NCA-GENL Question 17
Select 3A data science team is working with a large dataset containing customer transactions from an e-commerce platform. Their goal is to identify patterns in customer purchasing behavior to improve recommendations. Which of the following steps are part of the process to extract insights from this dataset using data mining and visualization techniques?
- A
Preprocessing the data by cleaning and normalizing it to ensure consistency
- B
Applying clustering algorithms to group customers based on purchasing similarities
- C
Training a neural network in an end-to-end manner without any preprocessing
- D
Using visualization tools to identify trends and correlations in customer behavior
- E
Deploying a pre-trained Large Language Model (LLM) directly on the dataset without any modifications
Show answer and explanation
Correct answers: A, B, D
Explanation
Extracting insights from large datasets involves a systematic process, including preprocessing the data, applying data mining techniques like clustering, and using visualization tools to discover patterns and trends. These steps help transform raw data into valuable insights, which can inform decisions such as improving recommendation systems.
- A. Correct.
Preprocessing the data is an essential step in ensuring data quality before applying data mining or visualization techniques. It helps remove inconsistencies and prepares the data for analysis.
- B. Correct.
Clustering algorithms are a common data mining technique used to group similar data points, such as customers with similar purchasing behaviors, to extract meaningful patterns.
- C. Incorrect.
Training a neural network without preprocessing is not aligned with standard practices for extracting insights using data mining and visualization. Preprocessing and applying specific analytical techniques are more appropriate for this task.
- D. Correct.
Visualization tools are crucial for identifying trends, correlations, and patterns in data, which can provide actionable insights into customer behavior.
- E. Incorrect.
Deploying a pre-trained LLM directly on the dataset is not relevant in this context. LLMs are designed for tasks like natural language understanding and generation, not for pattern discovery through data mining and visualization.