Ask any experienced data scientist what separates great predictive models from mediocre ones, and they’ll likely point to feature engineering. The raw data you start with rarely captures the patterns that drive predictions. It’s the features you create—transformations, combinations, and encodings—that unlock model performance.
Traditionally, feature engineering required programming skills and endless iteration. With Kanva, you get a guided workflow that puts this powerful technique in the hands of anyone who understands their data.
What Is Feature Engineering?
Feature engineering is the process of transforming raw data into inputs that better represent the underlying patterns for prediction. Consider a simple example:
You have a timestamp column in your sales data. A raw timestamp doesn’t help a model much. But extracting features like:
- Day of week
- Month
- Is weekend?
- Days until end of quarter
These derived features capture patterns that actually drive sales behavior.
Why Your Expertise Matters Here
Feature engineering is where domain knowledge becomes essential. The algorithm can find patterns, but you know which patterns make sense.
A model might discover that “customer ID modulo 7” correlates with churn. Statistically significant? Maybe. Meaningful? Obviously not. A domain expert catches this instantly. Pure automation doesn’t.
Kanva’s guided approach lets you apply what you know about your business while we handle the technical transformations.
The Kanva Feature Engineering Workflow
Step 1: Data Profiling
Before you engineer features, you need to understand what you have. Kanva automatically profiles your dataset:
- Data types: Numeric, categorical, datetime, text
- Missing values: Where and how much
- Distributions: Skew, outliers, ranges
- Cardinality: Unique value counts for categoricals
- Correlations: Initial relationships between columns
This profiling gives you the foundation for smart feature decisions.
Step 2: Guided Suggestions
Based on your data profile and target variable, Kanva suggests features that are likely to be valuable:
For datetime columns:
- Time-based extractions (hour, day, month, quarter)
- Cyclical encodings (for periodic patterns)
- Relative time features (days since event)
For numeric columns:
- Binning and discretization
- Log and power transforms
- Interaction terms with other numerics
For categorical columns:
- One-hot encoding
- Target encoding
- Frequency encoding
- Grouping rare categories
Step 3: You Decide
Not every suggested feature is a good feature. Kanva helps you evaluate:
- Preview impact: See how a feature correlates with your target
- Check for leakage: We warn if a feature might not be available at prediction time
- Balance complexity: More features isn’t always better
- Apply your judgment: Filter out features that don’t make business sense
We suggest. You decide.
Step 4: Validation
Before committing to your feature set, Kanva validates:
- No data leakage from the future
- All transformations are reproducible
- Missing values are handled consistently
- Categorical encodings are stable
Best Practices
Start with What You Know
The best features often come from understanding your business:
- What factors do experts think drive the outcome?
- What would a human look at when making this decision?
- What patterns have you observed over time?
Kanva’s guided workflow prompts you to consider these questions.
Be Careful with Time
When working with time-series or sequential data:
- Never use future information to predict the past
- Lag features correctly
- Account for seasonality explicitly
Our validation catches common temporal leakage issues.
Handle Categoricals Thoughtfully
High-cardinality categoricals (like product IDs or customer IDs) need special handling:
- Grouping rare values prevents overfitting
- Target encoding captures predictive power
- Interaction with other features often matters
Transform Before You Train
Many predictive models work better when features are:
- Scaled to similar ranges
- Normally distributed (for some algorithms)
- Free of extreme outliers
Kanva applies sensible transformations automatically while showing you what’s happening.
From Features to Predictions
Once you’ve engineered your features, Kanva moves you into model training. Your feature pipeline is saved and automatically applied to new data at prediction time—no manual work required.
This end-to-end approach means your carefully crafted features become part of a production-ready pipeline, not a one-off analysis.
Try It Yourself
Feature engineering is where domain expertise meets predictive power. With Kanva, you don’t need to be a programmer to apply these techniques. You just need to understand your data and your business.
Ready to transform your data into powerful predictive features? Kanva’s guided workflow is waiting.