ML Level#2

What is the primary objective of Regression analysis?

• a) To classify data into distinct categories.

• b) To predict continuous numerical values.

• c) To determine patterns and structures in unlabeled data.

• d) To optimize decision-making in real-time systems.

Problem Statement: Which of the following is an example of a Regression problem?

• a) Predicting whether an email is spam or not.

• b) Identifying handwritten digits from images.

• c) Estimating the price of a house based on its features.

• d) Grouping customers into distinct segments.

What is the main difference between Regression and Classification?

• a) Regression predicts continuous values, while Classification predicts discrete categories.

• b) Regression requires labeled data, while Classification can work with unlabeled data.

• c) Regression focuses on clustering similar data points, while Classification finds patterns in unlabeled data.

• d) Regression deals with time-series data, while Classification works with static datasets.

Problem Statement: In which scenario would Classification be a more appropriate choice than Regression?

• a) Predicting stock market prices.

• b) Sentiment analysis of customer reviews.

• c) Estimating the temperature based on historical data.

• d) Forecasting sales revenue for the next quarter.

What does the info() function in Python's pandas library provide?

• a) It returns a concise summary of the dataset, including the data types of each column and the number of non-null values.

• b) It displays the first few rows of the dataset.

• c) It calculates summary statistics such as mean, median, and standard deviation.

• d) It provides a graphical representation of the dataset using charts and plots.

What does the shape attribute of a pandas DataFrame represent?

• a) It returns the number of columns in the DataFrame.

• b) It returns the number of rows in the DataFrame.

• c) It returns a tuple representing the dimensions of the DataFrame in the format (rows, columns).

• d) It returns the names of all the columns in the DataFrame.

Which of the following statements is true about EDA?

• a) EDA is only performed on structured data, not unstructured data.

• b) EDA is a process of building predictive models using machine learning algorithms

• c) EDA involves cleaning and transforming data to prepare it for analysis.

• d) EDA focuses on interpreting the results of a statistical hypothesis test.

In EDA, what does the term "outliers" refer to?

• a) Values that are missing or null in the dataset.

• b) Extreme values that deviate significantly from the majority of the data points.

• c) Categorical variables that cannot be analyzed numerically.

• d) Variables that are highly correlated with each other.

What is the purpose of using a correlation matrix during EDA?

• a) To identify and handle missing values in the dataset.

• b) To calculate the statistical significance of relationships between variables.

• c) To summarize the main characteristics of the dataset using descriptive statistics.

• d) To visualize the strength and direction of linear relationships between variables.

What is the goal of data normalization in EDA?

• a) To identify outliers and anomalies in the dataset.

• b) To convert categorical variables into numerical values.

• c) To standardize the scale and range of numerical variables.

• d) To handle missing or null values in the dataset.