my-iris-model

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🌸 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

📦 Requirements

Dependencies listed in requirements.txt:

scikit-learn
pandas
joblib

License

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