Video Game Sales and Metacritic Score Analysis

Overview

This project analyzes video game sales and Metacritic scores using web-scraped data. The goal is to provide insights into industry trends, best-selling games, platform performance, and the relationship between critic reviews and commercial success.

Features

• Data Collection: Web-scraped video game sales and Metacritic scores.
• Data Cleaning & Processing: Handling missing data, normalization, and structuring.
• Visualization & Analysis:
  • Sales distribution by platform and genre
  • Correlation between Metacritic scores and sales
  • Trend analysis of video game popularity over time
• Business Insights: Actionable recommendations for game developers, publishers, and marketers.

Target Audience

• Large Video Game Companies: Insights on best-selling genres and platforms.
• Indie Game Developers: Understanding market trends and score-sales correlations.
• Marketers & Business Analysts: Identifying key drivers for commercial success.

Technologies Used

• Programming Languages: Python (Pandas, NumPy, Matplotlib, Seaborn)
• Data Processing: Jupyter Notebook, CSV/Excel data handling
• Visualization Tools: Matplotlib, Seaborn

Usage

• Open the Jupyter Notebook and execute the code cells step by step.
• Modify parameters to explore different insights.
• Use the generated visualizations to support research or business decisions.

Future Improvements

• Expand dataset with more recent sales data.
• Incorporate player reviews and social media sentiment analysis.
• Build interactive dashboards using Tableau or Plotly.

Contributions

Munish Khan

Contact

For questions or collaborations, reach out via [Your Email] or open an issue on GitHub.