examples_save1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#First import all the libraries needed\n",
    "\n",
    "import numpy as np #for linear algebra\n",
    "import pandas as pd #for chopping, processing\n",
    "import csv #for opening csv files\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt #for plotting the graphs\n",
    "from sklearn.linear_model import LogisticRegression #for logistic regression\n",
    "from sklearn.pipeline import Pipeline #to assemble steps for cross validation\n",
    "from sklearn.preprocessing import PolynomialFeatures #for all the polynomial features\n",
    "from sklearn import svm #for Support Vector Machines\n",
    "from sklearn.neighbors import NearestNeighbors #for nearest neighbor classifier\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier #for decision tree classifier\n",
    "from sklearn.naive_bayes import GaussianNB  #for naive bayes classifier\n",
    "from scipy import stats #for statistical info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split # to split the data in train and test\n",
    "from sklearn.cross_validation import KFold # for cross validation\n",
    "from sklearn.grid_search import GridSearchCV  # for tuning parameters\n",
    "from sklearn.neighbors import KNeighborsClassifier  #for k-neighbor classifier\n",
    "from sklearn import metrics  # for checking the accuracy \n",
    "from time import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#load data\n",
    "data = pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.8</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>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.6</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>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.9</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>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.8</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0  842302         M        17.99         10.38           122.8     1001.0   \n",
       "1  842517         M        20.57         17.77           132.9     1326.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "\n",
       "            ...             radius_worst  texture_worst  perimeter_worst  \\\n",
       "0           ...                    25.38          17.33            184.6   \n",
       "1           ...                    24.99          23.41            158.8   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "\n",
       "[2 rows x 32 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check out the first two rows to make sure it loaded correctly\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 569 cases in this dataset\n",
      "There are 31 features in this dataset\n",
      "There are 212 cases diagnosed as malignant tumor\n",
      "There are 357 cases diagnosed as benign tumor\n",
      "The percentage of malignant cases is: 37.2583%\n"
     ]
    }
   ],
   "source": [
    "#Description of the dataset\n",
    "\n",
    "#how many cases are included in the dataset\n",
    "length = len(data)\n",
    "#how many features are in the dataset\n",
    "features = data.shape[1]-1\n",
    "\n",
    "# Number of malignant cases\n",
    "malignant = len(data[data['diagnosis']=='M'])\n",
    "\n",
    "#Number of benign cases\n",
    "benign = len(data[data['diagnosis']=='B'])\n",
    "\n",
    "#Rate of malignant tumors over all cases\n",
    "rate = (float(malignant)/(length))*100\n",
    "\n",
    "print \"There are \"+ str(len(data))+\" cases in this dataset\"\n",
    "print \"There are {}\".format(features)+\" features in this dataset\"\n",
    "print \"There are {}\".format(malignant)+\" cases diagnosed as malignant tumor\"\n",
    "print \"There are {}\".format(benign)+\" cases diagnosed as benign tumor\"\n",
    "print \"The percentage of malignant cases is: {:.4f}%\".format(rate)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'B'], dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.diagnosis.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#make diagnosis column numerical\n",
    "data['diagnosis'] = data['diagnosis'].map({'M':1,'B':0})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>1</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>...</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>842517</td>\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>...</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>84300903</td>\n",
       "      <td>1</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>...</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>84348301</td>\n",
       "      <td>1</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>...</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>84358402</td>\n",
       "      <td>1</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>...</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>843786</td>\n",
       "      <td>1</td>\n",
       "      <td>12.45</td>\n",
       "      <td>15.70</td>\n",
       "      <td>82.57</td>\n",
       "      <td>477.1</td>\n",
       "      <td>0.12780</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>0.15780</td>\n",
       "      <td>0.08089</td>\n",
       "      <td>...</td>\n",
       "      <td>15.47</td>\n",
       "      <td>23.75</td>\n",
       "      <td>103.40</td>\n",
       "      <td>741.6</td>\n",
       "      <td>0.1791</td>\n",
       "      <td>0.5249</td>\n",
       "      <td>0.5355</td>\n",
       "      <td>0.1741</td>\n",
       "      <td>0.3985</td>\n",
       "      <td>0.12440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>844359</td>\n",
       "      <td>1</td>\n",
       "      <td>18.25</td>\n",
       "      <td>19.98</td>\n",
       "      <td>119.60</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>0.09463</td>\n",
       "      <td>0.10900</td>\n",
       "      <td>0.11270</td>\n",
       "      <td>0.07400</td>\n",
       "      <td>...</td>\n",
       "      <td>22.88</td>\n",
       "      <td>27.66</td>\n",
       "      <td>153.20</td>\n",
       "      <td>1606.0</td>\n",
       "      <td>0.1442</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.3784</td>\n",
       "      <td>0.1932</td>\n",
       "      <td>0.3063</td>\n",
       "      <td>0.08368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>84458202</td>\n",
       "      <td>1</td>\n",
       "      <td>13.71</td>\n",
       "      <td>20.83</td>\n",
       "      <td>90.20</td>\n",
       "      <td>577.9</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0.16450</td>\n",
       "      <td>0.09366</td>\n",
       "      <td>0.05985</td>\n",
       "      <td>...</td>\n",
       "      <td>17.06</td>\n",
       "      <td>28.14</td>\n",
       "      <td>110.60</td>\n",
       "      <td>897.0</td>\n",
       "      <td>0.1654</td>\n",
       "      <td>0.3682</td>\n",
       "      <td>0.2678</td>\n",
       "      <td>0.1556</td>\n",
       "      <td>0.3196</td>\n",
       "      <td>0.11510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>844981</td>\n",
       "      <td>1</td>\n",
       "      <td>13.00</td>\n",
       "      <td>21.82</td>\n",
       "      <td>87.50</td>\n",
       "      <td>519.8</td>\n",
       "      <td>0.12730</td>\n",
       "      <td>0.19320</td>\n",
       "      <td>0.18590</td>\n",
       "      <td>0.09353</td>\n",
       "      <td>...</td>\n",
       "      <td>15.49</td>\n",
       "      <td>30.73</td>\n",
       "      <td>106.20</td>\n",
       "      <td>739.3</td>\n",
       "      <td>0.1703</td>\n",
       "      <td>0.5401</td>\n",
       "      <td>0.5390</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.4378</td>\n",
       "      <td>0.10720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>84501001</td>\n",
       "      <td>1</td>\n",
       "      <td>12.46</td>\n",
       "      <td>24.04</td>\n",
       "      <td>83.97</td>\n",
       "      <td>475.9</td>\n",
       "      <td>0.11860</td>\n",
       "      <td>0.23960</td>\n",
       "      <td>0.22730</td>\n",
       "      <td>0.08543</td>\n",
       "      <td>...</td>\n",
       "      <td>15.09</td>\n",
       "      <td>40.68</td>\n",
       "      <td>97.65</td>\n",
       "      <td>711.4</td>\n",
       "      <td>0.1853</td>\n",
       "      <td>1.0580</td>\n",
       "      <td>1.1050</td>\n",
       "      <td>0.2210</td>\n",
       "      <td>0.4366</td>\n",
       "      <td>0.20750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302          1        17.99         10.38          122.80     1001.0   \n",
       "1    842517          1        20.57         17.77          132.90     1326.0   \n",
       "2  84300903          1        19.69         21.25          130.00     1203.0   \n",
       "3  84348301          1        11.42         20.38           77.58      386.1   \n",
       "4  84358402          1        20.29         14.34          135.10     1297.0   \n",
       "5    843786          1        12.45         15.70           82.57      477.1   \n",
       "6    844359          1        18.25         19.98          119.60     1040.0   \n",
       "7  84458202          1        13.71         20.83           90.20      577.9   \n",
       "8    844981          1        13.00         21.82           87.50      519.8   \n",
       "9  84501001          1        12.46         24.04           83.97      475.9   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760         0.30010              0.14710   \n",
       "1          0.08474           0.07864         0.08690              0.07017   \n",
       "2          0.10960           0.15990         0.19740              0.12790   \n",
       "3          0.14250           0.28390         0.24140              0.10520   \n",
       "4          0.10030           0.13280         0.19800              0.10430   \n",
       "5          0.12780           0.17000         0.15780              0.08089   \n",
       "6          0.09463           0.10900         0.11270              0.07400   \n",
       "7          0.11890           0.16450         0.09366              0.05985   \n",
       "8          0.12730           0.19320         0.18590              0.09353   \n",
       "9          0.11860           0.23960         0.22730              0.08543   \n",
       "\n",
       "            ...             radius_worst  texture_worst  perimeter_worst  \\\n",
       "0           ...                    25.38          17.33           184.60   \n",
       "1           ...                    24.99          23.41           158.80   \n",
       "2           ...                    23.57          25.53           152.50   \n",
       "3           ...                    14.91          26.50            98.87   \n",
       "4           ...                    22.54          16.67           152.20   \n",
       "5           ...                    15.47          23.75           103.40   \n",
       "6           ...                    22.88          27.66           153.20   \n",
       "7           ...                    17.06          28.14           110.60   \n",
       "8           ...                    15.49          30.73           106.20   \n",
       "9           ...                    15.09          40.68            97.65   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\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",
       "5       741.6            0.1791             0.5249           0.5355   \n",
       "6      1606.0            0.1442             0.2576           0.3784   \n",
       "7       897.0            0.1654             0.3682           0.2678   \n",
       "8       739.3            0.1703             0.5401           0.5390   \n",
       "9       711.4            0.1853             1.0580           1.1050   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \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",
       "5                0.1741          0.3985                  0.12440  \n",
       "6                0.1932          0.3063                  0.08368  \n",
       "7                0.1556          0.3196                  0.11510  \n",
       "8                0.2060          0.4378                  0.10720  \n",
       "9                0.2210          0.4366                  0.20750  \n",
       "\n",
       "[10 rows x 32 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.690000e+02</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.037183e+07</td>\n",
       "      <td>0.372583</td>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>...</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.250206e+08</td>\n",
       "      <td>0.483918</td>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>...</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.670000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.692180e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>...</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.060240e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>...</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.813129e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.113205e+08</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>...</td>\n",
       "      <td>36.040000</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id   diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
       "count  5.690000e+02  569.000000   569.000000    569.000000      569.000000   \n",
       "mean   3.037183e+07    0.372583    14.127292     19.289649       91.969033   \n",
       "std    1.250206e+08    0.483918     3.524049      4.301036       24.298981   \n",
       "min    8.670000e+03    0.000000     6.981000      9.710000       43.790000   \n",
       "25%    8.692180e+05    0.000000    11.700000     16.170000       75.170000   \n",
       "50%    9.060240e+05    0.000000    13.370000     18.840000       86.240000   \n",
       "75%    8.813129e+06    1.000000    15.780000     21.800000      104.100000   \n",
       "max    9.113205e+08    1.000000    28.110000     39.280000      188.500000   \n",
       "\n",
       "         area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
       "count   569.000000       569.000000        569.000000      569.000000   \n",
       "mean    654.889104         0.096360          0.104341        0.088799   \n",
       "std     351.914129         0.014064          0.052813        0.079720   \n",
       "min     143.500000         0.052630          0.019380        0.000000   \n",
       "25%     420.300000         0.086370          0.064920        0.029560   \n",
       "50%     551.100000         0.095870          0.092630        0.061540   \n",
       "75%     782.700000         0.105300          0.130400        0.130700   \n",
       "max    2501.000000         0.163400          0.345400        0.426800   \n",
       "\n",
       "       concave points_mean           ...             radius_worst  \\\n",
       "count           569.000000           ...               569.000000   \n",
       "mean              0.048919           ...                16.269190   \n",
       "std               0.038803           ...                 4.833242   \n",
       "min               0.000000           ...                 7.930000   \n",
       "25%               0.020310           ...                13.010000   \n",
       "50%               0.033500           ...                14.970000   \n",
       "75%               0.074000           ...                18.790000   \n",
       "max               0.201200           ...                36.040000   \n",
       "\n",
       "       texture_worst  perimeter_worst   area_worst  smoothness_worst  \\\n",
       "count     569.000000       569.000000   569.000000        569.000000   \n",
       "mean       25.677223       107.261213   880.583128          0.132369   \n",
       "std         6.146258        33.602542   569.356993          0.022832   \n",
       "min        12.020000        50.410000   185.200000          0.071170   \n",
       "25%        21.080000        84.110000   515.300000          0.116600   \n",
       "50%        25.410000        97.660000   686.500000          0.131300   \n",
       "75%        29.720000       125.400000  1084.000000          0.146000   \n",
       "max        49.540000       251.200000  4254.000000          0.222600   \n",
       "\n",
       "       compactness_worst  concavity_worst  concave points_worst  \\\n",
       "count         569.000000       569.000000            569.000000   \n",
       "mean            0.254265         0.272188              0.114606   \n",
       "std             0.157336         0.208624              0.065732   \n",
       "min             0.027290         0.000000              0.000000   \n",
       "25%             0.147200         0.114500              0.064930   \n",
       "50%             0.211900         0.226700              0.099930   \n",
       "75%             0.339100         0.382900              0.161400   \n",
       "max             1.058000         1.252000              0.291000   \n",
       "\n",
       "       symmetry_worst  fractal_dimension_worst  \n",
       "count      569.000000               569.000000  \n",
       "mean         0.290076                 0.083946  \n",
       "std          0.061867                 0.018061  \n",
       "min          0.156500                 0.055040  \n",
       "25%          0.250400                 0.071460  \n",
       "50%          0.282200                 0.080040  \n",
       "75%          0.317900                 0.092080  \n",
       "max          0.663800                 0.207500  \n",
       "\n",
       "[8 rows x 32 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#explore further\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#drop ID because we do not need the ID number as shown above\n",
    "\n",
    "data.drop('id',axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</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>...</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>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>...</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>1</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>...</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>1</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>...</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>1</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>...</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>12.45</td>\n",
       "      <td>15.70</td>\n",
       "      <td>82.57</td>\n",
       "      <td>477.1</td>\n",
       "      <td>0.12780</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>0.15780</td>\n",
       "      <td>0.08089</td>\n",
       "      <td>0.2087</td>\n",
       "      <td>...</td>\n",
       "      <td>15.47</td>\n",
       "      <td>23.75</td>\n",
       "      <td>103.40</td>\n",
       "      <td>741.6</td>\n",
       "      <td>0.1791</td>\n",
       "      <td>0.5249</td>\n",
       "      <td>0.5355</td>\n",
       "      <td>0.1741</td>\n",
       "      <td>0.3985</td>\n",
       "      <td>0.12440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>18.25</td>\n",
       "      <td>19.98</td>\n",
       "      <td>119.60</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>0.09463</td>\n",
       "      <td>0.10900</td>\n",
       "      <td>0.11270</td>\n",
       "      <td>0.07400</td>\n",
       "      <td>0.1794</td>\n",
       "      <td>...</td>\n",
       "      <td>22.88</td>\n",
       "      <td>27.66</td>\n",
       "      <td>153.20</td>\n",
       "      <td>1606.0</td>\n",
       "      <td>0.1442</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.3784</td>\n",
       "      <td>0.1932</td>\n",
       "      <td>0.3063</td>\n",
       "      <td>0.08368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>13.71</td>\n",
       "      <td>20.83</td>\n",
       "      <td>90.20</td>\n",
       "      <td>577.9</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0.16450</td>\n",
       "      <td>0.09366</td>\n",
       "      <td>0.05985</td>\n",
       "      <td>0.2196</td>\n",
       "      <td>...</td>\n",
       "      <td>17.06</td>\n",
       "      <td>28.14</td>\n",
       "      <td>110.60</td>\n",
       "      <td>897.0</td>\n",
       "      <td>0.1654</td>\n",
       "      <td>0.3682</td>\n",
       "      <td>0.2678</td>\n",
       "      <td>0.1556</td>\n",
       "      <td>0.3196</td>\n",
       "      <td>0.11510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>13.00</td>\n",
       "      <td>21.82</td>\n",
       "      <td>87.50</td>\n",
       "      <td>519.8</td>\n",
       "      <td>0.12730</td>\n",
       "      <td>0.19320</td>\n",
       "      <td>0.18590</td>\n",
       "      <td>0.09353</td>\n",
       "      <td>0.2350</td>\n",
       "      <td>...</td>\n",
       "      <td>15.49</td>\n",
       "      <td>30.73</td>\n",
       "      <td>106.20</td>\n",
       "      <td>739.3</td>\n",
       "      <td>0.1703</td>\n",
       "      <td>0.5401</td>\n",
       "      <td>0.5390</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.4378</td>\n",
       "      <td>0.10720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>12.46</td>\n",
       "      <td>24.04</td>\n",
       "      <td>83.97</td>\n",
       "      <td>475.9</td>\n",
       "      <td>0.11860</td>\n",
       "      <td>0.23960</td>\n",
       "      <td>0.22730</td>\n",
       "      <td>0.08543</td>\n",
       "      <td>0.2030</td>\n",
       "      <td>...</td>\n",
       "      <td>15.09</td>\n",
       "      <td>40.68</td>\n",
       "      <td>97.65</td>\n",
       "      <td>711.4</td>\n",
       "      <td>0.1853</td>\n",
       "      <td>1.0580</td>\n",
       "      <td>1.1050</td>\n",
       "      <td>0.2210</td>\n",
       "      <td>0.4366</td>\n",
       "      <td>0.20750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0          1        17.99         10.38          122.80     1001.0   \n",
       "1          1        20.57         17.77          132.90     1326.0   \n",
       "2          1        19.69         21.25          130.00     1203.0   \n",
       "3          1        11.42         20.38           77.58      386.1   \n",
       "4          1        20.29         14.34          135.10     1297.0   \n",
       "5          1        12.45         15.70           82.57      477.1   \n",
       "6          1        18.25         19.98          119.60     1040.0   \n",
       "7          1        13.71         20.83           90.20      577.9   \n",
       "8          1        13.00         21.82           87.50      519.8   \n",
       "9          1        12.46         24.04           83.97      475.9   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760         0.30010              0.14710   \n",
       "1          0.08474           0.07864         0.08690              0.07017   \n",
       "2          0.10960           0.15990         0.19740              0.12790   \n",
       "3          0.14250           0.28390         0.24140              0.10520   \n",
       "4          0.10030           0.13280         0.19800              0.10430   \n",
       "5          0.12780           0.17000         0.15780              0.08089   \n",
       "6          0.09463           0.10900         0.11270              0.07400   \n",
       "7          0.11890           0.16450         0.09366              0.05985   \n",
       "8          0.12730           0.19320         0.18590              0.09353   \n",
       "9          0.11860           0.23960         0.22730              0.08543   \n",
       "\n",
       "   symmetry_mean           ...             radius_worst  texture_worst  \\\n",
       "0         0.2419           ...                    25.38          17.33   \n",
       "1         0.1812           ...                    24.99          23.41   \n",
       "2         0.2069           ...                    23.57          25.53   \n",
       "3         0.2597           ...                    14.91          26.50   \n",
       "4         0.1809           ...                    22.54          16.67   \n",
       "5         0.2087           ...                    15.47          23.75   \n",
       "6         0.1794           ...                    22.88          27.66   \n",
       "7         0.2196           ...                    17.06          28.14   \n",
       "8         0.2350           ...                    15.49          30.73   \n",
       "9         0.2030           ...                    15.09          40.68   \n",
       "\n",
       "   perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n",
       "0           184.60      2019.0            0.1622             0.6656   \n",
       "1           158.80      1956.0            0.1238             0.1866   \n",
       "2           152.50      1709.0            0.1444             0.4245   \n",
       "3            98.87       567.7            0.2098             0.8663   \n",
       "4           152.20      1575.0            0.1374             0.2050   \n",
       "5           103.40       741.6            0.1791             0.5249   \n",
       "6           153.20      1606.0            0.1442             0.2576   \n",
       "7           110.60       897.0            0.1654             0.3682   \n",
       "8           106.20       739.3            0.1703             0.5401   \n",
       "9            97.65       711.4            0.1853             1.0580   \n",
       "\n",
       "   concavity_worst  concave points_worst  symmetry_worst  \\\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",
       "5           0.5355                0.1741          0.3985   \n",
       "6           0.3784                0.1932          0.3063   \n",
       "7           0.2678                0.1556          0.3196   \n",
       "8           0.5390                0.2060          0.4378   \n",
       "9           1.1050                0.2210          0.4366   \n",
       "\n",
       "   fractal_dimension_worst  \n",
       "0                  0.11890  \n",
       "1                  0.08902  \n",
       "2                  0.08758  \n",
       "3                  0.17300  \n",
       "4                  0.07678  \n",
       "5                  0.12440  \n",
       "6                  0.08368  \n",
       "7                  0.11510  \n",
       "8                  0.10720  \n",
       "9                  0.20750  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check that id is dropped\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.372583</td>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>...</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.483918</td>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>...</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>...</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>...</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>...</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>...</td>\n",
       "      <td>36.040000</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        diagnosis  radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
       "count  569.000000   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     0.372583    14.127292     19.289649       91.969033   654.889104   \n",
       "std      0.483918     3.524049      4.301036       24.298981   351.914129   \n",
       "min      0.000000     6.981000      9.710000       43.790000   143.500000   \n",
       "25%      0.000000    11.700000     16.170000       75.170000   420.300000   \n",
       "50%      0.000000    13.370000     18.840000       86.240000   551.100000   \n",
       "75%      1.000000    15.780000     21.800000      104.100000   782.700000   \n",
       "max      1.000000    28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       symmetry_mean           ...             radius_worst  texture_worst  \\\n",
       "count     569.000000           ...               569.000000     569.000000   \n",
       "mean        0.181162           ...                16.269190      25.677223   \n",
       "std         0.027414           ...                 4.833242       6.146258   \n",
       "min         0.106000           ...                 7.930000      12.020000   \n",
       "25%         0.161900           ...                13.010000      21.080000   \n",
       "50%         0.179200           ...                14.970000      25.410000   \n",
       "75%         0.195700           ...                18.790000      29.720000   \n",
       "max         0.304000           ...                36.040000      49.540000   \n",
       "\n",
       "       perimeter_worst   area_worst  smoothness_worst  compactness_worst  \\\n",
       "count       569.000000   569.000000        569.000000         569.000000   \n",
       "mean        107.261213   880.583128          0.132369           0.254265   \n",
       "std          33.602542   569.356993          0.022832           0.157336   \n",
       "min          50.410000   185.200000          0.071170           0.027290   \n",
       "25%          84.110000   515.300000          0.116600           0.147200   \n",
       "50%          97.660000   686.500000          0.131300           0.211900   \n",
       "75%         125.400000  1084.000000          0.146000           0.339100   \n",
       "max         251.200000  4254.000000          0.222600           1.058000   \n",
       "\n",
       "       concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "count       569.000000            569.000000      569.000000   \n",
       "mean          0.272188              0.114606        0.290076   \n",
       "std           0.208624              0.065732        0.061867   \n",
       "min           0.000000              0.000000        0.156500   \n",
       "25%           0.114500              0.064930        0.250400   \n",
       "50%           0.226700              0.099930        0.282200   \n",
       "75%           0.382900              0.161400        0.317900   \n",
       "max           1.252000              0.291000        0.663800   \n",
       "\n",
       "       fractal_dimension_worst  \n",
       "count               569.000000  \n",
       "mean                  0.083946  \n",
       "std                   0.018061  \n",
       "min                   0.055040  \n",
       "25%                   0.071460  \n",
       "50%                   0.080040  \n",
       "75%                   0.092080  \n",
       "max                   0.207500  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe again\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#explore some of the attribute for best understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum of radius mean is: 6.98 \n",
      "Maximum of radius mean is: 28.11 \n",
      "Average of radius mean is: 14.13 with a standard deviation of 3.52\n"
     ]
    }
   ],
   "source": [
    "#radius mean is the mean of distances from center to points on the perimeter of breast mass\n",
    "#so let's look at maximum, minimum, average, and standard deviation of radius mean\n",
    "min_radius = min(data['radius_mean'])\n",
    "max_radius = max(data['radius_mean'])\n",
    "average_radius = np.mean(data['radius_mean'])\n",
    "sd_radius = np.std(data['radius_mean'])\n",
    "\n",
    "print \"Minimum of radius mean is: {:,.2f} \".format(min_radius)\n",
    "print \"Maximum of radius mean is: {:,.2f} \".format(max_radius)\n",
    "print \"Average of radius mean is: {:,.2f} \".format(average_radius)+\"with a standard deviation of {:,.2f}\".format(sd_radius)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum of texture mean is: 9.71 \n",
      "Maximum of texture mean is: 39.28 \n",
      "Average of texture mean is: 19.29 with a standard deviation of 4.30\n"
     ]
    }
   ],
   "source": [
    "#texture mean is the standard deviation of gray scale value\n",
    "#so let's look at maximum, minimum, average, and standard deviation of texture mean\n",
    "min_texture = min(data['texture_mean'])\n",
    "max_texture = max(data['texture_mean'])\n",
    "average_texture = np.mean(data['texture_mean'])\n",
    "sd_texture = np.std(data['texture_mean'])\n",
    "\n",
    "print \"Minimum of texture mean is: {:,.2f} \".format(min_texture)\n",
    "print \"Maximum of texture mean is: {:,.2f} \".format(max_texture)\n",
    "print \"Average of texture mean is: {:,.2f} \".format(average_texture)+\"with a standard deviation of {:,.2f}\".format(sd_texture)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def histograms(df, variables, n_rows, n_cols):\n",
    "    fig=plt.figure()\n",
    "    for i, var_name in enumerate(variables):\n",
    "        ax=fig.add_subplot(n_rows,n_cols,i+1)\n",
    "        df[var_name].hist(bins=20,ax=ax)\n",
    "        ax.set_title(\" Distribution of \"+ var_name)\n",
    "    #fig.tight_layout()\n",
    "    plt.rcParams.update({'font.size': 6, 'font.weight':'bold'})\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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ZTowx49jJiNUsDA5vcXUv3ZWh87HTQ5Ina9e6l9oJHTlogzTNBk6LVtWpJI2re+muDNUP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\nHMdxKvDkwHEcx3GcCjw5cBzHcRyngv8DvbfjqloN5dUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b7199d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features=list(data.columns[1:5])\n",
    "histograms(data,features,2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uZyxXzwGgj5GRYW644YYI9dZPXPe4XfW2ioLdSOfiFhxD5d1IbcbdqdIbsi2m\nzf86xe9T3THo7++PWUN+m1/pESrtK7gdB1FTqZ4zyMc/WLFiRQx6axP/PW4vvS2kbXcjOZPK7eJZ\nCCwG7iooy+/iWVNy7hLgmpKyYX/uqpLyX5at29hdPHm9lXYjNVKPZUUl1Xa/nHPOOUVlUeyqqvd+\n9Pf3B9mNVG6nVCt2I+X9Pq7dSP39/UydOrVoN1K5ujZNVKsYx3sQcFdCXDS/6yDOXQlRyoh29bTR\nPK3alUAb7kaqf1dC1LsNQpxnfmU42nVXgmEY6SOxu5EMw6iOLT40DKNh1HYjGUbHYiMGhmEYhmFs\npemOgYhMF5GciGwSkUdE5JikBDopt9Cm8wlT50WLFgXRG+oep7NtJYXLU6U3ZFtMm/91it9HMWLw\nDC7R+H64LUqfZzTQydG4pbVXR6AncjolSlVjhKnzAQccEESvRUAzxmKRDztdd9r0Rk3THQNVfVRV\nfwqsw3USBikIdIJfiCQiIffoleWEE04IbUIAwtT5iCOOCKI31D1OZ9tKCv+YKr0h22La/K9T/D6S\nNQYi8jHcaME7ge+SmEAnhmEYhmEUEtXiwz6gGxfB46u4oCbBA52ECKxRfo6pfLriMGmXC4km7XK9\nAVva5X60ItBJK+phaZcNw4iFZgMh4MbH/gF4A7AC+BPwQRKQdvnOO++MRW57Bzi6M0I76g/E8o1v\nfCOWa12LuO5xu+q1tMv1BBA6N1Dgoij11h/gKFRbDKk7TXrbNcDRPsD3gXuAKcCHcLFE2z7QyQUX\nXBDahACEqfNVV10VRG+oe5zOtpUUfpgqvSHbYtr8r1P8PorFh32q+mpVnaSq+6nqSlXdrKpnquou\nvmwgCmOj5tprrw1tQgDC1Pn8888PojfUPU5n20oKZ6RKb8i2mDb/6xS/T3WAo0mTJoU2IQBh6vzS\nl740iN5Q9zidbSspbJ8qvSHbYtr8r1P8PtUdA8MwDMMwirGOgWEYhmEYW0l1x6B0u1g6CFPniy++\nOIjeUPc4nW0rKVyTKr2nnXYa2Wy25pHL5SLXnTb/6xS/T3V2xT333DO0CQEIU+fddtstiN5Q9zid\nbSspTO4YvYODg1U/f/zxx/nmN6/kiiuuqClr4sRJrF07GGnbTZv/dYrfp7pjcOaZacwOG6bOxx9/\nfBC9oe5xOttWUpgFhNg+G6Xex4EJzJlT707wPmB6lc8HGRmZw9DQUKT/3NLmf53i96nuGBiGYSST\njcAWav9Y6MbYAAAgAElEQVTDvxk415/TlklujTbEOgaGYRiJpdY//OpTDYZRjqiSKM0UkTUi8jcR\neUhEDhORCSKyRESeFJF7RaTtuqul8evTQZg6r1+/PojeUPc4nW0rKZTmEel0veFIm/91it9HtSth\nInA+8HbgaVw45BOB04Cjcd3WqyPSFRlnn332uL6Xy+Wqru6ttSAoLOOrc7NccsklQfSO9x4nVa9R\nD/0p0xuOtPlfp/h9JFMJqroi/1pEBoDjgcOBh1R1lYjsDhwrItNUNcxPxzJceumlDX8nl8vR1TWd\nkZHhGCxqBY3XOQpCOcx47nGS9Rr10MvYjKKdrDccafO/TvH7SOMYiMibgZOAK4Bdgef8R/m/u0ap\nr1nGs/p2aGjIdwr6gIEKx3kRWhk1YbbTTJkyJYhe27YUD0mdPnR0znbFdidt/tcpfh9Zx0BEXg+s\nBO4AzgGeAHbwH+/o/z5R6ftHHnlkUV75TCZDT08Py5cvLzpv5cqVZDKZMd+fN28eS5cuLSrLZrNk\nMhmGhoaKyhcuXMjixYuLynK5HJlMZswc0ZIlSyoErbgCGMYt/Mkfa4ElwLSSc2cDyxnL/DJl84Cf\nlZRlgQwwNOZsWFbyPufPLZ3PXMLY4Eab/N/SXzH9wNwyuhqtR2vux/DwMJlMhlWrVhXXor+fuXPH\n1mP27Nlt2a4arUdPTw/7779/kc/09vaOOS8mEjl9aBhGHUSRuxnYA9iA+w/zWmAqLv3yC8BM4Hpg\nsMJ3I8/LHif15XyvlU+92c/bRUa0eeON5okjN3utA/gabv/c1cADvuw4YDMwrcJ3Ivf7+nyz3nbb\n7udFrdP8NKnE4fNRjRi8y3cO9gMexv1sXYX7FXEjsC9QbySOllH66y4dhKnzsmXLgugNdY/T0raS\nNn3o+EHK9NbP4OBgpKGT0+Z/neL3kXQMVPVbqrqNPyb4v+tU9UxV3UVV91PVgSh0RcnwcFIXEDZD\nmDqPjIwE0RvqHqehbTU7fQjxTCG6WYxyU28LcR3jFwrK8lNvpdvMljA2t8GwP3dVSfkvy9Zt7NRb\nXm99U2/V61HayahUj5VldJWrx+OAMGfOHLq7u6seXV3T6evrq+t+DA8PB5l66+8fuwOkFVOIeb+P\nawqxv7+fqVOnFk0hzp9frj01SVRDD+M9sKmEmIYSbSohjbRqKoEmpg81Jr+3qYQozuvz51Y6+syf\n24w4fN4iHxqGMR7y04d74KYPFXg9o9OHj9KG04dGLSx0spHytMuGYYwPTej0oWEYtUl1x6B0rigd\nhKnzU089FURvqHuczraVFJ5Nmd5wpM3/OsXvU90xOOWUU0KbEIAwdf7sZz8bRG+oe5zOtpUUrkiZ\n3nCkzf86xe9TvcZg0aJFoU0IwKIgWk899dQgekPd43S2raTwfsKEJg6lN3rqyQczefLk1Plfp/h9\nqjsGM2akcZFNuDpns9my5ZMnT44tlGioe5zOtpUUSiOTdrreKHkcmMCcObXXlU6cOIm1a8MklDO/\nb45UdwyMVlD7QZJ/gHRKnHHD6Fw2AltwuWKmVzlvkJGROQwNDZlfJ5BIOgYisi1wJ9ANDKnq7iIy\nAfgyLn76H4G5qlr+J6PRwdR6kNgDxDCSh21r7GSiWnyouL3LdxSUnUSbJ1QZGzEtDYSq81qKE07l\nj2q/Opon1D1OZ9tKCj9Lmd5wlEYabBXm980RVUjkzap6IW5kIM8s4CFVXYXPlyAibTXJVmnOu7MJ\nVefSUK2tIdQ9TmfbSgq/T5necJSGBG4V5vfNEecag0oJVdbHqLMhLrvsstAmBCBUnc8JojXUPU5n\n24qH559/nmOOOY5HHllX9bxNm+rNx3EycFuzZo2DUHrDcfTRR9f8ZxnH4mPz++aIM45BQwlV4kim\nElfyDsd8xiZT6Qfmljm3NJlKoYwxNWHskGOW8slUAJaVvM8nU3mspHwJUFqPTf5v6RaqqOpROqxW\nuR6l2RfjSkICrUmm0op69PT0FCVTyWQy9Pb2jjkv6axbt45bb72ZdetmsG7dP1c8Hn30NaFNNbYy\nuui4VlKmN7yhix/96EeRZXQ0IiCqpAu41Mo3An8GuqgzoQqWRCmmJCrtkkSp1jmWZClKWpVEqdmj\nEb+/7777fJ1+VSMR0OIGEwYl+bx2tq3wvFpJmS5WmJBvsxWPiRMn6YYNG5pxjY6l3ZMoPeiNy78+\nDEuoYhiGkWJq7V4YpPb2R9u51Goim0rQ0UQq+eOn2uYJVcoNHXc+oepcPWf44OBgLMOIoe5xOttW\nUrgoZXpDUt3vR8l3IKLZuWR+3xypDnB0xhlnhDYhAKHqfFyF8ngDIIW6x+lsW0lhFmFCE4fSG5JK\nfh8v5vfNkeqOwaxZs0KbEIBQde6pUB5vAKRQ9zidbSspvCVlekNSye/jxfy+OVLdMTDaCYukZhiG\n0Q5Yx6AMuVyuYl7terKKGYZhGNFSb0ZHW6DYPKnuGCxfvpz3ve99RWW5XI6urumMjAwHsipulgPv\nq3lW9NxOiBGBcve4k/Ua9XB3yvSGJAq/bzyjYzabNb9vglR3DBYvXjzmJg4NDflOQaU575uBc1tg\nXVwsJkzHYBnwyZZrLXePO1mvUQ8/SJnekCyjeb9vPKNjLf+rNipcSKMjEJ3i96nuGOy6665VPq00\n5530qYRqdY6TVzb17VrDiJUcuPo9jo9Qeo162LH2KR2lNyTN+X0x9a9DquZ/jYwKb7/9RL773e8w\nZcqUquflnz+d4vexdgxE5L+BM4GngTNVdUWc+oxOpL5hxEoO/PTTT5PNZtm0aRPbb799xe/b3GR0\nmN8b7UztUeE8d7Jp0yc46qijasrMT2F0CrF1DETkYOCzwLHAPwLXiMgeqvq3uHQanUg9w4jVHbi7\nuxvYBthcUUszsRKMUczvjeQQRVRGd15+CqNTiHPEYBbwvKp+V0T+AHwcOAC3GiUWstks733v+xkZ\nqZxlbZddduGqq5ay3Xbbbf01WYjtOmhXqjlxNQeej4vOfW6Fz933owi5WjhvWa5tpWRUouV+bxiF\nDA4OlvW/ws8bo74pjFp6of5nQD1rIOJ8nsSddvl5//o5QCg/wT0RovmH3NfXx2OPbQBeV+GMv/PX\nvz7IwQfP5O9/fwHI/5osx82UX0/wixqf13NOs583I+MXwLcD2HlPgd64dKwv89kDwJuqfD5afvPN\nN1dthxMmTGDLli1lPxsaGmLBgnN48cXRTmlp29puu4nceGPt+cpmKLB/YmxKqhO53z/88MP+1beo\n3r+4w/+t1lYAHqrzvHraXiPnRak3atviOq+S3zcir16dqwHZOu1Y+dmeJ6q61q/3JS/ZngsvXMzk\nyZMrnlPuWVKO/PNk48aN+aLofD6qbEylB3Ae8Jx/fQDuJ90hZc47kRqZteyww46GjxPj8m3zezvs\naMsjMp8X76SRIyL/hOven4h7QHwIeI2qDpectwtwOPB7oHoXyTCMWkwE9gZWqOqTrVZufm8YLSdy\nn4+tYwAgIufisvY8g1udfEtsygzDaAvM7w0j2cTaMTAMwzAMI1lMCG2AYRiGYRjtg3UMDMMwDMPY\nSuwdAxH5bxH5s4g8JCKHl/n8wyLyBxHJicjJJZ+dKiJbROSaVukVkYNFZLWIDItI5Q2pEesWke1F\n5AYReUZE/iIin4pB73IR+ZuIbBaR7er9Xhx6RWSmiKzx5Q+JSMOJzMdbX/9ZnG2r0nVuqm2N8zo3\n3a7GQxP+F3dbLOd7e/m2sNkf98Wgt518r+n61tItItNF5F7f1nMiMqee78WlN+57LCI7i8j/icgm\nEfmjiJzeovqW1dt0fWPeunQwbrvSB4CLgCeBlxZ8vhfwIvBvuEAoLwBT/Wfb4VYs/w24phV6gZ2A\np4CrcFEtTm9VnXGZjbYApwJfw4Xp2yUqvf6cs4B+L3u7er8Xk97DcSvW9wV+C6yL8jpX0ht326pS\n36baVhN6m2pXrfT7uNtiFb17+e/tD+wO7NqiexPK95qqb53X+u24sNhduOxRw8BLWnCPK+mN9R4D\nLwOOAl4LrMQF+Yr9HlfR21x943gwFBh9HvCsf72/b5yHFHz+YV82GXiVr8hc/9mZwE3AOhp/eI9L\nL26L1WZ856SVdQbegtu29R5cSNm/ATtEpbfgvIUUPyTq+l7Ueks++xqwMcrrXE0vbsV8LG2rynU+\nqZm21YTeptpVi30g1rZYRW/+IfoELpLNsS26N0F8r9n6Nmo7cI7/fGfgc62ocxm9rbrHE3Adsbv9\n+5bUt0DvQBT3OO6phFpR0HYt+Oy5fJmITATOxsWxlVbpBV7jX98gIo+JyMUt1P0w8HNcL/e/gE+r\n6rMR6o36e5F8X0TejPuneXkDOset17et/yC+tlWJPfzf8bat8epttl2Nh/H6QNxtsZLep3E/Cg7B\nhSdcJiKviFBv1N9r9vvP0Fx969YtIrvjOuLfUdWNuE5Z7HUuo7fZOtfUKyL/jBuhOA64zhfHXt8S\nvdf64qbqG3fH4Ang5f71jgVllLzewR/5so/g4lHm41CKiDTyEB+v3r/iLnw/cDFwlogc1oDeZnSf\nDLwbN7x+EfA5EdkrQr1Rf6/p74vI63HDX3cAjc59j1dv3G2rEs22rfHqnUtz7Wo8jNcH4m6LZfWq\n6kZVvVZVHwCuxAWM2SdCvVF/r6nvq+pTTda3Lt0iMhm4DZcidW4zNjeh9xSIpM712P1T4G3A9cD/\niAvi1Yp7XKj3CyKyS7P1jbtjcCvwUhGZDczGzbHeLSJT/UKYn+CGOz4EzAH+jruh++Cys72IGxI5\nDvhMC/T+BDdU84I/1P9tRZ3VH5u8zm1xvc2o9CIi+zDa2+wSkR0rfO/XcesVkT18vf+Mm/OdIiKN\ntMfx1jfutlVJb7Nta7x6t9BcuxoP4/WBuNtiWb0icpiIzBWRLtzo1Qhumikqve3me83Wt6bugvpN\nwk3hvMLbFGudy+jd2ZfHeo9F5C3AP+B87EVcO9vUgvqW6t0MbGq6vo3OLY1jLupc3MP/YeAIYKY3\nfpb//MPAH/xxsi+biktnNQP4Iy6LxW5x6/XlH/E6nwD+p4V1noSb934aGAK+GIPe9f59/vhQue+1\nQi/QW/B+i/+7Zwv0tqJtVbrOTbWtcdb3pc22q1b5QIvaYjnf68Fl23oeN+x6TIf7XtP1raW74HWh\nf/9T3HWupDfuewwc6tvU37z8Oa1o05X0Nltfi3xoGIZhGMZWLMCRYRiGYRhbSXXHQER+Ly4Yxn0i\n8oCInCci2/vPpojI9VW+u5OIfKyG/FX+70wR+UGDth0tIvsWvP+MiBzaiIxGEZF+EblHRP4tTj3N\nUHhfROStIvKe0DYZYTD/HaOz7f03Luq5vv4+9rTKpiST6qkEEVkHdKvqUyIyCfg68KKqnlzHd/cG\nfqCqbynz2Taqurng/Uzgk6qaacC2K4Efqup36/1OM4jIbsCdqvr6VuiLAhHpBd6hqmeGtsVoPea/\nRfoS57+tRkQWAs+p6kWhbWl7xrPopFMO3MKcVxa83wHYyGhAjPt9+RtxK0mzwD24KFP9uL2jWWAx\nbkHIz4HvAWv89/KBKWbituT9EFgDfKVA57MFrz+A21rSg4tw9YiXP82Xv9+f9y5ffi/wDeAlBfVZ\nBAz4z95Qps7bA98E7vPnzfTl9+IWqmSBA0u+8yrgRl/31cABvvwTwP1e1r/5sr2AB4ErcItfbgG2\n95+9FrfK9h7gbl+vl+FWpN/tbXivP/d/KIgOiAvS8on8fcGtrt+AW5STxe0ueAgf1Q+3NfBhKkT5\n89fzK8BdwP/5e7TU2/7NgvMOA37p7bsOmFSwIOjXvu5fKzj/duAL/rM1pdfSDvNfUuy//vW/A7/x\nchZWuLfPAl/0NtzKqF+/Deez9wDfBXYq8Of3V7qOvl6P4xbqZYEDgX/x9V8N/KxKO+vFLeBdiVvZ\nPw+Y7+X8EtjZn7cP8GNcJNc78vcPF5nwV96elfgohP6aLMU9M/4Pl6I8uF+pqnUMKHiw+LLVwDt9\nQ7rPl10CnOBfb+udc+vnvnymb8x7FpQ9U/DZsP+O+Mbx/sJz/OsP4P8pFTb0wvdedw54rS//FnBW\nQX1O968/Bny9TJ0/AXzDv+7C/XPdrrQ+Jd+5tkCH4B7AM7zTTcQ9HB4A3urlvAC8xZ9/HXCif/0r\nIONfb+e/OwF4uS/bBXjYv35bobMCv8OF9iy8L73AJQXnnMvoA+4w4IYq9/5KfNRDIINbtf9G//5u\nYD9vzx34EKT4oFv+9c4Fsq4C/tm/vh240L9+D3Br6HbeqQfmv0nz36neLy8vsOUHwEFlbN4CHO9f\nn4v3c2/zQf71Z/C7bBjbMSi8jlf411s7J/79fcAU/3rHKu2sF/ejYxJuq+9G4CP+sy8WXNvbCu7r\n/sBP/OudCmR9mNHnw0JgFa5N7oLbNbRNaL9SjT/yYRIpF+zmLuC/RORsYG9V3VThu79R1VyVzzao\naxH9wEFV9FWjC5dX4BH//lu47Th5bvJ/B3BOXspBQB+Aqq7F5Qx4Qw2dhwJf9d9RdZHzDgJuUtUR\nVX0e94vkYH/+elW9v8COvUXk5cDuqvp9L+cFVR3BPVj+R0TuxTnW7iLyKlW9BxcFczcR2Q/4q6o+\nVsPOK4EP+ten+PfVyM8b3w/8SVUf9O9/B+wNHID7tfkLEVmN2+q1pz/nXSLyK3HJSQ4B3lQg98aC\nupe7B0Z8mP+OpV3891HcFrvDxCURy/rrUW76YzMuYA++vgf5GAU7qeoqX1567QopvI57VzhnFfAt\nEflX3D/natyuqsOqOoTrGPzQl9+Puz4vA/4RF9l0NS6S66v9Oa8RkRX+WfHvFD8rfqSqf1fVJ3Gj\nn6+mDah1MVKFiOyAc8aHcMORAKhqv4j8CjckdLOInIrrlZbyfJmyrWIqvC8sn1ivqVU+yz/0NlPf\n/a3nwVZqey0KH7ybGa1XOV0n4Xrhb1fVLSKyvuD8G3DBiHZjNMRoZSNV/yguC9khuF+NJ9Zp5xaK\nbd6Cu3ZbgJWqelLhl/wCt8uAGar6mJ+7LLx3jd4DIwLMfyvSTv4ruBgeX2/Qpnwd6u2I1byOqnq6\niLwT1y4GRGSGqj5VQ17elsJnx7a4DtJTqjqjzHeXAP+rqj/y61UWVpCblxUcGzHw+B7xZbhe9NMl\nn01T1fWqugQ3B7kfbthxh7GSisUWvP4HcakwJ+AiWN3py/8kIl2+/JiC859lNARmIWuBvcRFNAP3\nC/lnNSs4yp04Z0ZE3oDLD7G2jL2F/ATIp/Oc4HvudwLvE5GJvrd8TEGdxshR1eeAP4jI0V7OdiLy\nUlzWwb/4h8ohFP9Kuh44HjdEe0MZu8pdo6W4XxjX+1939VKu7r8CDhSR13qbJ4kL4zwR93B40reb\nf2lQrhEx5r9j7C2knfx3BXCK14mI7C4i5XIHbMOoX50ErFLVZ4C/isiBvvyDuKm+eim6JyKyj6r+\nVlUXAn9hNFdOw/hRmPUisvVZ4EdK8Drzo52949XRStLeMVDgdhG5H/dPYAPw0TLnHee3Q63GDQNd\npap/xQ0x3ycii6vIz/Mb4FLcMPUjqrrcl38K+BFuWKtwqPxaYIGIDIjItLwsPww6F/iOH77bzGgC\nonr+EX4F2MYPa/UDvar6Yo3vfxw4xH/nbmC6qq4GluEW2tyFm8e7t4acD+FyBNyLy1fwauDbwDt9\n2RxGcxjgh/Z3AP6oqn8uI+924I0ikhWRY33Z93FzpssqX4KyNmrpaz9seDLQ7+37JdDl//F8A3cv\nf4y7t/XINaLF/Ddh/quqtwLXAHd5e25gNBdAIc8D+/t7+/9wmUHB/WP9XxG5B7cmIl8+xn/L8APg\nGP+8OBC40N//+4BfqOp9Fb5XSiX5c4APi9sy+gBu7RK4tRDfEZHfUj1PQts8K1K9XdHoPETkHcBF\nqjoztC2GYYwPEXlWVWuN6Bgx0RbzGYYRBSLyH7hfjLXWFhiG0d7YL9aANDSVICLbishdIvKCiDxW\n8tn5IrJFRM737yeJi8S1UURWiQsoYhixoaqLVXWaqt6VLxOR/xSR1X74MP+30RTPqaac34vIbBFZ\nJyJ/80Onb/fl5vdG06hqufUZsSIiswqeEfnnRUsCVLUbDU0liMg2uH20s4A3qeruvnwybm7plcBi\nVf1PETkXl053FvAlYFhVLXytYSSMcn4vIifiMrr9H27udp2qHmp+bxjJp6ERA1XdrKoX4lLHFnIO\nbiV44WrWWcCvVDWLW5zzbv+AMQwjQZTze1W9RlVv8vvd1+B+FID5vWEknqbXGIjIFNy2kTfjfink\n2RV41L9+DtcJ2QW3LaTw+7sAh+MCdYw0a49hpJyJuIAuK3zQlFgRkXfhQvye5YvM7w2jtUTu81Es\nPvx3XIS5fGCI/KjBE4zuE94Rv++7zPcPx215MQwjOk7CbQuLDXGZ6m4ClqrqV32x+b1hhCEyn2+4\nYyAulejOuL20XbiRgnfj4sgDnC0iD+MSX5wlIt3AkcBtWpCxrIDfA/T19TF9+vTGa1AHxx13HNdf\nXzEDa9vKjlu+2R5GfpyyBwcHmTNnDni/igrv9zsx6vcTgZtxe+A/LyJTfcjblvr9448/zsaNGyt+\nvvPOOzNlypRxy4f421pUJMVOSI6tSbAzDp8fz4jBg4xuJXkQ+FdckA9wwTKWActxoR6n4+JnP4iP\nvFWGEYDp06czY0a5aJLN88Y3vjGRsuOWb7aHkR+37Z6oh+dL/f4O3IjAu3GBhRQXre4iWuT3uVyO\nAw88mJGR4YrnTJw4ibVrB9lzzz0rnlOLFt2vpkmKnZAcW5Nipycyn2+4Y6Cq1RYsli4yOqFR+YZh\ntB81/L7wvOdpkd8PDQ35TkEfri9SyiAjI3MYGhpqqmNgGGnDAhwZhpFwpuOyCBuGEQWpyJXw3HPP\nJVJ23PLN9jDy47bdiJak3K+k2AnJsTUpdkZNKjoGTz/9dO2T2lB23PLN9jDy47bdiJak3K+k2AnJ\nsTUpdkZNKjoGN910UyJlxy3fbA8jP27bjWhJyv1Kip2QHFuTYmfUpKJjEOfCo7gXNZntrZcdt3xb\nCJcsknK/kmInJMfWpNgZNanoGBiGYRiGUR/WMTAMwzAMYyup6BgsXrw4kbLjlm+2h5Eft+1GtCTl\nfiXFTkiOrUmxM2pS0TEYHq4cGa2dZcct32wPIz9u241oScr9SoqdkBxbk2Jn1Iiq1j4rTgNEZgAD\nAwMDSQo9aRhtSTabpbu7G6Dbpz5uS6Lw+9G6DlA+wFEW6MaeLUYnE4fPp2LEwDAMwzCM+rCOgWEY\nhmEYW2moYyAi24rIXSLygog85stmi8g6EfmbiNwjIm/35ZNEpF9ENorIKhHZO3rz62NoaCiRsuOW\nb7aHkR+37Ua0JOV+JcVOSI6tSbEzahodMVDgRlzK1TzbAJ8E9gd2xqVdxZcdBhwKbAa+2pSluDSr\n2Wy25pHL5Yq+d8oppzSruiJxyo5bvtkeRn7cthvRkpT7lRQ7ITm2JsXOyFHVhg/gSuCxMuW3APf4\n13cCP/SvzwZeBLYp850ZgA4MDGg1NmzYoBMnTlJc56TqMXHiJN2wYcPW79aS3Qxxyo5bvtkeRn7c\nsr0fzNBx+Harjnr9vr66DihomcN93uz1jrutRUVS7FRNjq1JsDMOn49sjYGIvAt4F3C5L9oVyKem\neg43OrHLeOUX514fqHL0MTIyXDQEFOeK5LhXO5vtrZcdt/ykrZCvMIU4QUSWiMiTInKv32XQVlOI\nUZGU+5UUOyE5tibFzqiJpGMgIj3ATcBSVc1PGTwB7OBf74jr0TxZScaRRx5JJpMpOnp6eli+fHnJ\nmRuBRbgfHIXHUmA1Lje7I5vNkslkxswTLVy4cEzgilwuRyaTYc2aNUXlS5YsYcGCBUVlw8PDZDIZ\nVq1aVVTe39/P3Llzx9Rt9uzZY+qxcuVKMpnMmHPnzZvH0qVLi8qsHlaPcvXo6elh//33L/KZ3t7e\nMedFQLkpxJOA04CjgUHgal8e+RSiYRgtptEhBmBf3EPiz0AX8FbgKWAF8Bpgqj/v08AQ0A38HLil\ngry6hhRrDxtGO3xoGEkkzqkECqYQcR2BB/zr43CdgGlEPIVYX13jnUowjHamXaYSHsT9SpjsX38J\nNyLwbmADkF/5dxFwK3AbboHi6ePQFQmlv/iSIjtu+WZ7GPlx294iSqcK82WRTiG2A0m5X0mxE5Jj\na1LsjJqGOwaqOkFVtyk4Di14PUFVt/HnPa+qJ6jqK1T1QFVdF7359ZHNxhcALk7Zccs328PIj9v2\nFlE6VZgvi2UKsdJUj6N0ujELZHADmaOMd6onf7/afcrqc5/7XGKm3rLZbCKmEAt9tR2mEPv7+5k6\ndWrRFOL8+fPH2N80UQ09jPfAphIMIzLimkpg7BTih4AXgJnA9cCDGsMUYn11takEI73E4fPbRt/V\nMAyjA3kQ9/DJvz4MtwPpRuBRXEcB3BTidNwU4oMEnEI0DGN8WMfAMIyaqGq5acefAmeWnPc8cEJL\njDIMIxYsV4JhGIZhGFtJRceg8qKl9pYdt3yzPYz8uG03oiUp9yspdkJybE2KnVGTio7BGWeckUjZ\nccs328PIj9t2I1qScr+SYickx9ak2Bk1qegYzJo1K5Gy45ZvtoeRH7ftRrQk5X4lxU5Ijq1JsTNq\nUtExMAzDMAyjPqxjYBiGYRjGVlLRMRibiCkZsuOWb7aHkR+37Ua0JOV+JcVOSI6tSbEzalLRMejv\n70+k7Ljlm+1h5MdtuxEtSblfSbETkmNrUuyMmlQEOLruuusSKTtu+WZ7GPlx294p5HK5MXHpCxkc\nHGyJHUm5X0mxE5Jja1LsjJqGOgYisi0urWo3MKSqu4vIBODLwInAH4G5qpoVkUnAUuA9wAPAHFX9\nfZTGG4bRmeRyObq6pjMyMhzaFMNIHY1OJSguNvodBWUnAafhUjEP4vK0A3wSF0/9UFyu9q82Zalh\nGKhBUHkAABzKSURBVKlhaGjIdwr6gIEKx3nhDDSMDqahEQNV3QxcKCJvBN7ki2cBD6nqKhHZHThW\nRKb58l/50YMfAZ8XkW28DMMwjDqYjkvEWI7WTCUYRtqIYvHhrsBz/vVzBWWl5ROAXSLQ1zDlcnMn\nQXbc8s32MPLjtt2IlqTcr6TYCcmxNSl2Rk0UHYMngB386x0LykrLFXiykpAjjzySTCZTdPT09JTZ\nLnIXUC5+9TzckoZRstksmUyGAw44oKh84cKFLF68uKgsl8uRyWRYs2ZNUfmSJUtYsGBBUdnw8DCZ\nTIZVq1YVRcbq7+8v25Bmz549ph4rV64sG4d73rx5LF06Wo9Zs2ZtrUfpQqxm6zFz5syt9Sgkqnq8\n5CUvKSqLsh6F173wfkRVj7z80vsRRT1mzZpVs13VU4+enh7233//Ip/p7e0dc57RHEmJfpcUOyE5\ntibFzshR1YYOYF/cOoM/A124POwvADOB64EH/XmfBoZwCxV/DtxSQd4MQAcGBrQaAwMDCigMKGiV\nw51XS55hdCKjfsIMbdC3W3nU8vv6/L2vxjn2LDA6nzh8fjzbFR/0RuRfHwZc7jsLj/qOAsBFuAnC\n2/x5p49Dl2EYhmEYLaThqQRVnaCq2xQcP1XVM1V1F1XdT1Xv9uc9r6onqOorVPVAVV0XvfmGYYRA\nRLYXkRtE5BkR+YuInCMiE0RkiYg8KSL3ikilVYOGYbQxbRP58Igj3ssee+xT8TjiiPeOW3bpnG2U\nxCk7bvlmexj5cdveIt4DfAD4d9xo4fm4WCYfZezW5USTlPuVFDshObYmxc6oaZuOwRNPHM6jjx5f\n8XjiiVeNW/YFF1wQoaWtkx23fLM9jPy4bW8Rj+DWFv0Bt95oE/B+YK2qrsJ1Fvb1W5cTTVLuV1Ls\nhOTYmhQ7o6aNQiKfQeX9ygB/Be4Zl+Rrr712XN8LLTtu+WZ7GPlx294iHsYtKv4BIMA5wLsov3V5\nfcuti5Ck3K+k2AnJsTUpdkZN24wYxMmkSZMSKTtu+WZ7GPlx294i5gLvxi02vgj4HJW3Llek0jbl\n22+/veTMlZTfpgxQuqU56899qqh0vNt78/crjm2xeaLYFnv88cc3vN06VD0mTZoU6bbxuOpR6Ktx\nbRtvpB79/f1MnTq1aJvy/Pnzx9jfNFFtbxjvgd+2VHsb4mm2XdEwatCq7Yq4MOibcesMPudfn0vx\n1uXBKt+37YqGEQHtsl3RMAzjauAI4JvAi7hEaucDr2J06/KcYNYZhjFuUjGVUDp0kxTZccs328PI\nj9v2VqCqw6p6jKrupKqTVfUTqrpZi7cuD4S2MwqScr+SYickx9ak2Bk1qegY7LnnnomUHbd8sz2M\n/LhtN6IlKfcrKXZCcmxNip1RI6pa+6w4DXBBUAZcGtVquxI+iguwWOu8LNDNwMAAM2ZYfBUjXWSz\nWbq7uwG6VTUb2p5K5P2+kp+O1qOav38bN1tR6Rx7FhidTxw+n4oRA8MwDMMw6sM6BoZhGIZhbCUV\nHYPSvaRJkR23fLM9jPy4bTeiJSn3Kyl2QnJsTYqdUdN0xyAJyVTOPvvsRMqOW77ZHkZ+3LYb0ZKU\n+5UUOyE5tibFzqiJIo5BPpnKR3ErgM4H/ujfH4KLdXw18KYIdI2LSy+9NJGy45ZvtoeRH7ftRrQk\n5X4lxU5Ijq1JsTNqophKaPtkKkneema2t1523PLTugUqqSTlfiXFTkiOrUmxM2qiGDFITTIVwzAM\nw+h0ohgxiCSZChyJS3xSePQwNkHKXZRPpjIPaC4JSTsl77B6WD1q1aOnp6comUomk6G3t3fMeYZh\nGA3RbLIFIkqmEmcSpS984QuRJKsoR5yy45ZvtoeRH6fsViVRavYgQUmU4m5rUZEUO1WTY2sS7GzX\nJEptn0xleHg4kbLjlm+2h5Eft+1GtCTlfiXFTkiOrUmxM2osJLJhdBAWErlICvYsMDodC4lsGIZh\nGEasWMfAMAzDMIytRLHGoC0ZHBzc+vqpp57iFa94xZhzJk+e3PQ+1aGhISZPntyUjFDyzfYw8uO2\nvVWIyMHAJUAXsAZ4B26N0Ym4IGdz23k6o16Scr+SYickx9ak2Bk1HThi8DgwgTlz5tDd3U13dzfv\nfve7t74uPLq6ppPL5ZrSdsopp0RjdgD5ZnsY+XHb3gpEZCfg+8D9QDfwDVyH4DTgaGAQtzA58STl\nfiXFTkiOrUmxM2o6cMRgI7AF6AOm+7LBgtdsLRsZmcPQ0FBTowaLFi0a93dDyzfbw8iP2/YWcRQu\nRsmnVPVRYFBErgYeUtVVIrI7cKyITFPVRAc2S8r9SoqdkBxbk2Jn1HRgxyDPdEZXKse3Ijnu1c5x\nyjfbw8jvkBXye/i/N4jI3rh4JbvSgRFPk3K/kmInJMfWpNgZNR04lWAYRgv4Ky4Eej/wJeAs4C00\nGPH0yCOPLIrcmMlk6Onp4fbbby85cyXlI57C2OioWX/uU0WlFmHT6pH0evT39zN16tSiiKfz588f\nY3/TRBUpabwHkUc+rBUNLdqoaIbRTrQq8iGwDy6g2WnAx3ERTz9IgxFPkxD50DDamTh8PiUjBktr\nnzJeyUvjkx23fLM9jPy4bW8FqroOOB0X/vy/gAtwEYcux2dUJXDE06hIyv1Kip2QHFuTYmfUpKRj\nEN+OqWw23t1Ycco328PIj9v2VqGqX1fVPVR1V1X9lKpuUdUzVXUXVd1PVQdC2xgFSblfSbETkmNr\nUuyMmg4MiVwrTGoeC5dqdB4WErlICtBNX18f06eX7koaJYp4JoYRijh8voN3JRiGkW5GY5pUY+LE\nSaxdO2idA8PwRDKVICIHi8hqERkWkayITBCRJSLypIjc638dGIZhtJDCmCYDFY4+RkaGx6w0N4w0\n0/SIQUEEtB/gIp8dwmgEtEOBM3AR0N7UrC7DMIzGKYxpYhhGLaIYMSiMgDaoql8BDsdHQMOvUBaR\naRHoGieV9j9HILnMHtikyDfbw8iP23YjWpJyv5JiJyTH1qTYGTVRdAwKI6A9JiIXUzkCWiDOiE/y\nGfHJjlu+2R5Gfty2G9GSlPuVFDshObYmxc6oiaJjEEkENDgS98u+8OhhbFSzuyg/AjCPsfEK8hHQ\nSocRFwLLikrGG6lq1qxZW8vjiLg1a9as2CJuHXTQQbFGDvvDH/5QVBZlPQqvexwR0PLy44iANmvW\nrEgiufX09BRFQMtkMvT29o45z2iOwrbWziTFTkiOrUmxM2qa3q4oIvsAa3E/y18KXAScjPsvfRju\nP/ZbVLXsfiHbrmgY0ZG07Yovecn2TJgw9vfJli1bePHFTTS3XbGeZ4E9B4xk05bbFVV1nYicjvsZ\nvj2jEdD2x60veJQOiYBmGEa0vPjiWcBrynzyY38YhtFqItmu2P4R0EqnIyKUvDw+2XHLN9vDyI/b\n9mRxPHBmmeMfQhpVRFLuV1LshOTYmhQ7oyYlIZH745PcH5/suOWb7WHkx227ES1JuV9JsROSY2tS\n7IyalHQMrotP8nXxyY5bvtkeRn7cthvRkpT7lRQ7ITm2JsXOqElJx8AwDMMwjHqwjoFhGIZhGFux\njoFhGIZhGFtJScdgbHCYyCSXCTyTFPlmexj5cdveSkTkVBHZIiLXdGrytOOOO45sNlv1yOVyoc1M\nVLtKiq1JsTNqUpJ2uXL0qsHBwZrfrpavPe7IWHHKN9vDyO+UaGoish3wn8AmX9RxydNyuRzLl3+P\nG264oep57ZC6OUntKim2JsXOqElJx+CEMmX15WoH2H77iXz3u99hypQpYz7r6uoim3XBpqp1IMbL\nCSeUs739Zcct32xvC04FVgNv9e+3Jk8Tkd2BY0VkmqquD2ZhkwwNDfHiiy/gUjeXDd4KDDIyMoeh\noaGgHYMktauk2JoUO6MmJR2DchTmaq/k8AB3smnTJzjqqKNqSmyHXw2G0QpEZCLwH8B7cCnXoXLy\ntMR2DEax1M1GekjJGoNq5B2+0jGZ0Q7EQJWjj5GR4TGJdQyjQ/kI8AsgPxcnwJNEljytdIpvJZXT\np5dGp8snT3u2pHwhsLik7HEA1q8v7ruUS3IFw17uqpLyW8pa1UzyNIg26VhRLWJIOmb1aE09+vv7\nmTp1alHytPnz54+xv2lUNeiB+++rMKCgVY7TtL7z+sqcd2ed59UrT8vIHlBABwYGNEruvPPOSOW1\nSnbc8s328gwMDPj2ygyN12+/BGzG9ZrzxyPAC8BM4HpgsMr3a/j9ojr8s5Zv1uPj1f129HqOX0a9\nbNiwQQcGBioeGzZsqPr9uH0iSpJiaxLsjMPnUzJicEFCZcMFF8QnP07Zccs324Pzv8A7gXcAj+F+\nNs/EpUC9EdgXS55WN7lcjq6u6XR3d1c8urqmV939kKR2lRRbk2Jn1ETWMWjvbUvXJlQ2XHttfPLj\nlB23fLM9LKr6qKpm/bGHqh6pqn/UtkqelhyGhoYYGRmm8pRl7anKJLWrpNiaFDujJpLFh+2/bWlS\nQmXDpEnxyY9TdtzyzXajMxn/Iscktauk2JoUO6MmqhGD/Lalx/37rduW8MOKIjItIl2GYRiGYcRE\n0x2Dgm1L5+JWJkPlbUuGYRiGYbQxUYwYxLxtqXQr0l2U37Y0D1haUpbftjSvpHwho1uv8+T8uWtK\nyleW0VW4bal468miRYvGnN3MNpkFCxbEtk3m4x//eKzbfY444oiisijrUViXOLYt5eXHsW1pwYIF\nkWy/6unpKdq2lMlk6O3tHXOe0RoGBweDhkweu72yfUmKrUmxM3Ka3dZA7NuW8kcz2xUviXG7Yl62\n2zLS19dXdctRPduOCrnkkkvqPrdR4pQdt3yzvTyt2q7Y7FHb71u7XbGS3/b11SPjhwoT8te97DFx\n4qSqfl97W2TtLZFx+0SUJMXWJNgZh89H4eBTGY0G9EfgZmAPYAlu5OA+oLvK91vQMWjFebUfDvU+\nJAxjvFjHoFHfrddv67Gjz59XevTV/KceRcfASCdx+HzTuxJU9VHgUf92j4KPzvRHSqg3xHJ7xFU3\nDANq++3NuOVT9WBhk43OIMW5EuLCHg6GkTwq+W3t7KuG0WmkJPJh6YLCpMhmzOK7pMiOW77ZbhjF\nJKldJcXWpNgZNSnpGJydSNm5XI7TTjut4krnZlc8n312nNclXvlmu2EUk6R2lRRbk2Jn1KRkKuHS\nxMnOx04fGRmmu7u76rnjTfd86aVxXpd45ZvthlFMktpVUmxNip1Rk5KOQZyL/OKRXRw7PZ7FjHEv\nfoxTvtlupJHBwdprHpLQvpJgIyTHzqhJSceg/ajl4KOf22JGwzAeByYwZ071hJXjHT00jEKsY9By\n6nNwwzCMUerZDm1boY1oSMniw8W1T2mZ7EIHL5deNX+c589fFomV5SgN35sk+Wa7kU7yI4jljl8H\ntKsxkuIDSbEzalIyYjDchrJrTRHkpxJGxim/NsPDcV6XeOWb7UbSqDZ9WM/agdrE96yImqT4QFLs\njJqUdAw+k1DZAB+NTfJnPhOv7XHKN9vDIiIzgcuBvYA/4DKV/QT4MnAiLjz6XFXNBjOybWjV9OFH\nga/HrCMakuIDSbEzalIylWAYRsRMBM4H3g48jesknAicBhyNG/K6Oph1bUU904fnVfy2YbSalIwY\nGIYRJaq6Iv9aRAaA44HDgYdUdZWI7A4cKyLTVHV9KDvbi2rThxZ62WgfIhkxEJGZIrJGRP4mIg+J\nyGEiMkFElojIkyJyr4gE3HM3lFDZAE/FJnloKF7b45RvtrcHIvJm4CTgCmBX4Dn/Uf7vriHsSifx\nPSuiJik+kBQ7oyaqqYQ2H1Y8JaGyAT4bm+RTTonX9jjlm+3hEZHXAyuBO4BzgCeAHfzHO/q/T1SX\nciSQKTl6GPsLeqX/rBzLS95n/bnPlpQvZOwuovyDv3RQYwmwoKRs2MtdVVL+ywp2zS5jW6V6LCtT\nlq9H6T+ncvXIAceNkbBkyRIWLCiux/DwMJlMhlWriuvR39/P3Llzx8iYPXs2y5cX12PlypVkMmPr\nMe//t3d+MVJddRz//GgE1LZpZdeEQkXamIUHiLIJKSalonExkU4wpjWSBlp4IBGwxb74AKH+SZSH\npjGBaILEiiC1lmZNYyON0QeEPtid+oe4kRpxB7Vpuo1spbBLQ48P5844O3Nn7pm558zcu/f3SU52\n9s653/nO2fmd/c25556zaxdHjx6d/S7KZUql0qx/stu3b+fAgQNNs/4rlQqlUqlpj4J+vY/6WI17\nH0BP38fJkydZsmQJa9eupVQqUSqV2Lt3b5P/1Pjav7lagB9gL6r9BDgfHXsQuAEsj6mfsC97tex0\n2Be91R7scee47NXuUm/Ms17cHu/t6rnv0z4xMWHGxsZq5fjx47N+r5aJiYkudgVvJuTe8aH3pc+r\n9xB7s8cV7BbrE8CrwN3AEmArcB24D3gWGG9zfkLcP+EQJ0mx5BJrc08jdGz4IA8ejcmHzxAx73WO\nQd2w4veB1cQPK170+ZpuhLyKEfoKSbvlkN2p33shCV+rp61ZE65tQmqH1g/tvUd8BpscLAVew3ZM\nH8OOFj4P/AvQVbx6ip++ohfkJQby4tM33u5KSD+s2GpIsXEo7mXih+J2AUcbjrUbinuh4Vglqtu4\nzeZLMa/VakgR4ImYY3FDir7ex9OzjrQawjp06FDd3gvVmdC/A9YDP6w7dpzp6avs3r27+V0EGFKE\n3g7FzaX3sW7dullDiqVSiW3btjXVC4Ex5sfGmJuiMi/6+XdjzB5jzCJjzGpjzFhPzCiK4hcfww6k\nGFakJ5cS5nI9t0sJ/x9u8qOnZJNeXUpIW5LjXi8ldK6hsVtEQsS8rxGD6rDiauywYgX7dbQ6rLiC\nvg4rNn4Dz4s2NI80+CSs98Zv2nnRDq0f2rtSVGxfMT4+Trlcji2VSqXPHi15iYG8+PSNl8TAZH5Y\nMeTia6EXdmu8tOGTsN7L5XD6IbVD64f2rhSVMtUVFoeHh2PL0NDKTCQHeYmBvPj0TUEWODqcU22w\n0zVC0dq7y9rtAwMDbScoHj4crm1CaofWD+1dKSpfBn5J6x0Ys7P7Yl5iIC8+fVOQxEBxx31dd937\nXVGySNIGbYrSHk0M5ghJ3/Ddd29z2fcdsvTtQ1EURfGHJga5J9TObfqtQ1EUpYgUZHfFVkupZl0b\nIGm5S5ed21rt3hbWe9y6AHnQDq0f2rtSVJ70olKpVFre1eDrzoa8xEBefPqmICMGzYv15EMb4tY/\njyfpG37cpYT03ttdoti4cSPlcjlxkmI3xC3AlBf90N6VojKCXUqme1xWSPUxtygvMZAXn74pSGIw\nklNtsKs/hiKN9/5OUhwZCdvuIfVDe1eKyiqnWu2S+fHx8boVUru/s6FSqbTdmXDFihVOXvtNUWO1\nIImB4h+dpKgo+aKT+UjdzzHq1aiDEg5NDJSU6CRFRckHLsn8i8D+VK8yOTnpZdRB6R8FSQxGgc05\n1Ab4LeH+8Yb2PgqECfwjR44wPDycWK/b+Q2jo6Ns3hymbUJqK0XmFcd67ZJ519ua075OPihqrBYk\nMThIuH+AIbXB7p74eCDt0N4PEmJlyEqlws6dO6ub8bSl2yHLgwcPBusQQmorRaZxx9hwJM1TmCsU\nNVYLkhgM5lQb4EMBtUN7D6M/OTkZJQXh5jcMDoZrm5DaSpG5NblKavytm5KUQIS4m6lTihqrQRMD\nEdkH7AGmgD3GmNMhX0/JLi7fImZmZliwYEEHWm5DlT72fQhF0uztKlnoJF3RuJ/L+Jin8DpAYnLh\nY4JiUnzlKa56SbDEQETuBb4JPAB8EvipiCw1xlwL9ZpKFunkG8ZNwI2+vPaCBQs5deo5Fi9eDMDU\n1FTszmquyYtLh+Mye7tKXmZxa9wXhTTzFC5HP9slF3a078yZM6xcGV8nKcZc4qsx7huZmpqiUqlk\nPu58E3LEYAR4xxhzSkQuAY8B92Bn0ymFwfW2xuq3jKR69XV9vfYZZma+xqZNm2YdjZ/c6Ja8uPwj\nT569XSVXs7g17hVH2iUXyUl9Uowlx1d83DcyNLQyF0m5T0ImBoPAO9HjK4AQf9F5of3xPO1n1f4l\n+vki7TPSszH1zgInHOq56jU+f8KjXmO9P9DsPY1er9qlWmdp9PhiGy2AfzvWq6/r6jFJ86/YBGIH\nUP3m8D3g0YZ6fwZ+0VAvjteZnj7KsWPHWL58ebOrs2c5ceIEFy9WfSX5s8+7XBKpq7MwsXIYPMV9\n9VjS56tdHdfP6FzRuJARH740GmOynvYxBjjEV1zcN/IU09PvtB256DdBYt4YE6RgF+e/Ej2+B/sX\n2BBTbwtgtGjR4rVsCRXbGvdatGSyeIt5cbnlqxtEZD12+HALtoPYCtxpjLnaUG8RsBH4BzAdxIyi\nFIeFwEeB08aYt3r94hr3itJzvMd8sMQAQET2Y3fqeRs7O/lXwV5MUZRMoHGvKPkmaGKgKIqiKEq+\nmNdvA4qiKIqiZAdNDBRFURRFqaGJgaIoiqIoNYInBiKyT0TeEJELIrIx5vkdInJJRCoi8rDreWn0\nRWSZiLwnIjei8qcutUdF5FqkMT+A9yZ9H95FZKWI/FFErkbt8pDLeWn1PXm/TUT+JiIzIvJPEfmK\nZ++x+r4+M1GdkUjrXIfndazt6ts3oeO+nz770aah+6J+e81am/roI/vpM3V7Br6n+V7sfcxfBJ4E\n3gLeX/f8MuBd7EoyjwHXgSVJ53nQXxadtxa4AxjsVDuq81XgJHYpvPmu56XUT+0d+AR2Lfsh7JZs\nV4H3eWz3Vvo+vH8Q2ATcDbyEXWXPW7u30ffymYnqvRy1ybkOPgvdaif6zlvcZ8BnT9vU8fPRdV+U\nEa+ZalNS9pEZ8JmqPYM1fGT6W8B/o8drow/Chrrnd0THBoAPR2/kkaTzPOhXG+1N7NJbD3SqXVfv\nQMMH3Iv3NvrevEfPfz16/jbg2z69x+j7bPd52M7lleh3r97r9Md8tjtwP1DG3ut/ztV7Cu1E33mL\n+wz47GmbdvCZ7aovyojXTLZp9HzHfWQGfKZqz9CXEpKWRx2se+5K3THXZVW71Z/CLsCyAbuO6NMi\ncnuH2q3w5b0Vb/vyLiJ3YO83f84YcxnbAXrzHqPvxbuIfB6bGT8I/Cw67M17g/4z0WFf7f4NYF/D\nMRfv3Wq7+PZN6Ljvt0+X/qOXPn2fl4aQ/ZpPQveR/faZqj1DJwZvAjdHj2+tO0bD41uiUj2WdF4q\nfWPMZWPMM8aY88CPsCtH3dWhdit8eY/FGPMfH95FZAD4NXa3kkd8e2/Q3+7TO/Ab4OPAs8B3xK6i\n57Pd6/W/KyKLfHgXkfuBGeA0NsAREXH01JW2o2/fhI57X4TsP3rp0/d5aQjZr/kkdB/pi5B9bWsC\nD9esxw5tfAl4Cnt95Bbsdbr52GUc3wUej8p14M4W533Ao/5nsX/oIeDn2Izs9k60ozp3AYeiequi\nP5wX7230U3uPdF7FLke7uu64r3Zvpe/D+yrgU1HbHIv+pjd79N5K34f3R6Pn36srx1y8p9BO9J23\nuM+Az562aZLPtH1RRrxmqk1J2UdmwGeq9gz2Aal7Y/uBN4DXgM8B90VvdCR6fgdwKSoPtzrPpz6w\nDjgfNdYF4Atdal+Mfq+WrZ69N+n78F73uPqP5Aaw3pf3VvqevH86+lteizQe8vmZaaXvyfsAdp/Z\nNcDvsVtnfsTVexfay1x95y3u++mzH23q4DNVX9Rvr1lrUzz0kf30mbY9dUlkRVEURVFq6AJHiqIo\niqLU0MRAURRFUZQamhgoiqIoilJDEwNFURRFUWpoYqAoiqIoSg1NDBRFURRFqaGJgaIoiqIoNTQx\nUBRFURSlhiYGiqIoiqLU0MRAURRFUZQa/wM7rAoI0UJhOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1208034d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features1=list(data.columns[5:9])\n",
    "histograms(data,features1,2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LF3Ho0LiKAOkp7QJIsHXr1qIjpKKc2SpLTulO0f07MTER//HfC7wVqLYY9jI1Ndl2K0HW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\nTmTn5wANAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dddb090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features2=list(data.columns[9:11])\n",
    "histograms(data,features2,1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#to see how distribution is in regard to the diagnosis, we need to first split\n",
    "#the dataset into two groups\n",
    "malignant = data[data['diagnosis'] ==1]\n",
    "benign = data[data['diagnosis'] ==0]\n",
    "\n",
    "#also bring features back, basically redefining them again- the first ten\n",
    "\n",
    "features = list(data.columns[1:11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</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>122.8</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.9</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
       "0        17.99         10.38           122.8     1001.0          0.11840   \n",
       "1        20.57         17.77           132.9     1326.0          0.08474   \n",
       "\n",
       "   compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "\n",
       "   fractal_dimension_mean  \n",
       "0                 0.07871  \n",
       "1                 0.05667  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#just to check that this works\n",
    "malignant[features].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dxVlnnZV0+zXXXMM111yT8r3GxsY2nz355JM555zPJsY799xzARg1alS7caYb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OAAAg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\nXY1YzDI5KQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121cfc2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 8, 'font.weight':'bold', 'font.family':'italics'})\n",
    "fig, axes = plt.subplots(nrows=5, ncols=2, figsize=(8,10))\n",
    "axes = axes.ravel()\n",
    "for x,y in enumerate(axes):\n",
    "    y.figure\n",
    "    binwidth= (max(data[features[x]]) - min(data[features[x]]))/30\n",
    "    y.hist([malignant[features[x]],benign[features[x]]], bins=np.arange(min(data[features[x]]), max(data[features[x]]) + binwidth, binwidth) , alpha=0.5,stacked=True, normed = True, label=['Malignant','Benign'],color=['silver','orangered'])\n",
    "    y.legend(loc='upper right')\n",
    "    y.set_title(features[x])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the figures above that the malignant types seem to have larger values in almost every attribute compared to the benign types. The malignant types are almost skewed on the right in every case with the exception of the fractal_dimension_mean.  By looking at this, I would say that concave points_mean, concavity_mean, compactness mean, area, perimeter, and radius are likely to be the most telling attributes for the types of cancer.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:py27]",
   "language": "python",
   "name": "conda-env-py27-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}