- 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?
- 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.