🌸 Iris Classification App

This project is a machine learning web application built with Streamlit that classifies Iris flowers using five different algorithms:

• Logistic Regression
• K-Nearest Neighbors (KNN)
• Support Vector Machine (SVM)
• Decision Tree
• Random Forest

The app visualizes Precision-Recall Curves to evaluate the models' performance and helps users interactively explore how different classifiers perform on the Iris dataset.

🚀 Live Demo

🔗 Click here to try the app

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📊 Features

• 🧠 Train and test five popular ML models on the Iris dataset.
• 📈 Visual comparison of performance using Precision-Recall Curves.
• 🔍 Interactive UI with options to choose model and visualize results.

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🛠️ Installation

1. Clone the repository:

   bash

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   git clone https://github.com/your-username/iris-classification-app.git cd iris-classification-app

2. Create a virtual environment (optional but recommended):

   bash

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   python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`

3. Install dependencies:

   bash

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   pip install -r requirements.txt

4. Run the app:

   bash

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   streamlit run app.py

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🧪 Evaluation Metrics

We use Precision-Recall Curves to evaluate model performance, especially useful for imbalanced datasets. The app displays:

• Precision
• Recall
• PR Curve (one-vs-rest for multi-class)

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📊 Dataset

• Source: UCI Machine Learning Repository
• Classes: Setosa, Versicolor, Virginica
• Features: Sepal length/width, Petal length/width

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📦 Dependencies

• scikit-learn
• matplotlib
• pandas
• numpy
• joblib
• streamlit

You can find all dependencies in the requirements.txt.

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📤 Deployment

To deploy the app using Streamlit Cloud:

1. Push the code to a GitHub repo.
2. Go to Streamlit Cloud, sign in with GitHub, and select the repo.
3. Set the main file path to 1_classification.py.
4. Click Deploy.

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🤝 Contributing

Contributions are welcome! Feel free to fork the repo, open issues, or submit PRs.

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📬 Contact

• Author: Apoorv Tripathi
• Email: apoorvtripathi537@gmail.com
• LinkedIn: @apoorv