically compared to campaign duration, which could skew the model toward weighting spend more heavily than duration. By standardizing these features to have a mean of 0 and variance of 1, both features contribute evenly to the model’s predictions.

This standardization ensures that campaign ROI is influenced appropriately by both factors, allowing for more balanced insights. For example, the model can better identify high-performing campaigns where a shorter duration achieved a higher ROI, regardless of spend size.

Encoding Categorical Variables: From Words to Numbers

Machine learning models work with numbers, so categorical features need transformation. Various encoding methods can represent these categories effectively.

• One-Hot Encoding: Creates binary columns for each category in a feature, marking 1 for presence and 0 for absence.

• Label Encoding: Assigns each category a unique numerical label, though this approach can introduce unintended ordinal relationships.

• Target Encoding (Mean Encoding): Replaces each category with the mean of the target variable within that category, useful for models where categories have a strong predictive relationship with the target.

Example: Target Encoding for Predicting Customer Response Rates

In a marketing dataset used to predict customer response rates to email campaigns, product categories like “beauty,” “electronics,” and “fitness” often influence customer engagement. Rather than expanding the dataset with one-hot encoding, which would add a new column for each category, target encoding assigns each category a numeric value based on average past response rates.

For instance, if customers engaging with “fitness” products historically show a 20% email response rate, “fitness” can be target-encoded with the value 0.2. This encoding approach allows the model to capture each product category's unique influence on engagement, without creating multiple columns. By understanding that customers in the “fitness” category are more responsive, the model can better predict overall campaign success and guide targeted marketing strategies.

Polynomial Features: Creating Interaction Terms

Polynomial features create new features by combining existing ones, which can capture relationships that simple features might miss.

Example: Interaction Terms for Predicting Customer Conversion

In a model predicting customer conversion likelihood, features like “ad frequency” (number of times an ad is shown) and “time spent on site” may not individually correlate linearly with conversions. However, their interaction—such as “ad frequency * time spent on site”—could reveal compounded effects, where high ad exposure combined with longer site engagement significantly boosts conversion probability.

For example, multiplying “ad frequency” by “time spent on site” captures cases where users exposed to ads more often and who spend longer on the site are more likely to convert. Similarly, squaring “time spent on site” could reveal that engagement time alone has an exponential impact on conversion likelihood. By adding these interaction terms, the model gains a deeper understanding of how multiple behaviors drive conversions, helping marketers optimize campaign strategies.

Feature Binning: Grouping Continuous Values

Binning groups continuous variables into discrete bins, creating categories that simplify patterns and reduce noise.

Example: Binning Monthly Usage Data for Subscription Renewal Prediction

To predict subscription renewal, monthly usage data can be simplified by binning it into “Low,” “Medium,” and “High” categories. This categorization reduces variability and highlights distinct usage patterns, making it easier for the model to detect trends in renewal likelihood based on customer engagement levels.

Feature Extraction with Domain Knowledge

Feature extraction is the process of creating new variables by combining existing data, often leveraging domain expertise.

Example: Feature Extraction for Predicting Customer Upsell Potential

In a marketing model aimed at predicting a customer’s likelihood to purchase an upsell product, transaction data might include details like “last purchase amount,” “category of product,” and “location.” By creating additional features—such as “time since last purchase” or “average spend per visit”—the model can capture valuable patterns in buying behavior that indicate upsell potential.

For example, a customer who frequently makes large purchases in quick succession might be more open to upsell offers, while a customer with longer gaps between purchases might require different marketing strategies. Similarly, extracting features like “total spend in the last 30 days” can help identify high-engagement customers who are prime targets for upsell opportunities. This enhanced dataset enables the model to detect nuanced behavior patterns and refine upsell targeting for greater campaign effectiveness.

Handling Outliers: Managing Data Extremes

Outliers can distort predictions, especially in linear models. Handling them carefully is essential to prevent models from overfitting to extremes.

• Capping/Flooring: Set outliers above or below certain thresholds to specific boundary values, preserving the data range while removing extreme values.

• Winsorization: Similar to capping but more controlled, it adjusts extreme values to a specified percentile range.

Example: Capping Outliers for Customer Lifetime Value Prediction

In a marketing model predicting Customer Lifetime Value (CLV), certain customers might have exceptionally high purchase values due to one-time events like seasonal promotions or bulk orders. To prevent these outliers from skewing the model, spending values above the 95th percentile can be capped, ensuring the model isn’t misled into overestimating typical customer value.

For instance, a handful of customers may make abnormally large purchases around holiday sales, but this spending doesn’t reflect their usual behavior. By capping these extreme values, the model can focus on predicting realistic lifetime value, providing a more accurate basis for customer segmentation and personalized marketing strategies.

Log Transformation: Reducing Data Skewness

Log transformation helps manage skewness in features where a few values are disproportionately large, enabling models to better capture underlying patterns.

Example: Log Transformation for Predicting Customer Spend Behavior

In a marketing model predicting customer spend behavior, transaction amounts often show a right-skewed distribution, with a small number of high-value purchases. Applying a log transformation to these values reduces skewness, enabling the model to better distinguish between typical spending patterns and higher purchase brackets.

For example, while most customers make moderate purchases, a few make significant, infrequent purchases that can heavily skew the data. The log transformation smooths these variations, allowing the model to identify spending trends more accurately across different customer segments and create more effective targeting strategies based on spend behavior.

Dimensionality Reduction: Principal Component Analysis (PCA)

Dimensionality reduction reduces the number of features, focusing on those with the highest impact. PCA transforms features into a smaller set of principal components, retaining variance while simplifying the dataset.

Example: Principal Component Analysis for Customer Segmentation

In a marketing model designed for customer segmentation, datasets often contain a wide array of features, like frequency of purchases, average transaction value, preferred product categories, and engagement metrics. Using Principal Component Analysis (PCA), these numerous purchasing behaviors can be condensed into a few principal components that capture the core patterns in customer behavior.

For example, instead of analyzing dozens of detailed metrics, PCA reduces them to a handful of components—like “spending intensity” or “category diversity”—which represent essential spending and engagement patterns. This simplification allows the model to segment customers effectively, grouping them based on meaningful behavioral patterns rather than a cluttered set of individual features.

Time-Based Feature Engineering: Adding Temporal Insight

Time-series data often benefits from time-based features that reveal seasonality, trends, or changes over time.

Example: Leveraging Temporal Features for Customer Purchase Prediction

In a marketing model predicting customer purchase behavior, temporal features like “day of the week” and “days since last interaction” provide valuable insights into buying patterns. By aggregating transaction data by week, month, or quarter, the model captures recurring trends, such as customers making purchases more frequently on weekends or during certain seasonal periods.

For instance, if data shows that customers are more likely to buy during the first week of the month, this pattern can guide when to launch campaigns or promotions. Aggregating and analyzing these time-based behaviors helps the model better predict future purchases, enabling marketers to optimize campaign timing for maximum impact.

Target Transformation: Adjusting the Outcome Variable

Transforming the target variable can also improve predictions, especially when it has skewed distributions.

• Log Transformation: Commonly applied to positive-skewed targets, enabling models to detect patterns across a wide range of values.

• Box-Cox Transformation: A more flexible approach that handles various types of skew, useful when log transformation isn’t sufficient.

Example: Log Transformation for Predicting Campaign ROI

In a marketing model aimed at predicting campaign ROI, revenue data can be highly skewed, with a few high-performing campaigns generating outsized returns. Applying a log transformation to the revenue feature ensures the model focuses on relative differences between campaigns, rather than being overwhelmed by large revenue values from a few outliers.

For example, instead of letting a few big campaigns dominate the dataset, the log transformation smooths the revenue distribution. This allows the model to capture meaningful trends across all campaigns, making it easier to identify which marketing strategies are consistently effective, regardless of their scale.

Feature Selection: Choosing the Most Relevant Features

Selecting relevant features and discarding others prevents model overfitting and simplifies interpretations.

• Filter Methods: Select features based on statistical properties like correlation with the target variable.

• Wrapper Methods: Evaluate subsets of features based on model performance.

• Embedded Methods: Automatically select features during the training process (e.g., Lasso regression).

Example: Feature Selection for Customer Churn Prediction

In a marketing model predicting customer churn, relevant features like “subscription length,” “monthly engagement,” and “number of support interactions” are selected based on their correlation with churn likelihood. Irrelevant features, such as “customer ID” or “signup source,” are dropped to reduce noise, allowing the model to focus on the data that directly impacts churn prediction.

By retaining only the most impactful features, the model improves its ability to accurately identify high-risk customers. This streamlined approach enables marketers to proactively address potential churn by tailoring retention strategies to customers who display patterns linked to leaving.

Conclusion

Feature engineering is a powerful tool in machine learning, transforming raw data into meaningful predictors. By using techniques like imputation, scaling, encoding, outlier handling, and transformation, data scientists can craft features that bring out the best in models. Each dataset and problem is unique, but applying these techniques thoughtfully can lead to models that offer accurate, reliable insights.