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Competition - predict hotel cancellation
  • AI Chat
  • Code
  • Report
  • The Data

    They have provided you with their bookings data in a file called hotel_bookings.csv, which contains the following:

    ColumnDescription
    Booking_IDUnique identifier of the booking.
    no_of_adultsThe number of adults.
    no_of_childrenThe number of children.
    no_of_weekend_nightsNumber of weekend nights (Saturday or Sunday).
    no_of_week_nightsNumber of week nights (Monday to Friday).
    type_of_meal_planType of meal plan included in the booking.
    required_car_parking_spaceWhether a car parking space is required.
    room_type_reservedThe type of room reserved.
    lead_timeNumber of days before the arrival date the booking was made.
    arrival_yearYear of arrival.
    arrival_monthMonth of arrival.
    arrival_dateDate of the month for arrival.
    market_segment_typeHow the booking was made.
    repeated_guestWhether the guest has previously stayed at the hotel.
    no_of_previous_cancellationsNumber of previous cancellations.
    no_of_previous_bookings_not_canceledNumber of previous bookings that were canceled.
    avg_price_per_roomAverage price per day of the booking.
    no_of_special_requestsCount of special requests made as part of the booking.
    booking_statusWhether the booking was cancelled or not.

    Source (data has been modified): https://www.kaggle.com/datasets/ahsan81/hotel-reservations-classification-dataset

    import pandas as pd
    hotels = pd.read_csv("data/hotel_bookings.csv")
    hotels

    The Challenge

    • Use your skills to produce recommendations for the hotel on what factors affect whether customers cancel their booking.

    Note:

    To ensure the best user experience, we currently discourage using Folium and Bokeh in Workspace notebooks.

    Judging Criteria

    CATEGORYWEIGHTINGDETAILS
    Recommendations35%
    • Clarity of recommendations - how clear and well presented the recommendation is.
    • Quality of recommendations - are appropriate analytical techniques used & are the conclusions valid?
    • Number of relevant insights found for the target audience.
    Storytelling35%
    • How well the data and insights are connected to the recommendation.
    • How the narrative and whole report connects together.
    • Balancing making the report in-depth enough but also concise.
    Visualizations20%
    • Appropriateness of visualization used.
    • Clarity of insight from visualization.
    Votes10%
    • Up voting - most upvoted entries get the most points.

    Checklist before publishing

    • Rename your workspace to make it descriptive of your work. N.B. you should leave the notebook name as notebook.ipynb.
    • Remove redundant cells like the judging criteria, so the workbook is focused on your work.
    • Check that all the cells run without error.

    Time is ticking. Good luck!

    hotels.info()