🌸 Iris Species Prediction App

A Streamlit-based interactive machine learning application for predicting the species of iris flowers using multiple classifiers. This app supports single predictions, batch predictions via CSV, visualizations, and SHAP-based interpretability.

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🚀 Features

• Model Selection: Random Forest, SVM, Logistic Regression
• Single Prediction: Enter feature values using sliders
• Batch Prediction: Upload CSV and get predictions with download option
• Data Visualizations: Pairplots, scatterplots
• Model Evaluation: Accuracy, confusion matrix, classification report
• SHAP Explanation: Visual force plot for Random Forest predictions
• Custom Training: Upload your own training dataset (with the same structure)

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📂 Project Structure

Streamlit_ml_app/
├── classification.py         # Main Streamlit app
├── requirements.txt          # Python dependencies
└── README.md                 # Project documentation

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⚙️ Setup Instructions

1. Clone the repository

git clone https://github.com/yourusername/Streamlit_ml_app.git
cd Streamlit_ml_app

2. Create and activate a virtual environment (optional but recommended)

python -m venv .venv
# Activate it
# On Windows:
.venv\Scripts\activate
# On macOS/Linux:
source .venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Run the app

streamlit run classification.py

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📌 How to Use

1. Choose a model from the sidebar.
2. Set hyperparameters and provide input for single prediction.
3. Upload a CSV file for batch prediction (same column format as iris dataset).
4. View model metrics and SHAP explanations if using Random Forest.
5. (Optional) Upload your own dataset for training under "Custom Training Data".

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🧲 Example CSV Format

sepal length (cm),sepal width (cm),petal length (cm),petal width (cm)
5.1,3.5,1.4,0.2
6.2,3.4,5.4,2.3
...

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🖼️ Screenshots

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📄 License

MIT License © 2025 Sameer017