{"id":24142008,"url":"https://github.com/khushi130404/k_means","last_synced_at":"2026-05-08T02:04:37.582Z","repository":{"id":270646669,"uuid":"911026277","full_name":"Khushi130404/K_Means","owner":"Khushi130404","description":"This repository showcases 2D, 3D, and custom K-Means clustering models with visualizations. 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The project includes interactive Jupyter notebooks (.ipynb files) and Python scripts (.py files) for ease of use and reproducibility.\n\n## 📊 Visualizations\n\n- 2D Clustering Graphs: Shows the separation of data points into distinct clusters.\n\n- 3D Clustering Graphs: Provides an interactive 3D view of clustered data points.\n\n- Iteration-wise Graphs: Displays how clusters evolve with each iteration of the K-Means algorithm.\n\n## 🚀 Features\n\n### 1. K-Means Model in 2D\n- Implementation of the K-Means clustering algorithm in a 2D space.\n- Interactive visualizations to show how clusters are formed.\n- Code available in both .ipynb format.\n\n### 2. K-Means Model in 3D\n- Extends the K-Means algorithm to 3D data.\n- 3D plots using libraries like Matplotlib and Plotly for enhanced visualization.\n- Code available in a Jupyter notebook for interactive exploration.\n\n### 3. Self-Created K-Means Model\n- A custom implementation of the K-Means algorithm from scratch.\n- Includes methods for initializing centroids, assigning clusters, and updating centroids.\n- Fully implemented in Python (.py file).\n\n### 4. Graphs and Visualization\n- Detailed visualizations of clustering results in both 2D and 3D.\n- Use of various Python libraries such as Matplotlib, Seaborn, and Plotly for dynamic and interactive plots.\n- Comparison of clustering outcomes with different initial centroids and number of clusters.\n\n## 🛠️ Technologies Used\n\n- Python: Core language used for all implementations.\n\n- Jupyter Notebooks: Interactive environment for coding and visualization.\n\n- Matplotlib: For creating 2D and 3D plots.\n\n- Seaborn: For enhancing visualizations.\n\n- Plotly: For interactive 3D plots.\n\n- NumPy: For efficient numerical computations.\n\n- scikit-learn: For using built-in K-Means functionality and data preprocessing.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkhushi130404%2Fk_means","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkhushi130404%2Fk_means","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkhushi130404%2Fk_means/lists"}