- Lab
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- Cloud
Feature Engineering for Training Ready Data in SageMaker
You are a data scientist working on a customer analytics use case where raw transactional data has been collected from multiple sources. However, the dataset is not directly usable for machine learning due to inconsistencies, missing values, and lack of a clearly defined target variable. In this lab, you will transform raw customer transaction data into a training-ready dataset using Amazon SageMaker Studio. You will apply feature engineering techniques, derive a meaningful target variable based on business logic (purchase success using return behaviour), and validate the dataset to ensure it is suitable for downstream machine learning workflows. This scenario reflects a real-world task where data scientists must prepare and validate data before model training.
Lab Info
Table of Contents
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Challenge
Apply transformations to convert raw data into training-ready features
- Load the raw dataset from a public GitHub URL into a SageMaker notebook
- Explore dataset structure (columns, data types, summary statistics)
- Encode categorical variables (e.g., gender) into numerical format
- Create new derived features
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Challenge
Refine feature values for consistency and modeling readiness
- Remove duplicate records from the dataset
- Handle missing values using appropriate imputation techniques
- Derive a target variable (e.g Purchased) using business logic based on return behavior
- Remove columns that can cause data leakage
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Challenge
Validate the feature set for data quality and modeling risks
- Check for duplicate records in the dataset
- Check for missing values
- Evaluate class balance of the target variable
- Document feature meaning and transformation logic
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