diff --git a/0_Input_cleaning.py b/0_Input_cleaning.py
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+# Business Data Challenge - Team 1
+
+import pandas as pd
+import numpy as np
+import os
+import s3fs
+import re
+import warnings
+
+# Create filesystem object
+S3_ENDPOINT_URL = "https://" + os.environ["AWS_S3_ENDPOINT"]
+fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})
+
+# Import cleaning and merge functions
+exec(open('0_Cleaning_and_merge_functions.py').read())
+
+# Output folder 
+BUCKET_OUT = "projet-bdc2324-team1"
+
+# Ignore warning
+warnings.filterwarnings('ignore')
+
+
+def export_dataset(df, output_name):
+    print('Exportation of temporary dataset :', output_name)
+    FILE_PATH_OUT_S3 = BUCKET_OUT + "/" + output_name
+    with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
+        df.to_csv(file_out, index = False)
+
+## 1 - Cleaning of the datasets
+
+# Cleaning customerplus
+df1_customerplus_clean = preprocessing_customerplus(directory_path = "1")
+
+## Exportation 
+export_dataset(df = df1_customerplus_clean, output_name = "0_Input/Company_1/customerplus_cleaned.csv")
+
+# Cleaning target area
+df1_target_information = preprocessing_target_area(directory_path = "1")
+## Exportation 
+export_dataset(df = df1_campaigns_information, output_name = "0_Input/Company_1/Campaigns dataset clean.csv")
+
+# Cleaning campaign area
+df1_campaigns_information = preprocessing_campaigns_area(directory_path = "1")
+## Exportation 
+export_dataset(df = df1_campaigns_information, output_name = "0_Input/Company_1/Campaigns dataset clean.csv")
+
+## Exportation 
+export_dataset(df = df1_campaigns_information, output_name = "0_Temp/Company 1 - Campaigns dataset clean.csv")
+
+# Cleaning product area
+df1_products_purchased_reduced = uniform_product_df(directory_path = "1")
+## Exportation 
+export_dataset(df = df1_campaigns_information, output_name = "0_Input/Company_1/Campaigns dataset clean.csv")
+#Exportation 
+export_dataset(df = df1_products_purchased_reduced, output_name = "0_Temp/Company 1 - Purchases.csv")