hotel_booking_demand

Analysis of Hotel Bookings

Exercise

To analyse the data for a hotel bookings, which shows

• type of hotel,
• arrival details,
• cancellation details,
• booking channels

Data

CSV is available in the dataset folder. Link

Task

To create a summary by analysing the factors available in the dataset

Solution

• Jupyter Notebook + Rmarkdown + Colab

Conclusion

• City hotels are twiced as much booked as Resorts.

• There is a huge percentage of cancellation in City hotels, i.e. people are more inclined to cancel a booking at a City hotel than Resorts

• 2016 was a high booking period, and the general preferences of the booking are -

  • Summer season
  • and weekends

  

• Types of visitors are mostly couples or individuals, with high number of bookings from Portugal, Uk, France, Germany, Spain

• marget segments are mostly Online/Offline Travel Agents & Travel Operators
• Most bookings do not have any sort of deposit, which can contribute to the high cancellation rate.
• Guests are not often repeated, which means either the experience is not good, or the segment has not been marketed yet.
• Price graph shows that the spike is prices during the summer season, (May - August). The surge is prices of Resorts are quite high than that of City Hotels.

Future steps

• Find reasons for low repitition of customers
• Add minimum deposit to see if it affects the number of bookings and cancellation.