❓ Question 2:
What is a z-score?

1. A standardized value that indicates the number of standard deviations an observation is from the mean.
2. The range between the highest and lowest values in a set of data.
3. A measure of the spread of a set of data.
4. A measure of central tendency of a set of data.

✅ Correct Response: 1

Explanation: In statistics, a z-score is a standardized value that indicates the number of standard deviations an observation is from the mean of a set of datIt is used to compare values from different normal distributions and to calculate probabilities.

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❓ Question 3:
The function ____ is used to load a CSV file into a DataFrame in Pandas.

Option 1: read_csv()
Option 2: to_csv()
Option 3: read_excel()
Option 4: load_csv()

✅ Correct Response: 1

Explanation: The read_csv() function is used to load a CSV file into a DataFrame in Pandas. It provides many parameters to read CSV data in a flexible way.

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❓ Question 4:
When creating a DataFrame in Pandas, which data structures can be used as input?

Option 1: Dictionaries
Option 2: NumPy ndarrays
Option 3: Series
Option 4: Python Lists

✅ Correct Response: 1, 2, 3, 4

Explanation: When creating a DataFrame in Pandas, various data structures can be used as input. These include Dictionaries, NumPy ndarrays, Pandas Series, and Python Lists. Each of these can be converted into a DataFrame, which then allows for flexible data manipulations in the form of a structured grid.

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❓Question 5:
The NumPy library is often used for performing ____ operations on large arrays and matrices.

Option 1: Mathematical
Option 2: Statistical
Option 3: Linear algebra
Option 4: All of the above
 
✅ Correct Response: 4

Explanation: The NumPy library is often used for performing mathematical, statistical, and linear algebra operations on large arrays and matrices. It provides efficient functions and operations to manipulate and analyze numerical data. NumPy is widely used in scientific computing and data analysis tasks

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❓ Question 6:
What is a decision tree regression?

1. A type of regression that uses multiple decision trees to make predictions.

2. A method for clustering data points into groups based on their similarity.

3. A technique for dimensionality reduction by projecting the data onto a lower-dimensional space.

4. A method for estimating the causal effects of interventions using graphical models.

✅ Correct Response: 1

Explanation: Decision tree regression is a type of regression that uses a decision tree to make predictions. The decision tree is constructed by recursively partitioning the input space into regions based on the values of the input variables, and then fitting a simple model, such as a constant or a linear function, in each region. Decision tree regression is a simple and interpretable model that can capture nonlinear relationships between the input variables and the response variable.

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❓ Question 7:
____ is a process of creating abstract classes and methods in Python.

Option 1: Inheritance
Option 2: Polymorphism
Option 3: Encapsulation
Option 4: Abstraction
 
✅ Correct Response: 4

Explanation: Abstraction is the process of creating abstract classes and methods in Python. Abstraction allows you to define the interface or contract for derived classes to follow, without providing the implementation details. This concept helps in achieving code modularity and creating more maintainable and flexible code.

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❓ Question 8:
What is a support vector regression (SVR)?

1. A technique for identifying the most important features in a dataset.

2. A regression method that finds the hyperplane that maximizes the margin between the data points and the hyperplane.

3. A clustering algorithm for grouping similar data points together.

4. A method for estimating the causal effects of interventions using graphical models.

✅ Correct Response: 2

Explanation: Support vector regression (SVR) is a regression method that finds the hyperplane that maximizes the margin between the data points and the hyperplane. SVR is commonly used for regression problems in which the data points have many irrelevant features and only a few relevant features. It is a type of kernel method that can be used with nonlinear data.

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