Description
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Project
Deadline: Sunday 30/11/2025 @ 23:59
[Total Mark for this Project is 14]
Group Details:
Name:
Name:
Name:
Name:
CRN:
ID:
ID:
ID:
ID:
Instructions:
• You must submit two separate copies (one Word file and one PDF file) using the Assignment Template on
Blackboard via the allocated folder. These files must not be in compressed format.
• It is your responsibility to check and make sure that you have uploaded both the correct files.
• Zero mark will be given if you try to bypass the SafeAssign (e.g., misspell words, remove spaces between
words, hide characters, use different character sets, convert text into image or languages other than English
or any kind of manipulation).
• Email submission will not be accepted.
• You are advised to make your work clear and well-presented. This includes filling your information on the cover
page.
• You must use this template, failing which will result in zero mark.
• You MUST show all your work, and text must not be converted into an image, unless specified otherwise by
the question.
• Late submission will result in ZERO mark.
• The work should be your own, copying from students or other resources will result in ZERO mark.
• Use Times New Roman font for all your answers.
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Project
Pg. 01
Learning Outcome(s):
CLO 1, 2, 5
1, Demonstrate an
understanding of the
concepts of decision
analysis and decision
support systems (DSS)
including probability,
modelling, decisions under
uncertainty, and real-world
problems.
Project
14 Marks
Teams & Datasets:
•
Teams: 3–4 students. Email team member names to your instructor by 5 Oct
2025. After that, teams will be randomly formed and datasets assigned.
•
Dataset: Each team must work on a unique dataset. Choose one from the link
below (or the instructor’s assigned set):
Saudi Open Data Platform:
Tools (pick what fits your dataset):
2, Describe advanced
Business Intelligence,
Business Analytics, Data
Visualization, and
Dashboards.
5, Improve hands-on skills
using Excel, and Orange
for building Decision
Support Systems.
•
Required: Microsoft Excel or Orange Data Mining for EDA/modelling.
•
For dashboards: Excel or Power BI (recommended).
Learning Outcomes:
You will:
1. Frame a data-driven decision problem with stakeholders and KPIs.
2. Clean and profile data; document data quality issues and fixes.
3. Perform descriptive statistics and EDA with appropriate visuals.
4. Test a quantitative hypothesis; interpret correlation/regression.
5. Train and evaluate at least two ML models aligned to the decision.
6. Build a dashboard summarizing actionable insights for decision-makers.
7. Produce clear recommendations, assumptions, and what-if takeaways.
Deliverables:
1.
Report (PDF + Word) which must incorporate all the following 7 tasks and
written using the provided template. (10 marks distributed among the below tasks).
2.
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Slide deck (10–12 slides in 6 mins) for in-class presentation. (4 marks).
Project
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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 (2 marks)
•
Decision context: Who is the decision-maker? What decision will the analysis support?
Define KPIs (2–4).
•
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:
o
Missing values
o
Duplicates
o
Outliers
o
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).
•
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Appropriate visuals (histograms/boxplots/density, bar/line where relevant).
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•
2–3 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, R², residuals check,
and practical interpretation linked to KPIs.
•
Conclusion: accept/reject hypothesis; implications for the decision.
Task 5 :Visual Analytics for Decision-Makers (1 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 1–2 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:
o
For classification: confusion matrix, accuracy, precision/recall, and 1 key
trade-off.
o
For regression: MAE/RMSE and an error plot.
o
For clustering: silhouette (or WCSS elbow) + business interpretation of
clusters.
•
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Brief model selection rationale tied to the decision.
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Task 7: Interactive Dashboard & Decision Support (2 marks)
•
Excel or Power BI dashboard with 3–5 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.
Report Template (section outline):
1. Executive Summary (½ page) – problem, method, 2–3 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)
Project Report:
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Project
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