Project Structure app_clustering.py: Web application for clustering guests based on booking behaviors using KMeans. app_classification.py: Web application for predicting booking cancellations using decision tree classification. decision_tree_model.pkl: Pre-trained Decision Tree model used for predicting cancellations. Jupyter Notebooks: Clustering.ipynb: Uses KMeans clustering to segment hotel guests based on their booking behavior. DecisionTreeClassification.ipynb: Implements Decision Tree classification to predict whether bookings will be canceled. EnsembleLearningMethods.ipynb: Demonstrates various ensemble learning techniques for classification. ExtraTreesOverFitting.ipynb: Focuses on overfitting issues using Extra Trees. IterativeImputerModel.ipynb: Demonstrates how to handle missing data. SeveralClassificationModels.ipynb: Implements and compares multiple classification models (Logistic Regression, Decision Trees, etc.). requirements.txt: Contains the Python packages required to run the project. Techniques Used Clustering

Goal: To segment hotel guests based on their booking behaviors and preferences, using unsupervised learning techniques. Model: KMeans Clustering, which groups customers into distinct clusters. Purpose: Helps in recognizing customer groups and tailoring marketing strategies for each group. Classification Goal: To predict guest behaviors such as cancellations and repeat bookings using supervised learning techniques. Model: Decision Tree with high accuracy. Purpose: Predicting guest cancellations helps the hotel adjust its operations, improving occupancy rates and minimizing the financial impact of last-minute cancellations. Machine Learning Models Clustering: KMeans: Used for clustering guests into distinct segments based on their booking behavior. Classification: Decision Tree: The primary model used for predicting cancellations, with high accuracy. Other Models Explored: Random Forest Extra Trees Ensemble Learning Methods Technologies Python: Core programming language. Scikit-learn: For building machine learning models. Pandas, NumPy: For data preprocessing. Matplotlib, Seaborn: For data visualization.