https://github.com/vinit714/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.
https://github.com/vinit714/player-retention-analysis
churn-prediction classification data-visualization eda feature-engineering game-analytics game-data-analysis gaming-analytics machine-learning model-interpretability nlp pandas player-retention python retention-analysis sckiit-learn shap streamlit wordcloud
Last synced: 3 months ago
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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.
- Host: GitHub
- URL: https://github.com/vinit714/player-retention-analysis
- Owner: vinit714
- License: mit
- Created: 2025-06-27T04:22:12.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-06-27T04:35:36.000Z (3 months ago)
- Last Synced: 2025-06-27T05:33:29.555Z (3 months ago)
- Topics: churn-prediction, classification, data-visualization, eda, feature-engineering, game-analytics, game-data-analysis, gaming-analytics, machine-learning, model-interpretability, nlp, pandas, player-retention, python, retention-analysis, sckiit-learn, shap, streamlit, wordcloud
- Language: Python
- Homepage:
- Size: 207 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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.
---
## 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)
---
## 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
```
---
## Installation
1. **Clone the repository:**
2. **Install dependencies:**
```bash
pip install -r requirements.txt
```
---
## Run the App
```bash
streamlit run app.py
```
This will launch the interactive dashboard in your browser.
---
## Running Tests
Run unit tests using:
```bash
pytest tests/
```
---
## Tech Stack
- **Python 3.10+**
- Streamlit
- scikit-learn
- SHAP
- Seaborn / Matplotlib
- WordCloud / TextBlob
- NLTK
- pandas / NumPy
---
## Screenshots
---