This project predicts machine failures before they happen using ML (Random Forest, XGBoost) and DL (LSTM/GRU). It includes data preprocessing, model training, evaluation, and a Streamlit dashboard.

🚀 Features

• Data preprocessing & feature engineering
• ML models: Random Forest, XGBoost
• DL models: LSTM / GRU for time-series failure prediction
• Model evaluation with confusion matrix, ROC-AUC, F1 score
• Interactive dashboard with failure probability & sensor monitoring
• Deployment-ready Streamlit app