This is a learning project that aims to build a predictive model to control quality in semiconductor manufacturing using machine learning techniques. The project uses the SECOM dataset and applies various models.

This repository presents a complete machine learning and deep learning pipeline for analyzing the UCI SECOM Dataset, a real-world dataset from semiconductor manufacturing. The objective is to predict whether a chip passes or fails quality control, enabling predictive quality assurance in manufacturing processes.

This project predicts pass/fail yield in semiconductor manufacturing using data cleaning, statistical analysis, and machine learning. It includes univariate/multivariate analysis, SMOTE balancing, model training, and evaluation to improve quality control and production efficiency.

An end-to-end machine learning project for predicting semiconductor wafer pass/fail outcomes using the UCI SECOM dataset. This project demonstrates comprehensive data science workflow from exploratory data analysis through model deployment, with a focus on handling extreme class imbalance in manufacturing quality control.