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🌸 Iris Deep Learning Classifier

This repository contains a deep learning-based classifier for the classic Iris flower dataset. The model is built using TensorFlow and Keras, and demonstrates how to apply neural networks to a simple multiclass classification problem.

📊 Dataset

The Iris dataset is a well-known dataset used for pattern recognition. It contains 150 samples of iris flowers, divided into three species: Setosa, Versicolor, and Virginica. Each sample has four features:

  • Sepal length
  • Sepal width
  • Petal length
  • Petal width

🧠 Model Overview

The model is a feedforward neural network (using Sequential from Keras) trained to classify the iris species based on the four input features. It uses:

  • Dense layers with ReLU activation
  • Categorical output with softmax
  • Evaluation via classification report

🚀 Installation

To run this project locally, follow the steps below:

1. Clone the Repository

git clone https://github.com/Kushan2k/simple-iris-dl-classifier.git
cd iris-dl-classifier

2. Setup a virtual environment(Optional)

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

Required Libraries:

  • numpy
  • matplotlib
  • seaborn
  • tensorflow
  • scikit-learn

If requirements.txt is missing, you can install manually:

pip install numpy matplotlib seaborn tensorflow scikit-learn

🧪 How to Run

You can run the notebook using:

jupyter notebook iris_dataset.ipynb

Or convert to a script:

jupyter nbconvert --to script iris_dataset.ipynb
python iris_dataset.py

📈 Results

After training, the model is evaluated using:

  • Accuracy
  • Confusion matrix
  • Classification report

You’ll also see visualizations of the training history and predictions using matplotlib and seaborn.

📁 File Structure

iris-dl-classifier/
│
├── iris_dataset.ipynb      # Main Jupyter notebook
├── README.md               # Project documentation
├── requirements.txt        # Dependencies (optional)
└── outputs/                # (Optional) Saved plots and model

✍️ Author

Kushan Gayantha Software Engineer @ Freelancer

📄 License

This project is open source and available under the MIT License.

Let me know if you want a matching `requirements.txt` generated for this project!

Thank you