🌸 Iris Classifier — AI-Powered Data Classification App

Production-ready Machine Learning App built with KNN, deployed on Streamlit

🚀 Real-time Predictions • 📊 Model Insights • ⚙️ Interactive Tuning

🔗 Live Demo • ⚙️ Setup • 📊 Performance

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🚀 Project Highlights

✅ End-to-end ML pipeline (Data → Model → Deployment) ✅ Clean IPO Architecture (Input → Process → Output) ✅ Real-time predictions with probability confidence ✅ Proper evaluation using F1 Score (not just accuracy) ✅ Interactive UI for model tuning (K selection) ✅ Deployed and accessible globally

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📸 Demo Preview

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🌐 Live Demo

👉 https://irisclassifier-project.streamlit.app/

✔ No installation required ✔ Works on mobile & desktop

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🔍 Overview

This project demonstrates how to build a production-ready machine learning application from scratch.

📊 Dataset

• 150 samples
• 3 classes
• 4 numerical features

Species	Setosa	Versicolor	Virginica
Emoji	🌷	🌼	🌺

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🚀 Features

🔮 Smart Prediction Engine

• Input flower measurements

• Get:

  • 🎯 Predicted class
  • 📊 Probability distribution
  • 🔥 Confidence score

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📊 Model Insights Dashboard

• Confusion Matrix
• F1 Score (per class)
• Classification report

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⚙️ Interactive Model Tuning

• Adjust K (1–20)
• Visualize performance curves

K Range	Behavior
1–2	⚠️ Overfitting
5	✅ Optimal
15+	⚠️ Underfitting

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📚 Dataset Explorer

• Raw data view
• Feature distributions
• Scatter plots

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📊 Model Performance

🔥 Test Results

Metric	Score
Accuracy	~96%
F1 Score (Macro)	~96%

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📉 Confusion Matrix

[[10 0 0]
 [ 0 10 0]
 [ 0  1 9]]

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🧠 Architecture (IPO Framework)

INPUT
- Load dataset
- Train/test split (80/20)
- Feature scaling (StandardScaler)

PROCESS
- KNN (K=5)
- Euclidean distance
- Majority voting

OUTPUT
- Predictions
- Metrics (Accuracy, F1)
- Confusion Matrix

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🛠 Tech Stack

Category	Tools
Frontend	Streamlit
ML	Scikit-learn
Data	Pandas, NumPy
Visualization	Matplotlib, Seaborn
Deployment	Streamlit Cloud

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📦 Installation (Local)

git clone https://github.com/yourusername/iris-classifier.git
cd iris-classifier

python -m venv venv
source venv/bin/activate      # Mac/Linux
# venv\Scripts\activate       # Windows

pip install -r requirements.txt
streamlit run app.py

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🖥 CLI Mode

python classifier.py

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📂 Project Structure

iris-classifier/
├── app.py
├── classifier.py
├── requirements.txt
└── .streamlit/

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🧪 ML Concepts Demonstrated

• Data preprocessing & scaling
• KNN (distance-based learning)
• Model evaluation (F1 Score)
• Confusion matrix interpretation
• Hyperparameter tuning (K selection)
• Probability-based predictions

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🌍 Deployment

Deployed on Streamlit Cloud

Steps:

1. Push to GitHub
2. Go to https://share.streamlit.io
3. Select repo
4. Deploy

✅ Auto redeploy on push

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👨‍💻 Author

Egwuatu Chibuike Dominion AI Engineer

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📧 Contact

📩 chibuikedominion7@gmail.com

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🏁 Final Note

💡 This project reflects my ability to build real-world AI applications — not just models, but complete user-facing systems.

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