Hotel Machine Learning

🏨 Hotel Booking — Machine Learning Project Developed a Machine Learning project for hotel booking analysis and prediction, using real-world booking data to uncover customer behavior, cancellation patterns, and pricing trends. The project focuses on two main prediction tasks: Cancellation Prediction: Predict whether a hotel booking will be canceled or not, helping hotels identify high-risk bookings and reduce cancellation losses. ADR Prediction: Predict the Average Daily Rate (ADR) of a booking to support smarter pricing and revenue-management decisions. The project includes data preprocessing, exploratory data analysis (EDA), feature engineering, correlation analysis, visualization, model building, and evaluation. 📊 The analysis explored factors such as lead time, market segment, deposit type, previous cancellations, reservation status, guest information, and ADR. 🎯 Model Accuracy: 94% 🛠️ Tools & Technologies: Python • Pandas • NumPy • Matplotlib • Seaborn • Scikit-learn • Jupyter Notebook 💡 Business Impact: The project demonstrates how Machine Learning can help hotels predict cancellation risks, improve pricing strategies, optimize revenue, and make data-driven decisions.