{"id":30280325,"url":"https://github.com/kiran8053/smart-machines-efficiency-prediction","last_synced_at":"2026-04-11T12:03:18.370Z","repository":{"id":309683483,"uuid":"1037155921","full_name":"Kiran8053/Smart-Machines-Efficiency-Prediction","owner":"Kiran8053","description":"An end-to-end MLOps project that predicts the efficiency of industrial smart machines—High, Medium, or Low—using real-time operational data. 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Flask application with prediction logic\n-  notebook.ipynb         # Model training and experimentation\n- requirements.txt       # Python dependencies\n- setup.py               # Package configuration\n- Dockerfile             # Docker image build instructions\n- Jenkinsfile            # Jenkins CI/CD pipeline script\n- deployment.yaml        # Kubernetes Deployment manifest\n- service.yaml           # Kubernetes Service manifest\n├── artifacts/\n   -- models/model.pkl   # Trained ML model\n   -- processed/scaler.pkl # Scaler for preprocessing\n   -- templates/\n    --- index.html         # HTML template for Flask UI\n    \n## Model Details\n\n# Input Features\n\nOperation Mode\nTemperature (°C)\nVibration (Hz)\nPower Consumption (kW)\nNetwork Latency (ms)\nPacket Loss (%)\nQuality Control Defect Rate (%)\nProduction Speed (units/hr)\nPredictive Maintenance Score\nError Rate (%)\nYear, Month, Day, Hour\n\n# Output Labels\n\nHigh efficiency\nMedium efficiency\nLow efficiency\n\n## Installation \u0026 Setup\n\n1️⃣ Clone the repository\nbash\nCopy\nEdit\ngit clone https://github.com/Kiran8053?tab=repositories.git\ncd \"Kiran8053/Smart Machines Efficiency Prediction\"\n\n2️⃣ Create \u0026 activate a virtual environment\nbash\nCopy\nEdit\npython -m venv venv\nsource venv/bin/activate   # macOS/Linux\nvenv\\Scripts\\activate      # Windows\n\n3️⃣ Install dependencies\nbash\nCopy\nEdit\npip install -r requirements.txt\n\n4️⃣ Run the application\nbash\nCopy\nEdit\npython application.py\nAccess the app at http://localhost:5000\n\n## Docker Deployment\n\nbash\nCopy\nEdit\n\n# Build the Docker image\n\ndocker build -t smart-machines-efficiency .\n\n# Run the container\n\ndocker run -p 5000:5000 smart-machines-efficiency\n\n☸ Kubernetes Deployment\n\nbash\nCopy\nEdit\nkubectl apply -f deployment.yaml\nkubectl apply -f service.yaml\nOnce deployed, the service will expose the prediction API/UI.\n\n## CI/CD Pipeline\n\nJenkinsfile defines automated stages:\n\nCode checkout\nBuild \u0026 test\nDocker image build \u0026 push\nKubernetes deployment\n\nCan be integrated with GitHub webhooks for continuous delivery.\n\n## Example Prediction\n\nSample Input:\n\nmakefile\nCopy\nEdit\nOperation_Mode: 1\nTemperature_C: 75\nVibration_Hz: 60\nPower_Consumption_kW: 120\nNetwork_Latency_ms: 10\nPacket_Loss_%: 0.2\nQuality_Control_Defect_Rate_%: 1.5\nProduction_Speed_units_per_hr: 500\nPredictive_Maintenance_Score: 0.85\nError_Rate_%: 0.3\nYear: 2025\nMonth: 8\nDay: 13\nHour: 14\n\nSample Output:\n\nmakefile\n\nCopy\n\nEdit\n\nEfficiency: High\n\n📜 License\nThis project is licensed under the MIT License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkiran8053%2Fsmart-machines-efficiency-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkiran8053%2Fsmart-machines-efficiency-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkiran8053%2Fsmart-machines-efficiency-prediction/lists"}