Wafer Fault Detection

Machine learning project using PyCharm is to identify defects in semiconductor wafers to ensure high-quality chip production and maximize yield based on the information given in .csv file. There are two classes: +1 and -1. +1 means that the wafer is in a working condition and it does not need to be replaced. Utilized two prominent algorithms, Random Forest Classifier and XG Boost Classifier for defect estimation then evaluated and compared the performance of the models in wafer defect.

PROTO-Customized AI Assistant for Personal Tasks

To develop an AI assistant to enhance user productivity and efficiency by providing customizable task automation, personalized assistance, and accurate information retrieval. Implementing Python libraries including speech recognition and pyttsx3 for analyzing user inputs, enhancing the assistant's ability to comprehend and respond to a wide range of queries with contextual understanding.