Git Product home page Git Product logo

customknn's Introduction

CustomKNN

In this project, I created my own K Nearest Neighbour classify and compared it performance with Scikit Learn KNN implementation. I tested the performance of the classify using iris dataset

Quickstart

To run this code, follow below instructions. Make sure you have python 3 installed 
  •  git clone https://github.com/ekmahama/CustomKNN.git 
  •  cd CustomKNN 
  •  pip install requirements.txt
  •  python KNN_Classify.py

Package dependencies

  • matplotlib==3.4.2
  • numpy==1.20.3
  • pyparsing==2.4.7
  • scikit-learn==0.24.2
  • scipy==1.6.3

Results

  • sk_knn and custom_knn agree by: 96.0
  • custom_knn_accuracy: 83.33333333333334
  • sk_knn_accuracy: 84.66666666666667

Plots of dataset and results on 2D grid

Dataset grid

original dataset grid

Prediction grid

predict grid

customknn's People

Contributors

ekmahama avatar

Watchers

avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.