📋 Exam Information

Block 1: Analytics Types & Applications (25 points)

Q1.1 (12 points)

Complete the comparison table for four analytics types:

💡 Click to View Answer & Solution

Memory Trick:

• Descriptive = Explain past
• Diagnostic = Investigate why
• Predictive = Estimate future
• Prescriptive = Execute action

Q1.2 (13 points)

Match each business problem to the correct analytics type and explain:

A: "Our sales dropped 15% last quarter. What caused this decline?"

B: "Which products should we recommend based on browsing patterns?"

C: "What is the optimal price point to maximize profit?"

D: "What were our top 5 selling products last month?"

💡 Click to View Answer & Solution

A: "Sales dropped 15%. What caused this?"

• Type: DIAGNOSTIC
• Reason: Investigating WHY something happened (root cause analysis)

B: "Which products to recommend based on browsing?"

• Type: PREDICTIVE
• Reason: Using patterns to predict what customers will want

C: "Optimal price point to maximize profit?"

• Type: PRESCRIPTIVE
• Reason: Optimization problem - determining best action

D: "Top 5 selling products last month?"

• Type: DESCRIPTIVE
• Reason: Simply summarizing historical data

Block 2: Analytics Lifecycle (25 points)

Q2.1 (15 points)

List and describe the SIX stages of Data Analytics Lifecycle in order.

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Stage 1: DISCOVERY

• Understand business problem and objectives
• Define key questions to answer
• Identify stakeholders and success criteria

Stage 2: DATA PREPARATION

• Collect data from various sources
• Clean data (handle missing values, outliers)
• Transform and integrate datasets

Stage 3: MODEL PLANNING

• Select appropriate techniques/algorithms
• Identify features (variables) to use
• Plan evaluation metrics

Stage 4: MODEL BUILDING

• Build and train models
• Test different algorithms
• Tune hyperparameters

Stage 5: COMMUNICATE RESULTS

• Present findings to stakeholders
• Create visualizations and reports
• Translate technical results to business insights

Stage 6: OPERATIONALIZE

• Deploy model to production
• Monitor performance over time
• Maintain and update as needed

Memory Trick: D-D-M-M-C-O = "Data Doctors Make Models, Communicate, Operate"

Q2.2 (10 points)

Identify which lifecycle stage each scenario describes:

A: "The team is cleaning missing values and removing outliers."

B: "Management wants to understand why churn increased. Team is defining specific questions."

C: "The model is deployed in production. Team monitors accuracy weekly."

D: "Data scientists are testing Random Forest, SVM, and Logistic Regression."

💡 Click to View Answer & Solution

A: Cleaning missing values and outliers

• Stage: DATA PREPARATION
• Reason: Data cleaning is core preparation activity

B: Defining specific questions to answer

• Stage: DISCOVERY
• Reason: Understanding problem and defining scope

C: Model deployed, monitoring accuracy

• Stage: OPERATIONALIZE
• Reason: Production deployment and monitoring

D: Testing multiple algorithms

• Stage: MODEL BUILDING
• Reason: Training and comparing different models

Block 3: Regression Analysis (25 points)

Scenario:

Real estate price prediction model:

$$\text{Price} = 50000 + 200 \times \text{SquareFeet} + 15000 \times \text{Bedrooms} - 5000 \times \text{Age}$$

Q3.1 (8 points)

Interpret each coefficient in plain business language.

💡 Click to View Answer & Solution

Constant (50,000):

• Base price when all other variables = 0
• Theoretical minimum house value

SquareFeet Coefficient (200):

• For each additional square foot, price increases by $200
• Holding bedrooms and age constant

Bedrooms Coefficient (15,000):

• Each additional bedroom adds $15,000 to price
• Holding square feet and age constant

Age Coefficient (-5,000):

• Each year older decreases price by $5,000
• Negative = older houses worth less
• Holding square feet and bedrooms constant

Key Phrase: Always say "holding other variables constant"

Q3.2 (8 points)

Interpret R² = 0.82. Calculate predicted price for a house with:

• 2,000 sq ft
• 3 bedrooms
• 10 years old

💡 Click to View Answer & Solution

R² Interpretation:

R² = 0.82 means 82% of the variation in house prices is explained by square feet, bedrooms, and age. The remaining 18% is due to other factors (location, condition, etc.). This is a good model for real estate.

Price Prediction: $\text{Price} = 50000 + 200(2000) + 15000(3) - 5000(10)$ $= 50000 + 400000 + 45000 - 50000$ $= 445000$

Predicted Price = $445,000

Q3.3 (9 points)

Compare two investment houses:

House A: 1,800 sq ft, 3 bedrooms, 5 years old House B: 1,500 sq ft, 4 bedrooms, 2 years old

Which has higher predicted price? Calculate the difference.

💡 Click to View Answer & Solution

House A: $= 50000 + 200(1800) + 15000(3) - 5000(5)$ $= 50000 + 360000 + 45000 - 25000$ $= 430000$

House B: $= 50000 + 200(1500) + 15000(4) - 5000(2)$ $= 50000 + 300000 + 60000 - 10000$ $= 400000$

Results:

• House A: $430,000
• House B: $400,000
• House A is higher by $30,000

Insight: Square footage has more impact than bedrooms. House A's extra 300 sq ft ($60,000 value) outweighs House B's extra bedroom ($15,000).

Block 4: Data Mining Algorithms (25 points)

Q4.1 (15 points)

Complete the algorithm comparison table:

💡 Click to View Answer & Solution

Q4.2 (10 points)

Scenario: Build email spam classifier with:

• 1 million emails (large dataset)
• Need real-time predictions (<100ms)
• Binary: Spam or Not Spam

Which algorithm? Why not the others?

💡 Click to View Answer & Solution

Best Choice: Naive Bayes

Reasons:

1. Very fast prediction - perfect for real-time (<100ms)
2. Works great for text classification (spam detection is classic NB use case)
3. Handles high-dimensional data well (many word features)
4. Scales to large datasets efficiently

Why NOT Decision Tree:

• Can overfit with many text features
• Large tree = slower prediction
• Less suited for text data

Why NOT KNN:

• Way too slow for 1 million emails
• Must compare against ALL training examples
• Real-time requirement impossible to meet
• Memory intensive (stores all data)

Summary:

• Naive Bayes: ✅ Fast, good for text, scalable
• Decision Tree: ⚠️ Possible but not optimal for text
• KNN: ❌ Too slow for large data + real-time

🎁 Bonus: Correlation vs Causation (5 extra points)

Explain the difference. Give an example where two variables are correlated but NOT causally related.

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Correlation: Two variables move together (positive or negative relationship)

Causation: One variable directly causes changes in another

Key Difference: Correlation ≠ Causation. Just because A and B move together doesn't mean A causes B (or B causes A).

Example:

• Ice cream sales and drowning deaths are positively correlated
• But ice cream doesn't CAUSE drowning!
• Confounding variable: Hot weather
  • Hot weather → More ice cream sales
  • Hot weather → More swimming → More drownings

Other Examples:

• Shoe size correlates with reading ability (age is the confounding variable)
• Nicolas Cage movies correlate with swimming pool drownings (pure coincidence)

🏁 End of Exam

📝 Quick Reference

Analytics Types:

• Descriptive: What happened?
• Diagnostic: Why?
• Predictive: What will happen?
• Prescriptive: What should we do?

Lifecycle: Discovery → Data Prep → Model Plan → Model Build → Communicate → Operationalize

Regression: Coefficient = change in Y per 1-unit change in X

R²: % of variance explained by model

Show your work for partial credit. Good luck!

ABW501 Mock Exam 2 - Analytics Edge

📋 Exam Information

Block 1: Analytics Strategy & Applications (25 points)

Q1.1 (12 points)

A hospital wants to reduce patient readmission rates. For each analytics approach, give a specific example of how it could help:

a) Descriptive Analytics b) Diagnostic Analytics c) Predictive Analytics d) Prescriptive Analytics

💡 Click to View Answer & Solution

a) Descriptive Analytics: "Dashboard showing readmission rates by department, age group, and diagnosis. Example: 'Cardiology has 15% readmission rate vs. 8% hospital average.'"

b) Diagnostic Analytics: "Root cause analysis to understand WHY readmissions happen. Example: 'Patients discharged on Friday have 20% higher readmission - likely due to weekend pharmacy closures.'"

c) Predictive Analytics: "ML model predicting which patients are likely to be readmitted. Example: 'Patient John has 78% probability of readmission within 30 days based on his diagnosis, age, and prior history.'"

d) Prescriptive Analytics: "Recommending specific interventions. Example: 'For high-risk patients, schedule follow-up call within 48 hours, arrange home nurse visits, and ensure medication delivery.'"

Key Pattern:

• Descriptive: Summarize what happened
• Diagnostic: Explain why
• Predictive: Forecast risk
• Prescriptive: Recommend actions

Q1.2 (13 points)

Explain the concept of data-driven decision making vs intuition-based decision making. Give TWO advantages and TWO disadvantages of each approach.

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Data-Driven Decision Making:

Intuition-Based Decision Making:

Best Practice: Combine both - use data to inform decisions, but let human judgment handle context and ethics.

Block 2: Data Quality & Preparation (25 points)

Q2.1 (12 points)

Explain FOUR common data quality issues and how to address each:

💡 Click to View Answer & Solution

1. Missing Values

• Problem: Empty cells in dataset
• Causes: Survey non-response, system errors, data not collected
• Solutions:
  • Delete rows (if few missing)
  • Impute with mean/median/mode
  • Use predictive models to estimate
  • Create "missing" category for categorical

2. Outliers

• Problem: Extreme values far from normal range
• Causes: Data entry errors, genuine rare events
• Solutions:
  • Remove if clearly erroneous
  • Cap at percentiles (winsorization)
  • Transform data (log scale)
  • Use robust algorithms

3. Inconsistent Formatting

• Problem: Same thing recorded differently
• Examples: "USA", "U.S.A.", "United States"
• Solutions:
  • Standardize formats
  • Create lookup tables
  • Use data validation rules
  • Regular expressions for cleaning

4. Duplicate Records

• Problem: Same entity recorded multiple times
• Causes: Multiple data sources, entry errors
• Solutions:
  • Exact matching (same ID)
  • Fuzzy matching (similar names)
  • Deduplication algorithms
  • Define business rules for merging

Q2.2 (13 points)

You receive a dataset with the following issues:

• Age column has values: 25, 30, -5, 150, 45, NULL, 35
• Gender column has: "M", "Male", "m", "F", "female", "Female"

a) Identify what's wrong with each column b) Propose specific cleaning steps

💡 Click to View Answer & Solution

a) Age Column Issues:

1. -5: Invalid (negative age impossible)
2. 150: Outlier (likely data entry error - no one is 150)
3. NULL: Missing value

Gender Column Issues:

1. Inconsistent case: "M" vs "m", "Male" vs "male"
2. Inconsistent format: "M" vs "Male" (abbreviation vs full word)
3. Inconsistent capitalization: "female" vs "Female"

b) Cleaning Steps:

For Age:

# Step 1: Replace invalid values with NaN
df.loc[df['Age'] < 0, 'Age'] = np.nan
 
# Step 2: Replace outliers (>120) with NaN
df.loc[df['Age'] > 120, 'Age'] = np.nan
 
# Step 3: Fill missing with median
df['Age'].fillna(df['Age'].median(), inplace=True)

For Gender:

# Step 1: Convert to lowercase
df['Gender'] = df['Gender'].str.lower()
 
# Step 2: Standardize to single format
df['Gender'] = df['Gender'].replace({
    'm': 'Male',
    'male': 'Male',
    'f': 'Female',
    'female': 'Female'
})

Result:

• Age: [25, 30, 32.5, 32.5, 45, 32.5, 35] (assuming median = 32.5)
• Gender: ['Male', 'Male', 'Male', 'Female', 'Female', 'Female']

Block 3: Model Evaluation & Interpretation (25 points)

Q3.1 (12 points)

Explain the following model evaluation concepts:

a) Accuracy, Precision, Recall b) When is accuracy NOT a good metric? c) What is the F1 Score and when to use it?

💡 Click to View Answer & Solution

a) Definitions:

Accuracy: $\text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN}$

• Overall correctness
• "Of all predictions, how many were right?"

Precision: $\text{Precision} = \frac{TP}{TP + FP}$

• Of positive predictions, how many were actually positive?
• "When I predict positive, how often am I right?"

Recall (Sensitivity): $\text{Recall} = \frac{TP}{TP + FN}$

• Of actual positives, how many did I catch?
• "Of all actual positives, how many did I find?"

b) When Accuracy Fails:

Imbalanced Classes!

Example: Fraud detection

• 99% legitimate, 1% fraud
• Model predicts "all legitimate" → 99% accuracy!
• But catches 0% of fraud (useless)

Rule: Use precision/recall for imbalanced datasets.

c) F1 Score:

$F1 = 2 \times \frac{Precision \times Recall}{Precision + Recall}$

• Harmonic mean of precision and recall
• Balances both metrics
• Range: 0 to 1 (higher is better)

Use When:

• Class imbalance exists
• Both false positives AND false negatives matter
• Need single metric to compare models

Q3.2 (13 points)

A spam detection model has the following confusion matrix:

Calculate: a) Accuracy b) Precision (for Spam) c) Recall (for Spam) d) F1 Score e) Which is more important for spam detection: Precision or Recall? Why?

💡 Click to View Answer & Solution

Confusion Matrix Values:

• TP (Spam correctly identified) = 85
• FN (Spam missed) = 15
• FP (Not spam marked as spam) = 10
• TN (Not spam correctly identified) = 890
• Total = 1000

a) Accuracy: $\text{Accuracy} = \frac{85 + 890}{1000} = \frac{975}{1000} = 97.5%$

b) Precision: $\text{Precision} = \frac{85}{85 + 10} = \frac{85}{95} = 89.47%$

c) Recall: $\text{Recall} = \frac{85}{85 + 15} = \frac{85}{100} = 85.0%$

d) F1 Score: $F1 = 2 \times \frac{0.8947 \times 0.85}{0.8947 + 0.85} = 2 \times \frac{0.760}{1.745} = 87.17%$

e) Precision is MORE important for spam detection

Reasoning:

• High precision = Few false positives
• False positive = Important email marked as spam
• This is WORSE than missing some spam (user might miss critical email!)

Trade-off:

• Prefer: Some spam in inbox (annoying but visible)
• Avoid: Important email in spam folder (might never be seen)

Block 4: Advanced Analytics Concepts (25 points)

Q4.1 (12 points)

Compare Supervised vs Unsupervised learning:

💡 Click to View Answer & Solution

Key Difference: Supervised has a "teacher" (labels), unsupervised discovers structure on its own.

Q4.2 (13 points)

Explain the concept of overfitting:

a) What is overfitting? b) How can you detect it? c) List FOUR techniques to prevent overfitting

💡 Click to View Answer & Solution

a) What is Overfitting?

Model learns training data TOO well, including noise and random fluctuations.

• Performs excellently on training data
• Performs poorly on new/unseen data
• Model has memorized rather than learned general patterns

Analogy: Student who memorizes test answers but can't solve new problems.

b) How to Detect Overfitting

1. Train-Test Gap:

• Training accuracy = 99%
• Test accuracy = 70%
• Large gap = overfitting

2. Learning Curves:

• Training error keeps decreasing
• Validation error increases or plateaus
• Lines diverge

3. Cross-Validation:

• High variance in scores across folds
• Some folds much worse than others

c) Four Techniques to Prevent Overfitting

1. Cross-Validation

• Split data into k folds
• Train on k-1, test on 1, rotate
• More reliable performance estimate

2. Regularization (L1/L2)

• Adds penalty for complex models
• L1 (Lasso): Can eliminate features
• L2 (Ridge): Shrinks coefficients

3. Early Stopping

• Monitor validation error during training
• Stop when validation error starts increasing
• Prevents over-training

4. Reduce Model Complexity

• Fewer features (feature selection)
• Simpler model (shallower tree)
• Less parameters

Bonus techniques:

• Dropout (neural networks)
• More training data
• Data augmentation
• Ensemble methods

🎁 Bonus: Ethics in Analytics (5 extra points)

A company wants to use ML for hiring decisions. The model was trained on historical hiring data (who was hired and who succeeded).

What ethical concerns should be considered?

💡 Click to View Answer & Solution

Ethical Concerns:

1. Historical Bias Perpetuation

• If past hiring was biased (e.g., fewer women in tech), model learns this
• Model will discriminate against groups historically underrepresented
• "The model is only as fair as its training data"

2. Proxy Discrimination

• Even without protected attributes (race, gender), proxies exist
• Zip code correlates with race
• Name style correlates with ethnicity
• Model may discriminate indirectly

3. Lack of Transparency

• Candidates can't understand why they were rejected
• "Black box" decisions are hard to appeal
• Legal requirement for explainable decisions

4. Feedback Loop

• Only hired people have success data
• Rejected candidates might have succeeded
• Model never learns from its mistakes

Recommendations:

• Audit model for bias regularly
• Use diverse training data
• Ensure human oversight
• Allow candidates to appeal/explain
• Be transparent about AI use

🏁 End of Exam

📝 Key Formulas Reference

Confusion Matrix:

                Predicted
              Pos    Neg
Actual Pos    TP     FN
       Neg    FP     TN

Show all working for partial credit. Good luck!