Iris Species Classification

A deep learning project implementing multi-class classification to predict iris flower species using a feedforward neural network with Keras and TensorFlow.

Overview

This project demonstrates a complete machine learning workflow including data preprocessing, model architecture design, training, and evaluation. The model predicts iris species (Setosa, Versicolor, Virginica) based on four morphological features using a neural network with 100% test accuracy.

Dataset

The Iris dataset contains 150 flower samples with 4 continuous features per sample:

• Sepal Length (cm)
• Sepal Width (cm)
• Petal Length (cm)
• Petal Width (cm)

Target: 3 iris species (categorical)

Project Structure

iris-classifier/
├── iris-species.ipynb          # Main implementation notebook
├── README.md                   # Project documentation
├── requirements.txt            # Python dependencies
└── .gitignore                  # Git ignore file

Methodology

1. Data Preprocessing

• Dataset normalization using Min-Max scaling (0-1 range)
• One-hot encoding of categorical target variable
• Train-test split: 70% training, 30% testing

2. Model Architecture

Input Layer (4 features)
    ↓
Dense Layer (64 neurons, ReLU activation)
    ↓
Dense Layer (64 neurons, ReLU activation)
    ↓
Output Layer (3 neurons, Softmax activation)

3. Training Configuration

• Optimizer: Adam (adaptive learning rate)
• Loss Function: Categorical Crossentropy
• Metrics: Accuracy
• Epochs: 100
• Batch Size: Default (32)

Results

Metric	Value
Test Accuracy	97.78%
Test Loss	0.02658
Training Samples	105
Testing Samples	45