Machine Failure Prediction

Developed a Machine Learning system that predicts machine failures before they occur using real-time sensor data helping businesses shift from reactive to predictive maintenance.

The Challenge

Unplanned machine failures lead to:

Production downtime

High emergency repair costs

Safety risks

Revenue loss

The Approach

Cleaned and preprocessed sensor data

Handled severe class imbalance using Random Oversampling

Applied MinMaxScaler for feature scaling

Trained and compared multiple models

Selected Random Forest as the best-performing model

Final Model Performance

98.1% Accuracy

98% Precision

98.1% Recall

Also deployed a Streamlit app for real-time failure prediction based on machine sensor inputs.

Projected Business Impact

30–40% maintenance cost reduction

50–70% downtime reduction

Improved safety and equipment lifespan