Analyze data when McDonald's ice cream machines are broken to determine how much revenue they lose.
Analysis is very inaccurate due to wild guesses on a lot of factors and variables. Even hourly distribution of customers, no necessary 4-hour maintenance period, all-day restaurant schedule, ice cream selling all-day - those are assumed.

• Use Apache Airflow for constructing pipelines for ETL
• Use a specialized data warehouse to store and query data more efficiently (Ex: BigQuery or Redshift)

python analyze.py --start_date=2022-09-22 --end_date=2022-09-23

--start_date - to specify analysis starting date
--end_date - to specify analysis ending date
python analyze.py -h for more help

python scrape.py

├─── data # Data folder
│    ├─── final # contains data warehouse csv
│    └─── processed # folder with processed hourly data (after transform)
├─── analyze.py # Perform analysis
├─── scrape.py # ETL data from website
├─── constants.py # Declare constants (contains explanation of variables)
├─── .gitignore
├─── README.md
└─── requirements.txt