CLassification Breast cancer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt # for data visualization purposes\n",
    "import seaborn as sns # for data visualization\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\mukes\\Downloads\\breast cancer.csv', header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(699, 11)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        0   1   2   3   4   5   6   7   8   9   10\n",
       "0  1000025   5   1   1   1   2   1   3   1   1   2\n",
       "1  1002945   5   4   4   5   7  10   3   2   1   2\n",
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       "4  1017023   4   1   1   3   2   1   3   1   1   2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_names = ['Id', 'Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', \n",
    "             'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses', 'Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = col_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Clump_thickness</th>\n",
       "      <th>Uniformity_Cell_Size</th>\n",
       "      <th>Uniformity_Cell_Shape</th>\n",
       "      <th>Marginal_Adhesion</th>\n",
       "      <th>Single_Epithelial_Cell_Size</th>\n",
       "      <th>Bare_Nuclei</th>\n",
       "      <th>Bland_Chromatin</th>\n",
       "      <th>Normal_Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
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      ],
      "text/plain": [
       "        Id  Clump_thickness  Uniformity_Cell_Size  Uniformity_Cell_Shape  \\\n",
       "0  1000025                5                     1                      1   \n",
       "1  1002945                5                     4                      4   \n",
       "2  1015425                3                     1                      1   \n",
       "3  1016277                6                     8                      8   \n",
       "4  1017023                4                     1                      1   \n",
       "\n",
       "   Marginal_Adhesion  Single_Epithelial_Cell_Size Bare_Nuclei  \\\n",
       "0                  1                            2           1   \n",
       "1                  5                            7          10   \n",
       "2                  1                            2           2   \n",
       "3                  1                            3           4   \n",
       "4                  3                            2           1   \n",
       "\n",
       "   Bland_Chromatin  Normal_Nucleoli  Mitoses  Class  \n",
       "0                3                1        1      2  \n",
       "1                3                2        1      2  \n",
       "2                3                1        1      2  \n",
       "3                3                7        1      2  \n",
       "4                3                1        1      2  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Id', axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 699 entries, 0 to 698\n",
      "Data columns (total 10 columns):\n",
      " #   Column                       Non-Null Count  Dtype \n",
      "---  ------                       --------------  ----- \n",
      " 0   Clump_thickness              699 non-null    int64 \n",
      " 1   Uniformity_Cell_Size         699 non-null    int64 \n",
      " 2   Uniformity_Cell_Shape        699 non-null    int64 \n",
      " 3   Marginal_Adhesion            699 non-null    int64 \n",
      " 4   Single_Epithelial_Cell_Size  699 non-null    int64 \n",
      " 5   Bare_Nuclei                  699 non-null    object\n",
      " 6   Bland_Chromatin              699 non-null    int64 \n",
      " 7   Normal_Nucleoli              699 non-null    int64 \n",
      " 8   Mitoses                      699 non-null    int64 \n",
      " 9   Class                        699 non-null    int64 \n",
      "dtypes: int64(9), object(1)\n",
      "memory usage: 54.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1     145\n",
      "5     130\n",
      "3     108\n",
      "4      80\n",
      "10     69\n",
      "2      50\n",
      "8      46\n",
      "6      34\n",
      "7      23\n",
      "9      14\n",
      "Name: Clump_thickness, dtype: int64\n",
      "1     384\n",
      "10     67\n",
      "3      52\n",
      "2      45\n",
      "4      40\n",
      "5      30\n",
      "8      29\n",
      "6      27\n",
      "7      19\n",
      "9       6\n",
      "Name: Uniformity_Cell_Size, dtype: int64\n",
      "1     353\n",
      "2      59\n",
      "10     58\n",
      "3      56\n",
      "4      44\n",
      "5      34\n",
      "6      30\n",
      "7      30\n",
      "8      28\n",
      "9       7\n",
      "Name: Uniformity_Cell_Shape, dtype: int64\n",
      "1     407\n",
      "3      58\n",
      "2      58\n",
      "10     55\n",
      "4      33\n",
      "8      25\n",
      "5      23\n",
      "6      22\n",
      "7      13\n",
      "9       5\n",
      "Name: Marginal_Adhesion, dtype: int64\n",
      "2     386\n",
      "3      72\n",
      "4      48\n",
      "1      47\n",
      "6      41\n",
      "5      39\n",
      "10     31\n",
      "8      21\n",
      "7      12\n",
      "9       2\n",
      "Name: Single_Epithelial_Cell_Size, dtype: int64\n",
      "1     402\n",
      "10    132\n",
      "2      30\n",
      "5      30\n",
      "3      28\n",
      "8      21\n",
      "4      19\n",
      "?      16\n",
      "9       9\n",
      "7       8\n",
      "6       4\n",
      "Name: Bare_Nuclei, dtype: int64\n",
      "2     166\n",
      "3     165\n",
      "1     152\n",
      "7      73\n",
      "4      40\n",
      "5      34\n",
      "8      28\n",
      "10     20\n",
      "9      11\n",
      "6      10\n",
      "Name: Bland_Chromatin, dtype: int64\n",
      "1     443\n",
      "10     61\n",
      "3      44\n",
      "2      36\n",
      "8      24\n",
      "6      22\n",
      "5      19\n",
      "4      18\n",
      "7      16\n",
      "9      16\n",
      "Name: Normal_Nucleoli, dtype: int64\n",
      "1     579\n",
      "2      35\n",
      "3      33\n",
      "10     14\n",
      "4      12\n",
      "7       9\n",
      "8       8\n",
      "5       6\n",
      "6       3\n",
      "Name: Mitoses, dtype: int64\n",
      "2    458\n",
      "4    241\n",
      "Name: Class, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for var in df.columns:\n",
    "    print(df[var].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Bare_Nuclei'] = pd.to_numeric(df['Bare_Nuclei'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clump_thickness                  int64\n",
       "Uniformity_Cell_Size             int64\n",
       "Uniformity_Cell_Shape            int64\n",
       "Marginal_Adhesion                int64\n",
       "Single_Epithelial_Cell_Size      int64\n",
       "Bare_Nuclei                    float64\n",
       "Bland_Chromatin                  int64\n",
       "Normal_Nucleoli                  int64\n",
       "Mitoses                          int64\n",
       "Class                            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clump_thickness                 0\n",
       "Uniformity_Cell_Size            0\n",
       "Uniformity_Cell_Shape           0\n",
       "Marginal_Adhesion               0\n",
       "Single_Epithelial_Cell_Size     0\n",
       "Bare_Nuclei                    16\n",
       "Bland_Chromatin                 0\n",
       "Normal_Nucleoli                 0\n",
       "Mitoses                         0\n",
       "Class                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clump_thickness                 0\n",
       "Uniformity_Cell_Size            0\n",
       "Uniformity_Cell_Shape           0\n",
       "Marginal_Adhesion               0\n",
       "Single_Epithelial_Cell_Size     0\n",
       "Bare_Nuclei                    16\n",
       "Bland_Chromatin                 0\n",
       "Normal_Nucleoli                 0\n",
       "Mitoses                         0\n",
       "Class                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0     402\n",
       "10.0    132\n",
       "2.0      30\n",
       "5.0      30\n",
       "3.0      28\n",
       "8.0      21\n",
       "4.0      19\n",
       "9.0       9\n",
       "7.0       8\n",
       "6.0       4\n",
       "Name: Bare_Nuclei, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Bare_Nuclei'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.drop('Class', axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking Distribution with pandas library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1440 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = df1.hist(figsize=(20, 20), bins = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replacing Class Values with (0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['Class']] = df[['Class']].replace(2, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['Class']] = df[['Class']].replace(4, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking each class Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    458\n",
       "1    241\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing Binary classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\mukes\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Class', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\KMSpico\\temp\\ipykernel_13044\\3691112722.py:1: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  df.Class.value_counts()/np.float(len(df['Class'])) * 100\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    65.522175\n",
       "1    34.477825\n",
       "Name: Class, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Class.value_counts()/np.float(len(df['Class'])) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Five Number Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Clump_thickness</th>\n",
       "      <td>699.0</td>\n",
       "      <td>4.417740</td>\n",
       "      <td>2.815741</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uniformity_Cell_Size</th>\n",
       "      <td>699.0</td>\n",
       "      <td>3.134478</td>\n",
       "      <td>3.051459</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uniformity_Cell_Shape</th>\n",
       "      <td>699.0</td>\n",
       "      <td>3.207439</td>\n",
       "      <td>2.971913</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marginal_Adhesion</th>\n",
       "      <td>699.0</td>\n",
       "      <td>2.806867</td>\n",
       "      <td>2.855379</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single_Epithelial_Cell_Size</th>\n",
       "      <td>699.0</td>\n",
       "      <td>3.216023</td>\n",
       "      <td>2.214300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bare_Nuclei</th>\n",
       "      <td>683.0</td>\n",
       "      <td>3.544656</td>\n",
       "      <td>3.643857</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bland_Chromatin</th>\n",
       "      <td>699.0</td>\n",
       "      <td>3.437768</td>\n",
       "      <td>2.438364</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal_Nucleoli</th>\n",
       "      <td>699.0</td>\n",
       "      <td>2.866953</td>\n",
       "      <td>3.053634</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mitoses</th>\n",
       "      <td>699.0</td>\n",
       "      <td>1.589413</td>\n",
       "      <td>1.715078</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <td>699.0</td>\n",
       "      <td>0.344778</td>\n",
       "      <td>0.475636</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             count      mean       std  min  25%  50%  75%  \\\n",
       "Clump_thickness              699.0  4.417740  2.815741  1.0  2.0  4.0  6.0   \n",
       "Uniformity_Cell_Size         699.0  3.134478  3.051459  1.0  1.0  1.0  5.0   \n",
       "Uniformity_Cell_Shape        699.0  3.207439  2.971913  1.0  1.0  1.0  5.0   \n",
       "Marginal_Adhesion            699.0  2.806867  2.855379  1.0  1.0  1.0  4.0   \n",
       "Single_Epithelial_Cell_Size  699.0  3.216023  2.214300  1.0  2.0  2.0  4.0   \n",
       "Bare_Nuclei                  683.0  3.544656  3.643857  1.0  1.0  1.0  6.0   \n",
       "Bland_Chromatin              699.0  3.437768  2.438364  1.0  2.0  3.0  5.0   \n",
       "Normal_Nucleoli              699.0  2.866953  3.053634  1.0  1.0  1.0  4.0   \n",
       "Mitoses                      699.0  1.589413  1.715078  1.0  1.0  1.0  1.0   \n",
       "Class                        699.0  0.344778  0.475636  0.0  0.0  0.0  1.0   \n",
       "\n",
       "                              max  \n",
       "Clump_thickness              10.0  \n",
       "Uniformity_Cell_Size         10.0  \n",
       "Uniformity_Cell_Shape        10.0  \n",
       "Marginal_Adhesion            10.0  \n",
       "Single_Epithelial_Cell_Size  10.0  \n",
       "Bare_Nuclei                  10.0  \n",
       "Bland_Chromatin              10.0  \n",
       "Normal_Nucleoli              10.0  \n",
       "Mitoses                      10.0  \n",
       "Class                         1.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "checking the distribution of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x2520 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (40, 35)\n",
    "df.plot(kind = 'hist', bins = 10, subplots = True, layout = (5, 2), sharex = False, sharey = False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multivariate plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Clump_thickness</th>\n",
       "      <th>Uniformity_Cell_Size</th>\n",
       "      <th>Uniformity_Cell_Shape</th>\n",
       "      <th>Marginal_Adhesion</th>\n",
       "      <th>Single_Epithelial_Cell_Size</th>\n",
       "      <th>Bare_Nuclei</th>\n",
       "      <th>Bland_Chromatin</th>\n",
       "      <th>Normal_Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Clump_thickness</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.644913</td>\n",
       "      <td>0.654589</td>\n",
       "      <td>0.486356</td>\n",
       "      <td>0.521816</td>\n",
       "      <td>0.593091</td>\n",
       "      <td>0.558428</td>\n",
       "      <td>0.535835</td>\n",
       "      <td>0.350034</td>\n",
       "      <td>0.716001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uniformity_Cell_Size</th>\n",
       "      <td>0.644913</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.906882</td>\n",
       "      <td>0.705582</td>\n",
       "      <td>0.751799</td>\n",
       "      <td>0.691709</td>\n",
       "      <td>0.755721</td>\n",
       "      <td>0.722865</td>\n",
       "      <td>0.458693</td>\n",
       "      <td>0.817904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uniformity_Cell_Shape</th>\n",
       "      <td>0.654589</td>\n",
       "      <td>0.906882</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.683079</td>\n",
       "      <td>0.719668</td>\n",
       "      <td>0.713878</td>\n",
       "      <td>0.735948</td>\n",
       "      <td>0.719446</td>\n",
       "      <td>0.438911</td>\n",
       "      <td>0.818934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marginal_Adhesion</th>\n",
       "      <td>0.486356</td>\n",
       "      <td>0.705582</td>\n",
       "      <td>0.683079</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.599599</td>\n",
       "      <td>0.670648</td>\n",
       "      <td>0.666715</td>\n",
       "      <td>0.603352</td>\n",
       "      <td>0.417633</td>\n",
       "      <td>0.696800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single_Epithelial_Cell_Size</th>\n",
       "      <td>0.521816</td>\n",
       "      <td>0.751799</td>\n",
       "      <td>0.719668</td>\n",
       "      <td>0.599599</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.585716</td>\n",
       "      <td>0.616102</td>\n",
       "      <td>0.628881</td>\n",
       "      <td>0.479101</td>\n",
       "      <td>0.682785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bare_Nuclei</th>\n",
       "      <td>0.593091</td>\n",
       "      <td>0.691709</td>\n",
       "      <td>0.713878</td>\n",
       "      <td>0.670648</td>\n",
       "      <td>0.585716</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.680615</td>\n",
       "      <td>0.584280</td>\n",
       "      <td>0.339210</td>\n",
       "      <td>0.822696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bland_Chromatin</th>\n",
       "      <td>0.558428</td>\n",
       "      <td>0.755721</td>\n",
       "      <td>0.735948</td>\n",
       "      <td>0.666715</td>\n",
       "      <td>0.616102</td>\n",
       "      <td>0.680615</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.665878</td>\n",
       "      <td>0.344169</td>\n",
       "      <td>0.756616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal_Nucleoli</th>\n",
       "      <td>0.535835</td>\n",
       "      <td>0.722865</td>\n",
       "      <td>0.719446</td>\n",
       "      <td>0.603352</td>\n",
       "      <td>0.628881</td>\n",
       "      <td>0.584280</td>\n",
       "      <td>0.665878</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.428336</td>\n",
       "      <td>0.712244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mitoses</th>\n",
       "      <td>0.350034</td>\n",
       "      <td>0.458693</td>\n",
       "      <td>0.438911</td>\n",
       "      <td>0.417633</td>\n",
       "      <td>0.479101</td>\n",
       "      <td>0.339210</td>\n",
       "      <td>0.344169</td>\n",
       "      <td>0.428336</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.423170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <td>0.716001</td>\n",
       "      <td>0.817904</td>\n",
       "      <td>0.818934</td>\n",
       "      <td>0.696800</td>\n",
       "      <td>0.682785</td>\n",
       "      <td>0.822696</td>\n",
       "      <td>0.756616</td>\n",
       "      <td>0.712244</td>\n",
       "      <td>0.423170</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Clump_thickness  Uniformity_Cell_Size  \\\n",
       "Clump_thickness                     1.000000              0.644913   \n",
       "Uniformity_Cell_Size                0.644913              1.000000   \n",
       "Uniformity_Cell_Shape               0.654589              0.906882   \n",
       "Marginal_Adhesion                   0.486356              0.705582   \n",
       "Single_Epithelial_Cell_Size         0.521816              0.751799   \n",
       "Bare_Nuclei                         0.593091              0.691709   \n",
       "Bland_Chromatin                     0.558428              0.755721   \n",
       "Normal_Nucleoli                     0.535835              0.722865   \n",
       "Mitoses                             0.350034              0.458693   \n",
       "Class                               0.716001              0.817904   \n",
       "\n",
       "                             Uniformity_Cell_Shape  Marginal_Adhesion  \\\n",
       "Clump_thickness                           0.654589           0.486356   \n",
       "Uniformity_Cell_Size                      0.906882           0.705582   \n",
       "Uniformity_Cell_Shape                     1.000000           0.683079   \n",
       "Marginal_Adhesion                         0.683079           1.000000   \n",
       "Single_Epithelial_Cell_Size               0.719668           0.599599   \n",
       "Bare_Nuclei                               0.713878           0.670648   \n",
       "Bland_Chromatin                           0.735948           0.666715   \n",
       "Normal_Nucleoli                           0.719446           0.603352   \n",
       "Mitoses                                   0.438911           0.417633   \n",
       "Class                                     0.818934           0.696800   \n",
       "\n",
       "                             Single_Epithelial_Cell_Size  Bare_Nuclei  \\\n",
       "Clump_thickness                                 0.521816     0.593091   \n",
       "Uniformity_Cell_Size                            0.751799     0.691709   \n",
       "Uniformity_Cell_Shape                           0.719668     0.713878   \n",
       "Marginal_Adhesion                               0.599599     0.670648   \n",
       "Single_Epithelial_Cell_Size                     1.000000     0.585716   \n",
       "Bare_Nuclei                                     0.585716     1.000000   \n",
       "Bland_Chromatin                                 0.616102     0.680615   \n",
       "Normal_Nucleoli                                 0.628881     0.584280   \n",
       "Mitoses                                         0.479101     0.339210   \n",
       "Class                                           0.682785     0.822696   \n",
       "\n",
       "                             Bland_Chromatin  Normal_Nucleoli   Mitoses  \\\n",
       "Clump_thickness                     0.558428         0.535835  0.350034   \n",
       "Uniformity_Cell_Size                0.755721         0.722865  0.458693   \n",
       "Uniformity_Cell_Shape               0.735948         0.719446  0.438911   \n",
       "Marginal_Adhesion                   0.666715         0.603352  0.417633   \n",
       "Single_Epithelial_Cell_Size         0.616102         0.628881  0.479101   \n",
       "Bare_Nuclei                         0.680615         0.584280  0.339210   \n",
       "Bland_Chromatin                     1.000000         0.665878  0.344169   \n",
       "Normal_Nucleoli                     0.665878         1.000000  0.428336   \n",
       "Mitoses                             0.344169         0.428336  1.000000   \n",
       "Class                               0.756616         0.712244  0.423170   \n",
       "\n",
       "                                Class  \n",
       "Clump_thickness              0.716001  \n",
       "Uniformity_Cell_Size         0.817904  \n",
       "Uniformity_Cell_Shape        0.818934  \n",
       "Marginal_Adhesion            0.696800  \n",
       "Single_Epithelial_Cell_Size  0.682785  \n",
       "Bare_Nuclei                  0.822696  \n",
       "Bland_Chromatin              0.756616  \n",
       "Normal_Nucleoli              0.712244  \n",
       "Mitoses                      0.423170  \n",
       "Class                        1.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation = df.corr()\n",
    "correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class                          1.000000\n",
       "Bare_Nuclei                    0.822696\n",
       "Uniformity_Cell_Shape          0.818934\n",
       "Uniformity_Cell_Size           0.817904\n",
       "Bland_Chromatin                0.756616\n",
       "Clump_thickness                0.716001\n",
       "Normal_Nucleoli                0.712244\n",
       "Marginal_Adhesion              0.696800\n",
       "Single_Epithelial_Cell_Size    0.682785\n",
       "Mitoses                        0.423170\n",
       "Name: Class, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation['Class'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting correlation heatmap among various features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "plt.title('correlation of Attribution with Class variable')\n",
    "a = sns.heatmap(correlation, square=True, annot = True, fmt = '.2f', linecolor='white')\n",
    "a.set_xticklabels(a.get_xticklabels(), rotation = 90)\n",
    "a.set_yticklabels(a.get_yticklabels(), rotation = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "replacing missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Bare_Nuclei'].fillna(df['Bare_Nuclei'].median(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Bare_Nuclei'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['Class'], axis = 1)\n",
    "y = df['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((559, 9), (559,))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 0)\n",
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Clump_thickness                0\n",
       " Uniformity_Cell_Size           0\n",
       " Uniformity_Cell_Shape          0\n",
       " Marginal_Adhesion              0\n",
       " Single_Epithelial_Cell_Size    0\n",
       " Bare_Nuclei                    0\n",
       " Bland_Chromatin                0\n",
       " Normal_Nucleoli                0\n",
       " Mitoses                        0\n",
       " dtype: int64,\n",
       " Clump_thickness                0\n",
       " Uniformity_Cell_Size           0\n",
       " Uniformity_Cell_Shape          0\n",
       " Marginal_Adhesion              0\n",
       " Single_Epithelial_Cell_Size    0\n",
       " Bare_Nuclei                    0\n",
       " Bland_Chromatin                0\n",
       " Normal_Nucleoli                0\n",
       " Mitoses                        0\n",
       " dtype: int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.isnull().sum(), X_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardizing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Clump_thickness</th>\n",
       "      <th>Uniformity_Cell_Size</th>\n",
       "      <th>Uniformity_Cell_Shape</th>\n",
       "      <th>Marginal_Adhesion</th>\n",
       "      <th>Single_Epithelial_Cell_Size</th>\n",
       "      <th>Bare_Nuclei</th>\n",
       "      <th>Bland_Chromatin</th>\n",
       "      <th>Normal_Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.028383</td>\n",
       "      <td>0.299506</td>\n",
       "      <td>0.289573</td>\n",
       "      <td>1.119077</td>\n",
       "      <td>-0.546543</td>\n",
       "      <td>1.858357</td>\n",
       "      <td>-0.577774</td>\n",
       "      <td>0.041241</td>\n",
       "      <td>-0.324258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.669451</td>\n",
       "      <td>2.257680</td>\n",
       "      <td>2.304569</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>3.106879</td>\n",
       "      <td>1.297589</td>\n",
       "      <td>-0.159953</td>\n",
       "      <td>0.041241</td>\n",
       "      <td>-0.324258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.202005</td>\n",
       "      <td>-0.679581</td>\n",
       "      <td>-0.717925</td>\n",
       "      <td>0.074148</td>\n",
       "      <td>-1.003220</td>\n",
       "      <td>-0.104329</td>\n",
       "      <td>-0.995595</td>\n",
       "      <td>-0.608165</td>\n",
       "      <td>-0.324258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.125209</td>\n",
       "      <td>-0.026856</td>\n",
       "      <td>-0.046260</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-0.546543</td>\n",
       "      <td>-0.665096</td>\n",
       "      <td>-0.159953</td>\n",
       "      <td>0.041241</td>\n",
       "      <td>-0.324258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.233723</td>\n",
       "      <td>-0.353219</td>\n",
       "      <td>-0.382092</td>\n",
       "      <td>-0.274161</td>\n",
       "      <td>-0.546543</td>\n",
       "      <td>-0.665096</td>\n",
       "      <td>-0.577774</td>\n",
       "      <td>-0.283462</td>\n",
       "      <td>-0.324258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Clump_thickness  Uniformity_Cell_Size  Uniformity_Cell_Shape  \\\n",
       "0         2.028383              0.299506               0.289573   \n",
       "1         1.669451              2.257680               2.304569   \n",
       "2        -1.202005             -0.679581              -0.717925   \n",
       "3        -0.125209             -0.026856              -0.046260   \n",
       "4         0.233723             -0.353219              -0.382092   \n",
       "\n",
       "   Marginal_Adhesion  Single_Epithelial_Cell_Size  Bare_Nuclei  \\\n",
       "0           1.119077                    -0.546543     1.858357   \n",
       "1          -0.622471                     3.106879     1.297589   \n",
       "2           0.074148                    -1.003220    -0.104329   \n",
       "3          -0.622471                    -0.546543    -0.665096   \n",
       "4          -0.274161                    -0.546543    -0.665096   \n",
       "\n",
       "   Bland_Chromatin  Normal_Nucleoli   Mitoses  \n",
       "0        -0.577774         0.041241 -0.324258  \n",
       "1        -0.159953         0.041241 -0.324258  \n",
       "2        -0.995595        -0.608165 -0.324258  \n",
       "3        -0.159953         0.041241 -0.324258  \n",
       "4        -0.577774        -0.283462 -0.324258  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = pd.DataFrame(X_train, columns = ['Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', \n",
    "             'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses'])\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = pd.DataFrame(X_test, columns = ['Clump_thickness', 'Uniformity_Cell_Size', 'Uniformity_Cell_Shape', 'Marginal_Adhesion', \n",
    "             'Single_Epithelial_Cell_Size', 'Bare_Nuclei', 'Bland_Chromatin', 'Normal_Nucleoli', 'Mitoses'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Clump_thickness</th>\n",
       "      <th>Uniformity_Cell_Size</th>\n",
       "      <th>Uniformity_Cell_Shape</th>\n",
       "      <th>Marginal_Adhesion</th>\n",
       "      <th>Single_Epithelial_Cell_Size</th>\n",
       "      <th>Bare_Nuclei</th>\n",
       "      <th>Bland_Chromatin</th>\n",
       "      <th>Normal_Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.125209</td>\n",
       "      <td>-0.679581</td>\n",
       "      <td>-0.382092</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-0.546543</td>\n",
       "      <td>-0.665096</td>\n",
       "      <td>-0.995595</td>\n",
       "      <td>-0.608165</td>\n",
       "      <td>-0.324258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.125209</td>\n",
       "      <td>-0.353219</td>\n",
       "      <td>-0.382092</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-0.546543</td>\n",
       "      <td>-0.665096</td>\n",
       "      <td>-0.577774</td>\n",
       "      <td>-0.608165</td>\n",
       "      <td>-0.324258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.592655</td>\n",
       "      <td>0.952231</td>\n",
       "      <td>0.961239</td>\n",
       "      <td>2.164005</td>\n",
       "      <td>1.280168</td>\n",
       "      <td>-0.665096</td>\n",
       "      <td>1.511331</td>\n",
       "      <td>1.664758</td>\n",
       "      <td>-0.324258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.233723</td>\n",
       "      <td>-0.679581</td>\n",
       "      <td>-0.717925</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-0.546543</td>\n",
       "      <td>-0.665096</td>\n",
       "      <td>-0.577774</td>\n",
       "      <td>-0.283462</td>\n",
       "      <td>-0.324258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.310519</td>\n",
       "      <td>1.278593</td>\n",
       "      <td>0.625406</td>\n",
       "      <td>2.512315</td>\n",
       "      <td>1.736846</td>\n",
       "      <td>1.577973</td>\n",
       "      <td>0.675689</td>\n",
       "      <td>0.690648</td>\n",
       "      <td>1.494406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Clump_thickness  Uniformity_Cell_Size  Uniformity_Cell_Shape  \\\n",
       "0        -0.125209             -0.679581              -0.382092   \n",
       "1        -0.125209             -0.353219              -0.382092   \n",
       "2         0.592655              0.952231               0.961239   \n",
       "3         0.233723             -0.679581              -0.717925   \n",
       "4         1.310519              1.278593               0.625406   \n",
       "\n",
       "   Marginal_Adhesion  Single_Epithelial_Cell_Size  Bare_Nuclei  \\\n",
       "0          -0.622471                    -0.546543    -0.665096   \n",
       "1          -0.622471                    -0.546543    -0.665096   \n",
       "2           2.164005                     1.280168    -0.665096   \n",
       "3          -0.622471                    -0.546543    -0.665096   \n",
       "4           2.512315                     1.736846     1.577973   \n",
       "\n",
       "   Bland_Chromatin  Normal_Nucleoli   Mitoses  \n",
       "0        -0.995595        -0.608165 -0.324258  \n",
       "1        -0.577774        -0.608165 -0.324258  \n",
       "2         1.511331         1.664758 -0.324258  \n",
       "3        -0.577774        -0.283462 -0.324258  \n",
       "4         0.675689         0.690648  1.494406  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n",
       "       1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
       "       0, 1, 1, 0, 0, 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = knn.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prob = knn.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, plot_confusion_matrix, classification_report\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating Accuracy, recall, precission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.9714285714285714 \n",
      "\n",
      "recall_score:  0.9636363636363636 \n",
      "\n",
      "precision_score:  0.9636363636363636\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy score: ', accuracy_score(y_test, y_pred),'\\n')\n",
    "print('recall_score: ', recall_score(y_test, y_pred),'\\n')\n",
    "print('precision_score: ', precision_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating Confussion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN confusion matrix values are : \n",
      " [[83  2]\n",
      " [ 2 53]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print('KNN confusion matrix values are : \\n', cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcdefaults()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting Confusion Matrtix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (6, 4))\n",
    "cm_matrix = pd.DataFrame(data = cm, columns = ['Actual positive: 1', 'Actual Negative: 0'],\n",
    "                        index = ['Predict Positive: 1', 'Predict Negative: 0'])\n",
    "sns.heatmap(cm_matrix, annot = True, fmt = 'd', cmap = 'YlGnBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting ROC_AUC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'False Positive Rate')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, color='red', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.axis('tight')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross Validation score for KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation mean score: 0.9624551971326165\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(knn, X_train, y_train, cv = 2, scoring = 'accuracy')\n",
    "print('Cross-validation mean score: {}'.format(np.mean(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lg = LogisticRegression()\n",
    "lg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "lg_y_pred = lg.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating Accuacy, precision, recall score for Logisitic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.9714285714285714\n",
      "precision_score:  0.9473684210526315\n",
      "recall_score:  0.9818181818181818\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy score: ', accuracy_score(y_test, lg_y_pred))\n",
    "print('precision_score: ', precision_score(y_test, lg_y_pred))\n",
    "print('recall_score: ', recall_score(y_test, lg_y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calulating Logistic Regression Confusion Matrix values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[82,  3],\n",
       "       [ 1, 54]], dtype=int64)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_cm_matrix = confusion_matrix(y_test, lg_y_pred)\n",
    "lg_cm_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting Confusion Matrix for Logisitc Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (6, 4))\n",
    "cm_matrix = pd.DataFrame(data = lg_cm_matrix, columns = ['Actual positive : 1', 'Actual negative : 0'], index = ['Predicted positive : 1', 'Predicted negative : 0'])\n",
    "sns.heatmap(cm_matrix, annot = True, fmt = 'd', cmap = 'YlGnBu')\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting AUC_ROC Curve For Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'False Positive Rate')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Fkqry5ctLyuietWnTxn08PT1de/bsuSSwXuy2226Tv7+/Pv/881xvEHEl3333nVJSUjRv3rws3akrLQu91ueoVKmSJGnjxo1X/IXC5V7/6/35uJJSpUrp8ccf1+OPP65jx46pYcOGeuONN9zBKadfL/Pv6tV+1gH4LmacAPiE1q1bq2nTpho9erSSk5MVGRmp1q1b69NPP9Xhw4cvOf/48ePu2/fee6/Wr1+vr7/++pLzMn/736VLFx08eFDjx4+/5Jxz5865d4e7nBo1aqhOnTqaNWuWZs2apVKlSmUJMf7+/rr33nv11VdfZfvG7sJ6c+uGG25Q+/btNXny5Cxbn2d66aWXtG3bNj333HOXdAK++eabLDNKK1eu1IoVK9xvWnPzOl+Jv7//JR2g//u//5PT6cxyLPOaTxcHquvRuHFjFS9eXOPHj1d6err7+PTp093L+a4kOjpa/fv31y+//KL/+7//u+TzLpdL7733ng4cOJCrujI7UhcvW5w8ebLHn+PWW29V4cKFNXLkSCUnJ2f53IWPDQ0NzXbp5PX+fGTH6XRe8rUiIyNVunTpLFucX66mi5UoUUI333yzJk2apH379mX5nKe6jwC8Gx0nAD7j2Wef1f33368pU6bo0Ucf1ZgxY3TTTTepTp066t+/vypWrKijR49q+fLlOnDggNavX+9+3Jw5c3T//ferb9++atSokU6ePKl58+Zp7Nixqlevnh588EHNnj1bjz76qBYtWqQbb7xRTqdTW7Zs0ezZs93Xz7mSrl27atiwYQoJCVG/fv0uuVjtW2+9pUWLFqlZs2bq37+/atasqZMnT2rNmjVasGCBTp48ec2vzdSpU9WuXTvddddd6tGjh1q2bKmUlBTNnTtXixcvVteuXfXss89e8rjKlSvrpptu0mOPPaaUlBSNHj1axYsX13PPPec+J6ev85XceeedmjZtmsLDw1WzZk0tX75cCxYsUPHixbOcV79+ffn7++vtt99WfHy8goOD3dcoulZBQUF65ZVX9OSTT6pt27bq0qWL9uzZoylTpqhSpUo56mi899572rlzp5566inNnTtXd955p4oWLap9+/bpyy+/1JYtW7J0GHPi1ltvVVBQkDp27KhHHnlEZ8+e1fjx4xUZGZltSL2e5wgLC9OoUaP08MMPq0mTJurRo4eKFi2q9evXKykpyX0dskaNGmnWrFkaPHiwmjRpokKFCqljx44e+fm42JkzZ1S2bFndd999qlevngoVKqQFCxZo1apVWTqTl6spOx9++KFuuukmNWzYUAMGDFCFChW0Z88e/fDDD1q3bl2u6gNgQ6bs5QcAeSRzS+tVq1Zd8jmn02lUqlTJqFSpknu76507dxq9evUySpYsaQQGBhplypQx7rzzTmPOnDlZHnvixAlj4MCBRpkyZYygoCCjbNmyRu/evbNsDZ6ammq8/fbbRq1atYzg4GCjaNGiRqNGjYwRI0YY8fHx7vMu3o480/bt2w1JhiRj6dKl2X5/R48eNZ544gkjOjraCAwMNEqWLGm0a9fOGDdunPuczG22v/zyy1y9dmfOnDFeeeUVo1atWkaBAgWMwoULGzfeeKMxZcqUS7ZjztzK+d133zXee+89Izo62ggODjZatmxprF+//pLnzsnrfKU/u1OnThl9+vQxIiIijEKFChkdOnQwtmzZku1rOX78eKNixYqGv79/lq3JL7cd+cWvU3bbVBuGYXz44YdG+fLljeDgYKNp06bGsmXLjEaNGhn/+te/cvDqGkZ6eroxYcIEo2XLlkZ4eLgRGBholC9f3ujTp0+WrcoztyM/fvx4lsdnvj67d+92H5s3b55Rt25dIyQkxIiJiTHefvttY9KkSZecV758eeOOO+7Itq6cPkfmuTfccINRoEABIywszGjatKnxxRdfuD9/9uxZo0ePHkaRIkUMSVm2Ac/pz4eky24xrgu2I09JSTGeffZZo169ekbhwoWN0NBQo169esbHH3+c5TGXq+lyf84bN2407r77bqNIkSJGSEiIUa1aNePll1/Oth4AvsVhGPSfAQC5s2fPHlWoUEHvvvuuhgwZYnY5pnC5XCpRooTuueeebJegAQDshRknAACuIjk5+ZI5l6lTp+rkyZNq3bq1OUUBAPIVM04AAFxFbGysBg0apPvvv1/FixfXmjVrNHHiRNWuXVv333+/2eUBAPIBwQkAgKuIiYlRdHS0PvzwQ508eVLFihVTr1699NZbbykoKMjs8gAA+YAZJwAAAAC4CmacAAAAAOAqCE4AAAAAcBU+N+Pkcrl06NAhFS5cOEcXLQQAAABgT4Zh6MyZMypduvQlF56/mM8Fp0OHDik6OtrsMgAAAABYxP79+1W2bNkrnuNzwalw4cKSMl6csLAwk6sBAAAAYJaEhARFR0e7M8KV+FxwylyeFxYWRnACAAAAkKMRHjaHAAAAAICrIDgBAAAAwFUQnAAAAADgKghOAAAAAHAVBCcAAAAAuAqCEwAAAABcBcEJAAAAAK6C4AQAAAAAV0FwAgAAAICrIDgBAAAAwFUQnAAAAADgKghOAAAAAHAVBCcAAAAAuAqCEwAAAABcBcEJAAAAAK6C4AQAAAAAV0FwAgAAAICrIDgBAAAAwFUQnAAAAADgKghOAAAAAHAVBCcAAAAAuAqCEwAAAABcBcEJAAAAAK6C4AQAAAAAV0FwAgAAAICrIDgBAAAAwFUQnAAAAADgKghOAAAAAHAVpgan3377TR07dlTp0qXlcDj0zTffXPUxixcvVsOGDRUcHKzKlStrypQpeV4nAAAAAN9manBKTExUvXr1NGbMmBydv3v3bt1xxx1q06aN1q1bp2eeeUYPP/ywfv755zyuFAAAAIAvCzDzi99222267bbbcnz+2LFjVaFCBb333nuSpBo1amjp0qUaNWqUOnTokFdlAgAAAJAkw5CcTik9XUpLy/qR22OdOknBwWZ/RzlmanDKreXLl6t9+/ZZjnXo0EHPPPPMZR+TkpKilJQU9/2EhIS8Kg8AAAD4h2FcW6C40jFPPte1fk1POXJEiory3PPlMa8KTkeOHFHURS9uVFSUEhISdO7cORUoUOCSx4wcOVIjRozIrxIBAABwLZxOa4eHa6nD6TT7Vc0/AQEZH4GBWT8uOOYKDJJf4AXn+XnXPnVeFZyuxdChQzV48GD3/YSEBEVHR5tYEQAAwHUwjH/enFshUHjquQzD7Fc2fzgclwSK7EJGro558rmu5WsGBGR8X1ewbEec3vxxsyb3aaLIwiH59GJ7llcFp5IlS+ro0aNZjh09elRhYWHZdpskKTg4WMFetHYSAAB4kMvlXeEhJ8fS081+VfOPv793B4rsjvn7m/2q5rul2+PU77NVSkl36eNFO/VKp1pml3RNvCo4tWjRQj/++GOWY7/++qtatGhhUkUAANhE5sC3NweK7O67XGa/svnH27sWFx8LCPC6pVy41G/bjqv/1D+Vku5Su+qRGnp7dbNLumamBqezZ89qx44d7vu7d+/WunXrVKxYMZUrV05Dhw7VwYMHNXXqVEnSo48+qo8++kjPPfec+vbtq//973+aPXu2fvjhB7O+BQCAL8oc+LZyeLiWOnyFn593B4rsjvn7X3WpFJDflpwPTanpLrWvEakxPRsqOMB7O26mBqc///xTbdq0cd/PnEXq3bu3pkyZosOHD2vfvn3uz1eoUEE//PCDBg0apA8++EBly5bVhAkT2IocAKwss4thlUDhiefytYFvbw4U2R2jiwHkucVbj2nAtNVKTXfplppRGtOjoYICvPtnz2EYvjKJlyEhIUHh4eGKj49XWFiY2eUAwD+uNPBtxfCQ02O+8r+ZzIFvbw4UFx/LwcA3AFzM6TJ02we/advRs7q1ZpQ+snBoyk028KoZJwBwyxz49uZAcfF9Xxz49tZAkd0xHxz4BoDs+Ps59Fnfpvp0yS69dEcNBfpbMzTlFsEJsLsLB769ITzk9JgvDnx7a6DIrovBUikAsJ24symKKJSxm3Wp8AJeu3ve5RCcgAtdOPDtrYEiu2O+4sKBb28MFJfrYrBUCgBgcQv+Pqonv1ird++vqzvrlja7nDxBcMK1y20Xw+zwkJNjvjrw7Y2BIrtjdDEAAMh3v/59VI9PX600p6FfNh0lOMFLbd0qffSRlJjo+RDjiwPfVgoZ1/NcdDEAAIAH/LzpiAbOWKM0p6E765bS+13qmV1SniE42d1rr0nTp+ff17tw4NtbuxYXH6OLAQAAcIn5GzNCU7rLUMd6pTWqSz0F2GQjiOwQnOzu5MmM/95/v9S0ad6HE7oYAAAAtjd/42ENnLFW6S5DneqV1vs2D00Swcn+zp3L+O9990lduphbCwAAAGxhxe6TSncZuqt+ab13v/1Dk0Rwsr/M4FSggLl1AAAAwDaG3VlTtUuHq3ODMvL3840VR/aPhr4uKSnjvwQnAAAAXIcVu04oNT3jOooOh0P3NirrM6FJIjjZHx0nAAAAXKfv1h9Sjwkr9MSMNe7w5GtYqmd3mcGpYEFz6wAAAIBX+nbdQQ2atU4uQypSINCnukwXIjjZHUv1AAAAcI0uDE1dGpfVW/fUlZ+PBieW6tkdS/UAAABwDb5Z+09o6tYk2qdDk0RwsjfDkJKTM24TnAAAAJBD36w9qMGzM0JT96bRevPuOj4dmiSW6tlbZmiSmHECAABAjpUKD1FwgL86NyijNzrX9vnQJBGc7C1zvkmi4wQAAIAca1axuL578iZVjAglNJ3HUj07y5xvCgyU/P3NrQUAAACW9vXaA9p8OMF9v3JkIULTBQhOdsZW5AAAAMiB2av2a/Ds9eo5YYUOx58zuxxLYqmenbGjHgAAAK5i5sp9emHuBklSp3qlVTIsxOSKrIngZGdcwwkAAABXMGPFPr34dUZo6nNjjIbdWVMOB8vzskNwsjM6TgAAALiM6Sv26qWvN0qS+t5YQS/fWYPQdAUEJztjxgkAAADZmL/xiDs09bupgv5zB6HpaghOdkbHCQAAANm4qUqEGpcvqgbliujF2wlNOUFwsjNmnAAAAJCNQsEB+vzhZgoO8CM05RDbkdsZHScAAACcN3nZbo1ZtMN9PyTQn9CUC3Sc7IwZJwAAAEiauHS3Xvv+b0lSw3JF1aJScZMr8j4EJzuj4wQAAODzJvy+S6//sFmS9HjrSmpesZjJFXkngpOdMeMEAADg0y4MTQPbVNa/b63K8rxrRHCyMzpOAAAAPmvcbzv15o9bJElPta2sQbcQmq4HwcnOmHECAADwSRsPxv8TmtpV0aD2VQhN14ngZGcs1QMAAPBJtcuEa9idNRV/Lk2Dbqlqdjm2QHCyM5bqAQAA+JSUdKeCA/wlSX1vqmByNfbCdZzsjOAEAADgM8Ys2qH7Plmu+KQ0s0uxJYKTnTHjBAAA4BM++t92vfvzVm04GK+fNx0xuxxbYqmenTHjBAAAYHsfLtyu93/dJkl6tkM1dWkSbXJF9kRwsjOW6gEAANja6AXbNHrBdknSc/+qpsdbVza5IvsiONkZS/UAAABsa9Sv2/TBwozQ9MJt1fVoq0omV2RvBCc7o+MEAABgS6cSU/XFyn2SpBdvr64BNxOa8hrByc6YcQIAALCloqFBmjmguZbtPKEHm5c3uxyfQHCyMzpOAAAAtmEYhnbHJapiiUKSpIolCrlvI++xHbmdMeMEAABgC4Zh6N2ft+pfH/yuJduOm12OTyI42VV6upR2/uJndJwAAAC8lmEYenv+Vn28eKdS013aE5dodkk+iaV6dpXZbZIITgAAAF7KMAy9NX+LPl2yS5I0olMt9b4hxtyifBTBya4uDE4hIebVAQAAgGtiGIZG/rRF437LCE2v3lVLvVrEmFuUDyM42dWFG0M4HObWAgAAgFwxDENv/LBZE5buliS9dlctPUhoMhXBya7YUQ8AAMBruQzp+NkUSdLrnWvrAbYcNx3Bya64hhMAAIDX8vdz6L376+n+RtG6qUqE2eVA7KpnX3ScAAAAvIphGPp23UE5XYYkKcDfj9BkIQQnu+IaTgAAAF7DMAy9Mm+Tnp65TkPn/iXDMMwuCRdhqZ5dsVQPAADAKxiGoWHfbtK02L1yOKTG5YvJweZelkNwsiuW6gEAAFiey2Vo2LyN+jx2nxwO6e1766pL42izy0I2CE52RXACAACwNJfL0MvfbtT0FRmh6Z176+p+QpNlEZzsihknAAAASxvx3SZ3aPrvffV0b6OyZpeEK2BzCLtixgkAAMDSbq5aQsEBfnq/C6HJG9BxsiuW6gEAAFhauxpR+v25NooMCzG7FOQAHSe7IjgBAABYistl6K2ftmjfiST3MUKT9yA42RUzTgAAAJbhdBl67qu/NHbJTvWcGKvkNKfZJSGXWKpnV8w4AQAAWILTZejZOes1d81B+fs59FyH6goJ9De7LOQSwcmuWKoHAABgOqfL0LNfrtfctRmh6YNu9XVn3dJml4VrQHCyK5bqAQAAmMrpMjTky/X6+nxo+rBbA91Rt5TZZeEaEZzsio4TAACAqT5YuF1frz2oAD+H/q97A91Wh9DkzQhOdsWMEwAAgKn63BCjxVuP6fHWlfSv2oQmb0dwsis6TgAAAPnOMAw5HA5JUtHQIH39+I3y93OYXBU8ge3I7YoZJwAAgHyV7nTpyS/WavqKve5jhCb7IDjZFR0nAACAfJPmdOnpmev0/V+HNWLe3zp0+pzZJcHDWKpnV8w4AQAA5Is0p0tPfbFWP208oiB/P33yQEOVLsJ7MLshONkVHScAAIA8l+Z06ckZazV/U0ZoGvtgQ7WtHmV2WcgDBCe7YsYJAAAgT6Wmu/TkF2v086ajCvL306cPNlKb6pFml4U8QnCyI8NgqR4AAEAem7/pSEZoCvDTuAcbqXU1QpOdEZzsKDU1IzxJBCcAAIA80rFuKe0+nqj65YqoVdUSZpeDPEZwsqNzF+ziQnACAADwmJR0p5wuQwWDAuRwOPR0+ypml4R8wnbkdpQZnPz9pcBAc2sBAACwiZR0px77fI36TlmlpNR0s8tBPiM42dGF800OLroGAABwvZLTnHp02mr9b8sxrdt/WluPnDG7JOQzlurZEVuRAwAAeExymlOPfr5ai7ceV0ignyb1bqIG5YqaXRbyGcHJjghOAAAAHpGc5tSAaav127bzoemhJrqhUoTZZcEEBCc74hpOAAAA1y05zan+U//U79vjVCDQX5MeaqIWlYqbXRZMQnCyI67hBAAAcN0OnErSXwfiVSDQX5P7NFHzioQmX0ZwsiOW6gEAAFy3ypGFNf3hZkpMSVczQpPPIzjZEUv1AAAArsm5VKd2xZ1VrdLhkqTaZcJNrghWwXbkdkTHCQAAINeSUtPVd8oqdRm7XKv3njS7HFgMwcmOmHECAADIlczQtHzXCUmSYZhcECyHpXp2RMcJAAAgx5JS09Vn8iqt2H1ShYID9FnfJmpUvpjZZcFiCE52xIwTAABAjiSmpKvPlFVa6Q5NTdWoPBe3xaUITnZExwkAAOCqElMyOk0r95xU4eAAfdavqRqWIzQhewQnO2LGCQAA4KoC/B0KDfZX4ZAATevXTPWji5hdEiyM4GRHdJwAAACuKjjAX5880Ej7TiapalRhs8uBxbGrnh0x4wQAAJCtM8lpmrJst4zz2+aFBPoTmpAjdJzsiKV6AAAAl0hITlPvSSu1dt9pnUxM1eBbq5ldErwIwcmOWKoHAACQRUJymnpNXKl1+08rvECgbq1V0uyS4GUITnZEcAIAAHCLP5emXpNWav3+0ypSMFCf92um2mXCzS4LXobgZEfMOAEAAEg6H5omrtD6A/EqUjBQ0x9uplqlCU3IPYKTHTHjBAAAIKfLUO9JK7X+QLyKFgzU9Iebq2bpMLPLgpdiVz07YqkeAACA/P0c6n1DeUUUCtaM/oQmXB86TnZEcAIAAJAk3d2grG6pWVKFgnnbi+tDx8mOmHECAAA+6lRiqgbOWKNjCcnuY4QmeAJ/i+yIGScAAOCDTiWmqueEFfr7cIKOn0nRzAHN5XA4zC4LNkHHyW5cLiklJeM2wQkAAPiIk4mp6nE+NEUUCtbrnWsTmuBRdJzsJvmftjTBCQAA+IKTianqMT5WW46cUYnCwfqif3NVjixkdlmwGYKT3WTON0kEJwAAYHsnzqao54QVhCbkOYKT3WTONwUFSf7+5tYCAACQx4bO3aAtR84osnCwvhjQXJVKEJqQNwhOdsNW5AAAwIeMuKuWTp9L01v31FFFQhPyEMHJbtiKHAAA2Fy606UA/4w9zkqFF9Asds9DPmBXPbuh4wQAAGzs2Jlk3fHhUn23/pD7GKEJ+YHgZDdcwwkAANjUsYRkdR8Xq61Hz+jt+VuUnOY0uyT4EJbq2Q0dJwAAYENHz4emXXGJKh0eoukPN1NIIBthIf8QnOyGGScAAGAzF4amMkUK6Iv+zVWuOO91kL8ITnbDUj0AAGAjR+KT1X18rHafD00zBzRXdDFCE/IfM052w1I9AABgI3NW7yc0wRLoONkNwQkAANjIE20qK91l6L5GZVW2KKEJ5iE42Q0zTgAAwMsdS0hWkYJBCgrwk8Ph0DPtq5pdEsBSPdthxgkAAHixA6eSdO/YP/TEjDVKTXeZXQ7gRsfJbliqBwAAvNSBU0nqNi5WB06dk0MOnU5KVWRYiNllAZLoONkPwQkAAHih/Sf/CU3lixfUrEeaE5pgKXSc7IYZJwAA4GUyQ9PB0+cUU7ygZg5ooZLhhCZYC8HJbphxAgAAXmTfiSR1H58RmipEhOqL/s0JTbAkgpPdsFQPAAB4kWNnknUqKVUVI0L1xYDmimJ5HiyK4GQ3BCcAAOBFGscU09S+TVWuWEFmmmBpBCe7YcYJAABY3J64RJ1Lc6pGqTBJGeEJsDp21bMbZpwAAICF7YlLVLdxseo5YYW2HjljdjlAjhGc7IalegAAwKJ2xyWq67jlOpKQrOKhQSoWGmR2SUCOsVTPbliqBwAALGjX8bPqNi5Wx86kqGpUIU1/uLlKFA42uywgxwhOdkPHCQAAWMzO42fV/XxoqhZVWNP7N1NEIUITvAvByW6YcQIAABaSOdN0/EyKqpcsrOkPN1NxQhO8EMHJbug4AQAAC4koHKzyxQqqeGgQoQlejeBkJ2lpUnp6xm1mnAAAgAUUCg7QlL5NlZruYjMIeDV21bOTzG6TRMcJAACYZtvRM5rw+y73/ULBAYQmeD06TnaSGZwcDimYNjgAAMh/W4+cUY/xsTqRmKrCIQHq2qSc2SUBHkHHyU4yg1NISEZ4AgAAyEdbjiS4Q1PtMmHqUKuk2SUBHmN6cBozZoxiYmIUEhKiZs2aaeXKlVc8f/To0apWrZoKFCig6OhoDRo0SMnJyflUrcVxDScAAGCSzYcT1GP8Cp1ITFWdMuGa3q+5ihRkeR7sw9TgNGvWLA0ePFjDhw/XmjVrVK9ePXXo0EHHjh3L9vwZM2bohRde0PDhw7V582ZNnDhRs2bN0osvvpjPlVsUW5EDAAAT/H0oo9N0MjFVdcuG6/OHmym8YKDZZQEeZWpwev/999W/f3/16dNHNWvW1NixY1WwYEFNmjQp2/P/+OMP3XjjjerRo4diYmJ06623qnv37lftUvkMtiIHAAD57HRSqnpOiNWppDTVKxuuaf2aKbwAoQn2Y1pwSk1N1erVq9W+fft/ivHzU/v27bV8+fJsH3PDDTdo9erV7qC0a9cu/fjjj7r99tsv+3VSUlKUkJCQ5cO2CE4AACCfFSkYpKfbVVH96CKa9jChCfZl2q56cXFxcjqdioqKynI8KipKW7ZsyfYxPXr0UFxcnG666SYZhqH09HQ9+uijV1yqN3LkSI0YMcKjtVsWM04AAMAED91YQT2bl1egv+nj80Ce8aq/3YsXL9abb76pjz/+WGvWrNHcuXP1ww8/6LXXXrvsY4YOHar4+Hj3x/79+/Ox4nzGjBMAAMgHGw7E64EJKxSflOY+RmiC3ZnWcYqIiJC/v7+OHj2a5fjRo0dVsmT2W1e+/PLLevDBB/Xwww9LkurUqaPExEQNGDBAL730kvz8Lv2BDQ4OVrCvXNOIpXoAACCPrd9/Wg9OXKGE5HS98/MWvXF3HbNLAvKFab8aCAoKUqNGjbRw4UL3MZfLpYULF6pFixbZPiYpKemScOTv7y9JMgwj74r1FgQnAACQh9btP60HzoemxuWLaujtNcwuCcg3pnWcJGnw4MHq3bu3GjdurKZNm2r06NFKTExUnz59JEm9evVSmTJlNHLkSElSx44d9f7776tBgwZq1qyZduzYoZdfflkdO3Z0ByifxowTAADII2v3nVKviSt1JiVdTWKKanKfpioUbOpbSSBfmfq3vWvXrjp+/LiGDRumI0eOqH79+po/f757w4h9+/Zl6TD95z//kcPh0H/+8x8dPHhQJUqUUMeOHfXGG2+Y9S1YCzNOAAAgD6zZd0q9z4empjHFNLlPE4USmuBjHIaPrXFLSEhQeHi44uPjFRYWZnY5nvXii9LIkdLTT0ujR5tdDQAAsAGny1CH0b9px7GzalahmCY9RGiCfeQmG7D9iZ0w4wQAADzM38+hcQ82Uqd6pek0wacRnOyEGScAAOAhiSnp7tsVSxTSh90bqGAQoQm+i+BkJ8w4AQAAD1i156RavrNIS7YdN7sUwDIITnbCUj0AAHCdVu4+qd6TVupkYqqm/rGHS74A59FvtROW6gEAgOuwYtcJ9ZmySkmpTt1UOUJjejaUw+EwuyzAEghOdsJSPQAAcI1id51Qn8mrdC7NqZZVIjS+V2OFBHKdTCATwclOWKoHAACuwR8749Rvyp86l+bUzVVLaNyDjQhNwEWYcbITghMAALgG89Yd0rk0p1oRmoDLouNkJ8w4AQCAa/B659qqVKKQHmxRntAEXAYdJzthxgkAAOTQ5sMJcroydswL8PdT/5srEpqAKyA42QlL9QAAQA78vv24Oo9Zpufm/OUOTwCujOBkJwQnAABwFUu2HVe/z/5USrpL8edSCU5ADjHjZBeGwYwTAAC4osVbj2nAtNVKTXfplppRGtOjoYIC+D06kBP8pNhFSkpGeJLoOAEAgEssuiA03UpoAnKNjpNdZHabJIITAADIYtGWY3pk2mqlOl3qUCtK/9ed0ATkFsHJLjKDk7+/FBhobi0AAMBSXIYhQ4Zuq11SH3ZvoEB/QhOQWwQnu2C+CQAAXEa7GlGa/UgL1S4TTmgCrhE/OXbBNZwAAMAFFm05pr0nEt33G5QrSmgCrgM/PXbBVuQAAOC8XzYd0YBpf6rbuFgdiU82uxzAFliqZxcEJwAAIGn+xiMaOGON0l2GGscUU0ShILNLAmyBjpNdMOMEAIDPm7/xsDs0dapXWqO61FMAy/MAj+AnyS6YcQIAwKf9tOGwnpixVukuQ53rl9b7hCbAo1iqZxcs1QMAwGct3npMA79YK6fL0N0Nyui/99eTv5/D7LIAWyE42QVL9QAA8Fn1yhZR1ajCqlGysN4lNAF5guBkFyzVAwDAZxUNDdLMAc1VKDiA0ATkERa+2gVL9QAA8Cnfrjuoz2P3uu+HFwgkNAF5iI6TXRCcAADwGd+sPajBs9fJZUgVS4TqhkoRZpcE2B4dJ7tgxgkAAJ/w9doD7tDUrUm0mlcobnZJgE+g42QXzDgBAGB7c9cc0L+/XC/DkLo3jdYbnevIj+V5QL6g42QXLNUDAMDW5qy+MDSVIzQB+YzgZBcEJwAAbOvvQwl6dk5GaOrRrJze6Fyb0ATkM5bq2QUzTgAA2FbN0mF6qm0VnUhM0audCE2AGQhOdsGMEwAAtuNyGe6Q9Ez7KpIkh4PQBJiBpXp2wVI9AABsZebKfXpg4golpaZLyghMhCbAPAQnuyA4AQBgGzNW7NMLczfoj50n9NXqA2aXA0AEJ/tgxgkAAFuYvmKvXvx6gySp740V9EDz8iZXBEBixsk+mHECAMDrTYvdq5e/2ShJ6ndTBf3njhoszwMsguBkFyzVAwDAq01dvkfDvt0kSerfsoJevJ3QBFgJwckuCE4AAHit00mpGvXrNknSgJsrauht1QlNgMUQnOyCGScAALxWkYJBmtavmRZuPqan2lUmNAEWRHCyA6dTSknJuE3HCQAAr3HsTLIiC4dIkmqXCVftMuEmVwTgcthVzw6Sk/+5TXACAMArTPh9l9r+d4lW7z1pdikAcoDgZAeZy/QkghMAAF5gwu+79PoPm3U2JV3LdpwwuxwAOcBSPTvI3Io8OFjyIwsDAGBl43/bpTd+3CxJeqptZT3ZtrLJFQHICYKTHbCjHgAAXuHTJTs18qctkqSn21XRoFuqmlwRgJwiONkBwQkAAMv7ZPFOvT0/IzQ9076KnmlPaAK8CcHJDtiKHAAAS3O6DK3cnTHLNKh9VT3dvorJFQHILYKTHWTOONFxAgDAkvz9HPrkgUb6edMR3VW/jNnlALgG7CRgByzVAwDAkn7fflyGYUiSQgL9CU2AFyM42QHBCQAAy/lw4XY9OHGlXv9hszs8AfBeLNWzA2acAACwlNELtmn0gu2SpOKFguRwOEyuCMD1IjjZATNOAABYxqhft+mDhRmh6fl/VddjrSuZXBEATyA42QFL9QAAMJ1hGBq1YLs+PB+aht5WXY+0IjQBdkFwsgOCEwAAprswNL10ew31v7miyRUB8CSCkx0w4wQAgOkqRBSUn0N68fYaergloQmwG4KTHTDjBACA6e5uUFZ1yhRR5chCZpcCIA+wHbkdsFQPAIB8ZxiGxv+2S0cTkt3HCE2AfRGc7IDgBABAvjIMQ2/9tEVv/LhZ3cfHKjnNaXZJAPIYS/XsgBknAADyjWEYGvnTFo37bZck6aEbYhQS6G9yVQDyGsHJDphxAgAgXxiGoTd+2KwJS3dLkl67q5YebBFjblEA8gXByQ5YqgcAQJ4zDEOvfb9Zk5adD02da+vB5uVNrgpAfiE42QHBCQCAPPfx4p3u0PTG3bXVsxmhCfAlbA5hB5lL9ZhxAgAgz9zbsKwqRITqzbvrEJoAH0THyQ7oOAEAkOdKhofop6dbshEE4KPoONkBwQkAAI8zDEOvzNuk79Yfch8jNAG+i+BkB2xHDgCAR7lchl7+dqOm/LFHg2ev04FTSWaXBMBkLNWzA7YjBwDAYzJD0/QV++RwSCPvqauyRfnlJODrCE52wFI9AAA8wuUy9NI3G/XFyozQ9N799XRPw7JmlwXAAghO3i4tTXI6M24TnAAAuGYZoWmDvli5X34O6b0u9XR3A0ITgAwEJ2+X2W2SmHECAOA6fL/hsDs0vd+lvjo3KGN2SQAshODk7TLnmxwOKSjI3FoAAPBiHeuW0pq9p9SgXBHdVZ/QBCArgpO3u3C+yeEwtxYAALyM02XI6TIUFOAnh8OhVzrVMrskABbFduTejo0hAAC4Jk6XoWfnrNfj09coNd1ldjkALI6Ok7fjGk4AAOSa02Xo2S/Xa+7ag/L3c2jtvlNqVrG42WUBsDCCk7fjGk4AAOSK02Xo37PX6Zt1h+Tv59D/dW9AaAJwVQQnb8dSPQAAcizd6dK/v1yvb9cdUsD50HRbnVJmlwXACxCcvB3BCQCAHEl3ujR49nrNW58Rmj7q0VD/ql3S7LIAeAmCk7djxgkAgBzZFZeoBZuPKsDPoTE9G6pDLUITgJwjOHk7ZpwAAMiRqlGFNfmhJkpITtctNaPMLgeAlyE4eTuW6gEAcFlpTpcOnT6n8sVDJYlNIABcM67j5O0ITgAAZCvN6dJTX6xV5zHLtPlwgtnlAPByBCdvl7lUjxknAADc0pwuPTljrX7aeESJKU4diU82uyQAXo6let6OjhMAAFmkprv05Bdr9POmowry99OnDzZSm+qRZpcFwMsRnLwdwQkAALfUdJeemLFGv/59VEEBfhr3YCO1rkZoAnD9CE7ejuAEAICkjND0+PQ1WrA5IzSN79VYraqWMLssADbBjJO3Y8YJAABJGXNN8edSFRzgpwmEJgAeRsfJ29FxAgBAkhQaHKDJfZpq65EzalS+qNnlALAZOk7ejuAEAPBhyWlOzVt/yH2/UHAAoQlAnqDj5O0ygxNL9QAAPiY5zalHP1+txVuP6+Cpc3qsdSWzSwJgYwQnb5c540THCQDgQ5LTnHpk2mot2XZcIYF+qlc23OySANgcwcnbsVQPAOBjktOc6j/1T/2+PU4FAv016aEmalGpuNllAbA5gpO3IzgBAHzIxaFpcp8mal6R0AQg7xGcvB0zTgAAH+F0Ge7QVDDIX5MfaqJmhCYA+YRd9bwdM04AAB/h7+dQu+qRCg3y15Q+TQlNAPIVHSdvx1I9AIAPeejGCrq9bilFFg4xuxQAPoaOkzczDIITAMDWklLTNfzbjTqdlOo+RmgCYAY6Tt4sJeWf28w4AQBsJik1XX0mr9KK3Se17ehZzejfTA6Hw+yyAPgogpM3y5xvkug4AQBsJTElXX2mrNLK3SdVODhAz/6rGqEJgKkITt4sc5leQEDGBwAANpCYktFpWrknIzRN7ddUDcoVNbssAD6Od9vejPkmAIDNnE1JV5/JK7VqzykVDgnQtH7NVD+6iNllAQDByatlLtVjvgkAYBPPf/WXOzR93q+Z6hGaAFgEu+p5MzpOAACbGXJrNVWNKqTpDxOaAFgLHSdvRnACANiAYRjujR8qRIRq/tM3y8+PjSAAWAsdJ29GcAIAeLmE5DR1GxerxVuPuY8RmgBYEcHJmzHjBADwYvHn0vTgxJVasfuknv/qLyWnOc0uCQAui6V63oyOEwDAS8UnpenBSSv014F4FS0YqMkPNVVIoL/ZZQHAZRGcvBnBCQDgheKT0vTAxBXacDBexUKDNP3hZqpRKszssgDgighO3iwzOLFUDwDgJU4npeqBiSu08WCCioUGaUb/ZqpektAEwPoITt4sc8aJjhMAwEtMXrZHGw8mqHhokGb0b65qJQubXRIA5AjByZuxVA8A4GWealdFp5NS1aNZeUITAK9CcPJmBCcAgBeIP5emQsEB8vdzyN/PoRF31Ta7JADINbYj92bMOAEALO5kYqq6frpcz85ZL6fLMLscALhmdJy8GTNOAAALO3E2RT0nrNCWI2cUdzZVRxOSVboI/88C4J0ITt6MpXoAAIuKO5uinuNXaOvRMypROFhf9G9OaALg1QhO3ozgBACwoLizKeoxPlbbjp5VZOFgfTGguSqVKGR2WQBwXQhO3owZJwCAxRw/kxGath87q6iwjE5TRUITABsgOHkzZpwAABaz7egZ7TmRqKiwYM0c0EIVIkLNLgkAPILg5M1YqgcAsJgbK0doXK/GiikeSmgCYCsEJ29GcAIAWMCxhGSdS3OqfPGMoNSmWqTJFQGA53EdJ2+WuVSPGScAgEmOJSSr2/hYdRsXq70nEs0uBwDyDMHJm9FxAgCY6GhCsrqNi9Wu44nyczjkkMPskgAgz7BUz5sRnAAAJjkSn6zu42O1Oy5RZYoU0MwBzRVdjBUQAOyL4OTNCE4AABMcjj+n7uNitedEEqEJgM8gOHkrp1NKTc24zYwTACCfHI4/d36eKUllixbQF/0JTQB8A8HJW2V2myQ6TgCAfBMS4K+CQQGKLpYRmsoWJTQB8A0EJ291YXAKCTGvDgCATykaGqTpDzdTcppTpYvwizsAvoNd9bxVZnAKDpb8+GMEAOSdA6eS9NXqA+77xUKDCE0AfA4dJ2/FNZwAAPlg/8kkdR8fqwOnzsnhkO5pWNbskgDAFAQnb8WOegCAPLb/ZJK6jYvVwdPnVCEiVDdUijC7JAAwjelrvMaMGaOYmBiFhISoWbNmWrly5RXPP336tJ544gmVKlVKwcHBqlq1qn788cd8qtZCCE4AgDx0cWj6on9zlQxnphaA7zK14zRr1iwNHjxYY8eOVbNmzTR69Gh16NBBW7duVWRk5CXnp6am6pZbblFkZKTmzJmjMmXKaO/evSpSpEj+F2+2zODEUj0AgIftO5GkbuOW61B8sipGhOqLAc0VFUZoAuDbTA1O77//vvr3768+ffpIksaOHasffvhBkyZN0gsvvHDJ+ZMmTdLJkyf1xx9/KDAwUJIUExOTnyVbR+aMEx0nAIAHxSel/ROaSoRqZv/miiQ0AYB5S/VSU1O1evVqtW/f/p9i/PzUvn17LV++PNvHzJs3Ty1atNATTzyhqKgo1a5dW2+++aacTudlv05KSooSEhKyfNgCS/UAAHkgvGCg7m8crUqEJgDIwrTgFBcXJ6fTqaioqCzHo6KidOTIkWwfs2vXLs2ZM0dOp1M//vijXn75Zb333nt6/fXXL/t1Ro4cqfDwcPdHdHS0R78P0xCcAAB5ZNAtVTVv4E2EJgC4gOmbQ+SGy+VSZGSkxo0bp0aNGqlr16566aWXNHbs2Ms+ZujQoYqPj3d/7N+/Px8rzkPMOAEAPGR3XKKe/GKtklLT3cdCg9l4FwAuZNq/ihEREfL399fRo0ezHD969KhKliyZ7WNKlSqlwMBA+fv7u4/VqFFDR44cUWpqqoKCgi55THBwsIKDgz1bvBUw4wQA8ICdx8+q+7hYHTuTosIhAXrz7jpmlwQAlmRaxykoKEiNGjXSwoUL3cdcLpcWLlyoFi1aZPuYG2+8UTt27JDL5XIf27Ztm0qVKpVtaLI1luoBAK7ThaGpWlRhDb6lqtklAYBlmbpUb/DgwRo/frw+++wzbd68WY899pgSExPdu+z16tVLQ4cOdZ//2GOP6eTJk3r66ae1bds2/fDDD3rzzTf1xBNPmPUtmIfgBAC4DjuOnVW386GpesnCmtG/mSIK2XCFBgB4iKkLmLt27arjx49r2LBhOnLkiOrXr6/58+e7N4zYt2+f/Pz+yXbR0dH6+eefNWjQINWtW1dlypTR008/reeff96sb8E8mUv1mHECAOTSjmNn1G3cCsWdzQxNzVUs1MdWbgBALpk++Tlw4EANHDgw288tXrz4kmMtWrRQbGxsHlflBeg4AQCugctl6LHP1yjubIpqlArT9IebEZoAIAe8alc9XIDgBAC4Bn5+Do3uVl83Vi6uGYQmAMgx0ztOuEYEJwBALqQ5XQr0z/h9aa3S4Zr+cHOTKwIA73JdHafk5GRP1YHcYsYJAJBDW44kqN17S7R670mzSwEAr5Xr4ORyufTaa6+pTJkyKlSokHbt2iVJevnllzVx4kSPF4jLoOMEAMiBzYcT1H1crPadTNJ/f94mwzDMLgkAvFKug9Prr7+uKVOm6J133sly7aTatWtrwoQJHi0OV0BwAgBcxd+HEtRjfKxOJaWpbtlwjX2wkRwOh9llAYBXynVwmjp1qsaNG6eePXvK39/ffbxevXrasmWLR4vDFRCcAABXsOlQvHpMyAhN9cqGa1q/ZgovEGh2WQDgtXK9OcTBgwdVuXLlS467XC6lpaV5pCjkADNOAIDL2HgwXj0nrFD8uTTVjy6iqf2aKiyE0AQA1yPXHaeaNWvq999/v+T4nDlz1KBBA48UhRyg4wQAuIyJS3cr/lyaGpQjNAGAp+S64zRs2DD17t1bBw8elMvl0ty5c7V161ZNnTpV33//fV7UiOwQnAAAl/HWvXUUFRaiJ9pUUmFCEwB4RK47TnfddZe+++47LViwQKGhoRo2bJg2b96s7777Trfcckte1IjsZAYnluoBACQdOJXk3jEvOMBfL9xWndAEAB50TRfAbdmypX799VdP14KcMox/ZpzoOAGAz1u//7QemLhC9zeK1st31mDnPADIA7nuOFWsWFEnTpy45Pjp06dVsWJFjxSFq0hLk1yujNsEJwDwaev2n9YDE1boTHK6Nhw8rZR0l9klAYAt5brjtGfPHjmdzkuOp6Sk6ODBgx4pCleRuUxPIjgBgA9bu++Uek1cqTMp6WoaU0yT+zRRSKD/1R8IAMi1HAenefPmuW///PPPCg8Pd993Op1auHChYmJiPFocLiMzOPn5SRdchBgA4DtW7z2l3pNW6mxKuppWKKbJDzVRaPA1rcAHAORAjv+F7dy5syTJ4XCod+/eWT4XGBiomJgYvffeex4tDpdx4XwT69gBwOes3ntSvSet0tmUdDWvWEyTHmqigkGEJgDISzn+V9Z1fqamQoUKWrVqlSIiIvKsKFwFW5EDgE87cOqcklLT1aJicU18qDGhCQDyQa7/pd29e3de1IHcIDgBgE+7q34ZhRcIVLMKxVUgiJkmAMgP1/QrqsTERC1ZskT79u1Tampqls899dRTHikMV5C5VI9rOAGAz1i995TKFi2gqLAQSVLrapEmVwQAviXXwWnt2rW6/fbblZSUpMTERBUrVkxxcXEqWLCgIiMjCU75gY4TAPiUFbtOqM+UVSoZFqKZjzRXZOEQs0sCAJ+T6+s4DRo0SB07dtSpU6dUoEABxcbGau/evWrUqJH++9//5kWNuBjBCQB8RuyuE3po8iolpTpVpmgBhYUEml0SAPikXAendevW6d///rf8/Pzk7++vlJQURUdH65133tGLL76YFzXiYgQnAPAJy3eeUJ/Jq3Quzambq5bQ+F6NuU4TAJgk18EpMDBQfn4ZD4uMjNS+ffskSeHh4dq/f79nq0P2mHECANv7Y0ec+kxZqXNpTrWqWkLjHmxEaAIAE+V6xqlBgwZatWqVqlSpolatWmnYsGGKi4vTtGnTVLt27byoERej4wQAtrZi1wn1/WyVktNcalOthD55gNAEAGbLdcfpzTffVKlSpSRJb7zxhooWLarHHntMx48f16effurxApENghMA2Fr54qEqGRaittUjNZZOEwBYQq47To0bN3bfjoyM1Pz58z1aEHKA4AQAtlYyPESzH22h8AKBCg4gNAGAFeS643Q5a9as0Z133umpp8OVMOMEALazZNtxfbvuoPt+ZOEQQhMAWEiugtPPP/+sIUOG6MUXX9SuXbskSVu2bFHnzp3VpEkTuVyuPCkSF6HjBAC2snjrMfWf+qcGzVqn5TtPmF0OACAbOV6qN3HiRPXv31/FihXTqVOnNGHCBL3//vt68skn1bVrV23cuFE1atTIy1qRieAEALaxaOsxPTJttVLTXbqlZpQalS9qdkkAgGzkuOP0wQcf6O2331ZcXJxmz56tuLg4ffzxx9qwYYPGjh1LaMpPBCcAsIVFW47pkakZoalDrSiN6dFQQQEeW0UPAPCgHHecdu7cqfvvv1+SdM899yggIEDvvvuuypYtm2fF4TKYcQIAr7dw81E99vkapTpd+letkvq/Hg0U6E9oAgCrynFwOnfunAqef6PucDgUHBzs3pYc+YyOEwB4tS1HEvTo56uV5jR0W+2S+rA7oQkArC5X25FPmDBBhQoVkiSlp6drypQpioiIyHLOU0895bnqkD2CEwB4tWpRhdWjaTnFnU3V6G71CU0A4AUchmEYOTkxJiZGDofjyk/mcLh327OqhIQEhYeHKz4+XmFhYWaXc22aN5dWrJDmzZM6djS7GgDANTAMQ06XoQBCEwCYJjfZIMcdpz179lxvXfCUzBknOk4A4DXmbzyieesPanTXBgoK8JPD4VCA/5V/IQkAsI5cLdWDRbBUDwC8yvyNhzVwxlqluww1Lr9XfW+qYHZJAIBcIjh5I4ITAHiNnzYc1sAv1srpMtS5fmn1alHe7JIAANeA4OSN2I4cALzCD38d1lMzM0LTPQ3K6N3768nfj+V5AOCNCE7eiI4TAFje938d0tMz12WEpoZl9O59hCYA8GZs5eNtDENKTs64TXACAEuKP5emoXM3yOkydG/DsoQmALCBawpOO3fu1H/+8x91795dx44dkyT99NNP2rRpk0eLQzYyQ5NEcAIAiwovEKiJvZvowebl9c59dQlNAGADuQ5OS5YsUZ06dbRixQrNnTtXZ8+elSStX79ew4cP93iBuEjmfJNEcAIAizmbku6+3bRCMb3WuTahCQBsItfB6YUXXtDrr7+uX3/9VUFBQe7jbdu2VWxsrEeLQzYy55sCA6UARtQAwCq+XntArd9dpM2HE8wuBQCQB3IdnDZs2KC77777kuORkZGKi4vzSFG4AjaGAADLmbvmgAbPXq+4s6n6eu1Bs8sBAOSBXAenIkWK6PDhw5ccX7t2rcqUKeORonAFBCcAsJQ5qw/o31+ul2FIPZqV0wv/qm52SQCAPJDr4NStWzc9//zzOnLkiBwOh1wul5YtW6YhQ4aoV69eeVEjLsQ1nADAMr78c7+enZMRmno2K6fX76otP2aaAMCWch2c3nzzTVWvXl3R0dE6e/asatasqZtvvlk33HCD/vOf/+RFjbgQHScAsITZf+7Xc1/9JcOQHmxeXq93JjQBgJ3leneBoKAgjR8/Xi+//LI2btyos2fPqkGDBqpSpUpe1IeLEZwAwHQul6GvVh+QYUi9WpTXiE615HAQmgDAznIdnJYuXaqbbrpJ5cqVU7ly5fKiJlwJwQkATOfn59DEh5royz/366EbYghNAOADcr1Ur23btqpQoYJefPFF/f3333lRE66EGScAMM3Gg/Hu24WCA9TnxgqEJgDwEbkOTocOHdK///1vLVmyRLVr11b9+vX17rvv6sCBA3lRHy5GxwkATDF9xV7d+X9L9X8Lt5tdCgDABLkOThERERo4cKCWLVumnTt36v7779dnn32mmJgYtW3bNi9qxIUITgCQ76bF7tVLX2+UJJ1KSpNhGCZXBADIb7kOTheqUKGCXnjhBb311luqU6eOlixZ4qm6cDmZwYmlegCQL6Yu36OXv8kITQ/fVEEv31mD5XkA4IOuOTgtW7ZMjz/+uEqVKqUePXqodu3a+uGHHzxZG7KTOeNExwkA8txnf+zRsG83SZIG3FxRL91BaAIAX5XrXfWGDh2qmTNn6tChQ7rlllv0wQcf6K677lJBOiD5g6V6AJAvpizbrVe+y9gE6ZFWFfXCv6oTmgDAh+U6OP3222969tln1aVLF0VERORFTbgSghMA5IvMkPRY60p6rkM1QhMA+LhcB6dly5blRR3IKbYjB4B80fuGGNUuE6aG5YoSmgAAOQtO8+bN02233abAwEDNmzfviud26tTJI4XhMug4AUCembvmgNpWj1SRgkGSpEbli5lcEQDAKnIUnDp37qwjR44oMjJSnTt3vux5DodDTqfTU7UhOwQnAMgT437bqTd/3KLaZcI059EbFBLob3ZJAAALyVFwcrlc2d6GCQhOAOBxny7ZqZE/bZEkta0epeCA67paBwDAhnL9f4apU6cqJSXlkuOpqamaOnWqR4rCFTDjBAAe9cnif0LT0+2qaPAtVZlpAgBcItfBqU+fPoqPj7/k+JkzZ9SnTx+PFIUroOMEAB4zZtEOvT0/IzQ9076KBt1S1eSKAABWletd9QzDyPY3cQcOHFB4eLhHisIVEJwAwCOmLNutd3/eKkkafEtVPdWuiskVAQCsLMfBqUGDBnI4HHI4HGrXrp0CAv55qNPp1O7du/Wvf/0rT4rEBQhOAOARrapFKmrJTj3QrLyeJDQBAK4ix8Epcze9devWqUOHDipUqJD7c0FBQYqJidG9997r8QJxEWacAMAjKkSE6udnbnZvPQ4AwJXkODgNHz5ckhQTE6OuXbsqJCQkz4rCFdBxAoBrNmbRDtUsHaY21SIlidAEAMixXM849e7dOy/qQE4RnADgmoz6dZs+WLhdQQF+Wji4laKL0bkHAORcjoJTsWLFtG3bNkVERKho0aJX3Kb15MmTHisOF0lPl9LSMm4TnAAgRwzD0KgF2/Xhwu2SpCG3ViU0AQByLUfBadSoUSpcuLD7Nte3MElmt0lixgkAcsAwDI36dZs+/N8OSdJ/7qihh1tWNLkqAIA3chiGYZhdRH5KSEhQeHi44uPjFRYWZnY5uXPsmBQVlXHb5ZIIsABwWYZh6L1ftumjRYQmAED2cpMNcn0B3DVr1mjDhg3u+99++606d+6sF198UampqbmvFjmX2XEKCSE0AcBV/LjhiDs0vXxnTUITAOC65Do4PfLII9q2bZskadeuXeratasKFiyoL7/8Us8995zHC8QF2BgCAHLsX7VL6u4GZTTszprqd1MFs8sBAHi5XAenbdu2qX79+pKkL7/8Uq1atdKMGTM0ZcoUffXVV56uDxfiGk4AcEWGYcjlyliB7u/n0Ptd6qkvoQkA4AG5Dk4Z/1NySZIWLFig22+/XZIUHR2tuLg4z1aHrOg4AcBlGYahN3/crCFz1st5PjyxmREAwFNyHZwaN26s119/XdOmTdOSJUt0xx13SJJ2796tqMyNC5A3CE4AkC3DMPTGD5s1/vfdmrvmoGJ3nTC7JACAzeQ6OI0ePVpr1qzRwIED9dJLL6ly5cqSpDlz5uiGG27weIG4AEv1AOAShmHote83a8LS3ZKkN+6urRsrR5hcFQDAbnJ0HacL1a1bN8uuepneffdd+fv7e6QoXAYdJwDIwjAMvfr935q8bI8k6c2766hHs3LmFgUAsKVcB6dMq1ev1ubNmyVJNWvWVMOGDT1WFC6D4AQAboZhaMR3f2vKH3skSSPvqaPuTQlNAIC8kevgdOzYMXXt2lVLlixRkSJFJEmnT59WmzZtNHPmTJUoUcLTNSITwQkA3LYdPasZK/bJ4ZDeuqeOujYhNAEA8k6uZ5yefPJJnT17Vps2bdLJkyd18uRJbdy4UQkJCXrqqafyokZkYsYJANyqlSysTx5oqLfvrUtoAgDkuVx3nObPn68FCxaoRo0a7mM1a9bUmDFjdOutt3q0OFyEjhMAH+dyGYpLTFFk4RBJUrsa7OYKAMgfue44uVwuBQYGXnI8MDDQfX0n5BGCEwAf5nIZ+s+3G3XXR8u090Si2eUAAHxMroNT27Zt9fTTT+vQoUPuYwcPHtSgQYPUrl07jxaHixCcAPgol8vQS99s0IwV+3QkIVl/HYg3uyQAgI/JdXD66KOPlJCQoJiYGFWqVEmVKlVShQoVlJCQoP/7v//LixqRiRknAD7I5TL04tcb9MXK/fJzSO93qaeO9UqbXRYAwMfkesYpOjpaa9as0cKFC93bkdeoUUPt27f3eHG4CB0nAD7G5TL0wty/NPvPA+dDU311blDG7LIAAD4oV8Fp1qxZmjdvnlJTU9WuXTs9+eSTeVUXskNwAuBDXC5Dz3/1l75cnRGaRnWtr7vqE5oAAObIcXD65JNP9MQTT6hKlSoqUKCA5s6dq507d+rdd9/Ny/pwIYITAB+SmJquTYcS5OeQRndroE4szwMAmCjHM04fffSRhg8frq1bt2rdunX67LPP9PHHH+dlbbgYM04AfEjhkEBNf7iZJvRuTGgCAJgux8Fp165d6t27t/t+jx49lJ6ersOHD+dJYcgGHScANud0GVq6Pc59v2hokNpW51pNAADz5Tg4paSkKDQ09J8H+vkpKChI5zLfzCPvEZwA2JjTZWjIl+v1wMQVmha71+xyAADIIlebQ7z88ssqeMEysdTUVL3xxhsKDw93H3v//fc9Vx2yIjgBsKl0p0v//nK9vl13SAF+DkWEBpldEgAAWeQ4ON18883aunVrlmM33HCDdu3a5b7vcDg8VxkuxYwTABtKd7o0ePZ6zVufEZo+6tFA/6pdyuyyAADIIsfBafHixXlYBnKEjhMAm0l3uvTMrHX6/q/DCvBzaEzPhupQq6TZZQEAcIlcXwAXJiI4AbARl8vQ07PW6Ye/DivQ36ExPRrqVkITAMCiCE7ewjBYqgfAVvz8HKoSWUiB/g593LORbqnJ7nkAAOsiOHmL1NSM8CTRcQJgG8+0r6qO9UqrUolCZpcCAMAV5Xg7cpjswm3fCU4AvFSa06UPFmxXUmq6+xihCQDgDQhO3iIzOPn5SYGB5tYCANcgNd2lgTPWaNSCbXr08zUyMrvoAAB4gWsKTr///rseeOABtWjRQgcPHpQkTZs2TUuXLvVocbjAhfNNbPsOwMtkhqafNx1VUICf+t4YwyUsAABeJdfB6auvvlKHDh1UoEABrV27VikpKZKk+Ph4vfnmmx4vEOexox4AL5Wa7tLj09fol78zQtP4Xo3Vulqk2WUBAJAruQ5Or7/+usaOHavx48cr8IIlYzfeeKPWrFnj0eJwAYITAC+Uku7U49NXa8HmowoO8NOEXo3VqmoJs8sCACDXcr2r3tatW3XzzTdfcjw8PFynT5/2RE3IDsEJgBcaOneDFmw+lhGaejdWyyqEJgCAd8p1x6lkyZLasWPHJceXLl2qihUreqQoZINrOAHwQv1uqqBS4SGa2LsJoQkA4NVy3XHq37+/nn76aU2aNEkOh0OHDh3S8uXLNWTIEL388st5USMkOk4AvFKt0uFaNKS1QgL9zS4FAIDrkuvg9MILL8jlcqldu3ZKSkrSzTffrODgYA0ZMkRPPvlkXtQIieAEwCskpzk1ePY69bmxgprEFJMkQhMAwBZyHZwcDodeeuklPfvss9qxY4fOnj2rmjVrqlAhLmCYpwhOACwuOc2p/lP/1O/b47Rqzyn9/lwbQhMAwDZyHZwyBQUFqWbNmp6sBVfCjBMAC7swNBUM8tdH3RsQmgAAtpLr4NSmTZsrXrTwf//733UVhMug4wTAos6lZoSmpTsyQtOUPk3VtEIxs8sCAMCjch2c6tevn+V+Wlqa1q1bp40bN6p3796eqgsXIzgBsKBzqU49PHWVlu04odAgf03p29Q92wQAgJ3kOjiNGjUq2+OvvPKKzp49e90F4TIITgAsaOySne7Q9FnfpmpMaAIA2FSur+N0OQ888IAmTZrkqafDxZhxAmBBj7eppE71ShOaAAC2d82bQ1xs+fLlCgkJ8dTT4WJ0nABYRHKaU8EBfnI4HAoO8NeH3RuYXRIAAHku18HpnnvuyXLfMAwdPnxYf/75JxfAzUsEJwAWkJiSrj6TV6lWmTANu7PmFTcLAgDATnIdnMLDw7Pc9/PzU7Vq1fTqq6/q1ltv9VhhuEjmUj2CEwCTnE1JV5/JK7VqzyltPpygvjdWUHQxlg8DAHxDroKT0+lUnz59VKdOHRUtWjSvakJ2MjtOzDgBMMHZlHQ9NGml/tx7SoVDAjStXzNCEwDAp+Rqcwh/f3/deuutOn36dB6Vg8tiqR4Ak5xJTlPvC0LT5/2aqX50EbPLAgAgX+V6V73atWtr165deVELroTgBMAEmaFp9d5TCgsJ0PSHm6keoQkA4INyHZxef/11DRkyRN9//70OHz6shISELB/II2xHDsAEf+45pXX7Tyu8QKCmP9xcdcsWMbskAABMkeMZp1dffVX//ve/dfvtt0uSOnXqlGU3JcMw5HA45HQ6PV8l6DgBMEWb6pEa1bW+KpUopNplwq/+AAAAbMphGIaRkxP9/f11+PBhbd68+YrntWrVyiOF5ZWEhASFh4crPj5eYWFhZpeTc2XKSIcOSWvWSA24ZgqAvBN/Lk0paU5FhnFtPgCAveUmG+S445SZr6wejGyLjhOAfBB/Lk29Jq7QmeR0fTGguaIITwAASMrljBMXOjQRM04A8lh8UpoenLhC6w/E61RSqk4lpZpdEgAAlpGr6zhVrVr1quHp5MmT11UQsuFySSkpGbfpOAHIA6eTUvXAxBXaeDBBxUKDNP3hZqpe0ouWMwMAkMdyFZxGjBih8HCGg/NdcvI/twlOADzsdFKqek5YoU2HMkLTjP6EJgAALpar4NStWzdFRkbmVS24nMz5JongBMCjTiVmhKa/DyeoeGiQZvRvrmolC5tdFgAAlpPj4MR8k4ky55uCgiR/f3NrAWArqU6XzqU5FVEoIzRVjSI0AQCQnVzvqgcTsKMegDwSFRaiL/o319mUNFWOJDQBAHA5OQ5OLpcrL+vAlRCcAHjQibMpWrvvtNrXjJIklQwPkcS24wAAXEmutiOHSQhOADzkxNkU9ZywQgOm/akfNxw2uxwAALwGwckbcA0nAB4QdzZFPcav0JYjZxRRKJhNIAAAyAVLBKcxY8YoJiZGISEhatasmVauXJmjx82cOVMOh0OdO3fO2wLNRscJwHXKCE2x2nr0jKLCgjVzQHNVKlHI7LIAAPAapgenWbNmafDgwRo+fLjWrFmjevXqqUOHDjp27NgVH7dnzx4NGTJELVu2zKdKTURwAnAdjp9JUfdxsdp29KxKhoVo5oAWqkhoAgAgV0wPTu+//7769++vPn36qGbNmho7dqwKFiyoSZMmXfYxTqdTPXv21IgRI1SxYsV8rNYkmUv1CE4Acin+XJq6j4/V9mOZoam5KkSEml0WAABex9TglJqaqtWrV6t9+/buY35+fmrfvr2WL19+2ce9+uqrioyMVL9+/a76NVJSUpSQkJDlw+tkdpyYcQKQS2EhAbqpcoRKhWeEphhCEwAA1yTH25Hnhbi4ODmdTkVFRWU5HhUVpS1btmT7mKVLl2rixIlat25djr7GyJEjNWLEiOst1Vws1QNwjRwOh4Z3rKmBbSsrolCw2eUAAOC1TF+qlxtnzpzRgw8+qPHjxysiIiJHjxk6dKji4+PdH/v378/jKvMAwQlALhxNSNYr8zYpJd0pKSM8EZoAALg+pnacIiIi5O/vr6NHj2Y5fvToUZUsWfKS83fu3Kk9e/aoY8eO7mOZF+YNCAjQ1q1bValSpSyPCQ4OVnCwl79hYDtyADl0JD5Z3cfHandcotKcLr1xdx2zSwIAwBZM7TgFBQWpUaNGWrhwofuYy+XSwoUL1aJFi0vOr169ujZs2KB169a5Pzp16qQ2bdpo3bp1io6Ozs/y8w8dJwA5cDj+nLqNW67dcYkqU6SAHm1V6eoPAgAAOWJqx0mSBg8erN69e6tx48Zq2rSpRo8ercTERPXp00eS1KtXL5UpU0YjR45USEiIateuneXxRYoUkaRLjtsKwQnAVRw6fU7dx8dq74kklS1aQF/0b67oYnSpAQDwFNODU9euXXX8+HENGzZMR44cUf369TV//nz3hhH79u2Tn59XjWJ5HsEJwBUcPH1O3cfFat/JJEUXywhNZYsSmgAA8CSHYRiG2UXkp4SEBIWHhys+Pl5hYWFml5Mz3bpJs2ZJH3wgPfWU2dUAsBCXy9Ad/7dUmw8nqFyxgvpiQHOVKcIvWQAAyIncZAMfb+V4CTpOAC7Dz8+hV++qpRqlwjST0AQAQJ4xfakecoDgBOAihmHI4XBIkprEFNMPT94kPz+HyVUBAGBfdJy8AcEJwAX2n0xSp4+WafPhBPcxQhMAAHmL4OQNuI4TgPP2nUhSt3Gx2nAwXv/5ZqN8bEwVAADTsFTPG9BxAiBp74lEdR8Xq0PxyaoYEaqPezZ0L9cDAAB5i+DkDQhOgM/beyJR3cbF6nB8siqWCNXM/s0VGRZidlkAAPgMlup5g8ylegQnwCftifsnNFUqEaqZAwhNAADkNzpO3iCz48SME+CT3vt1mw7HJ6tyZCHN6N9MkYUJTQAA5DeCkzdgqR7g0966p44KBvprSIdqKlE42OxyAADwSQQnq0tLk9LTM24TnACfcSoxVUVDgyRJocEBevu+uiZXBACAb2PGyeoyu00SwQnwETuOnVWH0b/p/xZuN7sUAABwHsHJ6i4MTiHMNQB2t+PYWXUfH6tjZ1L0w4bDOpfqNLskAAAglupZ34XzTVyvBbC1HcfOqNu4FYo7m6LqJQtrRv/mKhDkb3ZZAABABCfrY2MIwCdsP3pG3cfHKu5sqmqUCtP0h5up2PkZJwAAYD6Ck9VxDSfA9rYdPaPu42J1IjFVNc+HpqKEJgAALIXgZHVcwwmwvdV7T+lEYqpqlc4ITUUKEpoAALAagpPVsVQPsL3uTcupYJC/WlUtQWgCAMCi2FXP6ghOgC1tO3pGp5NS3ffvql+G0AQAgIURnKwuc8aJpXqAbfx9KEFdP12unhNWZAlPAADAughOVkfHCbCVTYfi1WNCrE4lpSnA308OLjMAAIBXYMbJ6ghOgG1sPBivnhNWKP5cmupHF9HUfk0VFhJodlkAACAH6DhZHcEJsIULQ1ODcoQmAAC8DR0nq2PGCfB6Gw/Gq8f4WCUkp6thuSL6rG9TFSY0AQDgVQhOVkfHCfB6ocEBKhDkrypRhTWlTxNCEwAAXojgZHUEJ8DrVYgI1ZeP3KBihYJUKJh/dgEA8EbMOFld5lI9ghPgVdbuO6VFW4+575crXpDQBACAFyM4WV1mx4kZJ8BrrNl3Sr0mrtQjU1dr1Z6TZpcDAAA8gOBkdSzVA7zK6r0ZoelMSrrqlyuimqXCzC4JAAB4AOtGrI7gBHiN1XtPqvekVTqbkq7mFYtp0kNNVDCIf2YBALAD/o9udcw4AV7hzz0n1XvSSiWmOtWiYnFNfKgxoQkAABvh/+pWx4wTYHk7jp1xh6YbKhXXxN5NVCDI3+yyAACABxGcrI6leoDlVYgopFtrldSxM8ma0IvQBACAHRGcrI7gBFiev59D/72/ntKcLoUEEpoAALAjdtWzOmacAEuK3XVCL369QU6XISkjPBGaAACwLzpOVseME2A5y3eeUN8pq3QuzamKEaF6uGVFs0sCAAB5jOBkdSzVAyzljx1x6vvZKiWnudSqagk90Ly82SUBAIB8wFI9KzMMghNgIcsuCE2tq5XQpw82YnkeAAA+go6TlaWkZIQniaV6gMmWbo9Tv89WKSXdpTbVSmjsg40UHEBoAgDAVxCcrCyz2yTRcQJMFH8uTY9NX62UdJfaVo/UJw80JDQBAOBjWKpnZZnByd9fCgw0txbAh4UXCNSH3Rro9jolCU0AAPgoOk5WxnwTYKo0p0uB/hm/X2pTPVJtqkeaXBEAADALHScry7yGE/NNQL5btPWY2r+/RHtPJJpdCgAAsACCk5XRcQJMsWjLMT0ydbX2nkjShN93m10OAACwAIKTlRGcgHy3cPNRPTJttVKdLt1Wu6SGdaxpdkkAAMACmHGyssylegQnIF8s+PuoHpu+WmlOQ7fXKakPujVwzzgBAADfxjsCK8vsODHjBOS5Xy8ITXfULUVoAgAAWfCuwMpYqgfkC5fL0JhFO5TmNHRn3VL6oGt9QhMAAMiCpXpWRnAC8oWfn0OTH2qiSct26+l2VRRAaAIAABfh3YGVMeME5Kn9J5Pct4uGBunft1YjNAEAgGzxDsHKmHEC8syPGw6r7XuLNW35HrNLAQAAXoDgZGUs1QPyxA9/HdaTX6xVmtPQ2v2nZRiG2SUBAACLIzhZGcEJ8Ljv/zqkp2auldNl6J6GZfTuffXkcDjMLgsAAFgcwcnKmHECPGre+kN6euY6OV2G7mtUVu/eV0/+foQmAABwdeyqZ2XMOAEe8+26gxo0a51chnR/o7J66966hCYAAJBjBCcrY6ke4DH7TiTJZUhdG0dr5D115EdoAgAAuUBwsjKCE+AxT7arouqlwtSueiShCQAA5BozTlaWOePEUj3gmizackyJKenu+7fUjCI0AQCAa0JwsjI6TsA1m7P6gPp+tkp9pqxScprT7HIAAICXY6melRGcgGvy5Z/79dxXf8kwpCqRhRTkz++IAADA9eHdhJURnIBcm73qn9D0QPNyeu2u2izPAwAA142Ok5Ux4wTkyqxV+/TC3A0yDKlXi/Ia0akWF7cFAAAeQXCyMjpOQI7NWX1Az3+1QZLUu0V5vUJoAgAAHkRwsjKCE5BjNUoVVpGCgepcv4yGd6xJaAIAAB5FcLKyzKV6BCfgqmqVDtcPT7VU6fAQQhMAAPA4NoewKqdTSk3NuM2ME5CtL1bu06o9J933yxQpQGgCAAB5guBkVcnJ/9ym4wRcYtryPRo6d4MemrRS+04kmV0OAACwOYKTVWXON0kEJ+AiU5fv0cvfbpIk9WxeXtHF+BkBAAB5ixknq8qcbwoKkvzIt0CmKct265Xv/pYkPdKqol74V3WW5wEAgDxHcLKqzI4T802A2+RluzXifGh6tFUlPf+vaoQmAACQLwhOVsVW5EAWv2w64g5Nj7eupGc7EJoAAED+IThZFcEJyKJVtRJqWz1SNUuF6d+3ViU0AQCAfEVwsiqu4QRIkgzDkMPhUHCAv8Y92Ej+fg5CEwAAyHfsOmBVzDgB+nTJTr36/d8yDEOSFODvR2gCAACmoONkVSzVg48bu2Sn3vppiySpdbVItapawuSKAACALyM4WRXBCT7s48U79M78rZKkQe2rEpoAAIDpCE5WxYwTfNSYRTv07s8Zoenft1TVk+2qmFwRAAAAwcm6mHGCD/rof9v131+2SZKG3FpVA9sSmgAAgDUQnKyKpXrwMduOntH7v2aEpmc7VNMTbSqbXBEAAMA/CE5WRXCCj6kaVVijutbXodPJeqx1JbPLAQAAyILgZFWZM04s1YONGYahsynpKhwSKEm6q34ZkysCAADIHtdxsio6TrA5wzA06tdtuuujZTqakGx2OQAAAFdEcLIqghNszDAMvffLNn34vx3aFZeoxVuPmV0SAADAFbFUz6rYjhw2ZRiG/vvLVo1ZtFOS9J87aqhrk3ImVwUAAHBlBCerYjty2JBhGHrn5636ZHFGaBp2Z031vamCyVUBAABcHcHJqliqB5sxDENvzd+iT5fskiQN71hTfW4kNAEAAO9AcLIqghNs5kxKun7ZdFSSNKJTLfW+IcbcggAAAHKB4GRVzDjBZsJCAvVF/+ZatiNO9zYqa3Y5AAAAucKuelbFjBNswDAMbTwY775fMjyE0AQAALwSwcmqWKoHL2cYhl7/YbM6fbRU3647aHY5AAAA14WlelZFcIIXMwxDr37/tyYv2yNJOpuSbm5BAAAA14ngZFXMOMFLGYahEd/9rSl/7JEkjbynjro35TpNAADAuxGcrIoZJ3ghwzA0fN4mTV2+V5L01j111I3QBAAAbIDgZEVpaZLTmXGbjhO8hGEYGvbtJk2L3SuHQ3r7nrrq0iTa7LIAAAA8guBkRZndJongBK/i7+fICE331lWXxoQmAABgHwQnK8qcb3I4pOBgc2sBcsjhcGh4x5rqVL+0GpYranY5AAAAHsV25FZ04Y56Doe5tQBX4HIZmha7VynpGUtLHQ4HoQkAANgSwcmK2IocXsDlMvTi1xv08jcb9eSMtTIMw+ySAAAA8gxL9ayI4ASLc7kMDZ27QbP+3C8/h3R7nVJy0B0FAAA2RnCyoswZJ7YihwW5XIae/+ovfbn6gPwc0qiu9XVX/TJmlwUAAJCnCE5WRMcJFuU8H5rmnA9No7s1UKd6pc0uCwAAIM8RnKyI4ASLemXeJs1ZfUD+fg6N7lpfHQlNAADAR7A5hBVlLtUjOMFiOtUvrbCQAH3QjdAEAAB8Cx0nK8rsODHjBItpElNMvz/fVuEFAs0uBQAAIF/RcbIilurBItKdLr38zUb9fSjBfYzQBAAAfBHByYoITrCAdKdLg2ev17TYveo9eaWSUtPNLgkAAMA0LNWzImacYLJ0p0uDZq/Xd+sPKcDPodc711bBIP65AAAAvot3QlbEjBNMlO506elZ6/TDX4cV6O/QmB4NdWutkmaXBQAAYCqCkxWxVA8mSXO69MzMdfphQ0Zo+rhnI91SM8rssgAAAExHcLIighNM8sninfphw2EF+fvpkwcaql0NQhMAAIBEcLImZpxgkn43VdCqPSfV58YYta1OaAIAAMhEcLIiZpyQj5wuQ/5+DklSaHCApvZtKofDYXJVAAAA1sJ25FbEUj3kk9R0lx79fLU+XLjdfYzQBAAAcCmCkxURnJAPUtKdenz6av3691F9tGiH9p1IMrskAAAAy2KpnhUx44Q8lpLu1OOfr9HCLccUHOCn8b0aq1xxloYCAABcDsHJiphxQh5KSXfqsc/X6H/nQ9OE3o3VskoJs8sCAACwNIKTFbFUD3kkOc2pxz5frUVbjys4wE8TezfRTVUizC4LAADA8ghOVkRwQh5Zuj1Oi7YeV0hgRmi6sTKhCQAAICcITlbEjBPySPuaUXqtc21VigjVDYQmAACAHCM4WREzTvCg5DSnUtJcCi8YKEl6sHl5kysCAADwPmxHbjWGwVI9eExymlP9p/6pHhNidTop1exyAAAAvBbByWqSk/+5TXDCdTiX6tTDn/2p37fHaXdconbHJZpdEgAAgNdiqZ7VZHabJIITrtm5VKf6fbZKf+w8odAgf03p21QNyhU1uywAAACvRXCymszgFBAgBQaaWwu8UlJquvpN+VPLd2WEps/6NlXjmGJmlwUAAODVCE5Ww3wTrkNSarr6Tlml2F0nVSg4QJ/1baJG5QlNAAAA14vgZDVsRY7rcOJsqvbEJZ0PTU3VqDzL8wAAADyB4GQ1bEWO6xBdrKC+GNBcp5NSmWkCAADwIIKT1bBUD7mUmJKuTYcS1LRCxpK8ChGhkkLNLQoAAMBm2I7caghOyIWzKel6aPJKPTBhhRZtPWZ2OQAAALZFcLIaZpyQQ2eS09R70kqt2nNKwYF+KlYwyOySAAAAbMsSwWnMmDGKiYlRSEiImjVrppUrV1723PHjx6tly5YqWrSoihYtqvbt21/xfK/DjBNyIDM0rd57SmEhAZr+cDPViy5idlkAAAC2ZXpwmjVrlgYPHqzhw4drzZo1qlevnjp06KBjx7JfdrR48WJ1795dixYt0vLlyxUdHa1bb71VBw8ezOfK8whL9XAVCclp6jVppdbsO63wAoGa0b+56pYtYnZZAAAAtmZ6cHr//ffVv39/9enTRzVr1tTYsWNVsGBBTZo0Kdvzp0+frscff1z169dX9erVNWHCBLlcLi1cuDCfK88jBCdcwdmUdPWauFJrz4em6Q83U+0y4WaXBQAAYHumBqfU1FStXr1a7du3dx/z8/NT+/bttXz58hw9R1JSktLS0lSsWPYX+UxJSVFCQkKWD0tjxglXUCDQXxUiQlWkIKEJAAAgP5m6HXlcXJycTqeioqKyHI+KitKWLVty9BzPP/+8SpcunSV8XWjkyJEaMWLEddeab5hxwhX4+zn03/vr6eCpcypXnL8jAAAA+cX0pXrX46233tLMmTP19ddfKyQkJNtzhg4dqvj4ePfH/v3787nKXGKpHi4Sn5SmDxZsl9NlSMoIT4QmAACA/GVqxykiIkL+/v46evRoluNHjx5VyZIlr/jY//73v3rrrbe0YMEC1a1b97LnBQcHKzg42CP15guCEy4Qn5SmByau0IaD8TqVlKpXOtUyuyQAAACfZGrHKSgoSI0aNcqysUPmRg8tWrS47OPeeecdvfbaa5o/f74aN26cH6XmH2accN7ppFT1nBirDQfjVSw0SN2aRptdEgAAgM8yteMkSYMHD1bv3r3VuHFjNW3aVKNHj1ZiYqL69OkjSerVq5fKlCmjkSNHSpLefvttDRs2TDNmzFBMTIyOHDkiSSpUqJAKFSpk2vfhMcw4QdKpxFT1nLBCfx9OUPHQIM3o31zVShY2uywAAACfZXpw6tq1q44fP65hw4bpyJEjql+/vubPn+/eMGLfvn3y8/unMfbJJ58oNTVV9913X5bnGT58uF555ZX8LD1vsFTP510YmiIKZYSmqlGEJgAAADOZHpwkaeDAgRo4cGC2n1u8eHGW+3v27Mn7gszEUj2f5nIZ6jNl1fnQFKwv+jdTFUITAACA6bx6Vz1bYqmeT/Pzc+jpdlVUpkgBzRxAaAIAALAKS3SccAGW6vm8NtUj9b8hrRQc4G92KQAAADiPjpPVEJx8TtzZFPWatFK74xLdxwhNAAAA1kJwshpmnHzK8TMp6j4uVr9tO65nZq2TYRhmlwQAAIBsEJyshhknn3H8TIp6jI/V9mNnFRUWrFFd6snhcJhdFgAAALLBjJPVsFTPJxw7k6we41dox7GzKhkWoi8GNFeFiFCzywIAAMBlEJyshuBke8cSktV9fKx2Hk9UqfAQfdG/uWIITQAAAJZGcLISp1NKTc24TXCyrVe//1s7jyeqdHhGp6l8cUITAACA1RGcrCSz2yQx42Rjr3eureQ0l4bdWVPlivPnDAAA4A0ITlZyYXAKCTGvDnhccppTIYEZW4wXKRikCb0bm1wRAAAAcoNd9awkMzgFB0t+/NHYxeH4c7rtg981bfkes0sBAADANeLduZVwDSfbOXT6nLqNi9XuuESN+32XklLTzS4JAAAA14ClelbCNZxsJTM07TuZpOhiBTRzQAsVDOJHDgAAwBvRcbIStiK3jYMXhKZyxQpq5oAWKlOEP1cAAABvxa+/rYTgZAsHTiWp+/hY7T95TuWLF9QX/ZurNKEJAADAqxGcrIQZJ1tY8PdR7T95TjHFC+qLAc1VKpw/TwAAAG9HcLISZpxs4aEbK0iS/lW7lEqGs608AACAHRCcrISlel7rwKkkFS0YpNDgjB+pzPAEAAAAe2BzCCthqZ5X2nciSV3GLlefKauUmMJ24wAAAHZEx8lK6Dh5nb0nEtVtXKwOxycrJMhfianp7q4TAAAA7IOOk5Uw4+RV9sT9E5oqlQjVzAHNFVmYmSYAAAA74lfjVkLHyWvsjktU93GxOpKQrMqRhfRF/+YqUTjY7LIAAACQRwhOVsKMk1fYdfysuo+P1dGEFFWJLKQZhCYAAADbIzhZCUv1vEJymksp6S5VjcoITRGFCE0AAAB2R3CyEpbqeYWapcM0c0BGYCI0AQAA+AaCk5UQnCxrx7EzOp2UpsYxxSRJ1UuGmVwRAAAA8hO76lkJM06WtP3oGXUbt0K9J63Uuv2nzS4HAAAAJiA4WQkzTpaz/egZdR8fq7izKSpXPFTlivFnAwAA4ItYqmclLNWzlG1Hz6j7uFidSExVzVJhmv5wMxUNDTK7LAAAAJiAjpOVEJwsY+uRf0JTrdKEJgAAAF9Hx8lKmHGyhD1xieo+PlYnE1NVu0yYPu/XTEUKEpoAAAB8GcHJSphxsoRSRULUILqIjp1J0ef9mim8YKDZJQEAAMBkBCcrYameJQQH+OvjBxoqOc2l8AKEJgAAADDjZC0EJ9NsOhSv//68VYZhSMoIT4QmAAAAZKLjZBWGwYyTSTYejNcDE1fodFKaioUGqe9NFcwuCQAAABZDcLKKtDTJ5cq4zYxTvtl4MF49J6xQ/Lk01Y8uovsalzW7JAAAAFgQwckqMpfpSXSc8smGA/HqOSFWCcnpalCuiKb2barCISzPAwAAwKWYcbKKzGV6DocUxNbXee2vA6fdoakhoQkAAABXQcfJKi7cGMLhMLcWm0tITlPvSSuVkJyuxuWLakrfpioUzI8CAAAALo+Ok1VwDad8ExYSqFc61VKzCsUITQAAAMgR3jFaBVuR5znDMOQ43827q34ZdaxbWn5+dPcAAABwdXScrIKtyPPUmn2ndNeYZTqakOw+RmgCAABAThGcrIKlenlm9d5T6jVxpf46EK/3ftlqdjkAAADwQizVswqW6uWJP/ecVO9JK5WY6lSLisX1SqdaZpcEAAAAL0RwsgqCk8et2nNSD50PTTdUKq6JvZuoQJC/2WUBAADACxGcrIIZJ49aufukHpq8UkmpTt1Yubgm9CI0AQAA4NoRnKyCGSePcbkMvfr9JiWlOnVT5QhN6N1YIYGEJgAAAFw7NoewCpbqeYyfn0MTezdR96blCE0AAADwCIKTVRCcrtvJxFT37aiwEI28pw6hCQAAAB5BcLIKZpyuyx874tTy7f/p23UHzS4FAAAANkRwsgpmnK7Zsh1x6jNllRJTnfpu/WEZhmF2SQAAALAZgpNVsFTvmizdHqe+U1YpJd2lttUjNaZnAzkcDrPLAgAAgM0QnKyC4JRrv207rn6fZYSmdtUj9ckDDRUcwEwTAAAAPI/tyK2CGadcWbLtuPpP/VOp6S61rxGlMT0bEJoAAACQZwhOVsGMU64s2xGn1HSXbqkZpTE9GioogOYpAAAA8g7BySpYqpcrQ2+rrsolCqlzgzKEJgAAAOQ53nFaBUv1rmrNvlNKSXdKkhwOh7o0iSY0AQAAIF/wrtMq6Dhd0cLNR9Xt01g9/vkapaa7zC4HAAAAPoalelbBjNNlLfj7qB6bvlppTkPBgX5it3EAAADkNzpOVkHHKVu/bDriDk131CmlD7o1UKA/f20BAACQv+g4WQUzTpf4edMRDZyxJiM01S2lD7rWVwChCQAAACYgOFkFHacsft50RE9MX6N0l6GO9UprVJd6hCYAAACYhuBkBYYhJSdn3GbGSZJULDRIQQF+ur1GlN4nNAEAAMBkBCcryAxNEh2n85rEFNM3T9yoihGhhCYAAACYjnekVpA53yT5dHCav/GwNh2Kd9+vGlWY0AQAAABL4F2pFWTONwUGSgG+2QT8/q9DemLGWvWcsEL7TyZd/QEAAABAPiI4WYGPbwzx3fpDenrmOjldhtpVj1LpIr75OgAAAMC6fLO9YTU+HJy+XXdQg2atk8uQ7m9UVm/dW1f+flzhFgAAANZCcLICH72G04WhqUvjsnrrnrryIzQBAADAgliqZwWZHScf2op88dZj7tDUrUk0oQkAAACWRsfJCnxwqV6TmGJqXL6YKkWG6o3OdQhNAAAAsDSCkxX4YHAKDQ7QZ32bKjjAj9AEAAAAy2OpnhX4yIzTnNUH9OHC7e77BYL8CU0AAADwCnScrMAHZpxm/7lfz3/1lwxDqlMmXG2qR5pdEgAAAJBjBCcrsPlSvdmr9uv5uRmhqVeL8mpdrYTZJQEAAAC5QnCyAhsv1Zu5cp9emLtBkvTQDTEa3rGmHA6W5wEAAMC7MONkBTbtOM1YQWgCAACAPdBxsgIbzjjtOHZGL32TEZr63BijYXcSmgAAAOC9CE5WYMOOU+XIwhrRqZb2nUjSS3fUIDQBAADAqxGcrMBGM06p6S4FBWSsAO3VIsbcYgAAAAAPYcbJCmzScfrsjz2655NlOp2UanYpAAAAgEcRnKzABjNOk5ft1vB5m7TxYIK+WXvQ7HIAAAAAj2KpnhV4ecdp0tLdevX7vyVJj7WupN43xJhbEAAAAOBhBCcr8OIZpwm/79LrP2yWJD3eupKe7VCNjSAAAABgOwQnK/DSpXoXhqaBbSrr37dWJTQBAADAlghOVuCFS/XOJKdp4tLdkqSn2lbWoFsITQAAALAvgpMVeGFwKhwSqC/6N9evfx/Vwy0rEJoAAABga+yqZwVeNOO070SS+3ZMRKj631yR0AQAAADbIzhZgZfMOH28eIfav79Ei7YcM7sUAAAAIF8RnKzAC5bqjVm0Q+/M36pUp0t/H04wuxwAAAAgXzHjZLb0dCktLeO2RYPT/y3crvd+3SZJGnJrVT3RprLJFQEAAAD5i+Bktsxuk2TJ4PTBgu0atSAjND3boRqhCQAAAD6J4GS2C4NTSIh5dWRj1K/b9MHC7ZKk5/5VTY+3JjQBAADANxGczJYZnEJCJD/rjJy5XIb2n8zYQe+F26rr0VaVTK4IAAAAMA/ByWwW3Yrcz8+hd++vpzvrlVLb6lFmlwMAAACYyjotDl9loR31DMPQjxsOy+kyJEn+fg5CEwAAACCCk/kscg0nwzD03i/b9Pj0NXr2y/UyDMPUegAAAAArYame2SzQcTIMQ+/+vFUfL94pSapVJlwOh8O0egAAAACrITiZzeQZJ8Mw9Pb8rRq7JCM0De9YU31urGBKLQAAAIBVEZzMZmLHyTAMvTV/iz5dskuSNKJTLfW+ISbf6wAAAACsjuBkNhNnnN79eas7NL16Vy31ahGT7zUAAAAA3oDNIcxmYsepSUwxBQX46TVCEwAAAHBFdJzMZuKMU5vqkVo8pLVKFzF/K3QAAADAyug4mS0fO06GYWj0gm3aHZfoPkZoAgAAAK6O4GS2fJpxMgxDI777W6MXbFfP8bFKSk3P068HAAAA2AlL9cyWDx0nwzD0yrxN+mz5Xjkc0tPtq6hgEH/0AAAAQE7x7tlseTzjZBiGhn27SdNiM0LT2/fUVZcm0XnytQAAAAC7IjiZLQ+X6rlchobN26jPY/dlhKZ766pLY0ITAAAAkFsEJ7Pl4VK9T3/b5Q5N795XT/c1KuvxrwEAAAD4AoKT2fIwOHVvGq2fNh5W7xYxupfQBAAAAFwzgpPZPDzjZBiGHA6HJKlIwSDNfewGBfizeSIAAABwPXhHbTYPzji5XIaGzt2gqcv3uI8RmgAAAIDrR8fJbB5aqudyGXr+q7/05eoD8vdz6OYqJRQTEeqBAgEAAADQjjCbB5bqOV2Gnjsfmvwc0vtd6hGaAAAAAA+i42S26+w4OV2Gnpvzl75ak9FpGt21vjrWK+3BAgEAAAAQnMx2HTNOTpehZ79cr7lrD8rfz6EPutXXnXUJTQAAAICnEZzMdh0dp4Wbj7pD04fdGuiOuqU8XBwAAAAAieBkLsO4rhmnW2uV1L9vqapKkYV0ex1CEwAAAJBXCE5mSk3NCE9SjoNTutOlVKdLBYMy/uiebFclr6oDAAAAcB676pkpc5melKMZp3SnS8/MWqeHJq9SYkp6HhYGAAAA4EIEJzNlBic/Pykw8IqnpjtdenrWOn3/12Gt3XdKGw7G50OBAAAAACSCk7kunG9yOC57WprTpadnrtMPfx1WoL9Dn/RspOYVi+dTkQAAAACYcTJTDnbUS3O69NQXa/XTxiMK8vfTJw80VLsaUflUIAAAAACJ4GSuq1zDKc3p0pMz1mr+pozQNPbBhmpbndAEAAAA5DeCk5mu0nE6eOqcVuw+oSB/P336YCO1qR6Zj8UBAAAAyERwMtNVruEUExGq6Q8317EzyWpdjdAEAAAAmIXgZKZsluqlpru08/hZ1SgVJkmqWTpMNRVmRnUAAAAAzmNXPTNdtFQvJd2px6ev1r2f/KFVe06aWBgAAACACxGczHRBcEpJd+qxz9doweZjcroMJac5za0NAAAAgJslgtOYMWMUExOjkJAQNWvWTCtXrrzi+V9++aWqV6+ukJAQ1alTRz/++GM+Veph52eckguG6tFpq/W/LccUEuinSQ81UcsqJUwuDgAAAEAm04PTrFmzNHjwYA0fPlxr1qxRvXr11KFDBx07dizb8//44w91795d/fr109q1a9W5c2d17txZGzduzOfKPeDcOSX7B+rR0u21aOvxjNDUu4lurBxhdmUAAAAALuAwDMMws4BmzZqpSZMm+uijjyRJLpdL0dHRevLJJ/XCCy9ccn7Xrl2VmJio77//3n2sefPmql+/vsaOHXvVr5eQkKDw8HDFx8crLMzcTReSX3lVj/xtaEnFxu5O0w2VCE0AAABAfshNNjC145SamqrVq1erffv27mN+fn5q3769li9fnu1jli9fnuV8SerQocNlz09JSVFCQkKWD6twnEtSgMulAoZTkx9qSmgCAAAALMrU4BQXFyen06moqKgsx6OionTkyJFsH3PkyJFcnT9y5EiFh4e7P6Kjoz1TvAcEl4vWx0cX68tSR9WiUnGzywEAAABwGabPOOW1oUOHKj4+3v2xf/9+s0v6x/+3d99hUV3pH8C/MwMzdAyKDiMoNsC1BmxojNElArGAJbBqFBWjiSDGEnWNisa1xLVnjRVFXRXEtUUNtogi6lpRY8ECxAbYIqBCBpizf/hzfhkpk0FhUL6f57l/zLnn3HkPvg/hzbn33JAQKOIPo/FXnxs7EiIiIiIiKoFRX4BbrVo1yGQyZGRk6LRnZGRAqVQWOUapVBrUX6FQQKFQvJmAiYiIiOitI4RAfn4+Cgr4upfKyNTUFDKZ7LWvY9TCSS6Xw8PDAwcPHoS/vz+AF5tDHDx4EKGhoUWO8fT0xMGDB/HVV19p2/bv3w9PT89yiJiIiIiI3iZqtRppaWl4/n+vgaHKRyKRwNHREVZWVq91HaMWTgAwevRoBAUFoUWLFmjVqhUWLlyIZ8+eYdCgQQCAAQMGoGbNmpg1axYAYOTIkejQoQPmzZuHLl26ICoqCqdPn8aKFSuMOQ0iIiIiqmA0Gg1SUlIgk8mgUqkgl8shkUiMHRaVIyEEHjx4gDt37qBBgwavtfJk9MIpMDAQDx48wJQpU5Ceno7mzZsjNjZWuwHErVu3IJX+/6NYbdu2xcaNGzFp0iRMnDgRDRo0wPbt29G4cWNjTYGIiIiIKiC1Wq191Y2FhYWxwyEjsbe3R2pqKvLy8l6rcDL6e5zKW0V6jxMRERERlZ3c3FykpKSgTp06MDMzM3Y4ZCQl5cFb8x4nIiIiIiKitwELJyIiIiIiIj1YOBEREREREenBwomIiIiIqAI6fvw4ZDIZunTpUuhcXFwcJBIJnjx5Uuics7MzFi5cqNN26NAhfPLJJ6hatSosLCzwl7/8BWPGjMHdu3fLKPoXzxaFhISgatWqsLKyQq9evQq9j/VVGRkZGDhwIFQqFSwsLODj44Pr169rz6empkIikRR5xMTElNlcABZOREREREQVUkREBEaMGIEjR47g3r17pb7O8uXL4eXlBaVSif/85z+4fPkyli1bhszMTMybN+8NRqxr1KhR+PHHHxETE4PDhw/j3r176NmzZ7H9hRDw9/dHcnIyduzYgXPnzqF27drw8vLCs2fPAABOTk5IS0vTOaZNmwYrKyv4+vqW2VyACrAdORERERFRuRACMNaLcC0sAAPeIfX06VNER0fj9OnTSE9PR2RkJCZOnGjw1965cwdhYWEICwvDggULtO3Ozs748MMPi1yxehMyMzMRERGBjRs3olOnTgCANWvWoGHDhjhx4gTatGlTaMz169dx4sQJ/PLLL2jUqBEAYOnSpVAqldi0aROGDBkCmUwGpVKpM27btm0ICAh47Rfc6sMVJyIiIiKqHJ4/B6ysjHMYWLBt3rwZbm5ucHV1xWeffYbVq1ejNG8RiomJgVqtxrhx44o8X6VKlWLH+vr6wsrKqtjjZXFTlDNnziAvLw9eXl7aNjc3N9SqVQvHjx8vcszvv/8OADpbhkulUigUChw9erTY70lMTERwcHCxsbwpXHEiIiIiIqpgIiIi8NlnnwEAfHx8kJmZicOHD+Ojjz4y6DrXr1+HjY0NHBwcDI5h1apVyMnJKfa8qalpsefS09Mhl8sLFWY1atRAenp6kWNeFlZ///vfsXz5clhaWmLBggW4c+cO0tLSihwTERGBhg0bom3btvon9JpYOBERERFR5WBhATx9arzv/pOSkpJw8uRJbNu2DQBgYmKCwMBAREREGFw4CSEgMeAWwT+qWbNmqcaVlqmpKbZu3Yrg4GDY2dlBJpPBy8sLvr6+Ra625eTkYOPGjZg8eXK5xMfCiYiIiIgqB4kEsLQ0dhR6RUREID8/HyqVStsmhIBCocC//vUv2NrawsbGBsCLZ4leXdV58uQJbG1tAQAuLi7IzMxEWlqawatOvr6+iI+PL/Z87dq1cenSpSLPKZVKqNVqPHnyRCe+jIyMQs8o/ZGHhwcSExORmZkJtVoNe3t7tG7dGi1atCjUd8uWLXj+/DkGDBjw5yf1GviMExERERFRBZGfn49169Zh3rx5SExM1B7nz5+HSqXCpk2bAAANGjSAVCrFmTNndMYnJycjMzMTLi4uAIDevXtDLpdjzpw5RX5fSZtDrFq1SieGV489e/YUO9bDwwOmpqY4ePCgti0pKQm3bt2Cp6en3p+Dra0t7O3tcf36dZw+fRp+fn6F+kRERKB79+6wt7fXe703gStOREREREQVxK5du/Dbb78hODhYu2r0Uq9evRAREYEvvvgC1tbWGDJkCMaMGQMTExM0adIEt2/fxvjx49GmTRvtMz9OTk5YsGABQkNDkZWVhQEDBsDZ2Rl37tzBunXrYGVlVeyW5K9zq56trS2Cg4MxevRo2NnZwcbGBiNGjICnp6fOjnpubm6YNWsWevToAeDFZhb29vaoVasWLl68iJEjR8Lf3x+dO3fWuf6NGzdw5MiREou3N40rTkREREREFURERAS8vLwKFU3Ai8Lp9OnTuHDhAgBg0aJFCAoKwvjx49GoUSMMHDgQTZs2xY8//qjzXNPw4cOxb98+3L17Fz169ICbmxuGDBkCGxsbjB07tszmsmDBAnTt2hW9evXChx9+CKVSia1bt+r0SUpKQmZmpvZzWloa+vfvDzc3N4SFhaF///7aVbY/Wr16NRwdHQsVVGVJIkqzr+FbLCsrC7a2tsjMzNTeG0pERERE757c3FykpKSgTp06OltcU+VSUh4YUhtwxYmIiIiIiEgPFk5ERERERER6sHAiIiIiIiLSg4UTERERERGRHiyciIiIiOidVsn2QqNXvKl/fxZORERERPROMjU1BQA8f/7cyJGQManVagCATCZ7revwBbhERERE9E6SyWSoUqUK7t+/DwCwsLDQeb8Rvfs0Gg0ePHgACwsLmJi8XunDwomIiIiI3llKpRIAtMUTVT5SqRS1atV67aKZhRMRERERvbMkEgkcHBxQvXp15OXlGTscMgK5XA6p9PWfUGLhRERERETvPJlM9trPuFDlxs0hiIiIiIiI9GDhREREREREpAcLJyIiIiIiIj0q3TNOL1+AlZWVZeRIiIiIiIjImF7WBH/mJbmVrnDKzs4GADg5ORk5EiIiIiIiqgiys7Nha2tbYh+J+DPl1TtEo9Hg3r17sLa2rhAvQMvKyoKTkxNu374NGxsbY4dDFRzzhQzFnCFDMWfIUMwZMlRFyhkhBLKzs6FSqfRuWV7pVpykUikcHR2NHUYhNjY2Rk8censwX8hQzBkyFHOGDMWcIUNVlJzRt9L0EjeHICIiIiIi0oOFExERERERkR4snIxMoVAgPDwcCoXC2KHQW4D5QoZizpChmDNkKOYMGeptzZlKtzkEERERERGRobjiREREREREpAcLJyIiIiIiIj1YOBEREREREenBwomIiIiIiEgPFk5lbMmSJXB2doaZmRlat26NkydPltg/JiYGbm5uMDMzQ5MmTbBnz55yipQqCkNyZuXKlWjfvj3ee+89vPfee/Dy8tKbY/TuMfT3zEtRUVGQSCTw9/cv2wCpwjE0Z548eYKQkBA4ODhAoVDAxcWF/32qZAzNmYULF8LV1RXm5uZwcnLCqFGjkJubW07RkrEdOXIE3bp1g0qlgkQiwfbt2/WOiYuLg7u7OxQKBerXr4/IyMgyj9NQLJzKUHR0NEaPHo3w8HCcPXsWzZo1g7e3N+7fv19k/2PHjqFPnz4IDg7GuXPn4O/vD39/f/zyyy/lHDkZi6E5ExcXhz59+uDQoUM4fvw4nJyc0LlzZ9y9e7ecIydjMTRnXkpNTcXYsWPRvn37coqUKgpDc0atVuPjjz9GamoqtmzZgqSkJKxcuRI1a9Ys58jJWAzNmY0bN2LChAkIDw/HlStXEBERgejoaEycOLGcIydjefbsGZo1a4YlS5b8qf4pKSno0qULOnbsiMTERHz11VcYMmQI9u7dW8aRGkhQmWnVqpUICQnRfi4oKBAqlUrMmjWryP4BAQGiS5cuOm2tW7cWw4YNK9M4qeIwNGdelZ+fL6ytrcXatWvLKkSqYEqTM/n5+aJt27Zi1apVIigoSPj5+ZVDpFRRGJozS5cuFXXr1hVqtbq8QqQKxtCcCQkJEZ06ddJpGz16tGjXrl2ZxkkVEwCxbdu2EvuMGzdONGrUSKctMDBQeHt7l2FkhuOKUxlRq9U4c+YMvLy8tG1SqRReXl44fvx4kWOOHz+u0x8AvL29i+1P75bS5Myrnj9/jry8PNjZ2ZVVmFSBlDZnvv32W1SvXh3BwcHlESZVIKXJmZ07d8LT0xMhISGoUaMGGjdujJkzZ6KgoKC8wiYjKk3OtG3bFmfOnNHezpecnIw9e/bgk08+KZeY6e3ztvwNbGLsAN5VDx8+REFBAWrUqKHTXqNGDVy9erXIMenp6UX2T09PL7M4qeIoTc68avz48VCpVIV++dC7qTQ5c/ToUURERCAxMbEcIqSKpjQ5k5ycjJ9//hn9+vXDnj17cOPGDQwfPhx5eXkIDw8vj7DJiEqTM3379sXDhw/xwQcfQAiB/Px8fPHFF7xVj4pV3N/AWVlZyMnJgbm5uZEi08UVJ6J3xOzZsxEVFYVt27bBzMzM2OFQBZSdnY3+/ftj5cqVqFatmrHDobeERqNB9erVsWLFCnh4eCAwMBDffPMNli1bZuzQqIKKi4vDzJkz8cMPP+Ds2bPYunUrdu/ejenTpxs7NKLXwhWnMlKtWjXIZDJkZGTotGdkZECpVBY5RqlUGtSf3i2lyZmX5s6di9mzZ+PAgQNo2rRpWYZJFYihOXPz5k2kpqaiW7du2jaNRgMAMDExQVJSEurVq1e2QZNRleb3jIODA0xNTSGTybRtDRs2RHp6OtRqNeRyeZnGTMZVmpyZPHky+vfvjyFDhgAAmjRpgmfPnmHo0KH45ptvIJXy/9uTruL+Braxsakwq00AV5zKjFwuh4eHBw4ePKht02g0OHjwIDw9PYsc4+npqdMfAPbv319sf3q3lCZnAGDOnDmYPn06YmNj0aJFi/IIlSoIQ3PGzc0NFy9eRGJiovbo3r27dhcjJyen8gyfjKA0v2fatWuHGzduaItsALh27RocHBxYNFUCpcmZ58+fFyqOXhbeQoiyC5beWm/N38DG3p3iXRYVFSUUCoWIjIwUly9fFkOHDhVVqlQR6enpQggh+vfvLyZMmKDtn5CQIExMTMTcuXPFlStXRHh4uDA1NRUXL1401hSonBmaM7NnzxZyuVxs2bJFpKWlaY/s7GxjTYHKmaE58yruqlf5GJozt27dEtbW1iI0NFQkJSWJXbt2ierVq4t//OMfxpoClTNDcyY8PFxYW1uLTZs2ieTkZLFv3z5Rr149ERAQYKwpUDnLzs4W586dE+fOnRMAxPz588W5c+fEr7/+KoQQYsKECaJ///7a/snJycLCwkJ8/fXX4sqVK2LJkiVCJpOJ2NhYY02hSCycytj3338vatWqJeRyuWjVqpU4ceKE9lyHDh1EUFCQTv/NmzcLFxcXIZfLRaNGjcTu3bvLOWIyNkNypnbt2gJAoSM8PLz8AyejMfT3zB+xcKqcDM2ZY8eOidatWwuFQiHq1q0rZsyYIfLz88s5ajImQ3ImLy9PTJ06VdSrV0+YmZkJJycnMXz4cPHbb7+Vf+BkFIcOHSry75OXeRIUFCQ6dOhQaEzz5s2FXC4XdevWFWvWrCn3uPWRCME1UyIiIiIiopLwGSciIiIiIiI9WDgRERERERHpwcKJiIiIiIhIDxZOREREREREerBwIiIiIiIi0oOFExERERERkR4snIiIiIiIiPRg4URERERERKQHCyciIiqVyMhIVKlSxdhhlJpEIsH27dtL7DNw4ED4+/uXSzxERFSxsXAiIqrEBg4cCIlEUui4ceOGsUNDZGSkNh6pVApHR0cMGjQI9+/ffyPXT0tLg6+vLwAgNTUVEokEiYmJOn0WLVqEyMjIN/J9xZk6dap2njKZDE5OThg6dCgeP35s0HVY5BERlS0TYwdARETG5ePjgzVr1ui02dvbGykaXTY2NkhKSoJGo8H58+cxaNAg3Lt3D3v37n3tayuVSr19bG1tX/t7/oxGjRrhwIEDKCgowJUrVzB48GBkZmYiOjq6XL6fiIj044oTEVElp1AooFQqdQ6ZTIb58+ejSZMmsLS0hJOTE4YPH46nT58We53z58+jY8eOsLa2ho2NDTw8PHD69Gnt+aNHj6J9+/YwNzeHk5MTwsLC8OzZsxJjk0gkUCqVUKlU8PX1RVhYGA4cOICcnBxoNBp8++23cHR0hEKhQPPmzREbG6sdq1arERoaCgcHB5iZmaF27dqYNWuWzrVf3qpXp04dAMD7778PiUSCjz76CIDuKs6KFSugUqmg0Wh0YvTz88PgwYO1n3fs2AF3d3eYmZmhbt26mDZtGvLz80ucp4mJCZRKJWrWrAkvLy98+umn2L9/v/Z8QUEBgoODUadOHZibm8PV1RWLFi3Snp86dSrWrl2LHTt2aFev4uLiAAC3b99GQEAAqlSpAjs7O/j5+SE1NbXEeIiIqDAWTkREVCSpVIrFixfj0qVLWLt2LX7++WeMGzeu2P79+vWDo6MjTp06hTNnzmDChAkwNTUFANy8eRM+Pj7o1asXLly4gOjoaBw9ehShoaEGxWRubg6NRoP8/HwsWrQI8+bNw9y5c3HhwgV4e3uje/fuuH79OgBg8eLF2LlzJzZv3oykpCRs2LABzs7ORV735MmTAIADBw4gLS0NW7duLdTn008/xaNHj3Do0CFt2+PHjxEbG4t+/foBAOLj4zFgwACMHDkSly9fxvLlyxEZGYkZM2b86TmmpqZi7969kMvl2jaNRgNHR0fExMTg8uXLmDJlCiZOnIjNmzcDAMaOHYuAgAD4+PggLS0NaWlpaNu2LfLy8uDt7Q1ra2vEx8cjISEBVlZW8PHxgVqt/tMxERERAEFERJVWUFCQkMlkwtLSUnv07t27yL4xMTGiatWq2s9r1qwRtra22s/W1tYiMjKyyLHBwcFi6NChOm3x8fFCKpWKnJycIse8ev1r164JFxcX0aJFCyGEECqVSsyYMUNnTMuWLcXw4cOFEEKMGDFCdOrUSWg0miKvD0Bs27ZNCCFESkqKACDOnTun0ycoKEj4+flpP/v5+YnBgwdrPy9fvlyoVCpRUFAghBDir3/9q5g5c6bONdavXy8cHByKjEEIIcLDw4VUKhWWlpbCzMxMABAAxPz584sdI4QQISEholevXsXG+vK7XV1ddX4Gv//+uzA3Nxd79+4t8fpERKSLzzgREVVyHTt2xNKlS7WfLS0tAbxYfZk1axauXr2KrKws5OfnIzc3F8+fP4eFhUWh64wePRpDhgzB+vXrtbeb1atXD8CL2/guXLiADRs2aPsLIaDRaJCSkoKGDRsWGVtmZiasrKyg0WiQm5uLDz74AKtWrUJWVhbu3buHdu3a6fRv164dzp8/D+DFbXYff/wxXF1d4ePjg65du6Jz586v9bPq168fPv/8c/zwww9QKBTYsGED/va3v0EqlWrnmZCQoLPCVFBQUOLPDQBcXV2xc+dO5Obm4t///jcSExMxYsQInT5LlizB6tWrcevWLeTk5ECtVqN58+Ylxnv+/HncuHED1tbWOu25ubm4efNmKX4CRESVFwsnIqJKztLSEvXr19dpS01NRdeuXfHll19ixowZsLOzw9GjRxEcHAy1Wl1kATB16lT07dsXu3fvxk8//YTw8HBERUWhR48eePr0KYYNG4awsLBC42rVqlVsbNbW1jh79iykUikcHBxgbm4OAMjKytI7L3d3d6SkpOCnn37CgQMHEBAQAC8vL2zZskXv2OJ069YNQgjs3r0bLVu2RHx8PBYsWKA9//TpU0ybNg09e/YsNNbMzKzY68rlcu2/wezZs9GlSxdMmzYN06dPBwBERUVh7NixmDdvHjw9PWFtbY1//vOf+O9//1tivE+fPoWHh4dOwfpSRdkAhIjobcHCiYiICjlz5gw0Gg3mzZunXU15+TxNSVxcXODi4oJRo0ahT58+WLNmDXr06AF3d3dcvny5UIGmj1QqLXKMjY0NVCoVEhIS0KFDB217QkICWrVqpdMvMDAQgYGB6N27N3x8fPD48WPY2dnpXO/l80QFBQUlxmNmZoaePXtiw4YNuHHjBlxdXeHu7q497+7ujqSkJIPn+apJkyahU6dO+PLLL7XzbNu2LYYPH67t8+qKkVwuLxS/u7s7oqOjUb16ddjY2LxWTERElR03hyAiokLq16+PvLw8fP/990hOTsb69euxbNmyYvvn5OQgNDQUcXFx+PXXX5GQkIBTp05pb8EbP348jh07htDQUCQmJuL69evYsWOHwZtD/NHXX3+N7777DtHR0UhKSsKECROQmJiIkSNHAgDmz5+PTZs24erVq7h27RpiYmKgVCqLfGlv9erVYW5ujtjYWGRkZCAzM7PY7+3Xrx92796N1atXazeFeGnKlClYt24dpk2bhkuXLuHKlSuIiorCpEmTDJqbp6cnmjZtipkzZwIAGjRogNOnT2Pv3r24du0aJk+ejFOnTumMcXZ2xoULF5CUlISHDx8iLy8P/fr1Q7Vq1eDn54f4+HikpKQgLi4OYWFhuHPnjkExERFVdiyciIiokGbNmmH+/Pn47rvv0LhxY2zYsEFnK+9XyWQyPHr0CAMGDICLiwsCAgLg6+uLadOmAQCaNm2Kw4cP49q1a2jfvj3ef/99TJkyBSqVqtQxhoWFYfTo0RgzZgyaNGmC2NhY7Ny5Ew0aNADw4ja/OXPmoEWLFmjZsiVSU1OxZ88e7QraH5mYmGDx4sVYvnw5VCoV/Pz8iv3eTp06wc7ODklJSejbt6/OOW9vb+zatQv79u1Dy5Yt0aZNGyxYsAC1a9c2eH6jRo3CqlWrcPv2bQwbNgw9e/ZEYGAgWrdujUePHumsPgHA559/DldXV7Ro0QL29vZISEiAhYUFjhw5glq1aqFnz55o2LAhgoODkZubyxUoIiIDSYQQwthBEBERERERVWRccSIiIiIiItKDhRMREREREZEeLJyIiIiIiIj0YOFERERERESkBwsnIiIiIiIiPVg4ERERERER6cHCiYiIiIiISA8WTkRERERERHqwcCIiIiIiItKDhRMREREREZEeLJyIiIiIiIj0+B+NYtO59THHFQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "fpr, tpr, thresholds = roc_curve(y_test, lg_y_pred)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, color='red', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.axis('tight')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross validation score for Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross validation mean accuracy for Logistic Regression is :  0.9642532467532469\n"
     ]
    }
   ],
   "source": [
    "score = cross_val_score(lg, X_train, y_train, cv = 10, scoring = 'accuracy')\n",
    "print('cross validation mean accuracy for Logistic Regression is : ',  np.mean(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "linear_svc = SVC(kernel = 'linear', C = 1.0)\n",
    "linear_svc.fit(X_train, y_train)\n",
    "svc_y_pred = linear_svc.predict(X_test) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating Accuacy, precision, recall score for SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.9714285714285714\n",
      "precision_score:  0.9473684210526315\n",
      "recall_score:  0.9818181818181818\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy score: ', accuracy_score(y_test, svc_y_pred))\n",
    "print('precision_score: ', precision_score(y_test, svc_y_pred))\n",
    "print('recall_score: ', recall_score(y_test, svc_y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calulating SVC Confusion Matrix values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix values for SVC: \n",
      " [[82  3]\n",
      " [ 1 54]]\n"
     ]
    }
   ],
   "source": [
    "svc_cm_matrix = confusion_matrix(y_test, svc_y_pred)\n",
    "print('confusion matrix values for SVC: \\n', svc_cm_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting Confusion matrix for SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (6, 4))\n",
    "cm_matrix = pd.DataFrame(data = svc_cm_matrix, columns = ['Actual positive : 1', 'Actual negative : 0'], index = ['Predicted positive : 1', 'Predicted negative : 0'])\n",
    "sns.heatmap(cm_matrix, annot = True, fmt = 'd', cmap = 'YlGnBu')\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting ROC_AUC Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "fpr, tpr, thresholds = roc_curve(y_test, svc_y_pred)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, color='red', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.axis('tight')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross validation mean accuracy for linear SVC is :  0.9642532467532469\n"
     ]
    }
   ],
   "source": [
    "score = cross_val_score(linear_svc, X_train, y_train, cv = 10, scoring = 'accuracy')\n",
    "print('cross validation mean accuracy for linear SVC is : ',  np.mean(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree = DecisionTreeClassifier()\n",
    "tree.fit(X_train, y_train)\n",
    "tree_y_pred = tree.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating Accuracy, precision, recall score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.9357142857142857\n",
      "precision_score:  0.9259259259259259\n",
      "recall_score:  0.9090909090909091\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy score: ', accuracy_score(y_test, tree_y_pred))\n",
    "print('precision_score: ', precision_score(y_test, tree_y_pred))\n",
    "print('recall_score: ', recall_score(y_test, tree_y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating Decision Tree Classifier Cnfusion Matrix values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusing Matrix values for Decsion Tree are: \n",
      " [[81  4]\n",
      " [ 5 50]]\n"
     ]
    }
   ],
   "source": [
    "tree_cm = confusion_matrix(y_test, tree_y_pred)\n",
    "print('Confusing Matrix values for Decsion Tree are: \\n', tree_cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting Confusion matrix for SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w4EB4e3trJvgdO3YMrVq1wsaNGxEUFKR4kKSsT5Zth10tayTFLUBJSSnMzc0w/eNvsXHbkQqPV6ksMDsyGN9+fxT3cu9XcbREhpWSfAXBwdPwoLAINWtaYcnnE9GkCVv99HSTnfwnTZqEyMhIzJw5U6t8+vTpmDRpkk7Jv7CwEIWFhVplQpRAkszlhkOV8Pq/OmFg3y4YOupznE+5itYt3fHx9MFIz7yLmO8Oah1bo4Y5/rtsDCRIGD3tKwNFTGQ4DT1csGXrx8i9l489e45h6pTPsfbrGfwDwNSZ+IQ/2d3+6enpGDx4cLnyQYMGIT09Xac6oqKioFartbbinPNyQ6FKmjstBJ8s+x6bdsTj1+Q/sWHLYXz25S5MfPdVreNq1DBHzLIxcGtQF/8KmctWP1VLlpYWcHd3RstnGyNifAiaezbE1+t+NHRYpG+SgpsRkp38X3zxRRw6dKhc+eHDh/HCCy/oVEdkZCSys7O1thp2XnJDoUqytrZEaanQKispLYWZ2f/+OTxK/I09nND7zTm4k5Vb1WESGSVRWoqiB0WGDoPoicju9n/11VcxefJkJCYmolOnTgAejvlv2rQJM2bMwPbt27WOrYhKpYJKpdIqY5d/1flx3ylMHtUXf16/jfMpf6Jty4YY/VYg1n27H8DDxL9+xVi0e9YD/YZ9BHNzMzjWUwMA7mTloqioxIDRE1WdhQti0LVrOzg710Ve3n388MNhJCScx6ov+ciryTPSiXpKkYQQ4p8P+5+/tg7/tmJJQkmJ7knC2i1YThj0BGxtrDB9Qn+8GuCNenXVSM+8i2+/P4q5n25GUVEJ3J6pi+Sjn1V47kv9Z+LQsd+qOOLqK/dKpKFDqNbem7YMx+LP4ebNu6hVqyaaNXfHW2/1gW/nNoYOrdozl1rrtf7GYZsUq+v31W8oVpdSZCd/fWHyJyqPyZ+oYvpO/o3eUi75//GlvOR/7do1TJ48Gbt27UJ+fj6aNGmC6OhoeHs/XHdCCIHp06dj1apVyMrKQufOnbF8+XI0bdpU52sY6ZuGiYiIqp+7d++ic+fOsLCwwK5du3D+/HksWLAAtWvX1hzz0UcfYcmSJVixYgWOHz8OGxsbBAQEaL1z559UaoU/IiIik2agMf/58+fD1dUV0dHRmjIPDw/NZyEEFi9ejPfeew99+vQBAKxbtw6Ojo7Ytm0bBg4cqNN12PInIiIqS5IU2woLC5GTk6O1lV3r5pHt27fD29sbb7zxBurXr4927dph1apVmv2pqanIyMiAv7+/pkytVqNjx46Ij4/X+faY/ImIiPSoorVtoqKiKjz2jz/+0Izf79mzByNGjMDo0aOxdu1aAEBGRgYAwNHRUes8R0dHzT5dsNufiIioLAW7/SMjIxEREaFVVvZx90dKS0vh7e2NuXMfvlytXbt2OHfuHFasWIEhQ4YoFpNOyT8nJ0fnCu3s7CodDBERkVFQsF+8orVtHsfZ2RleXtqL3rVo0QKbN28GADg5PXwhW2ZmJpydnTXHZGZmom3btjrHpFPyt7e3h6TjOsdynu0nIiKi/+ncuTOSk5O1ylJSUuDu7g7g4eQ/JycnxMbGapJ9Tk4Ojh8/jhEjRuh8HZ2Sf1xcnObz5cuXMWXKFAwdOlTzVr/4+HisXbv2sWMYRERETxUDvdhn3Lhx8PX1xdy5c9G/f38kJCTgiy++wBdffPH/YUkYO3YsZs+ejaZNm8LDwwPvv/8+XFxc0LdvX52vI3uRHz8/P7z11lsIDtZelGf9+vX44osvsH//fjnVaXCRH6LyuMgPUcX0vsjP6G2K1fXHkr6yjv/hhx8QGRmJixcvwsPDAxERERg+fLhm/6NFfr744gtkZWWhS5cuWLZsGZo1a6bzNWQn/5o1ayIpKancSkIpKSlo27Yt8vPz5VSnweRPVB6TP1HFTDn5VwXZUxpcXV21njl85Msvv4SrK99vTURETz8hSYptxkj2o36LFi1CUFAQdu3ahY4dOwIAEhIScPHiRc1sRCIioqeaia+CI/v2AgMDkZKSgldeeQV37tzBnTt38MorryAlJQWBgYH6iJGIiKhqmUnKbUaoUov8uLq6ahYgICIioqdLpTo2Dh06hEGDBsHX1xfXrl0DAHz99dc4fPiwosEREREZhIJr+xsj2cl/8+bNCAgIgLW1NU6dOqV5OUF2djZ7A4iIyDSYeLe/7OQ/e/ZsrFixAqtWrYKFhYWmvHPnzjh16pSiwREREZHyZI/5Jycno2vXruXK1Wo1srKylIiJiIjIsIyzwa4Y2S1/JycnXLp0qVz54cOH0ahRI0WCIiIiMiRhJim2GSPZyX/48OEYM2YMjh8/DkmScP36dcTExGDChAmyXipAREREhiG723/KlCkoLS2Fn58f8vPz0bVrV6hUKkyYMAGjRo3SR4xERERVy0hb7EqRnfwlScK0adMwceJEXLp0Cbm5ufDy8oKtra0+4iMiIqp6RvqInlJkd/uHhobi3r17sLS0hJeXF55//nnY2toiLy8PoaGh+oiRiIiIFCQ7+a9duxb3798vV37//n2sW7dOkaCIiIgMykzBzQjp3O2fk5MDIQSEELh37x6srKw0+0pKSvDjjz+ifv36egmSiIioSpl4t7/Oyd/e3h6SJEGSJDRr1qzcfkmSMGPGDEWDIyIiMghO+HsoLi4OQgj06NEDmzdvRp06dTT7LC0t4e7uDhcXF70ESURERMrROfl369YNAJCamgo3NzdIJt4lQkRE1ZiJt/xlT0X4+eef8d1335Ur37RpE9auXatIUERERIYkJEmxzRjJTv5RUVGoW7duufL69evzrX5ERERPAdmL/KSlpcHDw6Ncubu7O9LS0hQJioiIyKCM9BE9pci+vfr16+Ps2bPlypOSkuDg4KBIUERERAYlScptRkh28g8ODsbo0aMRFxeHkpISlJSU4Oeff8aYMWMwcOBAfcRIRERECpLd7T9r1ixcvnwZfn5+qFHj4emlpaUYPHgwx/yJiMg0mPhsf9nJ39LSEt988w1mzZqFpKQkWFtbo1WrVnB3d9dHfERERFWPyb9izZo1q3ClPyIiIjJuOiX/iIgIzJo1CzY2NoiIiPjbYxcuXKhIYERERAZj2g1/3ZL/6dOnUVRUpPn8OFz1j4iITIFgt//Ddf0r+kxERGSSTLwxa+LLGBAREVFZOrX8+/Xrp3OFW7ZsqXQwRERERoHd/oBardZ8FkJg69atUKvV8Pb2BgAkJiYiKytL1h8JRERERsu0c79uyT86OlrzefLkyejfvz9WrFgBc3NzAEBJSQneffdd2NnZ6SdKIiIiUozs5/y/+uorHD58WJP4AcDc3BwRERHw9fXFxx9/rGiAREREVc3MxGfEyb694uJiXLhwoVz5hQsXUFpaqkhQREREhmTi7/WRn/yHDRuGsLAwLFy4EIcPH8bhw4exYMECvPXWWxg2bJg+YiQiIqoWPvzwQ0iSpLV5enpq9hcUFCA8PBwODg6wtbVFUFAQMjMzZV9Hdrf/J598AicnJyxYsADp6ekAAGdnZ0ycOBHjx4+XHQAREZGxMWSLvWXLlti3b5/m+6OX6AHAuHHjsHPnTmzatAlqtRojR45Ev379cOTIEVnXkJ38zczMMGnSJEyaNAk5OTkAwIl+RERkUgy5Ym2NGjXg5ORUrjw7OxurV6/G+vXr0aNHDwAPJ+S3aNECx44dQ6dOnXS+RqWmNBQXF2Pfvn3YsGGD5gd0/fp15ObmVqY6IiIio6LkmH9hYSFycnK0tsLCwsde++LFi3BxcUGjRo0QEhKCtLQ0AA8fqy8qKoK/v7/mWE9PT7i5uSE+Pl7W/clO/leuXEGrVq3Qp08fhIeH4+bNmwCA+fPnY8KECXKrIyIiMmlRUVFQq9VaW1RUVIXHduzYEWvWrMHu3buxfPlypKam4oUXXsC9e/eQkZEBS0tL2Nvba53j6OiIjIwMWTHJ7vYfM2YMvL29kZSUBAcHB035a6+9huHDh8utjoiIyOgo2esfGRlZ7o24KpWqwmNffvllzefWrVujY8eOcHd3x7fffgtra2vFYpKd/A8dOoSjR4/C0tJSq7xhw4a4du2aYoEREREZiqTgc/4qleqxyf6f2Nvbo1mzZrh06RJ69uyJBw8eICsrS6v1n5mZWeEcgb8j+/ZKS0tRUlJSrvzq1auoVauW3OqIiIjoMXJzc/H777/D2dkZHTp0gIWFBWJjYzX7k5OTkZaWBh8fH1n1yk7+L730EhYvXqz5LkkScnNzMX36dAQGBsqtjoiIyOgYapGfCRMm4MCBA7h8+TKOHj2K1157Debm5ggODoZarUZYWBgiIiIQFxeHxMREDBs2DD4+PrJm+gOVfM6/V69e8PLyQkFBAd58801cvHgRdevWxYYNG+RWR0REZHQM9VK/q1evIjg4GLdv30a9evXQpUsXHDt2DPXq1QMALFq0CGZmZggKCkJhYSECAgKwbNky2deRhBBC7knFxcX45ptvkJSUhNzcXLRv3x4hISFPNBnB2i240ucSmarcK5GGDoHIKJlLrfVaf4vVBxWr67ewrorVpRRZLf+ioiJ4enrihx9+QEhICEJCQvQVFxERkcEY65r8SpGV/C0sLFBQUKCvWIiIiIyCqSd/2RP+wsPDMX/+fBQXF+sjHiIiItIz2RP+Tpw4gdjYWPz0009o1aoVbGxstPZv2bJFseCIiIgMwZBr+1cF2cnf3t4eQUFB+oiFiIjIKCi5yI8xkp38o6Oj9REHERGR0TDxhr/uY/6lpaWYP38+OnfujOeeew5TpkzB/fv39RkbERER6YHOyX/OnDmYOnUqbG1t0aBBA3z66acIDw/XZ2xEREQGYagV/qqKzsl/3bp1WLZsGfbs2YNt27Zhx44diImJQWlpqT7jIyIiqnJM/v8vLS1Na+1+f39/SJKE69ev6yUwIiIi0g+dJ/wVFxfDyspKq8zCwgJFRUWKB0VERGRIhlrbv6ronPyFEBg6dKjWO4kLCgrwzjvvaD3rz+f8iYjoaWes3fVK0Tn5DxkypFzZoEGDFA2GiIiI9E/n5M/n+4mIqLpgy5+IiKiakUx80N/EFzAkIiKistjyJyIiKoPd/kRERNUMkz8REVE1Y+rJn2P+RERE1Qxb/kRERGWY+GR/Jn8iIqKy2O1PREREJoUtfyIiojIkE28aM/kTERGVwW5/IiIiMils+RMREZUhmXjTn8mfiIioDBPP/ez2JyIiqm7Y8iciIirD1Fv+TP5ERERlMPlXkftpMwwdApHRcZ97xdAhEBmlK1P1W7+pL+/LMX8iIqJqxmha/kRERMaCLX8iIqJqxkwSim2VNW/ePEiShLFjx2rKCgoKEB4eDgcHB9ja2iIoKAiZmZny76/SUREREZFenDhxAitXrkTr1q21yseNG4cdO3Zg06ZNOHDgAK5fv45+/frJrp/Jn4iIqAwzSblNrtzcXISEhGDVqlWoXbu2pjw7OxurV6/GwoUL0aNHD3To0AHR0dE4evQojh07Ju/+5IdFRERk2swU3AoLC5GTk6O1FRYWPvba4eHh6N27N/z9/bXKExMTUVRUpFXu6ekJNzc3xMfHy74/IiIi0pOoqCio1WqtLSoqqsJjN27ciFOnTlW4PyMjA5aWlrC3t9cqd3R0REZGhqyYONufiIiojCeZqFdWZGQkIiIitMpUKlW54/7880+MGTMGe/fuhZWVlWLXrwiTPxERURlKPuqnUqkqTPZlJSYm4saNG2jfvr2mrKSkBAcPHsTnn3+OPXv24MGDB8jKytJq/WdmZsLJyUlWTEz+RERERsDPzw+//PKLVtmwYcPg6emJyZMnw9XVFRYWFoiNjUVQUBAAIDk5GWlpafDx8ZF1LSZ/IiKiMgwxIa5WrVp49tlntcpsbGzg4OCgKQ8LC0NERATq1KkDOzs7jBo1Cj4+PujUqZOsazH5ExERlWGsK/wtWrQIZmZmCAoKQmFhIQICArBs2TLZ9TD5ExERlSEpOOHvSezfv1/ru5WVFZYuXYqlS5c+Ub181I+IiKiaYcufiIioDGPt9lcKkz8REVEZpt4tbur3R0RERGWw5U9ERFSGkiv8GSNZyf/WrVv46quvEB8fr1lH2MnJCb6+vhg6dCjq1aunlyCJiIiqkqmP+evc7X/ixAk0a9YMS5YsgVqtRteuXdG1a1eo1WosWbIEnp6eOHnypD5jJSIiIgXo3PIfNWoU3njjDaxYsQKSpP0nkRAC77zzDkaNGiX7tYJERETGxtQnxOmc/JOSkrBmzZpyiR8AJEnCuHHj0K5dO0WDIyIiMgR2+/8/JycnJCQkPHZ/QkICHB0dFQmKiIiI9Efnlv+ECRPw9ttvIzExEX5+fppEn5mZidjYWKxatQqffPKJ3gIlIiKqKpzt///Cw8NRt25dLFq0CMuWLUNJSQkAwNzcHB06dMCaNWvQv39/vQVKRERUVUy921/Wo34DBgzAgAEDUFRUhFu3bgEA6tatCwsLC70ER0REZAic8FcBCwsLODs7Kx0LERERVQGu8EdERFQGx/yJiIiqGVMf8zf1YQ0iIiIqgy1/IiKiMky95c/kT0REVIapd4tX6v7MzMzQsmVLrbIWLVrA3NxckaCIiIhIfyrV8v/qq69gb2+vVRYVFYXs7GwlYiIiIjIozvavwNChQ8uV9e3b9wlDISIiMg6mPuZv6sMaREREVAYn/BEREZVh6i1jJn8iIqIyTL3bn8mfiIioDMnEJ/yZes8GERERlVGp5P/111+jc+fOcHFxwZUrVwAAixcvxvfff69ocERERIZgJim3GSPZyX/58uWIiIhAYGAgsrKyUFJSAgCwt7fH4sWLlY6PiIioypkpuBkj2XF99tlnWLVqFaZNm6a1op+3tzd++eUXRYMjIiIi5cme8Jeamop27dqVK1epVMjLy1MkKCIiIkMy9RX+ZLf8PTw8cObMmXLlu3fvRosWLZSIiYiIyKBMfcxfdss/IiIC4eHhKCgogBACCQkJ2LBhA6KiovDll1/qI0YiIiJSkOzk/9Zbb8Ha2hrvvfce8vPz8eabb8LFxQWffvopBg4cqI8YiYiIqpSxttiVUqlFfkJCQhASEoL8/Hzk5uaifv36SsdFRERkMKb+gnrZY/6zZ89GamoqAKBmzZpM/ERERApZvnw5WrduDTs7O9jZ2cHHxwe7du3S7C8oKEB4eDgcHBxga2uLoKAgZGZmyr6O7OS/adMmNGnSBL6+vli2bBlu3bol+6JERETGzEwSim1yPPPMM5g3bx4SExNx8uRJ9OjRA3369MGvv/4KABg3bhx27NiBTZs24cCBA7h+/Tr69esn+/4kIYTs5xl+/fVXxMTEYOPGjbh69Sp69uyJkJAQ9O3bFzVr1pQdxEMplTyPyHS5z71i6BCIjNKVqT31Wv+8pL2K1TWlzZPFWqdOHXz88cd4/fXXUa9ePaxfvx6vv/46AODChQto0aIF4uPj0alTJ53rrNTiQy1btsTcuXPxxx9/IC4uDg0bNsTYsWPh5ORUmeqIiIiMipKP+hUWFiInJ0drKyws/McYSkpKsHHjRuTl5cHHxweJiYkoKiqCv7+/5hhPT0+4ubkhPj5e3v3J/omUYWNjA2tra1haWqKoqOhJqyMiIjIpUVFRUKvVWltUVNRjj//ll19ga2sLlUqFd955B1u3boWXlxcyMjJgaWkJe3t7reMdHR2RkZEhK6ZKzfZPTU3F+vXrsX79eiQnJ6Nbt26YMWOGphuCiIjoaWau4KN+kZGRiIiI0CpTqVSPPb558+Y4c+YMsrOz8d1332HIkCE4cOCAcgGhEsm/U6dOOHHiBFq3bo1hw4YhODgYDRo0UDQoIiIiQ1LyOX+VSvW3yb4sS0tLNGnSBADQoUMHnDhxAp9++ikGDBiABw8eICsrS6v1n5mZKXvYXXby9/Pzw1dffQUvLy+5pxIREZFMpaWlKCwsRIcOHWBhYYHY2FgEBQUBAJKTk5GWlgYfHx9ZdcpO/nPmzJF7ChER0VPFUC/2iYyMxMsvvww3Nzfcu3cP69evx/79+7Fnzx6o1WqEhYUhIiICderUgZ2dHUaNGgUfHx9ZM/0BHZN/REQEZs2aBRsbm3LjFmUtXLhQVgBERETGxlDL+964cQODBw9Geno61Go1WrdujT179qBnz4ePCy5atAhmZmYICgpCYWEhAgICsGzZMtnX0Sn5nz59WjOT//Tp07IvQkRERP9s9erVf7vfysoKS5cuxdKlS5/oOjol/7i4uAo/ExERmSKu7V9GaGgo7t27V648Ly8PoaGhigRFRERkSEou8mOMZCf/tWvX4v79++XK79+/j3Xr1ikSFBEREemPzrP9c3JyIISAEAL37t2DlZWVZl9JSQl+/PFHvuGPiIhMgqFm+1cVnZO/vb09JEmCJElo1qxZuf2SJGHGjBmKBkdERGQISq7wZ4x0Tv5xcXEQQqBHjx7YvHkz6tSpo9lnaWkJd3d3uLi46CVIIiKiqmSsY/VK0Tn5d+vWDcDDdf1dXV1hZvbE7wQiIiIiA5C9wp+7uzsAID8/H2lpaXjw4IHW/tatWysTGRERkYGw5V/GzZs3MWzYMOzatavC/SUlJU8cFBERkSGZevKX3Xc/duxYZGVl4fjx47C2tsbu3buxdu1aNG3aFNu3b9dHjERERKQg2S3/n3/+Gd9//z28vb1hZmYGd3d39OzZE3Z2doiKikLv3r31EScREVGVMTfxR/1kt/zz8vI0z/PXrl0bN2/eBAC0atUKp06dUjY6IiIiAzBTcDNGsuNq3rw5kpOTAQBt2rTBypUrce3aNaxYsQLOzs6KB0hERETKkt3tP2bMGKSnpwMApk+fjl69eiEmJgaWlpZYs2aN0vERERFVOVOf8Cc7+Q8aNEjzuUOHDrhy5QouXLgANzc31K1bV9HgiIiIDIHJ/x/UrFkT7du3VyIWIiIiqgKyk39ERESF5ZIkwcrKCk2aNEGfPn20lv8lIiJ6mpj6bH/Zyf/06dM4deoUSkpK0Lx5cwBASkoKzM3N4enpiWXLlmH8+PE4fPgwvLy8FA+YiIhI30y921/2bP8+ffrA398f169fR2JiIhITE3H16lX07NkTwcHBuHbtGrp27Ypx48bpI14iIiK9M5OU24yRJISQ1bfRoEED7N27t1yr/tdff8VLL72Ea9eu4dSpU3jppZdw69YtGTWnyAmDqFpwn3vF0CEQGaUrU3vqtf4daRUvYV8Zr7i9rFhdSpHd8s/OzsaNGzfKld+8eRM5OTkAAHt7+3Iv/CEiInpamHrLX/aYf58+fRAaGooFCxbgueeeAwCcOHECEyZMQN++fQEACQkJaNasmaKBEhERVRVzI03aSpGd/FeuXIlx48Zh4MCBKC4uflhJjRoYMmQIFi1aBADw9PTEl19+qWykREREpAjZY/6P5Obm4o8//gAANGrUCLa2tk8YCsf8icrimD9RxfQ95v/TtR8Vq+ulBoGK1aWUSr9zICMjA+np6WjatClsbW1Ryb8hiIiIjA5f7FPG7du34efnh2bNmiEwMFCzzn9YWBjGjx+veIBERESkLNnJf9y4cbCwsEBaWhpq1qypKR8wYAB2796taHBERESGwNn+Zfz000/Ys2cPnnnmGa3ypk2b4soVjk8+rU6cOIfVq7fg3LnfcfPmHSxdOhX+/j6GDouoSo19oRHGvdBYq+zS7Tz4rTxa7ti1A9rhxcZ1Mfy7M/gp5WZVhUhVhLP9y8jLy9Nq8T9y584dqFQqRYKiqpefX4DmzT0QFNQTI0fONXQ4RAaTfDMXIesTNd+LS8vPZwp7zg2c5kRPM9nd/i+88ALWrVun+S5JEkpLS/HRRx+he/fuigZHVadbN2+MG/dv9OzJ1j5Vb8WlAjfzHmi2u/eLtPZ71bfF8I7umLjzVwNFSFXBTBKKbcZIdsv/o48+gp+fH06ePIkHDx5g0qRJ+PXXX3Hnzh0cOXJEHzESEVUZj9o1kTCqKwqLS3DqWjbm77+E6zkFAACrGmZY0rcV3t9zATfzuIqpKTPWsXqlyG75P/vss0hJSUGXLl3Qp08f5OXloV+/fjh9+jQaN278zxUQERmpM9eyMf6Hcxi88RSm7b4AV3trbPq3N2wszQEAH/RsjsSr2dh7kWP8po4T/iqgVqsxbdq0Sl+0sLAQhYWFWmUq1QOoVJaVrpOI6Ent/+O25vOFm7k4cz0bR8K74F8tHHE7vwi+7nUQuPqYASMkUkalkn9WVhYSEhJw48YNlJaWau0bPHjwP54fFRWFGTNmaJVNnz4SH344qjLhEBHpRU5hMVLv5MO9dk00r2cG99rW+GX8i1rHrOjXBgl/3sXAmMSKK6GnkrEuzqMU2cl/x44dCAkJQW5uLuzs7CBJ/+vTkCRJp+QfGRmJiIgIrTKVKk1uKEREelXTwhzutWtiy7l07PwtExuTrmnt3zvcFzP3JSOWwwAmRzJQd31UVBS2bNmCCxcuwNraGr6+vpg/fz6aN2+uOaagoADjx4/Hxo0bUVhYiICAACxbtgyOjo46X0f2Hzfjx49HaGgocnNzkZWVhbt372q2O3fu6FSHSqWCnZ2d1sYuf8PKy7uP3377A7/99vB9DVevZuK33/7A9evlX99MZKqm9WiKjm618YzaCh0aqPHF621QIgS2n8/AzbwHSLmZp7UBwPWcAvyZXWDgyMlUHDhwAOHh4Th27Bj27t2LoqIivPTSS8jLy9McM27cOOzYsQObNm3CgQMHcP36dfTr10/WdWS3/K9du4bRo0dX+Kw/Pb3OnbuEwYOnar5HRa0GALz2Wg/MmzfOUGERVSknOyt81qcV7K0tcCf/AU5czULfNQm4k1/0zyeTSTHUPL2yK+WuWbMG9evXR2JiIrp27Yrs7GysXr0a69evR48ePQAA0dHRaNGiBY4dO4ZOnTrpdB3ZyT8gIAAnT55Eo0aN5J5KRqxjx1ZITt5h6DCIDGrUtl9kHe8+d6+eIiFDM1S3f1nZ2dkAgDp16gAAEhMTUVRUBH9/f80xnp6ecHNzQ3x8vP6Sf+/evTFx4kScP38erVq1goWFhdb+V199VW6VREREJqviJ9xU/7gqbmlpKcaOHYvOnTvj2WefBfDwjbqWlpawt7fXOtbR0REZGRk6xyQ7+Q8fPhwAMHPmzHL7JElCSUmJ3CqJiIiMipKz/St+wm06Pvzww789Lzw8HOfOncPhw4cVjOYh2cm/7KN9REREpkZScFneip9w+/tW/8iRI/HDDz/g4MGDWi/Sc3JywoMHD5CVlaXV+s/MzISTk5POMZn6o4xEREQGVfETbhUnfyEERo4cia1bt+Lnn3+Gh4eH1v4OHTrAwsICsbGxmrLk5GSkpaXBx0f3d7NUapEfIiIiU2ao+X7h4eFYv349vv/+e9SqVUszjq9Wq2FtbQ21Wo2wsDBERESgTp06sLOzw6hRo+Dj46PzZD+AyZ+IiKgcQ832X758OQDgxRdf1CqPjo7G0KFDAQCLFi2CmZkZgoKCtBb5kYPJn4iIqAxDtfyF+Oe5BlZWVli6dCmWLl1a6etwzJ+IiKia0anln5OTo3OFdnZ2lQ6GiIjIGBjrq3iVolPyt7e313qBz9/hc/5ERPS0M/Hcr1vyj4uL03y+fPkypkyZgqFDh2oeK4iPj8fatWsRFRWlnyiJiIhIMTol/27dumk+z5w5EwsXLkRwcLCm7NVXX0WrVq3wxRdfYMiQIcpHSUREVIWMZW1/fZE94S8+Ph7e3t7lyr29vZGQkKBIUERERIYkKbgZI9nJ39XVFatWrSpX/uWXX8LV1VWRoIiIiEh/ZD/nv2jRIgQFBWHXrl3o2LEjACAhIQEXL17E5s2bFQ+QiIioqhlri10pslv+gYGBSElJwSuvvII7d+7gzp07eOWVV5CSkoLAwEB9xEhERFSlzCTlNmNUqRX+XF1dMXfuXKVjISIioipQqRX+Dh06hEGDBsHX1xfXrl0DAHz99dd6eecwERFRVeOEvzI2b96MgIAAWFtb49SpUygsLAQAZGdnszeAiIhMgiQJxTZjJDv5z549GytWrMCqVatgYWGhKe/cuTNOnTqlaHBERESGwJZ/GcnJyejatWu5crVajaysLCViIiIiIj2SnfydnJxw6dKlcuWHDx9Go0aNFAmKiIjIkCRJuc0YyU7+w4cPx5gxY3D8+HFIkoTr168jJiYGEyZMwIgRI/QRIxERUZUyU3AzRrIf9ZsyZQpKS0vh5+eH/Px8dO3aFSqVChMmTMCoUaP0ESMREREpSBJCVGoq4oMHD3Dp0iXk5ubCy8sLtra2TxhKyhOeT2R63OdeMXQIREbpytSe+q0/d4didbnbvqJYXUqR3SMRGhqKe/fuwdLSEl5eXnj++edha2uLvLw8hIaG6iNGIiKiKsXZ/mWsXbsW9+/fL1d+//59rFu3TpGgiIiISH90HvPPycmBEAJCCNy7dw9WVlaafSUlJfjxxx9Rv359vQRJRERUlYx1lr5SdE7+9vb2kCQJkiShWbNm5fZLkoQZM2YoGhwREZEhmHju1z35x8XFQQiBHj16YPPmzahTp45mn6WlJdzd3eHi4qKXIImIiEg5Oif/bt26AQBSU1Ph5uYGydT7RIiIqNoy1lfxKkX2hL+ff/4Z3333XbnyTZs2Ye3atYoERUREZEic7V9GVFQU6tatW668fv36fKsfERGZBL7Vr4y0tDR4eHiUK3d3d0daWpoiQREREZH+yE7+9evXx9mzZ8uVJyUlwcHBQZGgiIiIDMnUu/1lr+0fHByM0aNHo1atWppX+x44cABjxozBwIEDFQ+QiIioqpn6nHbZyX/WrFm4fPky/Pz8UKPGw9NLS0sxePBgjvkTERE9BWQnf0tLS3zzzTeYNWsWkpKSYG1tjVatWsHd3V0f8REREVU5E2/4y0/+jzRr1qzClf6IiIiedrInxD1ldEr+ERERmDVrFmxsbBAREfG3xy5cuFCRwIiIiEg/dEr+p0+fRlFRkebz43DVPyIiMgWmns50Sv5xcXEVfiYiIjJNpp39TX1Yg4iI6Klx8OBBvPLKK3BxcYEkSdi2bZvWfiEEPvjgAzg7O8Pa2hr+/v64ePGi7Ovo1PLv16+fzhVu2bJFdhBERETGRDJQyz8vLw9t2rRBaGhohbn3o48+wpIlS7B27Vp4eHjg/fffR0BAAM6fPw8rKyudr6NT8ler1ZrPQghs3boVarUa3t7eAIDExERkZWXJ+iOBiIjIWEmSYTrGX375Zbz88ssV7hNCYPHixXjvvffQp08fAMC6devg6OiIbdu2yVpoT6fkHx0drfk8efJk9O/fHytWrIC5uTkAoKSkBO+++y7s7Ox0vjAREZHxUq7lX1hYiMLCQq0ylUoFlUolq57U1FRkZGTA399fU6ZWq9GxY0fEx8fLSv6y/7T56quvMGHCBE3iBwBzc3NERETgq6++klsdERGRSYuKioJardbaoqKiZNeTkZEBAHB0dNQqd3R01OzTlexFfoqLi3HhwgU0b95cq/zChQsoLS2VWx0REZHRUXLMPzIystwaOXJb/UqTnfyHDRuGsLAw/P7773j++ecBAMePH8e8efMwbNgwxQMkIiKqesol/8p08VfEyckJAJCZmQlnZ2dNeWZmJtq2bSurLtnJ/5NPPoGTkxMWLFiA9PR0AICzszMmTpyI8ePHy62OiIiIdODh4QEnJyfExsZqkn1OTg6OHz+OESNGyKpLdvI3MzPDpEmTMGnSJOTk5AAAJ/oREZFJMdRs/9zcXFy6dEnzPTU1FWfOnEGdOnXg5uaGsWPHYvbs2WjatKnmUT8XFxf07dtX1nUq9WKf4uJi7N+/H7///jvefPNNAMD169dhZ2cHW1vbylRJRERkRAzznP/JkyfRvXt3zfdHcwWGDBmCNWvWYNKkScjLy8Pbb7+NrKwsdOnSBbt375b1jD8ASEIIIeeEK1euoFevXkhLS0NhYSFSUlLQqFEjjBkzBoWFhVixYoWsAP4npZLnEZku97lXDB0CkVG6MrWnXuvPKdqnWF12Fv7/fFAVk92vMWbMGHh7e+Pu3buwtrbWlL/22muIjY1VNDgiIiJDkBT8nzGS3e1/6NAhHD16FJaWllrlDRs2xLVr1xQLjIiIyFCMNWkrRXbLv7S0FCUlJeXKr169ilq1aikSFBEREemP7OT/0ksvYfHixZrvkiQhNzcX06dPR2BgoJKxERERGYiZgpvxqdRz/r169YKXlxcKCgrw5ptv4uLFi6hbty42bNigjxiJiIiqlCSZdre/7OTv6uqKpKQkfPPNN0hKSkJubi7CwsIQEhKiNQGQiIjo6cXkr1FUVARPT0/88MMPCAkJQUhIiL7iIiIiIj2RlfwtLCxQUFCgr1iIiIiMAmf7lxEeHo758+ejuLhYH/EQEREZAU7403LixAnExsbip59+QqtWrWBjY6O1f8uWLYoFR0RERMqTnfzt7e0RFBSkj1iIiIiMgql3+8tO/tHR0fqIg4iIyGiY+qN+Og9GlJaWYv78+ejcuTOee+45TJkyBffv39dnbERERKQHOif/OXPmYOrUqbC1tUWDBg3w6aefIjw8XJ+xERERGYik4GZ8dE7+69atw7Jly7Bnzx5s27YNO3bsQExMDEpLS/UZHxERUZWTYKbYZox0jiotLU1r7X5/f39IkoTr16/rJTAiIiLSD50n/BUXF8PKykqrzMLCAkVFRYoHRUREZFjG2V2vFJ2TvxACQ4cOhUql0pQVFBTgnXfe0XrWn8/5ExHR087UZ/vrnPyHDBlSrmzQoEGKBkNERGQcmPwB8Pl+IiIiUyF7kR8iIiJTZ6yz9JXC5E9ERFSOaXf7m/afNkRERFQOW/5ERERl8MU+RERE1YypP+rHbn8iIqJqhi1/IiKicky7bczkT0REVIapj/mb9p82REREVA5b/kREROWYdsufyZ+IiKgMU5/tz+RPRERUjmmPipv23REREVE5bPkTERGVYeqz/SUhhDB0EGQ8CgsLERUVhcjISKhUKkOHQ2QU+HtBpobJn7Tk5ORArVYjOzsbdnZ2hg6HyCjw94JMDcf8iYiIqhkmfyIiomqGyZ+IiKiaYfInLSqVCtOnT+ekJqK/4O8FmRpO+CMiIqpm2PInIiKqZpj8iYiIqhkmfyIiomqGyf8pJ0kStm3bZrDr79+/H5IkISsr62+Pa9iwIRYvXlwlMVH1ZOjfhSf14Ycfom3btoYOg6oJJn8dxcfHw9zcHL1795Z9riknPl9fX6Snp0OtVgMA1qxZA3t7+3LHnThxAm+//XYVR1feli1b8NJLL8HBwQGSJOHMmTOGDumpw9+FJ1fRHyoTJkxAbGysYQIqY//+/Wjfvj1UKhWaNGmCNWvWGDokUhiTv45Wr16NUaNG4eDBg7h+/bqhwzEalpaWcHJy+sd3X9erVw81a9asoqgeLy8vD126dMH8+fMNHcpTi78L+mFrawsHBwdDh4HU1FT07t0b3bt3x5kzZzB27Fi89dZb2LNnj6FDIyUJ+kf37t0Ttra24sKFC2LAgAFizpw55Y7Zvn278Pb2FiqVSjg4OIi+ffsKIYTo1q2bAKC1CSHE9OnTRZs2bbTqWLRokXB3d9d8T0hIEP7+/sLBwUHY2dmJrl27isTERK1zAIitW7c+NvZu3bqJ8PBwER4eLuzs7ISDg4N47733RGlpqeaYO3fuiH//+9/C3t5eWFtbi169eomUlBTN/suXL4t//etfwt7eXtSsWVN4eXmJnTt3CiGEiIuLEwDE3bt3NZ//uk2fPl0IIYS7u7tYtGiREEKI4OBg0b9/f604Hzx4IBwcHMTatWuFEEKUlJSIuXPnioYNGworKyvRunVrsWnTpsfep1ypqakCgDh9+rRidVYHT/vvwqhRo8TEiRNF7dq1haOjo+bf5yN3794VYWFhom7duqJWrVqie/fu4syZM1rHzJo1S9SrV0/Y2tqKsLAwMXnyZK34/ylWd3d3rZ/Bo/v8689hz549QqVSibt372pde/To0aJ79+6a74cOHRJdunQRVlZW4plnnhGjRo0Subm5j/0Z6GLSpEmiZcuWWmUDBgwQAQEBT1QvGRe2/HXw7bffwtPTE82bN8egQYPw1VdfQfxleYSdO3fitddeQ2BgIE6fPo3Y2Fg8//zzAB52Mz/zzDOYOXMm0tPTkZ6ervN17927hyFDhuDw4cM4duwYmjZtisDAQNy7d09W/GvXrkWNGjWQkJCATz/9FAsXLsSXX36p2T906FCcPHkS27dvR3x8PIQQCAwMRFFREQAgPDwchYWFOHjwIH755RfMnz8ftra25a7j6+uLxYsXw87OTnOvEyZMKHdcSEgIduzYgdzcXE3Znj17kJ+fj9deew0AEBUVhXXr1mHFihX49ddfMW7cOAwaNAgHDhx47H1++OGHaNiwoayfDcljCr8LNjY2OH78OD766CPMnDkTe/fu1ex/4403cOPGDezatQuJiYlo3749/Pz8cOfOHQBATEwM5syZg/nz5yMxMRFubm5Yvny5rFhPnDgBAIiOjkZ6errm+1/5+fnB3t4emzdv1pSVlJTgm2++QUhICADg999/R69evRAUFISzZ8/im2++weHDhzFy5MjH3v/ly5chSRL279//2GPi4+Ph7++vVRYQEID4+PjHnkNPIQP/8fFU8PX1FYsXLxZCCFFUVCTq1q0r4uLiNPt9fHxESEjIY8//a6v3EV1aO2WVlJSIWrVqiR07dmjKoENrp0WLFlot/cmTJ4sWLVoIIYRISUkRAMSRI0c0+2/duiWsra3Ft99+K4QQolWrVuLDDz+ssP6/tvyFECI6Olqo1epyx/31Z/DoZ7hu3TrN/uDgYDFgwAAhhBAFBQWiZs2a4ujRo1p1hIWFieDg4Mfe62effSZ69Ojx2P1/xZZ/5TztvwtdunTRKnvuuefE5MmThRAPW9F2dnaioKBA65jGjRuLlStXCiGE6NixowgPD9fa37lz53LxVybWsj+HMWPGaP17LtsbEBYWJt5++22tOg4dOiTMzMzE/fv3K4zl6tWronnz5uL48eOPjbdp06Zi7ty5WmU7d+4UAER+fv5jz6OnC1v+/yA5ORkJCQkIDg4GANSoUQMDBgzA6tWrNcecOXMGfn5+il87MzMTw4cPR9OmTaFWq2FnZ4fc3FykpaXJqqdTp05aY/I+Pj64ePEiSkpK8Ntvv6FGjRro2LGjZr+DgwOaN2+O3377DQAwevRozJ49G507d8b06dNx9uzZJ7qvGjVqoH///oiJiQHwcBz++++/17RoLl26hPz8fPTs2RO2traabd26dfj9998fW+/IkSONZsKUKTKF34XWrVtrfXd2dsaNGzcAAElJScjNzYWDg4PWv7vU1FTNv7vk5GRNT8YjZb8rFWtISAj279+vmVcRExOD3r17aybUJiUlYc2aNVqxBgQEoLS0FKmpqRXW2aBBA1y4cKFczFT91DB0AMZu9erVKC4uhouLi6ZMCAGVSoXPP/8carUa1tbWsus1MzPT6i4FoOlmf2TIkCG4ffs2Pv30U7i7u0OlUsHHxwcPHjyo3M1U0ltvvYWAgADs3LkTP/30E6KiorBgwQKMGjWq0nWGhISgW7duuHHjBvbu3Qtra2v06tULADTDATt37kSDBg20zuPa6oZjCr8LFhYWWt8lSUJpaSmAh//unJ2dK+wSr+gJlsdRKtbnnnsOjRs3xsaNGzFixAhs3bpVa9Z9bm4u/vOf/2D06NHlznVzc5N1rb9ycnJCZmamVllmZibs7Owq9f8vGSe2/P9GcXEx1q1bhwULFuDMmTOaLSkpCS4uLtiwYQOAh62Jv2txWlpaoqSkRKusXr16yMjI0PqPXtnHzo4cOYLRo0cjMDAQLVu2hEqlwq1bt2Tfx/Hjx7W+PxqHNDc3R4sWLVBcXKx1zO3bt5GcnAwvLy9NmaurK9555x1s2bIF48ePx6pVq3S+14r4+vrC1dUV33zzDWJiYvDGG29o/sPs5eUFlUqFtLQ0NGnSRGtzdXWVff/05Ezld+HvtG/fHhkZGahRo0a5f3d169YFADRv3rzcGH3Z77rEamFhodPvSUhICGJiYrBjxw6YmZlpPV7Zvn17nD9/vlysTZo0gaWlZWV/DPDx8Sn3/+HevXvh4+NT6TrJCBlyzMHYbd26VVhaWoqsrKxy+yZNmiS8vb2FEA/Hvc3MzMQHH3wgzp8/L86ePSvmzZunObZnz57i1VdfFVevXhU3b94UQghx/vx5IUmSmDdvnrh06ZL4/PPPRe3atbXGOdu1ayd69uwpzp8/L44dOyZeeOEFYW1trTVmCh3GOW1tbcW4cePEhQsXxPr164WNjY1YsWKF5pg+ffoILy8vcejQIXHmzBnRq1cv0aRJE/HgwQMhxMOxx927d4s//vhDJCYmio4dO2pm65cd8z9y5IgAIPbt2ydu3rwp8vLyhBAVj/VOmzZNeHl5iRo1aohDhw6V2+fg4CDWrFkjLl26JBITE8WSJUvEmjVrHnuvuoz53759W5w+fVozhrlx40Zx+vRpkZ6e/rfnVXem8rswZswYrbI+ffqIIUOGCCGEKC0tFV26dBFt2rQRe/bsEampqeLIkSNi6tSp4sSJE0IIIf773/8Ka2trsWbNGpGSkiJmzZol7OzsRNu2bWXF2rRpUzFixAiRnp4u7ty5I4SoeO7DxYsXBQDRunVrERYWprUvKSlJWFtbi/DwcHH69GmRkpIitm3bVm5Owl/pMub/xx9/iJo1a4qJEyeK3377TSxdulSYm5uL3bt3P/Ycevow+f+Nf/3rXyIwMLDCfcePHxcARFJSkhBCiM2bN4u2bdsKS0tLUbduXdGvXz/NsfHx8aJ169ZCpVKJv/69tXz5cuHq6ipsbGzE4MGDxZw5c7T+g3fq1Cnh7e0trKysRNOmTcWmTZvKJVFd/oP37rvvinfeeUfY2dmJ2rVri6lTp1b4qJ9arRbW1tYiICBA61G/kSNHisaNGwuVSiXq1asn/v3vf4tbt24JIconfyGEeOedd4SDg8NjH/V75Pz585pHnf4ajxAP/0O8ePFi0bx5c2FhYSHq1asnAgICxIEDBx57r9OnT//bSWJCPJyQiDKPm/01TqqYqfwu/F3yF0KInJwcMWrUKOHi4iIsLCyEq6urCAkJEWlpaZpjZs6cKerWrStsbW1FaGioGD16tOjUqZOsWLdv3y6aNGkiatSoUeGjfn/1/PPPCwDi559/LrcvISFB9OzZU9ja2gobGxvRunXrCh+/fOTRRNe/TtKsSFxcnOb/w0aNGono6Oi/PZ6ePnylr4l78cUX0bZtW66qRqQnPXv2hJOTE77++mtDh0KkM074IyLSUX5+PlasWIGAgACYm5tjw4YN2Ldvn9ZaAURPAyZ/IiIdSZKEH3/8EXPmzEFBQQGaN2+OzZs3l1sUh8jYsdufiIiomuGjfkRERNUMkz8REVE1w+RPRERUzTD5ExERVTNM/kRERNUMkz8REVE1w+RPRERUzTD5ExERVTNM/kRERNXM/wFHMBvIskiSogAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_matrixc = pd.DataFrame(data = tree_cm, columns = ['Actual values : 1', 'Predicted values : 0'], index = ['Predicted values : 1', 'Actual values : 0'])\n",
    "sns.heatmap(cm_matrix, annot = True, fmt = 'd', cmap = 'YlGnBu')\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting AUC_ROC Curve for Decsion Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 10))\n",
    "roc_auc = auc(fpr, tpr)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, tree_y_pred)\n",
    "plt.plot([0, 1], [0, 1], '--')\n",
    "plt.plot(fpr, tpr,color = 'red', label = roc_auc)\n",
    "plt.title('ROC_AUC curve for Decision Tree Classifier')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.axis('tight')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross validation score for Decision Tree "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross validation mean accuracy for decsion tree is :  0.9266558441558443\n"
     ]
    }
   ],
   "source": [
    "score = cross_val_score(tree, X_train, y_train, cv = 10, scoring = 'accuracy')\n",
    "print('cross validation mean accuracy for decsion tree is : ',  np.mean(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RandomForest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train, y_train)\n",
    "rf_y_pred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating Accuracy, precision, recall Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.9785714285714285\n",
      "precision_score:  0.9642857142857143\n",
      "recall_score:  0.9818181818181818\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy score: ', accuracy_score(y_test, rf_y_pred))\n",
    "print('precision_score: ', precision_score(y_test, rf_y_pred))\n",
    "print('recall_score: ', recall_score(y_test, rf_y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating Confusion matrix for Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusing Matrix values for Decsion Tree are: \n",
      " [[83  2]\n",
      " [ 1 54]]\n"
     ]
    }
   ],
   "source": [
    "rf_cm = confusion_matrix(y_test, rf_y_pred)\n",
    "print('Confusing Matrix values for Decsion Tree are: \\n', rf_cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting Random Forest confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 69,
     "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": [
    "cm_matrixc = pd.DataFrame(data = rf_cm, columns = ['Actual values : 1', 'Predicted values : 0'], index = ['Predicted values : 1', 'Actual values : 0'])\n",
    "sns.heatmap(cm_matrix, annot = True, fmt = 'd', cmap = 'YlGnBu')\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 10))\n",
    "roc_auc = auc(fpr, tpr)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, rf_y_pred)\n",
    "plt.plot([0, 1], [0, 1], '--')\n",
    "plt.plot(fpr, tpr,color = 'red', label = roc_auc)\n",
    "plt.title('ROC_AUC curve for Decision Tree Classifier')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.axis('tight')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN model classifcation report:\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.98      0.98        85\n",
      "           1       0.96      0.96      0.96        55\n",
      "\n",
      "    accuracy                           0.97       140\n",
      "   macro avg       0.97      0.97      0.97       140\n",
      "weighted avg       0.97      0.97      0.97       140\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('KNN model classifcation report:\\n ', classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression classification report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.96      0.98        85\n",
      "           1       0.95      0.98      0.96        55\n",
      "\n",
      "    accuracy                           0.97       140\n",
      "   macro avg       0.97      0.97      0.97       140\n",
      "weighted avg       0.97      0.97      0.97       140\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Logistic Regression classification report: \\n', classification_report(y_test, lg_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear SVC classification report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.96      0.98        85\n",
      "           1       0.95      0.98      0.96        55\n",
      "\n",
      "    accuracy                           0.97       140\n",
      "   macro avg       0.97      0.97      0.97       140\n",
      "weighted avg       0.97      0.97      0.97       140\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Linear SVC classification report: \\n', classification_report(y_test, svc_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Classifier Classification report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.95      0.95        85\n",
      "           1       0.93      0.91      0.92        55\n",
      "\n",
      "    accuracy                           0.94       140\n",
      "   macro avg       0.93      0.93      0.93       140\n",
      "weighted avg       0.94      0.94      0.94       140\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Decision Tree Classifier Classification report: \\n', classification_report(y_test, tree_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier Classification report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.98        85\n",
      "           1       0.96      0.98      0.97        55\n",
      "\n",
      "    accuracy                           0.98       140\n",
      "   macro avg       0.98      0.98      0.98       140\n",
      "weighted avg       0.98      0.98      0.98       140\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Random Forest Classifier Classification report: \\n', classification_report(y_test, rf_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Clump_thickness</th>\n",
       "      <th>Uniformity_Cell_Size</th>\n",
       "      <th>Uniformity_Cell_Shape</th>\n",
       "      <th>Marginal_Adhesion</th>\n",
       "      <th>Single_Epithelial_Cell_Size</th>\n",
       "      <th>Bare_Nuclei</th>\n",
       "      <th>Bland_Chromatin</th>\n",
       "      <th>Normal_Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.125209</td>\n",
       "      <td>-0.679581</td>\n",
       "      <td>-0.382092</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-0.546543</td>\n",
       "      <td>-0.665096</td>\n",
       "      <td>-0.995595</td>\n",
       "      <td>-0.608165</td>\n",
       "      <td>-0.324258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.125209</td>\n",
       "      <td>-0.353219</td>\n",
       "      <td>-0.382092</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-0.546543</td>\n",
       "      <td>-0.665096</td>\n",
       "      <td>-0.577774</td>\n",
       "      <td>-0.608165</td>\n",
       "      <td>-0.324258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.592655</td>\n",
       "      <td>0.952231</td>\n",
       "      <td>0.961239</td>\n",
       "      <td>2.164005</td>\n",
       "      <td>1.280168</td>\n",
       "      <td>-0.665096</td>\n",
       "      <td>1.511331</td>\n",
       "      <td>1.664758</td>\n",
       "      <td>-0.324258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.233723</td>\n",
       "      <td>-0.679581</td>\n",
       "      <td>-0.717925</td>\n",
       "      <td>-0.622471</td>\n",
       "      <td>-0.546543</td>\n",
       "      <td>-0.665096</td>\n",
       "      <td>-0.577774</td>\n",
       "      <td>-0.283462</td>\n",
       "      <td>-0.324258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.310519</td>\n",
       "      <td>1.278593</td>\n",
       "      <td>0.625406</td>\n",
       "      <td>2.512315</td>\n",
       "      <td>1.736846</td>\n",
       "      <td>1.577973</td>\n",
       "      <td>0.675689</td>\n",
       "      <td>0.690648</td>\n",
       "      <td>1.494406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Clump_thickness  Uniformity_Cell_Size  Uniformity_Cell_Shape  \\\n",
       "0        -0.125209             -0.679581              -0.382092   \n",
       "1        -0.125209             -0.353219              -0.382092   \n",
       "2         0.592655              0.952231               0.961239   \n",
       "3         0.233723             -0.679581              -0.717925   \n",
       "4         1.310519              1.278593               0.625406   \n",
       "\n",
       "   Marginal_Adhesion  Single_Epithelial_Cell_Size  Bare_Nuclei  \\\n",
       "0          -0.622471                    -0.546543    -0.665096   \n",
       "1          -0.622471                    -0.546543    -0.665096   \n",
       "2           2.164005                     1.280168    -0.665096   \n",
       "3          -0.622471                    -0.546543    -0.665096   \n",
       "4           2.512315                     1.736846     1.577973   \n",
       "\n",
       "   Bland_Chromatin  Normal_Nucleoli   Mitoses  \n",
       "0        -0.995595        -0.608165 -0.324258  \n",
       "1        -0.577774        -0.608165 -0.324258  \n",
       "2         1.511331         1.664758 -0.324258  \n",
       "3        -0.577774        -0.283462 -0.324258  \n",
       "4         0.675689         0.690648  1.494406  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n",
       "       1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
       "       0, 1, 1, 0, 0, 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  }
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