Creating data for observing available train tickets over half month to catch their sale patterns. Finding best intervals to buy tickets.
Scraping train ticket website by using Selenium framework on python.
Scraping process is running twice in a hour, in each process it scrapes available tickets for observing extinction of tickets.
Using chromedriver as webdriver with Chrome.
Holding data in MySQL data warehouse.
Relying on data visualizations below:
- Saturday is the most demanding day and tickets are consumpted 7 days before the trip date.
- A serious amount of ticket returns happening on the day before the trip.
- When there is a low demand to cancelled trip, returns create significant increment in empty seats.
- When there is a high demand to cancelled trip, returns are less and repurchases happen immediately, and there is no a pulse in the high demanding trips.
- Buy the ticket at least 5 days ago for an average day to travel without risk of being out of tickets.
