project Enhancing Hotel Reservation Accuracy with Machine Learning: A Predictive Model

In today's competitive hospitality industry, efficient management of hotel reservations and accurate forecasts of customer behavior are key to improving revenue and reducing cancellations. I'm excited to share my project, where I built a machine learning model to predict hotel booking outcomes using a real dataset. The project involved: Data Preprocessing: Handling missing values, encoding categorical features, and normalizing data. Outlier Detection: Utilizing Elliptic Envelope for robust outlier detection and removal to improve model performance. Model Training & Tuning: I applied various machine learning algorithms such as Logistic Regression, Decision Trees, Random Forest, and Gradient Boosting. I also used hyperparameter tuning techniques like GridSearchCV to achieve the best model performance. Balancing the Dataset: Implemented Random Oversampling to address class imbalance and ensure accurate predictions. Evaluation: Evaluated the models using cross-validation, confusion matrix, ROC-AUC curves, and accuracy metrics, optimizing for high accuracy and recall. This project demonstrates the potential of predictive analytics in transforming how hotels manage reservations, allowing for better decision-making, and reducing customer churn.