Data Transformation — Complete Guide
Data Transformation — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of ML.NET Tutorial on Toolliyo Academy.
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ML.NET Tutorial · Lesson 8 of 100
Data Transformation
Foundations → Models → NLP & advanced → MLOps
Foundations · 1 — Context & data · ~6 min · Module 1: ML.NET Foundations
What is this?
Transforms convert raw columns into features: encode categories, concatenate, normalize.
Why should you care?
AIPredict merchant categories and raw amounts must become numeric Features vectors.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
var pipeline = ml.Transforms.Categorical.OneHotEncoding("MerchantCatEncoded", "MerchantCategory")
.Append(ml.Transforms.Concatenate("Features", "Amount", "Hour", "MerchantCatEncoded"));What happened?
- Chain with Append.
- Fit learns encoding maps; Transform applies them at predict time via the saved model.
Practice next
- One-hot a category.
- Concatenate Features.
- Fit and peek output schema.
- Add LogMeanVarianceNormalize on Amount.
- Drop unused columns with DropColumns.
Remember
Encode → concatenate. Fit learns maps. Saved model carries transforms.
AIPredict feature transform
MerchantCategory becomes one-hot.
Outcome: Trainer receives a proper Features vector.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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