Analytics Projects That Overlook Data-Related Tasks 347965

Analytics Projects That Overlook Data Related Tasks Some Of The

Some of the most common metrics that make for analytics-ready data were mentioned. Choose three of these metrics and discuss them succinctly using resources. Your references should not be less than four in total. I am aware that all students have a Grammarly account.

I, therefore, request you all to use Grammarly to check your paper before you upload to iLearn, failing to do so will cause you to lose some points. The essence of doing this is to ensure that your paper is free from grammatical errors, conjugation, and spellings. Additionally, post some examples or find a related topic on the Internet. Reference: Sharda, R., Delen, Dursun, and Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. 11th Edition. By PEARSON Education. Inc. ISBN-13:

Paper For Above instruction

Effective analytics projects depend heavily on the quality and readiness of data. However, many projects overlook critical data-related tasks, leading to inaccurate insights and poor decision-making. Among the essential steps are selecting and correctly understanding metrics that can convert raw data into meaningful and actionable insights. In this discussion, three prevalent metrics are examined: accuracy, precision, and recall. These metrics are fundamental to evaluating models' performance in various analytical applications.

1. Accuracy

Accuracy is one of the most straightforward and widely used metrics in classification tasks. It measures the proportion of correctly predicted instances out of the total instances examined. Mathematically, it is expressed as:

Accuracy = (Number of correct predictions) / (Total predictions)

Accuracy is intuitive and easy to interpret, making it a popular choice for evaluating overall model performance. However, it can be misleading in cases of imbalanced datasets. For example, in a fraud detection system where fraudulent transactions constitute only 1% of the data, a naive model that classifies all transactions as legitimate would achieve 99% accuracy but would be useless for detecting fraud. Consequently, accuracy should be used with caution and supplemented with other metrics in imbalanced data scenarios (Manning et al., 2010).

2. Precision

Precision quantifies the number of true positive predictions among all positive predictions made by the model. It is particularly useful when the cost of false positives is high. The formula for precision is:

Precision = True Positives / (True Positives + False Positives)

High precision indicates that the model is accurate in identifying positive instances, minimizing false alarms. For example, in email spam detection, high precision ensures that legitimate emails are not wrongly flagged as spam. When false positives are costly or problematic, precision becomes a critical metric to optimize (Fawcett, 2006).

3. Recall

Recall, also known as sensitivity, measures the ability of a model to identify all actual positive instances. It is calculated as:

Recall = True Positives / (True Positives + False Negatives)

This metric is essential in contexts where missing positive cases is costly, such as in disease diagnosis or fraud detection. A high recall means fewer false negatives, which is crucial in scenarios where failing to identify positives can have severe consequences (Kohavi & Provost, 1998). However, optimizing for recall alone can lead to more false positives, which is why it is often balanced with precision using F1 scores.

Significance of Proper Metric Selection in Data Analysis

Choosing the appropriate metric is vital in ensuring the accuracy of data insights. Overlooking these metrics or misinterpreting their significance can lead to flawed conclusions, affecting strategic decisions. For example, relying solely on accuracy in an imbalanced dataset can create a false sense of success, masking the model's inability to detect minority class instances. Therefore, understanding the context and the cost of different types of errors guides the selection of suitable metrics, enhancing the reliability of the analytics outcomes (Sharda, Delen, & Turban, 2020).

Besides accuracy, precision, and recall, other metrics such as F1-score, ROC-AUC, and specificity also provide deeper insights into model performance. An integrated approach that considers multiple metrics is often recommended for comprehensive evaluation, especially when datasets are imbalanced or when different types of errors have varying impacts (Sokolova & Lapalme, 2009).

Conclusion

In conclusion, selecting the right metrics is a cornerstone of reliable analytics projects. Accuracy, precision, and recall are foundational in evaluating classification models, each providing unique insights into the model's performance in different contexts. Proper understanding and application of these metrics prevent the common pitfalls of oversimplification or misinterpretation that can derail data-driven decision-making. Ultimately, a nuanced approach that aligns metric selection with the specific goals and constraints of a project ensures more accurate, relevant, and timely insights.

References

• Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.
• Kohavi, R., & Provost, F. (1998). Glossary of terms. In IEEE International Conference on Data Mining.
• Manning, C. D., Raghavan, P., & Schütze, H. (2010). Introduction to Information Retrieval. Cambridge University Press.
• Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427-437.
• Sharda, R., Delen, D., & Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. 11th Edition. Pearson Education Inc.