You have the choice between two datasets: IMDb (or AirBnB.
Dataset 1: IMDb
IMDb (Internet Movie Database) is a go-to online platform for information about movies, TV shows, actors, directors, and more. It offers details like titles, release dates, cast info, ratings, and reviews, making it a popular resource for entertainment enthusiasts and professionals. Subsets of IMDb data can be accessed for personal/non-commercial purposes.
The IMDb data can allow you to answer a variety of research questions, such as:
- How have trends in genre popularity evolved over time in the entertainment industry?
- Is there a connection between the format of a title (movie, TV series, etc.) and its audience reception?
- Does the involvement of specific creators (directors, writers) impact the success of their projects?
- Is an individual’s fame related to his/her birth year?
- Are there patterns in viewer engagement with TV series episodes based on release timing?
- Are there differences in audience engagement between content targeted at adults and non-adults?
- Is there a connection between audience ratings and the financial performance of titles?
Download the data
Multiple datasets are available for download.
Dataset 2: AirBnB
Inside Airbnb is an independent, open-source data tool developed by community activist Murray Cox who aims to shed light on how Airbnb is being used and affecting neighborhoods in large cities. The tool provides a visual overview of the amount, availability, and spread of rooms across a city, as well as an approximation of the number of bookings and occupancy rate.
This data set allows for a variety of research questions, such as:
- How does the presence of specific neighborhood amenities impact the pricing of Airbnb listings in different cities?
- Do properties with a higher number of positive reviews command a price premium, and does this relationship differ across neighborhoods?
- Can the availability of Airbnb listings be predicted based on historical booking patterns, seasonal trends, and local events?
- What are the key factors influencing the popularity of certain neighborhoods for Airbnb stays, as indicated by booking frequency and review sentiment?
- To what extent do superhosts outperform regular hosts in terms of occupancy rates and pricing adjustments, and is this consistent across different city markets?
- How do different types of property amenities (e.g., pool, gym, balcony) impact occupancy rates and nightly prices across diverse neighborhoods?
- Does the presence of local events, such as concerts or festivals, influence the pricing strategy of Airbnb hosts in proximity to those events?
- Can machine learning models accurately predict the popularity of newly listed Airbnb properties based on their features and neighborhood characteristics?
Download the data
The data is available per city (e.g., Amsterdam) and entity (e.g., listings, calendar, reviews, neighbourhoods, etc.).
Pick the city that you find interesting!
Inside AirBnB offers datasets for various cities around the world. Feel free to explore the city/cities that spark your interest!