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Description
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Requirement
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Installation
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Directory Structure
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Usage
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.
- Python 3
- Jupyter Notebook
- Numpy
- Pandas
- Matplotlib
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Python
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Visit and download Python from https://www.python.org/downloads/
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Install and add Python to path
python3 -V
Install pip
sudo apt install python3-pip
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Numpy
Using pip,
pip install numpy
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Pandas
In command line, change directory to where pip is present
pip install pandas
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Jupyter notebook
Install the classic Jupyter Notebook using:
pip install notebook
To run the notebook
jupyter notebook
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Matplotlib
In command line, change directory to where pip is present
pip install -U matplotlib
. ├── src # Source files | ├── Distance_Based_Classification.ipynb # Jupyter Notebook | ├── iris.data | ├── iris.names ├── Distance-Based-Classification.docx ├── Project.md └── README.md
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.
Anagha-Sankar/Distance-Based-Classification
Classification of IRIS Dataset using various distance metrics.
Jupyter Notebook