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). Key fields per rule: - `card_scheme`: string — `GlobalCard`, `NexPay`, `SwiftCharge`, `TransactPlus` - `account_type`: list — `[]` means applies to ALL account types (R, D, H, F, S, O) - `merchant_category_code`: list — `[]` means applies to ALL MCCs - `aci`: list — `[]` means applies to ALL ACI values - `is_credit`: bool or `null` — `null` means applies to BOTH credit and debit - `intracountry`: float or `null` — `1.0` = domestic, `0.0` = international, `null` = both - `capture_delay`, `monthly_fraud_level`, `monthly_volume`: string or `null` — `null` = applies to all - `fixed_amount`: float (EUR), `rate`: integer **Filter rule:** For list fields, `[]` = applies to all → `not r['account_type']` is `True` for `[]`. For scalar/bool fields, `None` = applies to all → `r['is_credit'] is None`. ## Question Types ### Type A — Average Scenario Comparison *"In the average scenario, which card scheme would provide the cheapest/most expensive fee for X EUR?"* Use **ALL 1000 rules** grouped by scheme — **no additional filtering whatsoever**. ```python import json from collections import defaultdict with open('fees.json') as f: fees = json.load(f) txn = 100 # 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()} answer = min(avg_by_scheme, key=avg_by_scheme.get) # cheapest # answer = max(avg_by_scheme, key=avg_by_scheme.get) # most expensive print(answer) ``` ### Type B — Filtered Average (specific scheme + conditions) *"For [account type / credit / debit / MCC / combination], what is the average fee that [scheme] would charge for X EUR?"* Filter rules matching the scheme and all stated conditions, then compute the average. **Filter logic by field type:** | Field | Empty/null | Non-empty/non-null | |-------|-----------|-------------------| | `account_type` (list) | `not r['account_type']` → True, include | `account_type in r['account_type']` | | `merchant_category_code` (list) | `not r['merchant_category_code']` → True, include | `mcc_code in r['merchant_category_code']` | | `aci` (list) | `not r['aci']` → True, include | `aci_val in r['aci']` | | `is_credit` (bool/null) | `r['is_credit'] is None` → include | `r['is_credit'] == True/False` | | `capture_delay` (str/null) | `r['capture_delay'] is None` → include | `r['capture_delay'] == value` | ```python import json, pandas as pd with open('fees.json') as f: fees = json.load(f) scheme = 'GlobalCard' account_type = 'H' txn = 50 # is_credit = True # True for "credit", False for "debit" # If MCC is given as description, look it up first: mcc_df = pd.read_csv('merchant_category_codes.csv') keyword = 'Drinking Places' # distinctive keyword from the question's MCC description mcc_code = int(mcc_df[mcc_df['description'].str.contains(keyword, case=False)]['mcc'].iloc[0]) 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']) # and (r['is_credit'] is None or r['is_credit'] == is_credit) # add if question specifies ] fee_list = [r['fixed_amount'] + r['rate'] * txn / 10000 for r in applicable] print(round(sum(fee_list) / len(fee_list), 6)) ``` Add or remove filter conditions based on exactly what the question specifies. **Only filter on fields explicitly mentioned in the question.** ## Output Format - Numerical answers: **round to 6 decimal places** — `round(avg, 6)` - Card scheme name: exact case from fees.json — `GlobalCard`, `NexPay`, `SwiftCharge`, `TransactPlus` - Wrap in: `<answer>VALUE</answer>` ## Validation Checklist 1. Formula: `fixed_amount + rate * txn / 10000`? 2. Empty-list rules included? `not r['account_type']` → `True` for `[]`? 3. Null scalar fields included? `r['is_credit'] is None` → include? 4. "Average scenario": ALL rules used, NO extra filters? 5. MCC code looked up from CSV, not guessed? 6. Rounded to 6 decimal places? 7. At least 1 applicable rule found? (if 0, re-check filter logic)