Botanical_Iris_Classification-

Created Iris flower classification project using Python, scikit-learn, and algorithms like Decision Trees, K-Nearest Neighbors, SVM, and Logistic Regression to classify Iris species by petal and sepal measurements

Iris Flower Classification Project

Overview

This project focuses on the application of machine learning with Python programming for Iris flower classification. We use scikit-learn, a popular machine learning library, to implement various supervised and unsupervised learning algorithms. The primary goal is to classify different species of Iris flowers and measure the accuracy of the models.

Algorithms Used

• Decision Tree Classifier
• K-Nearest Neighbor Classifier (KNN)
• Support Vector Machine (SVM)
• Logistic Regression
• Evaluation Metrics
• Train-Test Split

Project Description

In this project, we leverage machine learning techniques to categorize Iris flowers into their respective species based on their unique characteristics. The Iris dataset is a classic example for beginners, as it provides a straightforward introduction to machine learning concepts.

This project is especially designed to be beginner-friendly and comprehensible for those new to machine learning. It aims to demonstrate the practical application of machine learning for solving real-world problems.

Usage

• Environment Setup: Ensure you have Python and the necessary libraries (scikit-learn, numpy, etc.) installed. You can install required packages using pip install -r requirements.txt.

• Data: The Iris dataset is included in the project. You can also access it here.

• Code: The project code is available in both Jupyter Notebook (iris_classification.ipynb) and Python script (iris_classification.py) formats.

Getting Started

1. Clone the project repository.
2. Run the Jupyter Notebook or Python script to explore the machine learning algorithms and their performance on the Iris dataset.

Contributions

Contributions, issues, and feature requests are welcome. If you find any issues or want to improve the project, please feel free to submit a pull request or open an issue.

Acknowledgments

We would like to thank the UCI Machine Learning Repository for providing the Iris dataset, making it accessible for educational purposes.