Game Churn Prediction AI

Overview

The Game Churn Prediction AI is a complete machine learning system built to predict online player churn. It evaluates gameplay engagement to determine the probability of a player abandoning the game. This project focuses on demonstrating end-to-end Machine Learning workflows, from data cleaning and processing, to model training, and delivering a clean user interface using Streamlit.

Project Structure

game-churn-prediction-ai/
├── data/
│   ├── raw_data.csv       # Raw Kaggle Game Online Behavior dataset
│   └── clean_data.csv     # Preprocessed data ready for ML training
├── notebooks/
│   ├── data_cleaning.ipynb # Jupyter notebook executing robust data processing actions
│   └── model_training.ipynb# Jupyter notebook showcasing ML evaluation procedures and results
├── models/
│   ├── churn_model.pkl     # Optimized Random Forest Classifier
│   └── model_features.pkl  # List of categorical bindings used for Streamlit mappings
├── app.py                  # Front-end system rendering ML execution with UI elements
├── requirements.txt        # Recommended Python modules/libraries constraint list
└── README.md               # User manual

Features

1. Data Cleaning & Engineering: Robust data cleaning techniques that address missing values, feature encoding, and redundant data elimination.
2. Machine Learning: Incorporates supervised classification architectures using Random Forest and Logistic Regression. Output is quantified using Accuracy, Precision, Recall, and F-1 metrics.
3. Interactive UI App: Seamless user experience leveraging streamlit to submit bulk game metrics and dynamically predict player churn probabilities and risk levels (Low, Medium, High).
4. Insights Dashboard: Highlights "Feature Importance" providing product-ready perspectives indicating which factors most heavily impact a player churning.

How to Run Locally

1. Requirements

Ensure you have Python 3.8+ installed.

2. Setup Virtual Environment (Optional but Recommended)

For Mac / Linux:

python3 -m venv venv
source venv/bin/activate

For Windows:

python -m venv venv
venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Build Dataset & Assets (Optional, models are pre-trained)

To manually generate the assets and run model training scripts:

python3 build_assets.py

5. Start the Application

Boot up the Streamlit interface using:

streamlit run app.py

Dataset Reference

Predict Online Gaming Behavior Dataset (from Kaggle): https://www.kaggle.com/datasets/rabieelkharoua/predict-online-gaming-behavior-dataset

Tech Stack

• Data Computation: pandas, numpy
• Machine Learning: scikit-learn
• Data Visualization: matplotlib, seaborn
• Application Server: streamlit

Notes for Beginners

The project heavily prioritizes human-readable comments. Each component showcases beginner-friendly steps such as defining evaluation metrics, standardizing the schema structures, and rendering user feedback immediately.