An end-to-end Machine Learning project for Predictive Maintenance using multiple classification models(Logistic Regression,Decision Tree, Random Forest, and XGBoost).The project includes EDA, data preprocessing, SMOTE, feature engineering, model evaluation,SHAP explainability,and model deployment using Joblib to predict machine failures accurately.
devabhinavraj/Predictive-Maintenance-using-Machine-Learning
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