A professional beginner-friendly Machine Learning project designed to classify iris flowers using the famous Iris Dataset.
This project demonstrates the fundamentals of supervised learning for classification, implemented with Linear Regression and enhanced by feature scaling to improve model accuracy and robustness.
It is intended as a guided, reproducible project for those starting in Machine Learning, while maintaining production-ready structure and best practices for deployment and experimentation.
- Introduce supervised learning concepts in a real-world dataset.
- Implement a classification task using Linear Regression.
- Apply feature scaling for improving prediction accuracy.
- Demonstrate reproducibility and professional project structure for ML workflows.
- Provide export options for models across formats (Scikit-learn, TensorFlow, PyTorch, etc.).
You have two ways to use this project:
- Visit the Iris Classifier Repository on GitHub.
- Click “Use this template” to create your own repository.
- On Kaggle, create a new Notebook and select “Link to GitHub” → choose your repository.
- Begin experimenting directly on Kaggle with zero installation overhead.
- Clone the repository:
git clone https://github.com/hatixntsoa/iris.classifier.git
cd iris.classifier-
Create a virtual environment:
python -m venv .venv
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Activate the environment:
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On Linux/Mac:
source .venv/bin/activate -
On Windows:
venv\Scripts\activate
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Install dependencies:
pip install -r requirements.txt
By completing this project, you will:
- Understand the Iris Dataset and its role in ML research.
- Learn how to perform data preprocessing and scaling.
- Train and evaluate a Linear Regression classifier for multi-class prediction.
- Explore export and deployment options for trained models.
- Gain confidence in building well-structured, reproducible ML pipelines.
This project is licensed under the MIT License.