NCA-GENL Question 80
Select 3A data science team is working on a large dataset collected from IoT sensors in a smart city project. They aim to identify traffic congestion patterns and provide actionable insights for city planners. Which of the following steps are critical for extracting insights using data mining and data visualization techniques?
- A
Preprocessing the data to handle missing values and outliers
- B
Using clustering algorithms to group similar traffic patterns
- C
Deploying the trained model directly without evaluating its performance
- D
Visualizing traffic flow patterns using heatmaps and interactive dashboards
- E
Ignoring data relationships and focusing only on raw numerical analysis
Show answer and explanation
Correct answers: A, B, D
Explanation
Extracting insights from large datasets requires a combination of data preprocessing, algorithmic analysis, and effective visualization. Preprocessing ensures data quality, clustering helps uncover patterns, and visualizations make the insights actionable and interpretable for stakeholders. Skipping model evaluation or ignoring data relationships can lead to poor or misleading results.
- A. Correct.
Preprocessing ensures the dataset is clean and reliable for analysis, which is a critical step in extracting meaningful insights.
- B. Correct.
Clustering algorithms can help group similar traffic patterns, enabling the identification of congestion hotspots or trends.
- C. Incorrect.
Deploying a model without evaluation can lead to unreliable or inaccurate insights, which is not a recommended practice.
- D. Correct.
Visualizations such as heatmaps and dashboards are crucial for effectively communicating insights derived from the data.
- E. Incorrect.
Ignoring data relationships can result in losing important context or patterns, making this approach unsuitable for extracting insights.