diff --git a/corrected_wrong b/corrected_wrong
new file mode 100644
index 0000000..4611ad9
--- /dev/null
+++ b/corrected_wrong
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+from bs4 import BeautifulSoup
+import requests
+import pandas as pd
+import numpy as np
+import sqlite3
+from datetime import datetime 
+
+url = 'https://web.archive.org/web/20230908091635/https://en.wikipedia.org/wiki/List_of_largest_banks'
+table_attribs = ["Name", "MC_USD_Billion"]
+db_name = 'Banks.db'
+table_name = 'Largest_banks'
+csv_path = './Largest_banks_data.csv'
+
+def log_progress(message):
+    timestamp_format = '%Y-%h-%d-%H:%M:%S' # Year-Monthname-Day-Hour-Minute-Second 
+    now = datetime.now() # get current timestamp 
+    timestamp = now.strftime(timestamp_format) 
+    with open("./code_log.txt","a") as f:
+        f.write(timestamp + ' : ' + message + '\n') 
+
+def extract(url, table_attribs):
+    page = requests.get(url).text
+    soup = BeautifulSoup(page,'html.parser')
+    df = pd.DataFrame(columns=table_attribs)
+    tables = soup.find_all('tbody')
+    rows = tables[0].find_all('tr')
+    for row in rows:
+        if row.find_all('td') is not None:
+            col = row.find_all('td')
+            bank_name = col[1].find_all('a')[1]['title']
+            market_cap = col[2].contents[0][:-1]
+            data_dict = {"Name": bank_name, "MC_USD_Billion": float(market_cap)}
+            df1 = pd.DataFrame(data_dict, index =[0])
+            df = pd.concat([df,df1], ignore_index = True)
+    return df
+
+def transform(df, csv_path):
+    exchange_rate = pd.read_csv(csv_path)
+    exchange_rate = exchange_rate.set_index('Currency').to_dict()['Rate']
+    df['MC_GBP_Billion']= [np.round(x*exchange_rate['GBP'],2) for x in df['MC_USD_Billion']]
+    df['MC_EUR_Billion']= [np.round(x*exchange_rate['EUR'],2) for x in df['MC_USD_Billion']]
+    df['MC_INR_Billion']= [np.round(x*exchange_rate['INR'],2) for x in df['MC_USD_Billion']]
+    return df
+
+def load_to_csv(df, output_path):
+    df.to_csv(csv_path)
+
+def load_to_db(df, sql_connection, table_name):
+    df.to_sql(table_name, sql_connection, if_exists='replace', index=False)
+
+def run_query(query_statement, sql_connection):
+    print(query_statement)
+    query_output = pd.read_sql(query_statement, sql_connection)
+    print(query_output)
+
+log_progress('Preliminaries complete. Initiating ETL process')
+df = extract(url, table_attribs)
+log_progress('Data extraction complete. Initiating Transformation process')
+df = transform(df,'exchange_rate.csv')
+log_progress('Data transformation complete. Initiating loading process')
+load_to_csv(df, csv_path)
+log_progress('Data saved to CSV file')
+sql_connection = sqlite3.connect('World_Economies.db')
+log_progress('SQL Connection initiated.')
+load_to_db(df, sql_connection, table_name)
+log_progress('Data loaded to Database as table. Running the query')
+query_statement = f"SELECT * from {table_name} WHERE MC_USD_Billion >= 100"
+run_query(query_statement, sql_connection)
+log_progress('Process Complete.')
+sql_connection.close()