Predicting a Hotel Booking Demand

Predicting a Hotel Booking Demand

Here in this article, I want to write about how to build a prediction model using Python with Jupyter Notebook. The data that I am using for this experiment is the Hotel Booking Demand dataset from Kaggle. In this article, I will only show you the modeling stage, with just the Logistic Regression model, but you can access the full document, including the Data Cleaning, Preprocessing, and Exploratory Data Analysis on my Github.

Here in this article, I want to write about how to build a prediction model using Python with Jupyter Notebook. The data that I am using for this experiment is the Hotel Booking Demand dataset from Kaggle.

In this article, I will only show you the modeling stage, with just the Logistic Regression model, but you can access the full document, including the Data Cleaning, Preprocessing, and Exploratory Data Analysis on my Github.

Without further ado, let’s do it.

Importing Libraries

Importing the Libraries

Loading the Dataset

Loading the Dataset

And here is how the dataset looks.

Image for post

Dataset

It has 32 columns, and the full version of it is:

['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',
       'arrival_date_month', 'arrival_date_week_number',
       'arrival_date_day_of_month', 'stays_in_weekend_nights',
       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',
       'country', 'market_segment', 'distribution_channel',
       'is_repeated_guest', 'previous_cancellations',
       'previous_bookings_not_canceled', 'reserved_room_type',
       'assigned_room_type', 'booking_changes', 'deposit_type', 'agent',
       'company', 'days_in_waiting_list', 'customer_type', 'adr',
       'required_car_parking_spaces', 'total_of_special_requests',
       'reservation_status', 'reservation_status_date']

predictions hotel data data-science machine-learning

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