Predictive modeling of hotel booking cancellations. Handling complex data preprocessing, feature engineering, and noise. Evaluating models for high F1-score and analyzing key features for interpretability.

This repository contains a data science project focused on predicting hotel booking cancellations. Additionally, it includes a web application built with Streamlit for user interaction and visualization of the prediction results.

Hotel Booking Cancellation predicition

Predict hotel booking cancellations using ML techniques. By analyzing historical booking data, the model helps hotels proactively manage cancellations, optimize revenue strategies, and improve operational efficiencies.

Booking cancellation prediction using hotel bookings data. Coded with Python on Jupyter Notebook.

Hotel Booking cancellations prediction

Hotel Booking Cancellation Prediction: A machine learning model that predicts the likelihood of hotel booking cancellations, deployed as a real-time web service using FastAPI and Docker. Enhance your hotel's operational efficiency, revenue optimization, and customer experience with data-driven decisions.

Predicting the chances of a Hotel Booking getting canceled with the help of Machine Learning techniques.

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 cancellations can cause issues for many businesses in the industry. Not only is there the lost revenue as a result of the customer cancelling, but this can also cause difficulty in coordinating bookings and adjusting revenue management practices. Data analytics can help to overcome this issue, in terms of identifying the customers who are most likely to cancel — allowing a hotel chain to adjust its marketing strategy accordingly. To investigate how machine learning can aid in this task, the ExtraTreesClassifer, logistic regression, and support vector machine models were employed in Python to determine whether cancellations can be accurately predicted with this model. For this example, both hotels are based in Portugal. The Algarve Hotel dataset available from Science Direct was used to train and validate the model, and then the logistic regression was used to generate predictions on a second dataset for a hotel in Lisbon.

Binary prediction for hotel booking cancellation.

Hotel Booking cancellation Prediction Project

Prediction of bookings cancellation in hotels

In recent years there has been a rapid increase in hotel cancellations. More and more frequently, guests tend to cancel their room reservation. In many cases the cancellation is free of charge up to 24 hours before arrival. Cancellations often present a big challenge for hotel managers. Especially since the rise of online travel agents such as Booking.com, Expedia and Co. it became more and more common to enforce free cancellations up to 24 hours before arrival. On one hand, this policy brings a new dimension of flexibility to hotel guests, on the other hand, it means an increasing financial risk for hotels and difficulties while planning their occupancy rate. Due to these developments, it got more and more important to analyze cancellation behavior of hotel guests and find a pattern in order to create a forecast.

The Hotel Booking Prediction project will explain the workflow for assessing someone, whether to cancel the booking or not. This assessment is based on a machine learning algorithm, which will provide predictions to the customer who makes a booking, then from the data provided it will be predicted about the cancellation. This project also contains various analyzes obtained through EDA and provides various insights on hotels to develop their business more effectively.

Predicting whether the user session lead to a booking of the hotel on a host website.

Machine learning project focused on predicting hotel room cancellations to optimize room availability and prevent overbooking. Includes data preprocessing, model building, and hyperparameter tuning to improve accuracy and precision.

This repository houses an artificial neural network (ANN) model developed specifically to tackle the hotel booking classification problem. The model is trained to predict whether a hotel booking will be canceled or not.

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Prediction of hotel booking cancellations (IITM MLP KA2)

This project aim to predict the possibility of a hotel customer to cancel a booking he/she has made. I was using booking data from a hotel in Portugal that is used to take insight and design a simple machine learning model.

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

The objective of the project is to predict the hotel booking status of the guest if it'll be canceled or not based on the various features like ADR (Average Daily Rate), booking changes, lead time, type of the hotel booked, and more. The Exploratory Data Analysis and Statistical Analysis is done for insights and feature engineering. Four Machine learning and Deep Learning Models are trained for that purpose.

Random forest algorithm can be used to analyze hotel booking data and predict booking behavior. This allows hotels to optimize pricing strategies, staffing, and identify potential cancellations for proactive guest communication.

• Researched and Performed Exploratory Data Analysis on 70k+ rows to identify potential gaps and KPI’s in marketing strategies for reducing the cancellation rate. • Developed predictive analytics solution to determine the likelihood of canceling a booking, generating insights for proactive recommendations. • By enhancing SVM we determined and trained the machine learning model to more than 75% balanced accuracy, and provided recommendations based on those determinants. • Developed Business Decisions, Strategies and presented to airline executives which helps to increase customer satisfaction and escalated profits.

"10-Step ML Model Lifecycle: Hotel Booking Prediction" - Jupyter Notebook project using Python. Explores Logistic Regression, Naive Bayes, Random Forest, KNN, Decision Tree for accurate hotel booking predictions. Complete model lifecycle, from data preprocessing to algorithm comparison. Contributions welcome!

In-depth data analytics on Hotel booking demand dataset | Booking cancellation prediction | Time series analysis for forecasting

Revenue Management with Hotel Booking Cancellation Predictions using a Machine Learning Classification Model; written in Python.

This project explores ML techniques across classification and regression. It includes penguin species classification, breast cancer prediction, and baseball performance prediction using regularization. After, I will develop an XGBoost model for hotel cancellation prediction, analyzing key booking factors and optimizing performance. (In Progress)

The aim is to develop an ML- based predictive classification model (logistic regression & decision trees) to predict which hotel booking is likely to be canceled. This is done by analysing different attributes of customer's booking details. Being able to predict accurately in advance if a booking is likely to be canceled will help formulate profitable policies for cancelations & refunds.