Data Exploration & Preprocessing
Inspect real data, handle missing values, encode features, avoid leakage, and build repeatable transformations.
Concept overview
What this lesson will help you understand.
Most model failures begin in data definition, splitting, or transformation; careful exploration often produces more value than a more complex algorithm.
Learn by doing
Explore the concept.
Make a prediction before changing a control. Run the experiment, explain what moved, and compare the result with the theory below.
Initializing Interactive Playground...
Complete lesson
Detailed concept deep dive.
Build intuition first, then work through implementation, mathematical derivations, failure analysis, and real system decisions.
7 guided chapters
Most model failures begin in data definition, splitting, or transformation; careful exploration often produces more value than a more complex algorithm.
Before working through the formal derivations, connect the vocabulary to one small, concrete example. The goal is not to memorize definitions in isolation. It is to understand what each idea represents, which assumptions make it valid, and how the pieces relate to one another.
Ideas to understand
- Rows, columns, features, labels, units, and data dictionaries
- Missingness, duplicates, outliers, imbalance, and inconsistent categories
- Encoding, scaling, imputation, and train-only fitting
Learn by doing
Make the idea concrete
Explain data exploration and preprocessing with a concrete example and no unexplained jargon.
Try this
- Define “Rows, columns, features, labels, units, and data dictionaries” in your own words, then annotate one concrete Data Exploration & Preprocessing input and output.
- Construct one valid case and one counterexample for “Missingness, duplicates, outliers, imbalance, and inconsistent categories”; explain which assumption separates them.
- Predict how “Encoding, scaling, imputation, and train-only fitting” will change one visible playground result, then test and record the before/after values.
Evidence of understanding
- You can explain the example without relying on jargon.
- You can name the assumptions and identify what would invalidate them.
- You can connect the example to at least one real ML use case.
What part of the Data Exploration & Preprocessing mental model still feels least intuitive, and what example would help clarify it?
Saved in your browser progress and included in exports.
Practice with code
Make the idea executable.
Complete the starter code, use progressive hints, and pass deterministic checks in the browser.
Runnable Python lab
Impute with training-only evidence
Compute a training mean and use it to fill missing values in both train and test partitions without reading test values into the statistic.
Keep learning
Papers, standards, and practical references.
Start with the free primary sources. Books are included where a longer, connected treatment is worth the investment.
NumPy: the absolute basics
Official NumPy guide to arrays, shapes, axes, indexing, and vectorized operations.
scikit-learn User Guide
Free reference for preprocessing, model selection, metrics, and classical ML algorithms.
Datasheets for Datasets
Research paper proposing documentation of dataset motivation, composition, collection, and recommended uses.
Test your understanding.
Answer explanations appear after every choice. Missed questions can be reviewed before a full retake.
Mastery requires 80% or higher.
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