This project was created to practice data analysis and visualization skills learned on the journey to mastering machine learning. Using Python and libraries such as NumPy, Pandas, and Matplotlib, we dived deep into global gaming behaviour to uncover trends and insights.
To make this project more engaging, we aim to prove the hypothesis:
Through detailed data analysis and visuals, we'll explore whether players who spend more time gaming also tend to spend more money on in-game purchases. This hypothesis is critical as it highlights the potential revenue opportunities for game developers targeting specific audiences.
Source: Kaggle
Metrics Explored: PlayTimeHours, InGamePurchases, GameDifficulty, EngagementLevel
Regions Covered: Asia, USA, Europe, and Other regions
Takeaway:
- The majority of gamers fall into the medium to high engagement categories.
- High engagement correlates with higher playtime and sessions.
Takeaway:
- Asia and the USA lead in high engagement, making them prime markets for immersive games.
Takeaway:
- The USA dominates in-game spending, showcasing a significant market for digital transactions.
Takeaway:
- Asia has the highest average playtime, indicating a deeply engaged gaming culture.
Using data correlations, we investigated the relationship between engagement levels and in-game purchases:
- Players with "High Engagement" were found to contribute significantly more to in-game spending, validating our hypothesis.
- This correlation was most evident in the USA and Europe, emphasizing the potential profitability of targeting highly engaged audiences in these regions.
- Focus on Engagement: Strategies to boost playtime and sessions can directly enhance revenue through in-game purchases.
- Regional Targeting: Tailored content and marketing in the USA and Europe can maximize returns.
- Data Analysis in ML: Data analysis is foundational to machine learning. Understanding trends and insights ensures better model performance and interpretability.
- Visualization Skills: Clear and impactful visuals make complex data understandable, an essential skill for ML practitioners.
- Proving Hypotheses: Validating hypotheses through real-world data bridges the gap between theory and practical application.
This project was created by:
| Name | Institution | ID | GitHub | Followers |
|---|---|---|---|---|
| Rajin Khan | North South University | 2212708042 |  |
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