average-fee-estimation

# Average Fee Estimation — Dabstep Dataset ## Core Formula ``` fee = fixed_amount + rate * transaction_value / 10000 ``` Fee rules live in `fees.json` (1000 rules). Each rule has: `card_scheme`, `account_type` (list), `merchant_category_code` (list), `is_credit` (bool/null), `aci` (list), `capture_delay`, `monthly_fraud_level`, `monthly_volume`, `intracountry`, `fixed_amount`, `rate`. **Empty list `[]` = null = applies to all values of that field.** A rule with `account_type: []` applies to every account type. Same for `merchant_category_code: []`, `aci: []`. ## Question Types ### Type 1 — Credit/Debit Average *"For credit transactions, what would be the average fee that [scheme] would charge for a transaction value of X EUR?"* ```python import json with open('fees.json') as f: fees = json.load(f) scheme = 'GlobalCard' # from question txn = 50 # from question is_credit = True # from question ("credit" → True, "debit" → False) applicable = [r for r in fees if r['card_scheme'] == scheme and (r['is_credit'] is None or r['is_credit'] == is_credit)] fee_list = [r['fixed_amount'] + r['rate'] * txn / 10000 for r in applicable] avg = sum(fee_list) / len(fee_list) print(round(avg, 6)) ``` ### Type 2 — Account Type Only Average *"For account type R, what would be the average fee that [scheme] would charge for a transaction value of X EUR?"* ```python scheme = 'NexPay' account_type = 'R' txn = 1000 applicable = [r for r in fees if r['card_scheme'] == scheme and (not r['account_type'] or account_type in r['account_type'])] fee_list = [r['fixed_amount'] + r['rate'] * txn / 10000 for r in applicable] avg = sum(fee_list) / len(fee_list) print(round(avg, 6)) ``` ### Type 3 — Account Type + MCC Average *"For account type H and the MCC description: Drinking Places..., what would be the average fee that [scheme] would charge for X EUR?"* First, look up the MCC numeric code from `merchant_category_codes.csv`: ```python import pandas as pd mcc_df = pd.read_csv('merchant_category_codes.csv') # Search by partial description match row = mcc_df[mcc_df['description'].str.contains('Drinking Places', case=False)] mcc_code = int(row['mcc'].iloc[0]) # e.g., 5813 ``` Then filter: ```python scheme = 'GlobalCard' account_type = 'H' txn = 50 applicable = [r for r in fees if r['card_scheme'] == scheme and (not r['account_type'] or account_type in r['account_type']) and (not r['merchant_category_code'] or mcc_code in r['merchant_category_code'])] fee_list = [r['fixed_amount'] + r['rate'] * txn / 10000 for r in applicable] avg = sum(fee_list) / len(fee_list) print(round(avg, 6)) ``` ### Type 4 — Average Scenario Comparison *"In the average scenario, which card scheme would provide the cheapest/most expensive fee for a transaction value of X EUR?"* Average ALL rules for each card scheme (no additional filtering): ```python from collections import defaultdict txn = 50 # from question scheme_fees = defaultdict(list) for r in fees: scheme_fees[r['card_scheme']].append(r['fixed_amount'] + r['rate'] * txn / 10000) avg_by_scheme = {s: sum(v)/len(v) for s, v in scheme_fees.items()} # cheapest: answer = min(avg_by_scheme, key=avg_by_scheme.get) # most expensive: answer = max(avg_by_scheme, key=avg_by_scheme.get) print(answer) ``` ## Output Format - Numerical answers: **round to 6 decimal places** (e.g., `0.315937`) - Card scheme name answers: exact name as in fees.json (e.g., `GlobalCard`, `NexPay`, `SwiftCharge`, `TransactPlus`) - Format: `<answer>VALUE</answer>` ## Key Rules Summary | Field | null or `[]` means | Non-empty means | |-------|-------------------|-----------------| | `account_type` | applies to all account types | must match one in list | | `merchant_category_code` | applies to all MCCs | must match one in list | | `aci` | applies to all ACI values | must match one in list | | `is_credit` | applies to credit and debit | must match exactly | | `capture_delay` | applies to all | must match exactly | | `monthly_fraud_level` | applies to all | must match exactly | | `monthly_volume` | applies to all | must match exactly | | `intracountry` | applies to all | must match exactly | ## MCC Lookup When the question gives an MCC *description* instead of a numeric code, look it up: ```python import pandas as pd mcc_df = pd.read_csv('merchant_category_codes.csv') # Use partial match for robustness: keyword = 'Drinking Places' # distinctive keyword from description mcc_code = int(mcc_df[mcc_df['description'].str.contains(keyword, case=False)]['mcc'].iloc[0]) ``` ## Validation Checklist 1. Used the correct fee formula? `fixed_amount + rate * txn / 10000` 2. Included rules where the list field is empty `[]` (= applies to all)? 3. For "credit transactions": filtered `is_credit == True or is_credit is None`? 4. For "average scenario": used ALL rules per scheme, no additional filters? 5. Rounded to 6 decimals? 6. MCC code looked up from CSV (not guessed)?