文件列表:

CAPSTONE_PROJECT_01.pdf (711521, 2022-12-26)
Hotel_Booking_Analysis_EDA_Capstone_Project.ipynb (671671, 2022-12-26)
Project_summary.pdf (161698, 2022-12-26)
Team_colab_notebook.ipynb (518900, 2022-12-26)
Technical_documentation.pdf (663392, 2022-12-26)

# Hotel-booking-analysis-EDA- Analyzing The Data of Hotel Booking(EDA-capstone project)  ## **Introduction:** Have you ever wondered the trends for hotel bookings? How long a person stays? How often people cancel? What the busiest months are? In this analysis I explore a large dataset to examine these questions. The data contains “booking due to arrive between the 1st of July of 2015 and the 31st of august 2017”. This dataset contains information on records for client stays at hotels. More specifically, it contains booking information for a city hotel and a resort hotel, and includes information such as when the booking was made, length of stay, the number of adults, children, and/or babies, and the number of available parking spaces, among other things. For the purpose of this post, I only focused on some of the important variables to examine. ## **Problem Statement:** The main objective of this project is to explore and visualize the dataset from hotel booking data using basic exploratory data analysis techniques. This will be done by finding out the information such as when the booking was made, length of stay, the number of adults, children, and/or babies, and the number of repeated guests, among other things. ## **Feature Description:** The dataset has around 119390 observations in it with 32 columns and it is mix between categorical and numerical values * Hotel ○ Resort hotel ○ City hotel * is_canceled ○ 1: Cancelled ○ 0: Not cancelled * lead_time ○ No of days that elapsed between entering date of booking into property management system and arrival date * arrival_date_year ○ Year of arrival date (2015-2017) * arrival_date_month ○ Month of arrival date (Jan - Dec) * arrival_date_week_numberr ○ Week number of year for arrival date (1-53) * arrival_date_day_of_month ○ Day of arrival date * stays_in_weekend_nights ○ No of weekend nights (Sat/Sun) the guest stayed or booked to stay at the hotel * stays_in_week_nights ○ No of week nights (Mon - Fri) the guest stayed or booked to stay at the hotel * Adults * Children * Babies * meal ○ Type of meal booked. Undefined/SC – no meal package; ○ BB – Bed & Breakfast; ○ HB – Half board (breakfast and one other meal – usually dinner); ○ FB – Full board (breakfast, lunch and dinner) * country * market_segment (a group of people who share one or more common characteristics, lumped together for marketing purposes) ○ TA: Travel agents ○ TO: Tour operators * distribution_channel (A distribution channel is a chain of businesses or intermediaries through which a good or service passes until it reaches the final buyer or the end consumer) ○ TA: Travel agents ○ TO: Tour operators * is_repeated_guest (value indicating if the booking name was from repeated guest) ○ 1: Yes ○ 0: No * previous_cancellations ○ Number of previous bookings that were cancelled by the customer prior to the current booking * previous_bookings_not_canceled ○ Number of previous bookings not cancelled by the customer prior to the current booking * reserved_room_type ○ Code of room type reserved. Code is presented instead of designation for anonymity reasons. * assigned_room_type ○ Code for the type of room assigned to the booking. Sometimes the assigned room type differs from the reserved room type due to hotel operation reasons (e.g., overbooking) or by customer request. Code is presented instead of designation for anonymity reasons. * booking_changes ○ Number of changes/amendments made to the booking from the moment the booking was entered on the PMS until the moment of check-in or cancellation * deposit_type ○ Indication on if the customer made a deposit to guarantee the booking. This variable can assume three categories: No Deposit – no deposit was made; Non- Refund – a deposit was made in the value of the total stay cost; Refundable – a deposit was made with a value under the total cost of stay. * agent -ID of the travel agency that made the booking * company ○ ID of the company/entity that made the booking or responsible for paying the booking. * day_in_waiting_list ○ Number of days the booking was in the waiting list before it was confirmed to the customer. * customer_type: ○ Contract - when the booking has an allotment or other type of contract associated to it; ○ Group – when the booking is associated to a group; ○ Transient – when the booking is not part of a group or contract, and is not associated to other transient booking; ○ Transient-party – when the booking is transient, but is associated to at least other transient booking * adr (average daily rate) * required_car_parking_spaces ○ Number of car parking spaces required by the customer * total_of_special_requests ○ Number of special requests made by the customer (e.g. twin bed or high floor) * reservation_status ○ Cancelled – booking was cancelled by the customer; ○ Check-Out – customer has checked in but already departed; ○ No-Show – customer did not check-in and did inform the hotel of the reason why * reservation_status_date ○ Date at which the last status was set. ## **Steps involved doing this project:-** Loading Data Load python libraries Load dataset Cleaning dataset Analyzing and Visualizing the Data ## **Conclusion:** 1. Majority of the hotels booked are city hotel. 2. Majority of the guests are from Western Europe.So target this area for more customers. 3. 2016 showed the highest rate of hotel bookings.(data from 2015-2017) 4. We should also target months between May to Aug.Those are peak months due to the summer period. 5. No deposit policies lead to a higher cancellation rates. 6. Since there are very few repeated guests,focus should be on retaining the customers after their first visit. 7. The majority of reservations convert into successful transactions. 8. More bookings occured on weekdays vs weekends. 9. Most bookings came from independent,transient customers.

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