This project analyzes video game sales data to uncover patterns and insights about the gaming industry. The dataset includes information on global sales, genre, platform, release year, and other relevant factors.
-
Data Cleaning:
- Handled missing values and corrected data types.
- Removed duplicates and irrelevant data entries.
-
Data Exploration and Visualization:
- Analyzed the distribution of video game sales across different platforms and genres.
- Visualized sales trends over the years.
- Created charts to showcase the top-selling games and platforms.
-
Statistical Analysis:
- Conducted correlation analysis to find relationships between different variables.
- Used summary statistics to understand the central tendencies and dispersion of sales data.
- Python: Main programming language used for analysis.
- Pandas: For data manipulation and cleaning.
- Matplotlib & Seaborn: For data visualization.
- Jupyter Notebook: For interactive coding and documentation.
The dataset used for this analysis contains the following columns:
Name: Name of the video game.Platform: Platform of the game release (e.g., PS4, Xbox, etc.).Year: Year of the game release.Genre: Genre of the game (e.g., Action, Adventure, etc.).Publisher: Publisher of the game.NA_Sales: Sales in North America (in millions).EU_Sales: Sales in Europe (in millions).JP_Sales: Sales in Japan (in millions).Other_Sales: Sales in other regions (in millions).Global_Sales: Total worldwide sales (in millions).
- Identified the most popular genres and platforms over the years.
- Highlighted the top-selling games and their contributing factors.
- Discovered sales trends and patterns in different regions.
- Developed models to predict future video game sales.
The analysis provides valuable insights into the video game industry, helping stakeholders make informed decisions. The findings can be used by game developers, marketers, and distributors to understand market trends and consumer preferences.