Player Retention Analysis

A complete Streamlit + Machine Learning + SHAP + NLP project to analyze, predict, and improve player retention in games. This project simulates a game environment, models churn behavior, and provides insights using SHAP, NLP word clouds, and strategy simulators.

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Features

• Interactive EDA Dashboard
• Churn Prediction using Random Forest
• SHAP Explainability for Feature Importance
• NLP Word Cloud from Player Reviews
• AI-based Retention Strategy Simulator
• Dynamic Churn Predictor UI (via Streamlit)

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Project Structure

player-retention-analysis/
├── app.py
├── requirements.txt
├── data/
│   └── processed/
│       └── reviews.csv
│   └── raw/
│       └── player_data_enhanced.csv
├── src/
│   ├── data_processing.py
│   ├── model.py
│   ├── explainability.py
│   ├── visualizations.py
│   ├── nlp_analysis.py
│   ├── strategy_simulator.py
│   └── utils.py
├── tests/
│   ├── test_data_processing.py
│   └── test_model.py
└── README.md

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Installation

1. Clone the repository:

2. Install dependencies:

pip install -r requirements.txt

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Run the App

streamlit run app.py

This will launch the interactive dashboard in your browser.

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Running Tests

Run unit tests using:

pytest tests/

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Tech Stack

• Python 3.10+
• Streamlit
• scikit-learn
• SHAP
• Seaborn / Matplotlib
• WordCloud / TextBlob
• NLTK
• pandas / NumPy

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Screenshots

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