This project aims to predict the final ranking (placement) of players in the PUBG (PlayerUnknown's Battlegrounds) game based on various in-game stats. The dataset includes player performance metrics, and the goal is to build a machine learning model that can predict the ranking of players given their gameplay data.
Key Features: Data Preprocessing: Handled missing values, outlier detection, and feature scaling. Exploratory Data Analysis (EDA): Visualized patterns and trends in player statistics to gain insights into key factors affecting ranking. Feature Engineering: Created additional relevant features from the dataset to improve model performance. Machine Learning Models: Trained multiple machine learning models (e.g., Linear Regression, Random Forest) to predict player ranks. Evaluation: Assessed model performance using metrics like R-squared and Mean Absolute Error (MAE). Tools and Technologies: Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn) Jupyter Notebook Objective: To develop a predictive model that can estimate a player’s performance and placement based on gameplay data.