{"id":25721367,"url":"https://github.com/parag000/predictive-machine-maintenance","last_synced_at":"2026-04-20T13:36:21.722Z","repository":{"id":273817269,"uuid":"920979794","full_name":"Parag000/Predictive-Machine-Maintenance","owner":"Parag000","description":"This project performs multiclass classification to identify machine failure types using a synthetic dataset with features like temperature, torque, and tool wear. 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By anticipating failures, timely maintenance can be performed, reducing downtime and saving costs.\n\n---\n\n## 📊 Dataset Overview\nThe synthetic dataset simulates real-world maintenance scenarios with:\n- **10,000 records** and **14 features**\n- **Target variable**:\n  - `Failure_Type`: Specifies the type of failure (multiclass labels).\n- **Features** include:\n  - Continuous: Temperature, rotational speed, torque, tool wear, etc.\n  - Categorical: Product quality, serial numbers, etc.\n\n---\n\n## 🚀 Project Workflow\n1. **EDA \u0026 Data Preperations**:\n   - Statistical Analysis\n   - Visualizations\n   - Handling missing values and outliers\n   - Skewness Analysis\n   - Correlation Analysis\n\n2. **Feature Engineering \u0026 Normalization**:\n   - Three new features were engineered\n   - Ordinal and Standard normalization \n\n3. **Model Training**:\n   - Multiclass classification using algorithms like Decision Trees, Gradient Boosting, etc.\n\n4. **Evaluation**:\n   - Metrics: Accuracy, Precision, Recall, F1-Score, Support\n   - Classification report\n\n---\n\n## 🛠️ Requirements\n- Python 3.8 or above\n- Libraries:\n  - pandas\n  - numpy\n  - scikit-learn\n  - matplotlib\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fparag000%2Fpredictive-machine-maintenance","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fparag000%2Fpredictive-machine-maintenance","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fparag000%2Fpredictive-machine-maintenance/lists"}