Hotel Booking demand prediction Using ML

Keywords: Hotel booking,machine learning, classification,algorithm

Background

The hotel industry is a dynamic and competitive sector that heavily relies on accurate demand forecasting and booking classification. Understanding and predicting hotel booking patterns can help hotels optimize their operations, improve revenue management strategies, and enhance customer satisfaction. This abstract provides an overview of the classification methods used to analyse hotel booking demand and predict customer behaviour. The study focuses on the application of machine learning and data analysis techniques to classify hotel bookings into different categories based on various factors, including customer demographics, booking channels, seasonality, and length of stay. By analysing historical data and extracting relevant features, predictive models can be developed to classify future bookings and provide insights into customer preferences and trends

Aim & Objectives

Aim
The main aim of this project is predicting the hotel booking patterns and predicts the customer’s behaviour. Using the machine learning techniques
Objective
• Data Cleaning and Pre-processing.
• Outlier Detection and imputing the outliers
• Perform the Statistical testing’s
• Build the required machine learning model

Research Methodology

Various classification Algorithms like KNN,RF,SVM,DT,XGBoost

Software & Tools

Jupyter, Google Collab

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