This project conducts thorough analysis of the gaming industry using a dataset of top-selling video games. It covers genres, platforms, sales trends, and publisher insights, with the aim of extracting valuable insights. For further details, please refer to the GitHub repository linked here.

Data Analyst Project: Gaming Industry Analysis

Overview

This data analyst project aims to analyze the gaming industry using a dataset of top-selling video games. We’ll explore various aspects of the gaming market, including genres, platforms, sales trends, and publisher insights. The goal is to derive meaningful insights from the data.

Dataset

The dataset used for this project is sourced from Kaggle and contains information about the top 100-selling video games, including details such as rank, name, platform, year, genre, publisher, and sales data. The dataset is provided in a CSV format (vgsales.csv).

Project Steps

1. Data Loading

• Load the dataset (vgsales.csv) into a pandas DataFrame.
• Check for missing values, data types, and summary statistics of numerical columns.

2. Data Cleaning

• Handle missing values, including dropping rows with missing values for Publisher and Year columns.
• Save the cleaned dataset to a new file (cleaned_vgsales.csv).

3. Genre Analysis

• Identify the gaming genre with the highest total global sales.
• Visualize the distribution of global sales by genre using a boxplot.

4. Platform Analysis

• Identify the gaming platform with the most games released.
• Visualize the distribution of global sales across different platforms using a boxplot.

5. Publisher Insights

• Identify the top publishers in terms of the number of games published.
• Identify the publisher with the highest total global sales.

6. Time Series Analysis

• Analyze how the number of game releases has changed over the years.
• Analyze trends in global sales over time.
• Identify significant spikes or drops in sales in specific years.

7. Regional Sales Analysis

• Determine the contribution of each region (North America, Europe, Japan, others) to global sales.
• Explore whether specific regions have preferences for certain genres or platforms.

8. Player Behavior and Preferences

• Analyze player preferences within specific genres or platforms.
• Draw conclusions about player preferences based on sales data.

Code Implementation

The code for this data analysis project is implemented in Python, utilizing the following libraries:

• Pandas: Data manipulation and analysis.
• NumPy: Numerical computations.
• Matplotlib and Seaborn: Data visualization.

Each analysis task is represented by Python code snippets, which can be found in the project’s main script.

Conclusion

The project is ongoing, and additional analyses and visualizations will be conducted to provide a comprehensive understanding of the gaming industry.

Please refer to the project code and Jupyter Notebook for detailed analysis and code implementation.

Author

• Name: Mohammed Mebarek Mecheter
• Email: mohammedmecheter@gmail.com
• GitHub: Mohammed Mebarek Mecheter
• Tableau Public: Mohammed Mebarek Mecheter
• Email: mohammedmecheter@gmail.com

Feel free to contact me for any questions or additional information about this project.