ML Iris Classification

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Description

This project demonstrates a complete Machine Learning workflow for classifying Iris flower species using a neural network implemented with TensorFlow/Keras.
The model is trained on the well-known Iris dataset, which includes measurements of sepal length, sepal width, petal length, and petal width.

The trained model achieves high accuracy on unseen test data and can be easily deployed for inference.

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Features

• Data loading and preprocessing using scikit-learn.
• One-hot encoding of target labels for multi-class classification.
• Train-test split with validation set.
• Fully connected neural network with multiple hidden layers.
• Model training with configurable epochs and batch size.
• Model saved via TensorFlow/Keras, compatible with HDF5 and future Keras native formats.
• Versioned model management using Git LFS for large files.

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Author

Nikabou Gaou Nadjombe

• Email: [your_email@example.com]
• GitHub: https://github.com/LucNIK
• Project License: MIT License (or specify another license)

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Project Structure

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Installation

1. Clone the repository:

git clone https://github.com/LucNIK/ml-iris-classification.git
cd ml-iris-classification

2. Create a virtual environment and activate it:

python -m venv venv
.\venv\Scripts\activate  # Windows
source venv/bin/activate # Linux/macOS

3. Install dependencies:

pip install -r requirements.txt

Usage

Train the model

python scripts/train_model.py

.The trained model will be saved in models/iris_model.h5.

.Training metrics and accuracy will be displayed in the console.

Load the model for inference

from tensorflow.keras.models import load_model

model = load_model("models/iris_model.h5")
predictions = model.predict(new_data)

Screenshots

License

© 2025 Nikabou Gaou Nadjombe All rights reserved.

This project is licensed under the MIT License — see the LICENSE file for details.

Future Work

.Implement hyperparameter tuning for optimal model performance.

.Add interactive web app for real-time inference.

.Include visualizations for data exploration and model evaluation.

.Extend to other datasets or classification tasks.