{"id":19044228,"url":"https://github.com/selcia25/iris-dataset-classification","last_synced_at":"2026-04-16T04:02:13.713Z","repository":{"id":220976538,"uuid":"753094053","full_name":"selcia25/iris-dataset-classification","owner":"selcia25","description":"☘This repository contains a Python script for classifying the Iris dataset using the Random Forest algorithm.","archived":false,"fork":false,"pushed_at":"2024-02-05T13:14:59.000Z","size":10,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-21T23:18:07.121Z","etag":null,"topics":["data-processing","iris-classification","pandas","random-forest-classifier","scikit-learn"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/selcia25.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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}},"created_at":"2024-02-05T13:10:01.000Z","updated_at":"2024-05-26T20:44:38.000Z","dependencies_parsed_at":null,"dependency_job_id":"0306ad65-80d7-4499-8e00-c8ab13800e17","html_url":"https://github.com/selcia25/iris-dataset-classification","commit_stats":null,"previous_names":["selcia25/iris-dataset-classification"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/selcia25/iris-dataset-classification","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/selcia25%2Firis-dataset-classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/selcia25%2Firis-dataset-classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/selcia25%2Firis-dataset-classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/selcia25%2Firis-dataset-classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/selcia25","download_url":"https://codeload.github.com/selcia25/iris-dataset-classification/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/selcia25%2Firis-dataset-classification/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31870516,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-15T15:24:51.572Z","status":"online","status_checked_at":"2026-04-16T02:00:06.042Z","response_time":69,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["data-processing","iris-classification","pandas","random-forest-classifier","scikit-learn"],"created_at":"2024-11-08T22:45:15.716Z","updated_at":"2026-04-16T04:02:13.694Z","avatar_url":"https://github.com/selcia25.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Iris Dataset Classification using Random Forest\n\nThis repository contains a Python script for classifying the Iris dataset using the Random Forest algorithm. The script covers data loading, exploration, preprocessing, model training, evaluation, and making predictions for new data points.\n\n## Usage\n1. **Clone the repository:**\n   ```bash\n   git clone https://github.com/selcia25/iris-dataset-classification.git\n   cd iris-dataset-classification\n   ```\n\n2. **Install Dependencies:**\n   ```bash\n   pip install scikit-learn pandas\n   ```\n\n3. **Download Dataset:**\n   - Download the Iris dataset (Iris.csv) or replace it with your dataset.\n   - Update the file name in the script accordingly.\n\n4. **Run the Script:**\n   ```bash\n   python object_recognition.py\n   ```\n\n## Dependencies\n- scikit-learn\n- pandas\n\n## Script Overview\n1. **Load and Explore Dataset:**\n   - Load the Iris dataset using pandas.\n\n2. **Explore Data:**\n   - Display first few rows, information, and summary statistics of the dataset.\n\n3. **Data Preprocessing:**\n   - Split features and target variables.\n   - Encode target variables to numerical values.\n   - Split the data into training and testing sets.\n\n4. **Choose Classification Algorithm and Train Model:**\n   - Use the Random Forest classifier with 100 estimators.\n\n5. **Evaluate Model's Performance:**\n   - Display accuracy score, classification report, and confusion matrix.\n\n6. **Make Predictions for New Data Points:**\n   - Provide sample data points and display predicted classes.\n\nThis script serves as a basic template for classification tasks on the Iris dataset and can be extended for other datasets or machine learning tasks.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fselcia25%2Firis-dataset-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fselcia25%2Firis-dataset-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fselcia25%2Firis-dataset-classification/lists"}