Customer Churn Prediction Using Machine Learning

Developed a machine learning workflow for predictive maintenance using the AI4I 2020 predictive maintenance dataset.

The project includes exploratory data analysis (EDA), data preprocessing, feature selection, categorical feature encoding, and model development.

K-Nearest Neighbors (KNN) and Decision Tree classifiers were used to predict machine failures. Model evaluation includes classification metrics, confusion matrices, and ROC-AUC analysis.

The goal is to help identify potential machine failures and support proactive maintenance decisions.