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Competition - predict hotel cancellation
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  • Predicting Hotel Cancellations

    🏨 Background

    You are supporting a hotel with a project aimed to increase revenue from their room bookings. They believe that they can use data science to help them reduce the number of cancellations. This is where you come in!

    They have asked you to use any appropriate methodology to identify what contributes to whether a booking will be fulfilled or cancelled. They intend to use the results of your work to reduce the chance someone cancels their booking.

    The Data

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

    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):

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

    The Challenge

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


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

    Judging Criteria

    • 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.
    • 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.
    • Appropriateness of visualization used.
    • Clarity of insight from visualization.
    • 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!

    !pip install -U scikit-learn
    !pip install hyperopt
    import missingno as mso
    import matplotlib.pyplot as plt
    import seaborn as sns
    import scipy.stats as stats
    import warnings
    from sklearn.experimental import enable_iterative_imputer
    from sklearn.impute import IterativeImputer
    R_SEED = 2051
    1. Create df_main as a copy of housing
    2. Cast Booking_ID column as a string dtype
    df_main = hotels.copy()

    Category Type Update

    1. Identify columns in a dataframe as category types
    2. List those columns and its unique values
    3. Cast the selected columns as category type
    #Manually update key features
    df_main['Booking_ID']                 = df_main['Booking_ID'].astype('string')
    df_main['arrival_year']               = df_main['arrival_year'].astype('category')
    df_main['arrival_month']              = df_main['arrival_month'].astype('category')
    df_main['arrival_date']               = df_main['arrival_date'].astype('category')
    def list_ctype(df):
        #List object columns and its unique values
        clist = df.select_dtypes(include=['object']).columns
        for i in clist:
            print(i,':', len(df[i].unique())) 
    def update_as_ctype(df):
        #update object column type as category
        clist = df.select_dtypes(include=['object']).columns
        for i in clist:
                df[i] = df[i].astype('category')
    # Object type to category type