Skip to content

About

The following algorithms are used to build models for the different datasets: k-Nearest Neighbour, Decision Tree, Support Vector Machine, Logistic Regression The results is reported as the accuracy of each classifier, using the following metrics when these are applicable: Jaccard index, F1-score, Log Loss. This project counts towards the final gra…

Resources

Stars

18 stars

Watchers

1 watching

Forks

Latest commit

 

History

12 Commits

Folders and files

NameName
Last commit message
Last commit date
 
 
 
 
 
 

Repository files navigation

Coursera-IBM-Machine-Learning-with-Python-Final-Project

The following algorithms are used to build models for the different datasets: k-Nearest Neighbour, Decision Tree, Support Vector Machine, Logistic Regression. The results is reported as the accuracy of each classifier, using the following metrics when these are applicable: Jaccard index, F1-score, Log Loss.

About

The following algorithms are used to build models for the different datasets: k-Nearest Neighbour, Decision Tree, Support Vector Machine, Logistic Regression The results is reported as the accuracy of each classifier, using the following metrics when these are applicable: Jaccard index, F1-score, Log Loss. This project counts towards the final gra…

Resources

Stars

18 stars

Watchers

1 watching

Forks

Releases

Packages

Used by

Contributors

Languages