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Навчання без вчителя.\n\n*З циклу [домашніх завдань Python Data Science](https://github.com/lexxai/goit_python_data_sciense_homework).*\n\n# Домашнє завдання\n\nЗавдання, що пропонуються, необхідно оформити у вигляді одного jupyter ноутбука.\n## Завдання 1\nУ цьому завданні вам потрібно завантажити [ось цей датасет](https://drive.google.com/file/d/1Zvz20Iqeia1eEtFbGa3NcIrt_SNSimP6/view?usp=share_link). Тут ви знайдете 2 файли - з двовимірним датасетом та датасетом `mnist`. Для кожного з них застосуйте алгоритм `K-means` для кластеризації. Щоб знайти оптимальну кількість кластерів, скористайтесь ліктевим методом.\n\n## Завдання 2\nВізуалізуйте результат роботи кластеризації. Для випадку з `mnist` датасетом, вам потрібно ще скористатись алгоримтом `PCA` щоб зменшити розмірність вашим даних до 2-вимірного варіанту.\n\n\n# Результат\n\n- [goit_python_ds_hw_06.ipynb](goit_python_ds_hw_06.ipynb)\n- [Collab goit_python_ds_hw_06.ipynb](https://colab.research.google.com/drive/1SzdJuZXEjNaTOgB4evRvrxaxKQ7QD9WK?usp=sharing)\n- [Collab mnist dataset - clustering.ipynb](https://colab.research.google.com/drive/1H4BcH3HgVvLkkLtb7ynjbbNrF56Vq2XX?usp=sharing)\n\n## Завдання 1\n\n### Резульат розбитя на кластери зорбражень з бази MNIST\n#### 784 ознак, k=19\n\n![](cluster_img.jpg)\n\n#### PCA (0.95), 114 ознак, k=13\n\n![](cluster_img_pca.jpg)\n\n## Завдання 2\n![](3d-2d-k2.png)\n![](3D-MNIST-k19.png)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flexxai%2Fgoit_python_ds_hw_06","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flexxai%2Fgoit_python_ds_hw_06","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flexxai%2Fgoit_python_ds_hw_06/lists"}