Introduction
In this case study, I explore how to predict Airbnb prices in Sydney using various features such as location, property attributes, and host characteristics. By building a machine learning model, I aim to uncover the key drivers behind listing prices and help hosts optimize their pricing strategy.
Objective
The goal of the project is to develop a machine learning model that can predict the price of Airbnb listings in Sydney, Australia, based on features like location, number of bedrooms, and available amenities.
Data Exploration
I conducted an exploratory data analysis (EDA) to understand the distribution of the key features in the dataset and how they correlate with prices.
Histograms of Key Attributes
- Minimum Nights: The majority of listings have a minimum night requirement of less than 100 nights, which suggests that Airbnb listings in Sydney are generally used for short-term stays.
- Security Deposit: The security deposit shows a highly skewed distribution, with most listings requiring no deposit or a very low deposit, while a few listings require large deposits.
- Cleaning Fee: Most listings have cleaning fees below $200, with a long tail of listings charging more. This may indicate that high-end or larger properties are charging extra for cleaning services.
- Accommodates: The number of guests that a listing can accommodate is concentrated between 2–4 guests, which likely reflects the common size of apartments and small homes in Sydney.
- Bedrooms: The number of bedrooms is heavily skewed, with most listings having 1–2 bedrooms.
- Bathrooms: Similar to bedrooms, most listings have 1–2 bathrooms.
Scatter Plot of Listings by Location
The scatter plot shows that listings are denser in central Sydney, and prices are likely higher in these clusters due to proximity to key locations like the Opera House and other tourist attractions.
Improved Visualization
The improved visualization color-codes the points based on the number of reviews for each listing. Listings with more reviews tend to be in higher-density areas closer to the city center, and they often have higher prices. This suggests that location and reputation (indicated by the number of reviews) are important factors in determining Airbnb pricing.
Correlation Analysis
Understanding the relationship between different attributes is crucial for building a predictive model.
Housing Prices Scatterplot
As expected, the scatter plot shows that listings with more bedrooms tend to have higher prices. However, there are some outliers, indicating that factors other than the number of bedrooms (such as luxury amenities or location) might play a significant role in pricing.
Correlation Matrix
The scatter matrix plot shows the pairwise relationships between variables like price, accommodates, bedrooms, and review scores.
- Price and Accommodates: Listings that can accommodate more guests tend to have higher prices, though there is variability.
- Price and Review Score: Higher review scores are correlated with higher prices, suggesting that quality and reputation may allow hosts to charge more.
- Bedrooms and Accommodates: Listings with more bedrooms are able to accommodate more guests, but the correlation is not perfectly linear, as other factors like the size of rooms or the availability of extra beds can impact this.
Key Takeaways
- Location: Properties closer to central Sydney and tourist hotspots command higher prices.
- Size: Larger properties (with more bedrooms and bathrooms) charge significantly more, likely due to their ability to accommodate more guests.
- Amenities: Additional features like cleaning services and security deposits are associated with higher rental prices, as they cater to a more luxury-oriented clientele.
- Host Reputation: Listings with more reviews tend to have higher prices, possibly due to trust factors and better service quality.
Model Building
After the exploratory analysis, I built several machine learning models to predict Airbnb listing prices. I experimented with:
- Linear Regression
- Decision Trees
- Random Forest
- Gradient Boosting
Conclusion
This project demonstrates how machine learning can be applied to predict Airbnb prices effectively. By analyzing various features, I was able to identify the key drivers of listing prices. Hosts looking to optimize their pricing strategy should focus on location, property size, and offering premium amenities. Future improvements could include analyzing seasonal trends and incorporating external factors like demand during events.
Code, Queries & Documentation
Find the complete code, and documentation on my GitHub: