PCA Step Through.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "# Principal Component Analysis\n",
    "***\n",
    "\n",
    "** Principal Component Analysis (PCA) is statistical procedure that uses right-angle (orthogonal) linear transformations to convert sets of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.**\n",
    "\n",
    "** Sklearn's - _Breast Cancer Wisconsin (Diagnostic) Database_ dataset was used to explore PCA and the features it offers.**\n",
    "\n",
    "** Using PCA we will attempt to identify which components are the most important in terms of variance.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading and Exploring Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "cancer = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['target', 'DESCR', 'target_names', 'feature_names', 'data'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breast Cancer Wisconsin (Diagnostic) Database\n",
      "=============================================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\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",
      "        largest values) of these features were computed for each image,\n",
      "        resulting in 30 features.  For instance, field 3 is Mean Radius, field\n",
      "        13 is Radius SE, field 23 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",
      "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",
      "\n"
     ]
    }
   ],
   "source": [
    "print(cancer['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<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",
       "</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",
       "   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  \\\n",
       "0                 0.07871           ...                    25.38   \n",
       "1                 0.05667           ...                    24.99   \n",
       "2                 0.05999           ...                    23.57   \n",
       "3                 0.09744           ...                    14.91   \n",
       "4                 0.05883           ...                    22.54   \n",
       "\n",
       "   worst texture  worst perimeter  worst area  worst smoothness  \\\n",
       "0          17.33           184.60      2019.0            0.1622   \n",
       "1          23.41           158.80      1956.0            0.1238   \n",
       "2          25.53           152.50      1709.0            0.1444   \n",
       "3          26.50            98.87       567.7            0.2098   \n",
       "4          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   worst compactness  worst concavity  worst concave points  worst symmetry  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \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",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(cancer['data'], columns=cancer['feature_names'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up SciKitLearn and the PCA model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_data = scaler.transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.09706398, -2.07333501,  1.26993369, ...,  2.29607613,\n",
       "         2.75062224,  1.93701461],\n",
       "       [ 1.82982061, -0.35363241,  1.68595471, ...,  1.0870843 ,\n",
       "        -0.24388967,  0.28118999],\n",
       "       [ 1.57988811,  0.45618695,  1.56650313, ...,  1.95500035,\n",
       "         1.152255  ,  0.20139121],\n",
       "       ...,\n",
       "       [ 0.70228425,  2.0455738 ,  0.67267578, ...,  0.41406869,\n",
       "        -1.10454895, -0.31840916],\n",
       "       [ 1.83834103,  2.33645719,  1.98252415, ...,  2.28998549,\n",
       "         1.91908301,  2.21963528],\n",
       "       [-1.80840125,  1.22179204, -1.81438851, ..., -1.74506282,\n",
       "        -0.04813821, -0.75120669]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Specifying that the number of components (n_components) we want to keep is 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_pca = pca.transform(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 2)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x77f63bcebe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "targets = cancer['target']\n",
    "colors = {0: '#607D8B', 1: '#E91E63'}\n",
    "labels = {0: 'Benign', 1: 'Malignant'}\n",
    "\n",
    "for t in np.unique(targets):\n",
    "    ix = np.where(targets == t)\n",
    "    plt.scatter(x_pca[ix,0], x_pca[ix,1],c = colors[t], label = labels[t] , alpha = 0.666)\n",
    "\n",
    "plt.title('PCA of the Breast Cancer Dataset\\n (2 Components)', fontdict = {'fontsize': 16})\n",
    "plt.xlabel('First Principal Component')\n",
    "plt.ylabel('Second Principal Component')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using 'pca.components' we get the below Numpy array. What we see is an array of the actual components, where each row represents a 'principal component (PC in PCA) and each column relates back to the original features.**\n",
    "\n",
    "**We can better visualize this with a heatmap.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<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>0.218902</td>\n",
       "      <td>0.103725</td>\n",
       "      <td>0.227537</td>\n",
       "      <td>0.220995</td>\n",
       "      <td>0.142590</td>\n",
       "      <td>0.239285</td>\n",
       "      <td>0.258400</td>\n",
       "      <td>0.260854</td>\n",
       "      <td>0.138167</td>\n",
       "      <td>0.064363</td>\n",
       "      <td>...</td>\n",
       "      <td>0.227997</td>\n",
       "      <td>0.104469</td>\n",
       "      <td>0.236640</td>\n",
       "      <td>0.224871</td>\n",
       "      <td>0.127953</td>\n",
       "      <td>0.210096</td>\n",
       "      <td>0.228768</td>\n",
       "      <td>0.250886</td>\n",
       "      <td>0.122905</td>\n",
       "      <td>0.131784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.233857</td>\n",
       "      <td>-0.059706</td>\n",
       "      <td>-0.215181</td>\n",
       "      <td>-0.231077</td>\n",
       "      <td>0.186113</td>\n",
       "      <td>0.151892</td>\n",
       "      <td>0.060165</td>\n",
       "      <td>-0.034768</td>\n",
       "      <td>0.190349</td>\n",
       "      <td>0.366575</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.219866</td>\n",
       "      <td>-0.045467</td>\n",
       "      <td>-0.199878</td>\n",
       "      <td>-0.219352</td>\n",
       "      <td>0.172304</td>\n",
       "      <td>0.143593</td>\n",
       "      <td>0.097964</td>\n",
       "      <td>-0.008257</td>\n",
       "      <td>0.141883</td>\n",
       "      <td>0.275339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0     0.218902      0.103725        0.227537   0.220995         0.142590   \n",
       "1    -0.233857     -0.059706       -0.215181  -0.231077         0.186113   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0          0.239285        0.258400             0.260854       0.138167   \n",
       "1          0.151892        0.060165            -0.034768       0.190349   \n",
       "\n",
       "   mean fractal dimension           ...             worst radius  \\\n",
       "0                0.064363           ...                 0.227997   \n",
       "1                0.366575           ...                -0.219866   \n",
       "\n",
       "   worst texture  worst perimeter  worst area  worst smoothness  \\\n",
       "0       0.104469         0.236640    0.224871          0.127953   \n",
       "1      -0.045467        -0.199878   -0.219352          0.172304   \n",
       "\n",
       "   worst compactness  worst concavity  worst concave points  worst symmetry  \\\n",
       "0           0.210096         0.228768              0.250886        0.122905   \n",
       "1           0.143593         0.097964             -0.008257        0.141883   \n",
       "\n",
       "   worst fractal dimension  \n",
       "0                 0.131784  \n",
       "1                 0.275339  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comp = pd.DataFrame(pca.components_,columns = cancer['feature_names'])\n",
    "df_comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x77f63bc91160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,10))\n",
    "sns.heatmap(df_comp, cmap = 'plasma');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "***\n",
    "**It would appear that our data was inverted somehwere along the way, which is okay because we can still see the values have the strongest influence on determining cancer. Since our colors are inverted, the darkest colors represent that values that have greatest impact in predicting breast cancer.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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