Tinder is an online dating and geosocial networking application. In Tinder, users "swipe right" to like or "swipe left" to dislike other users' profiles, which include their photos, a short bio, and a list of their interests.
Tinder was launched by Sean Rad at a hackathon held at the Hatch Labs incubator in West Hollywood in 2012.
As of 2021, Tinder has recorded more than 65 billion matches worldwide.
The goal of this project is to understand what factors contribute to people being interested in each other enough to go on a second date. The dataset used for this analysis comes from a speed dating experiment conducted by Tinder, where participants provided various information about themselves that could reflect on their dating profile. Each row in the dataset represents one speed date between two people and indicates whether each of them secretly agreed to go on a second date with the other person.
- Utilize the dataset to gain insights into the factors that influence people's interest in each other.
- Identify key attributes or characteristics.
Steam is a video game digital distribution service and storefront from Valve. It was launched as a software client in September 2003 to provide game updates automatically for Valve's games and expanded to distributing third-party titles in late 2005. Steam offers various features, like digital rights management (DRM), game server matchmaking with Valve Anti-Cheat measures, social networking, and game streaming services. Steam client's functions include game update automation, cloud storage for game progress, and community features such as direct messaging, in-game overlay functions, and a virtual collectible marketplace.
The goal of this project is to perform a global analysis of the games available on Steam's marketplace in order to better understand the video games ecosystem and today's trends. The analysis will focus on the "macro" level, including genres analysis and platform analysis.
1.Analysis at the "Macro" Level 2. Genres Analysis: Analyze the distribution of games across different genres to identify popular genres and trends in the market. 3. Platform Analysis: Explore the availability of games on different platforms (Windows, macOS, Linux) and analyze any patterns or preferences among users.
- This project was developed using Databricks.
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