Скачать или смотреть Day 15 | 24 Days Series of Microsoft Fabric | EDA & Feature Engineering in Microsoft Fabric

This is where Data Science actually begins.

In this video, we perform Exploratory Data Analysis (EDA) and Feature Engineering in Microsoft Fabric using Data Science notebooks. This is a critical step before building any Machine Learning model.

This is DAY 4 of the End-to-End Microsoft Fabric Data Science series.

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🎯 What you’ll learn in this video:
✔ What is Exploratory Data Analysis (EDA)
✔ How to analyze data patterns and trends in Fabric
✔ Understanding data types, missing values, and statistics
✔ Business-level EDA using PySpark
✔ Date-based feature engineering (year, month, day of week)
✔ Categorical feature engineering using StringIndexer
✔ Scaling numerical features using VectorAssembler & StandardScaler
✔ Creating an ML-ready feature dataset
✔ Saving features back to Lakehouse

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📊 Dataset Used:
We use the *sales_data_clean* table created in Day 3 and transform it into a Machine Learning–ready feature table.

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🧠 Why this video is important:
EDA and Feature Engineering decide how good your Machine Learning model will be.
Before MLflow, AutoML, or predictions — understanding and preparing data is the most important step.

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📌 Microsoft Fabric Data Science Flow:
Data Engineering → Lakehouse → EDA → Feature Engineering → Machine Learning → Power BI

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🚀 Upcoming Video (Day 5):
▶ Machine Learning Model Training in Microsoft Fabric
▶ Regression model using PySpark
▶ MLflow experiment tracking
▶ Model evaluation and predictions

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👥 Who should watch this?
• Power BI Developers moving into Data Science
• Data Analysts learning Machine Learning
• Microsoft Fabric beginners
• Azure / Fabric professionals
• DP-600 certification aspirants

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