FinalProject

Other possible questions/ topics/ ideas:

• Can we identify neighborhood clusters with similar listing patterns and pricing strategies?
• How do host attributes (e.g., superhost status, response rate, verifications) relate to listing prices and occupancy rates?
• What are the most important factors contributing to positive or negative guest reviews, and how do review scores influence listing demand?
• Mapping out areas with high demand, high prices, and high availability.
• How do prices and availability change with seasons?

Modeling Methods:

• Exploratory Data Analysis and Visualization: Analyze the distribution of variables, identify patterns, and visualize relationships between features and target variables.
• Feature Engineering: Create new features or transform existing ones to better capture relevant information (e.g., neighborhood clusters, time features).
• Regression Techniques: Apply basic regression techniques, tree-based methods (e.g., Decision Tree), and gradient boosting models to model and predict listing prices based on relevant features.
• Classification Techniques: Use basic classification algorithms, tree-based algorithms like decision trees, or ensemble methods (e.g., Random Forest, Gradient Boosting) to classify listings based on relevant features.
• Clustering Techniques: Apply k-means, hierarchical, or density-based clustering algorithms to group listings or neighborhoods based on similarities.
• Dimensionality Reduction: Use PCA to simplify the data and identify the most important features affecting price.