mohdshahgul/hotel-reservation-cancellation-prediction

The "Hotel Booking Cancellation Prediction" project is designed to address the needs of hotel owners seeking to optimize their operations and decision-making processes. The primary objective is to develop a robust machine learning model capable of predicting whether a customer is likely to honor or cancel their reservation.

Hotel Booking Cancellation Prediction

Objective

The primary goal of this project is to develop a machine learning model that can assist hotel owners in predicting whether a customer is likely to honor their reservation or cancel it. By understanding the likelihood of cancellations, hotel owners can make more informed decisions and optimize their operations.

Repository Contents

Jupyter File:

The Jupyter file (.ipynb) contains the Python code for data preprocessing, exploratory data analysis (EDA), feature engineering, model training, and evaluation. It is the core file where the machine learning model is developed.

Dataset - hotel_booking.csv

The hotel_booking.csv file contains the dataset used for training and evaluating the machine learning model. It includes information about hotel reservations and whether they were canceled or not. The dataset is essential for building a predictive model as it provides the necessary features and labels for training.

CSS for HTML Deployment:

This folder contains the Cascading Style Sheets (CSS) files that define the presentation style of the HTML file. These styles enhance the visual appeal and layout of the HTML deployment.

HTML File:

The HTML file serves as a user-friendly interface for deploying and interacting with the machine learning model. The HTML file might include forms for user input and display predictions.

Exported Model (.pkl file):

This file contains the trained machine learning model exported in a serialized format, commonly a .pkl file. This file can be loaded in another script or application for making predictions without the need to retrain the model.

Usage

Jupyter File:

Open the Jupyter file in a Jupyter Notebook environment or an Integrated Development Environment (IDE) that supports Python. Execute the cells in sequential order to run the code and train the machine learning model.

CSS for HTML Deployment:

These files are used to style the HTML interface. If deploying the model using HTML, ensure that these files are linked appropriately.

HTML File:

Deploy the HTML file using a web server or a local development environment. This interface allows users to input relevant information, and the trained model predicts the likelihood of a reservation being canceled.

Exported Model (.pkl file):

Use this file in other scripts or applications to load the trained model and make predictions.

Conclusion

This project aims to provide a practical tool for hotel owners to enhance their decision-making process regarding reservations. The machine learning model, trained on historical data, can offer valuable insights into the likelihood of booking cancellations. Users can deploy the model through the provided HTML interface for a user-friendly experience. The repository serves as a comprehensive package, including code, styling, and the trained model, making it accessible for both development and practical use.