For example, take a look at the NaN values on each columns, and think of how you take on every single on of them.
Many experts say that a company like Starbucks, which serves expensive coffee, is one of the first to suffer in a recession: …
The security regulators in China and the U.S. are now feeling the urgency of cooperating to discover and eliminate financial fraud. It now expects to earn between 6 cents to 21 cents, down from its prior forecast, released in June, of 11 cents to 36 cents. About 12% of orders were for delivery.Data is a real-time snapshot *Data is delayed at least 15 minutes. And then, every cell of this column is in a dictionary, so by using items method, we get its value.Even though it is not a straight line, we can see that the transactions are going up as time goes by.We can see there’s a trend as time goes by and more members are joining.The goal of my exploration & visualization of the Starbucks Capstone Challenge data files is to identify the preprocessing steps that I need to apply prior to combining them.From the histogram above, we can see that female’s member income is more evenly distributed than the male and other’s gender type.Here, we can see that most people end their offer as soon as they get them.From the histogram above, we can see that member’s income vary mainly around $40000 to $60000. This puts Starbucks in a challenging situation, as it can't support all of its employees while stores remain only partially open. Prior to Thinknum I worked inI am Thinknum's Finance Editor, covering public & private companies' alternative data - including social sentiment, hiring and foot traffic.
Transactions at locations open at least 13 months plunged 51%, but consumers spent more their orders, sending average check up by 23%.As the crisis continues to make it challenging for companies to predict future results, Starbucks revised its fourth-quarter forecast. /VCGTo be honest, for a long time I had no idea that Luckin Coffee was conducting financial fraud. This should result in more secure and transparent capital markets for investors in the future.Therefore, Luckin Coffee made a mistake when it claimed that the coffee market in China was very promising simply because Chinese people did not drink much coffee. Simply by using regex and a lambda function.Then, we finish the model testing with XGBoost Classifier from XGBoost library.Then, some of the column names are not align with each other, so we need to change them before merging by ‘offer_id’ and ‘customer_id’.From the histogram above, we can see that a lot of people in their 50-s are doing transactions from the offer.Then, we start by the simplest algorithm of all, which is Logistic Regression from sklearn library.From the search of hyperparameters, we get the feature importances of the data.Finally, in this part, we’re trying to explore the data by using visualizations.