01-Principal Component Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Principal Component Analysis\n",
    "\n",
    "Let's discuss PCA! Since this isn't exactly a full machine learning algorithm, but instead an unsupervised learning algorithm, we will just have a lecture on this topic, but no full machine learning project (although we will walk through the cancer set with PCA).\n",
    "\n",
    "## PCA Review\n",
    "\n",
    "Make sure to watch the video lecture and theory presentation for a full overview of PCA! \n",
    "Remember that PCA is just a transformation of your data and attempts to find out what features explain the most variance in your data. For example:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='PCA.png' />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data\n",
    "\n",
    "Let's work with the cancer data set again since it had so many features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _breast_cancer_dataset:\n",
      "\n",
      "Breast cancer wisconsin (diagnostic) dataset\n",
      "--------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 569\n",
      "\n",
      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
      "\n",
      "    :Attribute Information:\n",
      "        - radius (mean of distances from center to points on the perimeter)\n",
      "        - texture (standard deviation of gray-scale values)\n",
      "        - perimeter\n",
      "        - area\n",
      "        - smoothness (local variation in radius lengths)\n",
      "        - compactness (perimeter^2 / area - 1.0)\n",
      "        - concavity (severity of concave portions of the contour)\n",
      "        - concave points (number of concave portions of the contour)\n",
      "        - symmetry\n",
      "        - fractal dimension (\"coastline approximation\" - 1)\n",
      "\n",
      "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
      "        worst/largest values) of these features were computed for each image,\n",
      "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
      "        10 is Radius SE, field 20 is Worst Radius.\n",
      "\n",
      "        - class:\n",
      "                - WDBC-Malignant\n",
      "                - WDBC-Benign\n",
      "\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ===================================== ====== ======\n",
      "                                           Min    Max\n",
      "    ===================================== ====== ======\n",
      "    radius (mean):                        6.981  28.11\n",
      "    texture (mean):                       9.71   39.28\n",
      "    perimeter (mean):                     43.79  188.5\n",
      "    area (mean):                          143.5  2501.0\n",
      "    smoothness (mean):                    0.053  0.163\n",
      "    compactness (mean):                   0.019  0.345\n",
      "    concavity (mean):                     0.0    0.427\n",
      "    concave points (mean):                0.0    0.201\n",
      "    symmetry (mean):                      0.106  0.304\n",
      "    fractal dimension (mean):             0.05   0.097\n",
      "    radius (standard error):              0.112  2.873\n",
      "    texture (standard error):             0.36   4.885\n",
      "    perimeter (standard error):           0.757  21.98\n",
      "    area (standard error):                6.802  542.2\n",
      "    smoothness (standard error):          0.002  0.031\n",
      "    compactness (standard error):         0.002  0.135\n",
      "    concavity (standard error):           0.0    0.396\n",
      "    concave points (standard error):      0.0    0.053\n",
      "    symmetry (standard error):            0.008  0.079\n",
      "    fractal dimension (standard error):   0.001  0.03\n",
      "    radius (worst):                       7.93   36.04\n",
      "    texture (worst):                      12.02  49.54\n",
      "    perimeter (worst):                    50.41  251.2\n",
      "    area (worst):                         185.2  4254.0\n",
      "    smoothness (worst):                   0.071  0.223\n",
      "    compactness (worst):                  0.027  1.058\n",
      "    concavity (worst):                    0.0    1.252\n",
      "    concave points (worst):               0.0    0.291\n",
      "    symmetry (worst):                     0.156  0.664\n",
      "    fractal dimension (worst):            0.055  0.208\n",
      "    ===================================== ====== ======\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
      "\n",
      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
      "\n",
      "    :Donor: Nick Street\n",
      "\n",
      "    :Date: November, 1995\n",
      "\n",
      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
      "https://goo.gl/U2Uwz2\n",
      "\n",
      "Features are computed from a digitized image of a fine needle\n",
      "aspirate (FNA) of a breast mass.  They describe\n",
      "characteristics of the cell nuclei present in the image.\n",
      "\n",
      "Separating plane described above was obtained using\n",
      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
      "pp. 97-101, 1992], a classification method which uses linear\n",
      "programming to construct a decision tree.  Relevant features\n",
      "were selected using an exhaustive search in the space of 1-4\n",
      "features and 1-3 separating planes.\n",
      "\n",
      "The actual linear program used to obtain the separating plane\n",
      "in the 3-dimensional space is that described in:\n",
      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
      "Optimization Methods and Software 1, 1992, 23-34].\n",
      "\n",
      "This database is also available through the UW CS ftp server:\n",
      "\n",
      "ftp ftp.cs.wisc.edu\n",
      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
      "     San Jose, CA, 1993.\n",
      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
      "     July-August 1995.\n",
      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
      "     163-171.\n"
     ]
    }
   ],
   "source": [
    "print(cancer['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(cancer['data'],columns=cancer['feature_names'])\n",
    "#(['DESCR', 'data', 'feature_names', 'target_names', 'target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
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      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA Visualization\n",
    "\n",
    "As we've noticed before it is difficult to visualize high dimensional data, we can use PCA to find the first two principal components, and visualize the data in this new, two-dimensional space, with a single scatter-plot. Before we do this though, we'll need to scale our data so that each feature has a single unit variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.380</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.990</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.570</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.910</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.540</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>...</td>\n",
       "      <td>25.450</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>23.690</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>...</td>\n",
       "      <td>18.980</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>...</td>\n",
       "      <td>25.740</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>...</td>\n",
       "      <td>9.456</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0          17.99         10.38          122.80     1001.0          0.11840   \n",
       "1          20.57         17.77          132.90     1326.0          0.08474   \n",
       "2          19.69         21.25          130.00     1203.0          0.10960   \n",
       "3          11.42         20.38           77.58      386.1          0.14250   \n",
       "4          20.29         14.34          135.10     1297.0          0.10030   \n",
       "..           ...           ...             ...        ...              ...   \n",
       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
       "566        16.60         28.08          108.30      858.1          0.08455   \n",
       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
       "568         7.76         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0             0.27760         0.30010              0.14710         0.2419   \n",
       "1             0.07864         0.08690              0.07017         0.1812   \n",
       "2             0.15990         0.19740              0.12790         0.2069   \n",
       "3             0.28390         0.24140              0.10520         0.2597   \n",
       "4             0.13280         0.19800              0.10430         0.1809   \n",
       "..                ...             ...                  ...            ...   \n",
       "564           0.11590         0.24390              0.13890         0.1726   \n",
       "565           0.10340         0.14400              0.09791         0.1752   \n",
       "566           0.10230         0.09251              0.05302         0.1590   \n",
       "567           0.27700         0.35140              0.15200         0.2397   \n",
       "568           0.04362         0.00000              0.00000         0.1587   \n",
       "\n",
       "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
       "0                   0.07871  ...        25.380          17.33   \n",
       "1                   0.05667  ...        24.990          23.41   \n",
       "2                   0.05999  ...        23.570          25.53   \n",
       "3                   0.09744  ...        14.910          26.50   \n",
       "4                   0.05883  ...        22.540          16.67   \n",
       "..                      ...  ...           ...            ...   \n",
       "564                 0.05623  ...        25.450          26.40   \n",
       "565                 0.05533  ...        23.690          38.25   \n",
       "566                 0.05648  ...        18.980          34.12   \n",
       "567                 0.07016  ...        25.740          39.42   \n",
       "568                 0.05884  ...         9.456          30.37   \n",
       "\n",
       "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
       "0             184.60      2019.0           0.16220            0.66560   \n",
       "1             158.80      1956.0           0.12380            0.18660   \n",
       "2             152.50      1709.0           0.14440            0.42450   \n",
       "3              98.87       567.7           0.20980            0.86630   \n",
       "4             152.20      1575.0           0.13740            0.20500   \n",
       "..               ...         ...               ...                ...   \n",
       "564           166.10      2027.0           0.14100            0.21130   \n",
       "565           155.00      1731.0           0.11660            0.19220   \n",
       "566           126.70      1124.0           0.11390            0.30940   \n",
       "567           184.60      1821.0           0.16500            0.86810   \n",
       "568            59.16       268.6           0.08996            0.06444   \n",
       "\n",
       "     worst concavity  worst concave points  worst symmetry  \\\n",
       "0             0.7119                0.2654          0.4601   \n",
       "1             0.2416                0.1860          0.2750   \n",
       "2             0.4504                0.2430          0.3613   \n",
       "3             0.6869                0.2575          0.6638   \n",
       "4             0.4000                0.1625          0.2364   \n",
       "..               ...                   ...             ...   \n",
       "564           0.4107                0.2216          0.2060   \n",
       "565           0.3215                0.1628          0.2572   \n",
       "566           0.3403                0.1418          0.2218   \n",
       "567           0.9387                0.2650          0.4087   \n",
       "568           0.0000                0.0000          0.2871   \n",
       "\n",
       "     worst fractal dimension  \n",
       "0                    0.11890  \n",
       "1                    0.08902  \n",
       "2                    0.08758  \n",
       "3                    0.17300  \n",
       "4                    0.07678  \n",
       "..                       ...  \n",
       "564                  0.07115  \n",
       "565                  0.06637  \n",
       "566                  0.07820  \n",
       "567                  0.12400  \n",
       "568                  0.07039  \n",
       "\n",
       "[569 rows x 30 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data = scaler.transform(df)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PCA with Scikit Learn uses a very similar process to other preprocessing functions that come with SciKit Learn. We instantiate a PCA object, find the principal components using the fit method, then apply the rotation and dimensionality reduction by calling transform().\n",
    "\n",
    "We can also specify how many components we want to keep when creating the PCA object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=2)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "PCA(n_components=2)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(scaled_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can transform this data to its first 2 principal components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_pca = pca.transform(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 2)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pca.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! We've reduced 30 dimensions to just 2! Let's plot these two dimensions out!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Second Principal Component')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(x_pca[:,0],x_pca[:,1],c=cancer['target'],cmap='plasma')\n",
    "plt.xlabel('First principal component')\n",
    "plt.ylabel('Second Principal Component')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly by using these two components we can easily separate these two classes.\n",
    "\n",
    "## Interpreting the components \n",
    "\n",
    "Unfortunately, with this great power of dimensionality reduction, comes the cost of being able to easily understand what these components represent.\n",
    "\n",
    "The components correspond to combinations of the original features, the components themselves are stored as an attribute of the fitted PCA object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.21890244,  0.10372458,  0.22753729,  0.22099499,  0.14258969,\n",
       "         0.23928535,  0.25840048,  0.26085376,  0.13816696,  0.06436335,\n",
       "         0.20597878,  0.01742803,  0.21132592,  0.20286964,  0.01453145,\n",
       "         0.17039345,  0.15358979,  0.1834174 ,  0.04249842,  0.10256832,\n",
       "         0.22799663,  0.10446933,  0.23663968,  0.22487053,  0.12795256,\n",
       "         0.21009588,  0.22876753,  0.25088597,  0.12290456,  0.13178394],\n",
       "       [-0.23385713, -0.05970609, -0.21518136, -0.23107671,  0.18611302,\n",
       "         0.15189161,  0.06016536, -0.0347675 ,  0.19034877,  0.36657547,\n",
       "        -0.10555215,  0.08997968, -0.08945723, -0.15229263,  0.20443045,\n",
       "         0.2327159 ,  0.19720728,  0.13032156,  0.183848  ,  0.28009203,\n",
       "        -0.21986638, -0.0454673 , -0.19987843, -0.21935186,  0.17230435,\n",
       "         0.14359317,  0.09796411, -0.00825724,  0.14188335,  0.27533947]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this numpy matrix array, each row represents a principal component, and each column relates back to the original features. we can visualize this relationship with a heatmap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_comp = pd.DataFrame(pca.components_,columns=cancer['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(df_comp,cmap='plasma')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This heatmap and the color bar basically represent the correlation between the various feature and the principal component itself.\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "Hopefully this information is useful to you when dealing with high dimensional data!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}