Quiz on Boston Housing Dataset and Linear Regression

What is the primary objective of using the Boston Housing Dataset?

• To predict house prices based on various features

• To analyze crime rates in Boston

• To evaluate the performance of different machine learning algorithms

• To collect data on employment centers

How many records are contained in the Boston Housing Dataset?

• 506

• 250

• 750

• 1000

What is the target variable in the Boston Housing Dataset?

• Distance to employment centers

• Number of rooms

• MEDV (median home value)

• Crime rate

What is the purpose of data preprocessing in the data science workflow?

• To check for missing values and normalize features

• To retrieve data from libraries

• To visualize data relationships

• To apply machine learning models

Which algorithm is primarily used in this dataset to predict house prices?

• K-Nearest Neighbors

• Linear Regression

• Support Vector Machines

• Decision Trees

What does the term 'best-fit line' refer to in linear regression?

• A line that maximizes the distance from all data points

• A line that minimizes the difference between predicted and actual values

• A line that represents the average of all data points

• A line that connects the highest and lowest data points

What is the typical split ratio for training and testing sets mentioned in the notes?

• 90% training, 10% testing

• 60% training, 40% testing

• 80% training, 20% testing

• 70% training, 30% testing

What does the correlation matrix help to identify?

• The best-fit line for predictions

• The distribution of home prices

• The relationship between features and the target variable

• The number of records in the dataset