README.md

metadata

language: en
license: mit
tags:
  - sklearn
  - iris
  - classification
  - random-forest

🌸 Iris Flower Classifier

A simple Random Forest classifier trained on the classic Iris dataset.

Model Details

Property	Value
Algorithm	Random Forest
n_estimators	100
Test Accuracy	0.9000
Train samples	120
Test samples	30

Classes

The model predicts one of three Iris species:

• setosa
• versicolor
• virginica

Usage

import pickle, numpy as np

with open("model.pkl",  "rb") as f: model  = pickle.load(f)
with open("scaler.pkl", "rb") as f: scaler = pickle.load(f)

# sepal length, sepal width, petal length, petal width  (all in cm)
X = np.array([[5.1, 3.5, 1.4, 0.2]])
X_scaled = scaler.transform(X)
prediction = model.predict(X_scaled)
print(prediction)   # e.g. [0]  →  setosa

Per-class Metrics

Class	Precision	Recall	F1-score
setosa	1.0000	1.0000	1.0000
versicolor	0.8182	0.9000	0.8571
virginica	0.8889	0.8000	0.8421