Project Overview This repository contains an in-depth analysis of the Steam games dataset, exploring various facets of the gaming industry. The project uncovers insights into game popularity, success factors, audience targeting, platform preferences, and pricing strategies.

Key Features

1. Most Played Games Identifies the top 10 most played games based on different playtime metrics, revealing trends in popularity and engagement.

2. Success Analysis Examines the success rate of games, distribution across genres, and platform correlations, providing insights into the factors contributing to game success.

3. Audience Targeting Investigates audience targeting strategies, correlations with features such as price and platforms, and the distribution of games across audience categories.

4. Platform Preferences Explores the distribution of games across Windows, Mac, and Linux platforms, and how platforms correlate with genres and success.

5. Price Analysis Analyzes the distribution of game prices, offering insights into pricing trends and strategies within the gaming industry.

6. Data Visualization Employs various data visualization techniques to represent complex relationships within the data, enhancing interpretation and decision-making.

Technologies Used Python Pandas Matplotlib Seaborn

How to Run Clone the repository and open the Jupyter Notebook (Steam games dataset exploration.ipynb) in a Jupyter environment. Ensure that the required libraries are installed.

Dataset The dataset used in this project can be found on Kaggle.

Conclusion This project provides valuable insights into the gaming industry, serving as a resource for developers, marketers, and stakeholders to make informed decisions and strategies. The analysis is a commendable contribution to understanding the contemporary gaming landscape.

License This project is licensed under the MIT License - see the LICENSE.md file for details.