{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", " \n", "## [mlcourse.ai](https://mlcourse.ai) – Open Machine Learning Course \n", "\n", "Authors: [Olga Daykhovskaya](https://www.linkedin.com/in/odaykhovskaya/), [Yury Kashnitskiy](https://yorko.github.io). This material is subject to the terms and conditions of the [Creative Commons CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license. Free use is permitted for any non-commercial purpose." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#
Assignment #7 (demo)\n", "##
\n", "What is the minimum number of principal components required to cover the 90% of the variance of the original (scaled) data?" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Your code here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer options:**\n", "- 56 \n", "- 65\n", "- 66\n", "- 193" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Вопрос 2:**
\n", "What percentage of the variance is covered by the first principal component? Round to the nearest percent.\n", "\n", "**Answer options:**\n", "- 45\n", "- 51 \n", "- 56\n", "- 61" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Your code here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualize data in projection on the first two principal components." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Your code here\n", "# plt.scatter(, , c=y, s=20, cmap='viridis');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Question 3:**
\n", "If everything worked out correctly, you will see a number of clusters, almost perfectly separated from each other. What types of activity are included in these clusters?