NCA-AIIO Question 61
Select 3An organization is tasked with analyzing a large dataset containing customer transaction details from an e-commerce platform. The goal is to identify purchasing patterns and predict future trends. Which combination of techniques would be most appropriate for extracting actionable insights from this dataset?
- A
Use clustering algorithms to group customers with similar purchasing behaviors.
- B
Create a heatmap to visualize correlations between different product categories.
- C
Perform manual inspection of each transaction to identify patterns.
- D
Apply a decision tree model to predict future purchasing trends based on historical data.
- E
Generate static bar charts for individual transactions without aggregating data.
Show answer and explanation
Correct answers: A, B, D
Explanation
Extracting insights from large datasets requires a combination of data mining and visualization techniques. Clustering helps uncover hidden patterns, heatmaps reveal correlations, and predictive models like decision trees forecast future trends. Manual inspection and static visualizations are inefficient or insufficient for handling large-scale data analysis.
- A. Correct.
Clustering algorithms are effective for grouping customers with similar purchasing behaviors, which can help identify patterns and target specific customer segments.
- B. Correct.
A heatmap is a powerful visualization tool that can highlight correlations between product categories, aiding in understanding relationships in the dataset.
- C. Incorrect.
Manual inspection of each transaction is impractical for large datasets and does not leverage computational techniques for extracting insights.
- D. Correct.
Decision tree models are useful for predicting future trends based on historical data, making them appropriate for forecasting in this scenario.
- E. Incorrect.
Static bar charts for individual transactions do not provide a holistic view of the data and fail to aggregate insights across the dataset.