Project Overview
This project is an AI-powered predictive maintenance
analytics system built using Python, Pandas,
Matplotlib, Scikit-learn, and Power BI concepts. The project analyzes industrial machine
telemetry
data to identify machine failure patterns, engineer meaningful operational features, and train a
machine learning model capable of predicting machine failures.
The dataset contains 10,000 machine operation records with sensor-based telemetry data such as:
Air temperature
Process temperature
Rotational speed
Torque
Tool wear
Failure modes
The system simulates a real-world industrial monitoring workflow commonly used in:
Smart manufacturing
Industrial IoT systems
Automotive telemetry
Predictive maintenance platforms
AI-based monitoring systems