1️⃣ Question 1

What is the primary role of the data understanding phase?

• ❌ Creating data visualizations

• ✅ Assessing data quality and representativeness

• ❌ Building machine learning models

• ❌ Running complex algorithms

Explanation:
Data Understanding evaluates data quality, completeness, correctness, and relevance before any modeling begins.

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2️⃣ Question 2

How does the Data Preparation stage affect the next steps?

• ❌ Ensures visualization accuracy

• ❌ Determines project timeline

• ✅ Provides clean and formatted data for analysis

• ❌ Defines the problem statement

Explanation:
Models require properly formatted, consistent, and cleaned data. Data Preparation delivers this.

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3️⃣ Question 3

Why is Data Preparation time-consuming?

• ✅ It involves transforming data into a usable format

• ❌ Requires creating visualizations

• ❌ Involves running complex algorithms

• ❌ Needs deep ML knowledge

Explanation:
Cleaning, merging, formatting, encoding, and transforming data can take 60–80% of project time.

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4️⃣ Question 4

Purpose of feature engineering?

• ❌ Create models and algorithms

• ❌ Remove duplicates

• ❌ Address missing values

• ✅ Create meaningful characteristics for machine learning

Explanation:
Feature engineering creates new variables that help models learn patterns more effectively.

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5️⃣ Question 5

How does automation affect data collection & preparation time?

• ❌ Greatly reduces only data collection time

• ❌ Prolongs timeline

• ❌ Minimizes need for data understanding

• ✅ Automation can reduce data preparation time to as little as 50%

Explanation:
Automation speeds up cleaning, transforming, and validating data—reducing manual effort significantly.

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🧾 Summary Table