Skip to content

Implementing k – Nearest Neighbor classifier from scratch to classify data from the famous IRIS dataset of scikit-learn.

Notifications You must be signed in to change notification settings

danglingP0inter/Iris-flower-classification-using-ML-classifiers

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

7 Commits
 
 
 
 
 
 
 
 

Repository files navigation

Iris-flower-classification-using-ML-classifiers

Implementation of k – Nearest Neighbor classifier from scratch to classify data from the famous IRIS dataset of scikit-learn.

Some lines of the code have been commented deliberately. You can uncomment them to compare the accuracy of the model with default kNN implementation provided by scikit-learn.

Among all well-known ML classifiers, kNN is found to be performing best on this perticular dataset.

For 80:20 train-test split, kNN model has accuracy of 93.3%.

About

Implementing k – Nearest Neighbor classifier from scratch to classify data from the famous IRIS dataset of scikit-learn.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages