This project analyzes a dataset of the highest-grossing mobile games to identify factors contributing to their success. It includes exploratory data analysis (EDA), estimation of game industry KPIs (e.g., DAU, MAU, ARPU, retention rates), a simulated A/B test, and data-driven recommendations for game designers and marketing teams. The project was created to demonstrate my skills in data analytics for a Data Analytics Intern role in the gaming industry.
The dataset contains information on mobile games that have grossed at least $1 billion in revenue, including game name, publisher, category, release year, and global sales.
Credit: The dataset was sourced from Kaggle - Highest Grossing Mobile Games.
- Notebook:
Mobile_Game_Success_Analysis.ipynb- A Jupyter Notebook containing the full analysis, including data cleaning, EDA, KPI estimation, A/B testing, visualizations, and recommendations. - Outputs: Visualizations include revenue trends, estimated DAU by category, top publishers, and A/B test results.
- EDA: Analyzed revenue trends by game category, publisher, and release period.
- KPI Estimation: Estimated DAU, MAU, ARPU, and retention rates using industry benchmarks.
- A/B Testing: Simulated an A/B test to optimize an in-game purchase offer, improving conversion rates and revenue.
- Visualizations: Created bar charts and boxplots to present insights, mimicking a dashboard approach.
- Recommendations: Provided actionable insights for game designers and marketing teams to improve player engagement and monetization.
To run the notebook, install the following Python libraries:
pandasnumpymatplotlibseaborn
You can install them using:
pip install pandas numpy matplotlib seaborn- Clone this repository:
git clone https://github.com/your-username/mobile-game-success-analysis.git
- Navigate to the project directory:
cd mobile-game-success-analysis - Open the Jupyter Notebook:
jupyter notebook Mobile_Game_Success_Analysis.ipynb
- Run all cells to see the analysis and visualizations.
- Reyna Dai Luo