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🌸 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

🚀 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

📸 Demo Preview

Demo

🌐 Live Demo

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

✔ No installation required ✔ Works on mobile & desktop

🔍 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 🌷 🌼 🌺

🚀 Features

🔮 Smart Prediction Engine

  • Input flower measurements

  • Get:

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

📊 Model Insights Dashboard

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

⚙️ Interactive Model Tuning

  • Adjust K (1–20)
  • Visualize performance curves
K Range Behavior
1–2 ⚠️ Overfitting
5 ✅ Optimal
15+ ⚠️ Underfitting

📚 Dataset Explorer

  • Raw data view
  • Feature distributions
  • Scatter plots

📊 Model Performance

🔥 Test Results

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

📉 Confusion Matrix

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

🧠 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

🛠 Tech Stack

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

📦 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

🖥 CLI Mode

python classifier.py

📂 Project Structure

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

🧪 ML Concepts Demonstrated

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

🌍 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

👨‍💻 Author

Egwuatu Chibuike Dominion AI Engineer

📧 Contact

📩 chibuikedominion7@gmail.com

🏁 Final Note

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

⭐ Star this repo if you found it valuable!