{"id":26107043,"url":"https://github.com/sorna-fast/iris-classifier-pca","last_synced_at":"2026-04-16T10:31:31.650Z","repository":{"id":281518645,"uuid":"945500331","full_name":"sorna-fast/iris-classifier-pca","owner":"sorna-fast","description":"An interactive web application for Iris flower classification using Random Forest and PCA visualization, built with Streamlit. 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Adjust the test data size using the slider\r\n2. View model performance metrics\r\n3. Explore PCA visualization\r\n4. Use sidebar sliders to input feature values:\r\n   - Sepal Length\r\n   - Sepal Width\r\n   - Petal Length\r\n   - Petal Width\r\n5. Click \"Predict\" to see classification results\r\n\r\n## Technologies Used\r\n- Streamlit\r\n- Pandas\r\n- Matplotlib\r\n- Seaborn\r\n- Scikit-learn\r\n- PCA (Principal Component Analysis)\r\n- Random Forest Classifier\r\n\r\n## Dataset\r\nThe famous Iris dataset with:\r\n- 4 features (sepal length/width, petal length/width)\r\n- 3 classes (Setosa, Versicolor, Virginica)\r\n\r\n## License\r\nMIT License\r\n\r\n### Created with ❤️ by [Sorna](https://github.com/sorna-fast)\r\n\r\n### 📧 Contact Me  \r\nFor any questions or suggestions, feel free to reach out via email: masudpythongit@gmail.com \r\n-------------------\r\n\r\n# طبقه‌بندی مجموعه داده زنبق با PCA\r\n\r\n## توضیحات\r\nیک برنامه تحت وب تعاملی برای طبقه‌بندی گل زنبق با استفاده از Random Forest و تجسم PCA که با Streamlit ساخته شده است.\r\n\r\n## ویژگی‌ها\r\n- **آموزش مدل تعاملی**\r\n  - تنظیم اندازه داده تست (۱۰-۹۰٪)\r\n  - معیارهای دقت در لحظه\r\n  - نمایش ماتریس درهم‌ریختگی\r\n  - گزارش طبقه‌بندی دقیق\r\n\r\n- **تجسم PCA**\r\n  - نمودار پراکندگی ۲ بعدی\r\n  - نمایش تعاملی داده‌ها\r\n  - تجسم واضح جداسازی کلاس‌ها\r\n\r\n- **پیش‌بینی در لحظه**\r\n  - اسلایدرهای تعاملی برای ورود ویژگی‌ها\r\n  - نتایج پیش‌بینی فوری\r\n  - امتیازات احتمال برای هر کلاس\r\n\r\n## نصب و راه‌اندازی\r\nدستورات نصب و اجرا در بخش انگلیسی موجود است\r\n\r\n## نحوه استفاده\r\n۱. تنظیم اندازه داده تست با اسلایدر\r\n۲. مشاهده معیارهای عملکرد مدل\r\n۳. کاوش در تجسم PCA\r\n۴. استفاده از اسلایدرهای کناری برای ورود مقادیر ویژگی:\r\n   - طول کاسبرگ\r\n   - عرض کاسبرگ\r\n   - طول گلبرگ\r\n   - عرض گلبرگ\r\n۵. کلیک روی \"Predict\" برای دیدن نتایج طبقه‌بندی\r\n\r\n## تکنولوژی‌های استفاده شده\r\n- Streamlit\r\n- Pandas\r\n- Matplotlib\r\n- Seaborn\r\n- Scikit-learn\r\n- PCA (تحلیل مؤلفه‌های اصلی)\r\n- Random Forest Classifier (طبقه‌بند جنگل تصادفی)\r\n\r\n## مجموعه داده\r\nمجموعه داده معروف زنبق شامل:\r\n- ۴ ویژگی (طول و عرض کاسبرگ، طول و عرض گلبرگ)\r\n- ۳ کلاس (ستوسا، ورسیکالر، ویرجینیکا)\r\n\r\n## مجوز\r\nMIT لایسنس\r\n\r\n\r\n### Created with ❤️ by [Sorna](https://github.com/sorna-fast)\r\n\r\n### 📧 ارتباط با من | Contact\r\nبرای هرگونه سوال یا پیشنهاد، می‌توانید از طریق ایمیل با من تماس بگیرید: masudpythongit@gmail.com\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsorna-fast%2Firis-classifier-pca","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsorna-fast%2Firis-classifier-pca","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsorna-fast%2Firis-classifier-pca/lists"}