This project develops a data-driven Predictive Maintenance (PdM) system designed

for industrial milling operations. By leveraging Machine Learning through Logistic

Regression, the system analyzes real-time sensor data—including Torque,

Rotational Speed, and Tool Wear—to forecast machine failures before they occur.

The primary goal is to transition from reactive 'Run-to-Failure' strategies to

proactive monitoring, achieving an 86% AUC performance score. The project

focuses on identifying key failure drivers to minimize operational risks.

Technologies utilized include Excel for Exploratory Data Analysis (EDA) and Logistic

Regression for binary classification and predictive modeling.