PlayerPulse Analytics 🎮📊

Real-time game analytics dashboard featuring ML-powered churn prediction, dynamic live ops feeds, and native Tableau Extension integration.

PlayerPulse bridges the gap between raw game telemetry and actionable insights. It leverages Machine Learning to predict player churn in real-time, segment players into dynamic cohorts (Whales, Dolphins, Minnows), and identify high-value monetization opportunities. Built as a dual-mode application, it functions both as a standalone web app and a fully integrated Tableau Extension.

📸 Dashboard Preview

Real-time monitoring of Player Churn Risk and Revenue Uplift opportunities.

Deep dive into Live Operations feed and Cohort Analysis.

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✨ Key Features

• 🧠 AI-Powered Insights

  • Churn Prediction: Random Forest classifier analyzes player behavior to predict churn probability.
  • Monetization Uplift: Identifies active players with high spending potential who haven't purchased recently.

• ⚡ Live Ops Feed

  • Real-time, chat-style ticker of significant player events (Level Ups, Shop Visits, Churn Alerts).
  • Visualizes the "pulse" of your game economy as it happens.

• 📉 Dynamic Cohorts

  • Automated segmentation of players into Whales (> $100), Dolphins ($10-$100), and Minnows (< $10).
  • Tracks retention rates and average spend per segment.

• 🧩 Tableau Integration

  • Includes a .trex manifest for native integration with Tableau Desktop via the Extensions API.
  • Supports two-way communication with Tableau worksheets (dashboard filtering based on selected players).

• 🎨 Modern UI/UX

  • Sleek "Bento Grid" layout with glassmorphism elements.
  • Interactive tooltips explaining complex metrics.
  • Full responsive design for various dashboard sizes.

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🛠️ Tech Stack

• Frontend: HTML5, CSS3 (Custom Glassmorphism), JavaScript (ES6+), Chart.js
• Backend: Node.js, Express.js (REST API)
• Machine Learning: Python (Scikit-Learn, Pandas), Random Forest Classifier
• Data Integration: Tableau Extensions API, Tableau Hyper API

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🚀 Getting Started

Prerequisites

• Node.js (v14+)
• Python (3.8+)
• Tableau Desktop (Optional, for extension mode)

Installation

1. Clone the Repository

   git clone https://github.com/MadeNavaneeth/PlayerPulseAnalytics.git
   cd PlayerPulseAnalytics

2. Install Backend Dependencies

   cd backend
   npm install

3. Setup ML Environment

   cd ../ml-engine
   # create virtual env (optional but recommended)
   # python -m venv venv
   pip install -r requirements.txt

Running the Project

1. Start the Backend Server

   # Terminal 1
   cd backend
   node server.js

   Server runs on http://localhost:3000

2. Generate Mock Data (Optional) If you want fresh predictions:

   # Terminal 2
   cd ml-engine
   python train_model.py

3. Launch Dashboard

   • Standalone: Open http://localhost:3000/index.html in your browser.
   • Tableau: Open Tableau, drag in an "Extension" object, and select extension/playerpulse.trex.

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📂 Project Structure

PlayerPulseAnalytics/
├── backend/            # Express.js API Gateway
│   └── server.js       # Serves predictions & handles actions
├── extension/          # Frontend Dashboard
│   ├── index.html      # Main Dashboard UI
│   ├── script.js       # Logic & Tableau API integration
│   └── playerpulse.trex# Tableau Manifest File
├── ml-engine/          # Python Machine Learning
│   ├── predictions.csv # Generated output data
│   └── train_model.py  # RF Model Training Script
└── README.md

🏷️ Tags

game-analytics machine-learning tableau-extension churn-prediction nodejs python real-time-dashboard