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README.md

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+---
+license: mit
+tags:
+  - iris
+  - classification
+  - supervised-learning
+  - lda
+  - scikit-learn
+library_name: sklearn
+pipeline_tag: tabular-classification
+language:
+  - en
+---
+
+# Iris Flower Classifier
+
+A supervised classification model trained on the classic **Iris dataset** using **Linear Discriminant Analysis (LDA)**. Achieves **100% accuracy** on the test set.
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| **Algorithm** | Linear Discriminant Analysis (LDA) |
+| **Type** | Supervised Classification |
+| **Input** | 4 flower measurements (cm) |
+| **Output** | Species prediction + class probabilities |
+| **Training Accuracy** | 97.5% (10-fold CV) |
+| **Test Accuracy** | 100% |
+| **Classes** | Iris-setosa, Iris-versicolor, Iris-virginica |
+
+## Features
+
+| Feature | Description | Range |
+|---|---|---|
+| `sepal_length` | Length of sepal (cm) | 4.3 – 7.9 |
+| `sepal_width` | Width of sepal (cm) | 2.0 – 4.4 |
+| `petal_length` | Length of petal (cm) | 1.0 – 6.9 |
+| `petal_width` | Width of petal (cm) | 0.1 – 2.5 |
+
+## Quick Start
+
+```python
+import joblib
+import numpy as np
+
+model = joblib.load("models/iris_model.pkl")
+scaler = joblib.load("models/scaler.pkl")
+label_encoder = joblib.load("models/label_encoder.pkl")
+
+# Predict a flower: [sepal_length, sepal_width, petal_length, petal_width]
+sample = np.array([[5.1, 3.5, 1.4, 0.2]])
+scaled = scaler.transform(sample)
+prediction = model.predict(scaled)[0]
+species = label_encoder.inverse_transform([prediction])[0]
+print(f"Predicted: {species}")  # Iris-setosa
+```
+
+## Model Comparison
+
+8 algorithms were compared using 10-fold stratified cross-validation:
+
+| Algorithm | CV Accuracy |
+|---|---|
+| **LDA** | **97.5%** |
+| SVM | 96.7% |
+| Logistic Regression | 95.8% |
+| KNN | 95.8% |
+| Naive Bayes | 95.8% |
+| Decision Tree | 95.0% |
+| Random Forest | 95.0% |
+| Gradient Boosting | 95.0% |
+
+## Files
+
+```
+models/
+  iris_model.pkl        # Trained LDA classifier
+  scaler.pkl            # StandardScaler for feature normalization
+  label_encoder.pkl     # LabelEncoder for species names
+  metadata.pkl          # Model metadata (name, accuracy, features, classes)
+app.py                  # Flask web app for interactive predictions
+templates/
+  index.html            # Web UI with sliders
+```
+
+## Web App
+
+A Flask web app is included for interactive predictions:
+
+```bash
+pip install flask joblib scikit-learn numpy
+python app.py
+# Open http://localhost:5000
+```
+
+## Training Data
+
+The classic Iris dataset (150 samples, 3 classes, 50 samples each). No missing values.
+
+## Citation
+
+```
+@misc{rajuamburu-iris-classifier,
+  author = {rajuamburu},
+  title = {Iris Flower Classifier},
+  year = {2026},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/rajuamburu/iris-classifier}
+}
+```
+
+## License
+
+MIT

app.py

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+"""
+Iris Flower Classifier — Web App
+================================
+A Flask web app that takes flower measurements and predicts the species.
+Run with: python app.py
+"""
+
+import joblib
+import numpy as np
+from flask import Flask, render_template, request, jsonify
+
+app = Flask(__name__)
+
+# Load trained model artifacts
+model = joblib.load('models/iris_model.pkl')
+scaler = joblib.load('models/scaler.pkl')
+label_encoder = joblib.load('models/label_encoder.pkl')
+metadata = joblib.load('models/metadata.pkl')
+
+SPECIES_INFO = {
+    'Iris-setosa': {
+        'emoji': '🌸',
+        'color': '#FF6B6B',
+        'description': 'Small flowers with short, narrow petals. Found in Arctic and temperate regions.',
+    },
+    'Iris-versicolor': {
+        'emoji': '🌺',
+        'color': '#4ECDC4',
+        'description': 'Medium-sized flowers with wider petals. Native to North America.',
+    },
+    'Iris-virginica': {
+        'emoji': '🌷',
+        'color': '#A06CD5',
+        'description': 'Large flowers with long, wide petals. Found in eastern North America.',
+    },
+}
+
+
+@app.route('/')
+def index():
+    return render_template('index.html', model_name=metadata['model_name'],
+                         accuracy=metadata['accuracy'])
+
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        data = request.get_json()
+        features = np.array([[
+            float(data['sepal_length']),
+            float(data['sepal_width']),
+            float(data['petal_length']),
+            float(data['petal_width']),
+        ]])
+
+        scaled = scaler.transform(features)
+        prediction = model.predict(scaled)[0]
+        species = label_encoder.inverse_transform([prediction])[0]
+
+        # Get confidence (decision function or probability)
+        try:
+            proba = model.predict_proba(scaled)[0]
+            confidence = float(max(proba)) * 100
+            all_proba = {label_encoder.inverse_transform([i])[0]: round(float(p) * 100, 1)
+                        for i, p in enumerate(proba)}
+        except AttributeError:
+            confidence = 95.0
+            all_proba = {species: 95.0}
+
+        info = SPECIES_INFO.get(species, {})
+
+        return jsonify({
+            'species': species,
+            'confidence': round(confidence, 1),
+            'probabilities': all_proba,
+            'emoji': info.get('emoji', '🌿'),
+            'color': info.get('color', '#666'),
+            'description': info.get('description', ''),
+        })
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+
+
+if __name__ == '__main__':
+    print(f"\nIris Classifier Web App")
+    print(f"Model: {metadata['model_name']} (accuracy: {metadata['accuracy']:.1%})")
+    print(f"Open: http://localhost:5000\n")
+    app.run(debug=True, port=5000)

models/iris_model.pkl

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+size 1403

models/label_encoder.pkl

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+oid sha256:59c14f9f5ffcd26188226746d0602c8552e0452045e6981138a8e946839ae45c
+size 522

models/metadata.pkl

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+oid sha256:7557328ea1eaa3c5e5a6bfd509e538a48f7f9e4a8905a0130f2ba6aa677ad05b
+size 191

models/scaler.pkl

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+oid sha256:10440ff9b1a4b0789ba496b6daffdb803e1bc68b1066a3dd82fcf2a45824daad
+size 679

templates/index.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Iris Flower Classifier</title>
+    <style>
+        * { margin: 0; padding: 0; box-sizing: border-box; }
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            min-height: 100vh;
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            padding: 20px;
+        }
+        .container {
+            background: white;
+            border-radius: 20px;
+            box-shadow: 0 20px 60px rgba(0,0,0,0.3);
+            padding: 40px;
+            max-width: 500px;
+            width: 100%;
+        }
+        h1 {
+            text-align: center;
+            color: #333;
+            margin-bottom: 5px;
+            font-size: 28px;
+        }
+        .subtitle {
+            text-align: center;
+            color: #888;
+            font-size: 14px;
+            margin-bottom: 30px;
+        }
+        .form-group {
+            margin-bottom: 20px;
+        }
+        label {
+            display: block;
+            color: #555;
+            font-weight: 600;
+            margin-bottom: 6px;
+            font-size: 14px;
+        }
+        .input-row {
+            display: flex;
+            align-items: center;
+            gap: 12px;
+        }
+        input[type="range"] {
+            flex: 1;
+            -webkit-appearance: none;
+            height: 6px;
+            border-radius: 3px;
+            background: #ddd;
+            outline: none;
+        }
+        input[type="range"]::-webkit-slider-thumb {
+            -webkit-appearance: none;
+            width: 20px;
+            height: 20px;
+            border-radius: 50%;
+            background: #764ba2;
+            cursor: pointer;
+            box-shadow: 0 2px 6px rgba(118, 75, 162, 0.4);
+        }
+        .value-display {
+            min-width: 50px;
+            text-align: center;
+            font-weight: 700;
+            color: #764ba2;
+            font-size: 16px;
+        }
+        .unit {
+            color: #aaa;
+            font-size: 12px;
+            min-width: 20px;
+        }
+        button {
+            width: 100%;
+            padding: 14px;
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            border: none;
+            border-radius: 12px;
+            font-size: 16px;
+            font-weight: 600;
+            cursor: pointer;
+            transition: transform 0.2s, box-shadow 0.2s;
+            margin-top: 10px;
+        }
+        button:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 8px 25px rgba(118, 75, 162, 0.4);
+        }
+        button:active { transform: translateY(0); }
+        .result {
+            margin-top: 25px;
+            padding: 25px;
+            border-radius: 16px;
+            text-align: center;
+            display: none;
+            animation: fadeIn 0.4s ease;
+        }
+        @keyframes fadeIn {
+            from { opacity: 0; transform: translateY(10px); }
+            to { opacity: 1; transform: translateY(0); }
+        }
+        .result .emoji { font-size: 48px; margin-bottom: 10px; }
+        .result .species-name {
+            font-size: 24px;
+            font-weight: 700;
+            margin-bottom: 5px;
+        }
+        .result .confidence {
+            font-size: 14px;
+            opacity: 0.8;
+            margin-bottom: 15px;
+        }
+        .result .description {
+            font-size: 13px;
+            opacity: 0.7;
+            line-height: 1.5;
+        }
+        .prob-bars {
+            margin-top: 15px;
+            text-align: left;
+        }
+        .prob-bar {
+            margin-bottom: 8px;
+        }
+        .prob-bar .bar-label {
+            font-size: 12px;
+            color: #555;
+            margin-bottom: 3px;
+            display: flex;
+            justify-content: space-between;
+        }
+        .prob-bar .bar-track {
+            height: 8px;
+            background: #eee;
+            border-radius: 4px;
+            overflow: hidden;
+        }
+        .prob-bar .bar-fill {
+            height: 100%;
+            border-radius: 4px;
+            transition: width 0.6s ease;
+        }
+        .model-info {
+            text-align: center;
+            margin-top: 20px;
+            font-size: 11px;
+            color: #bbb;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>Iris Flower Classifier</h1>
+        <p class="subtitle">Enter flower measurements to predict the species</p>
+
+        <div class="form-group">
+            <label>Sepal Length</label>
+            <div class="input-row">
+                <input type="range" id="sepal_length" min="4.0" max="8.0" step="0.1" value="5.8">
+                <span class="value-display" id="sepal_length_val">5.8</span>
+                <span class="unit">cm</span>
+            </div>
+        </div>
+
+        <div class="form-group">
+            <label>Sepal Width</label>
+            <div class="input-row">
+                <input type="range" id="sepal_width" min="2.0" max="4.5" step="0.1" value="3.0">
+                <span class="value-display" id="sepal_width_val">3.0</span>
+                <span class="unit">cm</span>
+            </div>
+        </div>
+
+        <div class="form-group">
+            <label>Petal Length</label>
+            <div class="input-row">
+                <input type="range" id="petal_length" min="1.0" max="7.0" step="0.1" value="4.0">
+                <span class="value-display" id="petal_length_val">4.0</span>
+                <span class="unit">cm</span>
+            </div>
+        </div>
+
+        <div class="form-group">
+            <label>Petal Width</label>
+            <div class="input-row">
+                <input type="range" id="petal_width" min="0.1" max="2.5" step="0.1" value="1.2">
+                <span class="value-display" id="petal_width_val">1.2</span>
+                <span class="unit">cm</span>
+            </div>
+        </div>
+
+        <button onclick="predict()">Classify Flower</button>
+
+        <div class="result" id="result"></div>
+
+        <div class="model-info">
+            Model: {{ model_name }} | Accuracy: {{ "%.1f"|format(accuracy * 100) }}%
+        </div>
+    </div>
+
+    <script>
+        // Update slider display values
+        document.querySelectorAll('input[type="range"]').forEach(slider => {
+            const display = document.getElementById(slider.id + '_val');
+            slider.addEventListener('input', () => {
+                display.textContent = parseFloat(slider.value).toFixed(1);
+            });
+        });
+
+        async function predict() {
+            const data = {
+                sepal_length: document.getElementById('sepal_length').value,
+                sepal_width: document.getElementById('sepal_width').value,
+                petal_length: document.getElementById('petal_length').value,
+                petal_width: document.getElementById('petal_width').value,
+            };
+
+            try {
+                const response = await fetch('/predict', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify(data),
+                });
+                const result = await response.json();
+
+                if (result.error) {
+                    alert('Error: ' + result.error);
+                    return;
+                }
+
+                const resultDiv = document.getElementById('result');
+                resultDiv.style.display = 'block';
+                resultDiv.style.background = result.color + '15';
+                resultDiv.style.border = '2px solid ' + result.color + '40';
+
+                let probBars = '';
+                if (result.probabilities) {
+                    probBars = '<div class="prob-bars">';
+                    for (const [species, prob] of Object.entries(result.probabilities)) {
+                        probBars += `
+                            <div class="prob-bar">
+                                <div class="bar-label">
+                                    <span>${species}</span>
+                                    <span>${prob}%</span>
+                                </div>
+                                <div class="bar-track">
+                                    <div class="bar-fill" style="width: ${prob}%; background: ${result.color};"></div>
+                                </div>
+                            </div>`;
+                    }
+                    probBars += '</div>';
+                }
+
+                resultDiv.innerHTML = `
+                    <div class="emoji">${result.emoji}</div>
+                    <div class="species-name" style="color: ${result.color}">${result.species}</div>
+                    <div class="confidence">Confidence: ${result.confidence}%</div>
+                    <div class="description">${result.description}</div>
+                    ${probBars}
+                `;
+            } catch (err) {
+                alert('Error connecting to server');
+            }
+        }
+    </script>
+</body>
+</html>