iris-logreg-sklearn-demo

Iris Logistic Regression (scikit-learn)

This repository contains a simple Logistic Regression classifier trained on the classic Iris dataset using scikit-learn.

Details

• Library: scikit-learn
• Task: Multi-class classification (3 Iris species)
• Model: LogisticRegression(max_iter=200)
• Test accuracy: 0.9333 (93.33%)

Files

• model.pkl – a pickled Python dictionary containing:
  • model: trained LogisticRegression object
  • scaler: StandardScaler used for preprocessing
  • target_names: class names (setosa, versicolor, virginica)

Usage Example

import pickle
import numpy as np

# Load the model bundle
with open("model.pkl", "rb") as f:
    bundle = pickle.load(f)

model = bundle["model"]
scaler = bundle["scaler"]
target_names = bundle["target_names"]

# Example prediction: sepal_length, sepal_width, petal_length, petal_width
x_new = np.array([[5.1, 3.5, 1.4, 0.2]])
x_new_scaled = scaler.transform(x_new)

pred = model.predict(x_new_scaled)[0]
print("Predicted class:", target_names[pred])

Classes

1. Setosa
2. Versicolor
3. Virginica

Dataset

The Iris dataset is a classic machine learning dataset containing 150 samples with 4 features each:

• Sepal length (cm)
• Sepal width (cm)
• Petal length (cm)
• Petal width (cm)

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Created with: scikit-learn
Uploaded to: Hugging Face Hub