I want to build a native-Android simulation game that does more than entertain—it learns. While the player is immersed in the simulation, the app should continuously capture in-game actions, run them through a mobile-friendly machine-learning model, and return predictive analytics that can adapt gameplay or surface insights in real time. A browser panel is required as well. The user must be able to type any URL and have that page render inside the game, giving the title broad compatibility with popular online gaming sites. From an engineering standpoint the codebase needs to be future-proof. Design the data pipeline so it can scale to very large action logs; cloud-backed storage and auto-scaling functions (Firebase, AWS, or comparable) are perfectly acceptable as long as they slot in cleanly. Deliverables • Release-ready APK and full, well-commented source (Kotlin or Java) stored in a Git repository • A trained TensorFlow Lite (or similar) model that performs predictive analytics on in-game actions in ≤300 ms for a batch of 10 k rows • Modular browser panel integrated into the main activity and tested against the five gaming sites I will supply during QA • Setup & retraining guide detailing the data flow, model-update procedure, and extension points for new analytics The build must run crash-free on Android 9 through 14, pass basic unit tests, and deliver the analytics described above.