Data Analytics

What si the primary purpose of data preprocessing ?

• Data visualization

• Data storage

• Cleaning data

• Data analysis

What does PCA (Principal Component Analysis) do?

• Regression technique

• Dimensionality reduction method

• Clustering algorithm

• Classification method

Q3) What does cross-validation involve ?

• Evaluating models on a separate test set

• Preprocessing data technique

• Comparing model performance

• Unsupervised learning approach

What si the primary purpose of association rule mining in data analytics ?

• Predicting continuous variables

• Discovering interesting relationships in data

• Classifying data into predefined categories

• valuating model accuracy

Which algorithm is commonly used for association rule mining ?

• K-means

• Apriori

• Decision Trees

• Linear Regression

In the Apriori algorithm, what does the term "minimum support" refer to ?

• The minimum number of transactions containing an itemset

• The minimum number of features in a dataset

• The maximum confidence level for association rules

• The minimum number of iterations in the algorithm

What is the key advantage of the FP-growth algorithm over Apriori for association rule mining ?

• FP-growth is faster and requires les memory

• Apriori handles larger datasets more efficiently

• FP-growth si better suited for regression tasks

• Apriori has a higher accuracy rate

How does dimensionality reduction contribute to data analytics ?

• It increases the complexity of the dataset

• It enhances interpretability of the data

• It reduces the number of features while preserving important information

• It improves the performance of clustering algorithms

In dimensionality reduction, what is the curse of dimensionality ?

• The difficulty of visualizing high-dimensional data.

• The tendency of algorithms to overfit in low-dimensional spaces.

• The challenge of handling large datasets.

• The increase in computational complexity with higher dimensions.

Which of the following is a potential drawback of reducing dimensionality in a dataset ?

• Improved interpretability

• Loss of information

• Increased computational efficiency

• Enhanced model accuracy