{"id":50040090,"url":"https://github.com/cpeoples/powerpredict","last_synced_at":"2026-05-21T02:24:15.464Z","repository":{"id":222506591,"uuid":"757473523","full_name":"cpeoples/powerpredict","owner":"cpeoples","description":"🔮 AI-powered Powerball \u0026 Mega Millions lottery number prediction using deep learning (Transformer + LSTM), Markov chains, and statistical analysis. Built with TensorFlow/Keras 3.","archived":false,"fork":false,"pushed_at":"2025-12-24T16:26:24.000Z","size":61,"stargazers_count":5,"open_issues_count":0,"forks_count":3,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-03-01T20:55:52.941Z","etag":null,"topics":["artificial-intelligence","data-science","deep-learning","keras","lottery-prediction","lstm","machine-learning","markov-chain","megamillions","neural-network","powerball","python","scikit-learn","statistical-analysis","tensorflow","texas-lottery","transformer"],"latest_commit_sha":null,"homepage":"https://github.com/cpeoples/powerpredict","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/cpeoples.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2024-02-14T15:11:50.000Z","updated_at":"2025-12-24T16:26:27.000Z","dependencies_parsed_at":"2024-02-19T19:31:48.620Z","dependency_job_id":"ecdf2c3c-acd4-4955-a879-aec8b0a25994","html_url":"https://github.com/cpeoples/powerpredict","commit_stats":null,"previous_names":["cpeoples/powerpredict"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/cpeoples/powerpredict","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cpeoples%2Fpowerpredict","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cpeoples%2Fpowerpredict/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cpeoples%2Fpowerpredict/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cpeoples%2Fpowerpredict/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cpeoples","download_url":"https://codeload.github.com/cpeoples/powerpredict/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cpeoples%2Fpowerpredict/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33285709,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-20T15:12:43.734Z","status":"online","status_checked_at":"2026-05-21T02:00:07.181Z","response_time":62,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","data-science","deep-learning","keras","lottery-prediction","lstm","machine-learning","markov-chain","megamillions","neural-network","powerball","python","scikit-learn","statistical-analysis","tensorflow","texas-lottery","transformer"],"created_at":"2026-05-21T02:24:14.935Z","updated_at":"2026-05-21T02:24:15.456Z","avatar_url":"https://github.com/cpeoples.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🔮 PowerPredict - AI Lottery Number Prediction\n\n[![Python](https://img.shields.io/badge/Python-3.11+-blue.svg)](https://www.python.org/downloads/)\n[![TensorFlow](https://img.shields.io/badge/TensorFlow-2.18+-orange.svg)](https://www.tensorflow.org/)\n[![Keras](https://img.shields.io/badge/Keras-3.0+-red.svg)](https://keras.io/)\n[![Docker](https://img.shields.io/badge/Docker-Ready-2496ED.svg)](https://www.docker.com/)\n\n**PowerPredict** is an advanced lottery number prediction system that uses **deep learning**, **statistical analysis**, and **ensemble methods** to generate Powerball and Mega Millions number predictions based on historical drawing data.\n\n\u003e ⚠️ **Disclaimer**: Lottery outcomes are random. This tool is for educational and entertainment purposes only. Please gamble responsibly.\n\n---\n\n## ✨ Features\n\n- 🧠 **Deep Learning Ensemble** - Transformer + Bidirectional LSTM/GRU hybrid neural networks\n- 📊 **Multi-Strategy Analysis** - Frequency, gap, Markov chain, and pattern matching models\n- 🎯 **Smart Diversity** - Guaranteed unique predictions with Hamming distance enforcement\n- 📈 **Historical Data Analysis** - Analyzes 1,800+ historical lottery drawings\n- 🐳 **Docker Ready** - Containerized deployment with one command\n- ⚡ **Fast Predictions** - Optimized numpy/pandas operations\n\n---\n\n## 🚀 Quick Start\n\n### Using Docker (Recommended)\n\n```bash\n# Clone and build\ngit clone https://github.com/cpeoples/powerpredict.git\ncd powerpredict\ndocker build -t powerpredict .\n\n# Run predictions\ndocker run powerpredict powerball -n 5\ndocker run powerpredict megamillions -n 10 --analyze\n```\n\n### Using Python\n\n```bash\n# Clone repository\ngit clone https://github.com/cpeoples/powerpredict.git\ncd powerpredict\n\n# Create virtual environment\npython -m venv venv\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\n\n# Install dependencies\npip install -r requirements.txt\n\n# Run predictions\npython main.py powerball -n 5\npython main.py megamillions -n 10 --analyze\n```\n\n---\n\n## 📖 Usage\n\n```bash\n# Generate 5 Powerball predictions\npython main.py powerball -n 5\n\n# Generate 10 Mega Millions predictions with statistical analysis\npython main.py megamillions -n 10 --analyze\n\n# Quick mode (statistical only, no deep learning)\npython main.py powerball -n 5 --quick\n\n# Run 5 matrices for consensus analysis\npython main.py powerball -n 5 --matrix 5\n\n# Show help\npython main.py --help\n```\n\n### Command Line Options\n\n| Option                  | Description                                            |\n| ----------------------- | ------------------------------------------------------ |\n| `powerball`             | Predict Powerball numbers (1-69 + Power Ball 1-26)     |\n| `megamillions`          | Predict Mega Millions numbers (1-70 + Mega Ball 1-25)  |\n| `-n, --num-predictions` | Number of predictions to generate (default: 5)         |\n| `-a, --analyze`         | Show detailed statistical analysis                     |\n| `-q, --quick`           | Skip deep learning (faster, statistical only)          |\n| `-m, --matrix`          | Number of matrices for consensus analysis (default: 1) |\n\n---\n\n## 🧪 How It Works\n\nPowerPredict combines **four prediction strategies** into a master ensemble:\n\n### 1. Weighted Statistical Model\n\nAnalyzes historical frequency, gap patterns, and positional tendencies to score each number.\n\n### 2. Markov Chain Model\n\nUses transition probabilities to predict which numbers are likely to follow recent drawings.\n\n### 3. Pattern Matching Model\n\nGenerates combinations matching historical patterns (sum ranges, odd/even ratios, high/low distribution).\n\n### 4. Deep Learning Ensemble\n\n- **Transformer** with multi-head attention for sequence patterns\n- **Bidirectional LSTM/GRU** hybrid for temporal dependencies\n- Temperature-based sampling to prevent mode collapse\n\n### Master Ensemble\n\nCombines all models with weighted averaging and enforces diversity:\n\n- ✅ No repeated Power Balls across predictions\n- ✅ Minimum Hamming distance between number sets\n- ✅ Full range coverage (no clustering)\n\n---\n\n## 🛠️ Technology Stack\n\n\u003cp align=\"center\"\u003e\n  \u003cpicture\u003e\n    \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"https://raw.githubusercontent.com/devicons/devicon/master/icons/python/python-original.svg\"\u003e\n    \u003csource media=\"(prefers-color-scheme: light)\" srcset=\"https://raw.githubusercontent.com/devicons/devicon/master/icons/python/python-original.svg\"\u003e\n    \u003cimg alt=\"Python\" height=\"50\" src=\"https://raw.githubusercontent.com/devicons/devicon/master/icons/python/python-original.svg\"/\u003e\n  \u003c/picture\u003e\n  \u0026nbsp;\u0026nbsp;\n  \u003cpicture\u003e\n    \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"https://raw.githubusercontent.com/devicons/devicon/master/icons/tensorflow/tensorflow-original.svg\"\u003e\n    \u003csource media=\"(prefers-color-scheme: light)\" srcset=\"https://raw.githubusercontent.com/devicons/devicon/master/icons/tensorflow/tensorflow-original.svg\"\u003e\n    \u003cimg alt=\"TensorFlow\" height=\"50\" src=\"https://raw.githubusercontent.com/devicons/devicon/master/icons/tensorflow/tensorflow-original.svg\"/\u003e\n  \u003c/picture\u003e\n  \u0026nbsp;\u0026nbsp;\n  \u003cpicture\u003e\n    \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"https://raw.githubusercontent.com/devicons/devicon/master/icons/keras/keras-original.svg\"\u003e\n    \u003csource media=\"(prefers-color-scheme: light)\" srcset=\"https://raw.githubusercontent.com/devicons/devicon/master/icons/keras/keras-original.svg\"\u003e\n    \u003cimg alt=\"Keras\" height=\"50\" src=\"https://raw.githubusercontent.com/devicons/devicon/master/icons/keras/keras-original.svg\"/\u003e\n  \u003c/picture\u003e\n  \u0026nbsp;\u0026nbsp;\n  \u003cpicture\u003e\n    \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"https://upload.wikimedia.org/wikipedia/commons/d/d0/Pandas_logo_with_light_text.svg\"\u003e\n    \u003csource media=\"(prefers-color-scheme: light)\" srcset=\"https://raw.githubusercontent.com/devicons/devicon/master/icons/pandas/pandas-original-wordmark.svg\"\u003e\n    \u003cimg alt=\"Pandas\" height=\"50\" src=\"https://raw.githubusercontent.com/devicons/devicon/master/icons/pandas/pandas-original-wordmark.svg\"/\u003e\n  \u003c/picture\u003e\n  \u0026nbsp;\u0026nbsp;\n  \u003cpicture\u003e\n    \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"https://upload.wikimedia.org/wikipedia/commons/0/05/Scikit_learn_logo_small.svg\"\u003e\n    \u003csource media=\"(prefers-color-scheme: light)\" srcset=\"https://upload.wikimedia.org/wikipedia/commons/0/05/Scikit_learn_logo_small.svg\"\u003e\n    \u003cimg alt=\"Scikit-learn\" height=\"50\" src=\"https://upload.wikimedia.org/wikipedia/commons/0/05/Scikit_learn_logo_small.svg\"/\u003e\n  \u003c/picture\u003e\n  \u0026nbsp;\u0026nbsp;\n  \u003cpicture\u003e\n    \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"https://raw.githubusercontent.com/devicons/devicon/master/icons/docker/docker-original.svg\"\u003e\n    \u003csource media=\"(prefers-color-scheme: light)\" srcset=\"https://raw.githubusercontent.com/devicons/devicon/master/icons/docker/docker-original.svg\"\u003e\n    \u003cimg alt=\"Docker\" height=\"50\" src=\"https://raw.githubusercontent.com/devicons/devicon/master/icons/docker/docker-original.svg\"/\u003e\n  \u003c/picture\u003e\n\u003c/p\u003e\n\n| Technology   | Version | Purpose               |\n| ------------ | ------- | --------------------- |\n| Python       | 3.11+   | Core runtime          |\n| TensorFlow   | 2.18+   | Deep learning backend |\n| Keras        | 3.0+    | Neural network API    |\n| NumPy        | 1.26+   | Numerical computing   |\n| Pandas       | 2.2+    | Data manipulation     |\n| Scikit-learn | 1.5+    | ML utilities          |\n\n---\n\n## 📊 Sample Output\n\n```\n======================================================================\n🔮 POWERPREDICT - INTELLIGENT LOTTERY ANALYSIS SYSTEM\n======================================================================\n   Game: POWERBALL\n   Predictions: 5\n   Mode: Full (Statistical + Deep Learning)\n\n📥 Loading historical data...\n   ✓ Loaded 1885 historical drawings\n\n📊 Running comprehensive statistical analysis...\n   ✓ Analysis complete\n\n🧠 Training deep learning ensemble...\n   ✓ Training complete\n\n======================================================================\n⭐ MASTER ENSEMBLE PREDICTIONS (HIGHEST CONFIDENCE)\n======================================================================\n\n🎰 MASTER ENSEMBLE:\n--------------------------------------------------\n   #1: [ 6 - 22 - 32 - 51 - 57]  Power Ball: 22 (agreement: 46%)\n   #2: [ 6 - 19 - 39 - 53 - 66]  Power Ball: 12 (agreement: 44%)\n   #3: [ 7 - 30 - 40 - 49 - 63]  Power Ball: 18 (agreement: 57%)\n   #4: [ 9 - 22 - 42 - 49 - 58]  Power Ball: 15 (agreement: 54%)\n   #5: [ 8 - 13 - 21 - 42 - 53]  Power Ball:  7 (agreement: 47%)\n```\n\n---\n\n## 🤝 Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/AmazingFeature`)\n3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)\n4. Push to the branch (`git push origin feature/AmazingFeature`)\n5. Open a Pull Request\n\n---\n\n## ⚠️ Disclaimer\n\nThis software is provided for **educational and entertainment purposes only**. Lottery outcomes are determined by cryptographically secure random number generators and cannot be predicted by any statistical or machine learning method.\n\n- The probability of winning Powerball is 1 in 292,201,338\n- The probability of winning Mega Millions is 1 in 302,575,350\n\n**Please gamble responsibly.**\n\n---\n\n\u003cp align=\"center\"\u003e\n  Made with ❤️ and 🤖 by \u003ca href=\"https://github.com/cpeoples\"\u003ecpeoples\u003c/a\u003e\n\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcpeoples%2Fpowerpredict","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcpeoples%2Fpowerpredict","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcpeoples%2Fpowerpredict/lists"}