https://github.com/brazer27/iris-classification

A Python implementation of Naive Bayes algorithm for Iris flower classification. Features include cross-validation, data preprocessing, and prediction capabilities. Built from scratch without ML libraries, achieving ~95% accuracy on the classic Iris dataset.
https://github.com/brazer27/iris-classification

cross-validation data-science data-visualization flower-classification iris-dataset machine-learning naive-bayes python

Last synced: 27 days ago
JSON representation

A Python implementation of Naive Bayes algorithm for Iris flower classification. Features include cross-validation, data preprocessing, and prediction capabilities. Built from scratch without ML libraries, achieving ~95% accuracy on the classic Iris dataset.

• Host: GitHub
• URL: https://github.com/brazer27/iris-classification
• Owner: Brazer27
• Created: 2024-11-28T08:18:04.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-11-28T08:19:25.000Z (about 1 month ago)
• Last Synced: 2024-11-28T09:25:36.137Z (about 1 month ago)
• Topics: cross-validation, data-science, data-visualization, flower-classification, iris-dataset, machine-learning, naive-bayes, python
• Language: Python
• Homepage:
• Size: 6.84 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Iris Classification using Naive Bayes

A Python implementation of the Naive Bayes algorithm for classifying Iris flowers. This project provides two implementations:

- A comprehensive version with cross-validation and visualization

- A simplified version focused on making predictions

## Features

- Gaussian Naive Bayes implementation from scratch

- K-fold cross-validation

- Performance visualization using box plots

- Data preprocessing utilities

- Simple interface for making predictions on new data

Dataset: The classic Iris dataset containing 150 samples with 4 features (sepal length, sepal width, petal length, petal width) and 3 classes of Iris flowers.

Average accuracy: ~95% using 5-fold cross-validation