Anagha-Sankar / Distance-Based-Classification

Classification of IRIS Dataset using various distance metrics.

DISTANCE BASED CLASSIFICATION

CONTENTS:

• Description

• Requirement

• Installation

• Directory Structure

• Usage

  Description

  This project uses different distance metrics to classify the iris dataset. Also, aims to compare the performance of different distance metrics in terms of Misclassification Error Rate and Accuracy.

  Requirement

  1. Python 3
  2. Jupyter Notebook
  3. Numpy
  4. Pandas
  5. Matplotlib

  Installation

  1. Python

     • Visit and download Python from https://www.python.org/downloads/

     • Install and add Python to path

       python3 -V
       

       Install pip

       sudo apt install python3-pip
       

  2. Numpy

     Using pip,

     pip install numpy
     

  3. Pandas

     In command line, change directory to where pip is present

     pip install pandas
     

  4. Jupyter notebook

     Install the classic Jupyter Notebook using:

     pip install notebook
     

     To run the notebook

     jupyter notebook
     

  5. Matplotlib

     In command line, change directory to where pip is present

     pip install -U matplotlib
     

  Directory Structure

  .
  ├── src  # Source files
  |	├── Distance_Based_Classification.ipynb	# Jupyter Notebook
  |	├── iris.data
  |	├── iris.names
  ├── Distance-Based-Classification.docx
  ├── Project.md
  └── README.md
  

  Usage

  The source code (jupyter notebook) is present in the 'src' folder. The dataset used is iris dataset. The outputs are included in the word document 'Distance-Based-Classification.docx'

  Project.md explains the code used in this project.