{"id":27446303,"url":"https://github.com/mehtadigisha/iris-flower-classification","last_synced_at":"2026-05-03T01:43:36.209Z","repository":{"id":287940025,"uuid":"966292478","full_name":"mehtadigisha/Iris-Flower-Classification","owner":"mehtadigisha","description":"Iris Flower Classification","archived":false,"fork":false,"pushed_at":"2025-04-14T18:37:50.000Z","size":290,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-15T04:15:34.345Z","etag":null,"topics":["accuracy-score","classification-report","data-analysis","data-visualization","eda","iris-classification","machine-learning","matplotlib","pandas","prediction","python","scikit-learn","seaborn","svc-model","svm-model","visualization"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mehtadigisha.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-04-14T17:43:05.000Z","updated_at":"2025-04-14T18:37:55.000Z","dependencies_parsed_at":"2025-04-14T19:24:43.150Z","dependency_job_id":"88cad4ec-dd0e-4ea7-be89-bbd00de4dcbf","html_url":"https://github.com/mehtadigisha/Iris-Flower-Classification","commit_stats":null,"previous_names":["mehtadigisha/iris-flower-classification"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mehtadigisha%2FIris-Flower-Classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mehtadigisha%2FIris-Flower-Classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mehtadigisha%2FIris-Flower-Classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mehtadigisha%2FIris-Flower-Classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mehtadigisha","download_url":"https://codeload.github.com/mehtadigisha/Iris-Flower-Classification/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":249003963,"owners_count":21196793,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["accuracy-score","classification-report","data-analysis","data-visualization","eda","iris-classification","machine-learning","matplotlib","pandas","prediction","python","scikit-learn","seaborn","svc-model","svm-model","visualization"],"created_at":"2025-04-15T04:15:43.033Z","updated_at":"2025-10-30T05:37:07.547Z","avatar_url":"https://github.com/mehtadigisha.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":" # 🌸 Iris Flower Classification - Machine Learning Project\n\nThis project implements a **Supervised Machine Learning** model using **Support Vector Machine (SVM)** to classify iris flowers into three species: *Setosa*, *Versicolor*, and *Virginica*. The model is trained on the classic **Iris dataset**, and includes data visualization, model training, prediction, and evaluation.\n\n---\n\n## 🚀 Features\n\n- 📊 Exploratory Data Analysis with **Seaborn** and **Matplotlib**\n- 🤖 SVM (Support Vector Classification) for model building\n- 🧪 Model evaluation using **Accuracy Score** and **Classification Report**\n- 🔮 Predict the species of Iris flower based on input features\n- ✅ Beginner-friendly and well-commented code\n\n---\n\n## 📚 Dataset\n\nThe **Iris dataset** contains 150 samples of iris flowers, with the following features:\n\n- `Sepal Length`\n- `Sepal Width`\n- `Petal Length`\n- `Petal Width`\n\nTarget classes:\n- `Iris-setosa`\n- `Iris-versicolor`\n- `Iris-virginica`\n\n---\n\n## Libraries Used\n\n- `numpy`\n- `pandas`\n- `matplotlib`\n- `seaborn`\n- `scikit-learn`\n\n🧠 Model Training \u0026 Evaluation\n    The model is built using Support Vector Classifier (SVC) from sklearn.svm. It is evaluated using:\n\n✅ Accuracy Score\n\n📄 Classification Report (Precision, Recall, F1-score)\n\n### 💡 How to Use\n\n1. Install the libraries using:\n\n```bash\npip install numpy pandas matplotlib seaborn scikit-learn\n```\n\n2. Clone the repository:\n``` bash\nCopy\nEdit\ngit clone https://github.com/mehtadigisha/Iris-Flower-Classification\n```\n\n3. Run the Jupyter Notebook:\n``` bash\njupyter notebook iris_classification.ipynb\n```\n\n4. Follow the notebook cells to explore the data, train the model, and make predictions.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmehtadigisha%2Firis-flower-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmehtadigisha%2Firis-flower-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmehtadigisha%2Firis-flower-classification/lists"}