The given data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. Some users might not receive any offers during certain weeks. Not all users receive the same offer, and that is the challenge to solve with this data set.
The offers are three types: BOGO (Buy one get one), discount, and informative.
In this experiment, the offers, BOGO and discount, have durations, expiration terms. Hence, the offers need to be completed before the expiration dates.
The three files are given: profile.json, portfolio.json, and transcript.json.
Rewards program users (17000 users x 5 fields)
As I mentioned in the last post, We cannot install PyTorch MNIST smoothly.
If you stuck in installing the MNIST, check https://kazma-s-1306.medium.com/in-2021-as-it-turned-out-pytorch-mnist-cannot-be-installed-like-before-8b1083f80086.
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline# obtain a batch of training images
dataiter = iter(train_loader)
images, labels = dataiter.next()
images = images.numpy()fig = plt.figure(figsize=(20, 5))
for i in np.arange(20):
rows = 2
cols = 20/2
ax = fig.add_subplot(rows, cols, i+1, xticks=, yticks=)
Writer, programmer. Here to show what’s inside my head and to be understood.