← Introduction to Programming

Assignment 1: Smart Shopping Cart Calculator

Smart Shopping Cart with Discount Logic (Click to expand)

Assignment Description

Create a shopping cart program that calculates the final price with discount rules and membership status.

Requirements:

  1. Collect: 3 items (name, price, quantity), customer info
  2. Calculate: Subtotal, discounts (using boolean arithmetic), tax, shipping
  3. Display: Itemized receipt with all calculations
  4. Submit: GitHub repository

Key Technique - Boolean Arithmetic:

# Use True=1, False=0 to control calculations
is_member = True  # or False
discount = is_member * 0.10 * subtotal  # 10% if member, 0% if not

Important: Display ALL eligibility checks and metrics as True/False values. No if/else statements needed.

Variant 1.1: Electronics Store

Scenario: You’re building a checkout system for an electronics store.

Specific Requirements:

  • Collect: Item name, price, and quantity for 3 electronics items
  • Get customer information: Name, is_member (yes/no), total_previous_purchases (in sum)

Discount Rules: Calculate eligibility and amounts for each discount type:

  1. Member discount: 10% off subtotal (eligible if customer is a member)
  2. Bulk discount: 5% off (eligible if total items > 5)
  3. Loyalty discount: 3% off (eligible if total_previous_purchases >= 1000000)
  4. All discounts stack - apply them sequentially using boolean arithmetic

Other Calculations:

  • Tax rate: 12% applied to subtotal after all discounts
  • Shipping: 25000 sum (but 0 if subtotal > 500000)
    • Calculate using: shipping = (subtotal <= 500000) * 25000

Output must show:

  • Customer name and membership status
  • Itemized list of all 3 purchases (item, quantity, price, total)
  • Subtotal before discounts
  • Each discount type with:
    • Eligibility status (True/False)
    • Discount amount (will be 0 if not eligible)
  • Total discounts applied
  • Subtotal after discounts
  • Tax amount
  • Shipping cost with free shipping status (True/False)
  • Final total
  • Total amount saved
Variant 1.2: Restaurant Order System

Scenario: You’re creating an order calculator for a restaurant.

Specific Requirements:

  • Collect: Dish name, price, and quantity for 3 dishes
  • Get customer information: Name, has_student_id (yes/no), order_time (hour in 24-hour format, e.g., 14 for 2 PM)

Discount Rules: Calculate eligibility and amounts for each discount type:

  1. Student discount: 15% off subtotal (eligible if customer has student ID)
  2. Happy hour discount: 20% off subtotal (eligible if order_time >= 14 and order_time <= 17)
  3. Large order discount: 5% off (eligible if subtotal_before_discounts >= 150000)

Special Rule - Non-stacking discounts:

  • Student and Happy Hour discounts do NOT stack (customer gets the better one)
  • Calculate both amounts, then use: main_discount = student_discount * (student_discount >= happy_hour) + happy_hour * (happy_hour > student_discount)
  • Large order discount DOES stack with whichever main discount applies

Other Calculations:

  • Service charge: 10% of subtotal (applied AFTER all discounts)
  • Delivery fee: 15000 sum (but 0 if subtotal < 100000)
    • Calculate using: delivery = (subtotal >= 100000) * 15000

Output must show:

  • Customer name, student status, and order time
  • Itemized list of all 3 orders
  • Subtotal before discounts
  • Discount eligibility and amounts:
    • Student discount eligibility (True/False) and amount
    • Happy hour eligibility (True/False) and amount
    • Which discount was applied (the larger one)
    • Large order discount eligibility (True/False) and amount
  • Total discounts
  • Subtotal after discounts
  • Service charge amount
  • Delivery fee with free delivery status (True/False)
  • Final total
  • Total amount saved
Variant 1.3: Bookstore Purchase System

Scenario: You’re developing a checkout system for a university bookstore.

Specific Requirements:

  • Collect: Book title, price, and quantity for 3 books
  • Get customer information: Name, is_faculty_staff (yes/no), is_textbook_order (yes/no), number_of_books (total quantity)

Discount Rules: Calculate eligibility and amounts for each discount type:

  1. Faculty/Staff discount: 20% off subtotal (eligible if is_faculty_staff is True)
  2. Textbook discount: 25% off subtotal (eligible if is_textbook_order is True)
  3. Bulk book discount: 8% off (eligible if number_of_books >= 10)

Special Rules:

  • Faculty and Textbook discounts do NOT stack (customer gets the better one)
  • Calculate: main_discount = faculty_discount * (faculty_discount >= textbook_discount) + textbook_discount * (textbook_discount > faculty_discount)
  • Bulk discount DOES stack with whichever main discount applies
  • Small order fee: 10000 sum added if number_of_books < 3
    • Calculate using: small_order_fee = (number_of_books < 3) * 10000

Other Calculations:

  • Tax: 5% if is_textbook_order is False, 0% if True
    • Calculate using: tax = (is_textbook_order == False) * 0.05 * subtotal_after_discounts
  • Shipping: 20000 sum (but 0 if total_before_tax >= 200000)

Output must show:

  • Customer name, faculty status, and textbook order status
  • Itemized list of all 3 books
  • Subtotal before discounts
  • Discount eligibility and amounts:
    • Faculty discount eligibility (True/False) and amount
    • Textbook discount eligibility (True/False) and amount
    • Which discount was applied (the larger one)
    • Bulk discount eligibility (True/False) and amount
  • Total discounts
  • Small order fee (if applicable) with status (True/False for “fee applied”)
  • Subtotal after discounts and fees
  • Tax amount with textbook exemption status (True/False)
  • Shipping cost with free shipping status (True/False)
  • Final total
  • Net savings or extra fees (can be negative if fees exceed discounts)
Variant 1.4: Grocery Store Membership System

Scenario: Checkout system for a grocery store with loyalty cards and promotional discounts.

Specific Requirements:

  • Collect: Product name, price, and quantity for 3 grocery items
  • Get customer information: Name, has_loyalty_card (yes/no), is_senior_citizen (yes/no), day_of_week (1-7, where 1=Monday, 7=Sunday)

Discount Rules: Calculate eligibility and amounts for each discount type:

  1. Loyalty card discount: 8% off subtotal (eligible if has_loyalty_card is True)
  2. Senior citizen discount: 12% off subtotal (eligible if is_senior_citizen is True)
  3. Weekend special: 15% off subtotal (eligible if day_of_week >= 6)
  4. Large purchase discount: 5% off (eligible if subtotal_before_discounts >= 300000)

Special Rules:

  • Loyalty, Senior, and Weekend discounts do NOT stack (customer gets the best one)
  • Calculate all three, then use boolean arithmetic to select the largest:
    • main_discount = loyalty * (loyalty >= senior and loyalty >= weekend) * loyalty_amount + senior * (senior > loyalty and senior >= weekend) * senior_amount + weekend * (weekend > loyalty and weekend > senior) * weekend_amount
  • Large purchase discount DOES stack with whichever main discount applies
  • Bag fee: 5000 sum added if subtotal < 100000
    • Calculate using: bag_fee = (subtotal < 100000) * 5000

Other Calculations:

  • Tax: 15% applied to subtotal after all discounts and fees
  • Parking validation: Free (0) if subtotal_after_discounts >= 250000, otherwise 10000
    • Calculate using: parking = (subtotal_after_discounts < 250000) * 10000

Output must show:

  • Customer name, loyalty card status, senior status, and day of week
  • Itemized list of all 3 items (item, quantity, price, total)
  • Subtotal before discounts
  • Discount eligibility and amounts:
    • Loyalty discount eligibility (True/False) and amount
    • Senior discount eligibility (True/False) and amount
    • Weekend special eligibility (True/False) and amount
    • Which main discount was applied (show all three booleans for which one was selected)
    • Large purchase discount eligibility (True/False) and amount
  • Total discounts applied
  • Bag fee (if applicable) with status (True/False for “fee applied”)
  • Subtotal after discounts and fees
  • Tax amount
  • Parking fee with validation status (True/False for “free parking”)
  • Final total
  • Net savings or extra fees (can be negative if fees exceed discounts)

Assignment 2: Personal Finance Analyzer

Monthly Budget and Savings Goal Calculator (Click to expand)

Assignment Description

Create a personal finance analyzer that evaluates monthly income, expenses, and savings goals.

Requirements:

  1. Collect: Monthly income, expense categories, savings info, goal details
  2. Calculate: Total expenses, savings rate, projections, gaps
  3. Display: Comprehensive financial report with status indicators
  4. Submit: GitHub repository

Key Technique:

# Display all status checks as True/False
below_recommended = savings_rate < 20
print(f"Below Recommended (< 20%): {below_recommended}")
print(f"Savings Rate: {savings_rate:.2f}%")

Important: Show ALL metrics and indicators objectively, including negative values (shortfalls).

Variant 2.1: Young Professional Budget

Scenario: Budget analyzer for a young professional trying to save for an apartment down payment.

Required Inputs:

  • Name
  • Monthly net income
  • Fixed expenses: Rent, utilities, internet, phone bill
  • Variable expenses: Groceries, transportation, entertainment
  • Current savings amount
  • Apartment down payment goal
  • Months until desired purchase

Calculations Required:

  1. Total monthly expenses (sum of all categories)
  2. Monthly savings (income - expenses)
  3. Savings rate percentage (savings / income * 100)
  4. Projected savings after target months (monthly_savings * months)
  5. Total projected amount (current_savings + projected_savings)
  6. Monthly savings needed to reach goal (total_needed / months)
  7. Additional monthly savings needed (can be negative if already sufficient)
  8. Shortfall or surplus (total_projected - goal, negative = shortfall, positive = surplus)

Financial Health Status Indicators (all displayed as True/False):

  1. Critical warning: expenses >= income
  2. Below recommended: savings_rate < 20
  3. Good position: savings_rate >= 20 and savings_rate < 30
  4. Excellent position: savings_rate >= 30
  5. Goal achievable: total_projected >= down_payment_goal

Output Requirements:

  • Name and summary
  • Income: Monthly net income
  • Expense breakdown (all categories itemized)
    • Total fixed expenses
    • Total variable expenses
    • Total monthly expenses
  • Savings analysis:
    • Monthly savings amount
    • Savings rate percentage
    • Current savings
    • Projected savings in X months
    • Total projected
  • Goal analysis:
    • Target amount
    • Monthly savings needed for goal
    • Additional monthly savings needed (show actual number, can be negative)
  • All financial health indicators (True/False for each)
  • Goal achievability status (True/False)
  • Numeric gap: shortfall or surplus amount
Variant 2.2: Family Budget Planner

Scenario: Budget analyzer for a family trying to save for a vacation.

Required Inputs:

  • Family name
  • Combined monthly income
  • Fixed expenses: Mortgage/rent, insurance, car payment, utilities
  • Variable expenses: Groceries, childcare, family activities
  • Current vacation fund balance
  • Vacation cost target
  • Months until vacation date

Calculations Required:

  1. Total monthly expenses
  2. Monthly savings potential (income - expenses)
  3. Savings rate percentage
  4. Projected vacation fund at target date (current_fund + monthly_savings * months)
  5. Total needed (vacation_cost - current_fund)
  6. Monthly savings needed to reach goal
  7. Monthly shortfall or surplus (monthly_savings - monthly_needed, negative = need more)
  8. Total shortfall or surplus at vacation date

Financial Health Status Indicators (all displayed as True/False):

  1. Emergency status: savings_rate < 10
  2. Needs improvement: savings_rate >= 10 and savings_rate < 15
  3. Stable: savings_rate >= 15 and savings_rate < 25
  4. Strong: savings_rate >= 25
  5. Vacation affordable: projected_fund >= vacation_cost

Output Requirements:

  • Family name
  • Income and expense breakdown (itemized)
    • Total fixed expenses
    • Total variable expenses
    • Total monthly expenses
  • Savings metrics:
    • Monthly savings potential
    • Savings rate percentage
    • Current vacation fund
    • Projected fund at vacation date
  • Vacation goal analysis:
    • Target vacation cost
    • Total amount needed
    • Monthly savings needed
    • Monthly shortfall/surplus
  • All financial health indicators (True/False for each)
  • Vacation affordability status (True/False)
  • Total gap at vacation date (numeric value)
Variant 2.3: Student Living Expenses Tracker

Scenario: Budget analyzer for a university student managing scholarship money and part-time income.

Required Inputs:

  • Student name
  • Monthly income (scholarship + part-time job)
  • Fixed expenses: Dorm/rent, meal plan, textbooks (monthly average)
  • Variable expenses: Personal items, social activities, transportation
  • Current savings balance
  • Emergency fund goal (recommended: 3 months of expenses)
  • Has student loan (yes/no)

Calculations Required:

  1. Total monthly expenses
  2. Monthly net savings (income - expenses)
  3. Savings rate percentage
  4. Expense-to-income ratio (expenses / income)
  5. Three months of expenses (for emergency fund)
  6. Months needed to reach emergency fund (goal / monthly_savings)
  7. Emergency fund gap (goal - current_savings)
  8. Recommended minimum savings for loan holders (15% of income)

Financial Health Status Indicators (all displayed as True/False):

  1. Critical: expense_to_income_ratio > 0.95
  2. Risky: expense_to_income_ratio > 0.85 and expense_to_income_ratio <= 0.95
  3. Acceptable: expense_to_income_ratio > 0.70 and expense_to_income_ratio <= 0.85
  4. Healthy: expense_to_income_ratio <= 0.70
  5. Emergency fund adequate: current_savings >= emergency_fund_goal
  6. Has student loan: (from input)
  7. Savings below recommended for loan holder: has_student_loan and savings_rate < 15

Output Requirements:

  • Student name and loan status
  • Income and expense breakdown (itemized)
    • Total fixed expenses
    • Total variable expenses
    • Total monthly expenses
  • Savings metrics:
    • Monthly net savings
    • Savings rate percentage
    • Expense-to-income ratio
    • Current savings balance
  • Emergency fund analysis:
    • Emergency fund goal (3 months expenses)
    • Current progress toward goal
    • Gap remaining
    • Months needed at current rate
  • All financial health indicators (True/False for each)
  • Student loan consideration metrics:
    • Has loan: True/False
    • Recommended minimum savings (15% of income if has loan)
    • Meeting recommendation: True/False
Variant 2.4: Freelancer Income Stabilizer

Scenario: Budget analyzer for a freelancer with variable income trying to build an emergency fund.

Required Inputs:

  • Freelancer name
  • Average monthly income (over last 3 months)
  • Lowest monthly income (in last 6 months)
  • Fixed expenses: Rent, health insurance, internet, software subscriptions
  • Variable expenses: Utilities, groceries, transportation, professional development
  • Current emergency fund balance
  • Has business debt (yes/no)
  • Months of coverage desired for emergency fund (typically 6 months)

Calculations Required:

  1. Total monthly expenses (sum of all categories)
  2. Monthly savings (average_income - expenses)
  3. Savings rate percentage (savings / average_income * 100)
  4. Emergency fund goal (total_monthly_expenses * months_of_coverage)
  5. Emergency fund progress percentage (current_fund / goal * 100)
  6. Months needed to reach goal (remaining / monthly_savings)
  7. Income volatility (average_income - lowest_income)
  8. Volatility percentage (income_volatility / average_income * 100)
  9. Recommended buffer (total_expenses * 1.5 for freelancers)
  10. Shortfall or surplus (current_savings + projected_savings - goal)

Financial Health Status Indicators (all displayed as True/False):

  1. Critical warning: monthly_savings <= 0
  2. High risk: savings_rate < 15
  3. Moderate position: savings_rate >= 15 and savings_rate < 25
  4. Strong position: savings_rate >= 25
  5. High income volatility: volatility_percentage > 30
  6. Emergency fund adequate: emergency_fund_progress >= 100
  7. At least 3 months covered: emergency_fund_progress >= 50
  8. Has business debt: (from input)
  9. Debt with insufficient savings: has_business_debt and savings_rate < 20
  10. Buffer threshold met: monthly_savings >= (recommended_buffer / months_of_coverage)

Output Requirements:

  • Freelancer name and debt status
  • Income analysis:
    • Average monthly income
    • Lowest recent monthly income
    • Income volatility amount
    • Volatility percentage
  • Expense breakdown (itemized):
    • Total fixed expenses
    • Total variable expenses
    • Total monthly expenses
    • Recommended monthly buffer (1.5x expenses)
  • Savings metrics:
    • Monthly savings
    • Savings rate percentage
    • Current emergency fund
    • Emergency fund goal (X months coverage)
    • Emergency fund progress percentage
    • Months needed to reach goal at current rate
  • All financial health indicators (10 True/False values)
  • Gap analysis:
    • Amount needed to reach goal
    • Whether goal achievable at current rate

Assignment 3: Health and Fitness Metrics Calculator

Personal Fitness Assessment and Goal Tracker (Click to expand)

Assignment Description

Create a fitness tracker that calculates health metrics and tracks progress toward goals.

Requirements:

  1. Collect: Physical measurements, activity data
  2. Calculate: BMI, BMR, calorie needs, body composition, progress metrics
  3. Display: Comprehensive fitness report with status indicators
  4. Submit: GitHub repository

Key Technique - Gender-Based Calculations:

# Use boolean arithmetic for gender-specific formulas
is_male = gender.upper() == "M"
is_female = gender.upper() == "F"
bmr_male = 88.362 + (13.397 * weight) + (4.799 * height * 100) - (5.677 * age)
bmr_female = 447.593 + (9.247 * weight) + (3.098 * height * 100) - (4.330 * age)
bmr = (is_male * bmr_male) + (is_female * bmr_female)

Important: Display ALL health categories as True/False. Use ** for exponentiation (BMI = weight / height²).

Variant 3.1: Weight Loss Journey Tracker

Scenario: Track a person’s weight loss progress and calculate fitness metrics.

Required Inputs:

  • Name
  • Current weight (kg), height (m), age (years)
  • Gender (M/F)
  • Activity level (1-5: 1=Sedentary, 2=Light, 3=Moderate, 4=Active, 5=Very Active)
  • Starting weight (kg), goal weight (kg)
  • Weeks since started
  • Exercise hours per week

Calculations Required:

  1. Current BMI: weight / (height ** 2)
  2. Goal BMI: goal_weight / (height ** 2)
  3. BMR (Basal Metabolic Rate):
    • Men: 88.362 + (13.397 × weight) + (4.799 × height × 100) - (5.677 × age)
    • Women: 447.593 + (9.247 × weight) + (3.098 × height × 100) - (4.330 × age)
    • Use boolean arithmetic to select correct formula
  4. Activity multipliers by level: 1.2, 1.375, 1.55, 1.725, 1.9
    • Use: multiplier = (level==1)*1.2 + (level==2)*1.375 + ...
  5. TDEE (Total Daily Energy Expenditure): BMR × activity_multiplier
  6. Weight lost: starting_weight - current_weight
  7. Average weekly loss: weight_lost / weeks_since_started
  8. Remaining weight: current_weight - goal_weight
  9. Weeks to goal: remaining_weight / average_weekly_loss

Health Status Indicators (all displayed as True/False):

Current BMI Category:

  1. Underweight: current_bmi < 18.5
  2. Normal: current_bmi >= 18.5 and current_bmi < 25
  3. Overweight: current_bmi >= 25 and current_bmi < 30
  4. Obese: current_bmi >= 30

Goal BMI Category: 5. Goal underweight: goal_bmi < 18.5 6. Goal normal: goal_bmi >= 18.5 and goal_bmi < 25 7. Goal overweight: goal_bmi >= 25 and goal_bmi < 30 8. Goal obese: goal_bmi >= 30

Progress Indicators: 9. Goal is healthy: goal_bmi >= 18.5 and goal_bmi < 25 10. Progress rate healthy: average_weekly_loss >= 0.5 and average_weekly_loss <= 1.0 11. Exercise sufficient: exercise_hours >= 3 12. Overall on track: progress_rate_healthy and exercise_sufficient

Output Requirements:

  • Personal information (name, gender, age, height)
  • Weight information:
    • Starting, current, and goal weights
    • Weight lost so far
    • Remaining to goal
  • Current BMI with all 4 category indicators (True/False)
  • Goal BMI with all 4 category indicators (True/False)
  • Metabolic calculations:
    • BMR (calories/day)
    • Activity level and multiplier
    • TDEE (calories/day)
  • Progress metrics:
    • Weeks since started
    • Average weekly loss
    • Exercise hours per week
    • Estimated weeks to goal
  • All health status indicators (12 True/False values)
Variant 3.2: Athletic Performance Optimizer

Scenario: Analyze an athlete’s metrics for training and nutrition planning.

Required Inputs:

  • Name
  • Weight (kg), height (m), age (years)
  • Gender (M/F)
  • Sport type (1-3: 1=Endurance, 2=Strength, 3=Mixed)
  • Training hours per week
  • Protein intake (grams per day)
  • Current body fat percentage
  • Target body fat percentage

Calculations Required:

  1. BMI: weight / (height ** 2)
  2. BMR: (Use same gender-based formulas as Variant 1)
  3. Sport multipliers: 1.8, 1.9, 2.0 (for types 1-3)
    • Use: multiplier = (sport==1)*1.8 + (sport==2)*1.9 + (sport==3)*2.0
  4. Athlete TDEE: BMR × sport_multiplier
  5. Lean body mass: weight × (1 - body_fat_percentage / 100)
  6. Fat mass: weight - lean_body_mass
  7. Recommended protein: lean_body_mass × 2.2 (grams/day for athletes)
  8. Protein adequacy ratio: protein_intake / recommended_protein
  9. Body fat to lose/gain: (current_body_fat - target_body_fat) × weight / 100

Athletic Status Indicators (all displayed as True/False):

General Metrics:

  1. BMI appropriate for athlete: bmi >= 20 and bmi <= 27
  2. Training volume adequate: training_hours >= 5

Body Fat Categories (gender-specific using boolean arithmetic): 3. Current body fat in athlete range:

  • Calculate male_range: current_bf >= 6 and current_bf <= 17
  • Calculate female_range: current_bf >= 16 and current_bf <= 24
  • Result: (is_male * male_range) + (is_female * female_range) gives 1 or 0, convert to bool
  1. Target body fat realistic:
    • Same gender-specific approach for target

Nutrition: 5. Protein intake sufficient: protein_adequacy_ratio >= 0.9 and protein_adequacy_ratio <= 1.2 6. Protein intake low: protein_adequacy_ratio < 0.9 7. Protein intake high: protein_adequacy_ratio > 1.2

Performance Status: 8. Performance ready: (current_bf in range) and training_adequate and protein_sufficient

Output Requirements:

  • Athlete profile (name, gender, age, sport type)
  • Physical measurements:
    • Weight, height, BMI
    • Current and target body fat percentages
  • Body composition:
    • Lean body mass
    • Fat mass
    • Body fat to lose/gain
  • Metabolic calculations:
    • BMR
    • Sport type and multiplier
    • Athlete TDEE
  • Nutrition analysis:
    • Current protein intake
    • Recommended protein
    • Protein adequacy ratio
  • Training: hours per week
  • All athletic status indicators (8 True/False values)
Variant 3.3: Fitness Beginner Progress Tracker

Scenario: Help a fitness beginner track their first month and assess readiness for progression.

Required Inputs:

  • Name
  • Weight (kg), height (m), age (years)
  • Gender (M/F)
  • Days exercised in past 30 days
  • Average exercise duration per session (minutes)
  • Push-ups in one set (number)
  • Squats in one set (number)
  • Fitness goal (1=Weight loss, 2=Muscle gain, 3=General fitness)

Calculations Required:

  1. BMI: weight / (height ** 2)
  2. BMR: (Use same gender-based formulas as Variant 1)
  3. Sedentary TDEE: BMR × 1.2
  4. Active TDEE estimate: BMR × 1.375
  5. Exercise frequency percentage: (days_exercised / 30) × 100
  6. Total exercise minutes in month: days_exercised × avg_duration
  7. Fitness score: (push_ups × 2) + (squats × 1.5)
  8. Calorie targets by goal:
    • Calculate all three: weight_loss_target = active_TDEE - 500
    • muscle_gain_target = active_TDEE + 300
    • maintenance_target = active_TDEE
    • Select using boolean arithmetic based on goal value

Beginner Progress Indicators (all displayed as True/False):

Health Basics:

  1. BMI in reasonable range: bmi >= 18.5 and bmi < 30

Exercise Consistency: 2. Exercise frequency good: exercise_frequency >= 60 (at least 18 days) 3. Exercise frequency excellent: exercise_frequency >= 80 (at least 24 days) 4. Exercise duration adequate: avg_duration >= 30

Strength Baselines: 5. Basic push-up strength: push_ups >= 5 6. Basic squat strength: squats >= 10 7. Good push-up strength: push_ups >= 10 8. Good squat strength: squats >= 20

Overall Progress: 9. Consistency strong: exercise_frequency >= 70 and avg_duration >= 30 10. Basic strength present: push_ups >= 5 and squats >= 10 11. Ready for progression: consistency_strong and basic_strength_present

Output Requirements:

  • Beginner profile (name, gender, age)
  • Physical measurements:
    • Weight, height, BMI
  • Metabolic calculations:
    • BMR
    • Sedentary TDEE
    • Active TDEE
  • Monthly exercise statistics:
    • Days exercised (out of 30)
    • Exercise frequency percentage
    • Average duration per session
    • Total exercise minutes in month
  • Strength assessment:
    • Push-ups in one set
    • Squats in one set
    • Fitness score
  • Calorie targets for all three goals:
    • Weight loss target
    • Muscle gain target
    • General fitness target
    • Fitness goal selected (1, 2, or 3)
  • All progress indicators (11 True/False values)
Variant 3.4: Marathon Training Readiness Assessor

Scenario: Assess someone’s readiness to start training for a marathon based on fitness metrics and experience.

Required Inputs:

  • Name
  • Weight (kg), height (m), age (years)
  • Gender (M/F)
  • Current weekly running distance (km)
  • Longest single run completed in past month (km)
  • Running experience (1-4: 1=Beginner 0-6 months, 2=Intermediate 6-12 months, 3=Advanced 1-2 years, 4=Experienced 2+ years)
  • Previous injuries in past year (yes/no)
  • Resting heart rate (bpm)

Calculations Required:

  1. BMI: weight / (height ** 2)
  2. BMR: (Use gender-based formulas from Variant 3.1)
  3. Experience multipliers: 0.6, 1.0, 1.3, 1.6 (for experience levels 1-4)
    • Use: multiplier = (exp==1)*0.6 + (exp==2)*1.0 + (exp==3)*1.3 + (exp==4)*1.6
  4. Fitness level based on resting heart rate:
    • Use boolean arithmetic for gender-specific ranges:
    • Male excellent: rhr <= 60
    • Male good: rhr > 60 and rhr <= 70
    • Female excellent: rhr <= 65
    • Female good: rhr > 65 and rhr <= 75
    • Calculate: is_excellent_fitness = (is_male * male_excellent) + (is_female * female_excellent)
  5. Minimum weekly distance required for marathon training: 30 km
  6. Minimum long run required: 15 km
  7. Readiness score: (weekly_distance / 30) * 40 + (longest_run / 15) * 30 + (experience_multiplier) * 30
  8. Training volume percentage: (current_weekly_distance / 30) * 100

Marathon Readiness Indicators (all displayed as True/False):

Health Basics:

  1. BMI in athlete range: bmi >= 18.5 and bmi <= 27
  2. No recent injuries: previous_injuries == False

Cardiovascular Fitness (gender-specific using boolean arithmetic): 3. Excellent heart fitness: is_excellent_fitness (calculated above) 4. Good heart fitness: is_good_fitness (calculated similar to excellent)

Running Volume: 5. Weekly distance adequate: current_weekly_distance >= 30 6. Weekly distance insufficient: current_weekly_distance < 20 7. Long run adequate: longest_run >= 15 8. Long run insufficient: longest_run < 10

Experience Level: 9. Has sufficient experience: running_experience >= 2 10. Beginner level: running_experience == 1

Overall Readiness: 11. Ready for marathon training: readiness_score >= 80 12. Nearly ready: readiness_score >= 60 and readiness_score < 80 13. Not yet ready: readiness_score < 60 14. High risk start: (has_injuries or bmi > 27 or weekly_distance < 20)

Output Requirements:

  • Runner profile (name, gender, age)
  • Physical measurements:
    • Weight, height, BMI
    • Resting heart rate
  • Cardiovascular fitness assessment:
    • Gender-specific heart rate category (excellent/good/needs improvement)
    • BMR
  • Running experience and volume:
    • Experience level (1-4)
    • Experience multiplier
    • Current weekly distance
    • Training volume percentage (vs. 30km baseline)
    • Longest recent run
  • Marathon training requirements:
    • Minimum weekly distance (30 km)
    • Minimum long run (15 km)
    • Distance gap for weekly (if any)
    • Long run gap (if any)
  • Readiness analysis:
    • Readiness score (out of 100)
    • Previous injuries status
  • All readiness indicators (14 True/False values)
  • Recommendation summary based on readiness score

Submission Instructions (For All Assignments)

Click to expand submission instructions

GitHub Repository Setup

  1. Create a new repository named week2-assignment or similar

  2. Create a Python file named according to your assignment and variant:
    • Examples: shopping_cart_v1.py, budget_analyzer_v2.py, fitness_tracker_v3.py
  3. Initialize Git and make commits: 
    git init
git add .
git commit -m "Initial commit: Add program structure"
# ... make changes ...
git add .
git commit -m "Add input collection and calculations"
# ... make more changes ...
git add .
git commit -m "Add output formatting and final testing"
  4. Push to GitHub:
    • Create a new repository on GitHub
    • Follow GitHub’s instructions to push your local repository
  5. Test thoroughly:
    • Run your program with at least 3 different sets of inputs
    • Verify all calculations are correct
    • Check that all boolean values display properly
    • Ensure formatting looks professional
  6. Submit:
    • Make sure your repository is public or accessible
    • Copy your GitHub repository URL
    • Submit through the Google Form

Tips

Click to expand

Boolean Arithmetic:

is_member = True
discount = is_member * 0.10 * subtotal  # 10% if True, 0% if False

Display All Categories:

underweight = bmi < 18.5
normal = bmi >= 18.5 and bmi < 25
print(f"Underweight: {underweight}")
print(f"Normal: {normal}")

Select Between Values:

best_discount = (use_a * discount_a) + (use_b * discount_b)