BnBGenie - Unlocking Airbnb with EDA & ML Insights
Conducted EDA and applied ML models to uncover key factors influencing Airbnb listings, optimal pricing strategies, and demand trends.
BnBGenie: Unlocking Airbnb with EDA & ML Insights
🔍 GitHub Repository: BnBGenie on GitHub
Project Overview
- Conducted comprehensive Exploratory Data Analysis (EDA) on 38,000+ Airbnb listings, identifying key factors affecting listing appeal (e.g., room types, amenities, and location).
- Applied Machine Learning models including Linear Regression, XGBoost, and Random Forest to predict optimal pricing strategies, determining that XGBoost provided the highest R-squared value.
- Performed sentiment analysis on guest reviews and time-series analysis of demand trends, offering actionable insights for both hosts and tourists regarding pricing and listing features.
🔹 Key Insights & Impact
- Improved Pricing Strategies: Identified pricing factors that significantly impact booking success.
- Enhanced Guest Experience: Sentiment analysis revealed patterns in guest reviews, allowing hosts to optimize their listings.
- Market Demand Analysis: Time-series modeling uncovered seasonal trends affecting Airbnb occupancy rates.