🌸 Iris Species Classifier (Logistic Regression)

This repository provides a lightweight logistic regression model trained on the classic Iris dataset using scikit-learn. It is ideal for educational purposes, experimentation, and demonstration of inference on tabular data.

🧠 Model Overview

Algorithm: Logistic Regression
Framework: scikit-learn
Features:
- sepal_length
- sepal_width
- petal_length
- petal_width
Target Classes:
- setosa
- versicolor
- virginica

🚀 Usage Example

Python Code

import joblib
import pandas as pd

model = joblib.load("model.joblib")

sample = pd.DataFrame([[5.1, 3.5, 1.4, 0.2]],
                      columns=["sepal_length", "sepal_width", "petal_length", "petal_width"])

prediction = model.predict(sample)
print(f"🌸 Predicted class: {prediction[0]}")

🔄 How to load the model

from huggingface_hub import hf_hub_download
import joblib

model_path = hf_hub_download("DmytroSerbeniuk/my-iris-model", "model.joblib")
model = joblib.load(model_path)

CLI Inference

You can also use the provided inference.py script:

python3 inference.py 6.0 2.2 4.0 1.0

Expected Output:

🌸 Predicted class: versicolor

🧪 Sample Predictions

Sepal Length	Sepal Width	Petal Length	Petal Width	Prediction
5.1	3.5	1.4	0.2	setosa
6.0	2.2	4.0	1.0	versicolor
6.9	3.1	5.4	2.1	virginica

📦 Requirements

Dependencies listed in requirements.txt:

scikit-learn
pandas
joblib

License

my-iris-model is licensed under the Apache 2.0 license