Breast-Cancer-Prediction-ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "#%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<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>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",
       "      <th>Unnamed: 32</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>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</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",
       "      <td>NaN</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>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</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>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</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>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</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>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \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",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\yasha\\Desktop\\breastdata.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 33 columns):\n",
      "id                         569 non-null int64\n",
      "diagnosis                  569 non-null object\n",
      "radius_mean                569 non-null float64\n",
      "texture_mean               569 non-null float64\n",
      "perimeter_mean             569 non-null float64\n",
      "area_mean                  569 non-null float64\n",
      "smoothness_mean            569 non-null float64\n",
      "compactness_mean           569 non-null float64\n",
      "concavity_mean             569 non-null float64\n",
      "concave points_mean        569 non-null float64\n",
      "symmetry_mean              569 non-null float64\n",
      "fractal_dimension_mean     569 non-null float64\n",
      "radius_se                  569 non-null float64\n",
      "texture_se                 569 non-null float64\n",
      "perimeter_se               569 non-null float64\n",
      "area_se                    569 non-null float64\n",
      "smoothness_se              569 non-null float64\n",
      "compactness_se             569 non-null float64\n",
      "concavity_se               569 non-null float64\n",
      "concave points_se          569 non-null float64\n",
      "symmetry_se                569 non-null float64\n",
      "fractal_dimension_se       569 non-null float64\n",
      "radius_worst               569 non-null float64\n",
      "texture_worst              569 non-null float64\n",
      "perimeter_worst            569 non-null float64\n",
      "area_worst                 569 non-null float64\n",
      "smoothness_worst           569 non-null float64\n",
      "compactness_worst          569 non-null float64\n",
      "concavity_worst            569 non-null float64\n",
      "concave points_worst       569 non-null float64\n",
      "symmetry_worst             569 non-null float64\n",
      "fractal_dimension_worst    569 non-null float64\n",
      "Unnamed: 32                0 non-null float64\n",
      "dtypes: float64(31), int64(1), object(1)\n",
      "memory usage: 146.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "      <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>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   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",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   fractal_dimension_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "\n",
       "   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",
       "\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",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.iloc[:,2:32]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    M\n",
       "1    M\n",
       "2    M\n",
       "3    M\n",
       "4    M\n",
       "Name: diagnosis, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df.iloc[:,1]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Radius Mean')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['radius_mean'],bins=10,histtype='stepfilled')\n",
    "plt.style.use('seaborn-dark')\n",
    "plt.xlabel('Radius Mean')\n",
    "plt.title('Radius Mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Perimeter vs Area')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#elp(plt.scatter)\n",
    "plt.scatter(df['perimeter_mean'],df['area_mean'])\n",
    "plt.xlabel('Perimeter Mean')\n",
    "plt.ylabel('Area Mean')\n",
    "plt.title('Perimeter vs Area')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2', 'tableau-colorblind10', '_classic_test']\n"
     ]
    }
   ],
   "source": [
    "print(plt.style.available)#Available Styles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b8907eee48>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['radius_mean'],color='red')\n",
    "plt.plot(df['radius_se'],color='blue')\n",
    "plt.plot(df['radius_worst'],color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Radius Scatter Plots')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['radius_mean'],df['radius_worst'],color='red')\n",
    "plt.scatter(df['radius_se'],df['radius_worst'],color='blue')\n",
    "plt.scatter(df['radius_worst'],df['radius_worst'],color='green')\n",
    "plt.title('Radius Scatter Plots')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    357\n",
       "M    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['diagnosis'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "radius_mean                0\n",
       "texture_mean               0\n",
       "perimeter_mean             0\n",
       "area_mean                  0\n",
       "smoothness_mean            0\n",
       "compactness_mean           0\n",
       "concavity_mean             0\n",
       "concave points_mean        0\n",
       "symmetry_mean              0\n",
       "fractal_dimension_mean     0\n",
       "radius_se                  0\n",
       "texture_se                 0\n",
       "perimeter_se               0\n",
       "area_se                    0\n",
       "smoothness_se              0\n",
       "compactness_se             0\n",
       "concavity_se               0\n",
       "concave points_se          0\n",
       "symmetry_se                0\n",
       "fractal_dimension_se       0\n",
       "radius_worst               0\n",
       "texture_worst              0\n",
       "perimeter_worst            0\n",
       "area_worst                 0\n",
       "smoothness_worst           0\n",
       "compactness_worst          0\n",
       "concavity_worst            0\n",
       "concave points_worst       0\n",
       "symmetry_worst             0\n",
       "fractal_dimension_worst    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b89088e470>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Afv36ERwcrNAjIiJyB4YMPl5eXphMJmxtbQkNDWXixIn88MMPlJWVkZ6ezlNPPQVAmzZtsLG5u6bY/PnzmT9/PsOHD6egoOC272vfvj0nTpzg66+/pkePHuTl5XHw4EHatm1Lfn4+jo6ONGjQAIAOHTqQnp5eWfOvnUtERERuZMjgYzKZOHnyJLt27SIqKopJkyZRUVGBxWLB29ubI0eOAHDixAnKyspuGFu7dm1yc3MBOH78OAAlJSXs2LGD+fPns3r1ajZt2sT3339/27lbt27NypUree6552jfvj3vv/8+PXr0wNXVFbPZTE5ODgAHDhzg8ccfrxx3p7msrKyoqKio8rUSERF5mBhuj8/PHnvsMezt7enfvz+1atXC3d2dnJwchgwZwoQJEwgICMDb2xtbW9sbxvXq1YuQkBBSU1Np3bo1ALVq1cLFxYWXXnoJFxcXOnfufMeN0N27d2fChAm0aNGC5557js2bN9OhQwesrKyYPn06f/vb37CyssLFxYX33nuvsutzp7lat27NnDlz8PHxoVOnTr/PoomIiNRwVhaLxVLdRcjvKze3sLpLkCqkJzeLSE1RXU9udnd3uu01BZ/fSVJSElu3br3p9dDQUJ5++un7WouCj4iIGImCj8Ep+IiIiJHcKfgYcnOziIiIGJOCj4iIiBiGgo+IiIgYhoKPiIiIGIaCj4iIiBiGgo+IiIgYhoKPiIiIGIaCj4iIiBiGYc/qEqmpdGSFiNQE1XVcxS9Rx0dEREQMQ8FHREREDMNwwWf06NG/aXxqaionT56somqqVlJSEqWlpdVdhoiIyAPLcMFnyZIlv2n8xo0bycnJqaJqqtayZcuoqKio7jJEREQeWDV2c3NycjIpKSmYzWby8/N54403cHV1ZcGCBVhbW9O4cWOmTp3Kli1b2LhxIxUVFYwZM4awsDD27dtHUFAQzZs3Jz09HQcHB5555hn27t1LQUEBq1atwsHBgSlTppCZmUlFRQUhISHUqVOHL774guPHj9O0aVO++eYb4uLiMJlMtG/fnrCwMBYvXszhw4e5fv06M2bMwMfH54a6y8vL6dWrFx9//DF5eXn86U9/4ssvv6ROnTq8/PLLbNq0iVmzZnHo0CEA/vKXvzB06FDGjx/PlStXuHLlCtHR0YSEhGCxWCgtLeXdd9/l6NGj5Obm8tZbbxEdHV0dX4mIiMgDr8YGH4Dr168TGxtLXl4eAwcOxGQysX79etzc3IiKimLTpk3Y2Njg7OxMTEzMTeN9fX2JiIhg+PDh2NnZERsby7hx40hNTSUnJwdXV1dmzpxJfn4+gYGBbNu2jS5dutC7d28cHBxYvHgxGzduxN7enrFjx7Jv3z4AvL29iYiIuGXN1tbWtG/fniNHjpCZmckTTzzBV199RZ06dejcuTOfffYZFy5cYP369ZSVlTF48GA6deoEQKdOnQgODubzzz/HycmJefPmcfr0acxmMwMHDiQmJoYFCxb8fgsuIiJSw9Xo4NOhQwdMJhP16tXD3t6ezMxMQkJCACgqKqJz5840adIELy+vW45v1aoVAM7OzjRt2rTy7+LiYtLS0jh06BBHjx4FoKysjPz8/Mqx58+fJy8vjxEjRgBw7do1srKyAG4738969OjB7t27uXDhAm+99RYpKSmYTCYGDBjAgQMHeOaZZ7CyssLW1pY2bdpw5syZG+7btWtXzp07x+uvv46NjQ2jRo26p/UTERExmhodfI4fPw7A5cuXKS4upkmTJkRHR+Pk5ERKSgoODg5cunQJk+nXb2Xy9vbGw8ODkSNHUlRURExMDC4uLlhZWWGxWGjUqBENGzZk1apV2NrakpycTMuWLdm1a9cvzte5c2eWLVuGnZ0df/rTn1i0aBG2trb4+vry448/kpycTHBwMKWlpRw+fJh+/foBYGVlBcDXX39N/fr1WbVqFYcPH2b+/PnEx8djZWWlPT4iIiJ3UKODz+XLlxk6dCiFhYVMmTIFk8nEiBEjsFgs1KlThzlz5nDp0qV7ure/vz8REREEBgZiNpsZPHgwJpOJNm3aMHfuXKKioggODiYoKIjy8nI8PT3p1avXXd27Vq1aeHh48Oijj2IymfDy8uKRRx4B4IUXXuDAgQO8/PLLlJaW0rNnz8rO1M9atGjBW2+9xerVqzGZTLzxxhsAPPPMM4wYMYIPP/ywMiSJiIjI/7GyWCyW6i7iXiQnJ5ORkUFYWFh1l/LAy80trO4SpArpyc0iUhNU55Ob3d2dbnutRnd8HnSjR4/m6tWrN7zm6Oh4y43WInfrQX0MvIhITVBjOz5y99TxERERI7lTx8dwDzAUERER41LwEREREcNQ8BERERHDUPARERERw1DwEREREcNQ8BERERHDUPARERERw1DwEREREcNQ8BERERHD0JEVIjWMzuoSkQdFTTxCRx0fERERMQwFn4fImjVrqrsEERGRB5qCz0NEp76LiIjcmfb4/Buz2Ux4eDiFhYXk5+czcOBAtm/fjqurKwUFBSxfvpzIyEgyMzOpqKggJCSEjh07smPHDhISEirvs3DhQh555JGb7p+fn09wcDB///vfOXz4MH/961/56quvyM3NJTw8nKVLlzJx4kSysrIoLy9n2LBh9O7dm6CgoMoaJk+ezMSJE7GxscHa2po5c+aQnJzM1atXiYyMJDIy8j6umIiISM2h4PNvMjMz6dOnDz169CA7O5ugoCAaNGiAn58f3bt3Z+3atbi6ujJz5kzy8/MJDAxk27ZtnDt3juXLl2Nvb8/kyZPZu3cvffv2ven+rq6u1K1bl0uXLvHFF1/g4eHB8ePHOXbsGN26dSMpKQlXV1fef/99zGYz/fv3p1OnTgCVNSQkJNCqVSvGjx/PwYMHuXr1KqNGjWLNmjUKPSIiIneg4PNv6tWrx+rVq9m5cyeOjo6UlZUB4OXlBUBaWhqHDh3i6NGjAJSVlZGfn4+bmxvjxo2jTp06ZGRk0LZt29vO0b17d3bv3s3hw4d57bXX2LdvH4cPH2bGjBlER0fz7LPPAuDo6IiPjw9ZWVk31DBgwABWrFjBq6++ipOTE2+99dbvth4iIiIPE+3x+TerVq2ibdu2zJ07l549e2KxWACwsrICwNvbmz59+hAfH8+KFSvo2bMnNjY2LFq0iAULFjB9+nRq165dOe5WunXrxtatW3F0dKRr167s2rWLkpIS3N3d8fHx4eDBg8A/f3ZLS0ujUaNGN9SQkpJC+/btWb16NT179mTlypUAd5xTREREFHxu8sILL/Dhhx8SEBDA6tWrsba2pqSkpPK6v78/GRkZBAYG4u/vj6enJ46OjrRr145+/foxZMgQ7OzsyMnJue0cHh4eFBcX06lTJ1xcXLCxseH5558HYNCgQVy5coWAgABeeeUVRo8ejZub2w3jW7duTVRUFIMHDyYxMZHAwEAAfHx8CAsLq/pFEREReUhYWdQmeOjl5hZWdwlShfQAQxF5UDyoDzB0d3e67TXt8fmdlJSUMHz48Jte9/LyYurUqdVQkYiIiKjjYwDq+IiIiJHcqeOjPT4iIiJiGAo+IiIiYhgKPiIiImIYCj4iIiJiGAo+IiIiYhgKPiIiImIYCj4iIiJiGAo+IiIiYhgKPiIiImIYCj4iIiJiGDqrS6SG0SGlIsb0oB4IWtOo4yMiIiKGoeBzG7m5uURGRt7xPWvWrLk/xYiIiEiVUPC5DXd3918MPjExMfenGBEREakSVbrHp6ioiAkTJnDx4kVKS0uZNGkSrVu3ZuLEiWRlZVFeXs6wYcPo3bs3QUFBtGjRgvT0dMxmMwsXLsTT05Po6Gh27dpFeXk5AQEB+Pv7M2/ePL799luuXbuGj48P7733Hv3792fRokU0atSI7du3c+jQId58803Cw8PJz88HICIigubNm1fW9/XXX7N06VJMJhO5ubm8/PLLDBkyhBMnTjBt2jSsra2pXbs206ZNo6KigtDQUNavX4+fnx9/+MMfOHXqFFZWVkRHR7NmzRquXr1KZGQkQ4cOZcKECdjY2GBtbc2cOXNo0KDBLddo/Pjx2NjYcPHiRUpKSujduzefffYZly5dIjo6miZNmjBv3jxSU1OxWCwEBwfTq1cvDhw4wJIlSyrXefbs2dja2vL222/j4eFBVlYWTz31FO+++25VfqUiIiIPlSrt+CQmJuLp6UlSUhKzZs3im2++ISkpCVdXVxITE4mNjSUqKoq8vDwAfH19iYuLo3Pnzmzbto0TJ06wZ88eNmzYQGJiIqdPn6awsBBnZ2diY2NJTEzkyJEjZGdnM2DAADZv3gzApk2bGDRoEEuXLqVTp07Ex8czbdq0W3ZssrOziYmJYf369cTFxfHjjz8SERHB5MmTWbNmDQEBAcyaNeuGMdeuXaNPnz6sWbOG+vXrs2fPHkaNGoWLiwuRkZF8+eWXtGrVitjYWEaOHMnVq1fvuE6enp6sWrUKb29vLly4wIoVK+jRoweffvopu3fv5sKFCyQmJvLhhx+ydOlSCgoKSE9P5/333+fDDz/kxRdfZMeOHQCcO3eOGTNmsGHDBvbs2UNubm4VfJMiIiIPpyrt+GRkZNC16z93nTdr1oxmzZrx7rvv8uyzzwLg6OiIj48PWVlZADz55JMAeHh4cPnyZc6ePYuvry/W1tbY29sTERFBaWkpeXl5hIaG4uDgwPXr1yktLaVv374EBAQwcOBAzGYzzZo1Iy0tjf3797N9+3YACgoKbqrx6aefplatWgA88cQTnD9/npycHFq2bAlAhw4dmDdv3k3jfq61YcOGFBcX33BtwIABrFixgldffRUnJyfeeuutO67Tz/dydnbG29u78u+SkhLS0tI4fvw4QUFBAJSVlXHx4kUaNGjAjBkzcHBwIDs7m3bt2gHQpEkTHB0dgX/+PPfvtYmIiMj/qdKOj4+PD8eOHQMgKyuLt99+Gx8fHw4ePAiA2WwmLS2NRo0a3XK8t7c3J06coKKigtLSUoYNG8bu3bu5dOkS8+fPJzQ0lKKiIiwWC46OjrRu3bryZ6+fxwcHBxMfH09UVBR+fn43zfHdd99RXl7OTz/9xOnTp3nssceoX78+J0+eBCA1NZXHH3/8pnFWVlY3vWaxWABISUmhffv2rF69mp49e7Jy5co7rtOt7vWva9CxY0fi4+NZvXo1vXr1olGjRkRERDBz5kxmzZpF/fr1K+e+071ERETkRlXa8fH392fixIkEBgZSXl7OxIkTad68OZMmTSIgIIDi4mJGjx6Nm5vbLce3bNmSLl26EBAQQEVFBQEBAbRp04aYmBgGDRpErVq1aNy4MTk5OTRu3JiBAwfy6quvMnPmTABGjhxJeHg469evx2w2M3r06JvmKCsr47XXXuPKlSuMGjWKRx55hOnTpzNt2jQsFgvW1taV9/slPj4+hIWFMWbMGMaOHcvixYsxmUxMmDDhntfwxRdf5MCBAwwePJjr16/TrVs3HB0deemllxg0aBDOzs7Uq1ePnJyce55DRETEqKwsP7cODODrr78mMTGRBQsWVHcp91VubmF1lyBVSA8wFDEmPcDw7rm7O932mp7c/DsoKSlh+PDhN73u5eXF1KlTq6EiERERAYN1fIxKHR8RETGSO3V89ABDERERMQwFHxERETEMBR8RERExDAUfERERMQwFHxERETEMBR8RERExDAUfERERMQwFHxERETEMBR8RERExDAUfERERMQyd1SVSw+iQUpEHkw4RrRnU8RERERHDUPC5g9zcXCIjI+/4njVr1tyfYu7Cg1SLiIjIg0jB5w7c3d1/MfjExMTcn2LuwoNUi4iIyIOoyvf4FBUVMWHCBC5evEhpaSmTJk2idevWTJw4kaysLMrLyxk2bBi9e/cmKCiIFi1akJ6ejtlsZuHChXh6ehIdHc2uXbsoLy8nICAAf39/5s2bx7fffsu1a9fw8fHhvffeo3///ixatIhGjRqxfft2Dh06xJtvvkl4eDj5+fkARERE0Lx588r6vv76a5YuXYrJZCI3N5eXX36ZIUOGcOLECaZNm4a1tTW1a9dm2rRpVFRUEBoayvr16/Hz8+MPf/gDp06dwsrKiujoaNasWcPVq1eJjIxk6NChTJgwARsbG6ytrYzFVZ8AACAASURBVJkzZw4NGjS4aX3y8/MJDg7m73//O4cPH+avf/0rX331Fbm5uYSHh7N06dLbrpWrqysFBQVMnjyZiRMn3jBXcnJyZS2/FNZERESMqso7PomJiXh6epKUlMSsWbP45ptvSEpKwtXVlcTERGJjY4mKiiIvLw8AX19f4uLi6Ny5M9u2bePEiRPs2bOHDRs2kJiYyOnTpyksLMTZ2ZnY2FgSExM5cuQI2dnZDBgwgM2bNwOwadMmBg0axNKlS+nUqRPx8fFMmzbtliEgOzubmJgY1q9fT1xcHD/++CMRERFMnjyZNWvWEBAQwKxZs24Yc+3aNfr06cOaNWuoX78+e/bsYdSoUbi4uBAZGcmXX35Jq1atiI2NZeTIkVy9evWW6+Pq6krdunW5dOkSX3zxBR4eHhw/fpyUlBS6det2x7Xy8/MjLi6Or7766qa5/rUWERERubUqDz4ZGRm0bdsWgGbNmhEcHMyZM2fo0KEDAI6Ojvj4+JCVlQXAk08+CYCHhwfFxcWcPXsWX19frK2tsbe3JyIiAjs7O/Ly8ggNDWXy5Mlcv36d0tJS+vbtyyeffEJ2djZms5lmzZqRlpbGxo0bCQoKYtKkSRQUFNxU49NPP02tWrWws7PjiSee4Pz58+Tk5NCyZUsAOnToQHp6+k3jfq61YcOGFBcX33BtwIABuLq68uqrr5KQkIC1tfVt16h79+7s3r2bw4cP89prr7Fv3z52795Nt27d7rhWXl5ev3ouERER+T9VHnx8fHw4duwYAFlZWbz99tv4+Phw8OBBAMxmM2lpaTRq1OiW4729vTlx4gQVFRWUlpYybNgwdu/ezaVLl5g/fz6hoaEUFRVhsVhwdHSkdevWlT97/Tw+ODiY+Ph4oqKi8PPzu2mO7777jvLycn766SdOnz7NY489Rv369Tl58iQAqampPP744zeNs7Kyuuk1i8UCQEpKCu3bt2f16tX07NmTlStX3naNunXrxtatW3F0dKRr167s2rWLkpIS3N3d77hWP89/u7l+rkVERERurcr3+Pj7+zNx4kQCAwMpLy9n4sSJNG/enEmTJhEQEEBxcTGjR4/Gzc3tluNbtmxJly5dCAgIoKKigoCAANq0aUNMTAyDBg2iVq1aNG7cmJycHBo3bszAgQN59dVXmTlzJgAjR44kPDyc9evXYzabGT169E1zlJWV8dprr3HlyhVGjRrFI488wvTp05k2bRoWiwVra+vK+/0SHx8fwsLCGDNmDGPHjmXx4sWYTCYmTJhw2zE/d7c6deqEi4sLNjY2PP/88wAMGjToF9eqdevWt5zr51rmzp17V7WLiIgYjZXFYG2Cr7/+msTERBYsWFDdpdw3ubmF1V2CVCE9wFDkwaQHGD443N2dbntNT27+nZSUlDB8+PCbXvfy8mLq1KnVUJE8LPSPq4jIvTNcx8eI1PEREREjuVPHRw8wFBEREcNQ8BERERHDUPARERERw1DwEREREcNQ8BERERHDUPARERERw1DwEREREcNQ8BERERHDUPARERERw9CRFSI1jM7qEnlw6AiZmkcdHxERETEMBR8RERExDAUfERERMQwFHxERETEMbW6uAmazmfDwcAoLC8nPz2fgwIFs374dV1dXCgoKWL58OZGRkWRmZlJRUUFISAgdO3Zkx44dJCQkVN5n4cKFPPLII7ecY+fOnaxYsQIbGxs8PT2ZM2cO165dIzw8nPz8fAAiIiJo3rz5ffnMIiIiNZGCTxXIzMykT58+9OjRg+zsbIKCgmjQoAF+fn50796dtWvX4urqysyZM8nPzycwMJBt27Zx7tw5li9fjr29PZMnT2bv3r307dv3lnNs3bqV4OBg+vTpw+bNmzGbzSxbtoxOnToxePBgzp07x4QJE1i3bt19/vQiIiI1h4JPFahXrx6rV69m586dODo6UlZWBoCXlxcAaWlpHDp0iKNHjwJQVlZGfn4+bm5ujBs3jjp16pCRkUHbtm1vO8eECRNYtmwZ69atw9vbm27dupGWlsb+/fvZvn07AAUFBb/zJxUREanZFHyqwKpVq2jbti2DBw9m//797N69GwArKysAvL298fDwYOTIkRQVFRETE4ONjQ2LFi3i888/B2DYsGFYLJbbzpGUlMTf/vY33NzcmDx5Mv/4xz/w9vamb9+++Pn58eOPP7Jhw4bf/bOKiIjUZAo+VeCFF14gMjKSLVu2ULduXaytrSkpKam87u/vT0REBIGBgZjNZgYPHoyjoyPt2rWjX79+ODg44OzsTE5Ozm3n8PX1ZdiwYdStW5c6derw/PPP8/zzzxMeHs769esxm82MHj36fnxcERGRGsvKcqc2gzwUcnMLq7sEqUJ6crPIg0NPbn4wubs73faags8DpKSkhOHDh9/0upeXF1OnTr3n+yr4iIiIkSj4GJyCj4iIGMmdgo8eYCgiIiKGoeAjIiIihqHgIyIiIoah4CMiIiKGoeAjIiIihqHgIyIiIoah4CMiIiKGoeAjIiIihqHgIyIiIoahQ0pFahid1SVSNXTOljGp4yMiIiKGoeAjIiIihqHg8zsoLi7mxRdfBGDGjBlcvHixmisSERER0B6f3114eHh1lyAiIiL/n4LPXUpOTmbjxo1UVFTQs2dPUlJSKCsrw8nJicWLF1NaWkpYWBgFBQU0adKkclxQUBCRkZF8/PHH1KtXj4CAAM6cOUNkZCTx8fEsWLCA/fv3U1FRQZ8+fQgODr7l/MXFxbz55puYzWaKiooYO3YsHTt2ZPv27cTFxWEymWjfvj1hYWH3aUVERERqHv3U9Ss4OzuTkJBAYWEhcXFxrF27lrKyMo4dO8amTZto1qwZCQkJ+Pv73/U9N2/ezNy5c0lISMDOzu627zt//jyXL19m6dKlzJs3j6KiIq5cucLixYuJi4tj3bp1ZGdns2/fvqr4qCIiIg8ldXx+BS8vL0wmE7a2toSGhuLg4MAPP/xAWVkZ6enpdOnSBYA2bdpgY3N3Szt//nzmz5/P5cuXK8ffyhNPPMGQIUMIDQ2lrKyMoKAgzp8/T15eHiNGjADg2rVrZGVl/fYPKiIi8pBS8PkVTCYTJ0+eZNeuXWzYsIGffvqJ/v37Y7FY8Pb25siRI3Tr1o0TJ05QVlZ2w9jatWuTm5sLwPHjxwEoKSlhx44dzJ8/H4vFQp8+fejTpw+enp43zX3q1CmuXbvG8uXLycnJwd/fn48++oiGDRuyatUqbG1tSU5OpmXLlr//QoiIiNRQCj6/0mOPPYa9vT39+/enVq1auLu7k5OTw5AhQ5gwYQIBAQF4e3tja2t7w7hevXoREhJCamoqrVu3BqBWrVq4uLjw0ksv4eLiQufOnXn00UdvOe/jjz/Of//3f7N582ZsbW0ZM2YMjzzyCMHBwQQFBVFeXo6npye9evX63ddARESkprKyWCyW6i5Cfl+5uYXVXYJUIT25WaRq6MnNDy93d6fbXlPwecAkJSWxdevWm14PDQ3l6aefvqd7KviIiIiRKPgYnIKPiIgYyZ2Cj/47u4iIiBiGgo+IiIgYhoKPiIiIGIaCj4iIiBiGgo+IiIgYhoKPiIiIGIaCj4iIiBiGgo+IiIgYhoKPiIiIGIYOKRWpYXRWl8hvp3O6jEsdHxERETEMBZ9fKTk5mZSUFADWrFlTzdWIiIjIr6Hg8yv179+fP//5zwDExMRUczUiIiLya9TIPT5FRUVMmDCBixcvUlpaysSJE0lKSiIrK4vy8nKGDRtG7969CQoKokWLFqSnp2M2m1m4cCGenp5ER0eza9cuysvLCQgIwN/fn3nz5vHtt99y7do1fHx8eO+99+jfvz+LFi2iUaNGbN++nUOHDuHi4kK9evW4cuUKV69eJTIyksLCQvz8/Hj++ec5c+YMs2fPZvny5besPSgoiObNm5Oeno6DgwPPPPMMe/fupaCggFWrVuHg4MCUKVPIzMykoqKCkJAQOnbsyI4dO0hISKi8z8KFC0lPT2fFihXY2tpy4cIFevfuzahRo+7X1yAiIlLj1MiOT2JiIp6eniQlJTFr1iwOHDiAq6sriYmJxMbGEhUVRV5eHgC+vr7ExcXRuXNntm3bxokTJ9izZw8bNmwgMTGR06dPU1hYiLOzM7GxsSQmJnLkyBGys7MZMGAAmzdvBmDTpk0MGjSosoZRo0bh4uJCZGQkAwcOZNOmTQB89NFHDBgw4I71+/r6snr1akpKSrCzsyM2NpamTZuSmprKhg0bcHV1JSEhgejoaKZOnQrAuXPnWL58OfHx8Xh5ebF3714ALl68yOLFi0lKSmLlypVVvtYiIiIPkxoZfDIyMmjbti0AzZo1Izc3lw4dOgDg6OiIj48PWVlZADz55JMAeHh4UFxczNmzZ/H19cXa2hp7e3siIiKws7MjLy+P0NBQJk+ezPXr1yktLaVv37588sknZGdnYzabadas2S3r6dixIxkZGfz444/s27ePF1544Y71t2rVCgBnZ2eaNm1a+XdxcTFpaWns2bOHoKAgxowZQ1lZGfn5+bi5uTFu3DgmTJjAqVOnKCsrq/z8NjY2ODg4YGdn9xtXVkRE5OFWI3/q8vHx4dixY3Tr1o2srCy2bdtGrVq16N69O2azmbS0NBo1anTLsd7e3qxbt46KigrKy8sZMWIEQ4YM4dKlS5Wdon/84x9YLBYcHR1p3bp15c9e/85isQBgZWWFn58fM2bMoHPnztja2t7zZ/P29sbDw4ORI0dSVFRETEwMNjY2LFq0iM8//xyAYcOG3TC3iIiI3J0a2fHx9/fnwoULBAYG8s4777By5UquXLlCQEAAr7zyCqNHj8bNze2WY1u2bEmXLl0ICAhg8ODB+Pn50aZNG7Kyshg0aBBjxoyhcePG5OTkADBw4ED27NlD7969b7qXj48PYWFhwD83Pe/cufMXf+a6m8+WkZFBYGAg/v7+eHp64ujoSLt27ejXrx9DhgzBzs6usj4RERG5e1aWn1sH8ptkZ2fzzjvvsHr16uou5Sa5uYXVXYJUIT3AUOS30wMMH27u7k63vVYjf+p60HzyyScsWbKEGTNmAP/ccDxu3Lib3tehQwfGjBlzv8sTERGR/08dHwNQx0dERIzkTh2fGrnHR0REROReKPiIiIiIYSj4iIiIiGEo+IiIiIhhKPiIiIiIYSj4iIiIiGEo+IiIiIhhKPiIiIiIYSj4iIiIiGEo+IiIiIhh6KwukRpGh5SK/DY6oNTY1PERERERw1DwuQujR4/+TeNTU1M5efJkFVUjIiIi90rB5y4sWbLkN43fuHEjOTk5VVSNiIiI3CtD7fFJTk4mJSUFs9lMfn4+b7zxBq6urixYsABra2saN27M1KlT2bJlCxs3bqSiooIxY8YQFhbGvn37CAoKonnz5qSnp+Pg4MAzzzzD3r17KSgoYNWqVTg4ODBlyhQyMzOpqKggJCSEOnXq8MUXX3D8+HGaNm3KN998Q1xcHCaTifbt2xMWFsbixYs5fPgw169fZ8aMGfj4+NxU+6FDh5g9ezY2NjY4Ozszd+5cateufdN8HTt2rIaVFRERqRkMFXwArl+/TmxsLHl5eQwcOBCTycT69etxc3MjKiqKTZs2VYaLmJiYm8b7+voSERHB8OHDsbOzIzY2lnHjxpGamkpOTg6urq7MnDmT/Px8AgMD2bZtG126dKF37944ODiwePFiNm7ciL29PWPHjmXfvn0AeHt7ExERcdu6d+3aRffu3Rk+fDiffvopBQUFfP7557ecT0RERG7NcMGnQ4cOmEwm6tWrh729PZmZmYSEhABQVFRE586dadKkCV5eXrcc36pVKwCcnZ1p2rRp5d/FxcWkpaVx6NAhjh49CkBZWRn5+fmVY8+fP09eXh4jRowA4Nq1a2RlZQHcdr6fjRw5kqVLlzJ06FAaNGiAr6/vbedzdXW91+URERF5qBku+Bw/fhyAy5cvU1xcTJMmTYiOjsbJyYmUlBQcHBy4dOkSJtOv3/7k7e2Nh4cHI0eOpKioiJiYGFxcXLCyssJisdCoUSMaNmzIqlWrsLW1JTk5mZYtW7Jr165fnG/Lli3069ePcePGsWzZMtavX3/b+UREROTWDBd8Ll++zNChQyksLGTKlCmYTCZGjBiBxWKhTp06zJkzh0uXLt3Tvf39/YmIiCAwMBCz2czgwYMxmUy0adOGuXPnEhUVRXBwMEFBQZSXl+Pp6UmvXr3u6t5PPfUU48ePx8HBAVtbW6ZOnUqDBg1uOZ+IiIjcmpXFYrFUdxH3S3JyMhkZGYSFhVV3KfdVbm5hdZcgVUgPMBT5bfQAw4efu7vTba8ZruPzoBs9ejRXr1694TVHR8dbbrQWY9I/2iIi985QHR+jUsdHRESM5E4dH20IEREREcNQ8BERERHDUPARERERw1DwEREREcNQ8BERERHDUPARERERw1DwEREREcNQ8BERERHDUPARERERw9CRFSI1jM7qkgeFjk+RmkgdHxERETEMBZ97kJycTEpKCgBr1qyp5mr+qbi4mA0bNlR3GSIiIg80BZ970L9/f/785z8DPDCnpufm5ir4iIiI/IIau8enqKiICRMmcPHiRUpLS5k4cSJJSUlkZWVRXl7OsGHD6N27N0FBQbRo0YL09HTMZjMLFy7E09OT6Ohodu3aRXl5OQEBAfj7+zNv3jy+/fZbrl27ho+PD++99x79+/dn0aJFNGrUiO3bt3Po0CFcXFyoV68eV65c4erVq0RGRlJYWIifnx/PP/88Z86cYfbs2Sxfvvymur/77juioqJYtmwZW7duZfny5fzP//wPBw8e5O9//ztjx45l7NixmM1mysvLefPNN/njH//IX/7yFx5//HFq1arFkCFDmD17NjY2Njg7OzN37lyWLl3K6dOnWbJkCaNHj66Gb0REROTBV2M7PomJiXh6epKUlMSsWbM4cOAArq6uJCYmEhsbS1RUFHl5eQD4+voSFxdH586d2bZtGydOnGDPnj1s2LCBxMRETp8+TWFhIc7OzsTGxpKYmMiRI0fIzs5mwIABbN68GYBNmzYxaNCgyhpGjRqFi4sLkZGRDBw4kE2bNgHw0UcfMWDAgFvW3bJlSy5evEhxcTFffPEFVlZWXL58mU8//ZTu3bsTExPDs88+S0JCAgsXLiQ8PJyKigquX7/O66+/zvz589m1axfdu3dnzZo1DBgwgIKCAkaOHEnTpk0VekRERO6gxgafjIwM2rZtC0CzZs3Izc2lQ4cOADg6OuLj40NWVhYATz75JAAeHh4UFxdz9uxZfH19sba2xt7enoiICOzs7MjLyyM0NJTJkydz/fp1SktL6du3L5988gnZ2dmYzWaaNWt2y3o6duxIRkYGP/74I/v27eOFF164be3PPfccX3/9NZcuXcLPz48vv/ySgwcP8sc//pEzZ85Ufo4GDRrg6OhYGeC8vLwAGDlyJHl5eQwdOpQdO3ZgY1NjG3ciIiL3VY0NPj4+Phw7dgyArKwstm3bxsGDBwEwm82kpaXRqFGjW4719vbmxIkTVFRUUFpayrBhw9i9ezeXLl1i/vz5hIaGUlRUhMViwdHRkdatW1f+7PXvLBYLAFZWVvj5+TFjxgw6d+6Mra3tbWvv1q0bK1asoHnz5jz33HMkJCTw2GOPYWtri4+PT+XnyM7OpqCggLp16wJgMv3z69qyZQv9+vUjPj6eJ554gvXr12MymaioqLjH1RQRETGGGht8/P39uXDhAoGBgbzzzjusXLmSK1euEBAQwCuvvMLo0aNxc3O75diWLVvSpUsXAgICGDx4MH5+frRp04asrCwGDRrEmDFjaNy4MTk5OQAMHDiQPXv20Lt375vu5ePjQ1hYGPDPTc87d+687c9cP2vXrh1nz57lueeeo0WLFnz//ff06NEDgL/+9a/s37+fIUOG8PrrrzN16tSbOjpPPfUU48ePJzAwkP379/PSSy/h5uZGaWkp77///q9eSxEREaOwsvzcspDfLDs7m3feeYfVq1dXdyk3yM0trO4SpArpAYbyoNADDOVB5e7udNtr2hxSRT755BOWLFnCjBkzALh48SLjxo276X0dOnRgzJgx97s8ERERQR0fQ1DHR0REjOROHZ8au8dHRERE5NdS8BERERHDUPARERERw1DwEREREcNQ8BERERHDUPARERERw1DwEREREcNQ8BERERHDUPARERERw1DwEREREcPQWV0iNYwOKZX7TYeRysNEHR8RERExDMMGnzVr1lR3CSIiInKfGTb4xMTEVHcJIiIicp/d1z0+RUVFTJgwgYsXL1JaWsrEiRNJSkoiKyuL8vJyhg0bRu/evQkKCqJ58+akp6fj4ODAM888w969eykoKGDVqlWkpKSQkpKC2WwmPz+fN954g//4j/9gx44dJCQkVM63cOFC6taty/Tp0zl69CilpaX87W9/Iz09natXrxIZGYmvry+7d++mqKiI8+fP89prr9G/f39OnTrF9OnTAahbty4zZ86ktLSUkJAQLBYLpaWlvPvuuzz++OO8+eabmM1mioqKGDt2LB07drzl509OTuazzz6jqKiI3NxcXnnlFVJSUkhPT+edd96hW7dubN++nbi4OEwmE+3btycsLIwffviByMhIiouLuXLlCm+88QbdunXDz8+PP/zhD5w6dQorKyuio6NxcnK6L9+liIhITXRfOz6JiYl4enqSlJTErFmzOHDgAK6uriQmJhIbG0tUVBR5eXkA+Pr6snr1akpKSrCzsyM2NpamTZuSmpoKwPXr14mNjWXVqlXMmjWLsrIyzp07x/Lly4mPj8fLy4u9e/eSkpJCfn4+H330EStXruTYsWOMGjUKFxcXIiMjATCbzSxbtoyYmBiWL18OwKRJk5gyZQrx8fF07dqVlStXcvToUZycnFixYgURERGYzWbOnz/P5cuXWbp0KfPmzaOoqOiOa3Dt2jVWrFjBa6+9xrp161iyZAlTp04lOTmZK1eusHjxYuLi4li3bh3Z2dns27ePjIwMhg0bRmxsLJMmTaoMd9euXaNPnz6sWbOG+vXrs2ePNr2KiIjcyX3t+GRkZNC16z//d0CzZs1Yt24dzz77LACOjo74+PiQlZUFQKtWrQBwdnamadOmlX8XFxcD0KFDB0wmE/Xq1cPZ2Zm8vDzc3NwYN24cderUISMjg7Zt23L27Fnatm0LgLu7O2+99dZNdbVo0QKAhg0bUlJSAsCZM2d49913ASgtLcXLy4uuXbty7tw5Xn/9dWxsbBg1ahRPPPEEQ4YMITQ0lLKyMoKCgu64Bi1btgTA6f+xd+dhVdV7//+fm9EBEJxwTsAxuHFOT5bWrZlZ1onQBMUhzUsTB7g1RKEwFTVzKFMKySExQRPpqFnH6D5Zzpp9NUsRlISDCggpG2WLsH9/9Iv7dBRKj4m4Xo+/uPZe6/N5f9b2unxd7732+jg74+Xlhclkok6dOlgsFs6ePUt+fj5jx44Ffgk2mZmZdOnShZiYGD7++GNMJhPXr18vH+/BBx8sr/3XayMiIiI3d1eDj5eXF8eOHaNv375kZmayfft2HBwceOKJJzCbzaSmptKsWbM/NNbx48cByMvLw2w2U7NmTd555x3+8Y9/ADBq1CisViuenp589tlnABQWFjJlyhQ++OADrFZr+Vgmk+mG8T08PFiwYAFNmjTh8OHD5Obmsn//fho2bMiqVas4cuQIixcvJiIigqKiImJjY8nJyWHIkCE8/vjjFdZ9s7l+1axZMxo3bsyqVauwt7cnKSmJ9u3b8/bbbzNo0CB69+7N5s2b2bJlyx8aT0RERH7rrgafIUOGMGPGDIYNG0ZpaSlxcXGsX7+egIAALBYLwcHB1KtX7w+NlZeXx4gRIygsLOT111/HycmJzp078/zzz1OrVi1cXFzIycnBz8+PvXv3EhAQQGlpKRMmTAB+CWFTp04t7zj9u6ioKMLCwigtLQVg7ty5uLq6EhISwtq1a7GxsWHChAm0bNmS5cuXk5ycjL29PZMmTbrt61O3bl1GjhxJUFAQpaWlNG3alKeeeor+/fszd+5c3n//fRo3bkxBQcFtzyEiImJkJuu/tj6qiaSkJE6fPs3UqVOrupRqITe3sKpLkDtIDzCUu00PMJTqpkGDin/ooyc3/wmioqJIT0+/4fWVK1dSo0aNKqhIREREoJp2fOTWqOMjIiJGUlnHx7APMBQRERHjUfARERERw1DwEREREcNQ8BERERHDUPARERERw1DwEREREcNQ8BERERHDUPARERERw1DwEREREcNQ8BERERHD0F5dItWMNik1Nm0YKvKfUcdHREREDMPQwSc+Pr6qS7hjdu7cyYULF6q6DBERkXuaoYNPTExMVZdwx3z44YeYzeaqLkNEROSedtfv8SkuLiY8PJzs7GxKSkqYMWMGiYmJZGZmUlpayqhRoxgwYABBQUG0bduWU6dOUatWLbp27co333zD5cuXWbVqFSkpKaSkpGA2mykoKGDChAk8+eSTfPbZZ6xfv758vrfffhtXV1fmzJnD0aNHKSkpYeLEiZw6dYpLly4RFRWFr68vX331FcXFxZw9e5aXX34ZPz8/Tp48yZw5cwBwdXUlOjqakpISpkyZgtVqpaSkhFmzZtGyZUsmT56M2WymuLiYadOm0b1795uu//nnnycuLg4XFxe6d+9OfHw8Dz74IM8//zyJiYnEx8ezfft27Ozs6Nq1K9OmTWPZsmUcOXKEK1euMHfuXBYuXPibua5evcqPP/5IWFgYH330EQ4ODnflsxQREalu7nrwSUhIoGnTpixZsoTU1FS++OIL3Nzcyv8z9/Pzo0ePHgD4+voSERHB6NGjqVGjBqtXryYsLIyDBw8CcOXKFVavXk1+fj6DBg2iT58+ZGRkEBsbS82aNXnttdf45ptvqFmzJgUFBXz88cfk5uYSHx9PSEgI8fHx06dYgwAAIABJREFUREVFkZSUhNls5oMPPiAjI4Nx48bh5+dHZGQk0dHRtGrVik2bNhEXF0enTp1wdnZm0aJFpKWlYTabOXv2LHl5eaxZs4aLFy+SkZFR4fr79OnD119/TaNGjWjWrBm7d+/GwcGBli1bcubMGXbs2EFCQgJ2dnZMnDiR//3f/wXA09OTiIgITp06dcNcjz32GO3btycqKkqhR0REpBJ3PficPn2aXr1++VVCmzZt2LBhAw8//DAATk5OeHl5kZmZCYC3tzcALi4utGrVqvxvi8UCQLdu3bCxsaF+/fq4uLiQn59PvXr1CAsLo3bt2pw+fZqOHTty5swZOnbsCECDBg0ICQm5oa527doB0LhxY65duwZAeno6s2bNAqCkpAQPDw969epFRkYGr7zyCnZ2dowfP57WrVszdOhQQkNDuX79OkFBQRWuv1+/frz33ns0btyYkJAQ1q1bh9VqpV+/fpw+fZoOHTpgb28PQNeuXTl16hQAHh4eALc0l4iIiPzWXb/Hx8vLi2PHjgGQmZnJ9u3bOXToEABms5nU1FSaNWv2h8Y6fvw4AHl5eZjNZmrWrMk777zDkiVLmDNnDo6OjlitVjw9PcvnLCwsZPTo0QBYrdbysUwm0w3je3h4sGDBAtatW8e0adPo3bs3+/fvp2HDhqxatYrx48ezePFiTp48SVFREbGxscyfP5/Zs2dXWHObNm3Iysri6NGj9O7dmytXrpCSkkKvXr3w9PTk6NGjXL9+HavVysGDB8sDj43NLx9VRXOZTKbfrEdERERudNc7PkOGDGHGjBkMGzaM0tJS4uLiWL9+PQEBAVgsFoKDg6lXr94fGisvL48RI0ZQWFjI66+/jpOTE507d+b555+nVq1auLi4kJOTg5+fH3v37iUgIIDS0lImTJgA/BLCpk6dWt5x+ndRUVGEhYVRWloKwNy5c3F1dSUkJIS1a9diY2PDhAkTaNmyJcuXLyc5ORl7e3smTZpUad3dunUjKysLGxsbunXrRlpaGrVr16Zt27Y89dRTBAQEUFZWRpcuXejbty8nTpwoP7eiuTp16sSrr77KqlWrcHV1/UPXT0RExGhM1mraJkhKSuL06dNMnTq1qku55+XmFlZ1CXIH6QGGxqYHGIr8vgYNnCt8T09u/pNERUWRnp5+w+srV66kRo0aVVCRiIiIVNuOj/xx6viIiIiRVNbxMfQDDEVERMRYFHxERETEMBR8RERExDAUfERERMQwFHxERETEMBR8RERExDAUfERERMQwFHxERETEMBR8RERExDAUfERERMQwtFeXSDWjTUrvP9p4VOTuUcdHREREDEPB50+Qm5tLVFRUpcfEx8ffnWJERESknILPn6BBgwa/G3xiYmLuTjEiIiJS7p65x6e4uJjw8HCys7MpKSkhMjISHx8fZsyYQWZmJqWlpYwaNYoBAwYQFBREu3btOHXqFGazmbfffpumTZuyYsUKvvjiC0pLSwkICGDIkCEsWrSI77//nqKiIry8vJg3bx5+fn688847NGvWjB07dnD48GEmT57MzJkzKSgoACAiIoK2bduW17d//37ee+89bGxsyM3N5cUXX2To0KH88MMPzJ49G1tbWxwdHZk9ezZlZWWEhoayceNGBg4cyEMPPcTJkycxmUysWLGC+Ph4Ll26RFRUFCNGjCA8PBw7OztsbW158803cXd3v+k1+vvf/87KlSuxs7OjadOmvPnmmxQVFVVat4iIiPyfe6bjk5CQQNOmTUlMTGT+/Pn8v//3/0hMTMTNzY2EhARWr17N0qVLyc/PB8DX15c1a9bQs2dPtm/fzg8//MCuXbvYtGkTCQkJpKWlUVhYiIuLC6tXryYhIYHvvvuOCxcu4O/vT3JyMgBbtmxh8ODBvPfee/To0YN169Yxe/bsm3ZsLly4QExMDBs3bmTNmjVcvHiRiIgIXnvtNeLj4wkICGD+/Pm/OaeoqIinn36a+Ph4GjZsyK5duxg/fjx16tQhKiqKPXv24O3tzerVqxk3bhyXLl2q8Bpt27aNkSNHsmHDBh555BHMZvMfqltERER+cc90fE6fPk2vXr/8sqFNmza0adOGWbNm8fDDDwPg5OSEl5cXmZmZADz44IMANGrUiLy8PM6cOYOvry+2trbUrFmTiIgISkpKyM/PJzQ0lFq1anHlyhVKSkp49tlnCQgIYNCgQZjNZtq0aUNqair79u1jx44dAFy+fPmGGjt16oSDgwMArVu35uzZs+Tk5NC+fXsAunXrxqJFi24479daGzdujMVi+c17/v7+rFy5kjFjxuDs7ExISEiF1yg8PJz333+fDRs24OnpSd++ff9Q3SIiIvKLe6bj4+XlxbFjxwDIzMzkf/7nf/Dy8uLQoUMAmM1mUlNTadas2U3P9/T05IcffqCsrIySkhJGjRrFV199xblz51i8eDGhoaEUFxdjtVpxcnLCx8en/GuvX88fOXIk69atY+nSpQwcOPCGOX788UdKS0u5evUqaWlpPPDAAzRs2JATJ04AcPDgQVq2bHnDeSaT6YbXrFYrACkpKXTp0oW1a9fSv39/4uLiKrxGiYmJTJw4sfzG6J07d/6hukVEROQX90zHZ8iQIcyYMYNhw4ZRWlrKjBkzaNu2LZGRkQQEBGCxWAgODqZevXo3Pb99+/Y8+uijBAQEUFZWRkBAAB06dCAmJobBgwfj4OBA8+bNycnJoXnz5gwaNIgxY8YQHR0NwLhx45g5cyYbN27EbDYTHBx8wxzXr1/n5Zdf5ueff2b8+PHUrVuXOXPmMHv2bKxWK7a2tuXj/R4vLy+mTp3KpEmTmDZtGsuWLcPGxobw8PAKz/H19WXUqFG4urpSu3ZtHnvsMR577LHfrVtERER+YbL+2nqQSu3fv5+EhASWLFlS1aXcstzcwqouQe4gPcDw/qMHGIrcWQ0aOFf43j3T8ZFfXLt2jdGjR9/wuoeHB2+88UYVVCT3Gv0nKSJy+9TxMQB1fERExEgq6/jcMzc3i4iIiPzZFHxERETEMBR8RERExDAUfERERMQwFHxERETEMBR8RERExDAUfERERMQwFHxERETEMBR8RERExDC0ZYVINaO9uu492kZEpPpQx0dEREQM474PPhaLhU2bNt3SOTt37uTChQt/UkUiIiJSVe774JObm3vLwefDDz/EbDb/SRWJiIhIVbnv7/F57733SEtL49133yU1NZWCggIAIiIicHFxYcSIEcTHx5Oens6yZct46aWX+PHHHwkLC2PhwoWEhYWxceNGAAYPHszixYvZsmULR44c4cqVK8ydO5c9e/awbds2TCYTAwYMYPjw4RXW88QTT9CpUyd++uknevToQWFhIUePHsXDw4OFCxdy7tw5IiMjsVgsODo6Mnv2bBo3bsyiRYv4/vvvKSoqwsvLi3nz5rFs2TKysrK4ePEi2dnZhIeH8+ijj96V6yoiIlId3ffBZ9y4caSmpnL16lV69OhBYGAgGRkZhIeHs2HDBqZNm8b06dPJy8sjNjaWRo0a0b59e6KiorC3t69wXE9PTyIiIkhLS+PTTz/lo48+wmQyMXLkSB555BE8PT1vet4///lP1q5dS4MGDXjooYfYtGkTkZGR9OnTh8uXL7NgwQKCgoLo3bs3e/fu5a233mLWrFm4uLiwevVqysrKePrpp8u/inNwcCAuLo7du3ezatUqBR8REZFK3PfB51epqans27ePHTt2AHD58mUA+vbty5IlS3j44Ydp1KhRpWNYrdbyvz08PMrHzc7OZuTIkQBcunSJs2fPVhh8XF1dadKkCQC1atWiVatWADg7O2OxWEhNTeX9998nLi4Oq9WKvb09jo6O5OfnExoaSq1atbhy5QolJSUAtG/fHoBGjRpx7dq127k0IiIihnHfBx8bGxvKysrw9PTk2WefZeDAgVy8eLH8vp9Vq1bRs2dPjh07xnfffUfHjh0xmUxYrVYcHR25ePEipaWlFBUVkZWV9Ztx4ZfOT6tWrYiLi8NkMrFmzRratGlTYT0mk6nSej09PXnppZfo3Lkz6enpHDx4kF27dnHu3DmWLl1Kfn4+O3fuLA9hvzeeiIiI/J/7PvjUq1ePkpISioqK2LFjBxs3bsRsNhMcHMyxY8fYtm0biYmJZGZmMnHiRBITE+nUqROvvvpqeSjy9/enRYsWPPDAAzeM365dO/7yl78QEBDAtWvX8PX1xd3d/bbrDQsLIyoqCovFQnFxMTNnzqRZs2asWLGCwYMH4+DgQPPmzcnJyflPLouIiIghmaz/+v2N3JdycwurugS5g/QAw3uPHmAocm9p0MC5wvfu+45PVUhJSWHNmjU3vD58+HCeeOKJu1+QiIiIAOr4GII6PiIiYiSVdXzu+wcYioiIiPxKwUdEREQMQ8FHREREDEPBR0RERAxDwUdEREQMQ8FHREREDEPBR0RERAxDwUdEREQMQ8FHREREDEPBR0RERAxDe3WJVDPapPTu0gakIvcXdXxERETEMAwRfCwWC5s2bbqlc3bu3MmFCxf+pIruvOzsbL788suqLkNEROSeZojgk5ube8vB58MPP8RsNv9JFd15+/bt49tvv63qMkRERO5phrjH57333iMtLY13332X1NRUCgoKAIiIiMDFxYURI0YQHx9Peno6y5Yt46WXXuLHH38kLCyMhQsXEhYWxsaNGwEYPHgwixcvZsuWLRw5coQrV64wd+5c9uzZw7Zt2zCZTAwYMIDhw4fftJY1a9ZQWlrK6NGjee2113BwcCAiIoIVK1bQvHlzvLy8mD17Nra2tjg6OjJ79mzKysoYP348rq6u9OrVi1q1apGcnIyNjQ2dO3dm6tSpxMbGUlxcTKdOnejTp89du7YiIiLViSGCz7hx40hNTeXq1av06NGDwMBAMjIyCA8PZ8OGDUybNo3p06eTl5dHbGwsjRo1on379kRFRWFvb1/huJ6enkRERJCWlsann37KRx99hMlkYuTIkTzyyCN4enrecE6/fv2YMWMGo0eP5syZMxQXFwPwzTffEBsby/Dhw5k7dy7t27fniy++YP78+bz66qvk5uayefNmHBwceOGFF4iMjKRjx4589NFHWK1Wxo4dy+nTpxV6REREKmGIr7p+lZqayubNmwkKCiIyMpLLly8D0LdvX86fP89DDz1Eo0aNKh3DarWW/+3h4VE+bnZ2NiNHjmTEiBH8/PPPnD179qbnN2nShOLiYo4ePYqXlxdubm4cPXoUZ2dnnJycyMnJoX379gB069aNU6dOAdCsWTMcHBwAmDdvHgkJCQwbNozs7Ozf1CQiIiIVM0THx8bGhrKyMjw9PXn22WcZOHAgFy9eLL/vZ9WqVfTs2ZNjx47x3Xff0bFjR0wmE1arFUdHRy5evEhpaSlFRUVkZWX9Zlz4pfPTqlUr4uLiMJlMrFmzhjZt2lRYT+/evVm4cCEjRowgOzubOXPmMGjQIAAaNmzIiRMnaNeuHQcPHqRly5a/mQtg48aNzJo1C0dHR0aPHs2RI0fK1ygiIiIVM0TwqVevHiUlJRQVFbFjxw42btyI2WwmODiYY8eOsW3bNhITE8nMzGTixIkkJibSqVMnXn311fJQ5O/vT4sWLXjggQduGL9du3b85S9/ISAggGvXruHr64u7u3uF9fTr1493332XmJgYcnJymD9/Pu+99x4Ac+bMYfbs2VitVmxtbYmOjr7h/LZt2+Lv74+bmxvu7u506NABJycnYmJi8Pb25umnn75zF09EROQ+YrLqe5L7Xm5uYVWXIHeQHmB4d+kBhiLVT4MGzhW+Z4iOT1VISUlhzZo1N7w+fPhwnnjiibtfkIiIiKjjYwTq+IiIiJFU1vEx1K+6RERExNgUfERERMQwFHxERETEMBR8RERExDAUfERERMQwFHxERETEMBR8RERExDAUfERERMQwFHxERETEMBR8RERExDC0V5dINWP0TUq1aaiI/CfU8RERERHDUPC5DfHx8VVdgoiIiNwGBZ/bEBMTU9UliIiIyG2oNvf4FBcXEx4eTnZ2NiUlJcyYMYPExEQyMzMpLS1l1KhRDBgwgKCgINq2bcupU6eoVasWXbt25ZtvvuHy5cusWrWKlJQUUlJSMJvNFBQUMGHCBJ588kk+++wz1q9fXz7f22+/jaurK3PmzOHo0aOUlJQwceJETp06xaVLl4iKisLX15evvvqK4uJizp49y8svv4yfnx8nT55kzpw5ALi6uhIdHU1JSQlTpkzBarVSUlLCrFmzaNmyJZMnT8ZsNlNcXMy0adPo3r37Tdd/5swZwsPDsbOzw9bWljfffBN3d3cWLVrEwYMHsVqtjBw5kqeeeuqufB4iIiLVUbUJPgkJCTRt2pQlS5aQmprKF198gZubGwsXLsRsNuPn50ePHj0A8PX1JSIigtGjR1OjRg1Wr15NWFgYBw8eBODKlSusXr2a/Px8Bg0aRJ8+fcjIyCA2NpaaNWvy2muv8c0331CzZk0KCgr4+OOPyc3NJT4+npCQEOLj44mKiiIpKQmz2cwHH3xARkYG48aNw8/Pj8jISKKjo2nVqhWbNm0iLi6OTp064ezszKJFi0hLS8NsNnP27Fny8vJYs2YNFy9eJCMjo8L179mzB29vb6ZPn86hQ4e4dOkSJ06cICsri4SEBCwWC4MHD6Znz564uLjcjY9ERESk2qk2wef06dP06vXLrznatGnDhg0bePjhhwFwcnLCy8uLzMxMALy9vQFwcXGhVatW5X9bLBYAunXrho2NDfXr18fFxYX8/Hzq1atHWFgYtWvX5vTp03Ts2JEzZ87QsWNHABo0aEBISMgNdbVr1w6Axo0bc+3aNQDS09OZNWsWACUlJXh4eNCrVy8yMjJ45ZVXsLOzY/z48bRu3ZqhQ4cSGhrK9evXCQoKqnD9/v7+rFy5kjFjxuDs7ExISAipqakcP368/Lzr16+TnZ2t4CMiIlKBahN8vLy8OHbsGH379iUzM5Pt27fj4ODAE088gdlsJjU1lWbNmv2hsY4fPw5AXl4eZrOZmjVr8s477/CPf/wDgFGjRmG1WvH09OSzzz4DoLCwkClTpvDBBx9gtVrLxzKZTDeM7+HhwYIFC2jSpAmHDx8mNzeX/fv307BhQ1atWsWRI0dYvHgxERERFBUVERsbS05ODkOGDOHxxx+/ac0pKSl06dKF4OBgtm3bRlxcHH379qV79+7Mnj2bsrIyVqxY8YevgYiIiBFVm+AzZMgQZsyYwbBhwygtLSUuLo7169cTEBCAxWIhODiYevXq/aGx8vLyGDFiBIWFhbz++us4OTnRuXNnnn/+eWrVqoWLiws5OTn4+fmxd+9eAgICKC0tZcKECcAvIWzq1KnlHad/FxUVRVhYGKWlpQDMnTsXV1dXQkJCWLt2LTY2NkyYMIGWLVuyfPlykpOTsbe3Z9KkSRXW7OPjw7Rp01i2bBk2NjaEh4fz4IMPcuDAAQIDA7ly5Qp9+/bFycnpFq+siIiIcZis/9q+MICkpCROnz7N1KlTq7qUuyY3t7CqS5A7SA8w1AMMRaRyDRo4V/heten4GEVUVBTp6ek3vL5y5Upq1KhRBRWJiIjcPwzX8TEidXxERMRIKuv46AGGIiIiYhgKPiIiImIYCj4iIiJiGAo+IiIiYhgKPiIiImIYCj4iIiJiGAo+IiIiYhgKPiIiImIYCj4iIiJiGAo+IiIiYhjaq0ukmjHaJqXalFRE7iR1fERERMQwFHzukqSkJFJSUgCIj4+v4mpERESMScHnLvHz86NPnz4AxMTEVHE1IiIixmSoe3yKi4sJDw8nOzubkpISZsyYQWJiIpmZmZSWljJq1CgGDBhAUFAQ7dq149SpU5jNZt5++22aNm3KihUr+OKLLygtLSUgIIAhQ4awaNEivv/+e4qKivDy8mLevHn4+fnxzjvv0KxZM3bs2MHhw4epU6cO9evX5+eff+bSpUtERUVRWFjIwIEDeeyxx0hPT2fBggXExsbetPb169eTnJyMjY0NnTt3JiwsjHPnzhEZGYnFYsHR0ZHZs2fTuHHju3xVRUREqg9DdXwSEhJo2rQpiYmJzJ8/nwMHDuDm5kZCQgKrV69m6dKl5OfnA+Dr68uaNWvo2bMn27dv54cffmDXrl1s2rSJhIQE0tLSKCwsxMXFhdWrV5OQkMB3333HhQsX8Pf3Jzk5GYAtW7YwePDg8hrGjx9PnTp1iIqKYtCgQWzZsgWAjz/+GH9//wprT0pKYubMmSQmJtK8eXOuX7/OggULCAoKYt26dYwePZq33nrrT7x6IiIi1Z+hgs/p06fp2LEjAG3atCE3N5du3boB4OTkhJeXF5mZmQA8+OCDADRq1AiLxcKZM2fw9fXF1taWmjVrEhERQY0aNcjPzyc0NJTXXnuNK1euUFJSwrPPPsvnn3/OhQsXMJvNtGnT5qb1dO/endOnT3Px4kV2797N448/XmHt8+bNIyEhgWHDhpGdnY3VaiU1NZX333+foKAgli9fXh7aRERE5OYM9VWXl5cXx44do2/fvmRmZrJ9+3YcHBx44oknMJvNpKam0qxZs5ue6+npyYYNGygrK6O0tJSxY8cydOhQzp07V94p2rlzJ1arFScnJ3x8fMq/9vp3VqsVAJPJxMCBA5k7dy49e/bE3t6+wto3btzIrFmzcHR0ZPTo0Rw5cgRPT09eeuklOnfuTHp6OgcPHrwzF0pEROQ+ZajgM2TIEGbMmMGwYcMoLS0lLi6O9evXExAQgMViITg4mHr16t303Pbt2/Poo48SEBBAWVkZAQEBdOjQgZiYGAYPHoyDgwPNmzcnJyeH5s2bM2jQIMaMGUN0dPQNY3l5eTF16lTeeust/Pz8eOyxx/jkk08qrb1t27b4+/vj5uaGu7s7HTp0ICwsjKioKCwWC8XFxcycOfOOXCcREZH7lcn6a/tBqsSFCxd49dVXWbt27Z82R25u4Z82ttx9eoChiEjlGjRwrvA9Q3V87jWff/457777LnPnzgUgOzubsLCwG47r1q0bkyZNutvlyT1KQUBE5Pap42MA6viIiIiRVNbxMdSvukRERMTYFHxERETEMBR8RERExDAUfERERMQwFHxERETEMBR8RERExDAUfERERMQwFHxERETEMBR8RERExDC0ZYVINXM/7tWlbThE5G5Rx0dEREQMQ8FHREREDOO+Dz7Z2dl8+eWXAAQFBZGenl7FFYmIiEhVue+Dz759+/j222+rugwRERG5B1T5zc1nzpwhPDwcOzs7bG1teeGFF/jkk0+wt7fn/PnzDBkyhH379nHixAmGDx9OYGAgu3fvZunSpTg6OuLq6kp0dDQuLi7Mnz+fw4cPA/DMM88wbNgwYmNjKS4uplOnTgAsX76cvLw8rl69yuLFi8nOzmblypXY29uTlZXFgAEDGD9+POfOnSMyMhKLxYKjoyOzZ8+mbt26TJ48GbPZTHFxMdOmTaN79+5Mnz6ds2fPYrFYGD16NAMGDLjpWvfv309sbGylaztw4ABLlizB1taW5s2b88Ybb2CxWJg5cyaFhYUUFBQwaNAgAgMDCQoKol27dpw6dQqz2czbb79N06ZN79pnJyIiUt1UefDZs2cP3t7eTJ8+nUOHDpGens758+dJTk7m+PHjTJ48mZ07d3LhwgWCg4MJCAggMjKSDRs24O7uztq1a4mJieGhhx4iKyuLjRs3cv36dQIDA+nRowdjx47l9OnT9OnThzVr1tC7d2+ee+45li1bxmeffYavry/Z2dn87W9/49q1azz66KOMHz+eBQsWEBQURO/evdm7dy9vvfUW48aNIy8vjzVr1nDx4kUyMjIwm83s37+fzZs3A7B79+5K1/tH1vbRRx9Rr149li5dypYtW/D29ubpp5+mX79+XLhwgaCgIAIDAwHw9fVl5syZLFmyhO3btzN27Ng//TMTERGprqo8+Pj7+7Ny5UrGjBmDs7MzPXv2pHXr1tjb2+Ps7EyLFi1wcHCgTp06WCwWCgoKcHJywt3dHYBu3bqxePFi6tWrR9euXTGZTNjb29OhQ4eb3s/j4+MDQP369cnLywOgTZs22NnZYWdnR40aNQBITU3l/fffJy4uDqvVir29Pa1bt2bo0KGEhoZy/fp1goKCcHJyIjIyksjISMxmM88++2yl661sbfn5+eTk5DBlyhQAiouL6dmzJ71792bt2rX8/e9/x8nJievXr5eP9+CDDwLQqFGj8vWIiIjIzVV58ElJSaFLly4EBwezbds2Fi9eTIcOHSo83s3NDbPZTE5ODg0bNuTAgQO0bNkSLy8vkpKSGDlyJCUlJRw5coTnn3+eU6dOUVZWVmkNJpPphtc8PT156aWX6Ny5M+np6Rw8eJCTJ09SVFREbGwsOTk5DBkyBG9vb44fP87y5cuxWCzlHSU7u5tf2pvN9a9ra9SoEStWrMDZ2ZmUlBRq1arFqlWr6NixI4GBgezbt4+vvvqq0vWIiIjIzVV58PHx8WHatGksW7YMGxsbgoKCOHr0aIXHm0wm5syZw8SJEzGZTNSpU4d58+ZRt25dDhw4wIsvvkhJSQn9+/fH29sbk8lETEwM3t7et1RXWFgYUVFRWCwWiouLmTlzJi1btmT58uUkJydjb2/PpEmTaNCgAbm5ufz1r3+lVq1avPTSSxWGnt9jY2PDzJkzGTt2LFarldq1a/Pmm29iMpmIiopi69atuLq6Ymtry7Vr125rDhERESMzWa1Wa1UXIX+u3NzCqi5B7iA9uVlEpHINGjhX+J6Cz5/g3XffZf/+/Te8Hh0dTfPmze96PQo+IiJiJAo+BqfgIyIiRlJZ8LnvH2AoIiIi8isFHxERETEMBR8RERExDAUfERERMQwFHxERETEMBR8RERExDAUfERERMQwFHxERETEMBR8RERExjCrfpFREbs39tFeX9ugSkbsMDZwMAAAgAElEQVRNHR8RERExDAWfW7Rz504uXLhQ1WXc4OTJkxw8eLCqyxAREbmnKfjcog8//BCz2VzVZdzg73//O2lpaVVdhoiIyD2t2t3jc+bMGcLDw7Gzs8PW1pYmTZrg4+PD0KFDuXTpEqNGjSIsLIzY2Fjs7e05f/48Q4YMYd++fZw4cYLhw4cTGBjIwIED6dq1K6mpqXh4eFCvXj0OHTqEg4MDsbGxFBcXM3PmTAoKCgCIiIjg3Llz/Pjjj4SFhbFw4UImTZqEq6sr3bt3Jzk5mc8//xxbW1sWLlyIj48PTz311A31z507ly5dutC/f39Gjx7No48+ysiRI5k5cyYvvPACV69eZenSpTg6OuLq6kp0dDQ//vgjb731Fvb29gwePJgzZ86wb98+ysrKePrpp3nqqafYsmUL9vb2eHt74+vre7c/FhERkWqh2gWfPXv24O3tzfTp0zl06BBubm5ERkYydOhQtm3bxsCBAwE4f/48ycnJHD9+nMmTJ5d/RRUcHExgYCBFRUU888wz5SEkPDyckJAQhg0bRlpaGtu2baNHjx4EBgaSkZFBeHg4GzZsoH379kRFRWFvb09ubi6bN2/GwcGBzMxMvvnmGx555BF27drF5MmTb1p/v3792LJlC4899hiXL19mz549jBgxgh9++IE5c+bQp08fNmzYgLu7O2vXriUmJobHHnsMi8XCpk2bAOjduzfx8fG4u7uTlJSEu7s7zz//PPXr11foERERqUS1+6rL398fNzc3xowZw/r167G3t6d27dqkpaWxdetWnnvuOQBat26Nvb09zs7OtGjRAgcHB+rUqYPFYikfy9vbGwAXFxe8vLzK/7ZYLKSmprJ582aCgoKIjIzk8uXLN9TSrFkzHBwcABg0aBBJSUns2rWLhx9+uPz1f9elSxd++OEH9u/fT79+/cjPz+fQoUN07NiRgoICnJyccHd3B6Bbt26cOnUKAA8Pj/IxFi9ezOLFixk9evRN6xIREZGbq3bBJyUlhS5durB27Vr69+9PXFwcgwcPJiYmBnd3d+rWrQuAyWT63bEqO8bT05ORI0eybt06li5dWt5JMplMWK1WAGxs/u/yde3alczMTD7++GP8/f0rHNfGxgYfHx/i4uJ45JFH6NKlCwsXLqRfv364ublhNpvJyckB4MCBA7Rs2fI3c127do3PPvuMxYsXs3btWrZs2cI///lPTCYTZWVlv7tmERERI6t2wcfHx4elS5cSGBhIQkICw4YNo2/fvuzevbvSwHGrxo0bx44dOwgKCmLMmDG0bt0agE6dOvHqq69y6dKlG84ZOHAgeXl55cdW5IknniA9PZ127drxyCOP8NNPP9GtWzdMJhNz5sxh4sSJDBkyhL179/LKK6/85txfO1fPPfccI0aMoGfPnuX3Oa1fv559+/bdsWsgIiJyvzFZf21fVGNXr15l2LBhbNq06TddmLtt5cqVuLm53dEAdifk5hZWdQlyB+kBhiIilWvQwLnC96rdzc3/7ttvv+X1119nypQpVRp6pk+fTkFBAcuWLQMgMTGRbdu23XBcaGgonTp1utvliYiICPdJx0cqp46PiIgYSWUdn2p3j4+IiIjI7VLwEREREcNQ8BERERHDUPARERERw1DwEREREcNQ8BERERHDUPARERERw1DwEREREcNQ8BERERHDUPARERERw6j2e3WJGE113aRUG5KKyL1AHR8RERExDAWfW7Bz504uXLhQ1WWIiIjIbVLwuQUffvghZrO5qssQERGR21St7vE5c+YM4eHh2NnZYWtrS5MmTfDx8WHo0KFcunSJUaNGERYWRmxsLPb29pw/f54hQ4awb98+Tpw4wfDhwwkMDGTgwIF07dqV1NRUPDw8qFevHocOHcLBwYHY2FiKi4uZOXMmBQUFAERERHDu3Dl+/PFHwsLCWLhwIZMmTcLV1ZXu3buTnJzM559/jq2tLQsXLsTHx4ennnrqhvqzsrIICQmhcePGZGVl8fTTT3Pq1Cl++OEHHnvsMUJDQzl58iRz5swBwNXVlejoaGrVqsVrr73G+fPnKSgooFevXkyZMoXp06fj4ODAP//5T3Jycpg/fz7e3t539TMRERGpTqpV8NmzZw/e3t5Mnz6dQ4cO4ebmRmRkJEOHDmXbtm0MHDgQgPPnz5OcnMzx48eZPHly+VdUwcHBBAYGUlRUxDPPPEOXLl3o378/4eHhhISEMGzYMNLS0ti2bRs9evQgMDCQjIwMwsPD2bBhA+3btycqKgp7e3tyc3PZvHkzDg4OZGZm8s033/DII4+wa9cuJk+eXOEaMjMzWbVqFcXFxfTp04ddu3ZRs2ZNHn/8cUJDQ4mMjCQ6OppWrVqxadMm4uLiGDRoEB07dmTQoEFYLJby4APQpEkT3njjDTZu3EhiYiJvvPHGXfksREREqqNqFXz8/f1ZuXIlY8aMwdnZmZCQEGrXrk1aWhpbt25lxYoVnDp1itatW2Nvb4+zszMtWrTAwcGBOnXqYLFYysf6tTPi4uKCl5dX+d8Wi4XU1FT27dvHjh07ALh8+fINtTRr1gwHBwcABg0axLp16ygrK+Phhx8uf/1mmjdvjrOzMw4ODtSvXx9XV1cATCYTAOnp6cyaNQuAkpISPDw8cHV15dixY+zbtw8nJyeuXbtWPl779u0BaNSoEd9+++3tXVgRERGDqFbBJyUlhS5duhAcHMy2bduIi4tj8ODBxMTE4O7uTt26dYH/CxGVqewYT09Pnn32WQYOHMjFixfZtGlT+TlWqxUAG5v/uz2qa9euREdH8/HHH5d3Ym5nXgAPDw8WLFhAkyZNOHz4MLm5uSQlJeHs7Mwbb7zBTz/9xMaNG8vr+CNrFRERkV9Uq+Dj4+PDtGnTWLZsGTY2NoSHh9O6dWveeOMNFi5ceMfmGTduHDNnzmTjxo2YzWaCg4MB6NSpE6+++iqzZ8++4ZyBAwfy2Wef0bp16/9o7qioKMLCwigtLQVg7ty5eHl5ERoayuHDh6lZsyYPPPAAOTk5/9E8IiIiRmSy/to6qKauXr3KsGHD2LRp02+6MHfbypUrcXNzw9/fv8pqqEhubmFVlyB3kB5gKCJSuQYNnCt8r1p1fP7dt99+y+uvv86UKVOqNPRMnz6dgoICli1bBkBiYiLbtm274bjQ0FA6dep0t8sTERGR/1+17/jI71PHR0REjKSyjo8eYCgiIiKGoeAjIiIihqHgIyIiIoah4CMiIiKGoeAjIiIihqHgIyIiIoah4CMiIiKGoeAjIiIihqHgIyIiIoah4CMiIiKGUa336hIxontxk1JtQCoi1YU6PiIiImIYCj4iIiJiGIYIPtnZ2Xz55ZcABAUFkZ6eXsUV3XkHDx7kxIkTVV2GiIjIPc0QwWffvn18++23VV3Gn2rz5s3k5ORUdRkiIiL3tHvi5uYzZ84QHh6OnZ0dtra2vPDCC3zyySfY29tz/vx5hgwZwr59+zhx4gTDhw8nMDCQ3bt3s3TpUhwdHXF1dSU6OhoXFxfmz5/P4cOHAXjmmWcYNmwYsbGxFBcX06lTJwCWL19OXl4eV69eZfHixWRnZ7Ny5Urs7e3JyspiwIABjB8/nnPnzhEZGYnFYsHR0ZHZs2dTt25dJk+ejNlspri4mGnTptG9e3emT5/O2bNnsVgsjB49mgEDBtx0ra+88grjx4/nv/7rv3jyySeZOnUqTzzxBC+99BLz5s1j//79rF27FgcHB1q2bMkbb7zB1q1b2bx5M2VlZUyaNIlPPvnkN3O1aNGCr7/+muPHj9OqVSuaNGly1z47ERGR6uSeCD579uzB29ub6dOnc+jQIdLT0zl//jzJyckcP36cyZMns3PnTi5cuEBwcDABAQFERkayYcMG3N3dWbt2LTExMTz00ENkZWWxceNGrl+/TmBgID169GDs2LGcPn2aPn36sGbNGnr37s1zzz3HsmXL+Oyzz/D19SU7O5u//e1vXLt2jUcffZTx48ezYMECgoKC6N27N3v37uWtt95i3Lhx5OXlsWbNGi5evEhGRgZms5n9+/ezefNmAHbv3l3hWvv168euXbtwdXXF0dGR3bt306NHDywWCw4ODixbtowtW7bg5OREdHQ0iYmJ1KpVCxcXF2JiYjCbzcyYMeM3c/n4+PDoo48yYMAAhR4REZFK3BNfdfn7++Pm5saYMWNYv349tra2tG7dGnt7e5ydnWnRogUODg7UqVMHi8VCQUEBTk5OuLu7A9CtWzdOnTpFeno6Xbt2xWQyYW9vT4cOHW56P4+Pjw8A9evXp7i4GIA2bdpgZ2dHrVq1qFGjBgCpqam8//77BAUFsXz5cvLz82ndujVDhw4lNDSUWbNmUVZWhpOTE5GRkURGRhISEsK1a9cqXOvjjz/Onj17+Prrr3n55Zc5evQou3bt4vHHHyczM5NWrVrh5OT0m3UBeHh4ANzSXCIiIvJb90TwSUlJoUuXLqxdu5b+/fuzcuVKTCZThce7ublhNpvL72k5cOAALVu2xMvLq/xrrpKSEo4cOcIDDzyAjY0NZWVlldZws/k8PT2ZOnUq69atY9asWTz55JOcPHmSoqIiYmNjmT9/PrNnzyYnJ4fjx4+zfPlyYmNjWbhwIdevX7/pPHXq1KFGjRrs2LGDRx99lCZNmrB27Vr69etHs2bNSE9P58qVK+Xr+jXw2Nj88lFVNJfJZMJqtf7OlRYRETG2e+KrLh8fH6ZNm8ayZcuwsbEhKCiIo0ePVni8yWRizpw5TJw4EZPJRJ06dZg3bx5169blwIEDvPjii5SUlNC/f3+8vb0xmUzExMTg7e19S3WFhYURFRWFxWKhuLiYmTNn0rJlS5YvX05ycjL29vZMmjSJBg0akJuby1//+ldq1arFSy+9hJ1dxZe2T58+JCUl4erqyiOPPMJHH31EixYtAJg4cSLDhw/HxsaGFi1aMHXqVLZv315+bkVzdejQgbfeeotmzZrh5eV1S+sUERExCpNVbYL7Xm5uYVWXICIictc0aOBc4Xv3RMfnfvTuu++yf//+G16Pjo6mefPmVVCRiIiIqONjAOr4iIiIkVTW8bknbm4WERERuRsUfERERMQwFHxERETEMBR8RERExDAUfERERMQwFHxERETEMBR8RERExDAUfERERMQw9ORmkWqm26Jd5X8f/J9eVViJiEj1o46PiIiIGIaCj4iIiBjG7waf0tJSRo8eTUBAAJcuXbrlCX7++We2bt1a6TE9e/b8w+MNHjyYrKwskpKSSElJueV6bkdsbCxHjx69K3PdrsTEREpKSqq6DBERkXva7waf3NxcCgoK2LBhA3Xq1LnlCU6ePMmXX355W8VVxs/Pjz59+tzxcW9m7Nix+Pr63pW5btf7779PWVlZVZchIiJyT/vdm5sjIyPJyMjgtddeIysriytXrjB37lySk5P5/vvvKSoqwsvLi3nz5nHx4kWmT59OYWEhVquVBQsW8N5773HixAkSExPp1KkT8+fPp6ysjMuXLxMREUHnzp1/t8glS5bw9ddf06hRIwoKCgBYtmwZ9evXx9PTk9jYWOzt7Tl//jxDhgxh3759nDhxguHDhxMYGMiBAwdYsmQJtra2NG/enDfeeIOtW7fy1VdfUVxczNmzZ3n55Zfx8/Nj/fr1JCcnY2NjQ+fOnQkLC2P69OkMGDCAv/zlL8yYMYPMzExKS0sZNWoUAwYMICgoiHbt2nHq1CnMZjNvv/02TZs2vWEdpaWlPPXUU3z66afk5+fTu3dv9uzZQ+3atXnxxRfZsmUL8+fP5/DhwwA888wzjBgxgunTp/Pzzz/z888/s2LFCqZMmYLVaqWkpIRZs2Zx9OhRcnNzCQkJYcWKFbf6b0BERMQwfjf4vP7664SGhtKgQQMcHByIiIjAbDbj4uLC6tWrKSsr4+mnn+bChQusXLmS//7v/yYgIIC9e/dy9OhRxo0bR0JCAi+++CKffvopYWFhtG3blq1bt5KUlPS7wSc1NZWDBw/y8ccfc+XKFfr163fDMefPnyc5OZnjx48zefJkdu7cyYULFwgODiYgIIDIyEg++ugj6tWrx9KlS9myZQt2dnaYzWY++OADMjIyGDduHH5+fiQlJREZGUnHjh356KOPuH79evk8iYmJuLm5sXDhQsxmM35+fvTo0QMAX19fZs6cyZIlS9i+fTtjx469oU5bW1u6dOnCd999x08//UTr1q3Zu3cvtWvXpmfPnvzv//4vWVlZbNy4kevXrxMYGFg+fo8ePRg5ciT/+Mc/cHZ2ZtGiRaSlpWE2mxk0aBAxMTEsWbLk9z5OERERQ7uln7N7eHgA4OjoSH5+PqGhodSqVYsrV65QUlLCmTNn8Pf3B+Avf/kLAPv37y8/v2HDhqxYsYIaNWpQVFSEk5PT786ZlpaGj48PNjY2ODk50aZNmxuOad26Nfb29jg7O9OiRQscHByoU6cOFouF/Px8cnJymDJlCgDFxcX07NmTFi1a0K5dOwAaN27MtWvXAJg3bx6rVq3irbfeomPHjlit1vJ50tPTefjhhwFwcnLCy8uLzMxMAB588EEAGjVqRF5eXoXr6devH1999RVZWVmEhISQkpKCjY0N/v7+HDhwgK5du2IymbC3t6dDhw6kp6f/5tr36tWLjIwMXnnlFezs7Bg/fvzvXkMRERH5xS39qsvG5pfDd+3axblz51i8eDGhoaEUFxdjtVrx8vLi2LFjABw8eJCFCxdiY2NTfu/J3LlzmTRpEgsWLKBNmza/CRUV8fDw4OjRo5SVlXHlyhXS0tJuOMZkMlV4vpubG40aNWLFihWsW7eOcePG0b179wrP27hxI7NmzSI+Pp4ff/yRI0eOlL/n5eXFoUOHADCbzaSmptKsWbPfXcO/6tmzJwcPHqSgoIDevXtz/PhxTpw4ga+vL15eXuVfc5WUlHDkyBEeeOCB39S6f/9+GjZsyKpVqxg/fjyLFy8uf1/3+IiIiFTuth5g6Ovry4oVKxg8eDAODg40b96cnJwcxo0bx4wZM/jb3/4GQHR0NA4ODqSmprJmzRqeffZZXnnlFerVq/eb+3Uq0759e/r374+/vz8NGzakXr16t1SrjY0NM2fOZOzYsVitVmrXrs2bb77JuXPnbnp827Zt8ff3x83NDXd3dzp06EBSUhLwyy/KIiMjCQgIwGKxEBwcfMv1ODg40KhRI5o0aYKNjQ0eHh7UrVsXgMcff5wDBw7w4osvUlJSQv/+/fH29v7N+e3atSMkJIS1a9diY2PDhAkTAOjatStjx47lww8/rDQIioiIGJnJ+kfaLlKt5eYWVnUJcgfpyc0iIpVr0MC5wvfumeCTmJjItm3bbng9NDSUTp06VUFF/5ng4OAbnnvk5ORETEzMXa9FwUdERIykWgQf+fMo+IiIiJFUFny0ZYWIiIgYhoKPiIiIGIaCj4iIiBiGgo+IiIgYhoKPiIiIGIaCj4iIiBiGgo+IiIgYhoKPiIiIGIaCj4iIiBjGbW1SKiJ3xr/uu/VHaX8uEZHbp46PiIiIGIaCj4iIiBjGnxZ8SktLGT16NAEBATfsUv5H/Pzzz2zdurXSY3r27PmHxxs8eDBZWVkkJSWRkpJyy/XcjtjYWI4ePXpX5hIREZHf96fd45Obm0tBQQFJSUm3df7Jkyf58ssvGThw4B2ty8/P746OV5mxY8fetblERETk9/1pwScyMpKMjAxee+01srKyuHLlCnPnziU5OZnvv/+eoqIivLy8mDdvHhcvXmT69OkUFhZitVr5/9q796iq6vz/488DnIMhaEgIVjIJXjCNAJvyVrYqG8fUKURuipGaQ0ZqzNfAC2k2YZqlRV7CS6QrCk1jkrTGbNLGvGdpLu2Eo4GJgoLoQTmI7N8frfjlIDgaSHhej79gX9/vfVzb1/qcD3vPnDmThQsXcuDAAbKysggJCeHll1+mqqqK06dPM2XKFEJDQy9bw5w5c/jyyy/x9fWlpKQEgLS0NG666Sb8/f1JT0/HbDZz7NgxoqKi2Lp1KwcOHGD48OHExMSwfft25syZg7OzM23btmX69OmsWbOGjRs3Ul5eTl5eHk8++SRhYWG8++67ZGdn4+TkRGhoKElJSSQnJ9O/f3969OjBpEmTyM/P58KFCzzxxBP079+f2NhYAgMD+eGHH7DZbLz++uvccsstl+xl165dzJw5ExcXF1q0aMHs2bNxdXVl6tSp/Pjjj1RVVTF+/Hjuueeeev0cRUREricNFnymTp1KYmIi3t7eWCwWpkyZgs1mo0WLFrz99ttUVVXxyCOPcPz4cRYtWsQDDzxAdHQ0W7ZsYc+ePcTHx/P+++8TGRnJ2rVrSUpKolOnTqxZs4bVq1dfNvhYrVZ27NjBBx98wNmzZ3n44YdrbHPs2DGys7PZt28f48aNY/369Rw/fpyEhASio6NJSUkhMzMTLy8v5s6dy4cffoiLiws2m40lS5Zw+PBh4uPjCQsLY/Xq1aSkpBAcHExmZiaVlZXV58nKysLT05NXXnkFm81GWFgY3bt3ByAoKIjJkyczZ84cPv7441pHiT777DP69u3LyJEj+fzzzzl9+jRffPEFnp6epKamUlJSwrBhw/j4449/w6cmIiJyfbsmf87erl07AFxdXSkuLiYxMRE3NzfOnj3L+fPnOXToEOHh4QD06NEDgG3btlXv37p1a+bPn0+zZs0oKyvD3d39sufMzc2la9euODk54e7uTseOHWts06FDB8xmMx4eHvj5+WGxWGjZsiV2u53i4mIKCwsZP348AOXl5fTq1Qs/Pz8CAwMBaNOmDRUVFQDMmDGDpUuXMnv2bIKDgzEMo/o8Bw8epGfPngC4u7sTEBBAfn4+ALfffjsAvr6+nDhxotZ+4uPjWbhwIY8//jg+Pj4EBQVhtVrZtWtX9TyiyspKSkpK8PT0vOz1ERERcUTXJPg4Of08h3rTpk0UFBQwd+5ciouLWb9+PYZhEBAQwN69ewkMDGTHjh188cUX3H///VRVVQHw0ksvMXv2bAICAnjjjTf46aefLnvOdu3asWzZMqqqqigvLyc3N7fGNiaTqdb9PT098fX1Zf78+Xh4eLBhwwbc3NwoKCi45H4rVqzghRdewNXVlZEjR7J79+7qdQEBAezcuZO+fftis9mwWq3ceuutl+3h19asWcNjjz1GUlISb731FitWrMDf3x9fX1/i4+MpLy9nwYIFtGzZ8oqOKyIi4kiu6QMMg4KCmD9/PhEREVgsFtq2bUthYSHx8fFMmjSJjz76CIDU1FQsFgtWq5WMjAwGDRrEmDFj8PLyumi+Tl06d+5Mv379CA8Pp3Xr1nh5eV1RrU5OTkyePJnRo0djGAbNmzdn1qxZFBQUXHL7Tp06ER4ejqenJz4+Ptx5553VE7sjIiJISUkhOjoau91OQkLCFddzxx13kJycjJubG2azmenTp+Pj48OUKVMYNmwYNpuNmJiY6pApIiIiNZmMX38nI9eloqIzjV2C1EJPbhYRqX/e3h61rmvywScrK4ucnJwayxMTEwkJCWmEin6bhISEGs89cnd3Z8GCBVd9TAUfERFxJNd18JHLU/ARERFHUlfw0YQQERERcRgKPiIiIuIwFHxERETEYSj4iIiIiMNQ8BERERGHoeAjIiIiDkPBR0RERByGgo+IiIg4DAUfERERcRjX9CWlIteTq3nPVn3Qu7pERK6eRnxERETEYSj41MJut7Ny5cor2mf9+vUcP368gSoSERGR30rBpxZFRUVXHHyWLVuGzWZroIpERETkt1LwqcXChQvJzc3lzTffZOzYscTGxhIbG8v3339PQUEBDz/8MIWFhWzZsoWYmBg+++wz9u/fT1JSEocOHSIiIqL6WBERERw5coS0tDRGjBhBVFQUBw8eZPny5URGRhIVFcWyZcvqrCc5OZmYmBgGDx7M2rVrAdi+fTvR0dEMGzaMiRMncv78+Qa9JiIiIk2dJjfXIj4+HqvVyrlz5+jevTsxMTEcPnyYiRMn8t577zFhwgSSk5M5ceIE6enp+Pr60rlzZ6ZNm4bZbK71uP7+/kyZMoXc3FzWrl1LZmYmJpOJuLg4evfujb+/f419bDYb27ZtY9WqVQBs3rwZwzBISUkhMzMTLy8v5s6dy4cffnhR4BIREZGLKfhchtVqZevWraxbtw6A06dPA/DQQw8xZ84cevbsia+vb53HMAyj+ud27dpVH/fo0aPExcUBUFpaSl5e3iWDj7u7OykpKaSkpGCz2Rg0aBDFxcUUFhYyfvx4AMrLy+nVq9dv7ldEROR6puBTCycnJ6qqqvD392fQoEEMHDiQkydPVs/7Wbp0Kb169WLv3r188803BAcHYzKZMAwDV1dXTp48yYULFygrK+PIkSMXHRd+Hvlp3749ixcvxmQykZGRQceOHS9ZS2FhIfv27WPevHnY7Xb69OnDwIED8fX1Zf78+Xh4eLBhwwbc3Nwa/sKIiIg0YQo+tfDy8uL8+fOUlZWxbt06VqxYgc1mIyEhgb1795KTk0NWVhb5+fk888wzZGVlERISwnPPPVcdisLDw/Hz8+MPf/hDjeMHBgbSo0cPoqOjqaioICgoCB8fn0vW4u3tTVFREY8++ihubm6MGDECi8XC5MmTGT16NIZh0Lx5c2bNmtXQl0VERKRJMxm//h5GrktFRWcau4Trkh5gKCLy++Tt7VHrOo34/I5s2LCBjIyMGsuHDx9O3759r31BIiIi1xmN+DgAjfiIiIgjqWvER8/xEREREYeh4CMiIiIOQ8FHREREHIaCj4iIiDgMBR8RERFxGAo+IiIi4jAUfERERMRhKPiIiIiIw1DwEREREYeh4CMiIiIOQ+/qErlCjfVy0l/oJaUiIldPIz4iIiLiMBR8rpH169dz/Pjxxi5DRETEoSn4XCPLli3DZrM1dhkiIiIOzWHm+Bw6dIiJE/+ljT4AABS6SURBVCfi4uKCs7MzN998M127dmXo0KGUlpbyxBNPkJSURHp6OmazmWPHjhEVFcXWrVs5cOAAw4cPJyYmhoEDB3LXXXdhtVpp164dXl5e7Ny5E4vFQnp6OuXl5UyePJmSkhIApkyZQkFBAfv37ycpKYlXXnmFsWPHcuONN3LPPfeQnZ3Np59+irOzM6+88gpdu3blz3/+c4367XY748aNw2azUV5ezoQJE7jnnntYt24dGRkZODk50a1bN/7v//7vWl9aERGRJsNhgs9XX31Fly5dSE5OZufOnXh6epKSksLQoUPJyclh4MCBABw7dozs7Gz27dvHuHHjqr+iSkhIICYmhrKyMgYMGEC3bt3o168fEydO5Nlnn2XYsGHk5uaSk5ND9+7diYmJ4fDhw0ycOJH33nuPzp07M23aNMxmM0VFRaxatQqLxUJ+fj7//ve/6d27N5s2bWLcuHGXrD8vL48TJ06QkZHByZMnOXz4MKdOnSItLY1Vq1Zxww03MGHCBDZv3kyvXr2u5aUVERFpMhwm+ISHh7No0SJGjRqFh4cHzz77LM2bNyc3N5c1a9Ywf/58fvjhBzp06IDZbMbDwwM/Pz8sFgstW7bEbrdXH6tLly4AtGjRgoCAgOqf7XY7VquVrVu3sm7dOgBOnz5do5Zbb70Vi8UCwJAhQ1i+fDlVVVX07Nmzevl/69ChA0OHDiUxMZHKykpiY2PJy8ujuLiY0aNHA1BWVkZ+fn79XTQREZHrjMMEnw0bNtCtWzcSEhLIyclh8eLFREREsGDBAnx8fGjVqhUAJpPpsseqaxt/f38GDRrEwIEDOXnyJCtXrqzexzAMAJyc/v/UqrvuuovU1FQ++OADxo8fX+txv//+e8rKykhPT6ewsJCoqCg++OAD2rRpw9KlSzGbzaxevZrOnTv/T9dDRETEETnM5OauXbsyd+5cYmJieP/99xk2bBgPPfQQmzdvJjw8vN7OEx8fz7p164iNjWXUqFF06NABgJCQEJ577jlKS0tr7DNw4EBOnDhRve2l3HbbbWzfvp3w8HDGjRvH2LFjadWqFXFxccTGxjJkyBA2bdrEbbfdVm+9iIiIXG9Mxi/DEA7o3LlzDBs2jJUrV140CnOtLVq0CE9Pz3oNYL9WVHSmQY7rqPQAQxGR3zdvb49a1znMV13/7euvv2bq1KmMHz++UUNPcnIyJSUlpKWlAZCVlUVOTk6N7RITEwkJCbnW5cklKHiIiDRdDj3i4yg04iMiIo6krhEfh5njIyIiIqLgIyIiIg5DwUdEREQchoKPiIiIOAwFHxEREXEYCj4iIiLiMBR8RERExGEo+IiIiIjDUPARERERh+Gwr6yQpqOx3431e6NXZoiIXD2N+IiIiIjDUPARERERh6HgU4ejR4/y+eefAxAbG8vBgwcbuSIRERH5LRR86rB161a+/vrrxi5DRERE6kmTntx86NAhJk6ciIuLC87OzgwePJh//OMfmM1mjh07RlRUFFu3buXAgQMMHz6cmJgYNm/ezNy5c3F1deXGG28kNTWVFi1a8PLLL7Nr1y4ABgwYwLBhw0hPT6e8vJyQkBAA5s2bx4kTJzh37hyvvfYaR48eZdGiRZjNZo4cOUL//v156qmnKCgoICUlBbvdjqurKy+++CKtWrVi3Lhx2Gw2ysvLmTBhAvfccw/Jycnk5eVht9sZOXIk/fv3v2SvxcXFjB8/HsMwOH/+PC+88AKdOnVi+fLl5OTkYDKZ6N+/P8OHD79m119ERKSpadLB56uvvqJLly4kJyezc+dODh48yLFjx8jOzmbfvn2MGzeO9evXc/z4cRISEoiOjiYlJYX33nsPHx8f3nnnHRYsWMDdd9/NkSNHWLFiBZWVlcTExNC9e3dGjx7Nf/7zHx588EEyMjLo06cPf/nLX0hLS+OTTz4hKCiIo0eP8tFHH1FRUcG9997LU089xcyZM4mNjaVPnz5s2bKF2bNnEx8fz4kTJ8jIyODkyZMcPnwYm83Gtm3bWLVqFQCbN2+utdc9e/bg4eHBq6++Sm5uLjabjdzcXNauXUtmZiYmk4m4uDh69+6Nv7//tfoIREREmpQmHXzCw8NZtGgRo0aNwsPDg169etGhQwfMZjMeHh74+flhsVho2bIldrudkpIS3N3d8fHxAeCPf/wjr732Gl5eXtx1112YTCbMZjN33nnnJefzdO3aFYCbbrqJEydOANCxY0dcXFxwcXGhWbNmAFitVt566y0WL16MYRiYzWY6dOjA0KFDSUxMpLKyktjYWNzd3UlJSSElJQWbzcagQYNq7fW+++7j8OHDjBkzBhcXF5566imsVitHjx4lLi4OgNLSUvLy8hR8REREatGkg8+GDRvo1q0bCQkJ5OTk8Nprr3HnnXfWur2npyc2m43CwkJat27N9u3bue222wgICGD16tXExcVx/vx5du/ezWOPPcYPP/xAVVVVnTWYTKYay/z9/RkxYgShoaEcPHiQHTt28P3331NWVkZ6ejqFhYVERUXRpUsX9u3bx7x587Db7dUjSi4uNT+Wbdu20bp1a5YuXcru3bt57bXXmDx5Mu3bt2fx4sWYTCYyMjLo2LHjlV9IERERB9Gkg0/Xrl2ZMGECaWlpODk5ERsby549e2rd3mQy8fe//51nnnkGk8lEy5YtmTFjBq1atWL79u1ERkZy/vx5+vXrR5cuXTCZTCxYsIAuXbpcUV1JSUlMmzYNu91OeXk5kydP5rbbbmPevHlkZ2djNpsZO3Ys3t7eFBUV8eijj+Lm5saIESMuGXoAAgMDefbZZ3nnnXdwcnLi6aefJjAwkB49ehAdHU1FRQVBQUHVo1kiIiJSk8kwDKOxi5CGVVR0prFL+E305OaL6cnNIiJ18/b2qHWdgs/vzJtvvsm2bdtqLE9NTaVt27ZXdcymHnxERESuhIKPg1PwERERR1JX8NEDDEVERMRhKPiIiIiIw1DwEREREYeh4CMiIiIOQ8FHREREHIaCj4iIiDgMBR8RERFxGAo+IiIi4jAUfERERMRhNOmXlErj0LuzGpfe1SUicvU04iMiIiIOQ8FHREREHEadwefChQuMHDmS6OhoSktLr/jgp06dYs2aNXVu06tXr//5eBERERw5coTVq1ezYcOGK67naqSnp7Nnz55rci4RERFpWHXO8SkqKqKkpITVq1df1cG///57Pv/8cwYOHHhV+9cmLCysXo9Xl9GjR1+zc4mIiEjDqjP4pKSkcPjwYZ5//nmOHDnC2bNneemll8jOzua7776jrKyMgIAAZsyYwcmTJ0lOTubMmTMYhsHMmTNZuHAhBw4cICsri5CQEF5++WWqqqo4ffo0U6ZMITQ09LIFzpkzhy+//BJfX19KSkoASEtL46abbsLf35/09HTMZjPHjh0jKiqKrVu3cuDAAYYPH05MTAzbt29nzpw5ODs707ZtW6ZPn86aNWvYuHEj5eXl5OXl8eSTTxIWFsa7775LdnY2Tk5OhIaGkpSURHJyMv3796dHjx5MmjSJ/Px8Lly4wBNPPEH//v2JjY0lMDCQH374AZvNxuuvv84tt9xyyV7S0tL48ccfKSkpobS0lJiYGP75z39y6NAhZs6cSXBwMMuXLycnJweTyUT//v0ZPnw4Vqv1ktfu4YcfJjQ0lEOHDuHl5UVaWhrOzs5X8c9ARETEMdQZfKZOnUpiYiLe3t5YLBamTJmCzWajRYsWvP3221RVVfHII49w/PhxFi1axAMPPEB0dDRbtmxhz549xMfH8/777xMZGcnatWtJSkqiU6dOrFmzhtWrV182+FitVnbs2MEHH3zA2bNnefjhh2tsc+zYMbKzs9m3bx/jxo1j/fr1HD9+nISEBKKjo0lJSSEzMxMvLy/mzp3Lhx9+iIuLCzabjSVLlnD48GHi4+MJCwtj9erVpKSkEBwcTGZmJpWVldXnycrKwtPTk1deeQWbzUZYWBjdu3cHICgoiMmTJzNnzhw+/vjjOkeJmjVrxpIlS0hPT2fjxo0sXLiQVatW8fHHH+Pu7s7atWvJzMzEZDIRFxdH7969yc3NveS1y8/P55133qFNmzZERUWxd+9egoOD67ymIiIijux//nP2du3aAeDq6kpxcTGJiYm4ublx9uxZzp8/z6FDhwgPDwegR48eAGzbtq16/9atWzN//nyaNWtGWVkZ7u7ulz1nbm4uXbt2xcnJCXd3dzp27Fhjmw4dOmA2m/Hw8MDPzw+LxULLli2x2+0UFxdTWFjI+PHjASgvL6dXr174+fkRGBgIQJs2baioqABgxowZLF26lNmzZxMcHIxhGNXnOXjwID179gTA3d2dgIAA8vPzAbj99tsB8PX15cSJE3X29Mu2Hh4etG/fHqC6XqvVytGjR4mLiwOgtLSUvLy8Wq+dp6cnbdq0qe7Dbrdf9pqKiIg4sv85+Dg5/TwPetOmTRQUFDB37lyKi4tZv349hmEQEBDA3r17CQwMZMeOHXzxxRfcf//9VFVVAfDSSy8xe/ZsAgICeOONN/jpp58ue8527dqxbNkyqqqqKC8vJzc3t8Y2JpOp1v09PT3x9fVl/vz5eHh4sGHDBtzc3CgoKLjkfitWrOCFF17A1dWVkSNHsnv37up1AQEB7Ny5k759+2Kz2bBardx6662X7eFK6vX396d9+/YsXrwYk8lERkYGHTt25Omnn77ktavrWCIiIlLTFT/AMCgoiPnz5xMREYHFYqFt27YUFhYSHx/PpEmT+OijjwBITU3FYrFgtVrJyMhg0KBBjBkzBi8vr4vm69Slc+fO9OvXj/DwcFq3bo2Xl9cV1erk5MTkyZMZPXo0hmHQvHlzZs2aRUFBwSW379SpE+Hh4Xh6euLj48Odd95ZPbE7IiKClJQUoqOjsdvtJCQkXHE9lxMYGEiPHj2Ijo6moqKCoKAgfHx8ruraiYiISE0m49ff58h1qajoTL0eT09ublx6crOISN28vT1qXfe7CD5ZWVnk5OTUWJ6YmEhISEgjVPTbJCQk1Hjukbu7OwsWLGiUeuo7+IiIiPye/e6DjzQsBR8REXEkdQUfvbJCREREHIaCj4iIiDgMBR8RERFxGJrjIyIiIg5DIz4iIiLiMBR8RERExGEo+IiIiIjDUPCRJqO8vJxnnnmGmJgYnnzySYqLi2tsM3PmTCIjIxk8eDArVqxohCrrT1VVFc8//zyRkZHExsby448/XrR+xYoVhIWFERERwb/+9a9GqrL+Xa7vjIwMhgwZwpAhQ3jzzTcbqcr6d7m+f9lm1KhRvPfee41QYf27XM8bN24kIiKCiIgIpk2bxvUyJfVyfS9ZsoSwsDAGDx7M+vXrG6nKhvPtt98SGxtbY/nnn3/O4MGDiYyMbNj7tyHSRCxdutR44403DMMwjJycHOPFF1+8aP2WLVuMMWPGGIZhGHa73XjooYeMU6dOXfM668unn35qJCUlGYZhGLt37zbi4+Or1xUWFhoDBgww7Ha7cfr06eqfrwd19Z2Xl2c89thjRmVlpXHhwgUjMjLS2L9/f2OVWq/q6vsXr776qhEeHm5kZmZe6/IaRF09nzlzxnjkkUeMkydPGoZhGOnp6dU/N3V19V1aWmr06dPHsNvtxqlTp4z777+/scpsEOnp6caAAQOMIUOGXLS8oqKi+p5tt9uNsLAwo7CwsEFq0IiPNBm7du3i3nvvBeC+++5jy5YtF60PCQkhNTW1+vcLFy7g4nLF7+H93fh1v8HBwXz33XfV6/bs2UNISAgWiwUPDw/8/Pw4cOBAY5Var+rq29fXl8WLF+Ps7IyTkxOVlZW4uro2Vqn1qq6+AT755BNMJhP33Xf9vKutrp53795Nx44dmTlzJjExMdx00020atWqsUqtV3X1fcMNN3DzzTdz7tw5zp07h8lkaqwyG4Sfnx9paWk1lh88eBA/Pz9atmyJxWKhW7du7Ny5s0FqaLr/K8h1beXKlbzzzjsXLfPy8sLD4+fHkDdv3pwzZy5+FYerqyuurq6cP3+e5ORkIiMjad68+TWrub7ZbDbc3d2rf3d2dqayshIXFxdsNlv1tYCfr4fNZmuMMutdXX2bzWZatWqFYRjMmjWL22+/nXbt2jVitfWnrr6tVis5OTm88cYbzJs3rxGrrF919VxSUsK2bdvIzs7Gzc2NoUOHEhwcfF183nX1DdCmTRseeeQRLly4wF//+tfGKrNB/OlPf+LIkSM1ll/Le5qCj/wu/TKH49cSEhIoKysDoKysjBYtWtTYr7S0lLFjx3L33Xc3+RuGu7t7db/w87yAX26M/72urKzsoptGU1ZX3wB2u51JkybRvHlzpk6d2hglNoi6+s7Ozub48eM8/vjj/PTTT5jNZm655ZYmP/pTV8833ngjd9xxB97e3gDcdddd7N+//7oIPnX1vWnTJgoLC9mwYQMAI0eOJDQ0lKCgoEap9Vq5lvc0fdUlTUZoaCgbN24Efr45dOvW7aL15eXlxMXFMXjwYJ5++unGKLFehYaGsmnTJgC++eYbOnbsWL0uKCiIXbt2YbfbOXPmDAcPHrxofVNWV9+GYTBmzBg6derE9OnTcXZ2bqwy611dfT/33HOsXLmS5cuX89hjjxEXF9fkQw/U3XPXrl2xWq0UFxdTWVnJt99+S/v27Rur1HpVV98tW7akWbNmWCwWXF1d8fDw4PTp041V6jUTEBDAjz/+yKlTp6ioqGDnzp2EhIQ0yLk04iNNRnR0NElJSURHR2M2m3n11VcBmDVrFv369ePrr78mPz+flStXsnLlSgBSU1Np27ZtY5Z91fr27cvmzZuJiorCMAxSU1N5++238fPz48EHHyQ2NpaYmBgMw+DZZ5+9bua61NV3VVUV27dvp6Kigi+//BKAxMTEBrtBXkuX+7yvR5fr+W9/+xujRo0CoF+/ftdNuL9c31999RURERE4OTkRGhpKr169GrvkBrNmzRrOnj1LZGQkycnJjBw5EsMwGDx4MD4+Pg1yTr2yQkRERByGvuoSERERh6HgIyIiIg5DwUdEREQchoKPiIiIOAwFHxEREXEYCj4iIiLiMBR8RERExGEo+IiIiIjD+H9mWGGQKf7ccgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 12))\n",
    "X.corr()['radius_mean'].sort_values().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b8909c4860>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heatmap for top 10 Sales Price-features correlation\n",
    "k = 10\n",
    "cols = X.corr().nlargest(k, 'perimeter_mean')['perimeter_mean'].index\n",
    "k_corr_matrix = X[cols].corr()\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(k_corr_matrix, annot=True) #cmap=plt.cm.RdBu_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['concavity_mean'],df['radius_mean'],color='green')\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['concavity_mean'],df['concave points_mean'],color='red')\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['Benign','Malignant']\n",
    "sizes = [\n",
    "         df['diagnosis'].value_counts(),\n",
    "     \n",
    "        ]\n",
    "# print(sizes) # adds up to 1433, which is the total number of participants\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%', shadow=True)\n",
    "ax1.axis('equal')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelencoder_Y = LabelEncoder()\n",
    "Y = labelencoder_Y.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1,\n",
       "       1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1,\n",
       "       1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y #M-1 , B-0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into the Training set and Test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.35, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Models\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(classifier):\n",
    "    classifier.fit(X_train,Y_train)\n",
    "    y_pred=classifier.predict(X_test)\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_score(Result):\n",
    "    score = metrics.accuracy_score(Y_test, Result)\n",
    "    #confusion_matrix = metrics.confusion_matrix(Y_test,Result)\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_1 = LogisticRegression(random_state = 0)\n",
    "Result_1=predict(classifier_1)\n",
    "Accuracy_1 = (model_score(Result_1))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_2 = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
    "Result_2=predict(classifier_2)\n",
    "Accuracy_2 = (model_score(Result_2))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_3 = SVC(kernel = 'linear', random_state = 0)\n",
    "Result_3=predict(classifier_3)\n",
    "Accuracy_3 = (model_score(Result_3))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_4 = SVC(kernel = 'rbf', random_state = 0)\n",
    "Result_4=predict(classifier_4)\n",
    "Accuracy_4 = (model_score(Result_4))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_5=GaussianNB()\n",
    "Result_5=predict(classifier_5)\n",
    "Accuracy_5 = (model_score(Result_5))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_6=DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
    "Result_6=predict(classifier_6)\n",
    "Accuracy_6 = (model_score(Result_6))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_7 = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
    "Result_7=predict(classifier_7)\n",
    "Accuracy_7 = (model_score(Result_7))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = pd.DataFrame({'Model Name':[\"Logistic Regression\",\"KNN\",\"SVC Linear\",\"SVC RBF\",\"Naive Bayes\",\"Decision Tree\",\"Random Forest\"],\\\n",
    "                        'Accuracy':[Accuracy_1,Accuracy_2,Accuracy_3,Accuracy_4,Accuracy_5,Accuracy_6,Accuracy_7]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>97.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>95.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVC Linear</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC RBF</td>\n",
       "      <td>97.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>90.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Model Name  Accuracy\n",
       "0  Logistic Regression      97.5\n",
       "1                  KNN      95.5\n",
       "2           SVC Linear      96.0\n",
       "3              SVC RBF      97.5\n",
       "4          Naive Bayes      90.5\n",
       "5        Decision Tree      94.0\n",
       "6        Random Forest      96.0"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models"
   ]
  }
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