This project utilizes machine learning and data analysis techniques to analyze game data and provide insights into various aspects of the game. Additionally, it predicts the winner of the game based on the provided dataset. The dataset consists of parameters such as group_id, match_id, assists, damage done, kills, DBNOs (Down But Not Out), headshots, heals, match_duration, distance traveled, weapons acquired, etc.

Data analysis: Explore the dataset to understand different aspects of the game.

Visualization: Generate various charts and graphs to visualize the game data effectively.

Winner prediction: Utilize a CatBoost Model for training to predict the winner of the game.

Python 3.x

pandas

numpy

matplotlib

seaborn

scikit-learn

CatBoost

Testing Performance:

RMSE (Root Mean Squared Error): 0.08

R2 (R-squared): 0.93 These metrics indicate that the model is approximately 92-93% accurate in predicting the winner of the game based on the provided dataset.

Contributions are welcome! If you find any bugs or have suggestions for improvements, feel free to open an issue or submit a pull request.