GameSense AI by Ishita Yadav

GameSense AI is a machine learning–based gaming performance prediction system designed to predict Average FPS for different combinations of GPU, game, resolution, and graphics-quality settings without requiring every configuration to be physically benchmarked. The project uses a dataset of 158,960 gaming benchmark observations covering 186 GPUs and 104 games. GameSense AI (ishita yadav).pdf The project compares Linear Regression, Random Forest, XGBoost, and a Deep Neural Network (DNN) using evaluation metrics such as MAE, MSE, RMSE, and R². XGBoost achieved an R² of 0.9896, while the DNN achieved 0.9783, demonstrating that machine learning can accurately model the complex relationship between gaming hardware and performance. GameSense AI (ishita yadav).pdf Beyond FPS prediction, GameSense AI includes a Smart Target-FPS Optimizer that identifies suitable resolution and graphics settings for a desired FPS target, along with an NVIDIA GPU comparison module and generational performance trend analysis. The project also includes a backend designed for future Streamlit deployment, making the system suitable for an interactive gaming-performance prediction tool.