{"id":29244108,"url":"https://github.com/theveryhim/dimensionality-reduction-and-clustering","last_synced_at":"2025-09-10T20:33:15.920Z","repository":{"id":302536406,"uuid":"1012761027","full_name":"theveryhim/Dimensionality-Reduction-and-Clustering","owner":"theveryhim","description":"Simple ML-like data analysis and processing.","archived":false,"fork":false,"pushed_at":"2025-07-02T22:27:05.000Z","size":1138,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-07-02T23:28:18.245Z","etag":null,"topics":["autoencoder","clustering","data-analysis","dimensionality-reduction","pca"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MNIST\n\nSome of tasks done in this section which is part of a ML assignment:\n- PCA and LDA analysis on MNIST dataset\n- Implement an Auto-encoder network\n- Clustering with K-means\n- Clustering with GMM\n\n## PCA and LDA Analysis on MNIST Dataset\n\n| Method                          | Accuracy   |\n|---------------------------------|------------|\n| Original Data                   | 0.885      |\n| Manual PCA                      | 0.865      |\n| RBF Kernel PCA                  | 0.8175     |\n| Polynomial Kernel PCA           | 0.825      |\n| Linear Kernel PCA               | 0.865      |\n| LDA Projected Data              | 0.755      |\n\n## K-means Results\n\n| K (Number of Clusters) | Dunn Index |\n|------------------------|------------|\n| 3                      | 0.363      |\n| 4                      | 0.265      |\n| 5                      | 0.460      |\n\n# Cove data-set\n\nSome of tasks done in this section which is part of a MDA assignment:\n- Using the PCA algorithm, choose the value of k in such a way that at least 90% of the variance of the samples is maintained\n- Reduce the dimensions of the samples using the obtained eigenvectors.\n\u003cp align=\"center\"\u003e\n    \u003cimg src=\"images/1.png\" alt=\"Descriptive Alt Text\" class=\"fit-width-image\"\u003e\n\u003c/p\u003e\n\n- Divide the data(covtype.info) into three chunks and group the data into 7 clusters using one of the BRF or Cure algorithms.\n\u003cp align=\"center\"\u003e\n    \u003cimg src=\"images/2.png\" alt=\"Descriptive Alt Text\" class=\"fit-width-image\"\u003e\n\u003c/p\u003e\n\n- Evaluate using metrics *Silhouette Score* and *Davies-Bouldin Index*\n```markdown\nSilhouette Score: 0.42960626042271594\nDavies-Bouldin Index: 4.129832832292689\n```\n\n# Brain tumor: Description included in notebook\n\nSome of tasks done in this section which is part of a DeepLearning assignment:\n## Dimensionality Reduction\n- Load data\n- Flatten\n- Split\n- PCA\n- Reconstruction\n\u003cp align=\"center\"\u003e\n    \u003cimg src=\"images/3.png\" alt=\"Descriptive Alt Text\" class=\"fit-width-image\"\u003e\n\u003c/p\u003e\n\n## Classifier\nIn this section use Support Vector Machine (SVM) for predicting Tumor from features.\nOur purpose is comparing accuracy on test data before and after dimensionality reduction.\n\n```\nTest Accuracy before PCA: 0.8039\nTest Accuracy after PCA: 0.6470\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheveryhim%2Fdimensionality-reduction-and-clustering","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftheveryhim%2Fdimensionality-reduction-and-clustering","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheveryhim%2Fdimensionality-reduction-and-clustering/lists"}