This project focuses on analyzing the best mobile games released after 2015, with a special focus on their revenue. Using data from Kaggle, I conducted an in-depth analysis leveraging Python, Matplotlib, and Seaborn to discover trends and insights in the mobile gaming industry.
The dataset used for this analysis was obtained from Kaggle and includes a variety of information about mobile games, such as:
Release dates Game categories User ratings Revenue Pricing model (Free/Paid) Number of downloads
Python: For data preprocessing, analysis, and handling. Matplotlib: Used for data visualization and creating insightful charts. Seaborn: To create more advanced and aesthetic visualizations for in-depth analysis. Pandas: For data manipulation and cleaning.
Data Cleaning: Ensured only games released after 2015 were included in the analysis. The dataset was also cleaned for missing or irrelevant data. Revenue-Based Game Selection: Focused on identifying the top-performing games based on their revenue.
Popular game categories and their revenue trends. Relationships between user ratings, game downloads, and revenue. Insights into the impact of game pricing on revenue. Visualizations: Created various visualizations to help identify trends, patterns, and insights into the success factors of mobile games.