Challenge background
The dynamics of urban rental markets have seen a significant evolution with the advent of online platforms like Airbnb, where hosts can list their properties for short-term rentals. Determining an optimal price that appeals to potential guests while maximizing host revenue can be a challenging endeavor given the multitude of variables at play, such as location, property attributes, seasonal demand, and local events.
The project in focus aims to tackle this very issue - determining the optimal rental price for Airbnb listings. Currently, hosts may rely on intuition or manual comparisons with other local listings to set their prices. This can lead to inaccuracies, underpricing, or overpricing, resulting in lower occupancy rates and sub-optimal income.
Project timeline
- 1
Week 1
- Data collection - Data scraping and sourcing
- 2
Week 2
- Data preprocessing - Data cleaning
- 3
Week 3
- Data Exploration
- 4
Week 4
- Feature Selection
- 5
Week 5
- Model development and Training
- 6
Week 6
- Evaluating the Model
- 7
Week 7
- Model Integration
- 8
Week 8
- Model Deployment
What you'll learn
Participants will gain knowledge on the skills used machine learning skills and they will also gain people skills such as leadership and communications and others. They will also have a project to be proud of .They will network with other engineers across the globe.