24,99 €
inkl. MwSt.
Versandfertig in 6-10 Tagen
12 °P sammeln
Andere Kunden interessierten sich auch für
![Two Stroke Gate Operated Magnetic Engine]( "Two Stroke Gate Operated Magnetic Engine")
Two Stroke Gate Operated Magnetic Engine40,99 €![Analysis of Torsional Vibration in a Crankshaft of Four-Stroke Engine]( "Analysis of Torsional Vibration in a Crankshaft of Four-Stroke Engine")
Analysis of Torsional Vibration in a Crankshaft of Four-Stroke Engine19,99 €![Introduction to Rat Brain EEG Signal and its Significance]( "Introduction to Rat Brain EEG Signal and its Significance")
Introduction to Rat Brain EEG Signal and its Significance34,99 €![Automated Stock Market Trading using Machine Learning]( "Automated Stock Market Trading using Machine Learning")
Automated Stock Market Trading using Machine Learning23,99 €![The self management of chronic conditions]( "The self management of chronic conditions")
The self management of chronic conditions32,99 €![Machine Learning for Microeconomic Forecasting]( "Machine Learning for Microeconomic Forecasting")
Machine Learning for Microeconomic Forecasting29,99 €![Python at the Core: Machine Learning & Beyond]( "Python at the Core: Machine Learning & Beyond")
Python at the Core: Machine Learning & Beyond29,99 €
In the medical world, stroke is one of the most prominent diseases that threaten human life in the world. The most common causes of death in the world are stroke, heart disease and cancer. Fortunately, stroke can be prevented if high risks patients are identified before the occurrence of a stroke. A medical diagnosis for stroke prediction is a complex process. However, machine learning algorithms can be used to facilitate stroke prediction in the early stages. The performance of the classification algorithms depends on a balanced dataset hence this study aims to implement the imbalanced data techniques as undersampling, oversampling and hybrid techniques to enhance the classification models performance.