Practice Step 3: Data Preprocessing (18.3.3) - Data Science for Business and Decision- Making

Practice: Step 3: Data Preprocessing

Lesson 18.3.3Data Science for Business and Decision- Making

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9 questions

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3 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is data cleaning?
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Think about what happens when data is inaccurate.
Question 2.
What is feature engineering?
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Consider how we improve data for models.
3Question 3.
What is the primary goal of data preprocessing?
- To make data visually appealing
- To prepare data for analysis
- To secure data
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Remember, it's about ensuring the data is usable.
4Question 4.
True or False: Outlier treatment has no impact on the quality of data analysis.
- True
- False
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How does ignoring outliers affect results?
Question 5.
You are given a dataset with many missing values and some extreme outliers. Outline your approach to clean and prepare the data for analysis.
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Consider both statistical techniques and business context when dealing with anomalies.
Question 6.
Describe a scenario where feature engineering significantly improved a model's performance. What features would you create and why?
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Think about relationships between existing variables.

5 more questions in this section. Enroll free to practise them all.