NCA-AIIO Question 58
Select 3A data scientist is working on an AI project to predict customer churn for a subscription-based service. The dataset contains millions of rows with various customer attributes, subscription details, and usage patterns. To extract meaningful insights, which methods would be most effective for analyzing the data and presenting actionable results?
- A
Use clustering techniques to segment customers based on similar attributes.
- B
Apply histogram analysis to identify relationships between all dataset features.
- C
Generate heatmaps to visualize correlations between numerical variables.
- D
Train a deep neural network without exploring or visualizing the dataset.
- E
Leverage decision trees for feature importance evaluation.
Show answer and explanation
Correct answers: A, C, E
Explanation
Extracting insights from large datasets requires a combination of data mining and visualization techniques. Clustering helps identify customer segments, heatmaps provide a visual representation of correlations, and decision trees offer insights into feature importance. These methods ensure that the data is well-understood before moving to advanced modeling.
- A. Correct.
Clustering techniques, such as K-means, can effectively group customers into segments, which helps in understanding patterns and behaviors relevant to churn analysis.
- B. Incorrect.
Histogram analysis is useful for understanding distributions of individual features but does not effectively identify relationships across all dataset features.
- C. Correct.
Heatmaps are a powerful visualization tool for showing correlations between numerical variables, enabling the identification of key factors influencing churn.
- D. Incorrect.
Training a deep neural network without exploring or visualizing the dataset is not a recommended practice, as it skips critical steps of data understanding and preparation.
- E. Correct.
Decision trees can provide insights into which features are most important for predicting customer churn, making them valuable for feature analysis.