{"id":21288221,"url":"https://github.com/ruban2205/iris_classification","last_synced_at":"2025-08-05T09:16:56.786Z","repository":{"id":184255101,"uuid":"671173028","full_name":"Ruban2205/Iris_Classification","owner":"Ruban2205","description":"This repository contains the Iris Classification Machine Learning Project. 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Notebook","readme":"\u003c!-- To Bring back the link to top--\u003e \n\u003ca name=\"readme-top\"\u003e\u003c/a\u003e\n\n# 🌷 Iris Classification \n\n[![Contributors][contributors-shield]][contributors-url]\n[![Forks][forks-shield]][forks-url]\n[![Stargazers][stars-shield]][stars-url]\n[![Issues][issues-shield]][issues-url]\n[![MIT License][license-shield]][license-url]\n[![LinkedIn][linkedin-shield]][linkedin-url]\n[![Twitter][twitter-shield]][twitter-url]\n\n\u003c!-- MARKDOWN LINKS \u0026 IMAGES --\u003e\n\u003c!-- https://www.markdownguide.org/basic-syntax/#reference-style-links --\u003e\n[contributors-shield]: https://img.shields.io/github/contributors/Ruban2205/Iris_Classification.svg?style=for-the-badge\n[contributors-url]: https://github.com/Ruban2205/Iris_Classification/graphs/contributors\n[forks-shield]: https://img.shields.io/github/forks/Ruban2205/Iris_Classification.svg?style=for-the-badge\n[forks-url]: https://github.com/Ruban2205/Iris_Classification/network/members\n[stars-shield]: https://img.shields.io/github/stars/Ruban2205/Iris_Classification.svg?style=for-the-badge\n[stars-url]: https://github.com/Ruban2205/Iris_Classification/stargazers\n[issues-shield]: https://img.shields.io/github/issues/Ruban2205/Iris_Classification.svg?style=for-the-badge\n[issues-url]: https://github.com/Ruban2205/Iris_Classification/issues\n[license-shield]: https://img.shields.io/github/license/Ruban2205/Iris_Classification.svg?style=for-the-badge\n[license-url]: https://github.com/Ruban2205/Iris_Classification/blob/main/LICENSE\n[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge\u0026logo=linkedin\u0026colorB=555\n[linkedin-url]: https://linkedin.com/in/ruban-gino-singh\n[twitter-shield]: https://img.shields.io/badge/X.com%20(Twitter)%20-black.svg?style=for-the-badge\u0026logo=X\u0026colorB=555\n[twitter-url]: https://x.com/Rubangino\n\n\u003c!-- PROJECT LOGO --\u003e\n\u003cbr /\u003e\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://github.com/Ruban2205/Iris_Classification/\"\u003e\n    \u003cimg src=\"assets/logo.jpg\" alt=\"Logo\" width=\"160\" height=\"80\"\u003e\n  \u003c/a\u003e\n\n  \u003ch3 align=\"center\"\u003eIris-Classification\u003c/h3\u003e\n\n  \u003cp align=\"center\"\u003e\n    An Iris Classification project built with comparision of four different Machine Learning models\n    \u003cbr /\u003e\n    \u003ca href=\"https://github.com/Ruban2205/Iris_Classification/blob/main/iris_classification_main.ipynb\"\u003e\u003cstrong\u003eExplore the project »\u003c/strong\u003e\u003c/a\u003e\n    \u003cbr /\u003e\n    \u003cbr /\u003e\n    \u003ca href=\"https://irisclassifier.streamlit.app/\"\u003eView Demo\u003c/a\u003e\n    ·\n    \u003ca href=\"https://github.com/Ruban2205/Iris_Classification/issues\"\u003eReport Bug\u003c/a\u003e\n    ·\n    \u003ca href=\"https://github.com/Ruban2205/Iris_Classification/issues\"\u003eRequest Feature\u003c/a\u003e\n  \u003c/p\u003e\n\u003c/div\u003e\n\n\u003c!-- TABLE OF CONTENTS --\u003e\n\u003cdetails\u003e\n  \u003csummary\u003eTable of Contents\u003c/summary\u003e\n  \u003col\u003e\n    \u003cli\u003e\n      \u003ca href=\"#about-the-project-\"\u003eAbout The Project\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#project-workflow-\"\u003eProject Workflow\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#built-with-%EF%B8%8F\"\u003eBuilt With\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\n      \u003ca href=\"#getting-started-\"\u003eGetting Started\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#prerequisites-\"\u003ePrerequisites\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#installation-\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#usage-\"\u003eUsage\u003c/a\u003e\u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#contributing-\"\u003eContributing\u003c/a\u003e\u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#license-\"\u003eLicense\u003c/a\u003e\u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#acknowledgements-\"\u003eAcknowledgments\u003c/a\u003e\u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#contact-%EF%B8%8F\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n  \u003c/ol\u003e\n\u003c/details\u003e\n\n\u003c!-- About the project--\u003e\n## About the Project 💻\n\n[![Iris_Classification_Product_Screenshot](assets/output2.jpg)](https://irisclassifier.streamlit.app/)\n\nThe Iris Classification Machine Learning Project is a thorough investigation of multi-modal machine learning methods used to classify iris blossoms into several species according to their morphological traits. This project includes the collection of data, data preprocessing, feature scaling, model training, model assessment, and finally the creation and implementation of an intuitive interface using Streamlit.\n\n\u003cp align=\"right\"\u003e(\u003ca href=\"#readme-top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\n\n\u003c!--Built with Section--\u003e \n## Project Workflow 📚\n\nThe project follows a structured workflow:\n\n1) **Data Gathering:** Collecting the iris dataset, which includes measurements of sepal length, sepal width, petal length, petal width, and corresponding species labels.\n\n2) **Data Preprocessing:** Cleaning and preparing the data for training, including handling missing values, encoding categorical variables, and splitting into training and testing sets.\n\n3) **Feature Scaling:** Scaling the features to ensure that they have a consistent influence on the machine learning model.\n\n4) **Model Training:** Choosing a machine learning algorithm and training the model using the preprocessed data.\n\n5) **Model Evaluation:** Assessing the model's performance using various metrics such as accuracy, precision, recall, and F1-score to gauge its effectiveness in classifying iris species.\n\n6) **Model Building and Deployment:** Developing a user-friendly Streamlit application to interact with the trained model. Users can input iris measurements and receive predictions on the species of the flower.\n\n\u003cp align=\"right\"\u003e(\u003ca href=\"#readme-top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\n\n## Built With 🖥️\n\n[![Python](https://img.shields.io/badge/Python-FFD43B?style=for-the-badge\u0026logo=python\u0026logoColor=blue)](https://github.com/Ruban2205)\n[![Jupyter Notebook](https://img.shields.io/badge/Jupyter-F37626.svg?\u0026style=for-the-badge\u0026logo=Jupyter\u0026logoColor=white)](https://github.com/Ruban2205)\n\n[![Pandas](https://img.shields.io/badge/Pandas-2C2D72?style=for-the-badge\u0026logo=pandas\u0026logoColor=white)](https://github.com/Ruban2205)\n[![Numpy](https://img.shields.io/badge/Numpy-777BB4?style=for-the-badge\u0026logo=numpy\u0026logoColor=white)](https://github.com/Ruban2205)\n[![SciPy](https://img.shields.io/badge/SciPy-654FF0?style=for-the-badge\u0026logo=SciPy\u0026logoColor=white)](https://github.com/Ruban2205)\n[![Scikit_Learn](https://img.shields.io/badge/scikit_learn-F7931E?style=for-the-badge\u0026logo=scikit-learn\u0026logoColor=white)](https://github.com/Ruban2205)\n\n[![Streamlit](https://img.shields.io/badge/Streamlit-FF4B4B?style=for-the-badge\u0026logo=Streamlit\u0026logoColor=white)](https://github.com/Ruban2205)\n\n\u003cp align=\"right\"\u003e(\u003ca href=\"#readme-top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\n\n\u003c!--Getting Started Section--\u003e \n## Getting Started 🚀\n\nUsing this as an example, you may describe how to set up your project locally. Follow these easy sample steps to set up and operate a local copy.\n\n### Prerequisites 📋\n\nYou must have Python installed on your machine in order to use this project. Python may be downloaded from [this page](https://www.python.org/downloads/) if you don't already have it installed.\n\n### Installation 📋\n\n1. Clone the repository to your local machine\n```\ngit clone https://github.com/Ruban2205/Iris_Classification.git\n```\n\n2. Change directory into the repository\n```\ncd Iris_Classification\n```\n\n3. Explore the notebooks in the repository using a Jupyter Notebook or JupyterLab environment. You can launch the environment by running the following command:\n```\njupyter notebook\n```\nor\n```\njupyter lab\n```\n\n\u003cp align=\"right\"\u003e(\u003ca href=\"#readme-top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\n\n\u003c!--Usage--\u003e\n## Usage 📋\n\n1. Run the Streamlit application with the given command:\n```\nstreamlit run streamlitapi.py\n```\n\n2. Access the application in your web browser, input iris flower measurements, and receive predictions on the species.\n\n\u003cp align=\"right\"\u003e(\u003ca href=\"#readme-top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\n\n\u003c!--Contribution--\u003e\n## Contributing 🤝\n\nContributions to this repository are welcome! If you have any improvements, additional examples, or new topics you would like to add, please follow these steps:\n\n1) Fork the repository in GitHub.\n2) Create a new branch with a descriptive name for your changes.\n3) Make your modifications, additions, or improvements.\n4) Commit and push your changes to your forked repository.\n5) Submit a pull request to the original repository.\n\nPlease ensure your contributions adhere to the coding style and guidelines used in the repository.\n\n\u003cp align=\"right\"\u003e(\u003ca href=\"#readme-top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\n\n\u003c!--Licence--\u003e\n## License 📄\n\nThis repository is licensed under the [MIT LICENSE](/LICENSE). You are free to use, modify, and distribute the code and content within this repository for personal or commercial purposes. However, please provide attribution to the original repository by linking back to it.\n\n\u003cp align=\"right\"\u003e(\u003ca href=\"#readme-top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\n\n\u003c!--Acknowledgements--\u003e\n## Acknowledgements 🙏\n\nI want to express my appreciation to the people who created the [Iris dataset](https://www.kaggle.com/datasets/uciml/iris) and the larger machine learning and data science community for their insightful contributions.\n\nYou may learn more about the principles of machine learning, the use of models, and the actual applications of AI in the categorization of issues by investigating and participating in my Iris categorization Machine Learning Project.\n\n\u003cp align=\"right\"\u003e(\u003ca href=\"#readme-top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\n\n\u003c!--Contact--\u003e\n## Contact ☎️\n\nFor any questions or inquiries, please feel free to approach me through the following channels: \n\n- Ruban [info@rubangino.in](https://mailto:info@rubangino.in/)\n\n[![Website](https://img.shields.io/badge/website-000000?style=for-the-badge\u0026logo=About.me\u0026logoColor=white)](https://rubangino.in/)\n[![Mail](https://img.shields.io/badge/Email-D14836?style=for-the-badge\u0026logo=gmail\u0026logoColor=white)](mailto:info@rubangino.in)\n[![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge\u0026logo=linkedin\u0026logoColor=white)](https://www.linkedin.com/in/ruban-gino-singh/)\n[![Kaggle](https://img.shields.io/badge/Kaggle-20BEFF?style=for-the-badge\u0026logo=Kaggle\u0026logoColor=white)](https://www.kaggle.com/rubanginosingh)\n[![Hashnode](https://img.shields.io/badge/Hashnode-2962FF?style=for-the-badge\u0026logo=hashnode\u0026logoColor=white)](https://rubangino.hashnode.dev/)\n\nFeel free to report any issues or suggest improvements by creating an issue in the GitHub repository.\n\n\u003cp align=\"right\"\u003e(\u003ca href=\"#readme-top\"\u003eback to top\u003c/a\u003e)\u003c/p\u003e\n\n\n### Star ⭐ Some Of My Repositories for Future use 😉\n\nClick below to gift a book to me.\n\n[![BuyMeABook](https://img.shields.io/badge/Buy%20Me%20a%20Book-ffdd00?style=for-the-badge\u0026logo=buy-me-a-book\u0026logoColor=black)\n](https://bit.ly/3M5jxLd)\n\n**Thank You!!**\n\n\u003chr/\u003e\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fruban2205%2Firis_classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fruban2205%2Firis_classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fruban2205%2Firis_classification/lists"}