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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

    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.