基于 Hotel bookings dataset 的探索性数据分析(EDA)，并建立模型以预测预订取消情况

HOTEL PREDICTION PROJECT DESCRIPTION ## Problem Statement This model predicts the probability of a customer will cancel a booking before checking in the hotel. It would be nice for the hotels to have a model to predict if a guest will actually come. This can help a hotel to plan things like personnel and food requirements. Maybe some hotels also use such a model to offer more rooms than they have to make more money. ## Dataset Information: This data set 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. ## Attribute Information: * Hotel: Hotel (H1 = Resort Hotel or H2 = City Hotel) * is_canceled: Value indicating if the booking was cancelled (1) or not (0) * lead_time: Number of days that elapsed between the entering date of the booking into the PMS and the arrival date * arrival_date_year: Year of arrival date * arrival_date_month: Month of arrival date * arrival_date_week_number: Week number of year for arrival date * arrival_date_day_of_month: Day of arrival date * stays_in_weekend_nights: Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel * stays_in_week_nights: Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel * adults: Number of adults * children: Number of children * babies: Number of babies * meal: Type of meal booked. Categories are presented in standard hospitality meal packages: 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: Country of origin. Categories are represented in the ISO 3155–3:2013 format * market_segment: Market segment designation. In categories, the term “TA” means “Travel Agents” and “TO” means “Tour Operators” * distribution_channel: Booking distribution channel. The term “TA” means “Travel Agents” and “TO” means “Tour Operators” * is_repeated_guest: Value indicating if the booking name was from a repeated guest (1) or not (0) * 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. ID is presented instead of designation for anonymity reasons * days_in_waiting_list: Number of days the booking was in the waiting list before it was confirmed to the customer * customer_type: Type of booking, assuming one of four categories: 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 as defined by dividing the sum of all lodging transactions by the total number of staying nights * 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: Reservation last status, assuming one of three categories: Canceled – 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. This variable can be used in conjunction with the Reservation Status to understand when was the booking cancelled or when did the customer checked-out of the hotel ## Libraries Used: * pandas * numpy * matplotlib * plotpy * folium * sklearn * eli5 ## Machine Learning Models Used: (1) Random Forest Classifier (2)Logistic Regression ## Steps Involved: (1).Data Import (2). Exploratory Data Analysis (EDA) (3). Model Building (4).Prediction part

Analysis of Hotel Booking Cancellation dataset using statistical model and provide predictions on future booking cancelations using Logistic regression model.

In this project, data analysis is conducted on a real-world dataset named Hotel Bookings Demand to investigate patterns on hotel booking behaviors. The dataset is the booking records provided by two hotels, one located in city and the other located in resort, in Portugal from 2015 to 2017. It is of great importance for hotel managing purpose to figures out the answers to following two questions: (i) How to predict whether a customer would cancel the booking or not? (ii) Are there any patterns in customers’ booking behaviors, by which they can be segmented into groups that enable the managing team to propose distinct promotions? Hence, customer segmentation and cancelation prediction are investigated. Customer segment is done by dimension reduction via t-SNE, followed by hierarchical clustering. Cancelation prediction is done by applying Random Forest Classification. It is found that there are 7 customer groups with distinct booking patterns. The cancelation prediction has achieved an accuracy of 80% and 79% for train and test set, respectively.

Analysis and prediction of hotel booking dataset

Analysis of Hotel Booking Cancellation dataset using statistical model and provide predictions on future booking cancelations using Logistic regression model.

Comprehensive analysis of hotel pricing and booking behavior using Big Data and machine learning. Integrates JSON and CSV datasets, performs data cleaning, visualization, and price prediction with PySpark and Scikit-Learn, offering insights into hotel characteristics and booking trends.

Prediction and Exploratory Data Analysis of the Hotel Booking Deman with Python According to Antonio et al,.(2019) the Booking dataset composes of two hotel demand data. (H1 in Portugal) is a resort hotel and (H2 in the city of Lisbon) a city hotel. Each observation represents a hotel booking from 1rst of Jult 2015 to 31st of August 2017 including successful and canceled visit. Prediction The purpose of this project is to predict a hotel booking demand using the Hotel dataset. It can be downloded in links below: https://www.kaggle.com/jessemostipak/hotel-booking-demand and also from https://www.sciencedirect.com/science/article/pii/S2352340918315191#ec0010 Variables description.

This repository is the Final Data Science Project at Dibimbing.id. This project contains the results of analysis of the dataset of hotel booking/reservation history observations related to predictions of prospective guests who cancel reservations. This analysis involves Exploratory Data Analysis (EDA) and Machine Learning Classification.

This repository contains a group project for the Machine Learning course, focusing on the application of Neural Networks for analysis and prediction using multiple public datasets, including Online Retail II, Heart Failure Clinical Records, Air Quality, and Hotel Booking Demand.