How can EDA techniques help in detecting multicollinearity in a dataset?
- By applying dimensionality reduction techniques to the dataset
- By computing the eigenvalues of the correlation matrix
- By fitting a linear regression model to the dataset
- By generating scatterplots and calculating correlation coefficients between variables
EDA techniques, such as generating scatterplots and calculating correlation coefficients between variables, can help in detecting multicollinearity in a dataset. High correlation between predictor variables is an indication of multicollinearity.
Imagine you are dealing with a large dataset where outliers are sporadically distributed across multiple variables. How would you decide which outlier handling method to use?
- Apply different methods for different variables
- Use removal for all variables
- Use transformation for all variables
- nan
The best approach would be to apply different methods for different variables. The method of handling outliers may vary depending on the nature of the variable and the cause of the outliers.
What is the objective of the 'conclude' step in the EDA process?
- To clean data
- To draw conclusions from the explored data
- To formulate questions
- To visualize data
The 'conclude' step in the EDA process aims to draw insights or conclusions based on the findings from the 'explore' stage. This step might involve formal or informal hypothesis testing, and it helps in shaping further data analysis, reporting, or decision-making.
You are analyzing a data set and notice that the standard deviation is very high. What does this tell you about the data, and how might it affect your analysis?
- The data has a normal distribution
- The data values are all close to the mean
- The data values are skewed to the right
- The data values are spread out widely from the mean
If the standard deviation of a data set is very high, it implies that "The data values are spread out widely from the mean". This can make it harder to identify a "typical" value, and it suggests that there is high variability in the data.
_____ is a method used for handling missing data that replaces missing values with the mean, median, or mode of the available data.
- Listwise Deletion
- Mean/Median/Mode Imputation
- Pairwise Deletion
- Regression Imputation
'Mean/Median/Mode Imputation' is a basic method used for handling missing data that replaces missing values with the mean, median, or mode of the available data. It is simple to implement, but might introduce bias if the data is not missing at random.
You are given a dataset with a single continuous variable and asked to provide a detailed visualization. Which plots would you consider and why?
- Bar graph
- Histogram and Kernel Density Plot
- Line graph
- Scatter plot
For a single continuous variable, the Histogram and Kernel Density Plot are effective for providing a detailed visualization. They offer a clear visualization of the variable's distribution, density, and range of values.
How can you prevent specific functions or classes from being imported when the wildcard * is used with import?
- Define all
- Use a custom import
- Use a hidden prefix
- Use the @noimport decorator
You can prevent specific functions or classes from being imported when the wildcard * is used by defining a special variable named all in the module. all is a list of strings containing the names of symbols that should be imported when using from module import *.
How can you capture the error message of an exception into a variable?
- Using the 'get_error_message()' method
- Using the 'message' keyword
- Using the 'print()' statement
- Using the 'try' block and 'except'
To capture the error message of an exception into a variable, you can use a 'try' block and an 'except' clause. Inside the 'except' block, you can access the error message using the 'str()' function or by converting the exception object to a string. For example, error_message = str(exception).
You have a directory with multiple Python script files. You want it to be recognized as a Python package. Which file would you need to add to this directory?
- init.py
- main.py
- package.py
- setup.py
To make a directory recognized as a Python package, you need to add an 'init.py' file. This file can be empty but is necessary for package recognition.
The statement for x in y where y is a data structure, x refers to _______ of the data structure in each iteration.
- Elements/Items
- Keywords
- Length/Index
- Type
In the statement for x in y, where y is a data structure, x refers to the elements or items of the data structure in each iteration of the loop.
Which of the following is the correct way to define a function in Python?
- def my_function():
- define_function my_function():
- func my_function():
- function my_function():
The correct way to define a function in Python is as follows: 'def my_function():'. The 'def' keyword is used to define a function, followed by the function name and parentheses.
Which statement is used to skip the remainder of the code inside the current loop iteration and move to the next iteration?
- continue
- jump
- pass
- skip
The 'continue' statement is used to skip the remaining code inside the current loop iteration and move to the next iteration of the loop. It allows you to bypass specific conditions or processing in the loop.