Cleaned

app.py

@@ -1,31 +1,11 @@
 
-# Import necessary libraries.
 import pandas as pd
 from sklearn.metrics.pairwise import cosine_similarity
 
 # Read data source Excel files.
 df1 = pd.read_excel('Online_Retail.xlsx')
-
-# Check dataframe information.
-#df1.info()
-
-# Read header of dataframe.
-#df1.head()
-
-# Check any column containing the null value.
-#df1.isnull().any()
-
-# Count the number of null value records in the CustomerID column.
-#df1['CustomerID'].isna().sum()
-
 df1a = df1.dropna(subset=['CustomerID'])
 
-# Check dataframe information.
-#df1a.info()
-
-# Read header of dataframe.
-#df1a.head()
-
 # Create CustomerID vs Item (Purchased Items, by StockCode) matrix by pivot table function.
 CustomerID_Item_matrix = df1a.pivot_table(
     index='CustomerID', 
@@ -34,51 +14,30 @@ CustomerID_Item_matrix = df1a.pivot_table(
     aggfunc='sum'
 )
 
-# Display the shape of matrix, 4372 rows of CustomerID, 3684 columns of Item.
-#CustomerID_Item_matrix.shape
-
 # Update illustration of the matrix, 1 to represent customer have purchased item, 0 to represent customer haven't purchased.
 CustomerID_Item_matrix = CustomerID_Item_matrix.applymap(lambda x: 1 if x > 0 else 0)
 
-# Read header of CustomerID vs Item matrix.
-#CustomerID_Item_matrix.loc[12680:].head()
-
 # Create User to User similarity matrix. 
 user_to_user_similarity_matrix = pd.DataFrame(
     cosine_similarity(CustomerID_Item_matrix)
 )
 
-# Display header of User to User similarity matrix. 
-#user_to_user_similarity_matrix.head()
-
 # Update index to corresponding CustomerID.
 user_to_user_similarity_matrix.columns = CustomerID_Item_matrix.index
 user_to_user_similarity_matrix['CustomerID'] = CustomerID_Item_matrix.index
 user_to_user_similarity_matrix = user_to_user_similarity_matrix.set_index('CustomerID')
 
-# Display header of User to User similarity matrix. 
-#user_to_user_similarity_matrix.head()
-
-# Randomly pick CustomerID (12702) to display the most similar CustomerID.
-# The most similar CustomerID is 14608, which has 51% similarity.
-#user_to_user_similarity_matrix.loc[12702.0].sort_values(ascending=False)
-
 # Display CustomerID (12702) purchased items.
 items_purchased_by_X = set(CustomerID_Item_matrix.loc[12702.0].iloc[
     CustomerID_Item_matrix.loc[12702.0].to_numpy().nonzero()].index)
-#items_purchased_by_X
 
 # Display CustomerID (14608) purchased items.
 items_purchased_by_Y = set(CustomerID_Item_matrix.loc[14608.0].iloc[
     CustomerID_Item_matrix.loc[14608.0].to_numpy().nonzero()].index)
-#items_purchased_by_Y
 
 # Find out items which purchased by X (12702) but not yet purchased by Y (14608).
 items_to_recommend_to_Y = items_purchased_by_X - items_purchased_by_Y
 
-# Display the list of items recommended for Y (14608).
-#items_to_recommend_to_Y
-
 # Display the list of items recommended for Y (14608) with item Description.
 print(df1a.loc[
     df1a['StockCode'].isin(items_to_recommend_to_Y),