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 Prediction Project

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.

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 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.

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

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FDM Mini Project - Hotel Booking Cancellation Prediction System

A data science group project to predict hotel booking cancellations for both resort and city hotel

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Hotel Booking Analysis & Prediction is a machine learning project aimed at predicting hotel booking cancellations. It involves data cleaning, exploratory data analysis, and model training using various algorithms

Hotel Booking Cancellation Prediction is a data science project developed for the Introduction to Data Science (IDS f24) course. The project explores hotel booking data, performs exploratory data analysis (EDA), trains a Random Forest machine learning model, and deploys an interactive Streamlit web application to predict booking cancellations.

A machine learning pipeline project for hotel booking cancellation prediction using Genetic Algorithm feature selection and MLP neural network that includes a web application built with Flask for real-time cancellation risk assessment.

This project explores the prediction of hotel booking cancellations using data wrangling, exploratory data analysis (EDA), preprocessing techniques, and various machine learning models, including decision trees, random forests, linear regression, XGBoost, AdaBoost, gradient boosting, and k-nearest neighbors classifier.

Hotel Reservation Prediction project leverages ML to predict booking cancellations, automating data ingestion, model training, and hyperparameter tuning with MLflow. Jenkins handles CI/CD, while Docker ensures portability. Deployed on GKE, it scales for real-time predictions, offering a robust solution with seamless automation and cloud integration

The goal of this project is to build a robust data preprocessing pipeline for a hotel booking cancellation prediction model. The focus is not on training the final machine learning model but on ensuring that the dataset is clean, consistent, and ML-ready.

This project provides a machine learning solution to predict hotel booking cancellations using a Random Forest model. The system includes a Flask web application for uploading CSV files, processing data, and generating predictions, along with a Jupyter notebook detailing the data analysis and model training process.

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Hotel booking cancellation prediction project

Hotel Booking Cancellation Prediction Project

Hotel booking cancellation prediction project

python project "Hotel Booking Cancellation Prediction"

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Using machine learning techniques from Sklearn package to classify booking cancellations

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End-to-end hotel booking analytics using SQL & Power BI to analyze trends, revenue, and cancellation patterns.