End-to-End Data Preparation in SageMaker Canvas Data Wrangler | EDA, Preprocessing & Export on AWS

Описание: Ready to see SageMaker Data Wrangler in ACTION on a REAL project?
In this hands-on tutorial, we do a complete End-to-End Data Preparation
walkthrough using a real estate housing dataset — preparing it for an
ML model that estimates house prices!

Business Use Case:
Help real estate companies, investors & property platforms to:
💰 Price properties accurately
📊 Identify undervalued or overvalued homes
🏡 Support buying/selling decisions
📈 Improve investment strategy

📌 What You'll Build (Step-by-Step):
✅ Step 1 - Data Collection: Load SageMaker Canvas housing dataset
✅ Step 2 - EDA: Review table, histograms, boxplots, data quality insights
& missing value checks
✅ Step 3 - Data Preprocessing: Convert numeric columns, handle missing
values, remove outliers & one-hot encode ocean_proximity
✅ Step 4 - Clean Data Export: Export processed dataset for model training
✅ Step 5 - Cost Control: Stop Canvas after project completion

🔗 Useful Links:
► AWS SageMaker Data Wrangler Docs:
https://docs.aws.amazon.com/sagemaker...
► SageMaker Canvas Docs:
https://docs.aws.amazon.com/sagemaker...
► Previous Video - Data Wrangler Concepts: [Link]

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⏱️ Timestamps Below 👇
00:00 - Introduction & What We're Building
01:30 - Business Use Case: House Price Estimation
03:00 - Project Implementation Flow Overview
05:00 - Step 1: Data Collection (Load Housing Dataset)
08:00 - Step 2: EDA - Review Table & Create Histograms
11:30 - Step 2: EDA - Custom Boxplot & Outlier Detection
14:00 - Step 2: EDA - Data Quality Insights & Missing Values
17:00 - Step 3: Data Preprocessing - Convert Numeric Columns
20:00 - Step 3: Handle Missing Values
22:30 - Step 3: Remove Outliers
25:00 - Step 3: One-Hot Encode ocean_proximity
28:00 - Step 4: Clean Data Export to S3
31:00 - Step 5: Stop Canvas (Cost Control Tips)
33:00 - Outro & Next Steps
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