Now we are ready to start building a recommender system.
First, declare the imports:
import tensorflow as tfimport pandas as pdimport numpy as npimport scipy.sparse as spfrom tqdm import tqdm
Let us read the dataset:
df = pd.read_excel('Online Retail.xlsx')
Reading xlsx files may take a while. To save time when we next want to read the file, we can save the loaded copy into a pickle file:
import picklewith open('df_retail.bin', 'wb') as f_out: pickle.dump(df, f_out)
This file is a lot faster to read, so for loading, we should use the pickled version:
with open('df_retail.bin', 'rb') as f_in: df = pickle.load(f_in)
Once the data is loaded, we can have a look at the data. We can do this by invoking the head ...
