Advanced Code Example — Branching and Control Flow#

# ── Customer Record ─────────────────────────────────────────────────
customer = {
    'name': 'Alice Johnson',
    'region': 'Northwest',
    'total_spent': 1257.30,
    'purchase_count': 12,
    'avg_transaction': 104.78,
    'days_since_last_purchase': 8,
    'flagged_transactions': 0,
    'account_age_days': 540
}

name           = customer['name']
region         = customer['region']
total_spent    = customer['total_spent']
purchase_count = customer['purchase_count']
avg_txn        = customer['avg_transaction']
days_inactive  = customer['days_since_last_purchase']
flags          = customer['flagged_transactions']
account_age    = customer['account_age_days']

# ── 1. Loyalty Tier Classification ──────────────────────────────────
if total_spent >= 1500 and purchase_count >= 15:
    tier = 'Platinum+'
elif total_spent >= 1000 and purchase_count >= 10:
    tier = 'Platinum'
elif total_spent >= 500 or purchase_count >= 5:
    tier = 'Gold'
elif total_spent >= 200:
    tier = 'Silver'
else:
    tier = 'Standard'

# ── 2. Shipping Discount Logic ───────────────────────────────────────
if tier in ('Platinum+', 'Platinum') and region == 'Northwest':
    shipping_discount = 0.20
    shipping_label = '20% (Top Tier + Home Region)'
elif tier in ('Platinum+', 'Platinum'):
    shipping_discount = 0.15
    shipping_label = '15% (Top Tier)'
elif tier == 'Gold' and region in ('Northwest', 'Southwest'):
    shipping_discount = 0.10
    shipping_label = '10% (Gold + Regional)'
elif tier == 'Gold':
    shipping_discount = 0.07
    shipping_label = '7% (Gold Standard)'
else:
    shipping_discount = 0.00
    shipping_label = 'None'

# ── 3. Fraud Signal Evaluation ───────────────────────────────────────
fraud_signals = []

if flags > 0:
    fraud_signals.append(f'{flags} flagged transaction(s) on record')
if avg_txn > 500:
    fraud_signals.append('Unusually high average transaction amount')
if account_age < 30 and total_spent > 1000:
    fraud_signals.append('High spend on new account')
if purchase_count > 20 and days_inactive < 1:
    fraud_signals.append('Unusual purchase frequency')

fraud_risk = 'HIGH' if len(fraud_signals) >= 2 else ('MEDIUM' if fraud_signals else 'LOW')

# ── 4. Engagement Status ─────────────────────────────────────────────
if days_inactive <= 7:
    engagement = 'Active'
elif days_inactive <= 30:
    engagement = 'Recent'
elif days_inactive <= 90:
    engagement = 'At Risk'
else:
    engagement = 'Churned'

# ── Report Output ────────────────────────────────────────────────────
print("=" * 52)
print(f"  CUSTOMER EVALUATION: {name}")
print("=" * 52)
print(f"  Loyalty Tier    : {tier}")
print(f"  Engagement      : {engagement}")
print(f"  Shipping Disc.  : {shipping_label}")
print(f"  Fraud Risk      : {fraud_risk}")

if fraud_signals:
    print("\n  Fraud Signals Detected:")
    for signal in fraud_signals:
        print(f"    ⚠  {signal}")
else:
    print("\n  No fraud signals detected.")

print("=" * 52)

====================================================
  CUSTOMER EVALUATION: Alice Johnson
====================================================
  Loyalty Tier    : Platinum
  Engagement      : Recent
  Shipping Disc.  : 20% (Top Tier + Home Region)
  Fraud Risk      : LOW

  No fraud signals detected.
====================================================