Machine Learning
SVM Engine Failure Detection
SVM model detecting engine failures with 90% accuracy on 1000+ records. Includes EDA and training in Jupyter.
Overview
An industrial predictive maintenance machine learning system. It classifies engine health status based on sensor readings like temperature, vibration, and noise, helping prevent catastrophic mechanical failures.
Features
- Sensor feature correlation analysis & Exploratory Data Analysis (EDA)
- Support Vector Classifier hyperparameter tuning (GridSearchCV)
- Interactive classification reports and confusion matrices
- Feature importance ranking visualization
Technology
PythonScikit-LearnSeabornMatplotlibJupyter Notebook
Screenshots
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