A data analysis project exploring video game sales, critic scores, and user ratings to uncover trends in the gaming industry, including top-performing titles, platform dominance, and changes in game popularity over time.
- Python
- Pandas
- Matplotlib
- Seaborn
- Jupyter Notebook
- Analysis of video game sales and ratings
- Comparison of critic vs user scores
- Identification of top-performing games and publishers
- Trend analysis across different gaming eras
- Clear data visualizations for insights
- Explore video game sales and performance trends
- Compare critic scores with user ratings
- Identify best-selling games and platforms
- Understand how the gaming industry has evolved over time
- Collected and combined multiple video game datasets
- Cleaned and structured data using Python (Pandas)
- Performed exploratory data analysis (EDA)
- Created visualizations using Matplotlib and Seaborn
- Extracted meaningful insights from the data
- Data cleaning and preprocessing techniques
- Exploratory data analysis (EDA) in real datasets
- Data visualization for storytelling
- Understanding industry trends using data
- Working with multiple related datasets
git clone https://github.com/yourusername/video-game-analysis.git
cd video-game-analysis
pip install pandas matplotlib seaborn jupyter
jupyter notebook