🐍 Python Programming Quiz: Applied Business Logic & Data Transformation (Quiz 01)

An applied Python coding assessment demonstrating real-world algorithmic logic across e-commerce discount mechanics, ETL string parsing & KYC applicant validation, and enterprise performance-driven payroll compensation modeling.

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📌 Table of Contents

• 📖 Overview
• 🎯 Core Competencies Assessed
• 💼 Real-World Scenarios & Solutions
  • Scenario 1: E-Commerce Dynamic Checkout & Promotion Engine
  • Scenario 2: ETL Pipeline: String Parsing, Sanitization & KYC Validation
  • Scenario 3: HR & Payroll: KPI Quota Achievement & CSAT Bonus Model
• 📊 Business Logic Architecture
• 💻 Getting Started & Execution
• 👤 Author & Connect

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📖 Overview

In modern analytics engineering, Python is frequently used to transform dirty operational inputs, enforce validation boundaries, and calculate dynamic financial metrics. This repository showcases solutions to 3 realistic enterprise problem domains:

1. Dynamic Cart Invoicing: Handling multi-tiered discounts, string coupon normalization, and customer tier exemptions.
2. Data Cleansing & KYC Screening: Splitting raw delimited logs, typecasting, validating integrity, and executing compliance checks.
3. Incentive Compensation Systems: Multi-variable KPI rate calculations, CSAT score quality gates, and bonus payouts.

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🎯 Core Competencies Assessed

• String Manipulation & Sanitization: Using .strip(), .lower(), .title(), and .split() to parse unstructured or dirty strings into structured variables.
• Type Safety & Data Integrity: Guarding against casting crashes using .isdigit() prior to numeric conversion.
• Compound Financial Logic: Applying percentage-based thresholds and compounding promotional incentives.
• Conditional Quota Attainment: Calculating achievement ratios and segmenting employees into discrete performance tiers.

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💼 Real-World Scenarios & Solutions

Scenario 1: E-Commerce Dynamic Checkout & Promotion Engine

Business Case: Calculate an order's net checkout total by applying spending threshold discounts (10% on orders $\ge$ 1,000 EGP), verifying promotional coupon codes (save15), and waiving delivery fees for VIP subscribers.

total_spent = 1200.0
promo_code = " save15 "
is_vip = False

# 1. Base threshold discount
base_discount_rate = 0.0
if total_spent >= 1000:
    base_discount_rate = 0.10

# 2. Promo code normalization & application
promo_bonus = 0.0
cleaned_promo = promo_code.strip().lower()
if cleaned_promo == "save15":
    promo_bonus = 0.15

# 3. Discount calculation
total_discount_rate = base_discount_rate + promo_bonus
discount_amount = total_spent * total_discount_rate

# 4. VIP shipping fee policy
shipping_fee = 0.0
if not is_vip:
    shipping_fee = 50.0

final_total = total_spent - discount_amount + shipping_fee

print("=== CUSTOMER CHECKOUT SUMMARY ===")
print("Original Amount : EGP " + str(total_spent))
print("Discount Rate   : " + str(total_discount_rate * 100) + "%")
print("Discount Amount : EGP " + str(discount_amount))
print("Shipping Fee    : EGP " + str(shipping_fee))
print("Final Total     : EGP " + str(final_total))

Console Output:

=== CUSTOMER CHECKOUT SUMMARY ===
Original Amount : EGP 1200.0
Discount Rate   : 25.0%
Discount Amount : EGP 300.0
Shipping Fee    : EGP 50.0
Final Total     : EGP 950.0

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Scenario 2: ETL Pipeline: String Parsing, Sanitization & KYC Validation

Business Case: Ingest a raw, pipe-delimited data string representing applicant information (" 8801 | mohamed ali | 24 "), extract constituent fields, clean whitespace, title-case names, validate age datatype, and apply compliance age gates ($\ge 21$).

raw_user_record = " 8801 | mohamed ali | 24 "

# 1. Delimited tokenization & normalization
fields = raw_user_record.split("|")
user_id = fields[0].strip()
full_name = fields[1].strip().title()
age_str = fields[2].strip()

age_value = 0
application_status = ""

# 2. Datatype validation & compliance checks
is_valid_age = age_str.isdigit()
if is_valid_age:
    age_value = int(age_str)
if is_valid_age and age_value >= 21:
    application_status = "APPROVED"
if is_valid_age and age_value < 21:
    application_status = "REJECTED: Underage"
if not is_valid_age:
    application_status = "ERROR: Invalid Age Format"

print("=== APPLICANT VERIFICATION ===")
print("User ID      : " + str(user_id))
print("Full Name    : " + full_name)
print("Age          : " + str(age_value))
print("Application  : " + application_status)

Console Output:

=== APPLICANT VERIFICATION ===
User ID      : 8801
Full Name    : Mohamed Ali
Age          : 24
Application  : APPROVED

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Scenario 3: HR & Payroll: KPI Quota Achievement & CSAT Bonus Model

Business Case: Evaluate sales executive performance by calculating quota achievement percentage ($125,000 / $100,000 = 125%$). Assign tier ratings, calculate bonus percentages based on both sales volume and Customer Satisfaction (CSAT $\ge 4.5$), apply penalty deductions for poor service quality, and compute net salary payout.

base_salary = 15000.0
sales_target = 100000.0
sales_achieved = 125000.0
csat_score = 4.8

achievement_rate = sales_achieved / sales_target

performance_rating = "Needs Improvement"
bonus_percentage = 0.0

# Performance tier & bonus tier evaluation
if achievement_rate >= 1.00:
    performance_rating = "Meets Expectations"
    bonus_percentage = 0.10
if achievement_rate >= 1.20 and csat_score >= 4.5:
    performance_rating = "Exceeds Expectations"
    bonus_percentage = 0.20

calculated_bonus = base_salary * bonus_percentage

# Quality penalty thresholding
if csat_score < 3.0:
    calculated_bonus = calculated_bonus * 0.5

total_payout = base_salary + calculated_bonus

print("=== EMPLOYEE PAYROLL EVALUATION ===")
print("Achievement Rate : " + str(achievement_rate * 100) + "%")
print("Performance Tier : " + performance_rating)
print("Calculated Bonus : EGP " + str(calculated_bonus))
print("Total Payout     : EGP " + str(total_payout))

Console Output:

=== EMPLOYEE PAYROLL EVALUATION ===
Achievement Rate : 125.0%
Performance Tier : Exceeds Expectations
Calculated Bonus : EGP 3000.0
Total Payout     : EGP 18000.0

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📊 Business Logic Architecture

Scenario	Input Parameters	Validation / Logic Gates	Output Metrics
1. Checkout Engine	Spend, Promo Code, VIP Flag	Spend $\ge 1000 \to 10%$, Code == 'save15' $\to +15%$	Net Payable Total
2. KYC ETL Pipeline	Pipe-Delimited String	Whitespace trim, .isdigit(), Age $\ge 21$	Verification Status Flag
3. Payroll Model	Target, Actual Sales, CSAT Score	Rate $\ge 1.20 \land \text{CSAT} \ge 4.5 \to 20%$ Bonus	Total Compensation

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💻 Getting Started & Execution

Option 1: Run Online in Google Colab (Zero Setup)

Click the badge below to run the notebook instantly in Google Colab:

Option 2: Run Locally via Git & Jupyter

1. Clone the repository:

   git clone https://github.com/islamyasser424-design/Python-Quiz-01.git
   cd Python-Quiz-01

2. Launch Jupyter:

   jupyter notebook Python_Quiz_01.ipynb

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👤 Author & Connect

Islam Yasser
Data Analyst & Business Intelligence Specialist

• 🌐 Portfolio Website: islamyasser424-design.github.io/portfolio-
• 💼 LinkedIn Profile: linkedin.com/in/islam-yasser-55048b378
• 🐙 GitHub Profile: @islamyasser424-design
• ✉️ Email: islamyasser424@gmail.com

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_{Part of the Python Programming & Data Analytics Portfolio series. Built with clean code standards and verified business logic.}