101 lines
2.9 KiB
Python
101 lines
2.9 KiB
Python
from faker import Faker
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from dotenv import load_dotenv
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import os
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import pandas as pd
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import boto3
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import random
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from datetime import datetime
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fake = Faker()
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# ---- Load env ----
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load_dotenv()
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# ---- Hetzner S3 setup ----
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s3 = boto3.resource(
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"s3",
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endpoint_url=os.getenv("STORAGE_ENDPOINT"),
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aws_access_key_id=os.getenv("STORAGE_ACCESS_KEY"),
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aws_secret_access_key=os.getenv("STORAGE_SECRET_KEY")
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)
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bucket_name = os.getenv("STORAGE_BUCKET")
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customers_s3_key = "DataLab/customers/customers.csv"
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branches_s3_key = "DataLab/branches/branches.csv"
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# ---- Load branches from S3 ----
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branches_local = "../Data/branches.csv"
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s3.Bucket(bucket_name).download_file(branches_s3_key, branches_local)
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branches = pd.read_csv(branches_local)
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# ---- Helper functions ----
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def realistic_credit_score():
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"""Normal distribution around 680."""
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score = int(random.gauss(680, 60))
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return max(300, min(score, 850))
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def realistic_income():
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brackets = [
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(20000, 40000),
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(40000, 70000),
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(70000, 120000),
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(120000, 200000)
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]
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low, high = random.choice(brackets)
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return random.randint(low, high)
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def realistic_employment():
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return random.choices(
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["Employed", "Self-Employed", "Unemployed", "Student", "Retired"],
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weights=[50, 15, 10, 15, 10]
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)[0]
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def realistic_contact():
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return random.choice(["Email", "Phone", "SMS"])
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# ---- Generate Customers ----
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customers = []
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start_id = 100000 # Realistic banking customer IDs
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for i in range(50):
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first = fake.first_name()
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last = fake.last_name()
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dob = fake.date_between(start_date="-80y", end_date="-18y")
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age = (datetime.now().date() - dob).days // 365
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income = realistic_income()
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credit = realistic_credit_score()
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customers.append({
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"customer_id": start_id + i,
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"first_name": first,
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"last_name": last,
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"full_name": f"{first} {last}",
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"email": f"{first.lower()}.{last.lower()}@{fake.free_email_domain()}",
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"phone": fake.phone_number(),
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"date_of_birth": dob,
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"age": age,
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"gender": random.choice(["Male", "Female", "Other"]),
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"street_address": fake.street_address(),
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"city": fake.city(),
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"state": fake.state_abbr(),
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"zip_code": fake.zipcode(),
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"home_branch_id": random.choice(branches["branch_id"]),
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"customer_since": fake.date_between(start_date="-10y", end_date="today"),
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"employment_status": realistic_employment(),
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"annual_income": income,
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"credit_score": credit,
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"preferred_contact_method": realistic_contact()
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})
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df = pd.DataFrame(customers)
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# ---- Save locally ----
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local_file = "../Data/customers.csv"
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df.to_csv(local_file, index=False)
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print("Generated realistic customers.")
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# ---- Upload to S3 ----
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s3.Bucket(bucket_name).upload_file(local_file, customers_s3_key)
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print(f"Uploaded customers.csv to s3://{bucket_name}/{customers_s3_key}")
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