Iris Flower Classification

This project classifies the famous Iris flower dataset using two models:

1. Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel and GridSearchCV for hyperparameter tuning.
2. Neural Network implemented with TensorFlow/Keras.

Table of Contents

• Project Overview
• Dataset
• Models Used
• Installation
• Usage
• Results
• Contributing
• License

Project Overview

The Iris dataset contains 150 samples of Iris flowers with 4 features:

• Sepal Length
• Sepal Width
• Petal Length
• Petal Width

The goal of this project is to classify these flowers into three species:

• Iris Setosa
• Iris Versicolor
• Iris Virginica

Dataset

The dataset consists of 150 samples and 5 columns:

• SepalLengthCm, SepalWidthCm, PetalLengthCm, PetalWidthCm: Features of the flowers.
• Species: The target label (Iris-setosa, Iris-versicolor, Iris-virginica).

Models Used

1. Support Vector Machine (SVM)

• Kernel: Radial Basis Function (RBF)
• Hyperparameter Tuning: Grid Search with cross-validation using C and gamma values.
• Achieved an accuracy of 100% on the test data.

2. Neural Network

• A simple Sequential model built with TensorFlow/Keras.
• Architecture:
  • Input Layer: 64 neurons, ReLU activation
  • Hidden Layer: 32 neurons, ReLU activation
  • Output Layer: 3 neurons (one for each class), softmax activation.
• Achieved high accuracy after training with 60 epochs.

Installation

To run this project locally, clone the repository and install the dependencies:

git clone https://github.com/Sudo_User/iris-flower-classification.git
cd iris-flower-classification
pip install -r requirements.txt