Decision Support Systems

Project Tasks & Rubric (Total = 14 marks):

Evidence rule: every task must include screenshots/figures and a 2-4 sentence interpretation (what it shows, why it matters for the decision).

Task 1: Problem & Data Understanding (3 marks)

• Decision context: Who is the decision-maker? What decision will the analysis support? Define KPIs (24).
• Dataset description: source reliability, collection method, size, time span, unit of analysis.
• Data dictionary: list key features, types, and expected roles (predictor/target/ID).
• Hypothesis: one testable relationship between two numerical variables (directional, with rationale).

Task 2: Data Quality & Preparation (1 marks)

• Show tests and fixes with before/after evidence for:
  • Missing values
  • Duplicates
  • Outliers
  • Noise/irregularities (e.g., inconsistent categories, types, units)
• Include a Data Quality Log table: issue method action impact.

Task 3: Descriptive Statistics & EDA (2 marks)

• Central tendency (mean/median/mode) and distribution shape (variance, SD, skewness, kurtosis).
• Appropriate visuals (histograms/boxplots/density, bar/line where relevant).
• 23 insightful questions you posed from trends/patterns (and brief answers).

Task 4 : Hypothesis Testing & Relationship Analysis (2 marks)

• Correlation analysis (numeric pair; comment on strength/direction.
• Simple linear regression (or appropriate alternative): equation, R, residuals check, and practical interpretation linked to KPIs.
• Conclusion: accept/reject hypothesis; implications for the decision.

Task 5 :Visual Analytics for Decision-Makers (2 marks)

• A small, coherent visual story (3 – 4 charts) with correct chart types, clear labels, and callouts.
• Each chart must answer a stakeholder-relevant question; include a 12 sentence takeaway.

Task 6: Predictive/Descriptive Modeling (2 marks)

• Choose 1 2 models suitable for your data/task (e.g., Decision Tree, k-NN, Random Forest, SVM, k-means for segmentation if classification/regression is not applicable).
• Document training setup (feature set, split).
• Evaluation:
  • For classification: confusion matrix, accuracy, precision/recall, and 1 key trade-off.
  • For regression: MAE/RMSE and an error plot.
  • For clustering: silhouette (or WCSS elbow) + business interpretation of clusters.
• Brief model selection rationale tied to the decision.

Task 7: Interactive Dashboard & Decision Support (2 marks)

• Excel or Power BI dashboard with 35 tiles: KPIs, filters/slicers, and at least one what-if (e.g., price, volume, threshold).
• One paragraph on how a manager would use this dashboard to make or justify a decision.

1. Executive Summary ( page) problem, method, 23 key findings, recommendation.
2. Decision Context & KPIs
3. Data Understanding & Preparation (with Data Quality Log)
4. EDA & Descriptive Statistics
5. Hypothesis & Relationship Analysis
6. Visual Analytics for Decision-Makers
7. Modeling & Evaluation
8. Dashboard & Decision Use Case (with screenshot)
9. Recommendations, Sensitivity/What-If Notes, Limitations, Ethics
10. References (data source + any methods you cite)

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