https://github.com/tusharpandey003/iris-flower-classification

Iris flower classification using KNN and Random forest algorithm
https://github.com/tusharpandey003/iris-flower-classification

data-science iris iris-classification iris-data iris-dataset iris-detection iris-flower-classification iris-flowers knn-classification machine-learning-algorithms random-forest scikit-learn streamlit

Last synced: about 1 month ago
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Iris flower classification using KNN and Random forest algorithm

• Host: GitHub
• URL: https://github.com/tusharpandey003/iris-flower-classification
• Owner: tusharpandey003
• Created: 2024-03-30T12:07:36.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-04-12T13:48:56.000Z (8 months ago)
• Last Synced: 2024-04-12T21:14:48.904Z (8 months ago)
• Topics: data-science, iris, iris-classification, iris-data, iris-dataset, iris-detection, iris-flower-classification, iris-flowers, knn-classification, machine-learning-algorithms, random-forest, scikit-learn, streamlit
• Language: Jupyter Notebook
• Homepage:
• Size: 27.3 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Iris flower classification

Welcome to this GitHub repository, a comprehensive resource for Iris flower classification. The Iris flower dataset is a classic in the field of machine learning, featuring three species: Versicolor, Sentosa, and Virginica. Each species is characterized by four features: sepal length, sepal width, petal length, and petal width.

This repository presents two distinct approaches to classify the Iris flowers. The first approach is encapsulated in a Jupyter notebook titled ‘IRIS flower classification.ipynb’. This notebook employs the K-Nearest Neighbors (KNN) algorithm, a popular choice for classification tasks. The notebook meticulously outlines each step of the process, from loading the dataset to splitting it into training, validation, and testing sets. The model is then trained using KNN and its performance is evaluated using a classification report and confusion matrix. The final step involves testing the trained model on new data.

The second approach is implemented in an ‘app.py’ file. This file contains a Streamlit web application that uses the Random Forest algorithm to classify the Iris flowers. Random Forest is a robust and versatile algorithm known for its high accuracy and ability to prevent overfitting. The application provides an interactive platform for users to classify Iris flowers based on their features.

In conclusion, this repository serves as a valuable resource for both beginners and experienced practitioners in the field of machine learning. It not only provides practical implementations of two popular machine learning algorithms but also serves as a guide on how to handle real-world datasets and develop machine learning models. Explore the repository and delve into the fascinating world of Iris flower classification! 😊