removed old version

Scripts/Generate_customers.py

@@ -1,101 +0,0 @@
-from faker import Faker
-from dotenv import load_dotenv
-import os
-import pandas as pd
-import boto3
-import random
-from datetime import datetime
-
-fake = Faker()
-
-# ---- Load env ----
-load_dotenv()
-
-# ---- Hetzner S3 setup ----
-s3 = boto3.resource(
-    "s3",
-    endpoint_url=os.getenv("STORAGE_ENDPOINT"),
-    aws_access_key_id=os.getenv("STORAGE_ACCESS_KEY"),
-    aws_secret_access_key=os.getenv("STORAGE_SECRET_KEY")
-)
-
-bucket_name = os.getenv("STORAGE_BUCKET")
-customers_s3_key = "DataLab/customers/customers.csv"
-branches_s3_key = "DataLab/branches/branches.csv"
-
-# ---- Load branches from S3 ----
-branches_local = "../Data/branches.csv"
-s3.Bucket(bucket_name).download_file(branches_s3_key, branches_local)
-branches = pd.read_csv(branches_local)
-
-# ---- Helper functions ----
-def realistic_credit_score():
-    """Normal distribution around 680."""
-    score = int(random.gauss(680, 60))
-    return max(300, min(score, 850))
-
-def realistic_income():
-    brackets = [
-        (20000, 40000),
-        (40000, 70000),
-        (70000, 120000),
-        (120000, 200000)
-    ]
-    low, high = random.choice(brackets)
-    return random.randint(low, high)
-
-def realistic_employment():
-    return random.choices(
-        ["Employed", "Self-Employed", "Unemployed", "Student", "Retired"],
-        weights=[50, 15, 10, 15, 10]
-    )[0]
-
-def realistic_contact():
-    return random.choice(["Email", "Phone", "SMS"])
-
-# ---- Generate Customers ----
-customers = []
-start_id = 100000  # Realistic banking customer IDs
-
-for i in range(50):
-    first = fake.first_name()
-    last = fake.last_name()
-    
-    dob = fake.date_between(start_date="-80y", end_date="-18y")
-    age = (datetime.now().date() - dob).days // 365
-
-    income = realistic_income()
-    credit = realistic_credit_score()
-
-    customers.append({
-        "customer_id": start_id + i,
-        "first_name": first,
-        "last_name": last,
-        "full_name": f"{first} {last}",
-        "email": f"{first.lower()}.{last.lower()}@{fake.free_email_domain()}",
-        "phone": fake.phone_number(),
-        "date_of_birth": dob,
-        "age": age,
-        "gender": random.choice(["Male", "Female", "Other"]),
-        "street_address": fake.street_address(),
-        "city": fake.city(),
-        "state": fake.state_abbr(),
-        "zip_code": fake.zipcode(),
-        "home_branch_id": random.choice(branches["branch_id"]),
-        "customer_since": fake.date_between(start_date="-10y", end_date="today"),
-        "employment_status": realistic_employment(),
-        "annual_income": income,
-        "credit_score": credit,
-        "preferred_contact_method": realistic_contact()
-    })
-
-df = pd.DataFrame(customers)
-
-# ---- Save locally ----
-local_file = "../Data/customers.csv"
-df.to_csv(local_file, index=False)
-print("Generated realistic customers.")
-
-# ---- Upload to S3 ----
-s3.Bucket(bucket_name).upload_file(local_file, customers_s3_key)
-print(f"Uploaded customers.csv to s3://{bucket_name}/{customers_s3_key}")