diff --git a/Scripts/Generate_accounts.py b/Scripts/Generate_accounts.py
deleted file mode 100644
index 909427e..0000000
--- a/Scripts/Generate_accounts.py
+++ /dev/null
@@ -1,114 +0,0 @@
-from faker import Faker
-from dotenv import load_dotenv
-from datetime import datetime
-import os
-import random
-import pandas as pd
-import boto3
-
-# ---- Setup ----
-fake = Faker()
-load_dotenv()
-
-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_key = "DataLab/customers/customers.csv"
-accounts_s3_key = "DataLab/accounts/accounts.csv"
-
-# ---- Ensure local data folder exists ----
-os.makedirs("../Data", exist_ok=True)
-
-# ---- Download customers.csv from S3 ----
-local_customers_file = "../Data/customers.csv"
-try:
-    s3.Bucket(bucket_name).download_file(customers_key, local_customers_file)
-    print("Downloaded customers.csv from S3.")
-except Exception as e:
-    print("ERROR: Could not download customers.csv:", e)
-    raise SystemExit()
-
-# ---- Load customers DataFrame ----
-customers_df = pd.read_csv(local_customers_file)
-
-# Convert customer_since to actual date objects
-customers_df["customer_since"] = pd.to_datetime(customers_df["customer_since"]).dt.date
-
-# ---- Helper Functions ----
-
-def generate_account_id(branch_id):
-    """Generate realistic branch-coded account IDs (11–12 digits)."""
-    branch_part = str(branch_id).zfill(3)  # 3-digit branch ID
-    random_part = str(random.randint(10**8, 10**9 - 1))  # 8–9 random digits
-    return branch_part + random_part
-
-def generate_account_number():
-    """Generate realistic 11-digit bank account numbers."""
-    return str(random.randint(10**10, (10**11) - 1))
-
-def assign_account_types():
-    """
-    Assign 1–2 accounts per customer using realistic rules:
-      - ~50% Checking Only
-      - ~20% Savings Only
-      - ~30% Both
-    """
-    roll = random.random()
-
-    if roll < 0.50:
-        return ["Checking"]
-    elif roll < 0.70:
-        return ["Savings"]
-    else:
-        return ["Checking", "Savings"]
-
-def balance_for_type(account_type):
-    """Give realistic account balances."""
-    if account_type == "Checking":
-        return round(random.uniform(50, 7000), 2)
-    else:  # Savings
-        return round(random.uniform(200, 25000), 2)
-
-# ---- Generate accounts ----
-accounts = []
-
-for _, row in customers_df.iterrows():
-    customer_id = row["customer_id"]
-    customer_since = row["customer_since"]
-    home_branch_id = row["home_branch_id"]
-
-    # Determine which account types this customer owns
-    account_types = assign_account_types()
-
-    for acct_type in account_types:
-        accounts.append({
-            "account_id": generate_account_id(home_branch_id),
-            "account_number": generate_account_number(),
-            "customer_id": customer_id,
-            "account_type": acct_type,
-            "open_date": fake.date_between(
-                start_date=customer_since, end_date=datetime.today().date()
-            ),
-            "balance": balance_for_type(acct_type),
-            "branch_id": home_branch_id
-        })
-
-# ---- Convert to DataFrame ----
-accounts_df = pd.DataFrame(accounts)
-
-# ---- Save locally ----
-local_accounts_file = "../Data/accounts.csv"
-accounts_df.to_csv(local_accounts_file, index=False)
-print("Generated accounts.csv locally.")
-
-# ---- Upload to S3 ----
-try:
-    s3.Bucket(bucket_name).upload_file(local_accounts_file, accounts_s3_key)
-    print(f"Uploaded accounts.csv to s3://{bucket_name}/{accounts_s3_key}")
-except Exception as e:
-    print("ERROR: Could not upload accounts.csv to S3:", e)