Iris Classification

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Short description: A machine learning project to classify Iris flower species (Iris-setosa, Iris-versicolor, and Iris-virginica) using [your chosen language or framework, e.g., Python & scikit-learn].

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Table of Contents

• Project Overview
• Dataset
• Installation
• Usage
• Project Structure
• Model Training & Evaluation
• Results
• Technologies Used
• Future Work
• Contributing
• License

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Project Overview

Provide a high-level summary of why this project exists and what it does. E.g.:

This project implements a classification pipeline for the classic Iris dataset, training a model to predict the iris species based on sepal and petal measurements.

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Dataset

Explain the dataset details:

• Source: Iris dataset from UCI Machine Learning Repository (or loaded via scikit-learn.datasets.load_iris) :contentReference[oaicite:0]{index=0}.
• Samples: 150 total, evenly distributed among three species (Setosa, Versicolor, Virginica) :contentReference[oaicite:1]{index=1}.
• Features: Sepal length, sepal width, petal length, petal width—all in centimeters :contentReference[oaicite:2]{index=2}.

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Installation

Include setup steps:

git clone https://github.com/arpitaapatel/Iris-Classification.git
cd Iris-Classification
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt



Model Training & Evaluation

Summarize your modeling process:

Splitting dataset (e.g., 80% train, 20% test)

Feature scaling or encoding (if done)

Models tried: logistic regression, KNN, decision tree, Naive Bayes, Random Forest (common for Iris projects) 
upGrad
DataFlair
Medium
.

Evaluation metrics: accuracy, classification report, confusion matrix, recall, precision, F1-score 
Medium
Medium
.