Breast Cancer Logistic Prediction Project.ipynb

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       "  <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",
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       "  <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",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
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      ],
      "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": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "data = pd.read_csv(r\"C:\\Users\\Vai.Mathi\\Downloads\\archive (1)\\Breast Cancer Kaggle Data.csv\")\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "164c128e-4599-48b6-8bbf-15469676808e",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</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>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",
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       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
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       "  </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",
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       "      <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>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.037183e+07</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>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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.250206e+08</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>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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.670000e+03</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>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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.692180e+05</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>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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.060240e+05</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>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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.813129e+06</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>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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.113205e+08</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>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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
       "count  5.690000e+02   569.000000    569.000000      569.000000   569.000000   \n",
       "mean   3.037183e+07    14.127292     19.289649       91.969033   654.889104   \n",
       "std    1.250206e+08     3.524049      4.301036       24.298981   351.914129   \n",
       "min    8.670000e+03     6.981000      9.710000       43.790000   143.500000   \n",
       "25%    8.692180e+05    11.700000     16.170000       75.170000   420.300000   \n",
       "50%    9.060240e+05    13.370000     18.840000       86.240000   551.100000   \n",
       "75%    8.813129e+06    15.780000     21.800000      104.100000   782.700000   \n",
       "max    9.113205e+08    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  ...  texture_worst  perimeter_worst   area_worst  \\\n",
       "count     569.000000  ...     569.000000       569.000000   569.000000   \n",
       "mean        0.181162  ...      25.677223       107.261213   880.583128   \n",
       "std         0.027414  ...       6.146258        33.602542   569.356993   \n",
       "min         0.106000  ...      12.020000        50.410000   185.200000   \n",
       "25%         0.161900  ...      21.080000        84.110000   515.300000   \n",
       "50%         0.179200  ...      25.410000        97.660000   686.500000   \n",
       "75%         0.195700  ...      29.720000       125.400000  1084.000000   \n",
       "max         0.304000  ...      49.540000       251.200000  4254.000000   \n",
       "\n",
       "       smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "count        569.000000         569.000000       569.000000   \n",
       "mean           0.132369           0.254265         0.272188   \n",
       "std            0.022832           0.157336         0.208624   \n",
       "min            0.071170           0.027290         0.000000   \n",
       "25%            0.116600           0.147200         0.114500   \n",
       "50%            0.131300           0.211900         0.226700   \n",
       "75%            0.146000           0.339100         0.382900   \n",
       "max            0.222600           1.058000         1.252000   \n",
       "\n",
       "       concave points_worst  symmetry_worst  fractal_dimension_worst  \\\n",
       "count            569.000000      569.000000               569.000000   \n",
       "mean               0.114606        0.290076                 0.083946   \n",
       "std                0.065732        0.061867                 0.018061   \n",
       "min                0.000000        0.156500                 0.055040   \n",
       "25%                0.064930        0.250400                 0.071460   \n",
       "50%                0.099930        0.282200                 0.080040   \n",
       "75%                0.161400        0.317900                 0.092080   \n",
       "max                0.291000        0.663800                 0.207500   \n",
       "\n",
       "       Unnamed: 32  \n",
       "count          0.0  \n",
       "mean           NaN  \n",
       "std            NaN  \n",
       "min            NaN  \n",
       "25%            NaN  \n",
       "50%            NaN  \n",
       "75%            NaN  \n",
       "max            NaN  \n",
       "\n",
       "[8 rows x 32 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b6e7897-bafe-4cba-9b9e-24c55e0be246",
   "metadata": {},
   "source": [
    "<h2><b>Data Preprocessing</b></h2>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "64704b90-9fa9-475f-beaa-f538d4480a4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dgHj9+jWcnZ2xZcuWGs93794dq1fXvKFGUFAQoqOjERkZieTkZBQVFWHgwIGoqKiQtjqEEEIIaQBSD2H4+PjAx8enxvP+/v4AgEePHkk8X1BQgJ07d+Lnn38WbVu9b98+tGzZEqdOnaKEWoQQQuRTE9tI6qNPokxNTUVZWRk8PT1FZebm5nB0dMT58+c/dnUIIYQQftAQhmzl5uZCRUUFenp6YuUmJibIzc392NUhhBBC5N7WrVthbW0NNTU1uLi44OzZ2vfJ+OWXX+Ds7AwNDQ2YmZlhwoQJyM/Pl+qejWYZZ23puUtKSlBYWCh2yOn+V4QQQv6rGOPnkFJUVBSCgoLw9ddfIy0tDT179oSPjw+ysrIkXp+cnIxx48Zh0qRJuHXrFg4ePIiUlBRMnjxZqvt+9AaEqakpSktL8fLlS7HyvLw8mJiYSHxNaGgodHR0xA4mfPUxqksIIYTUTQMNYWzYsAGTJk3C5MmTYW9vj7CwMLRs2RLh4eESr7948SKsrKwQGBgIa2tr9OjRA1OnTsWVK1ekuu9Hb0C4uLhAWVkZ8fHxorKcnBzcvHkT3bp1k/iaBQsWoKCgQOwQKGh9rCoTQgghH42kXveSkhKJ15aWliI1NVVsXiEAeHp61jivsFu3bnjy5AlOnDgBxhiePXuGQ4cOYcCAAVLVU+oGRFFREa5duyba3yEzMxPXrl0TdZW8ePEC165dw+3btwEA9+7dw7Vr10TzG3R0dDBp0iTMnj0bf/75J9LS0vDZZ5/ByclJtCrjfaqqqtDW1hY7KJ03IYSQRoWnHghJve6hoaESb/n8+XNUVFRU68GvbV5ht27d8Msvv8DPzw8qKiowNTWFrq4uvv/+e6nertQNiCtXrqBjx47o2LEjACAkJAQdO3bEN99UJv+IiYlBx44dRS2ZUaNGoWPHjti2bZsoxsaNGzF06FCMHDkS3bt3h4aGBo4dOwZFRUVpq0MIIYQ0DkzIyyGp133BggW13vr9L9W1zSu8ffs2AgMD8c033yA1NRWxsbHIzMzEtGnTpHq7lI2TEEKI3GmM2TiLtwfzEkfj8411vra0tBQaGho4ePAgfH19ReWzZs3CtWvXkJSUVO01/v7+ePv2LQ4ePCgqS05ORs+ePZGdnQ0zM7M63bvRrMIghBBCiHRUVFTg4uIiNq8QAOLj42ucV1hcXAwFBfHHf9UIgDR9CpRMixBCCOFDA20CFRISAn9/f7i6uqJr167Yvn07srKyREMSCxYswNOnT7F3714AwKBBgzBlyhSEh4fDy8sLOTk5CAoKwieffAJzc/M635caEIQQQggfGmgraz8/P+Tn52P58uXIycmBo6MjTpw4AUtLSwCVKx3f3RNi/PjxePXqFbZs2YLZs2dDV1cXffr0wXfffSfVfWkOBCGEELnTKOdAhM/kJY7GF9KthmgovKfzXrp0Kezs7KCpqQk9PT14eHjg0qVLovMvXrzAzJkzYWtrCw0NDVhYWCAwMBAFBQWc3wwhhBDSYISMn0NO8J7Ou23bttiyZQvS09ORnJwMKysreHp64p9//gEAZGdnIzs7G+vWrUN6ejr27NmD2NhYTJo0ids7IYQQQhpSE0umxWkIQyAQIDo6GkOHDq3xmsLCQujo6ODUqVPo27evxGsOHjyIzz77DK9fv4aSUt2mZdAQBiGENF2Ncgjj+y95iaMxcysvcWRNppMoS0tLsX37dujo6MDZ2bnG6woKCqCtrV3nxgMhhBDS6MhR7wEfZPLEPn78OEaNGoXi4mKYmZkhPj4ehoaGEq/Nz8/HihUrMHXq1BrjlZSUVNsHvLZdtgghhJCPTj7XJNSbTDaScnd3x7Vr13D+/Hl4e3tj5MiRyMvLq3ZdYWEhBgwYgHbt2mHJkiU1xqNsnIQQQkjjIpMGhKamJmxsbNClSxfs3LkTSkpK2Llzp9g1r169gre3N5o1a4bo6GgoKyvXGI+ycRJCCGn0mtgkyo8y6YAxJjYEUVhYCC8vL6iqqiImJgZqamq1vl5VVRWqqqpiZTR8QQghpFGRoyWYfJC6AVFUVIS//vpL9HNVOm99fX0YGBhg1apVGDx4MMzMzJCfn4+tW7fiyZMnGDFiBIDKngdPT08UFxdj3759olznAGBkZEQZOQkhhMinBtqJsqFI3YC4cuUK3N3dRT+HhIQAAAICArBt2zbcvXsXEREReP78OQwMDPC///0PZ8+ehYODAwAgNTVVtLGUjY2NWOzMzExYWVnV970QQggh5CORugHh5uZWa7auw4cPc3o9IYQQIpdoCIMQQggh0mJyNAGSDzJZhUEIIYSQ/zbqgSCEEEL40MSGMHjPxjl+/HgIBAKxo0uXLhJjMcbg4+MjMQ4hhBAiV5iQn0NO8J6NEwC8vb2Rk5MjOk6cOCHxurCwMNrPgRBCCJFDUg9h+Pj4wMfHp9ZrVFVVYWpqWus1169fx4YNG5CSkgIzMzNpq0EIIYQ0LjSEwV1iYiKMjY3Rtm1bTJkypVoejOLiYowePRpbtmz5YEODEEIIkQu0lTU3Pj4+GDFiBCwtLZGZmYnFixejT58+SE1NFW1HHRwcjG7dumHIkCF1iknZOAkhhJDGhfcGhJ+fn+jPjo6OcHV1haWlJX7//XcMGzYMMTExOH36NNLS0uocMzQ0FMuWLRMrEyg0g0BRm7d6E0IIIZzQEAa/zMzMYGlpifv37wMATp8+jQcPHkBXVxdKSkpQUqpswwwfPhxubm4SY1A2TkIIIY1eE1uFIfN9IPLz8/H333+LJkrOnz8fkydPFrvGyckJGzduxKBBgyTGoGychBBCGr0m1gPBazZOfX19LF26FMOHD4eZmRkePXqEhQsXwtDQEL6+vgAAU1NTiRMnLSwsYG1tzeGtEEIIIeRj4TUbZ3h4ONLT07F37178+++/MDMzg7u7O6KioqClRUMOhBBC/ruaWi4M3rNxxsXFSV0Jys5JCCFE7jWxIQxKpkUIIYQQqVEyLUIIIYQPTawHghoQhBBCCB/kaAkmH3jPxvl+Js6qY+3atWLXXbhwAX369IGmpiZ0dXXh5uaGN2/ecHozhBBCCPk4eM/G+W4WzpycHOzatQsCgQDDhw8XXXPhwgV4e3vD09MTly9fRkpKCmbMmAEFBZqSQQghRE4JGT+HnOA9G+f7ezwcPXoU7u7uaNWqlagsODgYgYGBmD9/vqisTZs20laFEEIIaTSYHD38+SDTr/zPnj3D77//jkmTJonK8vLycOnSJRgbG6Nbt24wMTFB7969kZycLMuqEEIIIYRHMm1AREREQEtLC8OGDROVPXz4EACwdOlSTJkyBbGxsejUqRP69u0rypdBCCGEyB0awuDPrl27MHbsWKipqYnKhP9/p66pU6diwoQJAICOHTvizz//xK5duxAaGlotDqXzJoQQ0ug1sZ0oZdYDcfbsWdy7d69a4qyqpFrt2rUTK7e3t0dWVpbEWKGhodDR0RE7mPCVbCpOCCGE1EcT64GQWQNi586dcHFxgbOzs1i5lZUVzM3Nce/ePbHyjIwMWFpaSoxF6bwJIYSQxoXXbJwWFhYAgMLCQhw8eBDr16+v9nqBQIC5c+diyZIlcHZ2RocOHRAREYG7d+/i0KFDEu9J6bwJIYQ0enLUe8AHXrNx7tmzBwAQGRkJxhhGjx4tMUZQUBDevn2L4OBgvHjxAs7OzoiPj0fr1q3r8RYIIYSQhtfUEkMKmJy+YyWV5g1dBUIIIQ3kTfZZqa5XNmz14Ys4KpzqxUsc7R+lz2rdECgXBiGEEMIHGsIghBBCiNSaWAOCkk8QQgghRGrUA0EIIYTwgHJhfMCH0nk/e/YM48ePh7m5OTQ0NODt7V1ti+rc3Fz4+/vD1NQUmpqa6NSpU41LOAkhhBC5QBtJ1a62dN6MMQwdOhQPHz7E0aNHkZaWBktLS3h4eOD169ei6/z9/XHv3j3ExMQgPT0dw4YNg5+fH9LS0ri9G0IIIYR8FLym875//z4uXryImzdvwsHBAQCwdetWGBsb48CBA6JtrS9cuIDw8HB88sknAIBFixZh48aNuHr1Kjp27Fjf90IIIYQ0nKaVCoPfSZRVCa/eTZ6lqKgIFRUVsXTdPXr0QFRUFF68eAGhUIjIyEiUlJTAzc2Nz+oQQgghHw0TMl4OecFrA8LOzg6WlpZYsGABXr58idLSUqxevRq5ubnIyckRXRcVFYXy8nIYGBhAVVUVU6dORXR0dI07UZaUlKCwsFDskNP9rwghhPxX0RyI+lNWVsZvv/2GjIwM6OvrQ0NDA4mJifDx8YGioqLoukWLFuHly5c4deoUrly5gpCQEIwYMQLp6ekS41I2TkIIIaRx4bSVtUAgQHR0NIYOHVrtXEFBAUpLS2FkZITOnTvD1dUVP/zwAx48eAAbGxuxeRIA4OHhARsbG2zbtq1arJKSEtHwSBU9AztKqEUIIU1UY9zK+l8/9w9fVAe6UQm8xJE1me0DoaOjA6ByYuWVK1ewYsUKAEBxcTEAQEFBvPNDUVERQqHkGSiUjZMQQkhjJ0/zF/jAezrvgwcPwsjICBYWFkhPT8esWbMwdOhQeHp6AqicJ2FjY4OpU6di3bp1MDAwwJEjRxAfH4/jx4/z984IIYQQIjO8p/POyclBSEgInj17BjMzM4wbNw6LFy8WXa+srIwTJ05g/vz5GDRoEIqKimBjY4OIiAj079+fh7dECCGENIAmtoyT0nkTQgiRO41xDsQL3968xNGPTuIljqxRMi1CCCGESI2SaRFCCCF8aGJDGNSAIIQQQnjAmlgDQqohjNDQUPzvf/+DlpYWjI2NMXToUNy7d090vqysDPPmzYOTkxM0NTVhbm6OcePGITs7WyxOSUkJZs6cCUNDQ2hqamLw4MF48uQJP++IEEIIITInVQMiKSkJ06dPx8WLFxEfH4/y8nJ4enqKMm0WFxfj6tWrWLx4Ma5evYrDhw8jIyMDgwcPFosTFBSE6OhoREZGIjk5GUVFRRg4cCAqKir4e2eEEELIxyTk6ZATnFZh/PPPPzA2NkZSUhJ69eol8ZqUlBR88sknePz4MSwsLFBQUAAjIyP8/PPP8PPzAwBkZ2ejZcuWOHHiBLy8vOp0b1qFQQghTVdjXIXx3IefVRiGfzSBVRgFBQUAAH19/VqvEQgE0NXVBQCkpqairKxMtLEUAJibm8PR0RHnz5/nUh1CCCGk4TSxHoh6T6JkjCEkJAQ9evSAo6OjxGvevn2L+fPnY8yYMdDW1gYA5ObmQkVFBXp6emLXmpiYIDc3V2IcSbkwGGO0nTUhhBDSQOrdAzFjxgzcuHEDBw4ckHi+rKwMo0aNglAoxNatWz8Yr7YGAWXjJIQQ0tgxIT+HvKhXA2LmzJmIiYlBQkICWrRoUe18WVkZRo4ciczMTMTHx4t6HwDA1NQUpaWlePnypdhr8vLyYGJiIvF+CxYsQEFBgdghUNCqT9UJIYQQmWjIBsTWrVthbW0NNTU1uLi44OzZ2ueIlJSU4Ouvv4alpSVUVVXRunVr7Nq1S6p7StWAYIxhxowZOHz4ME6fPg1ra+tq11Q1Hu7fv49Tp07BwMBA7LyLiwuUlZURHx8vKsvJycHNmzfRrVs3ifdVVVWFtra22EHDF4QQQggQFRWFoKAgfP3110hLS0PPnj3h4+ODrKysGl8zcuRI/Pnnn9i5cyfu3buHAwcOwM7OTqr7SrUK48svv8T+/ftx9OhR2Nraisp1dHSgrq6O8vJyDB8+HFevXsXx48fFehT09fWhoqICAPjiiy9w/Phx7NmzB/r6+pgzZw7y8/ORmpoKRUXFOtWFVmEQQkjT1RhXYTxz52cVhkmCdKswOnfujE6dOiE8PFxUZm9vj6FDhyI0NLTa9bGxsRg1ahQePnxY6yKID5GqByI8PBwFBQVwc3ODmZmZ6IiKigIAPHnyBDExMXjy5Ak6dOggds27Kyw2btyIoUOHYuTIkejevTs0NDRw7NixOjceCCGEkEaHCXg5SkpKUFhYKHa8v5CgSmlpKVJTU8VWNgKAp6dnjSsbY2Ji4OrqijVr1qB58+Zo27Yt5syZgzdv3kj1dqVahfGhzgorK6sPXgMAampq+P777/H9999Lc3tCCCHkPy80NBTLli0TK1uyZAmWLl1a7drnz5+joqKi2hzC2lY2Pnz4EMnJyVBTU0N0dDSeP3+OL7/8Ei9evJBqHgTlwiCEEEJ4wNcKigULFiAkJESsTFVVtdbXvD8vsLaVjUKhEAKBAL/88gt0dHQAABs2bMCnn36KH374Aerq6nWqJzUgCCGEEB4wIT+T+1VVVT/YYKhiaGgIRUXFar0Nta1sNDMzQ/PmzUWNB6ByzgRjDE+ePEGbNm3qdG9OO1ESQgghpOGoqKjAxcVFbGUjAMTHx9e4srF79+7Izs5GUVGRqCwjIwMKCgoSt2aoCa/ZON83depUCAQChIWFicpevHiBmTNnwtbWFhoaGrCwsEBgYKBoW2xCCCFEHjXUPhAhISHYsWMHdu3ahTt37iA4OBhZWVmYNm0agMohkXHjxomuHzNmDAwMDDBhwgTcvn0bZ86cwdy5czFx4sQ6D18AUg5hVGXj/N///ofy8nJ8/fXX8PT0xO3bt6GpqSl27ZEjR3Dp0iWYm5uLlWdnZyM7Oxvr1q1Du3bt8PjxY0ybNg3Z2dk4dOiQNNUhhBBCGg3GGmZ/Ij8/P+Tn52P58uXIycmBo6MjTpw4AUtLSwCVey29uydEs2bNEB8fj5kzZ8LV1RUGBgYYOXIkVq5cKdV9ZZKN8+nTp+jcuTPi4uIwYMAABAUFISgoqMY4Bw8exGeffYbXr19DSalubRraB4IQQpquxrgPxJPOfXiJ0+LSaV7iyBrv2TiFQiH8/f0xd+5cODg41DmOtrZ2nRsPhBBCCGlYvGfj/O6776CkpITAwMA6xcnPz8eKFSswderUGq+hbJyEEEIaO75WYciLejcgqrJxJicni8pSU1OxadMmXL16tU4P98LCQgwYMADt2rXDkiVLarxO0qYaAoVmEChq1/AKQggh5OOq/4QA+cRrNs6zZ88iLy8PFhYWUFJSgpKSEh4/fozZs2fDyspKLMarV6/g7e2NZs2aITo6GsrKyjXej7JxEkIIIY2L1FtZz5w5E9HR0UhMTKyWjdPf3x8eHh5iZV5eXvD398eECRNEZYWFhfDy8oKqqipiYmKgpqZW630lbapBwxeEEEIaExrCqMX06dNF2Ti1tLREO19VZeM0MDColr5bWVkZpqamouydr169gqenJ4qLi7Fv3z5RohAAMDIyooRahBBC5BI1IGpRlSrUzc1NrHz37t0YP358nWKkpqbi0qVLAAAbGxuxc5mZmdWGOgghhBDS+PCajVOSR48eif3s5uZWrziEEEJIY9bUHm208QIhhBDCg6Y2hEHJtAghhBAiNeqBIIQQQnjQULkwGopMsnHeuXMHgwcPho6ODrS0tNClSxexRB5VGGPw8fGBQCDAkSNH6v0mCCGEkIbWUNk4G4pUDYiqbJwXL15EfHw8ysvL4enpidevX4uuefDgAXr06AE7OzskJibi+vXrWLx4scS9HsLCwmg/B0IIIf8JQibg5ZAXvGfjHDVqFJSVlfHzzz/X+trr169j4MCBSElJgZmZGaKjozF06NA635uycRJCSNPVGLNxZth78xKn7Z1YXuLIGq/ZOIVCIX7//Xe0bdsWXl5eMDY2RufOnasNTxQXF2P06NHYsmULTE1NuVSBEEIIaRQYE/ByyIt6NyAkZePMy8tDUVERVq9eDW9vb5w8eRK+vr4YNmwYkpKSRK8NDg5Gt27dMGTIEO7vgBBCCGkEmFDAyyEveM3GKRRWzv4YMmQIgoODAQAdOnTA+fPnsW3bNvTu3RsxMTE4ffo00tLS6nwvSudNCCGENC68ZuM0NDSEkpIS2rVrJ3a9vb29aBXG6dOn8eDBA+jq6ooydgLA8OHDq22RXSU0NBQ6OjpiBxO+qk/VCSGEEJlgjJ9DXkg1ifL9bJxt2rSpdk23bt3QunVrsUmUvr6+UFdXx/79+5Gbm4vnz5+LvcbJyQmbNm3CoEGDqmX4BCT3QOgZ2FEPBCGENFGNcRLl7dYDeInT7sHvvMSRNV6zcQLA3Llz4efnh169esHd3R2xsbE4duwYEhMTAQCmpqYSJ05aWFhIbDwAlM6bEEIIaWykGsIIDw9HQUEB3NzcYGZmJjqioqJE1/j6+mLbtm1Ys2YNnJycsGPHDvz222/o0aMH75UnhBBCGgvaB0JO0D4QhBDSdDXGIYx060G8xHHKPMZLHFmjZFqEEEIIkRol0yKEEEJ4IJ/9+fVHDQhCCCGEB/I0f4EP1IAghBBCeCBP21Dzgfd03kVFRZgxYwZatGgBdXV12NvbIzw8vFqsCxcuoE+fPtDU1ISuri7c3Nzw5s0bbu+GEEIIIR8F7+m8g4ODERsbi3379uHOnTsIDg7GzJkzcfToUdE1Fy5cgLe3Nzw9PXH58mWkpKRgxowZUFCgOZ2EEELkE+1EKQVJ6bwdHR3h5+eHxYsXi65zcXFB//79sWLFCgBAly5d0K9fP9HP9UHLOAkhpOlqjMs4r7QYyksc1ydHeIkja7ym8waAHj16ICYmBk+fPgVjDAkJCcjIyICXlxeAyoydly5dgrGxMbp16wYTExP07t1bLCkXIYQQQhq3ek+ilJTOGwA2b96MKVOmoEWLFlBSUoKCggJ27Ngh2ony4cOHAIClS5di3bp16NChA/bu3Yu+ffvi5s2bEvNrUDZOQgghjR1NoqyjqnTeBw4cECvfvHkzLl68iJiYGKSmpmL9+vX48ssvcerUKQD/l/J76tSpmDBhAjp27IiNGzfC1tYWu3btkngvysZJCCGksWtqW1nXqweiKp33mTNnxNJ5v3nzBgsXLkR0dDQGDKjMSta+fXtcu3YN69atg4eHB8zMzACg1pTf71uwYAFCQkLEyvQM7OpTdUIIIYTwQKoGxPvpvN/PnllWVoaysrJqqykUFRVFPQ9WVlYwNzevtvwzIyMDPj4+Eu9L2TgJIYQ0dnK0gIIXvKbz1tbWRu/evTF37lyoq6vD0tISSUlJ2Lt3LzZs2ACg8sE/d+5cLFmyBM7OzujQoQMiIiJw9+5dHDp0iP93SAghhHwE8jT8wAeplnHW9K1/9+7dGD9+PAAgNzcXCxYswMmTJ/HixQtYWlri888/R3BwsNjrV69ejR9++AEvXryAs7Mz1qxZI1XKb1rGSQghTVdjXMZ53mw4L3G65fzGSxxZo3TehBBC5E5jbECcM/2Ulzjdc+WjN55yYRBCCCE8EDZ0BT4yakAQQgghPGBoWnMgKPkEIYQQQqQmVQMiPDwc7du3h7a2NrS1tdG1a1f88ccfovOMMSxduhTm5uZQV1eHm5sbbt26JRYjNzcX/v7+MDU1haamJjp16kSrLwghhMg9IePnkBdSNSBatGiB1atX48qVK7hy5Qr69OmDIUOGiBoJa9aswYYNG7BlyxakpKTA1NQU/fr1w6tX/7drpL+/P+7du4eYmBikp6dj2LBh8PPzQ1paGr/vjBBCCPmIhBDwcsgLzqsw9PX1sXbtWkycOBHm5uYICgrCvHnzAFTmsDAxMcF3332HqVOnAgCaNWuG8PBw+Pv7i2IYGBhgzZo1mDRpUp3vS6swCCGk6WqMqzBOm4zkJU6fZ7/yEkfW6j0HoqKiApGRkXj9+jW6du2KzMxM5ObmwtPTU3SNqqoqevfujfPnz4vKevTogaioKLx48QJCoRCRkZEoKSmBm5sbpzdCCCGENCQGAS+HvJB6FUZ6ejq6du2Kt2/folmzZoiOjka7du1EjQQTExOx601MTPD48WPRz1FRUfDz84OBgQGUlJSgoaGB6OhotG7dusZ7UjZOQgghjV1TW8YpdQ+Era0trl27hosXL+KLL75AQEAAbt++LTr//kP9/Qf9okWL8PLlS5w6dQpXrlxBSEgIRowYgfT09BrvSdk4CSGEkMaF8xwIDw8PtG7dGvPmzUPr1q1x9epVdOzYUXR+yJAh0NXVRUREBB48eAAbGxvcvHkTDg4OYjFsbGywbds2ifeQ1AOhZ2BHPRCEENJENcY5ECdNRvESx/NZJC9xZI3zPhCMMZSUlMDa2hqmpqaIj48XnSstLUVSUhK6desGACguLq68aS3ZOiVRVVUVLR2tOqjxQAghpDER8nTIC6nmQCxcuBA+Pj5o2bIlXr16hcjISCQmJiI2NhYCgQBBQUH49ttv0aZNG7Rp0wbffvstNDQ0MGbMGACAnZ0dbGxsMHXqVKxbtw4GBgY4cuQI4uPjcfz4cZm8QUIIIeRjkKeHPx+kakA8e/YM/v7+yMnJgY6ODtq3b4/Y2Fj069cPAPDVV1/hzZs3+PLLL/Hy5Ut07twZJ0+ehJaWFgBAWVkZJ06cwPz58zFo0CAUFRXBxsYGERER6N+/P//vjhBCCCEyQdk4CSGEyJ3GOAfid5PRvMQZ8OwAL3FkjZJpEUIIITwQNrGpeZRMixBCCCFSox4IQgghhAfylMeCD7xm41y6dCns7OygqakJPT09eHh44NKlS2IxSkpKMHPmTBgaGkJTUxODBw/GkydP+Hk3hBBCSANhPB3ygtdsnG3btsWWLVuQnp6O5ORkWFlZwdPTE//8848oRlBQEKKjoxEZGYnk5GQUFRVh4MCBqKio4PedEUIIIURmeMvGKSmTZmFhIXR0dHDq1Cn07dsXBQUFMDIyws8//ww/Pz8AQHZ2Nlq2bIkTJ07Ay8urzvelVRiEENJ0NcZVGIdNx/ASZ1jufl7iyBpv2TjfV1paiu3bt0NHRwfOzs4AgNTUVJSVlYll7DQ3N4ejo6NYxk5CCCFE3ggFAl4OecFbNs4qx48fx6hRo1BcXAwzMzPEx8fD0NAQAJCbmwsVFRXo6emJxTQxMUFubi7Ht0IIIYSQj0XqBkRVNs5///0Xv/32GwICApCUlCRqRLi7u+PatWt4/vw5fvrpJ4wcORKXLl2CsbFxjTE/lJqb0nkTQghp7ORpAiQfpB7CUFFRgY2NDVxdXREaGgpnZ2ds2rRJdF5TUxM2Njbo0qULdu7cCSUlJezcuRMAYGpqitLSUrx8+VIsZl5eHkxMTGq8J6XzJoQQ0tg1tWRavGXjrMt5FxcXKCsri2XszMnJwc2bN0UZOyVZsGABCgoKxA6BghbXqhNCCCG8EQr4OeQFb9k4X79+jVWrVmHw4MEwMzNDfn4+tm7diidPnmDEiBEAAB0dHUyaNAmzZ8+GgYEB9PX1MWfOHDg5OcHDw6PG+6qqqkJVVVWsjIYvCCGEkIYjVQ9EVTZOW1tb9O3bF5cuXRJl41RUVMTdu3cxfPhwtG3bFgMHDsQ///yDs2fPwsHBQRRj48aNGDp0KEaOHInu3btDQ0MDx44dg6KiIu9vjhBCCPlYhBDwctTH1q1bYW1tDTU1Nbi4uODs2botcz137hyUlJTQoUMHqe9J2TgJIYTInca4D8Q+8894ifNZ9j6pro+KioK/vz+2bt2K7t2748cff8SOHTtw+/ZtWFhY1Pi6goICdOrUCTY2Nnj27BmuXbsm1X0pmRYhhBAixzZs2IBJkyZh8uTJsLe3R1hYGFq2bInw8PBaXzd16lSMGTNG4l5OdUENCEIIIYQHfE2iLCkpQWFhodhR02KF0tJSpKamim3QCACenp61btC4e/duPHjwAEuWLKn3+6UGBCGEEMIDvpZxStq6IDQ0VOI9nz9/joqKimpbIdS2QeP9+/cxf/58/PLLL1BSqn9SbkrnTQghhDQiCxYsQEhIiFjZ+ysR3/f+ysSaNlusqKjAmDFjsGzZMrRt25ZTPXlN5z1+/HgIBAKxo0uXLqLzL168wMyZM2FrawsNDQ1YWFggMDAQBQUFnN4EIYQQ0tD4Suetqqoqes5WHTU1IAwNDaGoqFitt6GmDRpfvXqFK1euYMaMGVBSUoKSkhKWL1+O69evQ0lJCadPn67z+5WqB6IqnbeNjQ0AICIiAkOGDEFaWppoqaa3tzd2794teo2Kioroz9nZ2cjOzsa6devQrl07PH78GNOmTUN2djYOHTokTVUIIYSQRqUhNoFSUVGBi4sL4uPj4evrKyqPj4/HkCFDql2vra2N9PR0sbKtW7fi9OnTOHToEKytret8b6kaEIMGDRL7edWqVQgPD8fFixdFDQhVVVWYmppKfL2joyN+++030c+tW7fGqlWr8Nlnn6G8vJzTWAwhhBDSFIWEhMDf3x+urq7o2rUrtm/fjqysLEybNg1A5ZDI06dPsXfvXigoKMDR0VHs9cbGxlBTU6tW/iH1fmJXVFTg4MGD1dJ5JyYmwtjYGLq6uujduzdWrVpVayKtgoICaGtrU+OBEEKIXGuoPBZ+fn7Iz8/H8uXLkZOTA0dHR5w4cQKWlpYAKlNGZGVl8X5fqTeSej+d9/79+9G/f38AlZtZNGvWDJaWlsjMzMTixYtRXl6O1NRUieM3+fn56NSpE/z9/bFy5coa7ykpG6eegR1tZ00IIU1UY9xI6scW/GwkNfWJdBtJNRSpGxClpaXIysoSpfPesWOHWDrvd+Xk5MDS0hKRkZEYNmyY2LnCwkJ4enpCT08PMTExUFZWrvGeS5cuxbJly8QrrtAMCora0lSdEELIf0RjbEBsa8lPA2La3/LRgOA9nfe7zMzMYGlpifv374uVv3r1Ct7e3mjWrBmio6NrbTwAlI2TEEIIaWw4TzyoLZ13fn4+/v77b5iZmYnKCgsL4eXlBVVVVcTExEBNTe2D96BsnIQQQhq7hpoD0VB4S+ddVFSEpUuXYvjw4TAzM8OjR4+wcOFCGBoaipaWvHr1Cp6eniguLsa+fftEW3QCgJGREWXkJIQQIreoAVGLqnTeOTk50NHRQfv27UXpvN+8eYP09HTs3bsX//77L8zMzODu7o6oqChoaVUON6SmpuLSpUsAINpLokpmZiasrKz4eVeEEEIIkSlK500IIUTuNMZJlN/zNIlyppxMoqTNFwghhBAeNMROlA2JsnESQgghRGrUA0EIIYTwoKlNouQ1G+f7mTirjrVr11aLxRiDj48PBAIBjhw5wvmNEEIIIQ1JyNMhL6RqQFRl47xy5QquXLmCPn36YMiQIbh16xaAyp0n3z127doFgUCA4cOHV4sVFhZGezkQQgghcorXbJzvZ+E8evQo3N3d0aqV+OzX69evY8OGDUhJSRHbZIoQQgiRV3K5pJED3rNxVnn27Bl+//13REREiJUXFxdj9OjR2LJlS41pvwkhhBB509RWYUjdgHg/G2d0dLTERFoRERHQ0tKqlkQrODgY3bp1w5AhQ+p8T0nZOBljNARCCCGk0ZCn+Qt8kHoZp62tLa5du4aLFy/iiy++QEBAAG7fvl3tul27dmHs2LFiuS5iYmJw+vRphIWFSXXP0NBQ6OjoiB1M+EraqhNCCCGEJ5x3ovTw8EDr1q3x448/isrOnj2LXr164dq1a3B2dhaVBwUFYfPmzVBQ+L92S0VFBRQUFNCzZ08kJiZKvIekHgg9AzvqgSCEkCaqMe5EGWrJz06UCx43kZ0oJWXj3LlzJ1xcXMQaDwAwf/58TJ48WazMyckJGzdurDZB812UjZMQQkhjJ2xi0yh5y8ZZpbCwEAcPHsT69eurvd7U1FTixEkLCwtYW1vXo/qEEEIIaQi8ZeOsEhkZCcYYRo8ezXtlCSGEkMaqqU2ipGychBBC5E5jnAOx3HIsL3G+efwLL3FkjZJpEUIIIURqlEyLEEII4UFTG8KgBgQhhBDCg6a2EyWv2TifPXuG8ePHw9zcHBoaGvD29sb9+/erxblw4QL69OkDTU1N6Orqws3NDW/evOH+bgghhBDyUfCWjZMxhqFDh+Lhw4c4evQo0tLSYGlpCQ8PD7x+/VoU48KFC/D29oanpycuX76MlJQUzJgxQ2xzKUIIIUTeCMF4OeQF51UY+vr6WLt2LXr27AlbW1vcvHkTDg4OACp3mTQ2NsZ3330n2kCqS5cu6NevH1asWMGp4rQKgxBCmq7GuArja6sxvMRZ9Wg/L3Fkrd5f+ysqKhAZGSnKxlm1G+W7uS8UFRWhoqKC5ORkAEBeXh4uXboEY2NjdOvWDSYmJujdu7foPCGEECKvhDwd8kLqBkR6ejqaNWsGVVVVTJs2TZSN087ODpaWlliwYAFevnyJ0tJSrF69Grm5ucjJyQEAPHz4EACwdOlSTJkyBbGxsejUqRP69u0rca5ElZKSEhQWFoodcrp9BSGEEPKfwFs2TmVlZfz222/IyMiAvr4+NDQ0kJiYCB8fHygqKgIAhMLKttXUqVMxYcIEdOzYERs3boStrS127dpV4z0pGychhJDGrqnNgZC6AaGiogIbGxu4uroiNDQUzs7O2LRpEwDAxcUF165dw7///oucnBzExsYiPz9flOfCzMwMANCuXTuxmPb29sjKyqrxngsWLEBBQYHYIVDQkrbqhBBCiMwwng55wXnpg6RsnDo6OjAyMsL9+/dx5coVDBkyBABgZWUFc3Nz3Lt3T+z6jIwMWFpa1ngPVVVV0dLRqoOycRJCCCENh9dsnAcPHoSRkREsLCyQnp6OWbNmYejQofD09ARQmYJ77ty5WLJkCZydndGhQwdERETg7t27OHToEP/vjhBCCPlI5GkCJB94zcaZk5ODkJAQPHv2DGZmZhg3bhwWL14sFiMoKAhv375FcHAwXrx4AWdnZ8THx6N169b8vStCCCHkI5On+Qt8oGychBBC5E5j3AcixGoUL3E2PIrkJY6sUS4MQgghhAdy+W2cA2pAEEIIITxoanMgKAEFIYQQQqRGPRCEEEIID1gTG8Tg1AMRGhoKgUCAoKAgUdnhw4fh5eUFQ0NDCAQCXLt2rdrrcnNz4e/vD1NTU2hqaqJTp060jJMQQohco1wYdZSSkoLt27ejffv2YuWvX79G9+7dsXr16hpf6+/vj3v37iEmJgbp6ekYNmwY/Pz8kJaWVt/qEEIIIQ2KtrKug6KiIowdOxY//fQT9PT0xM75+/vjm2++gYeHR42vv3DhAmbOnIlPPvkErVq1wqJFi6Crq4urV6/WpzqEEEII+cjq1YCYPn06BgwYUGsjoTY9evRAVFQUXrx4AaFQiMjISJSUlMDNza1e8QghhJCG1tRyYUg9iTIyMhJXr15FSkpKvW8aFRUFPz8/GBgYQElJCRoaGoiOjq5xN8qSkpJq+TYYY5QPgxBCSKMhT8MPfJCqB+Lvv//GrFmzsG/fPqipqdX7posWLcLLly9x6tQpXLlyBSEhIRgxYgTS09MlXk/pvAkhhJDGRaqtrI8cOQJfX18oKiqKyioqKiAQCKCgoICSkhLRuUePHsHa2hppaWno0KGD6PoHDx7AxsYGN2/ehIODg6jcw8MDNjY22LZtW7X7SuqB0DOwox4IQghpohrjVtZTrEbwEuenRwd5iSNrUg1h9O3bt1ovwYQJE2BnZ4d58+aJNSxqUlxcDABQUBDv/FBUVIRQKHkBi6qqKlRVVcXKqPFACCGkMWlq+0BI1YDQ0tKCo6OjWJmmpiYMDAxE5S9evEBWVhays7MBAPfu3QMAmJqawtTUFHZ2drCxscHUqVOxbt06GBgY4MiRI4iPj8fx48f5eE+EEEIIkTHet7KOiYlBx44dMWDAAADAqFGj0LFjR9HQhLKyMk6cOAEjIyMMGjQI7du3x969exEREYH+/fvzXR1CCCHko2hqG0lROm9CCCFypzHOgZhgNZyXOLsf/cZLHFmjZFqEEEIIkRol0yKEEEJ4IE/DD3ygBgQhhBDCA6F8zgioN96zcb5r6tSpEAgECAsLEysvKSnBzJkzYWhoCE1NTQwePBhPnjzhUhVCCCGkQTW1rax5z8ZZ5ciRI7h06RLMzc2rnQsKCkJ0dDQiIyORnJyMoqIiDBw4EBUVFfWtDiGEEEI+It6zcQLA06dPMWPGDPzyyy9QVlYWO1dQUICdO3di/fr18PDwQMeOHbFv3z6kp6fj1KlT9XsXhBBCSAOjdN51UFs2TqFQCH9/f8ydO1dsq+oqqampKCsrg6enp6jM3Nwcjo6OOH/+fH2qQwghhDQ4xtP/5AXv2Ti/++47KCkpITAwUOL53NxcqKioVOu5MDExQW5ursTXUDZOQgghpHGRqgFRlY3z5MmTErNxpqamYtOmTbh69arUD/faGgShoaFYtmyZWJlAoRkEitpS3YMQQgiRlaa2jFOqIYzU1FTk5eXBxcUFSkpKUFJSQlJSEjZv3gwlJSUkJiYiLy8PFhYWovOPHz/G7NmzYWVlBaAyJ0ZpaSlevnwpFjsvLw8mJiYS77tgwQIUFBSIHQIFrfq9Y0IIIUQGmtocCF6zcZqZmcHLy0vsvJeXF/z9/TFhwgQAgIuLC5SVlREfH4+RI0cCAHJycnDz5k2sWbNG4n0pGychhBDSuPCejdPAwEDsvLKyMkxNTWFrawsA0NHRwaRJkzB79mwYGBhAX18fc+bMgZOTk8RJmYQQQog8kKcJkHxokJ0oN27cCCUlJYwcORJv3rxB3759sWfPHigqKjZEdQghhBDOmtocCMrGSQghRO40xmycwywH8xLn8OMYXuLIGmXjJIQQQnjAGOPlqI+tW7fC2toaampqcHFxwdmzNTewDh8+jH79+sHIyAja2tro2rUr4uLipL4nNSAIIYQQHjTUKoyoqCgEBQXh66+/RlpaGnr27AkfHx9kZWVJvP7MmTPo168fTpw4gdTUVLi7u2PQoEFIS0uT6r40hEEIIUTuNMYhjEEWA3mJcyzruFTXd+7cGZ06dUJ4eLiozN7eHkOHDkVoaGidYjg4OMDPzw/ffPNNne8rk2ycd+7cweDBg6GjowMtLS106dJF1BJ68eIFZs6cCVtbW2hoaMDCwgKBgYEoKCjgUhVCCCHkP6GkpASFhYVix/u7MVcpLS1FamqqWHoIAPD09KxzegihUIhXr15BX19fqnryno3zwYMH6NGjB+zs7JCYmIjr169j8eLFop0rs7OzkZ2djXXr1iE9PR179uxBbGwsJk2aVN+qEEIIIQ2Or1wYoaGh0NHRETtq6kl4/vw5Kioqqm3EWFt6iPetX78er1+/Fu3NVFf1Wsb5bjbOlStXip37+uuv0b9/f7FNoVq1+r+uI0dHR/z222+in1u3bo1Vq1bhs88+Q3l5OZSUGmRlKSGEEMIJX7tILliwACEhIWJl72+m+L73N1esa76oAwcOYOnSpTh69CiMjY2lqiev2TiFQiF+//13tG3bFl5eXjA2Nkbnzp1x5MiRWuMVFBRAW1ubGg+EEEKaPFVVVWhra4sdNTUgDA0NoaioWK23obb0EFWioqIwadIk/Prrr/XayFHqBkRVNk5J3Sl5eXkoKirC6tWr4e3tjZMnT8LX1xfDhg1DUlKSxHj5+flYsWIFpk6dWuM9JY0HyencT0IIIf9RDbGMU0VFBS4uLoiPjxcrj4+PR7du3Wp83YEDBzB+/Hjs378fAwYMqNf75TUbp1BYuQ/XkCFDEBwcDADo0KEDzp8/j23btqF3795i1xcWFmLAgAFo164dlixZUuN9KRsnIYSQxq6hdqIMCQmBv78/XF1d0bVrV2zfvh1ZWVmYNm0agMohkadPn2Lv3r0AKhsP48aNw6ZNm9ClSxdR74W6ujp0dHTqfF9es3EaGBhASUkJ7dq1E3udvb19tfWor169gre3N5o1a4bo6GgoKyvXeF/KxkkIIYRI5ufnh7CwMCxfvhwdOnTAmTNncOLECVhaWgKoTFj57jP4xx9/RHl5OaZPnw4zMzPRMWvWLKnuK9U+EK9evcLjx4/Fyt7Nxuno6Ihu3bqhdevW+Pnnn0XX+Pr6Ql1dHfv37wdQ2fPg5eUFVVVVnDhxAhoaGlJVGqB9IAghpClrjPtAeLb05iXOyb9jeYkja7xn45w7dy78/PzQq1cvuLu7IzY2FseOHUNiYiKAykaIp6cniouLsW/fPtGcBgAwMjKihFqEEELkEl+rMOQF78sefH19sW3bNoSGhiIwMBC2trb47bff0KNHDwCVwyCXLl0CANjY2Ii9NjMzE1ZWVnxXiRBCCCE8o62sCSGEyJ3GOITRt4Xnhy+qgz+fnOQljqzRxguEEEIID2gIgxBCCCFSY02sAUHpvAkhhBAiNd6zcRYVFWHGjBlo0aIF1NXVYW9vL5Zi9F2MMfj4+EAgEHxwu2tCCCGkMRMyxsshL+o9hFFTNs7g4GAkJCRg3759sLKywsmTJ/Hll1/C3NwcQ4YMEbs2LCysTsk+CCGEkMZOfh79/KhXD8S72Tj19PTEzl24cAEBAQFwc3ODlZUVPv/8czg7O+PKlSti112/fh0bNmzArl276l97QgghhDQIXrNxAkCPHj0QExODp0+fgjGGhIQEZGRkwMvLS3RNcXExRo8ejS1btsDU1LT+tSeEEEIaCSEYL4e8kHoIoyobZ0pKisTzmzdvxpQpU9CiRQsoKSlBQUEBO3bsEG0kBVQOc3Tr1q3akAYhhBAir+Tp4c8HXrNxApUNiIsXLyImJgaWlpY4c+YMvvzyS5iZmcHDwwMxMTE4ffo00tLS6nzfkpISlJSUiJUxxmj+BCGEENJApNqJ8siRI/D19RXLV1FRUQGBQAAFBQUUFBRAT08P0dHRYvnFJ0+ejCdPniA2NhZBQUHYvHkzFBQUxGIoKCigZ8+eopwZ71q6dKnEdN4KlM6bEEKapMa4E2UXczde4lzMTuQljqxJ1QPRt29fpKeni5W9m42zoqICZWVlYo0DAFBUVIRQWJkpff78+Zg8ebLYeScnJ2zcuBGDBg2SeN8FCxYgJCRErEzPwE6aqhNCCCEyRUMYtahLNs7evXtj7ty5UFdXh6WlJZKSkrB3715s2LABAGBqaipx4qSFhQWsra0l3ldVVRWqqqpiZTR8QQghhDQc3reyjoyMxIIFCzB27Fi8ePEClpaWWLVqFaZNm8b3rQghhJBGo6ltZU3ZOAkhhMidxjgHwtWsJy9xruRI994aCiXTIoQQQnjQ1OZAUDItQgghhEiNeiAIIYQQHsjpjIB6owYEIYQQwgMawqjF0qVLIRAIxI53l2QyxrB06VKYm5tDXV0dbm5uuHXrVrU4Fy5cQJ8+faCpqQldXV24ubnhzZs33N8NIYQQQj4KqedAODg4ICcnR3S8u7HUmjVrsGHDBmzZsgUpKSkwNTVFv3798OrVK9E1Fy5cgLe3Nzw9PXH58mWkpKRgxowZ1TafIoQQQuQJ4+l/8kLqIQwlJSWJG0ExxhAWFoavv/4aw4YNAwBERETAxMQE+/fvx9SpUwFUJtIKDAzE/PnzRa9t06ZNfetPCCGENArCJjYHQuqv/ffv34e5uTmsra0xatQoPHz4EACQmZmJ3NxceHp6iq5VVVVF7969cf78eQBAXl4eLl26BGNjY3Tr1g0mJibo3bs3kpOTeXo7hBBCCPkYpGpAdO7cGXv37kVcXBx++ukn5Obmolu3bsjPz0dubi4AwMTEROw1JiYmonNVjY2lS5diypQpiI2NRadOndC3b1/cv3+/xvuWlJSgsLBQ7Ghqs10JIYQ0bk1tCEOqBoSPjw+GDx8OJycneHh44PfffwdQOVRR5f0cFe+m3a5KqDV16lRMmDABHTt2xMaNG2Fra4tdu3bVeN/Q0FDo6OiIHUz4qsbrCSGEkI9NyBgvh7zgNHNRU1MTTk5OuH//vmheRFVvQ5W8vDxRr4SZmRkAoF27dmLX2NvbIysrq8b7LFiwAAUFBWKHQEGLS9UJIYQQwgGnBkRJSQnu3LkDMzMzWFtbw9TUFPHx8aLzpaWlSEpKQrdu3QAAVlZWMDc3x71798TiZGRkwNLSssb7qKqqQltbW+ygbJyEEEIak6Y2hCHVKow5c+Zg0KBBsLCwQF5eHlauXInCwkIEBARAIBAgKCgI3377Ldq0aYM2bdrg22+/hYaGBsaMGQOgcnhj7ty5WLJkCZydndGhQwdERETg7t27OHTokEzeICGEEPIxyNPwAx+kakA8efIEo0ePxvPnz2FkZIQuXbrg4sWLot6Dr776Cm/evMGXX36Jly9fonPnzjh58iS0tP5vuCEoKAhv375FcHAwXrx4AWdnZ8THx6N169b8vjNCCCHkI5Kn3gM+UDpvQgghcqcxpvNuY+TCS5z7/6TyEkfWKBcGIYQQwgMawiCEEEKI1JraEAYloCCEEEKI1HjLxllWVoZ58+bByckJmpqaMDc3x7hx45CdnS0WIzc3F/7+/jA1NYWmpiY6depEKzAIIYTIPcaEvBzygrdsnMXFxbh69SoWL16Mq1ev4vDhw8jIyMDgwYPFXu/v74979+4hJiYG6enpGDZsGPz8/JCWlsbPOyKEEEIagBCMl0Ne8JaNU0dHR2wTKQD4/vvv8cknnyArKwsWFhYAKtN5h4eH45NPPgEALFq0CBs3bsTVq1fRsWPH+rwHQgghhHxkvGXjlKSgoAACgQC6urqish49eiAqKgovXryAUChEZGQkSkpK4ObmVp/6E0IIIY0CY4yXQ15I1QNRlY2zbdu2ePbsGVauXIlu3brh1q1bMDAwELv27du3mD9/PsaMGQNtbW1ReVRUFPz8/GBgYAAlJSVoaGggOjq61o2kSkpKUFJSIlb2bpIuQgghpKHJ0/ADH3jPxglUTqgcNWoUhEIhtm7dKnZu0aJFePnyJU6dOoUrV64gJCQEI0aMEM2lkISycRJCCCGNC+edKPv16wcbGxuEh4cDqGw8jBw5Eg8fPsTp06fFeiYePHgAGxsb3Lx5Ew4ODqJyDw8P2NjYYNu2bRLvIakHQs/AjnogCCGkiWqMO1E213P48EV18PTlLV7iyBqnjaSqsnH27NkTwP81Hu7fv4+EhIRqwxrFxcUAAAUF8Y4PRUVFCIU1L11RVVWFqqqqWBk1HgghhDQmtBNlLWrLxlleXo5PP/0UV69exfHjx1FRUYHc3FwAgL6+PlRUVGBnZwcbGxtMnToV69atg4GBAY4cOYL4+HgcP35cJm+QEEII+Ria2k6UvGXjfPToEWJiYgAAHTp0EHtdQkIC3NzcoKysjBMnTmD+/PkYNGgQioqKYGNjg4iICPTv35+3N0UIIYQQ2aJsnIQQQuROY5wDYaJjx0ucZwV3eYkja5RMixBCCOEBLeMkhBBCCPkA6oEghBBCeCCnMwLqjbdsnAAwfvz4aue7dOkiFqOkpAQzZ86EoaEhNDU1MXjwYDx58oSfd0MIIYQ0ECFjvBzygrdsnFW8vb3Fzp84cULsfFBQEKKjoxEZGYnk5GQUFRVh4MCBqKio4PZOCCGEEPLR8JaNs4qqqmqN5wsKCrBz5078/PPP8PDwAADs27cPLVu2xKlTp+Dl5SVtdQghhJBGgYYwPuBD2TgTExNhbGyMtm3bYsqUKcjLyxOdS01NRVlZGTw9PUVl5ubmcHR0xPnz5zm8DUIIIaRhCcF4OeSFVA2IqmyccXFx+Omnn5Cbm4tu3bohPz8fQGWyrV9++QWnT5/G+vXrkZKSgj59+ojyWOTm5kJFRQV6enpicU1MTES7VhJCCCGk8ZNqCMPHx0f0ZycnJ3Tt2hWtW7dGREQEQkJC4OfnJzrv6OgIV1dXWFpa4vfff8ewYcNqjPuh1NyUzpsQQkhjR0MYUtDU1ISTkxPu378v8byZmRksLS1F501NTVFaWoqXL1+KXZeXlwcTE5Ma70PpvAkhhDR2tApDClXZOM3MzCSez8/Px99//y067+LiAmVlZcTHx4uuycnJwc2bN9GtW7ca77NgwQIUFBSIHQIFLS5VJ4QQQnjFePqfvOAtG2dRURGWLl2K4cOHw8zMDI8ePcLChQthaGgIX19fAICOjg4mTZqE2bNnw8DAAPr6+pgzZw6cnJxEqzIkoXTehBBCSOPCWzbON2/eID09HXv37sW///4LMzMzuLu7IyoqClpa/9dbsHHjRigpKWHkyJF48+YN+vbtiz179kBRUZH3N0cIIYR8LPI0/MAHysZJCCFE7jTGbJxqaha8xHn7NouXOLJGybQIIYQQIjVKpkUIIYTwQJ4mQPKBGhCEEEIID+R0RkC90RAGIYQQIue2bt0Ka2trqKmpwcXFBWfP1j5HJCkpCS4uLlBTU0OrVq2wbds2qe9JDQhCCCGEB4wxXg5pRUVFISgoCF9//TXS0tLQs2dP+Pj4ICtL8mTMzMxM9O/fHz179kRaWhoWLlyIwMBA/Pbbb1Ldl1ZhEEIIkTuNcRUGX8+l8tKnUl3fuXNndOrUCeHh4aIye3t7DB06FKGhodWunzdvHmJiYnDnzh1R2bRp03D9+nVcuHChzvelHghCCCGkESkpKUFhYaHY8X4+qCqlpaVITU0Vy3INAJ6enjVmub5w4UK16728vHDlyhWUlZXVvaLsP+Tt27dsyZIl7O3btxS/ge5B8Sk+xaf4/9X4H8uSJUsYALFjyZIlEq99+vQpA8DOnTsnVr5q1SrWtm1bia9p06YNW7VqlVjZuXPnGACWnZ1d53r+pxoQBQUFDAArKCig+A10D4pP8Sk+xf+vxv9Y3r59ywoKCsSOmhpFVQ2I8+fPi5WvXLmS2draSnxNmzZt2LfffitWlpyczACwnJycOteTlnESQgghjYik/E81MTQ0hKKiInJzc8XKa8tybWpqKvF6JSUlGBgY1LmeNAeCEEIIkVMqKipwcXERy3INAPHx8TVmue7atWu160+ePAlXV1coKyvX+d7UgCCEEELkWEhICHbs2IFdu3bhzp07CA4ORlZWFqZNmwYAWLBgAcaNGye6ftq0aXj8+DFCQkJw584d7Nq1Czt37sScOXOkuu9/aghDVVUVS5YsqXPXT1OL/zHuQfEpPsWn+P/V+I2Vn58f8vPzsXz5cuTk5MDR0REnTpyApaUlACAnJ0dsTwhra2ucOHECwcHB+OGHH2Bubo7Nmzdj+PDhUt1XbveBIIQQQkjDoSEMQgghhEiNGhCEEEIIkRo1IAghhBAiNWpAEEIIIURq1IAghBBCiNSoAUE4EwqFyMjIQHJyMs6cOSN2kI/nr7/+QlxcHN68eQMA9UoL3JDkvf6ycubMGZSXl1crLy8v5+XfWKtWrZCfn1+t/N9//0WrVtwzWMqq/jk5Odi3bx9OnDiB0tJSsXOvX7/G8uXL6x2b1A0t46wjoVCIv/76C3l5eRAKhWLnevXq1UC1qps///wTf/75p8S679q1i1PsixcvYsyYMXj8+HG1D3yBQICKigpO8QHZ1v9jefDgAXbv3o0HDx5g06ZNMDY2RmxsLFq2bAkHBwdOsfPz8+Hn54fTp09DIBDg/v37aNWqFSZNmgRdXV2sX7+ec/1//vlnbNu2DZmZmbhw4QIsLS0RFhYGa2trDBkypFHXv1WrVkhJSam2Re+///6LTp064eHDh5ziT5w4EZs2bYKWlpZY+evXrzFz5kzOv6OKiorIycmBsbGxWHl+fj6MjY05/xtTUFBAbm5utfjPnj2DhYVFjVkg60oW9U9JSYGnpyeEQiHKysrQokULREdHi/4tPXv2DObm5rx8/pCaye1GUps3b67ztYGBgZzuJeuH5LNnzzBnzhzRQ/L9e3CJv2zZMixfvhyurq4wMzODQCDgVNf3TZs2Da6urvj9999lEl/W9a8iywdkUlISfHx80L17d5w5cwarVq2CsbExbty4gR07duDQoUOc4gcHB0NJSQlZWVmwt7cXlfv5+SE4OJjzAzg8PBzffPMNgoKCsGrVKtHvo66uLsLCwjj/95F1/R89eiTx31BJSQmePn3KKTYAREREYPXq1dUaEG/evMHevXs5NyAYYxJ/7/Pz86GpqVnvuDExMaI/x8XFQUdHR/RzRUUF/vzzT1hZWdU7fhVZ1H/hwoUYNmwYfvrpJ7x+/Rrz589H7969ER8fj44dO3KtMqkjuW1AbNy4Ueznf/75B8XFxdDV1QVQ+e1CQ0MDxsbGnBsQsn5Ijh8/HllZWVi8eDHv8bdt24Y9e/bA39+ft5jvun//Pg4dOgQbGxuZxJd1/QHZPyDnz5+PlStXIiQkROwh4+7ujk2bNnGKDVTuYR8XF4cWLVqIlbdp0waPHz/mHP/777/HTz/9hKFDh2L16tWicldXV6m3vpVEVvWX9QOysLAQrDKjMV69egU1NTWx+CdOnKj2rVsaw4YNA1D5JWX8+PFiuytWVFTgxo0bNeY6qIuhQ4eK4gcEBIidU1ZWhpWVFafGmyzrn5qaih9++AEKCgrQ0tLCDz/8AEtLS/Tt2xdxcXGwsLCod71J3cltAyIzM1P05/3792Pr1q3YuXMnbG1tAQD37t3DlClTMHXqVM73kvVDMjk5GWfPnkWHDh14j11aWsrpQ+ZDOnfujL/++ktm/21kXX9A9g/I9PR07N+/v1q5kZGRxLFnab1+/RoaGhrVyp8/f87Llr6ZmZkSv9Wpqqri9evXnOPLqv6yfkDq6upCIBBAIBCgbdu21c4LBAIsW7as3vGrGjyMMWhpaUFdXV10TkVFBV26dMGUKVPqHb9qONDa2hopKSkwNDSsdyxJZF3/t2/fiv381VdfQUFBAZ6ennIztCnv5LYB8a7Fixfj0KFDosYDANja2mLjxo349NNPMXbsWE7xZf2QbNmypcwmjE2ePBn79+/H4sWLZRJ/5syZmD17NnJzc+Hk5FQtk1v79u05xZd1/QHZPyB1dXWRk5MDa2trsfK0tDQ0b96cc/xevXph7969WLFiBYDKB5dQKMTatWvh7u7OOb61tTWuXbsm2le/yh9//IF27dpxji+r+sv6AZmQkADGGPr06YPffvsN+vr6onMqKiqwtLSEubl5vePv3r0bAGBlZYU5c+ZwGq6ozbtfxqr8+++/ot7c+pJl/R0dHXH+/Plqny9z5swBYwyjR4/m7V6kZv+JBkROTg7KysqqlVdUVODZs2ec48v6IRkWFob58+fjxx9/5GXM8V1v377F9u3bcerUKbRv375a3Tds2MApflXylYkTJ4rKBAKBaNyT6/wQWdcfkP0DcsyYMZg3bx4OHjwoejieO3cOc+bMEcuQV19r166Fm5sbrly5gtLSUnz11Ve4desWXrx4gXPnznGOP3fuXEyfPh1v374FYwyXL1/GgQMHEBoaih07djT6+svqAdm7d29RfAsLC5nNz/nqq6/EvmA8fvwY0dHRaNeuHTw9PTnH/+6772BlZQU/Pz8AwIgRI/Dbb7/BzMwMJ06cgLOzM6f4sqj/uHHjkJSUJMo2+a65c+eCMYbw8PB615nUEfsPGDhwIGvfvj1LSUlhQqGQMcZYSkoK69ChAxs0aBDn+AKBoNqhoKAg+n+udHV1mYqKClNQUGDNmjVjenp6YgcXbm5uNR7u7u6c6/7o0aNaD65kXX/GGNu1axdr3rw5i4yMZJqamuzAgQNs5cqVoj9zVVpaysaMGSP6nVFWVmYKCgrss88+Y+Xl5Ty8A8ZycnLYN998wwYMGMB8fHzY119/zbKzs3mJzRhj27dvZxYWFqLf/xYtWrAdO3bwFl+W9V+9ejWLjIwU/fzpp58ygUDAzM3N2bVr1zjH/+OPP9jZs2dFP2/ZsoU5Ozuz0aNHsxcvXnCO369fPxYeHs4YY+zly5fM2NiYtWjRgqmpqbGtW7dyjm9tbc3OnTvHGGPs5MmTTFdXl8XFxbFJkyaxfv36cY4v6/qThvOfaEDk5eUxHx8fJhAImIqKClNRUWECgYD5+Piw3NxczvFl/ZDcs2dPrQeRPVk/IBlj7MGDB+zgwYMsKiqKZWRk8Br7Y/nnn3/Ys2fPGroaUpH1A9LR0ZH9/vvvjDHGbty4wVRUVNiCBQtY586d2fjx4znHNzAwYDdv3mSMMfbTTz+x9u3bs4qKCvbrr78yOzs7zvHV1NRYVlYWY4yxwMBA9vnnnzPGGLt37x7T1dXlHF/W9ScN5z/RgKiSkZHBjh49yo4cOcLu3bvX0NVpUm7dusX++OMPdvToUbFD3nyMB2R5eTlLS0vj5dspY7L/BlxcXMxev34t+vnRo0ds48aNLC4ujnNsxmRff1k/IDU1NVlmZiZjjLElS5aw4cOHM8YYS01NZSYmJpzjq6urs8ePHzPGGBsxYgRbunQpY4yxrKwspq6uzjm+mZmZqIHVtm1b9uuvvzLGGLt79y7T0tLiHF9W9f/pp5/YuHHj2K5duxhjjEVGRjI7OztmbW3NvvnmG871Jh8mt3MgQkJCsGLFCmhqaiIkJKTa+aSkJNGf+RgnB4Dbt28jKyur2q5ngwcP5iU+ULl2/P35HNra2pxipqSk4ODBgxLrfvjwYU6xHz58CF9fX6Snp4vmPgAQjQfzsZGLLOsPVP43Z4xBQ0MDhoaGePz4McLCwngbYw4KCoKTkxMmTZqEiooK9O7dG+fPn4eGhgaOHz8ONzc3TvHnzp2L7777DkDlio+QkBDMnj0bp0+fRkhIiGgyW30NGTIEw4YNw7Rp0/Dvv//ik08+gYqKCp4/f44NGzbgiy++aNT119PTw99//42WLVsiNjYWK1euBFC5OoCP308VFRUUFxcDAE6dOiWa16Kvr4/CwkLO8W1sbHDkyBH4+voiLi4OwcHBAIC8vDzOnw1A5XLLMWPGoE2bNsjPz4ePjw8A4Nq1a7xMHJdF/cPCwrBo0SJ4eXnh66+/RnZ2NjZu3Ijg4GAIhUKsX78ezZs3x+eff865/qQWDdyAqTc3Nzf28uVL0Z9lOU7+4MED1r59e7G5D1V/5mMORFFREZs+fTozMjISxXz34OLAgQNMWVmZDRgwgKmoqLCBAwcyW1tbpqOjw0v36sCBA9mQIUNYXl4ea9asGbt9+zY7e/Ys++STT9iZM2c4x5d1/RmT/Rht8+bNWUpKCmOMsejoaGZmZsbu3bvHvv76a9atWzfO8WX9DVjWXdCyrv/06dOZpaUl8/DwYAYGBuzVq1eMscpvrB07duQcf+DAgczLy4stX76cKSsrsydPnjDGGIuLi2Nt2rThHP/gwYOieTPvDrl8++23zNvbm3P80tJStm7dOhYYGMiuXr0qKt+4cSP76aefOMeXRf3t7OzYL7/8whhj7OrVq0xJSUlsyHHXrl3MxcWFW8XJB8ltA+JjkvVD8ssvv2T29vbs4MGDTF1dne3atYutWLGCtWjRgu3bt49TbCcnJ7ZlyxbGGGPNmjVjDx48YEKhkE2ZMoWXbj4DAwN2/fp1xhhj2tra7O7du4wxxv7880/WoUMHzvFlXX/GZP+AVFVVZX///TdjjLEpU6awWbNmMcYYe/jwIS9dxHp6euzWrVuMMca6d+/OfvzxR8YYY5mZmbx0ccu6C13W9Zf1A/Lx48eiidzvPsSCgoLYzJkzOcdnrHKS6dWrV1lFRYWo7NKlS+zOnTuc4paWlrLx48ezBw8ecK1irfiu/7u/k4xV/hur+jfMGGP379/nZXiK1I4aEHUg64dky5YtWUJCAmOMMS0tLXb//n3GGGN79+5lPj4+nGJraGiIvt0ZGBiwGzduMMYYu337NjM1NeUUm7HKFSRVHz6tWrVip0+fZowx9tdff/Hy4S/r+jMm+wekhYUFi4uLY+Xl5axly5bs2LFjjDHGbt68ycuH3KBBg2T6DdjJyYlt2rSJZWVlMW1tbXb+/HnGGGNXrlzhpYdAlvWX9QOyrKyM7dmzh9cVL+/HV1RUZOnp6TKJzxhjOjo6Mv3vI4v6GxgYsNu3b4t+btGihdiE9vv377NmzZrxek9SHWXjrIOKigo0a9YMAGBoaIjs7GwAgKWlJe7du8c5/osXL0SbDGlra+PFixcAgB49enDOtqevr49Xr14BAJo3b46bN28CqFwHXzVuy4WjoyNu3LgBoHLDrTVr1uDcuXNYvnw5L5n8ZF1/4P/GaP/++2/ExcWJ5j3wNcY8YcIEjBw5Eo6OjhAIBOjXrx8A4NKlS7Czs+Mcf8uWLVBSUsKhQ4cQHh4u2pzqjz/+gLe3N+f433zzDebMmQMrKyt07twZXbt2BVC5BTUfeQdkWX9lZWVER0dzrmNNlJSU8MUXX1Sbm8NnfEtLS5kmhfL19cWRI0dkEltW9bezsxN97gDA33//LbaPy927d3nfU4dI0NAtGHnQo0cPFh0dzRhjbPTo0czb25slJyezcePGMQcHB87xnZycWGJiImOscjx+9uzZjDHGNm3axJo3b84p9ujRo9n69esZY4ytXLmSGRkZscmTJzNLS0vm6+vLreKMsdjYWPbbb78xxirnitjb2zOBQMAMDQ3Zn3/+yTm+rOvPmOzHmKvusWHDBtFQBmOVy3ePHDnCS/y6CA0NFc0bklZduqD//vtvsfN8q2/9x48fL/odkgU3NzfR54Ms7Nq1i/n4+LD8/HyZxF+5ciXT1dVlw4cPZ99++y3btGmT2MGVLOqfnJzM0tLSajz/ww8/sO+//563+xHJKJ13HcTFxeH169cYNmwYHj58iIEDB+Lu3bswMDBAVFQU+vTpwyn+xo0boaioiMDAQCQkJGDAgAGoqKhAeXk5NmzYgFmzZtU79osXL/D27VuYm5tDKBRi3bp1SE5Oho2NDRYvXgw9PT1Oda/pnnp6erzszPex6p+bm4ucnBw4OztDQaGyY+7y5cvQ1tYW9RI8efIE5ubmovN8c3JywokTJ9CyZUuZxNfW1sa1a9d46RmSp/irVq3CunXr0LdvX7i4uFTbUplrsr2DBw9i/vz5CA4Olhif6061HTt2xF9//YWysjJYWlpWi3/16lVO8d/fYv1dAoGAc7pzWdefNBxqQNQTnw/J92VlZeHKlSto3bo1521kP5a//voLDx48QK9evaCurl5jCl95JusHpJaWFq5fv07xeY4v6wekpAYln9u5fygh15IlSzjFlzV5rz+pmdzuA9EQ3n1I6uvryyQB1tu3b2FhYcFrOtoHDx5g9+7dePDgATZt2gRjY2PExsaiZcuWcHBw4BQ7Pz8fI0eOREJCAgQCAe7fv49WrVph8uTJ0NXV5ZTt8GPUXxrU1pZPknJhyFP8j/mAZe/t48IHaiD8d9EkyjrIz89H37590bZtW/Tv3x85OTkAKjNFzp49m3P8iooKrFixAs2bN0ezZs1E34gWL16MnTt3coqdlJQEJycnXLp0CYcPH0ZRUREA4MaNG7z8ww4ODoaysjKysrLEUjL7+fkhNjaWc3xZ1580Laxy5RmvMS0tLWs9+JKamop9+/bhl19+QVpaGm9xAWDv3r1wcnKCuro61NXV0b59e/z888+83kOW9ScNpIHmXsgVf39/5uXlxf7++2/RXgSMVS4za9euHef4y5YtY61atWL79u1j6urqovhRUVGsS5cunGJ36dJFNIHs3bpfvnyZmZubc6s4Y8zExESUkOjd+A8fPmSampqc48u6/tJ49/4UX77iR0REMEdHR6aqqspUVVWZk5MT27t3L291++uvv9iMGTNY3759mYeHB5s5cyb766+/eIn97Nkz5u7uzgQCAdPT02O6urpMIBCwPn36sLy8PM7x169fzzQ0NNhXX30lSgUwd+5cpqGhwTZs2NDo608aDjUg6kDWD8nWrVuzU6dOVYt/584dzvsEaGpqsocPH1aLnZmZyVRVVTnFropZlRjq/Qe8vr4+5/iyrr80GvMDsjHE19LSapT1l/UDMjY2lqmoqLBPPvmEBQcHs6CgIPbJJ58wVVVVdvLkSc7xR44cyVxcXMT2Pbh16xZzdXVlo0aN4hzfysqKRUREVCvfs2cPs7Ky4hxf1vUnDYcaEHUg64ekmpqaaBOUd+PfunWLcwOlefPmokQ578Y+fPgwa9WqFafYjDHWv39/tmjRIlH8hw8fsoqKCjZixAjRlsRcyLr+0misD8i68vHxkdmGR4w13vrL+gHZoUMHNm/evGrl8+bN42WrbG1tbXb58uVq5ZcuXWI6Ojqc46uqqoo2r3tXRkYGL410Wdc/KSlJtFV8lZSUFJaUlMQ5NqkdzYGog169emHv3r2inwUCAYRCIdauXQt3d3fO8R0cHHD27Nlq5QcPHuS8Uc+YMWMwb9485Obmiup97tw5zJkzR5T0h4u1a9fixx9/hI+PD0pLS/HVV1/B0dERZ86cESVIasz1lwarx9h5WVkZ3N3dkZGR8cFrf/zxR5iYmEh9j6tXryI9PV3089GjRzF06FAsXLhQbIOjEydOwMzMTOr4e/bsqdOmXbdv367XmL+bmxv27t2LN2/e1Hpdfeufk5ODbt26VSvv1q2baD4TF3fu3MGkSZOqlU+cOBG3b9/mHF8oFEJZWblaubKyMoRCIef4NjY2+PXXX6uVR0VFoU2bNpzjy7r+bm5u1T4L/P39eflsJh/Q0C0YeXDr1i1mZGTEvL29mYqKCvv000+Zvb09MzEx4WWcMyYmhuno6LDVq1czDQ0NtnbtWjZ58mSmoqLCuQu0tLSUjRkzRpQErGrDpM8++4yVl5dzrjtjlZsMffPNN2zAgAHMx8eHff3117x90/0Y9X9fQUEBi46OFutyZaxya+v63NPQ0FDUgyULrq6u7NChQ4yxys281NTU2OjRo5mNjY0o7wYXpqamTEtLi02cOFHUG8SnkJAQZmJiwrS1tdnkyZPZhQsXeI3v4ODAVq1aVa18xYoVzNHRkXP8Fi1aiFJgvysqKoq1bNmSc/zBgwezXr16sadPn4rKnjx5wnr37s2GDh3KOf6hQ4eYoqKiaDvxFStWMC8vL6akpMQOHz7MOb6s6//o0SOx2Iwx9vTpU7GtrYlsUAOijmT5kGSschy1V69eTFNTk6mrq7Pu3buzuLg43uL/9ddf7ODBgywqKkqmDzNZkWX9R4wYIdq1rri4mLVp04YpKyszJSUl0YOZi5CQEIld3HzR1tYWNWRXr17NPD09GWOVu/W1aNGCc/zy8nJ29OhR5uvry1RUVJitrS1bvXo1y8nJ4Rz73XscOXKEDRkyhCkrKzN7e3u2du1alpubyzm2rB+Qy5YtY7q6umz16tXszJkz7OzZsyw0NJTp6uqyFStWcI6flZXFOnbsyJSVlVmrVq1Y69atmbKyMuvUqZPYzqZcXLlyhY0dO5Z16tSJdezYkY0dO1Ys8RgXH6P+pGHQRlKEs7dv3+LGjRvIy8ur1iU5ePDgBqpV3ZmamiIuLg7Ozs7Yv38/lixZguvXryMiIgLbt2/nvORs5syZ2Lt3L2xsbODq6lptJ74NGzZwiq+trY3U1FS0adMG/fr1w8CBAzFr1ixkZWXB1tb2g0MD0sjLy8O+ffuwZ88e3L17F97e3pg0aRIGDRrE2w6d//zzD3788UesWrUKFRUV6N+/PwIDAznt+JqamoqNGzfizp07YIyhXbt2mD17Ni+5PBhjCAsLw/r160V5cszNzTF37lwEBgbytqdCfHw87t69K6q/h4cHL3E/FnmvP6mOGhB19LEekkVFRdXic0noxBjDoUOHkJCQILHuhw8frndsAIiNjcW4cePw/Pnzauf42IVP1vUHAHV1dWRkZKBly5YYN24czM3NsXr1amRlZaFdu3aivSfqq7axWIFAgNOnT3OK36dPH7Rs2RIeHh6YNGkSbt++DRsbGyQlJSEgIACPHj3iFP99ly5dwq5duxAREQEzMzP8+++/0NXVxe7du+Hm5sYp9uXLl7F7924cOHAAOjo6GD9+PHJycvDLL7/giy++wLp16/h5EzJSlfhNS0uLt5jFxcVie6zwbezYsXBzc4Obmxsvcx7ex3f9pdkBuCoxIZEN2omyDmT9kMzMzMSMGTOQmJiIt2/fisoZD1vhzpo1C9u3b4e7uztMTEx43156xowZGDFiBL755pt6TQD8EFnXHwBatmyJCxcuQF9fH7GxsYiMjAQAvHz5EmpqapzjJyQkcI5Rm7CwMIwdOxZHjhzB119/DRsbGwDAoUOHJE4erI9nz57h559/xu7du/Hw4UMMHToUx48fh4eHB968eYNFixYhICAAjx8/ljp2Xl6eKPb9+/cxaNAgREZGwsvLS/T3PXLkSAwdOrReDQhZPyB/+uknUWw+Gw5VdHV14erqKnoP3bt3r9aLxUWzZs2wfv16TJ06Faampujduzd69+4NNzc3XrLF8l3/sLAw0Z/z8/OxcuVKeHl5ibLEXrhwAXFxcVi8eDHXqpMPaaChE7nSunVr9uWXX/IyHitJ165dWdeuXVlkZCRLSEhgiYmJYgcXenp67Pfff+epptVpaWnxtmGOJLKuP2OVmfuUlJSYrq4uc3Z2FmWU3Lx5M3Nzc+PtPvfv32exsbGsuLiYMcaYUCjkLbYkb968YaWlpZzjDBw4kCkrKzMHBwe2ceNGiVkVnz59ygQCQb3iKysrMzs7O7ZmzZoaNxYqKCio99/F559/zmxtbZlAIGBmZmZs1KhRLDw8XCyTKBfvx962bRtvsRlj7Pz58yw0NJR5eXkxLS0tpqyszDp37szmzZvHTpw4wdt9cnJy2IEDB9jUqVOZnZ0dU1BQYKamppzjyrL+w4YNk5h18/vvv2dDhgzhFJt8GDUg6kDWD0lNTU129+5dmcS2srLi9cPsfRMmTGA7duyQWXxZ179KSkoKO3z4MHv16pWo7Pjx4yw5OZlz7OfPn7M+ffowgUDAFBQURHslTJw4kYWEhHCOn5WVJTYZ7dKlS2zWrFnsxx9/5Bybscp6nj9/vtZrhEJhvWa9C4VClpSUxF6/fl3f6tWZrB6QVbH379/Ppk6dymxtbZmCggIzMTFhfn5+vMSvUl5ezi5cuMACAgKYkpISU1BQ4C12UVERi42NZfPnz2ddunRhKioqrEOHDrzFZ4z/+mtqata4hwUfm/yR2lEDog5k/ZB0c3Nj8fHxMom9Z88eNmrUKNG3Xr69fv2a9e/fnwUEBLB169axTZs2iR1cybr+H4Ost0Lv0aOHaFvmnJwcpq2tzbp27coMDAzYsmXLOMePiIhgb9++rVZeUlIicYMmaVRUVDBlZeWPsjLoYzwgq+4xfvx4pqSkxBQVFXmJe+fOHRYeHs5GjRrFTE1NmYGBAfP19WVhYWGcY3/11Vesc+fOTE1Njbm6urKQkBB29OhR9vLlS+4V//9kVX8LCwu2Zs2aauVr1qxhFhYWnGKTD6NJlHVQXFyMESNGwMjICE5OTtU2RQkMDOQU/8GDB5g2bRo+++wzODo6Vovfvn37escuLi7GsGHDcO7cOVhZWVWLffXq1XrHBoAdO3Zg2rRpUFdXh4GBgdgcBT5SJcu6/kDlhj+12bVrF6f4767yeDcldWZmJpycnDhP0tTT08PFixdha2uLzZs3IyoqCufOncPJkycxbdo0zn8HioqKyMnJgbGxsVh5fn4+jI2NOc8BcnBwwM6dO9GlSxdOcWoyb948JCUl4fr163B0dESvXr3Qu3dv9OrVC7q6upzj//HHH0hKSkJiYiKuX78OBwcH9OrVC25ubujZsyf09PQ4xTc1NUVZWRn69OkDNzc39OrVC05OTpzrXUVBQQFGRkYIDg7GkCFDYG9vz1tsQLb137NnDyZNmgRvb2/RHIiLFy8iNjYWO3bswPjx43m5D5GMJlHWwf79+xEXFwd1dXUkJiZWe0hybUD8888/ePDgASZMmCAWl/EwiXL8+PFITU3FZ599JpNJiIsWLcLy5csxf/583pbxvUvW9QcqJ0u+q6ysDDdv3sS///7LaelgldevX0uchf78+XOoqqpyjl9WViaKc+rUKdGqIDs7O152Wqz6PXzfkydPoKOjwzn+mjVrMHfuXISHh8PR0ZFzvPetXbsWRkZGWLJkiUwekAMGDICRkRFmz56NuLg4Xv6bvMvU1BR37txBVlYWsrKy8OTJE1hbW6NZs2a8xE9LSxM1gNavXw9FRUXRJEo3NzfO/71kWf/x48fD3t4emzdvxuHDh0VLRM+dO4fOnTtzjk9qRz0QdWBqaorAwECZPSTbtWsHe3t7fPXVVxIfklxSAmtqaiIuLg49evTgWk2J9PX1kZKSgtatW8skvqzrXxOhUIgvv/wSrVq1wldffcUp1oABA9CpUyesWLECWlpauHHjBiwtLTFq1CgIhUIcOnSIU/zOnTvD3d0dAwYMgKenJy5evAhnZ2dcvHgRn376KZ48eVKvuB07doRAIBB9q1ZS+r/vGxUVFcjMzIS3t7fEbZCloaenh+LiYpSXl0NFRQXq6upi57kuxbt+/broAXn27FneH5BhYWE4c+aMTGJX+ffff3HmzBkkJSUhKSkJt27dQvv27eHu7o7Vq1fzco8q169fR1hYGPbt2wehUMi5hwn4uPUnHw81IOrgYzwkr1+/Llp+xyc7Ozv8+uuvnIZBahMcHAwjIyMsXLhQJvFlXf/a3Lt3D25ubpy/xd++fRtubm5wcXHB6dOnMXjwYNy6dQsvXrzAuXPnOP9eJSYmwtfXF4WFhQgICBANuSxcuBB3796t914Zy5YtE/3/7Nmzxb4xqqiowMrKCsOHD4eKigqn+u/Zs6fWnqWAgABO8d8niwdklfT0dCQlJSEhIQHHjh2DgYEBL71AVV68eIHExEQcPXoU+/fv563+aWlpSExMFDWyCgsL0aFDB7i7u2Pt2rU81LySLOr/4MED0fLisLAwGBsbIzY2Fi1btoSDgwNPNSeSUAOiDmT9kBw0aBDGjx+P4cOH8x77999/x/fff49t27bBysqK9/iBgYHYu3cvnJ2d0b59+2pzFLjusijr+tfmxIkTCAgIwD///MM5Vm5uLsLDw5GamgqhUIhOnTph+vTp9UoOJUlFRQUKCwvFxtsfPXoEDQ2NanMXpBUREQE/Pz9e9sRoKB/jAVl1j4SEBJw9exavXr1Cx44dkZKSwiludHS0qO63bt2CgYEBevbsCTc3N7i7u3N+SOrp6aGoqAjOzs6inpNevXpx2sDuY9U/KSkJPj4+6N69O86cOYM7d+6gVatWWLNmDS5fvsy5d4/UjhoQdSDrh+T27duxcuVKTJw4UeIkTS47Xb7bPayhoVEtNtfuYVnvsijr+gNASEiI2M+MMeTk5OD3339HQEAAtmzZwil+VlYWWrZsKfFbdlZWFiwsLDjFB4Dy8nIkJibiwYMHGDNmDLS0tJCdnQ1tbW3exsplRdaTNGX9gBw8eDCSk5NFjRK+72FsbCyalOnm5sb7PJHjx4/Xqa5PnjyBubm51MO4sqx/165dMWLECISEhIhNUE5JScHQoUPx9OlT3u5FqqMGRB3I+iFZ2z9IrpMoIyIiaj3Pd/dwTer74fMx6v/+32/VrPQ+ffpg4sSJYmP/9SHrB+Tjx4/h7e2NrKwslJSUICMjA61atUJQUBDevn2Lbdu2SR1TX18fGRkZMDQ0/ODWwVwbcQoKCsjNza323yc7OxutW7fmnMtD1g/IOXPm8N4oqY/Vq1dj2rRpvKwskURbWxvXrl1Dq1atZBK/PvVv1qwZ0tPTYW1tLdaAePToEezs7MR29iX8o1UYdVDXrYjr+wH0fn4HPtX1ASvrD5927drV68PnY9Rf1ltN17SKoaioiJdhgVmzZsHV1RXXr1+HgYGBqNzX1xeTJ0+uV8yNGzeKtmXeuHGjTFa/bN68GUBlI3nHjh1iPSUVFRU4c+YML1spDxw4sE7X1fd3tK7bazs5OeHEiRNo2bKlVPHr6ttvv8XIkSNl9m9Y1t8161N/XV1d5OTkwNraWqw8LS0NzZs357mG5H3UgOBRfT+A6kqWH0BN8cNH1qqGRgQCARYvXiy2lLOiogKXLl1Chw4dON8nOTkZ586dqzaZ0dLSst5duO823GS1ln7jxo0AKn83tm3bBkVFRdG5qkma9ek9qS9Z/44+evQIZWVlMosv753J9an/mDFjMG/ePBw8eBACgQBCoRDnzp3DnDlzMG7cOBnUkryLGhA8kucPoKb24dOpUyf8+eef0NPTEy1XrEl9N6uqSgPOGEN6errYA15FRQXOzs6YM2dOvWK/q6aZ7E+ePOEluZO7uzs+++wzfPrpp7zucZCZmSmKf/jwYc4bLpGmZ9WqVRg/fjyaN28u2gOioqICY8aMwaJFixq6ev951IAgTdKQIUNEmy8NHTpUJveoGhqZMGECNm3aJLPx8X79+iEsLAzbt28HUNnjUVRUhCVLlqB///6c4zs5OWHRokWYMWMG+vfvD39/f/Tv35/z8s0qVf+dSktLkZmZidatW3Oed0KaBmVlZfzyyy9Yvnw50tLSIBQK0bFjR5lkXSUSfJwds5uGd/McyFt8ea77x4jPB1ll43z69Clr27Yts7e3Z0pKSqxLly7MwMCA2drasmfPnvFyj4qKChYXF8cCAgKYtrY209PTY1OmTOGcLZYxxoqLi9nEiROZoqIiU1RUFP09zpw5k4WGhnKOX1fy/jsq6/haWlpyXX/CP2rmk49CFpPw5MWLFy8wYsQIJCQkQCAQ4P79+2jVqhUmT54MXV1drF+/nlN8c3NzXLt2DQcOHMDVq1chFAoxadIkjB07ttqujvWloKAAT09PeHp6Ytu2bTh27BhWrVqFnTt3cl5FMn/+fFy/fh2JiYnw9vYWlXt4eGDJkiWYP38+1+rXSVP+Ha0L1giHORljOHToEBISEpCXl1dtQnp9N1EjdUMNCB7RB1DNGtuHz4eWJr6L6zLFoKAgKCsrIysrS2xrYz8/PwQHB3NuQACAuro6Jk6c+MHEYFzl5uYiMjIS+/btw40bN/C///2Pc8wjR44gKioKXbp0Efs7adeuHR48eMA5fl01tt9RafXs2bNeDcY9e/Zg5MiREvO1vOv27dswNzevb/U+qD71nzVrFrZv3w53d3eZ5cohNaMGBI/k+QOoqX34hIWFif6cn5+PlStXwsvLS5TR78KFC4iLi8PixYs51+3kyZOIi4tDixYtxMrbtGmDx48fc44PABkZGUhMTJT4Leybb77hFLuwsBC//fYb9u/fj8TERLRq1QpjxoxBZGQkL9uv//PPPxJ3y3z9+rVMHgiFhYU4ffo0bG1txRp09fkdLSsrg6enJ3788Ue0bdu21mt//PFHmJiYSF3fq1evQllZWZTB8ujRo9i9ezfatWuHpUuXiuainDhxQurYALBgwQIEBgZixIgRmDRpErp16ybxuvqu/nJzc8PEiRMxYsSIWv+N1qf++/btw+HDh3mZ60PqoUEHUORUQUEBi46OZrdv3xYrz8rKYuXl5VLFKi0tZW5ubuzevXsfvPaXX35hRUVFUsVPTU1lN27cEP185MgRNmTIELZgwQJWUlIiVSxJTE1NmZaWFps4cSI7d+4c53jv6927N4uIiBDNG5CFYcOGse+//75a+ffff8+GDBnCOX6zZs1YRkaG6M9V47yXL19m+vr6nONv376dKSoqMhMTE+bs7Mw6dOggOjp27Mg5vpqaGjMzM2OzZs1ily9f5hzvfb169WKbN29mjFX+93n48CFjjLHp06czLy8vzvFHjBgh+vstLi5mbdq0YcrKykxJSYkdOnSIc3xDQ0PR368suLq6iur54MEDpqamxkaPHs1sbGzYrFmzOMcvLy9nR48eZb6+vkxFRYXZ2tqy1atXs5ycHM6xGWMsJCSEmZiYMG1tbTZ58mR24cIFXuIyxpiVlRW7c+cOb/GIdKgBUQfy/AFEHz4fpqmpye7fv1+tPCMjg2lqanKO379/f7Zo0SLG2P89ICsqKtiIESPY8OHDOce3sLBgq1ev5hynJnFxcayiokJm8c+dO8e0tLTYtGnTmJqaGps1axbz8PBgmpqa7MqVK5zjm5iYsGvXrjHGKhvhNjY27PXr12zr1q2sQ4cOnOOHhISwefPmcY5TE21tbfbXX38xxhhbvXo18/T0ZIwxlpyczFq0aMHrvZ49e8bWr1/PnJycmLKyMhs0aBA7cuQI57//8vJy0ZcXZWVlZm9vz9auXctyc3M5xd2zZw8bNWqUTL9gkJpRA6IO5PkDiD58PszCwoKtWbOmWvmaNWuYhYUF5/i3bt1iRkZGzNvbm6moqLBPP/2U2dvbMxMTE9HfDReynh3/Mdy4cYONGzeOOTg4MHt7ezZ27FixnjMu1NTUWFZWFmOMMX9/f9G/tcePH/PSQJwxYwbT1tZmnTp1Yp9//jkLDg4WO7jS0tISfcHw8PBgYWFhjLHK+qupqXGO/76LFy+yzz//nKmqqjIrKyumq6vLrKysWEJCAi/x8/Ly2IoVK5iamhpTVlZmQ4YMYX/++We9Yr1+/Zp5eXmxZs2aMUdHR9axY0exg8gWzYGog4KCAujr6wMAYmNjMXz4cGhoaGDAgAGYO3cu5/ilpaXYsWMH4uPj4erqCk1NTbHzXJJ1McZEY+KnTp0SbevbsmVLPH/+vP6VlsDY2Bjdu3fHvXv3kJGRgfT0dIwfPx66urrYvXs33Nzc6hVXUVERQ4YMwZAhQ/DPP//gxx9/xOLFi7Fw4UL0798fgYGB6NOnT73rvWzZMkyaNAmJiYmiORAXL15EbGwsduzYUe+4Vdq1a4cbN24gPDwcioqKeP36NYYNG8ZbNs4RI0bg5MmTmDZtGudYNTl06BB+/fVXZGVlobS0VOxcfTfaepeTk9MH857UV8uWLXHhwgXo6+sjNjYWkZGRAICXL1/yspX4zZs30alTJwCVc1HexcccDldXV6xcuRIeHh5ISkpCeHg4gMqNuOozp0KSZ8+e4eeffxalxR46dCiOHz8ODw8PvHnzBosWLUJAQADnOTuXL1/G7t27ceDAARgbG2P8+PHIycnBoEGD8MUXX9R5W/Aq48ePR2pqKj777DOaRNkQGroFIw/atGnDoqKiWFFRETMyMhK1lq9du8YMDAw4x3dzc6vxcHd35xTb3d2djRs3ju3du5cpKyuLuuoTExOZpaUl57ozxlhubi5bu3Yta9euHVNTU2OjRo1i8fHxjLHKIZ+QkBBevslfunSJTZs2jeno6DALCwv2zTffsClTpjANDQ02e/ZsTrEvXrzIxowZwzp27Mg6dOjAxowZwy5evMi5zh/Dt99+ywwNDVlAQABbt24d27Rpk9jB1aZNm1izZs3Y9OnTmYqKCps6dSrz8PBgOjo6bOHChTy8g0rPnj1j6enp7Pr162IHVz/88ANTUlJiurq6zNnZWdQjtnnzZubm5sY5vqxdv36dOTo6Mm1tbbZ06VJR+YwZM9jo0aM5xx84cCBTVlZmDg4ObOPGjSw/P7/aNU+fPmUCgaBe8Z89e8bWrVvHHBwcmIqKChs+fDj7448/xPZBiY+Pr1dvkIaGBjt79my96kW4owZEHcjzBxB9+DQOb968YZcuXWLHjh1jR48eFTu4srKyqvGwtrbmHN/W1pbt37+fMSY+CXTx4sVs+vTpnONfuXKFOTg4MAUFBSYQCMQOBQUFzvEZYywlJYUdPnyYvXr1SlR2/PhxlpyczEt8xmS3UVhN3rx5w0pLSznHmThxIjt//nyt1wiFQvbo0aN6xVdWVmZ2dnZszZo1LC8vT+I1BQUF9fostbW15aWRSeqHGhB19F/7AKIPH8mKi4tZQUGB2MHVH3/8wYyMjKo9HPl8QMqSurq66O/PyMhINB8oIyODl1UkTk5OzNfXl128eJFlZmayR48eiR2N3fPnz1mfPn1Ef59VDayJEyeykJAQzvGzsrLY33//Lfr50qVLbNasWezHH3/kHJsxxiIiItjbt2+rlZeUlLCIiAhOsYVCIUtKSmKvX7/mFKcmx48fZ15eXiwzM1Mm8UntqAHRCMjyA4g+fD7s9evXbPr06czIyIgpKChUO7hq3bo1+/LLL3mb9PmxWVtbs9TUVMZY5aqebdu2McYqV2fo6elxjt+sWTOJq2D4MmHChFoPrvz9/ZmXlxf7+++/xXpo4uLiWLt27TjH79GjB9u7dy9jjLGcnBymra3NunbtygwMDNiyZcs4x1dQUJC45fnz5885//5XVFQwZWVlma0y09XVZSoqKkxBQYE1a9aM6enpiR1EtmgSZR18aHe/Xbt2cYofHBwss50Kx4wZg88//xz+/v7Izc1Fv3794ODggH379iE3N5fzJkMTJkyAt7d3tY2AXr16hQkTJnBKqcsYg4eHB27duiXT5Dhz585FQkICtm7dinHjxuGHH37A06dP8eOPP2L16tWc4+fl5SEkJIS3CW/vq6iowJ49e/Dnn39K3Ejq9OnTnOL36dMHx44dQ6dOnTBp0iQEBwfj0KFDuHLlCoYNG8YpNgD07dsX169f52VTKklevnwp9nNZWRlu3ryJf//9l9Pk2yqy3ijs5s2b+OSTTwAAv/76KxwdHXHu3DnRxFmu/4YZYxInHz558oRz9lUFBQW0adMG+fn5Mvk3/O6GcOTjowZEHcjzBxB9+HzYsWPHsHfvXtGOeT179oSNjQ0sLS3xyy+/YOzYsZzif/rpp0hMTETr1q15qrG4WbNmYc+ePRgwYAAcHR15n4m+fft2UaNk2rRp0NfXR3JyMgYNGsTLyo8dO3YgICAAN2/ehKOjI5SVlcXODx48mFP86OjoamVCoRBffvklWrVqxSk2ULljpqSdWJ8/fy7K+MpFWVmZKM6pU6dE/z3s7OyQk5NT77hVaewFAgH69u0rlgG1oqICmZmZYrlJ6mvNmjWYO3cuwsPD4ejoyDneuwICAniNR6RDDYg6kOcPIPrw+bAXL17A2toaAKCtrS3KfdGjRw988cUXnONv2bIFI0aMwNmzZ+Hk5FTtARkYGMgpfmRkJH799VeZbeeroKAABQUF0c8jR47EyJEjeYt//vx5JCcn448//qh2TiAQcE7WJYmCggKCg4Ph5uaGr776ilOsXr16Ye/evVixYgWAyjoLhUKsXbsW7u7unOvq4OCAbdu2YcCAAYiPjxfdJzs7GwYGBvWOW5XG/tq1a/Dy8kKzZs1E51RUVGBlZYXhw4dzqjsAfPbZZyguLoazszNUVFSqbWfNNddMlTdv3qCsrEysTFtbm5fYRDJqQNSTvHwA0YfPh7Vq1QqPHj2CpaUl2rVrh19//RWffPIJjh07Bl1dXc7x9+/fj7i4OKirqyMxMVGsh0AgEHBuQKioqMis+x8Adu/ejWbNmmHEiBFi5QcPHkRxcTHnb4GBgYHw9/fH4sWLZTbMI8mDBw9QXl7OOc7atWvh5uaGK1euoLS0FF999RVu3bqFFy9e4Ny5c5zjf/fdd/D19cXatWsREBAAZ2dnAEBMTIyod7E+lixZAgCwsrKCn58fL3tiSLJx40aZ7c/w+vVrzJs3D7/++ivy8/OrnZdF45P8HwFjcpwBqoGdOHECAQEB+OeffzjFuX37Ntzc3ODi4oLTp09j8ODBYh9AXLq+ExMT4evri8LCQgQEBIjmayxcuBB3797lnO42IiJCph8+e/bsqfXDh48uzI0bN0JRURGBgYFISEjAgAEDUFFRgfLycmzYsAGzZs3iFN/U1BSBgYGYP3++2Dd5vqxfvx4PHz7Eli1bZPJBbWtri23btlVrzCYlJeHzzz/HvXv3OMXX0tLCtWvXZDbEExISIvYzYww5OTn4/fffERAQgC1btnC+R25uLsLDw5GamgqhUIhOnTrxtlEYUPkgLCwshJ6enqjs0aNH0NDQkJiIrKmYPn06EhISsHz5conzl7gOP5LaUQOiDuT9A4g+fGomKZtiVlYWrly5gtatW4u+7XGhr6+PlJQUXh+Q709ePH36NPT19eHg4FBtiIRrI1FNTQ13796FlZWVWPmjR49gb2+PN2/ecIofEBCAnj17YvLkyZzi1OT9ho+CggKMjIzQp08fTJw4UWz4rT6ysrLQsmVLiY23rKwsWFhYcIoPAOXl5UhMTMSDBw8wZswYaGlpITs7G9ra2mK9f3Wlr6+PjIwMGBoafjC1PddePkVFReTk5FT7rMnPz4exsTGnXgILCwvR/CVtbW1cvXoVNjY2+Pnnn3HgwIF6ZygldUNDGHWQlpYm9nPVB9D69es/uEKjLqo+gJYtWybxHNcPIMYYUlNTxT58VFRUPpiCuyb/lQ8fAFBWVsbNmzfF3oOFhQUvH/pVAgICEBUVhYULF/IW8/0Jqr6+vrzFfp+xsTFu3LhRrQFx/fp1TsNgVdq2bYsFCxYgOTlZJnNEEhISOL3+Q6ytrWv8HbW2tub8O/r48WN4e3sjKysLJSUl6NevH7S0tLBmzRq8ffsW27Ztkzrmxo0boaWlJfqzLLeAruk7aklJiSgVeX3Jev4SqR01IOpAnj+A6MPnw8aNG4edO3fysmRTkoqKCqxZswZxcXFo3759tQdkfXKd7N69m6/qfdCoUaMQGBgILS0t9OrVC0Dl8MWsWbMwatQozvF37NiBZs2aISkpCUlJSWLn+JgjIms1rUQqKiriZWhv1qxZcHV1rdZg8/X1rXevzbtDf+PHj+daRYk2b94MoPLvsOrvuEpFRQXOnDkDOzs7TveQ9fwlUjtqQDQCsvwAog+fD5NlMjMASE9PR8eOHQFULqt9Fx+Nrz59+uDw4cPVPjALCwsxdOhQzvtArFy5Eo8fPxZbbSMUCjFu3Dh8++23nGIDlUmh+NapUyf8+eef0NPTE60Yqkl9k4FVDW0KBAIsXrxYrEevoqICly5dQocOHeoV+13Jyck4d+5ctQazpaUlnj59yjm+u7s7PvvsM3z66aecl16/a+PGjQAqP9+2bdsGRUVF0bmqidb1+QLzrgkTJuD69evo3bs3FixYgAEDBuD7778XzV8iskUNiBr8Vz6A6MPnw2SdTVHWPViJiYnVMmQCwNu3b3H27FnO8VVUVBAVFYUVK1bg+vXrUFdXh5OTEywtLTnHlpUhQ4aIli9XrRjiW9XQJmMM6enpYv/GVFRU4OzsjDlz5nC+j1AolNgL+eTJE1FPIBdOTk5YtGgRZsyYgf79+8Pf3x/9+/fn3MNX1TB0d3fH4cOHxeZg8SU4OFj0Z3d3d9y9e5fX+UukdtSAqMF/5QOIPnw+TNYPeFm5ceOG6M+3b99Gbm6u6OeKigrExsaiefPmvN2vbdu2oommfGKM4dChQ0hISJC4k2Z9JoFWLVF8/898qvq9mTBhAjZt2iSzPQf69euHsLAwbN++HUBlo7aoqAhLlizhZe+PzZs3IywsDKdOncL+/fsREBAARUVFfPrppxg7dix69+7NKX7Vf6fS0lJkZmaidevWnCeu1oTv+UukdrQKoxGQ5QeQn58fdHR0sH37dmhpaeHGjRswMjLCkCFDYGFhwctYulAoFH34REdH8/rhU+VjfPjwadiwYdizZw+0tbU/uN1zfVdJKCgoiHpIJP0zVldXx/fff895oq+st8oODAzE9u3b4e7uDhMTk2q9Ph9zvgcXf/31Fx48eIBevXpBXV29xqFJaWVnZ8Pd3R2Kioq4f/8+XF1dcf/+fRgaGuLMmTO8r6R6+/Ytjh07hlWrViE9PZ3zJNA3b95gxowZiIiIAFDZy9eqVSsEBgbC3Nwc8+fP5xT/zz//rPF3k2uaAVI7akA0IrL4AKIPn4YxYcIEbN68GVpaWpgwYUKt19b3Afn48WMwxtCqVStcvnwZRkZGonMqKiowNjYWG/qprxkzZoi2yjYzM6v2O1k13FRf+vr62LdvH687aX5oddC7uK4UevHiBUaMGIGEhAQIBALcv38frVq1wqRJk6Crq8spl02VN2/e4MCBA7h69apomffYsWOrbazGVW5uLiIjI7Fv3z5cvXoV//vf/3Dp0iVOMWfNmoVz584hLCwM3t7euHHjBlq1aoWYmBgsWbKk2io3aSxbtgzLly+Hq6urxN9NSbsIEx59pKRdckdX9/+1d+9hUVXrH8C/A3K/CA6giVxEBAnFWxJKpYGKHn1SMDjoUVTQJA08iAKeCjTzzh07WogoejSRFE4alwxEQZEOhlcyG4E8BYIWlgweA97fHzzMj3HGG3tvBnB9nmf+YEHvXg8Nr2vWXvt9jRQ6uz3uxdXdu3cFbQcslUopJSWFVqxYQe+++y4lJyfLWobzqaamhuLi4mjs2LEkEonI2dmZc8zg4GAaO3YsnTlzhvT09GS/m6ysLBo1ahTn+EJrb2UudEdRIYnFYjpx4oRg8a2tramiooLXmHv37pW9YmJiyNjYmHx9fSkhIYESEhLI19eXjI2NKTY2lvO1hO7GKbR79+7Rnj17aPLkydSnTx+ys7OjdevW8dYh1dLSks6dO0dEJPf7uXHjBhkYGHCKPWDAAFmnUqbrdf+9YBXp2OXt7t27+Pjjj+Hh4YHx48cDAM6dO4fc3Fx8+OGHnK/197//XbBunEDbVra/vz8vNSse9fvvv+OLL77AwYMHcerUKdjY2GDevHn4/PPPeSmvnJmZicOHD8PFxUXu08XLL78MiUTCOb7QiAhDhw4VvKOoRCJBfHw8KioqIBKJ4ODggJUrV/JSvEroUtnr1q3D+vXrsWfPHt4+UXd8UmjOnDn46KOP8N5778nGgoODsWPHDpw8eVLuIF5nCN2NE2jbeTt16pTSbXquDfH69+8PY2Nj+Pj4YNOmTRg3bhyneI+qr69XutPZ2NjIeYf14cOHmDBhAqcYTOexBcRj9KYExJKP6nRFR9Hc3Fy89dZbGDVqFFxdXUFEOHv2LBwdHfHll19iypQpnOKHhoYiISFBsFLZ3t7eOHToEMzMzGBtba1QJ6OzTzm1y83NxdatWxXGPTw8eLkFJnQ3zuTkZLz77rswMTHBgAEDFHqpcP0bzsrKwuTJkwUpsw4A48aNw4kTJxAUFATg/59sSk5Oln0g66wlS5bg4MGDvHyQY54fW0A8g56cgFjyUT2hO4pGREQgJCREoRBWREQEwsPDOS8gioqKUFBQgOzsbEFKZS9atAhlZWWYP3++0kOUXInFYhw7dgxr1qyRG8/MzOSlkqbQ3Tg//vhjbNy4EeHh4ZxjKTN16lRB4rbbvHkzpk2bhmvXrqG5uRkJCQm4evUqzp07p1A47Hk9ePAAn332GU6ePMlbkTbm2bFDlM/AysoK7733nkIC2r59O3bs2MF5l2DGjBkYM2YMNmzYIHtSwsrKCr6+vmhtbUVGRganuS9fvlyw5CO0s2fPYtq0afjb3/6GvXv3YtmyZXLJZ+zYsaqe4lMZGxtDKpWiublZkI6i2trauHz5ssIOxw8//AAnJyc8ePCAU3yhDoG209PTQ25uLl577TVOcR5n7969CAgIwLRp02SLzpKSEuTk5GD37t2ci6EJ2QwPaCvRXF5eDhsbG05xniQjIwPp6en46aefFGqKcN0BAtqKqUVHR8v1+gkPD8eIESM4xX3SAk0kEnF+Qoh5MrYD8QzWr1+PgIAAnDp1SmkC4krIdsC//fabQhtmvgmZfCZMmIDi4mJER0djyJAhyMvLw5gxY3Du3DnOyaerdDxPIwRTU1OUl5crLCDKy8t5ecpG6McoLSwsBKuhALTtcDg4OCAxMRFHjx4FEeHll19GcXExXn31Vc7xX375ZVy6dAk7d+6Euro6Ghsb4eXlxVszPG9vb+Tl5SEwMJBzLGUSExPx/vvvY+HChcjKysLixYshkUjw7bffYsWKFbxcY8SIEbInqfjUU2u49BZsB+IZnT9/HomJiaioqJAloODgYF4SECBcN86AgACMGzeuS5JPcnKyQvLZuHGjINdl/t9HH32EuLg4REREYMKECRCJRCgqKsLWrVsRGhqKDz74gJfr1NfX4/r16xCJRLCzs5N7bJSLEydOICkpCbt27VJo2MW03QKIjY3FjBkzBGk2NmzYMERFRWHu3LkwMDDAxYsXYWNjg8jISPz666+8dBsGgLq6OqXnsJycnHiJz3Q9toDo5Vjy6R4kEglSU1MhkUiQkJAAMzMz5OTkwMLCAo6OjpxiExHi4+MRExODX375BQAwcOBArFmzBsHBwZzPFDQ2NiIoKAhpaWmy37+6ujr8/PyQlJTU6a6u7Tre4tHV1VV4j3K9xdNRU1MT/vzzT7kxPnY/Hjx4gEuXLil9j7711lucYrd3m1RGJBLh5s2bnOLr6uqioqICVlZWMDMzw9dff42RI0fixo0bcHFxwd27dznFLysrw8KFC2UfvjoSiUScasU0NjZiy5Ytjy0kxfV3wzwZW0A8p56WgFjyUb3CwkJMnz4drq6uOH36NCoqKmBjY4Nt27ahtLSU0xmXR/3xxx8AwEuZ8nbLli3DyZMnsWPHDri6ugJoO1gZHByMKVOmYOfOnZzi792794mLnI5PRHWGVCpFWFgY0tPTlb4fub6HcnJy4Ofnhzt37ih8rye8R21sbJCRkYExY8Zg3LhxWLJkCZYtW4a8vDz4+vpyXsA5OTnB1tYW4eHhSg/JcumpMnfuXBQWFmLBggVKC0mtXLmy07GZZ6CC2hM9TmNjI61YsYJMTU1JTU1N4cVVdnY2mZqakkgkUnjxEV9IgwcPprKyMiIieuWVV2jXrl1E1FZEh48iWyNGjCBPT08qKSmhyspKqqqqknv1BC4uLhQTE0NE8oV0SktLaeDAgbxdp66ujs6cOUNFRUVUX1/PW1yxWEwFBQUK4/n5+WRiYsLbdYSyfPlycnBwoCNHjpCOjg7t2bOHNmzYQIMGDaIDBw5wjj9kyBBavnw51dbW8jDbrhcQEEDr1q0jIqKdO3eSjo4OTZ48mYyMjMjf359zfH19fd6KUj2qb9++VFRUJEhs5unYAuIZsAT0eD05+XQVPT09unnzJhHJLyAqKytJS0uLc/z79+/T4sWLSV1dXbbw7NOnD/n7+/NSAVNHR4euXbumMH7lyhXS1dXlHH/ixIm0b98+QaqjEhFZWFjIFkAGBgay91NaWhpNnz6dc3wDAwP68ccfOcd5nObmZtq9ezfNnTuX3N3d6c0335R7cdXS0kJ//vmn7OvDhw9TUFAQJSQk0P/+9z/O8WfNmkUZGRmc4yhjbW2t9L3JdA12C+MZWFpaIi0tDZMmTYKhoSEuXLgAW1tb7N+/H4cOHcJXX33FKb6hoSG+++47XqoGPkroRkitra1obW2VNbhKT09HUVERbG1tERgYyLkr5+zZs7FgwQLMmTOHUxxVGjRoENLT0zFhwgS5cyLHjh3D6tWrOVfUFPoWg7u7O8RiMdLS0qCtrQ2g7VbewoUL8euvv+LkyZOc4oeGhuJf//oXmpqa4OPjg4CAALi4uHCK2ZG+vj6uXr0KKysrDBo0CEePHoWzszMqKysxYsQI3L9/n1N8f39/uLq6IiAggKcZyxO6F4nQ7ty5g4ULF8LZ2RnDhw9XOOPC5RbtgQMHkJWVhX379nE+i8M8P7aAeAY9OQGx5KN6YWFhOHfuHI4cOQI7OztcuHABt2/fhp+fH/z8/Di3mzYxMUFGRgYmTZokN15QUAAfHx/U19dzin/58mVMnz4dDx48wMiRIyESiVBeXg4tLS3k5eVxPgQKtC10jx8/jtTUVHz11VewtbWFv78/FixYgP79+3OK7eTkhKSkJEycOBFTp06Fk5MToqOjkZiYiG3btuG///0vp/hSqRTe3t4wNTUV5KCyiYkJ0tLSeG021lFqair09fUVHvc+cuQIpFIp5zMo//73v7FgwQLZ+ZyOuJ4RGT16NCQSCYhIkCqmzJOxBcQz6MkJiCUf1fvzzz+xaNEifP755yAi9OnTB83NzbLiWFw7Zurq6qKsrEyujwoAXL16Fc7OzmhsbOQUH2jbcThw4AC+//572WPMQnSDBNoeF/3000+xceNGtLS04C9/+QuCg4Ph5ubWqXhxcXFQV1dHcHAwCgoKMGPGDLS0tKC5uRmxsbGcD9rt3r0bgYGB0NHRgVgsVqj2yvWg8sCBA3Hq1CnY2dlxivM49vb22LVrl0JRpsLCQrzzzju4fv06p/jW1taYOXMmPvzwQ86LwUetX7/+id/nujhnnkJlN096kNjYWEpISCCitoNjOjo6pKmpSWpqahQfH885fnJyMqmrq5O+vj5ZWVmRtbW17DV48GBOsV966SW6fv065zk+jp2dHeXn5yuMnzp1iuzs7DjHt7KyohUrVvTI8yGPkkgkdPjwYUpPT+f1XIebmxt5e3tTU1OTbEwqlZK3tze5u7tzjr9p0yZKSUlRGE9JSaEtW7Zwjt/R+fPnKTAwkPr27UuWlpYUGRlJS5cuJV1dXQoNDX3ueA8fPqRJkybJ/Q1UV1fTF198QeXl5bzMuX///rRx40ZqaWnhJd6joqOjafny5dTa2ipIfC0tLaqsrFQYr6ysJG1tbc7x9fX1BT0jwqgOW0A8RU9PQCz5dA+7d+8mR0dH0tTUJE1NTXJ0dKTk5GReYl++fJnMzc1JLBaTm5sbubu7k1gsJnNzc7py5Qrn+FZWVlRcXKwwXlJSQtbW1pzj3759m6Kjo2W/nzlz5lB2drbce/brr78mPT29TsU3MTGhH374gfM8H8fY2Jj396inp6fcq2/fvjR48GCaOXOmwve4srCwoKysLIXxzMxMMjc35xzfz8+Pt/c6072wUtZPoaGhgStXrshtS1paWsLS0pK3azx8+BB//etfeWtI5eXlJfd1fn6+YI2QzMzMcOnSJYUKghcvXuSlUZGXlxcKCgoEOWDaVT788EPExcUhKChIrh18SEgIqqqq8PHHH3OKP3z4cNy4cUPuFoOvry9vtxhqa2uVVkQ1NTVFTU0N5/iDBg3CkCFD4O/vj0WLFimtcOns7NzpTq9+fn5ISUlRaDbGl4ULF+Lw4cP4xz/+wVvMvn37yn3t6enJW+xH+fr6Ijg4GAYGBnjjjTcAtN2+WLlyJXx9fTnHt7Ozw9q1a1FUVMTbLVo1NTWltUMMDQ1hb2+PsLAwhTzI8I+dgXgGoaGh0NDQECwBhYSEwNTUlLcE9LTmRx1x7XPQXqAnNTVVLvn4+/vj7bffRnR0NKf4GzduRHx8vGCVNLuCiYkJkpKSMHfuXLnxQ4cOISgoSGkBou5k6NChiIqKwvz58+XG9+/fj6ioKM73+E+fPo2xY8dCT08PAFBdXY1jx47BwcEBHh4enGIDkFXRtLW1xSuvvCK7TjuuHRuDg4ORlpaGkSNH9siOkA8fPsSCBQtw5MgR2dNUra2t8PPzw65duzg/SSVEMbusrCyl4w0NDSgtLUVqair27dsneB+gFx1bQDwDloAerycmn65mbGyM0tJSpd0ynZ2d0dDQwPkaP//8M4qLi5U+qst1kbV161Zs374d27dvlx1k/OabbxAWFobQ0FCsXbuWU/ypU6fCy8sLgYGBaGhogL29PTQ1NXHnzh3Exsbi3Xff5RRf6I6NQsd3c3PD0aNHYWRkJDf++++/Y/bs2bx1nPzhhx9w8eJF6OjoYMSIEZwqRKraJ598grS0NJw/f17VU+nV2ALiGfTkBMSSj+oFBQVBQ0NDYSG4evVqNDU14ZNPPuEUPzU1VVZzQ4inAIgIERERSExMlHVb1dbWRnh4OCIjIznFBtp2aAoLC+Ho6Ijdu3cjKSkJ3333Hb744gtERkaioqKC8zV6MjU1NdTW1ip0Vq2rq4O5ublCaX0GuHHjBpydnfHbb7+peiq9GltA9HIs+ahe+w6WhYWFrEBSSUkJbt26BT8/P7kdp87sNllYWCAwMBBr167l7RyNMvfv30dFRQV0dHQwdOhQaGlp8RJXV1cX33//PSwtLeHj4wNHR0dERUXh1q1bsLe3h1Qq5eU6Pc2lS5cAAKNGjUJ+fj769esn+15LSwtycnLw6aefoqqqitN1hC42R0TIyMhAQUGB0vhcz2Epc+nSJXh4ePByRod5PHaIspdqTz4AcO3aNdTW1sq+bk8+5ubmnK/TG5MP365cuYIxY8YAgKzqpKmpKUxNTXHlyhXZz3W2a6ZUKoWvr6+giwegraBaZw8yPomtrS0yMzPh6emJ3NxchISEAGhb5PLRqE4IXl5e2Lt3LwwNDZ96WK+z79FRo0ZBJBJBJBIprYGho6ODpKSkTsXuaOXKlbJic8OHD+fcvVVZ/M8++wxvvvmm0mZaQkhOTsbo0aMFv86Lji0gVEToBMSST/dRUFAgaPyAgAAcOXIEERERgl5HKJGRkZg3bx5CQkLg7u4ue1IlLy+v2/4j0LdvX9l78dEnJvhSWVkJIoKNjQ1KS0vlnk7R1NSEmZkZ5yJkAPD5558jPT1dsGJzBw4cwNGjR3mNv2rVKqXj9+7dw3/+8x9IJBKcOXOGt+sxyrFbGCqyePFiJCYmwsDA4KlPTXTmSYnq6uouST5CV7rs168fDhw4IFj83qClpQUzZ85EU1OT0idVuvMh3Ha1tbWoqanByJEjZTsppaWlMDQ0xLBhw1Q8u8cjIvz0008wNTXtsb0YhK50OXjwYGRnZ/P6//Fx58ba3y/Lly9n57C6AFtAqFhPT0A9Mfn0Nhs2bEBUVBTs7e0Vdmn4OOTLPF5rayu0tbVx9epVhads+CSRSBAfH4+KigqIRCI4ODhg5cqVvNRHiYmJwc2bN7Fjxw5Bdvj27duHnJwc7NmzR5DS54zqsAWEinVFAmLJp3czNjZGXFwcFi1apOqpvJAcHR2RkpLCawfRjnJzc/HWW29h1KhRcHV1BRHh7NmzuHjxIr788ktMmTKFU3xPT08UFBSgX79+ghSbk0ql8PLyQnFxMWt41cuwMxAqpqamhqFDh+Lu3buCLCAel3wcHR15ST5FRUUoKCgQrNKlt7c3Dh06BDMzM5Z8HkNLS0vWxpvpetu2bcOaNWuwc+dODB8+nPf4ERERCAkJUShkFxERgfDwcM5/w0ZGRoJWuly0aBHKysowf/78HnuOiVGO7UB0AydOnMCWLVsESUCjR4+Gh4eH0uSTl5fH+R9gIc5vdOTj44OCggK8/fbbSpMP67YHbN68GTU1NUhMTFT1VF5IxsbGkEqlaG5uhqampsJO2a+//sopvra2Ni5fvqy0EJmTkxMePHjAKb7Q9PT0kJubi9dee03VU2F4xnYguoH58+dDKpVi5MiRvCegiooKpKenK4z7+/sjPj6+03HbcV0gPM2JEydY8nmK0tJS5Ofn4/jx44LsAjFPxsff0ZOYmpqivLxcYQFRXl6uUN+Fi/r6ely/fh0ikQh2dnZKe5J0hoWFRbd9HJfhhi0gugEhExBLPr2fkZERaxykQgsXLhQ0/tKlS/HOO+/g5s2bmDBhAkQiEYqKirB161aEhoZyjt/Y2CgrdtZeZ0VdXR1+fn5ISkrifLg7JiYGYWFh2LVrl0LTPaaH66q2n4xqrF+/noyMjGjLli10+vRpOnPmDG3evJmMjIxow4YNnOPfv3+fFi9eTOrq6iQSiUgkElGfPn3I39+fGhsbOcc/fvw4eXh4KG0ZzrSRSqV0//592deVlZUUFxdHOTk5KpzVi+XHH3+k999/n3x9fen27dtERJSdnc1LO/XW1laKjY0lc3Nz2d+Yubk5xcfHy7U876x33nmHbGxs6KuvvqJ79+7RvXv36MSJEzRkyBAKDAzkHN/IyIg0NTVJTU2N9PX1ydjYWO7F9FzsDEQ3IZFIkJqaColEgoSEBJiZmSEnJwcWFhZwdHTsdFwiQnx8PGJiYvDLL78AaHv0cs2aNQgODuZ8oGnZsmU4efIkduzYITvIV1RUhODgYEyZMgU7d+7kFL/j/WVdXV2F7Xmu95d7g0ebUQ0bNgwaGhq8NaNinqywsBDTp0+Hq6srTp8+jYqKCtjY2GDbtm0oLS1FRkYGb9f6448/AAAGBga8xTQxMUFGRgYmTZokN15QUAAfHx/U19dzir93794n5hmhd3AY4bAFRDfQVQmIJZ/eiTWjUq3x48fD29sbq1atgoGBAS5evAgbGxt8++23mD17Nn7++WdertPxNqG9vT1MTEx4iaurq4uysjI4ODjIjV+9ehXOzs5obGzk5TpML6TS/Q+GiIhcXFwoJiaGiIj09fVJIpEQEVFpaSkNHDiQl2vU1dXRmTNnqKioiOrr63mJSUSko6ND165dUxi/cuUK6erq8nYd5vF0dHSourqaiIi8vb1p3bp1RET0008/kY6Ojiqn9kLQ09OjmzdvEpH8329lZSVpaWlxji/0bUI3Nzfy9vampqYm2ZhUKiVvb29yd3fnHH/ixIm0b98+kkqlnGMx3Yuw3XeYZ3L58mWlz2Gbmpri7t27nGI3NjbC398fL730Et544w28/vrreOmllxAQEMBLl8Px48cjKipK7lGypqYmrF+/XtbTgItJkyYhLS0NTU1NnGP1Vu3NqG7duoXc3FxMnToVQPduRtWbGBkZKe36+N133/HSsG7VqlUoLCzEl19+iYaGBjQ0NCArKwuFhYW8HKKMj4/H2bNnMWjQILi7u2Py5MmwsLBAcXExEhISOMcfO3YswsLCMGDAACxduhQlJSWcYzLdhKpXMAyRubk5FRcXE5H8J5ijR4+SjY0Np9hCH5C6dOkSmZubk1gsJjc3N3J3dyexWEwDBw7k5QDZqlWrqH///mRoaEhLliyhc+fOcY7Z2xw5coQ0NDRITU2NpkyZIhvftGkTTZs2TYUzezGsWbOGXnvtNaqpqSEDAwO6ceMGFRUVkY2NjWw3iAuxWEwFBQUK4/n5+WRiYsI5PlHbjsNnn31Gq1atopCQEEpOTuZ1x6C5uZkyMzNp1qxZpKGhQQ4ODrR9+3aqra3l7RpM12MLiG5AyATEks+Loaamhi5cuEAtLS2ysfPnz1NFRYUKZ/ViePjwIc2bN4/U1NRIJBKRhoYGiUQimj9/PjU3N3OOL/Rtwk2bNlFKSorCeEpKCm3ZsoVz/EfV1dXRhg0bSFtbmzQ0NGjWrFn0zTff8H4dRnhsAdENCJmAWPJhmK4hkUjo8OHDlJ6eTjdu3OAtrtBnFKysrGQ7oB2VlJSQtbU15/gdnT9/ngIDA6lv375kaWlJkZGRtHTpUtLV1aXQ0FBer8UIjy0guhEhEhBLPgwjvN27d5OjoyNpamqSpqYmOTo6UnJyMi+xL1++rPQ2obm5OS+3CbW0tGSHQDuSSCS8HAK9ffs2RUdHy34/c+bMoezsbLkaFl9//TXp6elxvhbTtdgCopsQKgGx5MMwwvrggw9IT0+PIiIiKCsri7KysigiIoL09fXp/fff5+UaQt4mtLW1pf379yuMp6Wl0eDBgznH19DQoGHDhtG2bduorq5O6c/cu3ePJk2axPlaTNdiC4huQOgExJIPwwhHLBbTwYMHFcYPHjxIYrFYBTN6Plu2bCGxWEx79uyhqqoqqqqqopSUFBKLxbRp0ybO8QsLC+UqpVZVVbFKqb0EKyTVDZiYmCApKQlz586VGz906BCCgoJw584dFc3s6bZu3Yrt27dj+/btcHNzAwB88803CAsLQ2hoKNauXcsp/unTpzF27Fjo6ekBAKqrq3Hs2DE4ODjAw8OD8/wZhitjY2OUlpYq7Zbp7OyMhoYGztf4+eefUVxcjLq6Olm/inbBwcGcYhMRIiIikJiYiIcPHwJo6wAaHh6OyMhITrEBxUqp9vb20NTUZJVSewG2gOgGhE5ALPkwjHCCgoKgoaGB2NhYufHVq1ejqakJn3zyCaf4qampCAwMhKamJsRisVxlVpFIhJs3b3KK3+7+/fuoqKiAjo4Ohg4dCi0tLV7iskqpvRdbQHQDQiYglnwYRljtnSwtLCzg4uICACgpKcGtW7fg5+cn17/l0b/xZ2FhYYHAwECsXbsWamo9r/afrq4uvv/+e1haWsLHxweOjo6IiorCrVu3YG9vz0tBO0Y12AKiGxAyAbHkwzDCevPNN5/p50QiEfLz8587vlgsRmlpKYYMGfLc/2134OTkhCVLlsDT0xPDhw9HTk4Oxo8fj7KyMsyYMQO1tbWqniLTSWwB0Q0ImYBY8mGYni0sLAz9+vVDRESEqqfSKRkZGZg3bx5aWlrg7u6OvLw8AMDmzZtx+vRpZGdnq3iGTGexBUQvx5IPw/RsLS0tmDlzJpqamjBixAiFlvaduS3S1Wpra1FTU4ORI0fKdkJLS0thaGiIYcOGqXh2TGexBUQvx5IPw/RsGzZsQFRUFOzt7dG/f3+Fc0yduS3CMHxgC4hejiUfhunZjI2NERcXh0WLFql6Kgwjhy0gejmWfBimZxswYADOnDmj8Jg3w6hazzuWzzwXLS0tuLq6qnoaDMN00sqVK5GUlKTqaTCMArYD0ctt3rwZNTU1SExMVPVUGIbpBE9PT+Tn50MsFsPR0VHhHNPRo0dVNDPmRddH1RNghFVaWor8/HwcP36cJR+G6YGMjIzg5eWl6mkwjAK2gOjlWPJhmJ7tn//8J1pbW2X9YKqqqpCZmcn6wTAqx25h9HJNTU0s+TBMD/ZoP5hhw4ZBQ0OD9YNhVI4douzlZs2ahf379wMAGhoa4OLigpiYGMyePRs7d+5U8ewYhnmaCxcu4PXXXwfQVlitf//+qK6uRlpaGjvbxKgUW0D0ciz5MEzPJpVKYWBgAADIy8uDl5cX1NTU4OLigurqahXPjnmRsQVEL8eSD8P0bLa2tsjMzMStW7eQm5uLqVOnAgDq6upgaGio4tkxLzK2gOjlWPJhmJ4tMjISq1evhrW1NV599VWMHz8eQNsHgtGjR6t4dsyLjB2i7OVYMyqG6flYPximO2ILiBcASz4MwzAM39gCgmEYhmGY58bOQDAMwzAM89zYAoJhGIZhmOfGFhAMwzAMwzw3toBgGIZhGOa5sQUEwzAMwzDPjS0gGIZhGIZ5bmwBwTAMwzDMc/s/Azg8g9lDcjMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0ad41bae-44d5-460e-8538-aa8263154133",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d5c88718-d495-4242-b096-08698da90230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0</th>\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>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>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>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>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>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>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>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>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>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0         M        17.99         10.38          122.80     1001.0   \n",
       "1         M        20.57         17.77          132.90     1326.0   \n",
       "2         M        19.69         21.25          130.00     1203.0   \n",
       "3         M        11.42         20.38           77.58      386.1   \n",
       "4         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",
       "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0         0.2419  ...         25.38          17.33           184.60   \n",
       "1         0.1812  ...         24.99          23.41           158.80   \n",
       "2         0.2069  ...         23.57          25.53           152.50   \n",
       "3         0.2597  ...         14.91          26.50            98.87   \n",
       "4         0.1809  ...         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 31 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop([\"Unnamed: 32\", \"id\"], axis = 1, inplace = True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e971159f-4fd7-4c85-a86f-aa8c6c8982e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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>0.3001</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>0.0869</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>0.1974</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>0.2414</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>0.1980</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 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",
       "\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",
       "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0         0.2419  ...         25.38          17.33           184.60   \n",
       "1         0.1812  ...         24.99          23.41           158.80   \n",
       "2         0.2069  ...         23.57          25.53           152.50   \n",
       "3         0.2597  ...         14.91          26.50            98.87   \n",
       "4         0.1809  ...         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 31 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.diagnosis = [1 if value == \"M\" else 0 for value in data.diagnosis]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9aa48f63-56e9-4ec1-b851-55371480b819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='diagnosis'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['diagnosis'] = data['diagnosis'].astype('category', copy = False)\n",
    "data['diagnosis'].value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "60133098-e3bb-43f7-b8d9-990346245216",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['diagnosis']\n",
    "x = data.drop(['diagnosis'], axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5288a116-dc93-4646-b91c-33f79509ce38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.380</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.990</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.570</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.910</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.540</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>...</td>\n",
       "      <td>25.450</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>23.690</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>...</td>\n",
       "      <td>18.980</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>...</td>\n",
       "      <td>25.740</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>...</td>\n",
       "      <td>9.456</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     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",
       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
       "566        16.60         28.08          108.30      858.1          0.08455   \n",
       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
       "568         7.76         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "0             0.27760         0.30010              0.14710         0.2419   \n",
       "1             0.07864         0.08690              0.07017         0.1812   \n",
       "2             0.15990         0.19740              0.12790         0.2069   \n",
       "3             0.28390         0.24140              0.10520         0.2597   \n",
       "4             0.13280         0.19800              0.10430         0.1809   \n",
       "..                ...             ...                  ...            ...   \n",
       "564           0.11590         0.24390              0.13890         0.1726   \n",
       "565           0.10340         0.14400              0.09791         0.1752   \n",
       "566           0.10230         0.09251              0.05302         0.1590   \n",
       "567           0.27700         0.35140              0.15200         0.2397   \n",
       "568           0.04362         0.00000              0.00000         0.1587   \n",
       "\n",
       "     fractal_dimension_mean  ...  radius_worst  texture_worst  \\\n",
       "0                   0.07871  ...        25.380          17.33   \n",
       "1                   0.05667  ...        24.990          23.41   \n",
       "2                   0.05999  ...        23.570          25.53   \n",
       "3                   0.09744  ...        14.910          26.50   \n",
       "4                   0.05883  ...        22.540          16.67   \n",
       "..                      ...  ...           ...            ...   \n",
       "564                 0.05623  ...        25.450          26.40   \n",
       "565                 0.05533  ...        23.690          38.25   \n",
       "566                 0.05648  ...        18.980          34.12   \n",
       "567                 0.07016  ...        25.740          39.42   \n",
       "568                 0.05884  ...         9.456          30.37   \n",
       "\n",
       "     perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n",
       "0             184.60      2019.0           0.16220            0.66560   \n",
       "1             158.80      1956.0           0.12380            0.18660   \n",
       "2             152.50      1709.0           0.14440            0.42450   \n",
       "3              98.87       567.7           0.20980            0.86630   \n",
       "4             152.20      1575.0           0.13740            0.20500   \n",
       "..               ...         ...               ...                ...   \n",
       "564           166.10      2027.0           0.14100            0.21130   \n",
       "565           155.00      1731.0           0.11660            0.19220   \n",
       "566           126.70      1124.0           0.11390            0.30940   \n",
       "567           184.60      1821.0           0.16500            0.86810   \n",
       "568            59.16       268.6           0.08996            0.06444   \n",
       "\n",
       "     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",
       "..               ...                   ...             ...   \n",
       "564           0.4107                0.2216          0.2060   \n",
       "565           0.3215                0.1628          0.2572   \n",
       "566           0.3403                0.1418          0.2218   \n",
       "567           0.9387                0.2650          0.4087   \n",
       "568           0.0000                0.0000          0.2871   \n",
       "\n",
       "     fractal_dimension_worst  \n",
       "0                    0.11890  \n",
       "1                    0.08902  \n",
       "2                    0.08758  \n",
       "3                    0.17300  \n",
       "4                    0.07678  \n",
       "..                       ...  \n",
       "564                  0.07115  \n",
       "565                  0.06637  \n",
       "566                  0.07820  \n",
       "567                  0.12400  \n",
       "568                  0.07039  \n",
       "\n",
       "[569 rows x 30 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "aaaf4197-1834-4d18-8cd2-3ee96a403f34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "564    1\n",
       "565    1\n",
       "566    1\n",
       "567    1\n",
       "568    0\n",
       "Name: diagnosis, Length: 569, dtype: category\n",
       "Categories (2, int64): [0, 1]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8fd22b4-13a2-4043-81f8-f303db8fce9f",
   "metadata": {},
   "source": [
    "<h2><b>Normalize the Data</b></h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a5927235-7b8a-4bd2-bb82-19cae764de11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.09706398, -2.07333501,  1.26993369, ...,  2.29607613,\n",
       "         2.75062224,  1.93701461],\n",
       "       [ 1.82982061, -0.35363241,  1.68595471, ...,  1.0870843 ,\n",
       "        -0.24388967,  0.28118999],\n",
       "       [ 1.57988811,  0.45618695,  1.56650313, ...,  1.95500035,\n",
       "         1.152255  ,  0.20139121],\n",
       "       ...,\n",
       "       [ 0.70228425,  2.0455738 ,  0.67267578, ...,  0.41406869,\n",
       "        -1.10454895, -0.31840916],\n",
       "       [ 1.83834103,  2.33645719,  1.98252415, ...,  2.28998549,\n",
       "         1.91908301,  2.21963528],\n",
       "       [-1.80840125,  1.22179204, -1.81438851, ..., -1.74506282,\n",
       "        -0.04813821, -0.75120669]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "x_scaled = scaler.fit_transform(x)\n",
    "x_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb1749a9-00ba-43c9-9764-276cab5fbd3a",
   "metadata": {},
   "source": [
    "<h2><b>Select Data to Train and Test the Model</b></h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3184268f-0fef-47bc-9394-f4aade0fa4ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_scaled, y, test_size = 0.3, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1a2e3f11-b7d3-4169-a938-5f054c9c7c91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "       1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0,\n",
       "       1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression()\n",
    "\n",
    "lr.fit(x_train, y_train)\n",
    "\n",
    "y_pred = lr.predict(x_test)\n",
    "\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e03a7347-e68f-4883-9ad5-db0180e7b39f",
   "metadata": {},
   "source": [
    "<h2><b>Evaluate the Model</b></h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4877f6da-7caa-4618-85cf-dbf742c0bbc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.98\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy: .2f}\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "73d1ea3f-488f-428e-a719-1212b9c08ff2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99       108\n",
      "           1       0.97      0.98      0.98        63\n",
      "\n",
      "    accuracy                           0.98       171\n",
      "   macro avg       0.98      0.98      0.98       171\n",
      "weighted avg       0.98      0.98      0.98       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "023fb3c7-c402-4630-a7a9-2455082a6fed",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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