PCA Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis Breast Cancer Data using PCA to find out strongest influencers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = load_breast_cancer()\n",
    "ds.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(ds['data'],columns=ds['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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",
       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>564</td>\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",
       "      <td>565</td>\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",
       "      <td>566</td>\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",
       "      <td>567</td>\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",
       "      <td>568</td>\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scale = scaler.transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9.19283683,  1.94858307],\n",
       "       [ 2.3878018 , -3.76817174],\n",
       "       [ 5.73389628, -1.0751738 ],\n",
       "       ...,\n",
       "       [ 1.25617928, -1.90229671],\n",
       "       [10.37479406,  1.67201011],\n",
       "       [-5.4752433 , -0.67063679]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(df_scale)\n",
    "pca_df=pca.transform(df_scale)\n",
    "pca_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "461"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pca_df[pca_df[:,0]==16.31923323103201]\n",
    "pca_df[:,0].argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a22161250>"
      ]
     },
     "execution_count": 14,
     "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": [
    "# list out strong influences\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(x=pca_df[:,0],y=pca_df[:,1],c=ds['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.00000000e+00, 4.64950044e-32])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 30)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
       "      <td>0</td>\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",
       "      <td>1</td>\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  worst texture  worst perimeter  \\\n",
       "0                0.064363  ...      0.227997       0.104469         0.236640   \n",
       "1                0.366575  ...     -0.219866      -0.045467        -0.199878   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0    0.224871          0.127953           0.210096         0.228768   \n",
       "1   -0.219352          0.172304           0.143593         0.097964   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0              0.250886        0.122905                 0.131784  \n",
       "1             -0.008257        0.141883                 0.275339  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Camp=pd.DataFrame(pca.components_,columns=df.columns)\n",
    "df_Camp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a222ea310>"
      ]
     },
     "execution_count": 18,
     "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": [
    "# find out the strongest factor among all variables\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(df_Camp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.46299273, -0.81023729, -0.34724455,  0.        ,  0.09259855,\n",
       "         0.        ],\n",
       "       [-0.88575179,  0.43746009,  0.14712928,  0.        , -0.04924835,\n",
       "         0.        ]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}