It is an End-to-end MLOps pipeline for hotel reservation prediction leveraging GCP, LightGBM, MLFlow, Jenkins and Docker. This system automates data ingestion, preprocessing, feature selection, model training, and tracking, with a Flask-based frontend for real-time inference.

Hotel Reservation Cancellation Prediction

No description available

Hotel Reservation Cancellation Prediction using MLFlow, Jenkins and GCP

Utilizing machine learning algorithms in Python to predict hotel reservation cancellations. The goal is to determine whether a hotel booking is likely to be canceled based on various booking features. This project aims to improve hotel management strategies and reduce cancellation-related losses.

End-to-end MLOps pipeline for a Hotel Reservation Cancellation Prediction using Docker, Jenkins, Kubernetes, MLflow, Kubeflow, Prometheus, and Grafana on GCP.

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

Hotel Reservations ML prediction

Hotel reservation cancellation prediction

Hotel Reservation Cancellation Prediction

Hotel Reservations Prediction with R

Hotel Reservation Prediction using XGBoost

Hotel Reservations Predictions using Logistic Regression

Hotel Reservation Prediction - Machine learning model capable of predicting the cost per room in a hotel reservation. API service developed to load the trained model from S3 and perform inference.

No description available

No description available

No description available

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

No description available

No description available

No description available

Hotel Reservation Prediction is a smart machine learning tool for hotels and travel businesses. It predicts if a guest will cancel their booking using key reservation details—helping you plan better, set prices smarter, and manage resources more efficiently.

Multiple models implemented to check if hotel reservations will be canceled based on features. Dataset extracted by Kaggle.

An MLOps project for predicting hotel reservation cancellations using LightGBM. It includes an automated Jenkins CI/CD pipeline, MLflow experiment tracking, Docker containerization, and a Flask web app for making live predictions from a cloud-hosted dataset on GCP.

No description available

No description available

No description available

No description available

No description available

This project focuses on predicting customer reservation cancellations, involving essential data cleaning, exploratory analysis, and model training. Notably, the RandomForestClassifier exhibited impeccable accuracy, reaching 100%.