NCA-GENL Question 14
Select 3A data scientist is analyzing a large dataset containing customer purchase histories to identify trends and predict future behaviors. Which combination of techniques would be most appropriate to extract actionable insights from this dataset?
- A
Applying clustering algorithms to group customers with similar purchasing patterns
- B
Creating data visualizations to identify trends and anomalies in customer behavior
- C
Manually inspecting individual customer records to understand purchasing decisions
- D
Performing sentiment analysis on customer reviews to complement purchase data
- E
Using reinforcement learning to dynamically adapt the dataset without human intervention
Show answer and explanation
Correct answers: A, B, D
Explanation
To extract actionable insights from large datasets, a combination of data mining techniques like clustering, data visualization to identify patterns, and sentiment analysis to provide additional context is most effective. These methods enable the identification of trends, anomalies, and customer behaviors at scale. Manual inspection is inefficient, and reinforcement learning is not applicable in this scenario.
- A. Correct.
Clustering algorithms are a key data mining technique for grouping customers with similar behaviors, which can uncover patterns and trends in large datasets.
- B. Correct.
Data visualizations are essential for identifying trends, patterns, and outliers in the dataset, making it easier to derive actionable insights.
- C. Incorrect.
Manually inspecting individual records is impractical and inefficient for large datasets and doesn't align with data mining or visualization best practices.
- D. Correct.
Sentiment analysis on customer reviews provides additional context and insights, enriching the dataset and enabling better trend prediction.
- E. Incorrect.
Reinforcement learning is not suitable for this context as it focuses on decision-making in dynamic environments rather than extracting insights from static datasets.