KNN_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Classified Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>WTT</th>\n",
       "      <th>PTI</th>\n",
       "      <th>EQW</th>\n",
       "      <th>SBI</th>\n",
       "      <th>LQE</th>\n",
       "      <th>QWG</th>\n",
       "      <th>FDJ</th>\n",
       "      <th>PJF</th>\n",
       "      <th>HQE</th>\n",
       "      <th>NXJ</th>\n",
       "      <th>TARGET CLASS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.913917</td>\n",
       "      <td>1.162073</td>\n",
       "      <td>0.567946</td>\n",
       "      <td>0.755464</td>\n",
       "      <td>0.780862</td>\n",
       "      <td>0.352608</td>\n",
       "      <td>0.759697</td>\n",
       "      <td>0.643798</td>\n",
       "      <td>0.879422</td>\n",
       "      <td>1.231409</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.635632</td>\n",
       "      <td>1.003722</td>\n",
       "      <td>0.535342</td>\n",
       "      <td>0.825645</td>\n",
       "      <td>0.924109</td>\n",
       "      <td>0.648450</td>\n",
       "      <td>0.675334</td>\n",
       "      <td>1.013546</td>\n",
       "      <td>0.621552</td>\n",
       "      <td>1.492702</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.721360</td>\n",
       "      <td>1.201493</td>\n",
       "      <td>0.921990</td>\n",
       "      <td>0.855595</td>\n",
       "      <td>1.526629</td>\n",
       "      <td>0.720781</td>\n",
       "      <td>1.626351</td>\n",
       "      <td>1.154483</td>\n",
       "      <td>0.957877</td>\n",
       "      <td>1.285597</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.234204</td>\n",
       "      <td>1.386726</td>\n",
       "      <td>0.653046</td>\n",
       "      <td>0.825624</td>\n",
       "      <td>1.142504</td>\n",
       "      <td>0.875128</td>\n",
       "      <td>1.409708</td>\n",
       "      <td>1.380003</td>\n",
       "      <td>1.522692</td>\n",
       "      <td>1.153093</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.279491</td>\n",
       "      <td>0.949750</td>\n",
       "      <td>0.627280</td>\n",
       "      <td>0.668976</td>\n",
       "      <td>1.232537</td>\n",
       "      <td>0.703727</td>\n",
       "      <td>1.115596</td>\n",
       "      <td>0.646691</td>\n",
       "      <td>1.463812</td>\n",
       "      <td>1.419167</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0       WTT       PTI       EQW       SBI       LQE       QWG  \\\n",
       "0           0  0.913917  1.162073  0.567946  0.755464  0.780862  0.352608   \n",
       "1           1  0.635632  1.003722  0.535342  0.825645  0.924109  0.648450   \n",
       "2           2  0.721360  1.201493  0.921990  0.855595  1.526629  0.720781   \n",
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       "4           4  1.279491  0.949750  0.627280  0.668976  1.232537  0.703727   \n",
       "\n",
       "        FDJ       PJF       HQE       NXJ  TARGET CLASS  \n",
       "0  0.759697  0.643798  0.879422  1.231409             1  \n",
       "1  0.675334  1.013546  0.621552  1.492702             0  \n",
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       "3  1.409708  1.380003  1.522692  1.153093             1  \n",
       "4  1.115596  0.646691  1.463812  1.419167             1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(df.drop('TARGET CLASS', axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_features = scaler.transform(df.drop('TARGET CLASS', axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        -1.82805088,  0.63675862],\n",
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       "        -0.68249379, -0.37784986],\n",
       "       ...,\n",
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       "         0.3369971 ,  0.01034996]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sat_sam = sc.fit_transform(df.drop('TARGET CLASS', axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [ 1.73031962, -0.38765353, -0.59589427, ..., -0.56778932,\n",
       "         0.3369971 ,  0.01034996]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat_sam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_feat = pd.DataFrame(scaled_features, columns=df.columns[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.730320</td>\n",
       "      <td>-0.123542</td>\n",
       "      <td>0.185907</td>\n",
       "      <td>-0.913431</td>\n",
       "      <td>0.319629</td>\n",
       "      <td>-1.033637</td>\n",
       "      <td>-2.308375</td>\n",
       "      <td>-0.798951</td>\n",
       "      <td>-1.482368</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.726856</td>\n",
       "      <td>-1.084836</td>\n",
       "      <td>-0.430348</td>\n",
       "      <td>-1.025313</td>\n",
       "      <td>0.625388</td>\n",
       "      <td>-0.444847</td>\n",
       "      <td>-1.152706</td>\n",
       "      <td>-1.129797</td>\n",
       "      <td>-0.202240</td>\n",
       "      <td>-1.828051</td>\n",
       "      <td>0.636759</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.723391</td>\n",
       "      <td>-0.788702</td>\n",
       "      <td>0.339318</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.755873</td>\n",
       "      <td>2.031693</td>\n",
       "      <td>-0.870156</td>\n",
       "      <td>2.599818</td>\n",
       "      <td>0.285707</td>\n",
       "      <td>-0.682494</td>\n",
       "      <td>-0.377850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.719927</td>\n",
       "      <td>0.982841</td>\n",
       "      <td>1.060193</td>\n",
       "      <td>-0.621399</td>\n",
       "      <td>0.625299</td>\n",
       "      <td>0.452820</td>\n",
       "      <td>-0.267220</td>\n",
       "      <td>1.750208</td>\n",
       "      <td>1.066491</td>\n",
       "      <td>1.241325</td>\n",
       "      <td>-1.026987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.716463</td>\n",
       "      <td>1.139275</td>\n",
       "      <td>-0.640392</td>\n",
       "      <td>-0.709819</td>\n",
       "      <td>-0.057175</td>\n",
       "      <td>0.822886</td>\n",
       "      <td>-0.936773</td>\n",
       "      <td>0.596782</td>\n",
       "      <td>-1.472352</td>\n",
       "      <td>1.040772</td>\n",
       "      <td>0.276510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "   Unnamed: 0       WTT       PTI       EQW       SBI       LQE       QWG  \\\n",
       "0   -1.730320 -0.123542  0.185907 -0.913431  0.319629 -1.033637 -2.308375   \n",
       "1   -1.726856 -1.084836 -0.430348 -1.025313  0.625388 -0.444847 -1.152706   \n",
       "2   -1.723391 -0.788702  0.339318  0.301511  0.755873  2.031693 -0.870156   \n",
       "3   -1.719927  0.982841  1.060193 -0.621399  0.625299  0.452820 -0.267220   \n",
       "4   -1.716463  1.139275 -0.640392 -0.709819 -0.057175  0.822886 -0.936773   \n",
       "\n",
       "        FDJ       PJF       HQE       NXJ  \n",
       "0 -0.798951 -1.482368 -0.949719 -0.643314  \n",
       "1 -1.129797 -0.202240 -1.828051  0.636759  \n",
       "2  2.599818  0.285707 -0.682494 -0.377850  \n",
       "3  1.750208  1.066491  1.241325 -1.026987  \n",
       "4  0.596782 -1.472352  1.040772  0.276510  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_feat\n",
    "y = df['TARGET CLASS']\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1,\n",
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      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[145  14]\n",
      " [ 12 129]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.91      0.92       159\n",
      "           1       0.90      0.91      0.91       141\n",
      "\n",
      "    accuracy                           0.91       300\n",
      "   macro avg       0.91      0.91      0.91       300\n",
      "weighted avg       0.91      0.91      0.91       300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "print(confusion_matrix(y_test, pred))\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate = []\n",
    "\n",
    "for i in range(1, 40):\n",
    "    \n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(X_train, y_train)\n",
    "    pred_i = knn.predict(X_test)\n",
    "    error_rate.append(np.mean(pred_i != y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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dC88+Cxnt0uTCQvtIn6am2LQrIiIioVGSFSN//CMMHw7vvx/9fX/wATQ2Bt42aJBdwmHXrui3KyIiIqFTkhUjsZz8fuONMHAgHDwYuN0vfhH+9a/otysiIiKhU5IVA14vPHS/j940Mur8JvJzGykr9eH1dn3fTU3w2GN2Un16+uHbR4yADz+0o2jR5vVCWamP/NxG0tOie1wiIiI9jZKsKKushOLBDRy5opxtFOGjF+vrisiqKKd4cAOVlV3b/8aNdpSq7V2FbbUs5xBtLceVVVHO+roifCa6xyUiItLT6O7CKPJ6YeK4BtbsvZDhVH/+vocd3OKfwyX+Rxk9rorqre6Iny24apWd7H7xxR2X+dWvYOdOqKiIrI324nFcIiIiPY1GsqJowR0+pvjvPiQRaWs41Uz2L2Lhnb6I9m+MTbLOPx/69eu43Dvv2MftREusj0tERKQnUpIVRcsfaGKSf3HQMpP9i1h+f4AZ66G2sdyOVAVTWGjvQPzww4ibObTNOByXiIhIT6MkK4r21GcygJ1By/RnF3vqI3uCs+PAWWfB2WcHLxftOxtjfVwiIiI9kZKsKMrL8bGTAUHL7KI/eTn7Itr/r38NGzZ0Xm7QIPs9WklWrI9LRESkJ1KSFUXjr0xjqWta0DIVrumMnxBg7YVOvPMO/Pzn8NxznZft39+OePXqFXYzAcXyuERERHoqJVlRNHN2JktcpWygOOD2DRRT4ZrOjLLMsPfd8tDnjpZuaCs9HTZtggkTwm4moFgel4iISE+lJCuKPB5YttLN6Owq5rrm4aUAPxl4KeA65nEhVSy8N7JlDlatsnOtTjkl+nF3pu1xXcehxzXXNY/R2VUsW6nlG0RERNpSkhVlJSVQvdWNb+osRuTWkJXmY0RuDR9cMYtf3Orm8svD3+eHH8Lzz4c2itXiD3+Ao4+2zzGMhpbj8v9oFuf0qaE3Ps7MqME3dRbVW92UlESnHRERkZ5Ci5HGgMcD8xdkMn9ByzvZh2xvaoK0MNLbt96CI48ML8nKzoZ//9vWPf300OsF4/HAXYszuWsxXHEFVFdntzlGERERaUsjWXH24IMwbBg0NoZep7gY3n3XTmYPVcsyDm+8EV58wSxdCjfcYF8PHWpXlX///ejtX0REpCdRkhVnRx0FmzfDzTeHVv7gwdaRr3CeS3jyybZ8tJZxAJsgrl1rX190Ecyda1ehFxERkcMpyYqzUaNg4kS49VbYtq3z8o8/bpdkeOut8NrJzoYBA6KXZDU1wUsv2VE4gMGD4ZZb4Etfis7+RUREeholWQlwxx3Qty/86Ec2eQlm1SrYuxcGDgy/nauuak2Kuuqtt+DTT+1lwhYNDfDaa9HZv4iISE+jJCsB8vJsorV+Paxb13E5v9+OZF1yCbhc4bfzy1/CT34SaZSH2rjRfm+btP3kJ3DeebpkKCIiEoiSrASZONEuGHrBBR2XeeEF+PhjuPTSyNtpbIT9+yOv36K+Hk44ofWRPWATro8+gh07ur5/ERGRnkZJVoK0POwZ4O23A5dZtQqysuAb34isjRdfBLcbnnkmsvptTZ9uk6n0Nk/OaRnVahnlEhERkVZKshLs6afhpJPsZcH2vvMduO02O4k9EgUF9lJeNO8wbOu002wSqCRLRETkcEqyEuyrX7VrWs2YYS/JtXX++TBzZuT7zsuDL36x62tlbd5sY2yfTGVkwFe+oiRLREQkECVZCdarF9xzD7zzDsyaBWWlPvJzG0lPa+LInEbKSn14vZHt23FsctTVkawXX4Tt2yE///BtN98M8+d3bf8iofB6D/185Od27fMhIhJrSrKSwDnnwMUXw8N/bCBzSTnr64rwmV5UNxSRVVFO8eAGKisj23c0kqxNm+xjffr3P3zb174GZ5/dtf2LdKayEooHN5BV0fr5WF/X9c+HiEgs6dmFScDrhY3rGqjiQoYfqP78fQ87uMU/h0v8jzJ6XBXVW914POHt+3vfg6Ki8J+X2NbGjXaSe6AV5w8ehMceg2OPVbIlseH1wsRxDazZeyHDie7nQ0QkljSSlQQW3OFjiv/uQ/6AtDWcaib7F7HwTl/Y+/7616GsLPIE67PP7JyujhY1TUuDKVNgyZLI9i/SmVh+PkREYklJVhJY/kATk/yLg5aZ7F/E8vsPhr1vY+zSC7t3RxZbQ4NdOb6j9bwcxyZgmvwusRLLz4eISCwpyUoCe+ozGcDOoGX6s4s99b3D3ndTE5x6KpSXRxbb0UfDvffCued2XGboUPt4nYaGyNoQCSaWnw8RkVhSkpUE8nJ87GRA0DK76E9ezr6w952eDiefHPkyDu+/3/ljc4YNs8ncli2RtSESTCw/HyIisaQkKwmMvzKNpa5pQctUuKYzfkJ60DId6codhmedBZMnBy/T8tDozZsja0MkmFh/PkREYkVJVhKYOTuTJa5SNlAccPsGiqlwTWdGWWZE+y8stPOy9oX5H/1//cvO5Tr99ODl8vPtHWA//nFE4YkEFevPh4hIrCjJSgIeDyxb6WZ0dhVzXfPwUoCfDLwUMNc1j9HZVSxbGfnt6YWF9nJebW149TZtst9bRqqCKSgIvMSDSFd5PHDBt92Mooqfpkf/8yEiEitKspJESQlUb3XjmzqLEbk1ZKX5GJFbg2/qLKq3uikpiXzfI0fC//0fHHdcePU2brRzus48s/Oyr74KP/wh7NkTUYgiHaqpgUcfhZLvuPFPa/58OD6G50Tn8yEiEitajDSJeDwwf0Em8xe0vBPhk6HbOeYYGDcu/HobN9pLhaE8oPrTT+1diOPG2dXrRaKhqQl+9CP4whfs46eOOCL6nw8RkVjRSFaK2LQJnnkmvDo/+Qn8/Oehlf3KV+zCpC2XGEWi4eBBO8pbXg5HHNH6/u7d8NRTiYtLRCQUGslKET//OXzwQXh3AIYzIpWTY9fj0qKkEk0uV+BEf8kS+PWv7dpsvbU8logkKY1kpYiWZRyamkIr/49/wPr1diQhVC0rv3e2rpZIKGbNgscfD7yt5WaOt96Kb0wiIuFQkpUiBg2CvXvh3XdDK794MVx0UXgJ09ln20s6H30UWYwiLdasgQULYNu2wNsHDbLfI13/TUQkHpRkpYjCQvs91JXfN26086wywrigPGWK/aPXdu6MSLjq6mDGDCgqguuuC1zm5JPtkiFKskQkmSnJShEtSVYof5T8fvuInGHDwmtD62RJNPzsZ3bEdckSOycrkOxsGDAg8sdFiYjEgya+p4ijjoING+zk9M7U1IDPF36SBTBnjl309NFHw68r8vrr8PvfQ2kpFAde4P1zDzwAX/pSfOISEYmEkqwU4Tid/9Fq0bIMQyRJ1v79sHYtHDgQ3qVGEbBzrR54AL71rc7LjhgR+3hERLpClwtTSHU1/OY3nZebMAH+9jcYODD8NoYOtRPsX389/LqS2vbts/8ZGD8e+vbtvPy778Ldd9ulSUREkpGSrBTywgtwww3wySfBy2Vn21GCSOZYtYx+ab0sCcfbb9s5Vk88EXodr9dOkN+yJXZxiYh0hZKsFBLK5Pf6epuIRTqh+MQT7SNQlGQdzuuFslIf+bmNpKc1kZ/bSFmpD6830ZHFX/u+GHxSIw0f+UIawWoRzs0cIiKJEFKS5TjONx3H2e44Tq3jONcH2J7pOM5DzdtfdBxnYPP7P3Ac55U2X02O45wR3UOQUIWyttDmzfaS4ttvR9aG48CkSaFNsE8llZVQPLiBrIpy1tcV4TO9WF9XRFZFOcWDG6isTHSE8ROoL145WMSMpnK+883Q++LII6FfP91hKCLJq9OpyY7jpAMLgYuA3cAmx3HWGGPazrqZBHxsjDnRcZwrgFuB7xlj/gT8qXk/pwOPGWNeifZBSGgGDoRevYL/UWoZgRo6NPJ2br898ro9kdcLE8c1sGbvhQyn+vP3PezgFv8cLvE/yuhxVVRvdePxJDDQOAjWF7c2zeHSvaH3hePY/zhoJEtEklUoI1nDgFpjzA5jzH5gBTCmXZkxwH3Nr1cCoxznsBk93wce7Eqw0jUZGXDSSQS9PLVxI5xwgh0l6Aqfz156FFhwh48p/rsPSSraGk41k/2LWHinL86RxV+0+6LlcVEiIskolCTrWOCdNj/vbn4vYBljzAHgU6D9ut/fQ0lWwj3/PPzf/3W8fePGyJZuaOvDD6FPH7uYpMDyB5qY5F8ctMxk/yKW3x/GgyK7qWj3xS236PmFIpK8QlnJKNA9Zu2faBe0jOM4ZwN7jTEBn0TmOM5UYCpAfn4+69atCyEsq76+PqzyPVlX+6K+Pp0PPxzOF7/4T9at292lWPr1K+aJJz7lzDMTM2Emmc6LPfXnMYCdQcv0Zxd76jJjErP6wkqmfkg09UUr9UUr9YUV1X4wxgT9AoYDa9v8PBeY267MWmB48+sMYA/gtNl+J3BDZ20ZYxgyZIgJx7PPPhtW+Z4slL547TVjrrnGGK838PaDB43Zt6/rsVx2mTEeT9f3E6lkOi+O6rPX1FJgjH3edsCvWgpMfm5DTNrvyX1RX2/M3LnGPP1052WTqR8STX3RSn3RSn1hhdsPwEumg5wmlMuFm4CTHMc5wXGcXsAVwJp2ZdYAVzW/Hgc809wwjuOkAZdj53JJgjU2wr33wssvB96elgaZmV1vZ9gwO/frww+7vq/ubvyVaSx1TQtapsI1nfET0uMUUeKMvzKNiozo9UXv3nDHHfYpAyIiyabTJMvYOVYzsaNVbwAPG2NecxznJsdxRjcXWwoc4ThOLXAt0HaZh/OA3caYHdENXSJxyin2e6DJwjNnwk03RaedlnldL70Unf11ZzNnZ7LEVcoGAj/XaAPFVLimM6MsCtltkisty2RBU/T6Ij0dTj5ZyziISHIKaZ0sY8yTxpiTjTEeY8zNze/daIxZ0/x6nzHmcmPMicaYYW0TKmPMOmNMiE/Nk1jLyYHjjz88yTIGHnwQdu2KTjtDhsCtt9q7GVOdxwPLVroZnV3F9a55eCnATwZeCriOeVzkVLHkTz1/+QawN17UN7m5uFcVc9v1xVzXPEZnV7FsZXh9oTsMRSRZacX3FFRYePj//HfsgI8+6vqdhS369IE5c6CgIDr76+5KSqB6q5v9U2cxIreGrDQfI3Jr2DVmFg3GnTIr5NfVwde/Dptec+Nr1xe+qbOo3uqmpCS8fRYW2vPX1/NXwBCRbkZJVgoaPPjw91r+yEcryQI7H+vJJ+0omcD27fDBp5m8/nY2Bw6m8e9Ps3l4dSZXXw3z58P77yc6wtj7yU/gL3+xj1+avyCTf3/a2hfzF2RGNJo3aBC43faB0SIiyURJVgq6/fbD50pt2mQnEZ92WvTaeegh+Na3oncJsrt76il45BHIzT30/dtvtw/vzs9PTFzx8Mwz8Nhj9nUkDx4P5nvfsw8916ipiCSbUNbJkhTQrx9ceim4XNHbZ8uo2MaNMGBA9PbbXW3aBF/5il15v60jjrBfAB980PXV9pNNQ4N9nmXv3nDxxdE9x8BOfhcRSUYayUpBDQ12XsyyZa3v/fznduJ7NA0ebJ+VmCrzjYLx+2HLluCXY3//e3ujwHvvxS+uePh//w/++U+4557oJ1gtrrsOfvGL2OxbRCRSSrJSUHa2TXyqmx8fd/BgbOZN9eoFZ56pJAtg2zbYty94knXxxXby9n/9V/ziirVXXrHzzSZPhq9+NXbt1NTAn/8cu/2LiERCSVYKcpxDb3v/wx/g6KNjM/F62DDYvNkmcqnss8+gqCh4kuXx2NGYRx6BNe2X++2GDh6EqVMhLw9uuy22bQ0aZM/npqbYtiMiEg4lWSmqbZK1caO9nHXUUdFv58c/hhdfjP5k5+7ma1+zoy2dTc6ePRtOPx1mzLDLHXRnaWkwaxYsWmTn/MVSYaG9DK47DEUkmSjJSlGDBsG//gWffmqTrGHDYpMIeTz2jsW0FD/TQr0c63LZuUuffmpHALszx4EJE2Ds2Ni3VVhov2tRUhFJJin+py91feUrMGqU/Z//tm3RXR+rvQcfhOXLY7f/ZFdfb0cJ295oEExxMbzzDowcGdOwYsYY+MEPYPHi+LU5aBCceirs3x+/NkVEOqMkK0VddBFUVdkFQ5uaYOjQ2LW1dKl9iG+q2p6kV8AAACAASURBVLIF9uyxc5NC1bevTVZWr7aXcruTRx+1SXV9ffzazM+H116z67KJiCQLJVkpyOuFslIf+bmNjPxaE317NfLEIz683ti01fCRjze2NJKe1kR+biNlpbFrq+W4Rl1wXkzbCkfL3ZXhJrLPP28vtV10nj2mcPovkr5oWyfUttrXOSq3kckTfQwaZFd3FxFJZUqyUkxlJRQPbiCropz1dUX4TC827y/ii38qp3hwA5WV0W/ra1vLqcG2tb6uiKyK2LXV9rhi1Va4Nm6EgQPDX2S0oQH6pDdwVnV4xxRJX0Srzoa6IibtLef9txv461/DO96uuu02u2SIiEjSMMYk1deQIUNMOJ599tmwyvdknfVFba0xedn1Zj3FxtirUYd8rafY5GXXm9rarsfSU9uKxIABxnz3u+HVifSYIqkXrzqxduuttvmPPw68Xb8rWqkvWqkvWqkvrHD7AXjJdJDTaCQrhSy4w8cU/90Mpzrg9uFUM9m/iIV3+tRWlBw8CN//Plx+eXj1QjmmSf5F3DXPx759dqHTlnqTw6z3u9tCq9PSfz5faHXi3eeDBtnv27fHrUkRkeA6yr4S9aWRrMh11hdH9dlraikIOPLQ8lVLgcnPbehyLD21rXgJ9ZiyaDBgTEZGZPXAmN6E13/nnBN+nXh4803b9L33Bt6u3xWt1Bet1Bet1BdWNEey9IDoFLKnPpMB7Axapj+72FPfW21Fye7d8MUv2kcZhSPUY/I5vfnNLa3rkIVbD+B/5obXf9OmwdXrk6/PTzjBPspJa2WJSLLQ5cIUkpfjYycDgpbZRX/ycvaprSiZOhXOOSf8eqEe05F99nH99TBnTmT1rr8e8vqE138TJoRfJx4yMuCqq+xDtkVEkoGSrBQy/so0lrqmBS1T4ZrO+Anp3a6tiji1FQ5j7J2FQ4aEXzfS/oukXrzqxMM998CkSXFtUkSkYx1dR0zUl+ZkRS6V7y78giu57nQzxhiv14aweHH4dXV3YeT27TPm4MHD39fvilbqi1bqi1bqC0t3F0pEPB5YttLN6Owq5rrm4aUAPxl4KWCuax6js6tYttKNxxPbtv7bmceFVPG7e6LTlssFjWluvpFexfUxPq5wtCxCGskjiyL9t4qkXrzqxMOqVeB2a16WiCQHJVkppqQEqre68U2dxYjcGrLSfIzIrcE3dRbVW92UlMS+rQ/HzyK9j5ucnK63YQyUlkJ6OjzxjJv9LW05+2J2XKHatAl694aiosjqR/pvdVi9EPoikrbieS6F6vjj7bIZSrJEJCl0NMSVqC9dLoxcd+qLzz6Lzn42brRXqO6449D3n3zyOfPMM8bs3RuddiLx6qvG/OlPiWu/RXc6L7qqrs6eDzfffPi2VOqHzqgvWqkvWqkvLF0ulG6vTx87CvXYY117kPDQobB+PfzXfx36/pYt/bjgAvtw5kQZPBjGj09c+6koJweOOw7eeCPRkYiI6HKhJNDrr8Oll8IvfhFZ/Xfftd+HD7e377dVWFgHtM6Lird337Xzg+rqEtN+Kiss1OVCEUkOSrIkYU47zS5s+bvfwebN4dX9+9/tQ5fXrAm8/Ygj9nP88YlLsior4TvfgfffT0z7qeyaa+DqqxMdhYiIkixJsN/8Bo46yi7aeeBAaHX277fljzkGLrig43LDhtnJ54mwcSP060fc764Te4l2xoxERyEioiRLEuwLX4Dycjt3qrw8tDrz5tlLjXffTdA7FIcOBa8XPvwwOrGGY+NGm+Q5TvzbTnVNTbBzZ2L+3UVE2lKSJQk3bhz88If29vvOvPkm/OpXcPnl8K1vBS87fjy8+CLk5kYnzlA1NMC2bZGtjyVd9/779lLygw8mOhIRSXV6QLQknOPA0qWhld20Cfr2hbvu6rzs8ceHlrhF28sv27Wahg6Nf9sCX/qSTaw1+V1EEk0jWZI0DhyAO+6ARx/tuMwPfgA7dsDRR4e2z7VroaIiOvGF6pxz7Ijb+efHt12xHAcGDdIyDiKSeEqyJGk4jr3EU1oKH3986LYPPoDHH7ev3e7Q97liBdxwg12TK17S0uCkk4LPF5PY0jIOIpIMlGRJ0khPh3vugf/8By4410d+biPpaU3k5zZywbk+vvMdeOed8PY5bJhN0HbujE3MgcyZA3/9a/zak8MVFsJ778Gnn0ZW3+uFstJDz8GyUh9eb3TjFJGeTUmWJJX334c+6Q2Mer2c9XVF+Ewv1tcV8Y03y3E7DWzbFt7+Wiafx2sphz177N2PL78cn/YksDFj7KioyxV+3cpKKB7cQFbFoedgVkU5xYMbqKyMfrwi0jNp4rskDa8XJo5r4C8HLmQ41Z+/72EHtzOHy/yPMnpcFdVb3SGvP3X66ZCZaZdUuPzyGAXeRksypzsLE2vQIPsVrpZzcM3ew8/BW/xzuCSCc1BEUpdGsiRpLLjDxxT/3Yf8cWtrONVM9i9i4Z2+kPfZqxeccUb85uds3Gjnlg0ZEp/2pGMvvggvvRRenVicgyKSupRkSdJY/kATk/yLg5aZ7F/E8vsPhrXftWs7fvxOtG3aBKeeah+ALYl1zTVw883h1YnVOSgiqUlJliSNPfWZDCD4DPX+7GJPfe+w9tu3b/xWXv/kE10qTBaR3GEYq3NQRFKTkixJGnk5PnYyIGiZXfQnL2dfWPutq4OJE2HVqq5EF5q//Q2WLIl9O9K5wkKorQW/P/Q6sToHRSQ1KcmSpDH+yjSWuqYFLVPhms74Celh7dfttpcL167tSnShSw8vPImRQYPsArc7doReZ/yVaVTE4BwUkdSkJEuSxszZmSxxlbKB4oDbN1BMhWs6M8oyw9pvWpp9xM3GjdGIsmO33GLvYIznwqfSscJC+z2cld9nzs5kEdE/B0UkNSnJkqTh8cCylW5GZ1cx1zUPLwX4ycBLAXNd8xidXcWylZHdOj9sGNTUQGNj9ONusXYt7N4dv/lfElxREbzwAowaFXqdgwehwbj5Rvrh5+D1XTwHRST1KMmSpFJSAtVb3fimzmJEbg1ZaT5G5NbgmzqL6q1uSkoi2+/QofbS0SuvRDfeFgcO2OUCNOk9eWRlwbnnhn6npzEwbZq9vPzU39qcg46P4e4afFO6dg6KSOrRYqSSdDwemL8gk/kLWt7J7vI+hw2zIxv19V3eVUBvvAF79yrJSjbPPQfbt8PUqZ2X3bcPBgyAK66A4mIoLo7uOSgiqUdJlqSEY46xlwtjpWW+19ChsWtDwrdyJdx3H0yZ0nnZrCy4997A2zZsgEcfhdtu0+VgEQmdLhdKSonVpPS+feEb34ATT4zN/iUyhYV2CY9//St4uV//OvjzJl99FW6/HV5/PbrxiUjPpiRLUsaKFZCXBx99FP19jxsHf/mLvZNRkkfLHYbBFiX961/h5z8Pvo7amDF2BCsea62JSM+hPwmSMo46yiZY4T7PrjMHDoBPj7JLSi0Pie4oydq71052P/lkuOGGjvdz9NF2npaSLBEJh5IsSRlDhtjRiGivl7VxI+TmwrPPRne/0nVHH23vLty+PfD2X/3KLla6eDH07uRJOZdeClu2wK5d0Y9TRHomJVmSMvr2hVNOiU2StX9/66UpSR6OYxOsO+88fNu2bTBvnn2Q9Pnnd76vsWPh+OPh7bejH6eI9Ey6u1BSyrBhdtFQY6J3l9imTXDccXbURJJPR/8uJ51kR7JCWd6hpfzOnbq7UERCpyRLUsp3vmP/6Pr90KtXdPa5caOWbkhmGzfah3aPHdv6vEFjIDMT5s4Nb1+OA01Ndh5etM4fEem5dLlQUsqYMfDb30bvD+RHH0FtrRYhTWbvvgsVFfDOO9mf/zx4MKxfH/6+du+2a64tXx7lIEWkR1KSJSln796uT172eqGs1EfhgEbSaOKOXzdSVurD641OjBIdXi889rCP3jRSOu1M8nMbufCrPt58E/Lzw9/fscfaETDdZSgSnpbfmfm5jaSnNZGfG9rvzEjqRdpWLCjJkpRzwQVw1VWR16+shOLBDWRVlLOhvggfvahuKCKropziwQ1UVkYvVolcy7/T0Y+Usw3777S+rohvvV1OlmngzTfD36fj2LsMn3oKGhqiH7NIT9T2d+b6uiJ8xn4WO/udGUm9SNuKGWNMp1/AN4HtQC1wfYDtmcBDzdtfBAa22TYY2AC8BtQAvYO1NWTIEBOOZ599NqzyPZn6olWwvpgxw5g+fYw5cCD8/dbWGpOXXW/WU2yMndpzyNd6ik1edr2prY089mhLxfMilv9Ozzxjd/PII9GPO15S8ZzoiPqiVSz6ItLPYiT1ovW5D7cfgJdMBzlNpyNZjuOkAwuBEuBU4PuO45zartgk4GNjzInAncCtzXUzgAeAacaY04CRgD/CfFAkKoYOtY9a6WjtpGAW3OFjiv9uhlMdcPtwqpnsX8TCO7U6aSLF8t/pq1+FI47QJUORUET6WYykXjL+fg7lcuEwoNYYs8MYsx9YAYxpV2YMcF/z65XAKMdxHODrwFZjzKsAxpgPjTEHoxO6SGRaJqlHsl7W8geamORfHLTMZP8ilt+v0zyRYvnvlJEB8+fDD38YaXQiqSPUz2LF/x5k3LjWh7SHW2/cOFi6JPl+PzumkyfmOo4zDvimMWZy888TgLONMTPblNnWXGZ3889e4GzgSmAIcBRwJLDCGHNbgDamAlMB8vPzh6xYsSLkA6ivrycnJyfk8j2Z+qJVsL5oaoJLLjmXCy98n7Kyt8La76gLzsNnepFBxx9SPxlkOfuoeuaFsPYdK6l4XnTHf6d4SsVzoiPqi1ax6ItQP4u92Uf/gfv4+tf/zfe//07Y9QB2/jOL/XT9cx9uP5x//vmbjTFnBdzY0XXEli/gcqCizc8TgN+3K/MacFybn73AEcB1wNtAHpCNnZs1Klh7mpMVOfVFq8764qGHjNmyJfz9HtVnr6mlIOD1/pavWgpMfm5DZIHHQCqeF/H4d3rpJWNWr45i0HGUiudER9QXrWLRF5F+FiOpF63PfVznZAG7gePb/Hwc8F5HZZrnYfUFPmp+/zljzB5jzF7gSeArIbQpElPf/S6ceWb49cZfmcZS17SgZSpc0xk/IT1oGYmtePw73XILlJbakVERCSzSz2Ik9ZLx93MoSdYm4CTHcU5wHKcXcAWwpl2ZNUDLTfHjgGeas7u1wGDHcbKbk6+vAa9HJ3SRyDU02InLO3eGV2/m7EyWuErZQHHA7RsopsI1nRllmVGIUiIVj3+nsWPhvffsY5VEJLBIP4uR1EvG38+dJlnGmAPATGzC9AbwsDHmNcdxbnIcZ3RzsaXAEY7j1ALXAtc31/0YmI9N1F4Bthhj/hz9wxAJzyef2EfsrGn/34VO+P3wi1vdjM6uYq5rHl4K8JOBlwLmuuYxOruKZSvdeDyxiVtC4/HAspWx/Xf61rfsJPjVq6MXt0hP4/HA965xM4oq5qSH/lmM5DMcj899uEJ6dqEx5knspb62793Y5vU+7NytQHUfwC7jIJI0jj3WPsMwnDsMm5pg0iS7mvBzm9xU3D2LEfeXsqe+N3k5+xg/IZ3qskwlWEmipASqt7pZeGfzv1NdJnl9fFH7d+rXD0aOtCOiv/lNVEIW6ZFuuAF69XLj981ixAOh/8487DMcQr1I6sSSHhAtKWvYsPCSrHvusc+7u+8+OPVUmL8gk/kLWrZmxyJE6SKPp/Xfad26dYwcOTKq+x87FubMsZcNjzkmqrsW6RGMsZ+N+fMBMrlzYcuW0H5ntv0Mh1ovkjqxosfqSMoaNgzefNNeOuzMe+/BT38Ko0bBhAmxj026h6uvhj17lGCJBPLYY3D++fD++4mOJHGUZEnKalmU9KWXOi/74x/D/v2weLF9fp0IQHY29O6d6ChEkk9dHcycCR99BF/8YqKjSRwlWZKyRoywI1mjRgUv19QEp50Gv/oVnHhifGKT7uOFF+DLX4Z33kl0JCLJ42c/g3fftdMsXK5ER5M4mpMlKSsrC046qfNyaWnwy1/GPBzppo48ErZutZdGZs7svLxIT7dpE/z+9zB9OhQHXk0hZWgkS1Las89CWVnH23/7W3jiifjFI91PYSGccoqWchBp8etfw5e+ZBfsTXVKsiSl1dTA735nh7Xbe+kl+J//gT9rZTfpxNixsG6dnX8ikuqWL4cnn4S+fRMdSeIpyZKU1jL5vf1SDgcOwJQpkJ+vNZCkc2PHwsGDGvWU1Paf/8C+feB2wxlnJDqa5KAkS1LaGWfYVbvbJ1l33QWvvALl5fCFLyQmNuk+zjrLLudw/PGdFhXpkYyBiRPh3HP1PM+2NPFdUlrv3vbOsLZJ1rvvwo03wiWXwGWXJS426T7S0uDeexMdhUjirFgBa9fa/6Cmafjmc+oKSWleL5h9Pl5c10h6WhP5uY3Mu9nHjTfCggVaE0tC5/XC5Ak+jsxpPZfKSn14vYmOTKRjXi+UlfrIz21k1AXnhXTetq2TntbEUX0amf5DH6efDjNmxC/27kBJlqSsykooHtzA198s59WmInymF+vrisiuKOf2mxp47bVERyjdRcu59IUHyqluaD2XsirKKR7cQGVloiMUOVzLeZtVUc76utDO20B1NtQXMXlfOe++1cBTT8X/OJKZLhdKSvJ6YeK4BtbsvZDhVH/+vocd3OKfwyX+Rxk9rorqrfF9Yrt0PzqXpDuK5LwNVud25nDZPp3r7WkkS1LSgjt8TPHffcgviraGU81k/yIW3umLc2TS3ehcku4okvNW53r4lGRJSlr+QBOT/IuDlpnsX8Ty+w/GKSLprnQuSXcU6nl7/x/tefvZZ3DvUp3r4VKSJSlpT30mA9gZtEx/drGnXk//leB0Lkl3FOp5+9Fee97W1cFn+3Wuh0tJlqSkvBwfOxkQtMwu+pOXsy9OEUl3pXNJuqNQz9sj+9jz9phj4Mg+OtfDpSRLUtL4K9NY6poWtEyFazrjJ6THKSLprnQuSXc0/so0KsI4bx1H53oklGRJSpo5O5MlrlI2EPgR8RsopsI1nRllmXGOTLobnUvSHc2cncliJ7zzVud6+JRkSUryeGDZSjejs6uY65qHlwL8ZOClgLmueYzOrmLZSt2GLJ0Ldi7NZh7fzNC5JMmnb184mOnm62lVXB/i70D93gyfkixJWSUlUL3VjW/qLEbk1pCV5mNEbg2+qbOo3uqmpCTREUp30dG59PzgWXx2wK3nX0rSmT3bPsz54Sfc7G85b519nf4O1O/N8GgxUklpHg/MX5DJ/AUt72QnMhzpxgKdS3V1cNppMHUqbNkCLlciIxSxDhywX3Pm2KSppMSet+vWrWPkyJGd1tfvzdBpJEtEJEb69IGFC+0aQ2+/nehoRKyMDPjTn+CmmxIdSc+nJEtEJIYuuQS2b4eTT050JCKwdCls22ZfpykDiDl1sYhIjPXuDT4f3HsvGJPoaCRV1dTAtGkwf36iI0kdSrJEROLgoYfghz+E++9PdCSSipqa7NzAvn3httsSHU3qUJIlIhIHV14Jw4fDtdfCnj2JjkZSzeLFUF0Nd94JeXmJjiZ1KMkSEYmDtDS45x749FN7+7xIvLz3HsydC6NG2WRf4kdJlohInBQV2dvmly2Dp59OdDSSKvr1g5kz7WiW4yQ6mtSidbJEROLoZz+D2lo48shERyKpIisLbr450VGkJo1kiYjEUVaWnQQ/eHCiI5Gerq4ORo6E559PdCSpS0mWiEgCfPQRXH45TLzCR35uI+lpTeTnNlJW6sPrTXR0XeP1QllpzzuueImk/wLVuWCEj+ee05MGEklJlohIAjz1FDy5soH8h8pZX1eEz/RifV0RWRXlFA9uoLIy0RFGprISigc3kFXRs44rXiLpv47qfK2mnNyMBj75JP7HIZbmZImIxJnXC7MmNVDFhQyn+vP3PezgFv8cLvE/yuhxVVRvdePxJDDQMHm9MHFcA2v29qzjipdI+i9YnduZw2UH1OeJpJEsEZE4W3CHjyn+uw/5o9jWcKqZ7F/Ewjt9cY6sa3rqccVLJP2nPk9uSrJEROJs+QNNTPIvDlpmsn8Ry+8/GKeIoqOnHle8hNN/n30Gq1fDsj+qz5OZLheKiMTZnvpMBrAzaJn+7GJPfe84RRQdPfW44iWc/tu1C8aOBQf1eTLTSJaISJzl5fjYyYCgZXbRn7ycfXGKKDp66nHFSzj95/HAyy/DEW71eTJTkiUiEmfjr0xjqWta0DIVrumMn5Aep4iio6ceV7yE039ZWXDGGXDlRPV5MlOSJSISZzNnZ7LEVcoGigNu30AxFa7pzCjLjHNkXdNTjyteIuk/9XlyU5IlIhJnHg8sW+lmdHYVc13z8FKAnwy8FHAd8/i6U8W9D3W/W+5bjuuSrCqub3dcc13zGJ1dxbKV3e+44sXjgWEj3VxIFT/NCK3/gp1L6vPEU5IlIpIAJSVQvdWNb+osRuTWkJXmY0RuDW99Yxb1xs327YmOMDIlJTDyYjf/6zr0uJ4+dRb9jnUzalSiI0xe69fbhUW/e7Ub/48O7T/f1FlUb3VTUnJ4vY7OpWB1JD50d6GISIJ4PDB/QSbzF7S8k40xMGYMVFTAj38MGd3st/SBA7BuHVx8aSZ/+lPLu9k8/jiMHg233w433JDAAJPU/v0wdSocdxz8/veQk3PoedGZQOeSJJ5GskREkojj2ARr06bul2AB/P3v8OGHdnmBti65BMaNg5tugrfeSkxsyWztWnjtNVi4EHJyEh2NRIuSLBGRJHPUUfYPrc8HNTWJjiY8q1ZBZiZ885uHb7vrLrtt2jQwJv6xJbNLLoGtW+136TmUZImIJKmrr4aLLoKPP050JKExxq5CftFFgUdjjjkGbr3Vru/k9cY/vmRkDPzjH/b16acnNhaJPiVZIiJJas4c2LMHfvrTREcSukcegV/+suPtU6fC9u1w4olxCymp3X8/nHaanfQuPY+SLBGRJHXmmVBWBkuWwAsvJDqazjkODBlivzqSlgZHHglNTVBVFb/YktGePXDttVBcbL+k51GSJSKSxH75SxgwwI4A+XyJjia4G2+E6urQyi5ebC8rPv10bGNKZrNnw6efwv/+r00+pefRP6uISBJzu2HRIujTBz74INHRdKy2Fn71K9iwIbTy11xjLxlOmwaNjbGNLRk9/TQsW2YvBRcVJToaiRUlWSIiSa6kxI4QHXdcoiPp2OrV9vull4ZWPivLjuDU1sKvfx27uJLVW2/ZuVj/8z+JjkRiSUmWiEg3kJZm5/D85jfJufzBqlXw5S/DCSeEXueCC+Cqq+C222DbttjFloymTbN3WWZlJToSiSUlWSIi3cSaNXa19HvvTXQkh/r3v+1lwvYLkIbi9tth6FCor49+XMnotdfg8cfta5crsbFI7CnJEhHpJq6+Gr76VXvH4bRrfOTnNpKe1kR+biNlpb6ga095vVBWauuMuuC8kOqEqrYWvvSlyJKsvDy7fMGRR7bGF8kxhVonUpG21b7e2V9u5MrLfbzySvRjlOQTUpLlOM43HcfZ7jhOreM41wfYnuk4zkPN2190HGdg8/sDHcdpdBznleavxdENX0QkdaSlwQ9+AAc+ayD3vnLW1xXhM71YX1dEVkU5xYMbqKw8vF5lJRQPbiCrIvQ64Tj3XNi9O/LFNFvic/1v8hxTNNoKVO/Vg0VMO1DORSOiG6MkKWNM0C8gHfACBUAv4FXg1HZlSoHFza+vAB5qfj0Q2NZZG22/hgwZYsLx7LPPhlW+J1NftFJftFJfWD2hH2prjcnLrjfrKTbGTs065Gs9xSYvu97U1natTjj8fmMOHuy+x9TZeRFpW7Hu91joCZ+RaAi3H4CXTAc5TSgjWcOAWmPMDmPMfmAFMKZdmTHAfc2vVwKjHMdxIk38RETkcAvu8DHFfzfDCbwY1XCqmbR/Ebf80se+ffa9+b/xMXl/8DqT/YtYeGdki3A98ggcfzy8/XZE1UM7Jr89ph07YMcOuPkXsT2mcOOb7F/EXfNa44t3jJK8QkmyjgXeafPz7ub3ApYxxhwAPgWOaN52guM4LzuO85zjOF/tYrwiIilr+QNNTPIHn3Ux5cAiHnzgIFu22J8f/FMTkw8ErzPZv4jl9x+MKKZVq+DAAejfP6LqoR2T3x6TxwMeT+yPKdz4JreLL94xSvJyTCf3AjuOcznwDWPM5OafJwDDjDGz2pR5rbnM7uafvdgRsHogxxjzoeM4Q4DVwGnGmM/atTEVmAqQn58/ZMWKFSEfQH19PTmBnkSagtQXrdQXrdQXVk/oh1EXnIfP9CKDjv8w+8mgN/tY+Wg1/fr5GXX+efjovE6Ws4+qZ8J7ds/+/Q5jx47g/PP/w3XXvRlW3RbhHNOc620bt/32lKgdU2fnRajxZTn7+O+ftvZBNGOMl57wGYmGcPvh/PPP32yMOSvgxo6uI7Z8AcOBtW1+ngvMbVdmLTC8+XUGsIfmBK5duXXAWcHa05ysyKkvWqkvWqkvrJ7QD0f12WtqKQg4x6flq5YCk5/b0KU6oXrySbuLP/+5+x5TZ+dFpG3Fst9jpSd8RqIh3nOyNgEnOY5zguM4vbAT29e0K7MGuKr59TjgGWOMcRznSMdx0gEcxykATgJ2hNCmiIi0M/7KNJa6pgUtU+GazvgJ6V2qE6pVq+zjfkaNCrvq55LtmKIRX7xjlCTWUfbV9gu4GHgTe5fh/zS/dxMwuvl1b+D/gFpgI1DQ/P5lwGvYOxK3AJd01pZGsiKnvmilvmilvrB6Qj8k+k689p5+2pjFi7v3MenuwlY94TMSDdEcyQopyYrnl5KsyKkvWqkvWqkvrJ7SD08+af94X++aZ2opMPvJMLUUmOtd80xedr158snQ61znzDPZ1Jv774//cYQSXyTHFKxOIKGcF08+acwRvevNbMJrK1oxxktP+Yx0VbwvF4qISJIoKYHqrW58U2cxIreGrDQfI3Jr8E2dRfVW4Ut5VgAADJ5JREFUNyUlIdRx9jEit4YPx8/iQC83jz0Wfhx/+QtRW7U8KscUQp2uxHdVqZtyZnFOn9DbimeMkpwyEh2AiIiEx+OB+Qsymb+g5Z3ssOqsW7eOkSNH2vcHwc9+ZlcnD/WPvjEwYwaccgo8+WREhxA0Piu8YwJoaMhm3DioqrLboun55+ErZ2dS/fmyV53HFyjGUOtJz6CRLBGRFPbf/w3z5sHXvhZ6nZoau+BmJM8qjCW3G/75T3j44eju95134KWX4NJLo7tf6fmUZImIpLBeveC66yA7Gw6GuC7m6tXgODB6dGxji8TYsfDcc/Dhh9HbZ8vl1GRLKiX5KckSERG2bYNBg2Dz5s7LrloF55wD+fmxjytcY8faZPGJJ6K3z+HD4Ze/tJdHRcKhJEtERDj+eKivhylT7GNyOrJnD7z5ZvKO6gwZAscea0fbornPX/wievuT1KGJ7yIiQt++UF4Ol18Od90Fs2cHLpeXBx98EPqlxXhLS4Nrr7WXM6PhpZdg/347mhWtfUrqUJIlIiIAXHYZfPvbcOON9vXAgYHLZSf5DXLXXhu9fd18M2zaBLt2KcmS8OlyoYiIADaJWLjQfl+06PDtH3wAZ51lJ5Ynu/p6mxx1xd69sHatvaswTX8tJQIayRIRkc/17w8bNsBppx2+bc0aOzG+b9/4xxWuWbPsXYH/+Q9kRPiXbu1aaGzU0g0SOeXmIiJyiNNPtyM3778Pn3zS+v7q1TBgAHz5y4mLLVSjR8PHH9tFRCO1ejX06xfeGmIibSnJEhGRw3z2mV3S4fxzfOTnNpKe1sTTTzRyZF8fO3YkOrrOfeMbkJVll5uIhDE2Qfv2t8Hlim5skjqUZImIyGH+/nc4WNfAqDfKWV9XhM/0ooYiRr1eTvHgBiorEx1hcNnZNtFavdomTOFyHPjHP+D226Mfm6QOJVkiInIIrxcmjmvgLwcu5Hbm4GEHGRzEww5+e2AOa/ZeyMRxDXi9iY40uEsvhd27I3+QdWYmHHVUdGOS1KIkS0REDrHgDh9T/HcznOqA24dTzWT/Ihbe6YtzZOEZOxZefRXOOCO8esbARRfB8uWxiUtSh5IsERE5xPIHmpjkXxy0zGT/Ipbfn6QrkjbLzYXBg8Nf32rzZqiqAr8/NnFJ6lCSJSIih9hTn8kAdgYt059d7KnvHaeIIrdjB1xzDbz1Vuh1Vq2C9HQ76V2kK5RkiYjIIfJyfOxkQNAyu+hPXs6+OEUUOZcL/vhHePTR0OusXg3nnQdHHBGzsCRFKMkSEZFDjL8yjaWuaUHLVLimM35Cepwiitzxx9tV6kNdyuHNN+H115P3AdjSvSjJEhGRQ8ycnckSVykbKA64fQPFVLimM6MsM86RRWbsWHjxRXjvvc7L+v32uY1jxsQ+Lun5lGSJiMghPB5YttLN6Owq5rrm4aUAPxl4KWCuax6js6tYttKNx5PoSEPT8licxx7rvOxpp8HKlfbxQiJdpSRLREQOU1IC1Vvd+KbOYkRuDVlpPkbk1uCbOovqrW5KShIdYegGDYJRozq/y/CTT+gWq9lL96EHRIuISEAeD8xfkMn8BS3vZCcynIg5jl2SoTMPPgilpXZe1kknxT4u6fk0kiUiIinh4EH46KOOt69aZZOrE0+MX0zSsynJEhGRlDBsGEydGnjbJ5/As8/aSfLhLl4q0hElWSIikhLOPhsqK6Gx8fBtf/4zHDigpRskupRkiYhIShg7Fvbuhb/+9fBtq1fD0Ufb0S6RaFGSJSIiKeFrX4O+fW1C1d6iRfDII5Cmv4oSRbq7UETk/7d3/7FW13Ucx5+vLpcrP2RmYGsiGHds6ZgSq4ajNWKOQRnWRlua5R8aachMLJPWVrGRawY4h9ESCbTwx8iCWqwEdPmH19Ikro1aXgMyEcLSguH1Cu/++H7pHOjes9HO93zu+X5fj+3unu/3fO/23mvvnfO+38/n3GuVMHJk9v8It27NlgZH1L0Djh+ffZk1k2d2MzOrjKVLYdOmUze3r1wJ69enq8nKy0OWmZlVxowZMHcudOT/dvH4cXHHHbBjR9q6rJw8ZJmZWaXs2QMrVkAE9PaO49VX/alCK4aHLDMzq5QtW2D51/o57+xjLL3lUkZxjCd+2U9fX+rKrGw8ZJmZWWVs2wbfWX6UJdxNz9FpvMlIepnGuI13M/OSo2zblrpCKxN/utDMzCqhrw8+u/AoPzt2OZfR89/z3bzItwZu42MDj7Jg4XZ6do+huzthoVYavpNlZmaVsGZlP58b+O4pA1a9y+jh+oG13LO6v8WVWVl5yDIzs0rY9MMTXDfwvYbXXD+wlk0PHG9RRVZ2HrLMzKwSDh/pYjL7Gl4zif0cPnJWiyqysvOQZWZmlTB+bD/7mNzwmv1MYvzYN1pUkZWdhywzM6uEq695G/d13tDwmnWdN3L1ZzpaVJGVnYcsMzOrhJtu7eLezi/wFDMHff4pZrKu80YW39LV4sqsrDxkmZlZJXR3w/2bx7Bg9HaWdd5JH1MYYAR9TGFZ550sGL2d+zf7zzdY83jIMjOzypg/H3p2j6F/0RJmjetllN5g1rhe+hctoWf3GObPT12hlYmHLDMzq5Tubli1potXXh/N9p1P8srro1m1pst3sKzpPGSZmZmZFcBDlpmZmVkBPGSZmZmZFcBDlpmZmVkBPGSZmZmZFcBDlpmZmVkBPGSZmZmZFcBDlpmZmVkBFBGpaziFpL8D+87gR8YDhwsqp904ixpnUeMsMs6hxlnUOIsaZ5E50xwmR8SEwZ4YdkPWmZL0TES8L3Udw4GzqHEWNc4i4xxqnEWNs6hxFplm5uDlQjMzM7MCeMgyMzMzK0AZhqzvpy5gGHEWNc6ixllknEONs6hxFjXOItO0HNp+T5aZmZnZcFSGO1lmZmZmw05bD1mS5kn6k6QXJN2eup6UJO2V1Ctpl6RnUtfTSpLWSzok6fm6c+dKekzSn/Pvb09ZYysMkcM3JP0t74tdkj6SssZWkXSBpMcl7ZH0B0k35+cr1RcNcqhcX0g6S9JvJP0+z+Kb+fl3S3o674mHJY1MXWvRGmSxQdJf6vpieupaW0FSh6TnJP08P25aT7TtkCWpA7gHmA9cDFwl6eK0VSX34YiYXsGP4G4A5p127nZgR0RMBXbkx2W3gf/NAWB13hfTI+IXLa4plbeAWyPiImAmsDh/fahaXwyVA1SvL/qBORFxKTAdmCdpJvBtsiymAv8ErktYY6sMlQXAl+v6Yle6ElvqZmBP3XHTeqJthyzgA8ALEfFiRLwJPARcmbgmSyAifg3847TTVwIb88cbgY+3tKgEhsihkiLiQET8Ln/8b7IX0POpWF80yKFyInMkP+zMvwKYA2zOz5e+J6BhFpUjaSLwUWBdfiya2BPtPGSdD/y17vglKvrikQvgV5KelbQodTHDwDsj4gBkbzTAeYnrSekmSbvz5cRSL48NRtKFwHuBp6lwX5yWA1SwL/JloV3AIeAxoA94LSLeyi+pzPvI6VlExMm+WJH3xWpJXQlLbJW7gNuAE/nxO2hiT7TzkKVBzlVyEs/NiogZZMuniyV9KHVBNiysBbrJlgQOACvTltNaksYCPwa+GBH/Sl1PKoPkUMm+iIjjETEdmEi2GnLRYJe1tqo0Ts9C0jRgGfAe4P3AucBXEpZYOElXAIci4tn604Nc+n/3RDsPWS8BF9QdTwReTlRLchHxcv79EPATsheQKjso6V0A+fdDietJIiIO5i+mJ4B7qVBfSOokGyx+FBGP5qcr1xeD5VDlvgCIiNeAJ8j2qZ0jaUT+VOXeR+qymJcvL0dE9AM/oPx9MQtYIGkv2ZajOWR3tprWE+08ZP0WmJp/CmAk8Clga+KakpA0RtLZJx8Dc4HnG/9U6W0Frs0fXwtsSVhLMicHitwnqEhf5Psq7gP2RMSquqcq1RdD5VDFvpA0QdI5+eNRwOVke9QeBxbml5W+J2DILP5Y9wuIyPYhlbovImJZREyMiAvJZoidEfFpmtgTbf3HSPOPHd8FdADrI2JF4pKSkDSF7O4VwAhgU5WykPQgMJvsP6cfBL4O/BR4BJgE7Ac+GRGl3hQ+RA6zyZaEAtgLfP7knqQyk/RB4Emgl9pei6+S7UeqTF80yOEqKtYXki4h28TcQXaD4ZGIWJ6/fj5Etjz2HHBNfientBpksROYQLZktgu4oW6DfKlJmg18KSKuaGZPtPWQZWZmZjZctfNyoZmZmdmw5SHLzMzMrAAesszMzMwK4CHLzMzMrAAesszMzMwK4CHLzMzMrAAesszMzMwK4CHLzMzMrAD/AaOXENzTWc0pAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range(1, 40), error_rate, color = 'blue', linestyle = 'dashed', marker = 'o', markerfacecolor = 'red', markersize = 10)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[152   7]\n",
      " [ 13 128]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.96      0.94       159\n",
      "           1       0.95      0.91      0.93       141\n",
      "\n",
      "    accuracy                           0.93       300\n",
      "   macro avg       0.93      0.93      0.93       300\n",
      "weighted avg       0.93      0.93      0.93       300\n",
      "\n",
      "-----------------------------------------------------\n",
      "[[145  14]\n",
      " [ 12 129]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.91      0.92       159\n",
      "           1       0.90      0.91      0.91       141\n",
      "\n",
      "    accuracy                           0.91       300\n",
      "   macro avg       0.91      0.91      0.91       300\n",
      "weighted avg       0.91      0.91      0.91       300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "knn2 = KNeighborsClassifier(n_neighbors=20)\n",
    "knn2.fit(X_train, y_train)\n",
    "pred_new = knn2.predict(X_test)\n",
    "\n",
    "print(confusion_matrix(y_test, pred_new))\n",
    "print(classification_report(y_test, pred_new))\n",
    "\n",
    "print('-----------------------------------------------------')\n",
    "\n",
    "\n",
    "print(confusion_matrix(y_test, pred))\n",
    "print(classification_report(y_test, pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[152   7]\n",
      " [ 13 128]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.96      0.94       159\n",
      "           1       0.95      0.91      0.93       141\n",
      "\n",
      "    accuracy                           0.93       300\n",
      "   macro avg       0.93      0.93      0.93       300\n",
      "weighted avg       0.93      0.93      0.93       300\n",
      "\n",
      "-----------------------------------------------------\n",
      "\n",
      "\n",
      "[[145  14]\n",
      " [ 12 129]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.91      0.92       159\n",
      "           1       0.90      0.91      0.91       141\n",
      "\n",
      "    accuracy                           0.91       300\n",
      "   macro avg       0.91      0.91      0.91       300\n",
      "weighted avg       0.91      0.91      0.91       300\n",
      "\n",
      "-----------------------------------------------------\n",
      "\n",
      "\n",
      "[[152   7]\n",
      " [ 13 128]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.96      0.94       159\n",
      "           1       0.95      0.91      0.93       141\n",
      "\n",
      "    accuracy                           0.93       300\n",
      "   macro avg       0.93      0.93      0.93       300\n",
      "weighted avg       0.93      0.93      0.93       300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "knn3 = KNeighborsClassifier(n_neighbors=20)\n",
    "knn3.fit(X_train, y_train)\n",
    "pred_3 = knn2.predict(X_test)\n",
    "\n",
    "print(confusion_matrix(y_test, pred_3))\n",
    "print(classification_report(y_test, pred_3))\n",
    "\n",
    "print('-----------------------------------------------------')\n",
    "print('\\n')\n",
    "\n",
    "print(confusion_matrix(y_test, pred))\n",
    "print(classification_report(y_test, pred))\n",
    "\n",
    "print('-----------------------------------------------------')\n",
    "\n",
    "print('\\n')\n",
    "print(confusion_matrix(y_test, pred_new))\n",
    "print(classification_report(y_test, pred_new))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}