{"id":30344428,"url":"https://github.com/keneandita/iris-intel","last_synced_at":"2026-05-06T02:31:40.507Z","repository":{"id":293874375,"uuid":"984948682","full_name":"KeneanDita/Iris-Intel","owner":"KeneanDita","description":"Iris Flower Classifier is a simple web app built with Streamlit that predicts the species of an Iris flower based on user-input flower features. 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On Linux/Mac\n   .\\venv\\Scripts\\activate      # On Windows\n   ```\n\n3. **Install dependencies**\n\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n4. **Run the Streamlit app**\n\n   ```bash\n   streamlit run Stream.py\n   ```\n\n---\n\n## 📂 Project Structure\n\n```project-structure\nIris-Intel/\n│── Exported Models/\n│   ├── Logistic_regression.joblib\n│   ├── KNN.joblib\n│   ├── svm_model.joblib\n│   ├── Decision_Tree.joblib\n│\n│── Iris_Classification.ipynb   # ML training notebook \n│── Stream.py              # Main Streamlit app\n│── requirements.txt       # Dependencies\n│── README.md              # Project documentation\n```\n\n## Demo\n\n**Input example**:\n\n* Sepal Length: `5.1 cm`\n* Sepal Width: `3.5 cm`\n* Petal Length: `1.4 cm`\n* Petal Width: `0.2 cm`\n\n**Output**:\n\n```sample_output\nThe predicted class is Setosa using Logistic Regression!\n```\n\nConfidence Scores:\n\n* Setosa: 97.3%\n* Versicolor: 2.6%\n* Virginica: 0.1%\n\nAuthor: [Kenean Dita](https://github.com/keneandita/)# 🌸 Iris Flower Classifier\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkeneandita%2Firis-intel","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkeneandita%2Firis-intel","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkeneandita%2Firis-intel/lists"}