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.

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.

Developed a predictive model for hotel booking status using historical data. 2. Conducted data preprocessing, feature engineering, and model selection. 3. Documented project process and findings for reproducibility. 4. Utilized Python, pandas, scikit-learn, and Jupyter Notebook.

Project: Hotel Booking Prediction System (Learn Classification problem)

Machine Learning (Classification) and Data Analysis Problem. Hotel booking cancellation prediction

Prediction the hotel booking. Project descriptionPrediction the hotel booking whether : booking is going to cancel or not with ML classification models.

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.

This GitHub repository hosts a predictive analytics case study aimed at forecasting hotel booking cancellations. It includes EDA, machine learning models (KNN, Decision Trees), and SMOTE for balancing classes. Complete with code, datasets, and a report, it serves as a resource for understanding data science applications in hotel booking management.

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Machine learning model for INN Hotels Group to predict booking cancellations using EDA, feature engineering, and classification (LogReg, SVM, Decision Tree, Random Forest). Achieved 90% accuracy; provided actionable business insights to reduce revenue loss.

solving prediction of hotel booking cancelation by using machine learning classification models

This project uses machine learning classification algorithms—Random Forest and XGBoost—to predict hotel booking statuses. The best-performing model is deployed as an interactive web application using Streamlit for real-time customer booking predictions.

Hotel demand prediction is a classification problem statement where the model predicts whether the Hotel booking is cancelled or not

Supervised Classification Machine Learning Model Building #1.2 : Improvement to the previous project of hotel booking cancellation prediction

Hotel Booking Cancelations Prediction notebook. Machine learning techniques: supervised learning and classification using Logistic Regression, K-NN, LDA and QDA

A binary classification model to predict whether a hotel guest will cancell their reservations. Analyze, useful plots, dataset, model training and predictions included. Dataset is from: kaggle.com (https://www.kaggle.com/datasets/youssefaboelwafa/hotel-booking-cancellation-prediction)

Groceries market basket analysis using Association rule mining methods and Hotel booking prediction system using classification model. Deploy with the Flask framework and integrate as a one web Application.

This project is a machine learning-based implementation for predicting hotel booking statuses (canceled or confirmed) using various classification algorithms. The model is trained on a dataset of hotel reservations and leverages several preprocessing techniques to enhance prediction accuracy.

This project utilizes the Random Forest algorithm on a hotel reservation dataset from Kaggle, built using the Streamlit framework. The system provides classification and prediction features, analyzing booking patterns to predict reservation statuses and improve decision-making in hotel management.

End-to-end pipeline for hotel booking cancellation prediction and ADR forecasting. Includes data cleaning, feature engineering, time-based validation, supervised classification, regression, and customer segmentation with K-Means. Tech stack: Python, scikit-learn, pandas, Seaborn.

A machine learning-based hotel booking prediction system that leverages LightGBM for classification and MLflow for model tracking. The system is containerized using Docker and deployed on Google Cloud Platform (GCP) for scalability and ease of deployment.

In this project I analyze a hotel booking data set. I apply Random Forest, K-Means, and NN for prediction and classification/clustering of clients and their decisions. More specifically, I focus on the aspect of cancellation of reservations.

This project predicts hotel booking cancellations using various machine learning models. The repository includes Jupyter notebooks that implement Decision Trees, Ensemble Learning methods, and several other classification models. It also contains a streamlit based Python app for making real-time predictions.

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.