Airbnb is a global online housing marketplace, popular with local and foreign tourists. The platform's popularity has grown significantly over the years, with millions of hosts and guests using Airbnb for their travel needs.
The aim of this project is to create a recommender system that will enable stakeholders to have a better strategy in decision making such as proper timing for property renovations.
A South-African based housing company wants to venture into the Airbnb business and needs to create a sustainable and profitable business model that can compete with established players in the market. The company's stakeholders aim at ensuring customer retention,customer satisfaction and boost their business as a new party entity in the Airbnb Platform. As Data Scientists, we are expected to address questions as well as provide recommendations.
1.What is the best month to visit Cape Town if you are on a budget?
2.What is the best time to list your property on Airbnb? And how do set price rates according to the time of the year?
3.What is the best time in the year when owners can take down their listing for maintenance and repair?
4.When is the best time to lure clients with offers in the case of an upcoming low season: Time series analysis
We extracted the data from InsideAirbnb which has data from the Airbnb platform. Link to the dataset: "http://insideairbnb.com/get-the-data/"
Content Based Filtering Recommendetion System
Sentiment Analysis
Nurulain Abdi: https://github.com/Nurul-ain2022
Amos Kibet: https://github.com/AmosMaru
Mercy Onduso: https://github.com/MercyMoraa
Beth Mithamo: https://github.com/Mythamor