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Wednesday, February 18, 2026
Tuesday, February 3, 2026
MDM Assignments
On Unit 1 and 2
1. Explain the concept, importance, and lifecycle of predictive analytics with real-world examples.
2. Describe key applications of predictive analytics in healthcare, finance, e-commerce, and social media.
3. Discuss how recommender systems and targeted marketing utilize predictive analytics in online retail.
4. Analyze the role, benefits, and ethical challenges of personalization and predictive modeling in retail strategies.
On Unit 3 and 4
1. Explain the different data types used in predictive analytics and their significance.Include examples of structured, unstructured, and semi-structured data, and discuss how recognizing data types influences the choice of analysis techniques.
2. Describe the complexities involved in raw data analysis.Discuss issues like missing data, noise, high dimensionality, and data variability. Include strategies for exploring and cleaning raw data to prepare it for modeling.
3. Discuss how understanding data complexities can improve predictive modeling outcomes.Include real-world examples where addressing data issues led to better model accuracy and insights.
On Unit 5 and 6
1. Select a real-world case study (e.g., Google Flu Trends, Twitter earthquake prediction).Summarize the problem, data used, modeling approach, and results.
2. Explain how clustering techniques like K-means or nearest neighbors help in identifying patterns in data.Use a practical example, such as customer segmentation or document classification.
3. Analyze the importance of feature extraction and similarity measures in building effective predictive models.Discuss how these techniques improve model performance and insights.
On Unit 7 and 8
1. Describe how classification algorithms like decision trees and support vector machines are used to predict future outcomes.Provide examples in domains like healthcare or finance.
2. Develop a step-by-step plan to build a predictive model, including data preparation, model selection, testing, and deployment.Use a hypothetical or real case example.
3. Discuss the role of ensemble methods, neural networks, and regression in improving prediction accuracy.Include benefits, limitations, and suitable scenarios for each method.
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