{"id":25056545,"url":"https://github.com/ascender1729/iris-flower-classification-2024","last_synced_at":"2025-07-20T17:34:35.852Z","repository":{"id":233186026,"uuid":"782104519","full_name":"ascender1729/iris-flower-classification-2024","owner":"ascender1729","description":"An exploratory data analysis and machine learning project using the Iris dataset to classify flower species with a K-Nearest Neighbors classifier. It includes data visualization, feature scaling, model training, and evaluation with 100% accuracy on the test set.","archived":false,"fork":false,"pushed_at":"2024-04-11T19:34:38.000Z","size":801,"stargazers_count":5,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-07T10:53:39.130Z","etag":null,"topics":["classification","data-analysis","iris-dataset","k-nearest-neighbors","machine-learning","matplotlib","pandas","python","scikit-learn","seaborn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/ascender1729.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,"zenodo":null}},"created_at":"2024-04-04T16:39:53.000Z","updated_at":"2025-01-25T20:10:00.000Z","dependencies_parsed_at":null,"dependency_job_id":"d48b55df-080a-4455-b668-48065168dbac","html_url":"https://github.com/ascender1729/iris-flower-classification-2024","commit_stats":null,"previous_names":["ascender1729/iris-flower-classification-2024"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ascender1729/iris-flower-classification-2024","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ascender1729%2Firis-flower-classification-2024","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ascender1729%2Firis-flower-classification-2024/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ascender1729%2Firis-flower-classification-2024/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ascender1729%2Firis-flower-classification-2024/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ascender1729","download_url":"https://codeload.github.com/ascender1729/iris-flower-classification-2024/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ascender1729%2Firis-flower-classification-2024/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":266166276,"owners_count":23886753,"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":["classification","data-analysis","iris-dataset","k-nearest-neighbors","machine-learning","matplotlib","pandas","python","scikit-learn","seaborn"],"created_at":"2025-02-06T13:30:59.048Z","updated_at":"2025-07-20T17:34:35.847Z","avatar_url":"https://github.com/ascender1729.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Iris Flower Classification Analysis\n\nThe Iris Flower Classification Analysis is a comprehensive machine learning project that leverages the Iris dataset along with its Bezdek's variant to predict Iris species using the K-Nearest Neighbors (KNN) algorithm. This project includes enhanced data handling, visualization, and model evaluation techniques.\n\n## Table of Contents\n\n- [Project Overview](#project-overview)\n- [Features](#features)\n- [Data Description](#data-description)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Contributing](#contributing)\n- [License](#license)\n- [Contact](#contact)\n\n## Project Overview\n\nThis project offers a detailed exploration and analysis of the Iris flower dataset, including data integrity checks, feature scaling, and dimensionality reduction through PCA to optimize classification performance. Enhanced visualization techniques aid in understanding the intricate relationships within the data.\n\n## Features\n\n- *Data Integration*: Utilizes Google Colab for seamless integration and data manipulation.\n- *Dual Dataset Analysis*: Analysis includes both the original and Bezdek's Iris datasets to ensure robustness.\n- *Advanced Data Handling*: Includes detection and removal of duplicate entries.\n- *Feature Scaling and PCA*: Implements StandardScaler for normalization and PCA for reducing dimensionality.\n- *Enhanced Visualization*: Uses Seaborn and Matplotlib to visualize data in reduced dimensions.\n- *Precision Modeling*: Applies a KNN model with optimized parameters for superior prediction accuracy.\n- *Model Evaluation*: Assesses the model's accuracy through advanced metrics.\n\n## Data Description\n\nTwo Iris datasets are utilized, each containing 150 samples of Iris flowers with features:\n- Sepal Length\n- Sepal Width\n- Petal Length\n- Petal Width\n- Species (Iris-setosa, Iris-versicolor, Iris-virginica)\n\n## Installation\n\nSetup for Google Colab:\n\n```bash\nfrom google.colab import drive\ndrive.mount('/content/drive')\n```\nClone the repository and navigate to the project directory:\n\n```bash\ngit clone https://github.com/ascender1729/iris-flower-classification-2024.git\ncd iris-flower-classification-2024\n```\n\n## Usage\n\nInstall the required libraries:\n\n```bash\npip install pandas numpy seaborn matplotlib scikit-learn\n```\n\nRun the Jupyter notebook via Google Colab for a comprehensive walkthrough.\n\n## Contributing\n\nContributions are welcome to extend the analysis or improve the existing methodologies.\n\n## License\n\nThis project is licensed under the MIT License - see the `LICENSE` file for details.\n\n## Contact\n\nPavan Kumar - pavankumard.pg19.ma@nitp.ac.in\n\nLinkedIn: [@ascender1729](https://www.linkedin.com/in/im-pavankumar)\n\nProject Link: [iris-flower-classification-2024](https://github.com/ascender1729/iris-flower-classification-2024)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fascender1729%2Firis-flower-classification-2024","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fascender1729%2Firis-flower-classification-2024","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fascender1729%2Firis-flower-classification-2024/lists"}