ML 2.16 : Implement KNN without using any standard ML library like scikit-learn or more (D)

Description

📌 Issues for Week 2

Welcome to 'ML' Team, good to see you here

:arrow_forward: This issue will helps readers in acquiring all the knowledge that one needs to know about ----

Implement KNN without using any standard ML library like scikit-learn or more.

:red_circle: To get assigned to this issue, add your Batch Numbers mentioned in the spreadsheet of "Machine Learning", the approach one would follow and choice you prefer (Documentation, Audio, Video). You can go with all three or any number of options you're interested to work on.

✔️ Domain : Machine Learning

:red_circle::yellow_circle: Points to Note :

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Changes should be made inside the Machine Learning/Machine_Learning/Supervised_Machine_Learning Branch.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'GWOC' contributors of 'Machine Learning' domain.
Dataset to be used : IRIS DATASET. (No other datasets will be entertained.)

:white_check_mark: To be Mentioned while taking the issue :

Full name
Batch Number
GitHub Profile Link
Which type of Contribution you want to make :
- [ ] Documentation (Coding in .ipynb file, show the implementation)
  
  Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

Domain

Machine Learning

Type of Contribution

Documentation

Code of Conduct

[X] I follow Contributing Guidelines & Code of conduct of this project.

girlscript / winter-of-contributing