Add fraud detection model plan

plan/code.py

@@ -0,0 +1,69 @@
+# from duckdb.experimental.spark.sql import SparkSession
+import pandas as pd
+import numpy as np
+from faker import Faker
+import uuid
+import datetime
+
+fake = Faker()
+
+def main() -> None:
+    NUM_USERS_TO_GENERATE = 500
+    NUM_TRANSFERS_TO_GENERATE = 2000
+#     spark = SparkSession.builder.getOrCreate()
+    users_dataframe = generate_user_data(NUM_USERS_TO_GENERATE)
+    transfers_dataframe = generate_transfer_data(NUM_TRANSFERS_TO_GENERATE, users_dataframe)
+
+def generate_user_data(num_users: int) -> pd.DataFrame:
+    print(f"Generating {num_users} user records...")
+    COUNTRIES = ['GB', 'US', 'DE', 'AU', 'SG', 'BE', 'FR', 'CA']
+    COUNTRY_PROBS = [0.3, 0.2, 0.15, 0.1, 0.1, 0.05, 0.05, 0.05]
+    ACCOUNT_TYPES = ['Personal', 'Business']
+    ACCOUNT_TYPE_PROBS = [0.85, 0.15] # 85% Personal, 15% Business
+    KYC_STATUSES = ['Verified', 'Pending', 'Rejected']
+    KYC_STATUS_PROBS = [0.9, 0.07, 0.03] # Most users are verified
+    users_data = []
+    for _ in range(num_users):
+        user = {
+            'user_id': str(uuid.uuid4()),
+            'first_name': fake.first_name(),
+            'last_name': fake.last_name(),
+            'email': fake.unique.email(),
+            'country_of_residence': np.random.choice(COUNTRIES, p=COUNTRY_PROBS),
+            'account_type': np.random.choice(ACCOUNT_TYPES, p=ACCOUNT_TYPE_PROBS),
+            'kyc_status': np.random.choice(KYC_STATUSES, p=KYC_STATUS_PROBS),
+            'registration_date': fake.date_time_between(start_date='-3y', end_date='now', tzinfo=datetime.timezone.utc),
+            'label': np.random.choice([0, 1], p=[0.9, 0.1])
+        }
+        users_data.append(user)
+    return pd.DataFrame(users_data)
+
+
+def generate_transfer_data(num_transfers: int, users_df: pd.DataFrame) -> pd.DataFrame:
+    print(f"\nGenerating {num_transfers} transfer records...")
+    CURRENCIES = ['GBP', 'EUR', 'USD', 'AUD', 'JPY', 'CAD']
+    TRANSFER_STATUSES = ['COMPLETED', 'PENDING', 'CANCELLED', 'FAILED']
+    TRANSFER_STATUS_PROBS = [0.92, 0.03, 0.03, 0.02] # Most transfers complete
+    exchange_rate = np.random.normal(loc=1.15, scale=0.05)
+    user_ids = users_df['user_id'].tolist()
+    transfers_data = []
+    for _ in range(num_transfers):
+        sender_id, recipient_id = np.random.choice(user_ids, size=2, replace=False)
+        source_amount = np.random.lognormal(mean=np.log(100), sigma=1.5)
+        transfer = {
+            'transfer_id': str(uuid.uuid4()),
+            'sender_id': sender_id,
+            'recipient_id': recipient_id,
+            'source_currency': np.random.choice(CURRENCIES),
+            'target_currency': np.random.choice(CURRENCIES),
+            'source_amount': round(source_amount, 2),
+            'target_amount': round(source_amount * exchange_rate, 2),
+            'status': np.random.choice(TRANSFER_STATUSES, p=TRANSFER_STATUS_PROBS),
+            'created_at': fake.date_time_between(start_date='-2y', end_date='now', tzinfo=datetime.timezone.utc)
+        }
+        transfers_data.append(transfer)
+    return pd.DataFrame(transfers_data)
+
+
+if __name__ == "__main__":
+    main()