{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from io import StringIO\n", "import numpy.linalg as la\n", "import matplotlib.pyplot as plt\n", "from matplotlib import cm as cm\n", "import seaborn as sns\n", "sns.set(font_scale=2)\n", "plt.style.use('seaborn-whitegrid')\n", "%matplotlib inline\n", "\n", "from sklearn.decomposition import PCA\n", "from sklearn.preprocessing import scale" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example from:\n", "https://analyticsdefined.com/implementing-principal-component-analysis/" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | Fresh | \n", "Milk | \n", "Grocery | \n", "Frozen | \n", "Detergents_Paper | \n", "Delicassen | \n", "
---|---|---|---|---|---|---|
0 | \n", "12669 | \n", "9656 | \n", "7561 | \n", "214 | \n", "2674 | \n", "1338 | \n", "
1 | \n", "7057 | \n", "9810 | \n", "9568 | \n", "1762 | \n", "3293 | \n", "1776 | \n", "
2 | \n", "6353 | \n", "8808 | \n", "7684 | \n", "2405 | \n", "3516 | \n", "7844 | \n", "
3 | \n", "13265 | \n", "1196 | \n", "4221 | \n", "6404 | \n", "507 | \n", "1788 | \n", "
4 | \n", "22615 | \n", "5410 | \n", "7198 | \n", "3915 | \n", "1777 | \n", "5185 | \n", "