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Machine Learning EDA

Total questions: 20

Worksheet time: 12mins

Name
Class
Date
1.

What is a key requirement for a time series column?

a)

It must be numeric

b)
  • It must be in datetime format

c)

It must be a categorical feature

d)
  • It must be unique

2.

What plot is most commonly used to visualize trends over time?

a)
  • a) Bar chart

b)
  • b) Scatter plot

c)
  • c) Line plot

d)
  • d) Histogram

3.

What is the purpose of Exploratory Data Analysis (EDA)?

a)

To find patterns and relationships in the data

b)

To build predictive models

c)

To evaluate model performance

d)

None of the above

4.

What is the purpose of data preprocessing in EDA?

a)

To remove missing values from the data

b)

To transform the data into a suitable format

c)

To handle outliers in the data

d)

All of the above

5.

Which of the following techniques can be used to handle missing values in the data during EDA when you are working on personal data?

a)

Deleting rows or columns with missing values

b)

Replacing missing values with the mean or median

c)

None of the above

d)

Both A and B

6.

What is the goal of feature engineering during EDA?

a)

To create new features from the existing data

b)

To remove irrelevant features from the data

c)

To transform the data into a suitable format for analysis

d)

All of the above

7.

What is the purpose of descriptive statistics in EDA?

a)

To summarize the data using measures like mean and standard deviation

b)

To identify patterns and relationships in the data

c)

To clean and preprocess the data

d)

To build predictive models

8.

Which of the following is an example of a categorical variable?

a)

Age of a person

b)

Height of a building

c)

Gender of an individual

d)

Temperature of a room

9.
  1. What role does domain knowledge play in EDA?

a)

It helps in preprocessing and cleaning the data

b)

It guides the selection of appropriate visualization techniques

c)

It provides insights into the data patterns and relationships

d)

It enables the building of predictive models

10.

What is Machine Learning?

a)

A computer program that learns from data

b)

A set of rules and instructions for a computer

c)

A type of computer hardware

d)

A way to store and retrieve information

11.

In Exploratory Data Analysis (EDA), what does a histogram visualize?

a)

Correlation between variables

b)

Distribution of a single variable

c)

Relationships between categorical variables

d)

Outliers in the dataset

12.

Which library provides OneHotEncoder as a class?

a)

pandas

b)

numpy

c)

sklearn

d)

seaborn

13.

Which of the following is NOT a common step in data cleaning?

a)

Handling missing values

b)

Encoding categorical variables

c)

Removing duplicates

d)

Correcting inconsistent data

14.

Which of these is NOT typically extracted from a datetime column?

a)

Year

b)

Day of week

c)

Temperature

d)

Month

15.

Which of these is NOT a numerical feature summary statistic?

a)

Mean

b)

Median

c)

Standard deviation

d)

Countplot

16.

What will df['column'].mean() calculate?

a)

Median value

b)

Average

c)

Standard Deviation

d)

Mode

17.

Which Python library provides functions like dropna(), fillna(), and duplicated() for data cleaning?

(a)  

18.

A bar plot helps to visualize:

a)

Trend over time

b)

Relationship between two numerical variables

c)

Outliers

d)

Missing values

19.

What is a categorical feature?

a)

A numeric feature

b)

A feature with text or limited set of values

c)

A missing feature

d)

A datetime feature

20.

value_counts() function is used for:

a)

Summing numerical features

b)

Counting the frequency of each category

c)

Filling missing values

d)

Calculating mean