Stroke prediction is critical in healthcare, as it can significantly impact patient outcomes. This study explores the use of machine learning approaches to predict strokes among both young and elderly patients. By analyzing different factors, we aim to develop a robust model to aid in early detection and prevention. According to the World Health Organization (WHO), stroke is the second leading cause of death globally, accounting for about 11% of all deaths. Thus, the stroke prediction technique with high accuracy would be essential.Machine Learning technique would be the most common technique used to the early prediction in health care area, thus, we aim to use and compare different machine learning method to predict stroke occurrence.

The project goal is to develop predictive models using Machine Learning that leverage employee demographic information, job related insights, performance metrics and other key factors to enable Human Resource Department (HR) to make well-informed, data decisions.

Group 12 - Programming For Data Science (WQD7004) Without evaluation part

Group Member: Kau Zhi Wei Chang Qi Han Cheah Yining Chee Zi Yaw Zhuang Zhen Yu

Our weekly roundup, from 06th to 10th of January 2025.

Tugas Statistika Dasar 14,Materi Uji Hipotesis

This is group assignment of WQD7004 Programming for Data Science, one of the major course in Master of Data Science in University of Malaysia.