Business Understanding This project presents the findings of our analysis conducted on a dataset comprising various features of Airbnb listings. We built six predictive models, including linear regression, logistic regression, Random forest, Bagging, Ridge and Lasso, to identify the features most strongly related to perfect rating score (having a 100% rating). The goal of this analysis is to provide valuable insights for individuals and businesses seeking to optimize their Airbnb listings and increase booking rates. Our models offer actionable information that can be utilized to attract potential guests and maximize the revenue potential of Airbnb listings. Target Audience and Potential Users: The predictive models we developed cater to a wide range of individuals and businesses within the Airbnb ecosystem. The target audience includes:

1. Individual Airbnb hosts: Individual hosts can leverage the predictions to gain insights into the factors most strongly associated with perfect rating score. By identifying these features, hosts can optimize their listings, improve their descriptions, and make targeted improvements to their properties to increase bookings.
2. Property management companies: Companies specializing in managing multiple Airbnb listings can utilize the models to identify common characteristics of high-performing properties. This information can guide their acquisition strategies, property improvement investments, and marketing efforts to maximize their revenue potential.
3. Real estate investors: Investors looking to purchase properties for Airbnb rentals can benefit from the predictive models by identifying the key features associated with perfect rating score. This enables them to make informed decisions about property selection and optimization, maximizing their return on investment.
4. Airbnb platform itself: Airbnb can leverage the insights derived from our models to provide recommendations to hosts. By offering tailored suggestions for optimizing listings, Airbnb can enhance the guest experience and increase overall booking rates, resulting in increased revenue for both hosts and the platform. 4 Business Actions and Value Generation The predictions generated by our models offer actionable insights that can drive strategic business actions and generate substantial value for the target audience. Here are some potential actions and value propositions based on the output of the models:
5. Listing optimization: Hosts can leverage the model predictions to identify the features that have the greatest impact on perfect rating score. By emphasizing these features in their listings, hosts can increase their attractiveness to potential guests and ultimately boost their booking rates and revenue.
6. Property improvements: Hosts and property management companies can invest in targeted property improvements based on the features identified as significant predictors of perfect rating score. For example, if amenities such as swimming pools or outdoor spaces are found to be strong predictors, hosts can consider investing in these areas to enhance their property's appeal.
7. Pricing strategy: Hosts can adjust their pricing strategies based on the model predictions. If certain features are strongly associated with perfect rating score, hosts can consider charging a premium for listings that possess those features, maximizing their revenue potential.
8. Marketing and guest targeting: Property management companies and hosts can utilize the identified features to tailor their marketing efforts and target specific guest segments. By understanding the preferences and priorities of guests associated with perfect rating score, targeted marketing campaigns can be developed to attract similar individuals or demographics.

Note: Refer to file - 'Airbnb listing rating prediction project report.pdf' for more details