A beautifully interactive machine learning web app built with Streamlit that predicts Iris flower species based on user inputs or batch CSV upload. This project showcases the end-to-end deployment of a trained Random Forest Classifier with real-time predictions, model confidence visualization, and powerful data insights.
🌐 View on Streamlit Cloud →
(No installation needed — try it instantly!)
- ✅ Manual & Batch prediction modes
- ✅ Real-time output with prediction confidence
- ✅ Interactive visualizations (scatter plots, pair plots)
- ✅ Downloadable prediction reports
- ✅ Modular code for scalability & clean architecture
- ✅ Powered by Streamlit + scikit-learn
Iris-ML/ │ ├── app.py # 🌐 Main Streamlit web app ├── train_model.py # 🧠 Script to train & save Random Forest model ├── predictor.py # 🔮 Loads model and returns predictions ├── utils.py # 📊 Charts & report helpers ├── sample_input.csv # 📥 Sample input CSV for batch prediction ├── requirements.txt # 📦 Python dependencies ├── README.md # 📘 Project documentation └── model/ └── iris_rf.pkl # ✅ Trained Random Forest model
yaml Copy Edit
| Layer | Tech |
|---|---|
| Frontend | Streamlit |
| ML Framework | scikit-learn |
| Model Type | Random Forest Classifier |
| Visualization | Seaborn, Matplotlib |
| Format | .pkl for ML model |
| Detail | Value |
|---|---|
| Dataset | Iris Dataset (150 samples) |
| Features | Sepal & Petal Length/Width |
| Algorithm | Random Forest Classifier |
| Accuracy | ~97% on test set |
| Output | Iris species (Setosa, Versicolor, Virginica) |
- Clone the Repository
git clone https://github.com/MrKratik/Iris-ML.git cd Iris-ML
Install Dependencies
bash Copy Edit pip install -r requirements.txt Train the Model (optional, already included)
bash Copy Edit python train_model.py Run the App
bash Copy Edit streamlit run app.py 🧪 Usage Use the web interface to enter values manually OR upload a CSV file.
Test with the provided sample_input.csv file.
View real-time predictions with confidence scores.
Download results for offline use.
🌐 Deploy on Streamlit Cloud (Free) Push code to a public GitHub repo (✅ already done).
Go to https://streamlit.io/cloud
Click "New App" and connect your repo: MrKratik/Iris-ML
Set main file to app.py, and branch to main
Click "Deploy"
Visit your live app: 👉 https://celebal-ml-devlopment.streamlit.app/
👨💻 Author Kratik Jain 📧 kratikjain121@gmail.com 🔗 https://github.com/MrKratik/Iris-ML
📄 License This project is licensed under the MIT License.