heart_diseases.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f5ccfcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "from sklearn.preprocessing import MinMaxScaler,LabelEncoder,OneHotEncoder\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6208c1a8",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>chest pain type</th>\n",
       "      <th>resting bp s</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>fasting blood sugar</th>\n",
       "      <th>resting ecg</th>\n",
       "      <th>max heart rate</th>\n",
       "      <th>exercise angina</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>ST slope</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>male</td>\n",
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       "      <td>False</td>\n",
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       "      <td>4</td>\n",
       "      <td>138</td>\n",
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       "      <td>339</td>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "      <td>False</td>\n",
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       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>136</td>\n",
       "      <td>164</td>\n",
       "      <td>False</td>\n",
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       "      <td>male</td>\n",
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       "      <td>204</td>\n",
       "      <td>False</td>\n",
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       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>49</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
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       "      <td>42</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>115</td>\n",
       "      <td>211</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    age     sex  chest pain type  resting bp s  cholesterol  \\\n",
       "0    40    male                2           140          289   \n",
       "1    49  female                3           160          180   \n",
       "2    37    male                2           130          283   \n",
       "3    48  female                4           138          214   \n",
       "4    54    male                3           150          195   \n",
       "5    39    male                3           120          339   \n",
       "6    45  female                2           130          237   \n",
       "7    54    male                2           110          208   \n",
       "8    37    male                4           140          207   \n",
       "9    48  female                2           120          284   \n",
       "10   37  female                3           130          211   \n",
       "11   58    male                2           136          164   \n",
       "12   39    male                2           120          204   \n",
       "13   49    male                4           140          234   \n",
       "14   42  female                3           115          211   \n",
       "\n",
       "    fasting blood sugar  resting ecg  max heart rate  exercise angina  \\\n",
       "0                 False            0             172                0   \n",
       "1                 False            0             156                0   \n",
       "2                 False            1              98                0   \n",
       "3                 False            0             108                1   \n",
       "4                 False            0             122                0   \n",
       "5                 False            0             170                0   \n",
       "6                 False            0             170                0   \n",
       "7                 False            0             142                0   \n",
       "8                 False            0             130                1   \n",
       "9                 False            0             120                0   \n",
       "10                False            0             142                0   \n",
       "11                False            1              99                1   \n",
       "12                False            0             145                0   \n",
       "13                False            0             140                1   \n",
       "14                False            1             137                0   \n",
       "\n",
       "    oldpeak  ST slope  target  \n",
       "0       0.0         1   False  \n",
       "1       1.0         2    True  \n",
       "2       0.0         1   False  \n",
       "3       1.5         2    True  \n",
       "4       0.0         1   False  \n",
       "5       0.0         1   False  \n",
       "6       0.0         1   False  \n",
       "7       0.0         1   False  \n",
       "8       1.5         2    True  \n",
       "9       0.0         1   False  \n",
       "10      0.0         1   False  \n",
       "11      2.0         2    True  \n",
       "12      0.0         1   False  \n",
       "13      1.0         2    True  \n",
       "14      0.0         1   False  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"heart_disease.csv\")\n",
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9463120d",
   "metadata": {},
   "outputs": [],
   "source": [
    "le=LabelEncoder()\n",
    "for column in df.columns :\n",
    "    df.loc[:,column]=le.fit_transform(df.loc[:,column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5e1a55a8",
   "metadata": {},
   "outputs": [
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       "<p>1190 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  sex  chest pain type  resting bp s  cholesterol  \\\n",
       "0      12  1.0                1            41          147   \n",
       "1      21  0.0                2            55           40   \n",
       "2       9  1.0                1            31          141   \n",
       "3      20  0.0                3            39           72   \n",
       "4      26  1.0                2            49           53   \n",
       "...   ...  ...              ...           ...          ...   \n",
       "1185   17  1.0                0            14          122   \n",
       "1186   40  1.0                3            45           51   \n",
       "1187   29  1.0                3            31            9   \n",
       "1188   29  0.0                1            31           94   \n",
       "1189   10  1.0                2            39           35   \n",
       "\n",
       "      fasting blood sugar  resting ecg  max heart rate  exercise angina  \\\n",
       "0                       0            0              98                0   \n",
       "1                       0            0              82                0   \n",
       "2                       0            1              25                0   \n",
       "3                       0            0              34                1   \n",
       "4                       0            0              48                0   \n",
       "...                   ...          ...             ...              ...   \n",
       "1185                    0            0              58                0   \n",
       "1186                    1            0              67                0   \n",
       "1187                    0            0              41                1   \n",
       "1188                    0            2             100                0   \n",
       "1189                    0            0              99                0   \n",
       "\n",
       "      oldpeak  ST slope  target  \n",
       "0        10.0         1       0  \n",
       "1        20.0         2       1  \n",
       "2        10.0         1       0  \n",
       "3        25.0         2       1  \n",
       "4        10.0         1       0  \n",
       "...       ...       ...     ...  \n",
       "1185     22.0         2       1  \n",
       "1186     42.0         2       1  \n",
       "1187     22.0         2       1  \n",
       "1188     10.0         2       1  \n",
       "1189     10.0         1       0  \n",
       "\n",
       "[1190 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "\n",
    "imputer.fit(df.iloc[:, :])\n",
    "df.iloc[:, :] = imputer.transform(df.iloc[:, :])  \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cfc84e75-cbc3-46bf-92cc-7a26af618217",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of class 1 = >629\n",
      "length of class 0 => 561\n"
     ]
    }
   ],
   "source": [
    "class_1=sum(df[\"target\"]==1)\n",
    "print(f\"length of class 1 = >{class_1}\")\n",
    "print(f\"length of class 0 => {1190-class_1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1f591d6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "96079fd1",
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>chest pain type</th>\n",
       "      <th>resting bp s</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>fasting blood sugar</th>\n",
       "      <th>resting ecg</th>\n",
       "      <th>max heart rate</th>\n",
       "      <th>exercise angina</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>ST slope</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>39</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>918 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  chest pain type  resting bp s  cholesterol  \\\n",
       "0     12  1.0                1            41          147   \n",
       "1     21  0.0                2            55           40   \n",
       "2      9  1.0                1            31          141   \n",
       "3     20  0.0                3            39           72   \n",
       "4     26  1.0                2            49           53   \n",
       "..   ...  ...              ...           ...          ...   \n",
       "913   17  1.0                0            14          122   \n",
       "914   40  1.0                3            45           51   \n",
       "915   29  1.0                3            31            9   \n",
       "916   29  0.0                1            31           94   \n",
       "917   10  1.0                2            39           35   \n",
       "\n",
       "     fasting blood sugar  resting ecg  max heart rate  exercise angina  \\\n",
       "0                      0            0              98                0   \n",
       "1                      0            0              82                0   \n",
       "2                      0            1              25                0   \n",
       "3                      0            0              34                1   \n",
       "4                      0            0              48                0   \n",
       "..                   ...          ...             ...              ...   \n",
       "913                    0            0              58                0   \n",
       "914                    1            0              67                0   \n",
       "915                    0            0              41                1   \n",
       "916                    0            2             100                0   \n",
       "917                    0            0              99                0   \n",
       "\n",
       "     oldpeak  ST slope  target  \n",
       "0       10.0         1       0  \n",
       "1       20.0         2       1  \n",
       "2       10.0         1       0  \n",
       "3       25.0         2       1  \n",
       "4       10.0         1       0  \n",
       "..       ...       ...     ...  \n",
       "913     22.0         2       1  \n",
       "914     42.0         2       1  \n",
       "915     22.0         2       1  \n",
       "916     10.0         2       1  \n",
       "917     10.0         1       0  \n",
       "\n",
       "[918 rows x 12 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.reset_index(drop=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "39d78356",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 918 entries, 0 to 917\n",
      "Data columns (total 12 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   age                  918 non-null    int64  \n",
      " 1   sex                  918 non-null    object \n",
      " 2   chest pain type      918 non-null    int64  \n",
      " 3   resting bp s         918 non-null    int64  \n",
      " 4   cholesterol          918 non-null    int64  \n",
      " 5   fasting blood sugar  918 non-null    int64  \n",
      " 6   resting ecg          918 non-null    int64  \n",
      " 7   max heart rate       918 non-null    int64  \n",
      " 8   exercise angina      918 non-null    int64  \n",
      " 9   oldpeak              918 non-null    float64\n",
      " 10  ST slope             918 non-null    int64  \n",
      " 11  target               918 non-null    int64  \n",
      "dtypes: float64(1), int64(10), object(1)\n",
      "memory usage: 86.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "500d9b14-6ef8-4ae2-96fb-95ead859d35a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "females=df[df['sex']==0]\n",
    "males=df[df['sex']==1]\n",
    "\n",
    "explode=(0.1,0.2,0.1,0.1)\n",
    "\n",
    "p_female=sum(females['target']==1)\n",
    "p_male=sum(males['target']==1)\n",
    "n_female=sum(females['target']==0)\n",
    "n_male=sum(males['target']==0)\n",
    "\n",
    "plt.pie([p_male,p_female,n_male,n_female],autopct='%1.0f%%',\n",
    "        explode = explode , shadow=True , colors= ['r','g','b',\"#96D1D8\"],labeldistance=1.11\n",
    ")\n",
    "plt.title(\"sex\")\n",
    "plt.legend(labels=['positive males','positive females','negative males','negative females'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1dfa205e-602a-4de9-b1fd-b16dbb016e3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "patient=df[df['target']==1]\n",
    "\n",
    "have_bs=sum(patient['fasting blood sugar']==1)\n",
    "n_have_bs=sum(patient['fasting blood sugar']==0)\n",
    "\n",
    "\n",
    "plt.pie([have_bs,n_have_bs],\n",
    "        \n",
    "        autopct='%1.0f%%',\n",
    "        explode = (0.1,0.2) , shadow=True , colors= ['#593473',\"#96D1D8\"]\n",
    ")\n",
    "\n",
    "plt.legend(labels=['Have Blood suger ',\"Not Have Blood suger \"])\n",
    "\n",
    "plt.title(\"Heart Disease with blod suger \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "da8e2fbe-8ab2-455c-9a3a-d080e5f318ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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27NFNN93U5jh5eXmaMWOGb7u+vl4JCQkdVzgAAAiqDgsoH330kQ4ePKjVq1dfcO5NN92k0NBQlZaWnjWgOJ1OOZ3OjigTAABYqMPuQVm8eLEGDhyo9PT0C849cOCAWlpaFB8f31HlAACATiTgKyiNjY364osvfNtlZWX6+OOPFR0drcTEREl/ewnmj3/8o+bNm9dm/7/85S9asWKFfvzjH8vlcumzzz7TzJkz9cMf/lC33XbbZZwKAADoKgIOKLt27dLw4cN929/eG5Kbm6ulS5dKklatWiVjjB544IE2+4eFhenDDz/Uq6++qsbGRiUkJOjuu+/W7NmzFRIScomnAQAAupKAA0pmZqaMMeed8+ijj+rRRx8961hCQoKKi4sDfVgAAHAV4bt4AACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrBBxQtmzZotGjR8vj8cjhcGjdunV+4xMnTpTD4fBrQ4YM8Zvj9Xo1bdo0uVwuRUREaMyYMTp8+PBlnQgAAOg6Ag4ox48fV3p6uhYuXHjOOaNGjVJlZaWvvfvuu37j06dP19q1a7Vq1Spt3bpVjY2NysnJUWtra+BnAAAAupzuge6QnZ2t7Ozs885xOp1yu91nHaurq9PixYv19ttv684775QkLV++XAkJCdq4caPuuuuuQEsCAABdTIfcg1JUVKTY2Fj169dPkyZNUnV1tW9s9+7damlpUVZWlq/P4/EoNTVV27ZtO+vxvF6v6uvr/RoAAOi62j2gZGdna8WKFdq0aZPmzZunnTt3asSIEfJ6vZKkqqoqhYWFqXfv3n77xcXFqaqq6qzHLCgoUFRUlK8lJCS0d9kAAMAiAb/EcyHjxo3z/ZyamqpBgwYpKSlJ77zzjsaOHXvO/YwxcjgcZx3Ly8vTjBkzfNv19fWEFAAAurAOf5txfHy8kpKSVFpaKklyu91qbm7W0aNH/eZVV1crLi7urMdwOp3q1auXXwMAAF1XhweU2tpaVVRUKD4+XpI0cOBAhYaGqrCw0DensrJS+/fvV0ZGRkeXAwAAOoGAX+JpbGzUF1984dsuKyvTxx9/rOjoaEVHRys/P1/33Xef4uPjdejQIT399NNyuVy69957JUlRUVF65JFHNHPmTMXExCg6OlqzZs1SWlqa7109AADg6hZwQNm1a5eGDx/u2/723pDc3Fy9/vrr2rdvn9566y0dO3ZM8fHxGj58uFavXq3IyEjfPi+//LK6d++u+++/X01NTbrjjju0dOlShYSEtMMpAQCAzi7ggJKZmSljzDnH33///Qseo0ePHlqwYIEWLFgQ6MMDAICrAN/FAwAArENAAQAA1iGgAAAA6xBQAACAddr9k2SBrqy8vFw1NTXBLiMgLpdLiYmJwS4DAAJCQAEuUnl5ufpff4NONp0IdikB6RHeUwc/LyGkAOhUCCjARaqpqdHJphOKyZmp0JjO8V1QLbUVqt0wTzU1NQQUAJ0KAQUIUGhMgpzu64JdBgB0adwkCwAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWCfggLJlyxaNHj1aHo9HDodD69at8421tLToV7/6ldLS0hQRESGPx6OHH35YR44c8TtGZmamHA6HXxs/fvxlnwwAAOgaAg4ox48fV3p6uhYuXNhm7MSJE9qzZ4+effZZ7dmzR2vWrNH//M//aMyYMW3mTpo0SZWVlb725ptvXtoZAACALqd7oDtkZ2crOzv7rGNRUVEqLCz061uwYIFuueUWlZeXKzEx0dffs2dPud3uQB8eAABcBTr8HpS6ujo5HA5dc801fv0rVqyQy+XSgAEDNGvWLDU0NJzzGF6vV/X19X4NAAB0XQFfQQnEyZMn9dRTT+nBBx9Ur169fP0PPfSQkpOT5Xa7tX//fuXl5emTTz5pc/XlWwUFBXruuec6slQAAGCRDgsoLS0tGj9+vE6fPq1Fixb5jU2aNMn3c2pqqlJSUjRo0CDt2bNHN910U5tj5eXlacaMGb7t+vp6JSQkdFTpAAAgyDokoLS0tOj+++9XWVmZNm3a5Hf15GxuuukmhYaGqrS09KwBxel0yul0dkSpAADAQu0eUL4NJ6Wlpdq8ebNiYmIuuM+BAwfU0tKi+Pj49i4HAAB0QgEHlMbGRn3xxRe+7bKyMn388ceKjo6Wx+PRT37yE+3Zs0cbNmxQa2urqqqqJEnR0dEKCwvTX/7yF61YsUI//vGP5XK59Nlnn2nmzJn64Q9/qNtuu639zgwAAHRaAQeUXbt2afjw4b7tb+8Nyc3NVX5+vtavXy9JuvHGG/3227x5szIzMxUWFqYPP/xQr776qhobG5WQkKC7775bs2fPVkhIyGWcCgAA6CoCDiiZmZkyxpxz/HxjkpSQkKDi4uJAHxYAAFxF+C4eAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWKd7sAsAgDOVl5erpqYm2GUExOVyKTExMdhlAF0GAQWAVcrLy9X/+ht0sulEsEsJSI/wnjr4eQkhBWgnBBTgKlBSUhLsEi5aSUmJTjadUEzOTIXGJAS7nIvSUluh2g3zVFNTQ0AB2gkBBejCWhuPSg6HJkyYEOxSAhYakyCn+7pglwEgSAgoQBd22tsoGdOprkY0fblLdR8tD3YZAIKMgIKg6Ww3Qnaml0nO1JmuRrTUVgS7BAAWIKAgKDrrjZAAgCuDgIKgqKmp6XQ3QvLSAwBcOQQUBBUvPQAAzoZPkgUAANYhoAAAAOsQUAAAgHUIKAAAwDoBB5QtW7Zo9OjR8ng8cjgcWrdund+4MUb5+fnyeDwKDw9XZmamDhw44DfH6/Vq2rRpcrlcioiI0JgxY3T48OHLOhEAANB1BBxQjh8/rvT0dC1cuPCs43PnztX8+fO1cOFC7dy5U263WyNHjlRDQ4NvzvTp07V27VqtWrVKW7duVWNjo3JyctTa2nrpZwIAALqMgN9mnJ2drezs7LOOGWP0yiuv6JlnntHYsWMlScuWLVNcXJxWrlypyZMnq66uTosXL9bbb7+tO++8U5K0fPlyJSQkaOPGjbrrrrsu43QAAEBX0K73oJSVlamqqkpZWVm+PqfTqWHDhmnbtm2SpN27d6ulpcVvjsfjUWpqqm/Ombxer+rr6/0aAADouto1oFRVVUmS4uLi/Prj4uJ8Y1VVVQoLC1Pv3r3POedMBQUFioqK8rWEhM7xyaMAAODSdMi7eBwOh9+2MaZN35nONycvL091dXW+VlHBJ3oCANCVtWtAcbvdktTmSkh1dbXvqorb7VZzc7OOHj16zjlncjqd6tWrl18DAABdV7sGlOTkZLndbhUWFvr6mpubVVxcrIyMDEnSwIEDFRoa6jensrJS+/fv980BAABXt4DfxdPY2KgvvvjCt11WVqaPP/5Y0dHRSkxM1PTp0zVnzhylpKQoJSVFc+bMUc+ePfXggw9KkqKiovTII49o5syZiomJUXR0tGbNmqW0tDTfu3oAAMDVLeCAsmvXLg0fPty3PWPGDElSbm6uli5dqieffFJNTU164okndPToUQ0ePFgffPCBIiMjffu8/PLL6t69u+6//341NTXpjjvu0NKlSxUSEtIOpwQAADq7gANKZmamjDHnHHc4HMrPz1d+fv455/To0UMLFizQggULAn14AABwFeC7eAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDrdg10AAHQVJSUlwS4hYC6XS4mJicEuA2iDgAIAl6m18ajkcGjChAnBLiVgPcJ76uDnJYQUWIeAAgCX6bS3UTJGMTkzFRqTEOxyLlpLbYVqN8xTTU0NAQXWIaAAQDsJjUmQ031dsMsAugRukgUAANYhoAAAAOu0e0D53ve+J4fD0aZNmTJFkjRx4sQ2Y0OGDGnvMgAAQCfW7veg7Ny5U62trb7t/fv3a+TIkfrpT3/q6xs1apSWLFni2w4LC2vvMgAAQCfW7gHl2muv9dt+8cUX1bdvXw0bNszX53Q65Xa7L/qYXq9XXq/Xt11fX3/5hQIAAGt16D0ozc3NWr58uX7+85/L4XD4+ouKihQbG6t+/fpp0qRJqq6uPu9xCgoKFBUV5WsJCZ3nbXwAACBwHRpQ1q1bp2PHjmnixIm+vuzsbK1YsUKbNm3SvHnztHPnTo0YMcLvCsmZ8vLyVFdX52sVFRUdWTYAAAiyDv0clMWLFys7O1sej8fXN27cON/PqampGjRokJKSkvTOO+9o7NixZz2O0+mU0+nsyFIBAIBFOiygfPXVV9q4caPWrFlz3nnx8fFKSkpSaWlpR5UCAAA6mQ57iWfJkiWKjY3V3Xfffd55tbW1qqioUHx8fEeVAgAAOpkOCSinT5/WkiVLlJubq+7d//8iTWNjo2bNmqXt27fr0KFDKioq0ujRo+VyuXTvvfd2RCkAAKAT6pCXeDZu3Kjy8nL9/Oc/9+sPCQnRvn379NZbb+nYsWOKj4/X8OHDtXr1akVGRnZEKQAAoBPqkICSlZUlY0yb/vDwcL3//vsd8ZAAAKAL4bt4AACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA63QPdgEAgOAqKSkJdgkBcblcSkxMDHYZ6GAEFAC4SrU2HpUcDk2YMCHYpQSkR3hPHfy8hJDSxRFQupDO9K+gzlQr0FWd9jZKxigmZ6ZCYxKCXc5FaamtUO2GeaqpqSGgdHEElC6gs/4rCIAdQmMS5HRfF+wyAD8ElC6gM/4rqOnLXar7aHmwywAAWIqA0oV0pn8FtdRWBLsEAIDFeJsxAACwDgEFAABYp90DSn5+vhwOh19zu92+cWOM8vPz5fF4FB4erszMTB04cKC9ywAAAJ1Yh1xBGTBggCorK31t3759vrG5c+dq/vz5WrhwoXbu3Cm3262RI0eqoaGhI0oBAACdUIfcJNu9e3e/qybfMsbolVde0TPPPKOxY8dKkpYtW6a4uDitXLlSkydPPuvxvF6vvF6vb7u+vr4jygYAAJbokCsopaWl8ng8Sk5O1vjx4/Xll19KksrKylRVVaWsrCzfXKfTqWHDhmnbtm3nPF5BQYGioqJ8LSGhc7yVFgAAXJp2DyiDBw/WW2+9pffff1+/+93vVFVVpYyMDNXW1qqqqkqSFBcX57dPXFycb+xs8vLyVFdX52sVFbxFFQCArqzdX+LJzs72/ZyWlqZbb71Vffv21bJlyzRkyBBJksPh8NvHGNOm7+85nU45nc72LhUAAFiqw99mHBERobS0NJWWlvruSznzakl1dXWbqyoAAODq1eEBxev1qqSkRPHx8UpOTpbb7VZhYaFvvLm5WcXFxcrIyOjoUgAAQCfR7i/xzJo1S6NHj1ZiYqKqq6v1wgsvqL6+Xrm5uXI4HJo+fbrmzJmjlJQUpaSkaM6cOerZs6cefPDB9i4FAAB0Uu0eUA4fPqwHHnhANTU1uvbaazVkyBDt2LFDSUlJkqQnn3xSTU1NeuKJJ3T06FENHjxYH3zwgSIjI9u7FAAA0Em1e0BZtWrVeccdDofy8/OVn5/f3g8NAAC6CL6LBwAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDrdg10AAACBKikpCXYJAXG5XEpMTAx2GZ0KAQUA0Gm0Nh6VHA5NmDAh2KUEpEd4Tx38vISQEgACCgCg0zjtbZSMUUzOTIXGJAS7nIvSUluh2g3zVFNTQ0AJAAEFANDphMYkyOm+LthloANxkywAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHXaPaAUFBTo5ptvVmRkpGJjY3XPPffo4MGDfnMmTpwoh8Ph14YMGdLepQAAgE6q3QNKcXGxpkyZoh07dqiwsFCnTp1SVlaWjh8/7jdv1KhRqqys9LV33323vUsBAACdVLt/UNt7773nt71kyRLFxsZq9+7duv322339TqdTbrf7oo7p9Xrl9Xp92/X19e1TLAAAsFKH34NSV1cnSYqOjvbrLyoqUmxsrPr166dJkyapurr6nMcoKChQVFSUryUkdI6PNwYAAJemQz/q3hijGTNmaOjQoUpNTfX1Z2dn66c//amSkpJUVlamZ599ViNGjNDu3bvldDrbHCcvL08zZszwbdfX1xNSAACdCt/AHJgODShTp07Vp59+qq1bt/r1jxs3zvdzamqqBg0apKSkJL3zzjsaO3Zsm+M4nc6zBhcAAGzHNzBfmg4LKNOmTdP69eu1ZcsW9enT57xz4+PjlZSUpNLS0o4qBwCAoOAbmC9NuwcUY4ymTZumtWvXqqioSMnJyRfcp7a2VhUVFYqPj2/vcgAAsALfwByYdr9JdsqUKVq+fLlWrlypyMhIVVVVqaqqSk1NTZKkxsZGzZo1S9u3b9ehQ4dUVFSk0aNHy+Vy6d57723vcgAAQCfU7ldQXn/9dUlSZmamX/+SJUs0ceJEhYSEaN++fXrrrbd07NgxxcfHa/jw4Vq9erUiIyPbuxwAANAJdchLPOcTHh6u999/v70fFgAAdCF8Fw8AALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDpBDSiLFi1ScnKyevTooYEDB+qjjz4KZjkAAMASQQsoq1ev1vTp0/XMM89o7969+tGPfqTs7GyVl5cHqyQAAGCJoAWU+fPn65FHHtEvfvEL3XDDDXrllVeUkJCg119/PVglAQAAS3QPxoM2Nzdr9+7deuqpp/z6s7KytG3btjbzvV6vvF6vb7uurk6SVF9f3+61NTY2/u0xq77Q6eaT7X78jtBSWyGJmjsaNV8Z1HzldMa6qfnKaPnrYUl/+5vYnn9rvz2WMebCk00QfP3110aS+c///E+//l//+temX79+bebPnj3bSKLRaDQajdYFWkVFxQWzQlCuoHzL4XD4bRtj2vRJUl5enmbMmOHbPn36tP76178qJibmrPMvR319vRISElRRUaFevXq167Fx6VgXO7EudmJd7MS6/O3vfENDgzwezwXnBiWguFwuhYSEqKqqyq+/urpacXFxbeY7nU45nU6/vmuuuaYjS1SvXr2u2v+AbMa62Il1sRPrYqerfV2ioqIual5QbpINCwvTwIEDVVhY6NdfWFiojIyMYJQEAAAsErSXeGbMmKGf/exnGjRokG699Vb99re/VXl5uR577LFglQQAACwRtIAybtw41dbW6vnnn1dlZaVSU1P17rvvKikpKVglSfrby0mzZ89u85ISgot1sRPrYifWxU6sS2AcxlzMe30AAACuHL6LBwAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgof2fRokVKTk5Wjx49NHDgQH300UfBLumqkp+fL4fD4dfcbrdv3Bij/Px8eTwehYeHKzMzUwcOHAhixV3Tli1bNHr0aHk8HjkcDq1bt85v/GLWwev1atq0aXK5XIqIiNCYMWN0+PDhK3gWXc+F1mXixIltnj9Dhgzxm8O6tL+CggLdfPPNioyMVGxsrO655x4dPHjQbw7PmUtDQPk/q1ev1vTp0/XMM89o7969+tGPfqTs7GyVl5cHu7SryoABA1RZWelr+/bt843NnTtX8+fP18KFC7Vz50653W6NHDlSDQ0NQay46zl+/LjS09O1cOHCs45fzDpMnz5da9eu1apVq7R161Y1NjYqJydHra2tV+o0upwLrYskjRo1yu/58+677/qNsy7tr7i4WFOmTNGOHTtUWFioU6dOKSsrS8ePH/fN4TlziS7/u4m7hltuucU89thjfn3XX3+9eeqpp4JU0dVn9uzZJj09/axjp0+fNm6327z44ou+vpMnT5qoqCjzxhtvXKEKrz6SzNq1a33bF7MOx44dM6GhoWbVqlW+OV9//bXp1q2bee+9965Y7V3ZmetijDG5ubnmH/7hH865D+tyZVRXVxtJpri42BjDc+ZycAVFUnNzs3bv3q2srCy//qysLG3bti1IVV2dSktL5fF4lJycrPHjx+vLL7+UJJWVlamqqspvjZxOp4YNG8YaXUEXsw67d+9WS0uL3xyPx6PU1FTWqoMVFRUpNjZW/fr106RJk1RdXe0bY12ujLq6OklSdHS0JJ4zl4OAIqmmpkatra1tvkk5Li6uzTcuo+MMHjxYb731lt5//3397ne/U1VVlTIyMlRbW+tbB9YouC5mHaqqqhQWFqbevXufcw7aX3Z2tlasWKFNmzZp3rx52rlzp0aMGCGv1yuJdbkSjDGaMWOGhg4dqtTUVEk8Zy5H0L6Lx0YOh8Nv2xjTpg8dJzs72/dzWlqabr31VvXt21fLli3z3ezHGtnhUtaBtepY48aN8/2cmpqqQYMGKSkpSe+8847Gjh17zv1Yl/YzdepUffrpp9q6dWubMZ4zgeMKiiSXy6WQkJA2SbW6urpN6sWVExERobS0NJWWlvrezcMaBdfFrIPb7VZzc7OOHj16zjnoePHx8UpKSlJpaakk1qWjTZs2TevXr9fmzZvVp08fXz/PmUtHQJEUFhamgQMHqrCw0K+/sLBQGRkZQaoKXq9XJSUlio+PV3Jystxut98aNTc3q7i4mDW6gi5mHQYOHKjQ0FC/OZWVldq/fz9rdQXV1taqoqJC8fHxkliXjmKM0dSpU7VmzRpt2rRJycnJfuM8Zy5D0G7PtcyqVatMaGioWbx4sfnss8/M9OnTTUREhDl06FCwS7tqzJw50xQVFZkvv/zS7Nixw+Tk5JjIyEjfGrz44osmKirKrFmzxuzbt8888MADJj4+3tTX1we58q6loaHB7N271+zdu9dIMvPnzzd79+41X331lTHm4tbhscceM3369DEbN240e/bsMSNGjDDp6enm1KlTwTqtTu9869LQ0GBmzpxptm3bZsrKyszmzZvNrbfear773e+yLh3s8ccfN1FRUaaoqMhUVlb62okTJ3xzeM5cGgLK33nttddMUlKSCQsLMzfddJPvbWK4MsaNG2fi4+NNaGio8Xg8ZuzYsebAgQO+8dOnT5vZs2cbt9ttnE6nuf32282+ffuCWHHXtHnzZiOpTcvNzTXGXNw6NDU1malTp5ro6GgTHh5ucnJyTHl5eRDOpus437qcOHHCZGVlmWuvvdaEhoaaxMREk5ub2+Z3zrq0v7OtiSSzZMkS3xyeM5fGYYwxV/qqDQAAwPlwDwoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArPO/11gxcr+2pZoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HHnhA0hdnSxISEvTII49o2rRpqq6uVs+ePbV69WqNHz9eknT06FElJibqpZde0ogRIy5puzU1NYqOjlZ1dbWioqJa2v45HT58WNded73+WX+qVT+3rXUPC9eB998jpAAArBXI8TugMyj/qqGhQWvWrNGsWbPkcDh08OBBeTwe5eTk+GqcTqfS09O1bds2TZs2TWVlZTp9+rRfjdvtVnJysrZt23begOL1euX1ev12sK1UVlbqn/WnFDdqtkLiEttsO63p9PEjOv7iY6qsrCSgAAC6hBYHlOeff14nTpzQ5MmTJUkej0eSlJCQ4FeXkJCgTz75xFcTGhqqmJiYZjVn3n8uhYWFWrhwYUtbbZGQuEQ5Xde06zYBAMAXWvwUT3FxsUaOHCm32+037nA4/F4bY5qNne1iNfPnz1d1dbVvOXLkSEvbBgAAnUCLAsonn3yizZs3a+rUqb4xl8slSc3OhBw7dsx3VsXlcqmhoUFVVVXnrTkXp9OpqKgovwUAAHRdLQooK1asUK9evfTtb3/bN9a/f3+5XC6VlJT4xhoaGlRaWqq0tDRJUkpKikJCQvxqKioqtHfvXl8NAABAwPegNDU1acWKFZo0aZKCg///7Q6HQ7m5uSooKFBSUpKSkpJUUFCg8PBwTZgwQZIUHR2tKVOmaPbs2YqLi1NsbKzmzJmjQYMGKSsrq/X2CgAAdGoBB5TNmzfr8OHD+s///M9m6+bOnav6+npNnz5dVVVVSk1N1aZNm3y/A0WSFi9erODgYI0bN0719fXKzMzUypUrrfkdKAAAoON9qd+D0lHa8veg7Nq1SykpKXJNWtJpnuLxej6SZ1WuysrKNHTo0I5uBwCAcwrk+M138QAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsE3BA+fvf/67vfe97iouLU3h4uL72ta+prKzMt94Yo/z8fLndboWFhSkjI0P79u3z+wyv16uZM2cqPj5eERERGjNmjMrLy7/83gAAgC4hoIBSVVWlm2++WSEhIXr55Ze1f/9+PfbYY/rKV77iqykqKtKiRYu0dOlS7dixQy6XS9nZ2aqtrfXV5ObmauPGjVq/fr22bt2quro6jRo1So2Nja22YwAAoPMKDqT4kUceUWJiolasWOEb69evn++/jTFasmSJ8vLyNHbsWEnSqlWrlJCQoLVr12ratGmqrq5WcXGxVq9eraysLEnSmjVrlJiYqM2bN2vEiBGtsFsAAKAzC+gMygsvvKBhw4bpzjvvVK9evTRkyBA988wzvvWHDh2Sx+NRTk6Ob8zpdCo9PV3btm2TJJWVlen06dN+NW63W8nJyb6as3m9XtXU1PgtAACg6woooBw8eFDLly9XUlKSXn31Vd177736/ve/r1//+teSJI/HI0lKSEjwe19CQoJvncfjUWhoqGJiYs5bc7bCwkJFR0f7lsTExEDaBgAAnUxAAaWpqUlDhw5VQUGBhgwZomnTpum//uu/tHz5cr86h8Ph99oY02zsbBeqmT9/vqqrq33LkSNHAmkbAAB0MgEFlN69e2vgwIF+Y9dff70OHz4sSXK5XJLU7EzIsWPHfGdVXC6XGhoaVFVVdd6aszmdTkVFRfktAACg6woooNx88806cOCA39gHH3ygvn37SpL69+8vl8ulkpIS3/qGhgaVlpYqLS1NkpSSkqKQkBC/moqKCu3du9dXAwAALm8BPcXzgx/8QGlpaSooKNC4ceP09ttv6+mnn9bTTz8t6YtLO7m5uSooKFBSUpKSkpJUUFCg8PBwTZgwQZIUHR2tKVOmaPbs2YqLi1NsbKzmzJmjQYMG+Z7qAQAAl7eAAsrXv/51bdy4UfPnz9dPfvIT9e/fX0uWLNE999zjq5k7d67q6+s1ffp0VVVVKTU1VZs2bVJkZKSvZvHixQoODta4ceNUX1+vzMxMrVy5UkFBQa23ZwAAoNNyGGNMRzcRqJqaGkVHR6u6urrV70fZtWuXUlJS5Jq0RE7XNa362W3F6/lInlW5Kisr09ChQzu6HQAAzimQ4zffxQMAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwTkABJT8/Xw6Hw29xuVy+9cYY5efny+12KywsTBkZGdq3b5/fZ3i9Xs2cOVPx8fGKiIjQmDFjVF5e3jp7AwAAuoSAz6B89atfVUVFhW/Zs2ePb11RUZEWLVqkpUuXaseOHXK5XMrOzlZtba2vJjc3Vxs3btT69eu1detW1dXVadSoUWpsbGydPQIAAJ1ecMBvCA72O2tyhjFGS5YsUV5ensaOHStJWrVqlRISErR27VpNmzZN1dXVKi4u1urVq5WVlSVJWrNmjRITE7V582aNGDHiS+4OAADoCgI+g/Lhhx/K7Xarf//+uuuuu3Tw4EFJ0qFDh+TxeJSTk+OrdTqdSk9P17Zt2yRJZWVlOn36tF+N2+1WcnKyr+ZcvF6vampq/BYAANB1BRRQUlNT9etf/1qvvvqqnnnmGXk8HqWlpen48ePyeDySpISEBL/3JCQk+NZ5PB6FhoYqJibmvDXnUlhYqOjoaN+SmJgYSNsAAKCTCSigjBw5UnfccYcGDRqkrKws/fGPf5T0xaWcMxwOh997jDHNxs52sZr58+erurratxw5ciSQtgEAQCfzpR4zjoiI0KBBg/Thhx/67ks5+0zIsWPHfGdVXC6XGhoaVFVVdd6ac3E6nYqKivJbAABA1/WlAorX69V7772n3r17q3///nK5XCopKfGtb2hoUGlpqdLS0iRJKSkpCgkJ8aupqKjQ3r17fTUAAAABPcUzZ84cjR49WldeeaWOHTumhx9+WDU1NZo0aZIcDodyc3NVUFCgpKQkJSUlqaCgQOHh4ZowYYIkKTo6WlOmTNHs2bMVFxen2NhYzZkzx3fJCAAAQAowoJSXl+vuu+9WZWWlevbsqZtuuknbt29X3759JUlz585VfX29pk+frqqqKqWmpmrTpk2KjIz0fcbixYsVHByscePGqb6+XpmZmVq5cqWCgoJad88AAECn5TDGmI5uIlA1NTWKjo5WdXV1q9+PsmvXLqWkpMg1aYmcrmta9bPbitfzkTyrclVWVqahQ4d2dDsAAJxTIMdvvosHAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYJ0vFVAKCwvlcDiUm5vrGzPGKD8/X263W2FhYcrIyNC+ffv83uf1ejVz5kzFx8crIiJCY8aMUXl5+ZdpBQAAdCEtDig7duzQ008/rcGDB/uNFxUVadGiRVq6dKl27Nghl8ul7Oxs1dbW+mpyc3O1ceNGrV+/Xlu3blVdXZ1GjRqlxsbGlu8JAADoMloUUOrq6nTPPffomWeeUUxMjG/cGKMlS5YoLy9PY8eOVXJyslatWqVTp05p7dq1kqTq6moVFxfrscceU1ZWloYMGaI1a9Zoz5492rx5c+vsFQAA6NRaFFBmzJihb3/728rKyvIbP3TokDwej3JycnxjTqdT6enp2rZtmySprKxMp0+f9qtxu91KTk721ZzN6/WqpqbGbwEAAF1XcKBvWL9+vXbt2qUdO3Y0W+fxeCRJCQkJfuMJCQn65JNPfDWhoaF+Z17O1Jx5/9kKCwu1cOHCQFsFAACdVEBnUI4cOaL7779fa9asUffu3c9b53A4/F4bY5qNne1CNfPnz1d1dbVvOXLkSCBtAwCATiaggFJWVqZjx44pJSVFwcHBCg4OVmlpqR5//HEFBwf7zpycfSbk2LFjvnUul0sNDQ2qqqo6b83ZnE6noqKi/BYAANB1BRRQMjMztWfPHu3evdu3DBs2TPfcc492796tq666Si6XSyUlJb73NDQ0qLS0VGlpaZKklJQUhYSE+NVUVFRo7969vhoAAHB5C+gelMjISCUnJ/uNRUREKC4uzjeem5urgoICJSUlKSkpSQUFBQoPD9eECRMkSdHR0ZoyZYpmz56tuLg4xcbGas6cORo0aFCzm24BAMDlKeCbZC9m7ty5qq+v1/Tp01VVVaXU1FRt2rRJkZGRvprFixcrODhY48aNU319vTIzM7Vy5UoFBQW1djsAAKATchhjTEc3EaiamhpFR0erurq61e9H2bVrl1JSUuSatERO1zWt+tltxev5SJ5VuSorK9PQoUM7uh0AAM4pkOM338UDAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsE5AAWX58uUaPHiwoqKiFBUVpeHDh+vll1/2rTfGKD8/X263W2FhYcrIyNC+ffv8PsPr9WrmzJmKj49XRESExowZo/Ly8tbZGwAA0CUEFFD69Omjn/3sZ9q5c6d27typW2+9Vf/+7//uCyFFRUVatGiRli5dqh07dsjlcik7O1u1tbW+z8jNzdXGjRu1fv16bd26VXV1dRo1apQaGxtbd88AAECnFVBAGT16tG677TYNGDBAAwYM0E9/+lP16NFD27dvlzFGS5YsUV5ensaOHavk5GStWrVKp06d0tq1ayVJ1dXVKi4u1mOPPaasrCwNGTJEa9as0Z49e7R58+Y22UEAAND5tPgelMbGRq1fv14nT57U8OHDdejQIXk8HuXk5PhqnE6n0tPTtW3bNklSWVmZTp8+7VfjdruVnJzsqzkXr9ermpoavwUAAHRdAQeUPXv2qEePHnI6nbr33nu1ceNGDRw4UB6PR5KUkJDgV5+QkOBb5/F4FBoaqpiYmPPWnEthYaGio6N9S2JiYqBtAwCATiTggHLttddq9+7d2r59u/7nf/5HkyZN0v79+33rHQ6HX70xptnY2S5WM3/+fFVXV/uWI0eOBNo2AADoRAIOKKGhobrmmms0bNgwFRYW6oYbbtDPf/5zuVwuSWp2JuTYsWO+syoul0sNDQ2qqqo6b825OJ1O35NDZxYAANB1fenfg2KMkdfrVf/+/eVyuVRSUuJb19DQoNLSUqWlpUmSUlJSFBIS4ldTUVGhvXv3+moAAACCAyn+0Y9+pJEjRyoxMVG1tbVav369tmzZoldeeUUOh0O5ubkqKChQUlKSkpKSVFBQoPDwcE2YMEGSFB0drSlTpmj27NmKi4tTbGys5syZo0GDBikrK6tNdhAAAHQ+AQWUTz/9VBMnTlRFRYWio6M1ePBgvfLKK8rOzpYkzZ07V/X19Zo+fbqqqqqUmpqqTZs2KTIy0vcZixcvVnBwsMaNG6f6+nplZmZq5cqVCgoKat09AwAAnZbDGGM6uolA1dTUKDo6WtXV1a1+P8quXbuUkpIi16QlcrquadXPbitez0fyrMpVWVmZhg4d2tHtAABwToEcv/kuHgAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYJ7ijGwAAoKs7fPiwKisrO7qNgMTHx+vKK6/ssO0TUAAAaEOHDx/Wtdddr3/Wn+roVgLSPSxcB95/r8NCCgEFAIA2VFlZqX/Wn1LcqNkKiUvs6HYuyenjR3T8xcdUWVlJQAEAoCsLiUuU03VNR7fRaQR0k2xhYaG+/vWvKzIyUr169dLtt9+uAwcO+NUYY5Sfny+3262wsDBlZGRo3759fjVer1czZ85UfHy8IiIiNGbMGJWXl3/5vQEAAF1CQAGltLRUM2bM0Pbt21VSUqLPP/9cOTk5OnnypK+mqKhIixYt0tKlS7Vjxw65XC5lZ2ertrbWV5Obm6uNGzdq/fr12rp1q+rq6jRq1Cg1Nja23p4BAIBOK6BLPK+88orf6xUrVqhXr14qKyvTt771LRljtGTJEuXl5Wns2LGSpFWrVikhIUFr167VtGnTVF1dreLiYq1evVpZWVmSpDVr1igxMVGbN2/WiBEjWmnXAABAZ/Wlfg9KdXW1JCk2NlaSdOjQIXk8HuXk5PhqnE6n0tPTtW3bNklSWVmZTp8+7VfjdruVnJzsqzmb1+tVTU2N3wIAALquFgcUY4xmzZqlb3zjG0pOTpYkeTweSVJCQoJfbUJCgm+dx+NRaGioYmJizltztsLCQkVHR/uWxMTOcRc0AABomRYHlPvuu0/vvvuu1q1b12ydw+Hwe22MaTZ2tgvVzJ8/X9XV1b7lyJEjLW0bAAB0Ai0KKDNnztQLL7yg119/XX369PGNu1wuSWp2JuTYsWO+syoul0sNDQ2qqqo6b83ZnE6noqKi/BYAANB1BRRQjDG677779Nxzz+m1115T//79/db3799fLpdLJSUlvrGGhgaVlpYqLS1NkpSSkqKQkBC/moqKCu3du9dXAwAALm8BPcUzY8YMrV27Vv/7v/+ryMhI35mS6OhohYWFyeFwKDc3VwUFBUpKSlJSUpIKCgoUHh6uCRMm+GqnTJmi2bNnKy4uTrGxsZozZ44GDRrke6oHAABc3gIKKMuXL5ckZWRk+I2vWLFCkydPliTNnTtX9fX1mj59uqqqqpSamqpNmzYpMjLSV7948WIFBwdr3Lhxqq+vV2ZmplauXKmgoKAvtzcAAKBLCCigGGMuWuNwOJSfn6/8/Pzz1nTv3l1PPPGEnnjiiUA2DwAALhNf6vegAAAAtAUCCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1Ag4of/7znzV69Gi53W45HA49//zzfuuNMcrPz5fb7VZYWJgyMjK0b98+vxqv16uZM2cqPj5eERERGjNmjMrLy7/UjgAAgK4j4IBy8uRJ3XDDDVq6dOk51xcVFWnRokVaunSpduzYIZfLpezsbNXW1vpqcnNztXHjRq1fv15bt25VXV2dRo0apcbGxpbvCQAA6DKCA33DyJEjNXLkyHOuM8ZoyZIlysvL09ixYyVJq1atUkJCgtauXatp06apurpaxcXFWr16tbKysiRJa9asUWJiojZv3qwRI0Z8id0BAABdQaveg3Lo0CF5PB7l5OT4xpxOp9LT07Vt2zZJUllZmU6fPu1X43a7lZyc7Ks5m9frVU1Njd8CAAC6rlYNKB6PR5KUkJDgN56QkOBb5/F4FBoaqpiYmPPWnK2wsFDR0dG+JTExsTXbBgAAlmmTp3gcDoffa2NMs7GzXahm/vz5qq6u9i1HjhxptV4BAIB9WjWguFwuSWp2JuTYsWO+syoul0sNDQ2qqqo6b83ZnE6noqKi/BYAANB1tWpA6d+/v1wul0pKSnxjDQ0NKi0tVVpamiQpJSVFISEhfjUVFRXau3evrwYAAFzeAn6Kp66uTh999JHv9aFDh7R7927FxsbqyiuvVG5urgoKCpSUlKSkpCQVFBQoPDxcEyZMkCRFR0drypQpmj17tuLi4hQbG6s5c+Zo0KBBvqd6AADA5S3ggLJz507dcsstvtezZs2SJE2aNEkrV67U3LlzVV9fr+nTp6uqqkqpqanatGmTIiMjfe9ZvHixgoODNW7cONXX1yszM1MrV65UUFBQK+wSAADo7AIOKBkZGTLGnHe9w+FQfn6+8vPzz1vTvXt3PfHEE3riiScC3TwAALgM8F08AADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOt0aEBZtmyZ+vfvr+7duyslJUVvvPFGR7YDAAAs0WEBZcOGDcrNzVVeXp7eeecdffOb39TIkSN1+PDhjmoJAABYosMCyqJFizRlyhRNnTpV119/vZYsWaLExEQtX768o1oCAACWCO6IjTY0NKisrEzz5s3zG8/JydG2bdua1Xu9Xnm9Xt/r6upqSVJNTU2r91ZXV/fFNj0fqanhn63++W3h9GflkqSysjJf/51Bt27d1NTU1NFtBISe2wc9t5/O2Hdn6/nAgQOSOudxpa6urlWPtWc+yxhz8WLTAf7+978bSebNN9/0G//pT39qBgwY0Kx+wYIFRhILCwsLCwtLF1iOHDly0azQIWdQznA4HH6vjTHNxiRp/vz5mjVrlu91U1OTPvvsM8XFxZ2z/suoqalRYmKijhw5oqioqFb9bPw/5rl9MM/tg3luP8x1+2ireTbGqLa2Vm63+6K1HRJQ4uPjFRQUJI/H4zd+7NgxJSQkNKt3Op1yOp1+Y1/5ylfaskVFRUXxw98OmOf2wTy3D+a5/TDX7aMt5jk6OvqS6jrkJtnQ0FClpKSopKTEb7ykpERpaWkd0RIAALBIh13imTVrliZOnKhhw4Zp+PDhevrpp3X48GHde++9HdUSAACwRIcFlPHjx+v48eP6yU9+ooqKCiUnJ+ull15S3759O6olSV9cTlqwYEGzS0poXcxz+2Ce2wfz3H6Y6/Zhwzw7jLmUZ30AAADaD9/FAwAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOpdlQFm2bJn69++v7t27KyUlRW+88cYF60tLS5WSkqLu3bvrqquu0lNPPdVOnXZugczzc889p+zsbPXs2VNRUVEaPny4Xn311XbstvMK9Of5jDfffFPBwcH62te+1rYNdhGBzrPX61VeXp769u0rp9Opq6++Wr/61a/aqdvOK9B5fvbZZ3XDDTcoPDxcvXv31n/8x3/o+PHj7dRt5/TnP/9Zo0ePltvtlsPh0PPPP3/R93TIcbBVvv2vE1m/fr0JCQkxzzzzjNm/f7+5//77TUREhPnkk0/OWX/w4EETHh5u7r//frN//37zzDPPmJCQEPO73/2unTvvXAKd5/vvv9888sgj5u233zYffPCBmT9/vgkJCTG7du1q5847l0Dn+YwTJ06Yq666yuTk5JgbbrihfZrtxFoyz2PGjDGpqammpKTEHDp0yPzlL39p9gWp8BfoPL/xxhumW7du5uc//7k5ePCgeeONN8xXv/pVc/vtt7dz553LSy+9ZPLy8szvf/97I8ls3LjxgvUddRy87ALKjTfeaO69916/seuuu87MmzfvnPVz58411113nd/YtGnTzE033dRmPXYFgc7zuQwcONAsXLiwtVvrUlo6z+PHjzc//vGPzYIFCwgolyDQeX755ZdNdHS0OX78eHu012UEOs+PPvqoueqqq/zGHn/8cdOnT58267GruZSA0lHHwcvqEk9DQ4PKysqUk5PjN56Tk6Nt27ad8z1vvfVWs/oRI0Zo586dOn36dJv12pm1ZJ7P1tTUpNraWsXGxrZFi11CS+d5xYoV+tvf/qYFCxa0dYtdQkvm+YUXXtCwYcNUVFSkK664QgMGDNCcOXNUX1/fHi13Si2Z57S0NJWXl+ull16SMUaffvqpfve73+nb3/52e7R82eio42CH/ar7jlBZWanGxsZm35ickJDQ7JuVz/B4POes//zzz1VZWanevXu3Wb+dVUvm+WyPPfaYTp48qXHjxrVFi11CS+b5ww8/1Lx58/TGG28oOPiy+uvfYi2Z54MHD2rr1q3q3r27Nm7cqMrKSk2fPl2fffYZ96GcR0vmOS0tTc8++6zGjx+vf/7zn/r88881ZswYPfHEE+3R8mWjo46Dl9UZlDMcDoffa2NMs7GL1Z9rHP4Cnecz1q1bp/z8fG3YsEG9evVqq/a6jEud58bGRk2YMEELFy7UgAED2qu9LiOQn+empiY5HA49++yzuvHGG3Xbbbdp0aJFWrlyJWdRLiKQed6/f7++//3v66GHHlJZWZleeeUVHTp0iC+dbQMdcRy8rP4XKj4+XkFBQc3S+LFjx5qlwzNcLtc564ODgxUXF9dmvXZmLZnnMzZs2KApU6bot7/9rbKystqyzU4v0Hmura3Vzp079c477+i+++6T9MWB1Bij4OBgbdq0Sbfeemu79N6ZtOTnuXfv3rriiisUHR3tG7v++utljFF5ebmSkpLatOfOqCXzXFhYqJtvvlk//OEPJUmDBw9WRESEvvnNb+rhhx/mDHcr6ajj4GV1BiU0NFQpKSkqKSnxGy8pKVFaWto53zN8+PBm9Zs2bdKwYcMUEhLSZr12Zi2ZZ+mLMyeTJ0/W2rVruYZ8CQKd56ioKO3Zs0e7d+/2Lffee6+uvfZa7d69W6mpqe3VeqfSkp/nm2++WUePHlVdXZ1v7IMPPlC3bt3Up0+fNu23s2rJPJ86dUrduvkfxoKCgiT9///h48vrsONgm96Ca6Ezj7EVFxeb/fv3m9zcXBMREWE+/vhjY4wx8+bNMxMnTvTVn3m86gc/+IHZv3+/KS4u5jHjSxDoPK9du9YEBwebJ5980lRUVPiWEydOdNQudAqBzvPZeIrn0gQ6z7W1taZPnz7mu9/9rtm3b58pLS01SUlJZurUqR21C51CoPO8YsUKExwcbJYtW2b+9re/ma1bt5phw4aZG2+8saN2oVOora0177zzjnnnnXeMJLNo0SLzzjvv+B7ntuU4eNkFFGOMefLJJ03fvn1NaGioGTp0qCktLfWtmzRpkklPT/er37JlixkyZIgJDQ01/fr1M8uXL2/njjunQOY5PT3dSGq2TJo0qf0b72QC/Xn+VwSUSxfoPL/33nsmKyvLhIWFmT59+phZs2aZU6dOtXPXnU+g8/z444+bgQMHmrCwMNO7d29zzz33mPLy8nbuunN5/fXXL/jvrS3HQYcxnAcDAAB2uazuQQEAAJ0DAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArPN/xvpII5tDCUEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features=df.columns\n",
    "n_features=len(features)\n",
    "\n",
    "for i in range(n_features):\n",
    "    plt.hist(df.iloc[:,i],edgecolor='black')\n",
    "    plt.title(features[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7237cb7c-8180-4b1c-a1d1-98496a3198e3",
   "metadata": {},
   "source": [
    "--preprocessing--"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1b6a84b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of class 1 => 508\n",
      "length of class 0 => 410\n"
     ]
    }
   ],
   "source": [
    "class_1=sum(df[\"target\"]==1)\n",
    "print(f\"length of class 1 => {class_1}\")\n",
    "print(f\"length of class 0 => {918-class_1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "09bb3529",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before OverSampling  # 1 = 364\n",
      "Before OverSampling  # 0 = 278\n",
      "-----------------------------------------\n",
      "After OverSampling  # 1 = 364\n",
      "After OverSampling  # 0 = 364\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE \n",
    "\n",
    "from sklearn.model_selection import train_test_split  \n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "\n",
    "\n",
    "x= df.iloc[:, :-1] \n",
    "  \n",
    "y= df.iloc[:, -1]\n",
    "\n",
    "x_train, x_test, y_train, y_test= train_test_split(x, y, test_size= 0.3)  \n",
    "\n",
    "print(\"Before OverSampling  # 1 =\",sum(y_train == 1)) \n",
    "print(\"Before OverSampling  # 0 =\",sum(y_train == 0)) \n",
    "\n",
    "\n",
    "sm = SMOTE()\n",
    "x_train, y_train = sm.fit_resample(x_train, y_train)\n",
    "\n",
    "print(\"-----------------------------------------\")\n",
    "print(\"After OverSampling  # 1 =\",sum(y_train == 1)) \n",
    "print(\"After OverSampling  # 0 =\",sum(y_train == 0)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "27b04369-499e-4b8b-a4e6-ec9214b70afd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "sns.heatmap(df.corr(), square=True, cmap='Blues', annot=True, fmt='0.1f');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eb3c0581-1603-4532-9194-982f59030cea",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.244898</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.621212</td>\n",
       "      <td>0.665158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.830508</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.180995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.694915</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.183673</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.469697</td>\n",
       "      <td>0.638009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.211864</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.408163</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.325792</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.288136</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.480769</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.530612</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.742424</td>\n",
       "      <td>0.239819</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.406780</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>0.346939</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.212121</td>\n",
       "      <td>0.552036</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.491525</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.423077</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>0.816327</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.567797</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.807692</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>0.591837</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.469697</td>\n",
       "      <td>0.040724</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.347458</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.423077</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>0.591837</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.469697</td>\n",
       "      <td>0.425339</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.847458</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>0.204082</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.158371</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.838983</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>918 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0    1         2         3         4    5    6         7    8   \\\n",
       "0    0.244898  1.0  0.333333  0.621212  0.665158  0.0  0.0  0.830508  0.0   \n",
       "1    0.428571  0.0  0.666667  0.833333  0.180995  0.0  0.0  0.694915  0.0   \n",
       "2    0.183673  1.0  0.333333  0.469697  0.638009  0.0  0.5  0.211864  0.0   \n",
       "3    0.408163  0.0  1.000000  0.590909  0.325792  0.0  0.0  0.288136  1.0   \n",
       "4    0.530612  1.0  0.666667  0.742424  0.239819  0.0  0.0  0.406780  0.0   \n",
       "..        ...  ...       ...       ...       ...  ...  ...       ...  ...   \n",
       "913  0.346939  1.0  0.000000  0.212121  0.552036  0.0  0.0  0.491525  0.0   \n",
       "914  0.816327  1.0  1.000000  0.681818  0.230769  1.0  0.0  0.567797  0.0   \n",
       "915  0.591837  1.0  1.000000  0.469697  0.040724  0.0  0.0  0.347458  1.0   \n",
       "916  0.591837  0.0  0.333333  0.469697  0.425339  0.0  1.0  0.847458  0.0   \n",
       "917  0.204082  1.0  0.666667  0.590909  0.158371  0.0  0.0  0.838983  0.0   \n",
       "\n",
       "           9         10   11  \n",
       "0    0.192308  0.333333  0.0  \n",
       "1    0.384615  0.666667  1.0  \n",
       "2    0.192308  0.333333  0.0  \n",
       "3    0.480769  0.666667  1.0  \n",
       "4    0.192308  0.333333  0.0  \n",
       "..        ...       ...  ...  \n",
       "913  0.423077  0.666667  1.0  \n",
       "914  0.807692  0.666667  1.0  \n",
       "915  0.423077  0.666667  1.0  \n",
       "916  0.192308  0.666667  1.0  \n",
       "917  0.192308  0.333333  0.0  \n",
       "\n",
       "[918 rows x 12 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn .preprocessing import MinMaxScaler \n",
    "mini_max_scale = MinMaxScaler()\n",
    "df=pd.DataFrame(mini_max_scale.fit_transform(df))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bf5095f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.9, 'degree': 1, 'kernel': 'linear'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=6)\n",
    "\n",
    "pram_grad={'kernel':['linear','poly','sigmoid','rbf'],\n",
    "           'C':[0.1,0.3,0.5,0.7,0.9,1],\n",
    "           'degree':[1,2,3,4,5]\n",
    "          }\n",
    "grid=GridSearchCV(SVC(),pram_grad)\n",
    "grid.fit(x_train,y_train)\n",
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "353146cd-8057-4392-b555-127e37daf410",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "svc=SVC(kernel=\"linear\",C=0.9,degree=1)\n",
    "svc.fit(x_train,y_train)\n",
    "prediction_scv=svc.predict(x_test)\n",
    "\n",
    "acc_svc=accuracy_score(y_test,prediction_scv)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aeb35dc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 28 candidates, totalling 140 fits\n",
      "{'n_neighbors': 28}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "kf=KFold(n_splits=5,shuffle=True,random_state=42)\n",
    "parameter={'n_neighbors': np.arange(2, 30, 1)}\n",
    "\n",
    "knn=KNeighborsClassifier()\n",
    "knn_cv=GridSearchCV(knn, param_grid=parameter, cv=kf, verbose=1)\n",
    "knn_cv.fit(x_train, y_train)\n",
    "print(knn_cv.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "edffc69a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn=KNeighborsClassifier(n_neighbors=28)\n",
    "knn.fit(x_train,y_train)\n",
    "prediction_knn=knn.predict(x_test)\n",
    "\n",
    "acc_knn=accuracy_score(y_test,prediction_knn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "983445d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rfc=RandomForestClassifier(n_estimators=17)\n",
    "rfc.fit(x_train,y_train)\n",
    "rfc_prd= rfc.predict(x_test)\n",
    "acc_rfc= accuracy_score(y_test,rfc_prd)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f2365ddf-a18e-45ff-97bd-536eca966524",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt=DecisionTreeClassifier(criterion=\"entropy\")\n",
    "dt.fit(x_train,y_train)\n",
    "dt_pred=dt.predict(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "60e664fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_svc=confusion_matrix(y_test,prediction_scv)\n",
    "svc_conf_matrix=pd.DataFrame(data=cm_svc ,index=['Actual Positive:1', 'Actual Negative:0'], \n",
    "                                 columns=['Predict Positive:1', 'Predict Negative:0'])\n",
    "\n",
    "sns.heatmap(svc_conf_matrix,cmap='coolwarm',annot=True,fmt='d')\n",
    "plt.title(\"SVM \")\n",
    "plt.show()\n",
    "\n",
    "#################################################################\n",
    "cm_knn=confusion_matrix(y_test,prediction_knn)\n",
    "\n",
    "conf_matrix_knn=pd.DataFrame(cm_knn,index=['Actual Positive:1', 'Actual Negative:0'], \n",
    "                                 columns=['Predict Positive:1', 'Predict Negative:0'])\n",
    "sns.heatmap(conf_matrix_knn,annot=True,fmt='d')\n",
    "plt.title(\"KNN \")\n",
    "plt.show()\n",
    "#################################################################\n",
    "cm_rfc=confusion_matrix(y_test,rfc_prd)\n",
    "rfc_conf_matrix=pd.DataFrame(data=cm_rfc, index=['Actual Positive:1', 'Actual Negative:0'], \n",
    "                                 columns=['Predict Positive:1', 'Predict Negative:0'])\n",
    "\n",
    "sns.heatmap(rfc_conf_matrix,cmap='winter',annot=True,fmt='d')\n",
    "plt.title(\"RandomForest Classifier\")\n",
    "plt.show()\n",
    "#############################################################\n",
    "cm_dt=confusion_matrix(y_test,dt_pred)\n",
    "dt_conf_matrix=pd.DataFrame(data=cm_dt ,index=['Actual Positive:1', 'Actual Negative:0'], \n",
    "                                 columns=['Predict Positive:1', 'Predict Negative:0'])\n",
    "\n",
    "sns.heatmap(dt_conf_matrix,cmap='coolwarm',annot=True,fmt='d')\n",
    "plt.title(\"Decision Tree\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6c070bc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Report of SVM \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.81      0.82       118\n",
      "           1       0.86      0.87      0.87       158\n",
      "\n",
      "    accuracy                           0.84       276\n",
      "   macro avg       0.84      0.84      0.84       276\n",
      "weighted avg       0.84      0.84      0.84       276\n",
      "\n",
      "##################################################\n",
      "Report of KNN \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.75      0.75       118\n",
      "           1       0.81      0.80      0.81       158\n",
      "\n",
      "    accuracy                           0.78       276\n",
      "   macro avg       0.78      0.78      0.78       276\n",
      "weighted avg       0.78      0.78      0.78       276\n",
      "\n",
      "##################################################\n",
      "Report of SVM \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.77      0.82       118\n",
      "           1       0.84      0.92      0.88       158\n",
      "\n",
      "    accuracy                           0.86       276\n",
      "   macro avg       0.86      0.85      0.85       276\n",
      "weighted avg       0.86      0.86      0.86       276\n",
      "\n",
      "##################################################\n",
      "Report of SVM \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.81      0.80       118\n",
      "           1       0.85      0.85      0.85       158\n",
      "\n",
      "    accuracy                           0.83       276\n",
      "   macro avg       0.83      0.83      0.83       276\n",
      "weighted avg       0.83      0.83      0.83       276\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"Report of SVM \\n{classification_report(y_test,prediction_scv)}\")\n",
    "print(\"#\"*50)\n",
    "print(f\"Report of KNN \\n{classification_report(y_test,prediction_knn)}\")\n",
    "print(\"#\"*50)\n",
    "print(f\"Report of SVM \\n{classification_report(y_test,rfc_prd)}\")\n",
    "print(\"#\"*50)\n",
    "print(f\"Report of SVM \\n{classification_report(y_test,dt_pred)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5011b98",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afc9eec0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42f310e2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60e16a6f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59f4339e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c11b8f74",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ea35842",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d956d7f1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40014f93",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a420eeeb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75ea379c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2ab7391",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19388573",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9b55f14",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8f05d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install scikit-learn  "
   ]
  },
  {
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