# Online Gaming Behavior Prediction

## Project Overview

This project predicts player engagement levels based on gaming behavior and demographic features using Machine Learning techniques.

## Algorithms Used

- Decision Tree

- Random Forest

- AdaBoost

- XGBoost

- Voting Classifier

- Stacking Classifier

## Project Workflow

1. Data Collection

2. Data Preprocessing

3. Feature Engineering

4. Model Training

5. Model Evaluation

6. Model Comparison

7. Best Model Selection

## Evaluation Metrics

- Accuracy Score

- Confusion Matrix

- Classification Report

## Technologies Used

- Python

- Pandas

- NumPy

- Scikit-Learn

- XGBoost

- Matplotlib

- Seaborn

## Results

The best-performing model achieved high classification accuracy in predicting gaming engagement levels.

## Future Improvements

- Hyperparameter Tuning

- Deep Learning Models

- Real-Time Prediction System

- Streamlit Deployment