classification_on_wine_color.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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.model_selection import train_test_split , GridSearchCV , cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler , MinMaxScaler\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report , confusion_matrix , roc_curve , roc_auc_score \n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "white = pd.read_csv('winequality-white.csv' , delimiter = ';')\n",
    "red = pd.read_csv('winequality-red.csv' , delimiter = ';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "white.drop(['quality'] , axis = 1 , inplace=True)\n",
    "red.drop(['quality'] , axis = 1 , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "white['label'] = 1\n",
    "red['label'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "\n",
       "   alcohol  label  \n",
       "0      8.8      1  \n",
       "1      9.5      1  \n",
       "2     10.1      1  \n",
       "3      9.9      1  \n",
       "4      9.9      1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  label  \n",
       "0      9.4      0  \n",
       "1      9.8      0  \n",
       "2      9.8      0  \n",
       "3      9.8      0  \n",
       "4      9.4      0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine = pd.concat([white , red]).reset_index()\n",
    "del wine['index']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6497 entries, 0 to 6496\n",
      "Data columns (total 12 columns):\n",
      "fixed acidity           6497 non-null float64\n",
      "volatile acidity        6497 non-null float64\n",
      "citric acid             6497 non-null float64\n",
      "residual sugar          6497 non-null float64\n",
      "chlorides               6497 non-null float64\n",
      "free sulfur dioxide     6497 non-null float64\n",
      "total sulfur dioxide    6497 non-null float64\n",
      "density                 6497 non-null float64\n",
      "pH                      6497 non-null float64\n",
      "sulphates               6497 non-null float64\n",
      "alcohol                 6497 non-null float64\n",
      "label                   6497 non-null int64\n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 609.2 KB\n"
     ]
    }
   ],
   "source": [
    "wine.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = wine.drop(['label' , 'density' , 'citric acid' , 'chlorides' , 'pH' , 'sulphates' , 'alcohol'], axis = 1 ).values\n",
    "y = wine['label'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X , y  , test_size = 0.3 , random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_and_plot_k_neighbors(X_train, X_test, y_train, y_test):\n",
    "    \n",
    "    neighbors = np.arange(1, 10)\n",
    "    train_accuracy = np.empty(len(neighbors))\n",
    "    test_accuracy = np.empty(len(neighbors))\n",
    "    \n",
    "    for i, k in enumerate(neighbors):\n",
    "        knn = KNeighborsClassifier(n_neighbors= k)\n",
    "        knn.fit(X_train , y_train)\n",
    "        train_accuracy[i] = knn.score(X_train, y_train)    \n",
    "        test_accuracy[i] = knn.score(X_test, y_test)\n",
    "        \n",
    "    plt.title('k in kNN analysis')\n",
    "    plt.plot(neighbors, test_accuracy, label = 'Testing Accuracy')\n",
    "    plt.plot(neighbors, train_accuracy, label = 'Training Accuracy')\n",
    "    plt.legend()\n",
    "    plt.xlabel('Number of Neighbors')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kNN_algorithm(X_train , y_train , X_test , y_test):\n",
    "    \n",
    "    global y_pred_kNN\n",
    "    global kNN_pipeline\n",
    "    \n",
    "    steps = [('impute' , SimpleImputer(missing_values = 0, strategy='mean')),\n",
    "             ('sclaer', MinMaxScaler()),\n",
    "             ('kNN', KNeighborsClassifier(n_neighbors = 1))]\n",
    "    \n",
    "    kNN_pipeline = Pipeline(steps)\n",
    "    \n",
    "    kNN_pipeline.fit(X_train , y_train)\n",
    "    \n",
    "    y_pred_kNN = kNN_pipeline.predict(X_test)\n",
    "    \n",
    "    print(classification_report(y_test , y_pred_kNN))\n",
    "    print('kNN algorithm acuracy is : {0:.2f} %'.format(kNN_pipeline.score(X_test , y_test) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SVM_algorithm(X_test , y_test , X_train , y_train):\n",
    "    \n",
    "    global y_pred_SVM\n",
    "    global SVM_pipeline\n",
    "    global y_prob_SVM\n",
    "    \n",
    "    steps = [('scaler', StandardScaler()),\n",
    "             ('SVM', SVC(probability=True))]\n",
    "    \n",
    "    SVM_pipeline = Pipeline(steps)\n",
    "    \n",
    "    parameters = {'SVM__C':[1, 10, 100],\n",
    "                  'SVM__gamma':[0.1, 0.01]}\n",
    "    \n",
    "    cv = GridSearchCV(SVM_pipeline , param_grid = parameters)\n",
    "    \n",
    "    cv.fit(X_train , y_train)\n",
    "    \n",
    "    y_pred_SVM = cv.predict(X_test)\n",
    "    \n",
    "    y_prob_SVM = cv.predict_proba(X_test)\n",
    "    \n",
    "    print(\"Accuracy: {0:.2f} %\".format(cv.score(X_test, y_test) * 100))\n",
    "    print(classification_report(y_test, y_pred_SVM))\n",
    "    print(\"Tuned Model Parameters: {}\".format(cv.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cf_matrix , y_test):\n",
    "    \n",
    "    group_names = ['True Negative','False Positive','False Negative','True Positive']\n",
    "    group_counts = ['{0:0.0f}'.format(value) for value in cf_matrix.flatten()]\n",
    "    labels = ['{}\\n{}'.format(v1 ,v2) for v1, v2 in zip(group_names,group_counts)]\n",
    "    labels = np.asarray(labels).reshape(2,2)\n",
    "    \n",
    "    plt.figure(figsize=(8, 5))\n",
    "    sns.heatmap(\n",
    "        cf_matrix,\n",
    "        annot=labels,\n",
    "        cmap=sns.cubehelix_palette(100, as_cmap=True, hue=1, dark=0.30),\n",
    "        fmt='',\n",
    "        linewidths=1.5,\n",
    "        vmin=0,\n",
    "        vmax=len(y_test),\n",
    "    )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_AUC_ROC_kNN(X_test , y_test , pipeline):\n",
    "    \n",
    "    probs = pipeline.predict_proba(X_test)\n",
    "    preds = probs[:,1]\n",
    "    fpr, tpr, threshold = metrics.roc_curve(y_test, preds)\n",
    "    roc_auc = metrics.auc(fpr, tpr)\n",
    "    \n",
    "    plt.title('Receiver Operating Characteristic')\n",
    "    plt.plot(fpr, tpr, 'b', label = 'AUC = {0:.2f}'.format(roc_auc_score(y_test, preds)))\n",
    "    plt.legend(loc = 'lower right')\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "    plt.xlim([0, 1])\n",
    "    plt.ylim([0, 1])\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.show()\n",
    "    \n",
    "    print('ROC AUC score is ' + '{0:.2f}'.format(roc_auc_score(y_test, preds)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_AUC_ROC_SVM(X_test , y_test , y_prob_SVM):\n",
    "    \n",
    "    probs = y_prob_SVM\n",
    "    preds = probs[:,1]\n",
    "    fpr, tpr, threshold = metrics.roc_curve(y_test, preds)\n",
    "    roc_auc = metrics.auc(fpr, tpr)\n",
    "    \n",
    "    plt.title('Receiver Operating Characteristic')\n",
    "    plt.plot(fpr, tpr, 'b', label = 'AUC = {0:.2f}'.format(roc_auc_score(y_test, preds)))\n",
    "    plt.legend(loc = 'lower right')\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "    plt.xlim([0, 1])\n",
    "    plt.ylim([0, 1])\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.show()\n",
    "    \n",
    "    print('ROC AUC score is ' + '{0:.2f}'.format(roc_auc_score(y_test, preds)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.93      0.94       477\n",
      "           1       0.98      0.98      0.98      1473\n",
      "\n",
      "   micro avg       0.97      0.97      0.97      1950\n",
      "   macro avg       0.96      0.96      0.96      1950\n",
      "weighted avg       0.97      0.97      0.97      1950\n",
      "\n",
      "kNN algorithm acuracy is : 97.13 %\n"
     ]
    }
   ],
   "source": [
    "kNN_algorithm(X_train , y_train , X_test , y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf_matrix_knn = confusion_matrix(y_test, y_pred_kNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(cf_matrix_knn , y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GFLRqlfu3Th14911YvtySRAEpnSis2cmYFPbDD24a0pYt3dzVAH37QtWqgYYVjyxRGGNSz7vvuiqvb74J99wDHTsGHVFcS8lEYVOgGpPChg+HgQPdF8CCBW72OeuwDislO7N374Z9+yxRGJMyQov4nXoqNG8Ot98OFVLyK7DEfD2jEJFeIrJGRNaJyN2FPF9fRGaIyGIRWSYivf2MJ48NjTUmhWzc6DqnX3/dPR4xAu66y5JECfiWKESkPPAscB7QArhERFoUWO3PwNuq2g4YDDznVzyhLFEYkwJycuDpp10Rv7lz888qTIn5eUbREVinqhtU9VdgPNCvwDoKVPfu1wC2+BjPQVu8vdhcFMYkqdWr4Ywz4JZboFs3V6dp+PCgo0pYfp571QE2hzxOBzoVWOd+4FMRuQmoApxV2IZEZAQwAqB+/fplDsymQDUmya1b5wr5/fvfMGSIXTBVRkGPeroEeFVV6wK9gX+LyG9iUtUXVTVNVdNq165d5p1mZEDNmjbQwZiksmgRvPyyu9+3r+ubuOwySxJR4GeiyADqhTyu6y0LdRXwNoCqfglUBmr5GJMLzIbGGpM89u2Du++GTp3gr3/NL+JXvXr415mI+ZkoFgBNRaSRiFTEdVZPLrDOJuBMABFpjksUO3yMCbBEYUzSmDUL2rSBRx5xfRCLF1tTgQ98SxSqmg3cCHwCrMaNblopIg+KyAXearcD14jIUmAcMFzV/6EJliiMSQIZGXDmmZCdDZ99BmPHpnQpcD/5OpBYVacCUwss+0vI/VXAaX7GUNCBAzYFqjEJbflyOOUU90f8/vuu4muVKkFHldSC7syOua1b3XBqSxTGJJidO2HoUGjdOr+IX58+liRiIOUuTbSL7YxJMKrwzjtw442QmQn33ec6rk3MpFyisIvtjEkww4a56yHS0mDaNNfsZGIq5RKFnVEYkwBCi/h16+aam2691eozBSTl+igyMqBiRajl+9UaxphS2bABzjoLXn3VPb7qKrjjDksSAUrJRGFToBoTh3Jy4KmnXNPSggVQLuW+nuJWyqVou4bCmDi0ahVceSXMmwfnnw8vvAB16wYdlfGkXMq2RGFMHNq4Edavh7fegilTLEnEmZRKFDYFqjFxZMECGDPG3T//fNc3cckl1i4ch1IqUfz4I/zyiyUKYwL1yy+uc7pzZ3j44fwiftWqBRuXKVJKJQq7hsKYgM2c6Ya6Pv44XHONFfFLECnVmW3XUBgToPR0OPtsaNAApk93NZpMQkipMwpLFMYEYOlS92/dujBpEixbZkkiwaRkorCmJ2NiYMcOuPRSaNsWPv/cLevdG444Iti4TImlXNPT0UfD4YcHHYkxSUwVxo+Hm292I0geeAC6dAk6KlMGESUKb4a6+qq6zud4fGVDY42JgaFD4c03XYXXl16Cli2DjsiUUbFNTyJyPrAc+K/3uK2IvO93YH6wRGGMT3Jz8wv59egBTzwBc+ZYkkgSkfRRPAh0AnYDqOoSoImfQfnFEoUxPli3zk1J+sor7vFVV8GoUVC+fLBxmaiJJFEcUNXdBZb5Pq91tGVnw7Zt1pFtTNRkZ8Njj7kifosXu7LMJilF0kexWkQGAeVEpBFwMzDX37Ci7/vvbQpUY6JmxQq44gpYuBD69YPnnrNfYUkskjOKG4EOQC7wHrAfuMXPoPxg11AYE0WbNsF337nRTe+/b0kiyUVyRnGuqt4F3JW3QEQuxCWNhGGJwpgymjfPXTw3YoS7HmLDBqhaNeioTAxEckbx50KW3RPtQPxmicKYUtq7F267zV0L8Y9/wP79brkliZRR5BmFiJwL9ALqiMgTIU9VxzVDJZSMDDjsMJsC1ZgSmT7dFe/bsAGuvx7+/neoVCnoqEyMhWt62g6sALKAlSHL9wB3+xmUH/KmQLXZFY2JUHo6nHsuNGrkSnB07Rp0RCYgRSYKVV0MLBaRN1U1K4Yx+cKuoTAmQosXQ7t2rojflCnQrZvVvUlxkfy+riMi40VkmYiszbv5HlmUWaIwphjbtsHFF0P79vlF/Hr1siRhIkoUrwKvAAKcB7wN/MfHmHyxZYuN4DOmUKrwxhvQogVMnAgPPQSnnhp0VCaORJIojlDVTwBUdb2q/hmXMBLGTz/Bzz/bGYUxhbr0UlfI7+STYckSuOceN/LDGE8k11HsF5FywHoRuQ7IABJqclsbGmtMAbm5IOJu55zjhr7ecIPVZzKFiuSMYhRQBVe64zTgGuBKP4OKNksUxoRYu9ZVeH35Zff4iivc3BGWJEwRij2jUNV53t09wFAAEUmor1xLFMbgivg98QTcdx9Urmyd1CZiYc8oROR3ItJfRGp5j1uKyOvAvHCvizeWKEzKW7YMOneGu+6C886DVatc34QxESgyUYjIw8CbwBDgYxG5H5gBLAVOikl0UZKRAUcdZT+gTApLT4fNm+Gdd+Ddd+H444OOyCSQcE1P/YA2qrpPRI4GNgOnqOqGSDcuIr2AfwLlgbGq+vdC1hkE3I+b42Kpqkb9Z45dQ2FS0v/+584krrsuv4hflSpBR2USULimpyxV3Qegqj8Aa0uYJMoDz+KG0rYALhGRFgXWaQr8EThNVVsCt5Yw/ojYNRQmpfz8M9xyC5x+Ojz+eH4RP0sSppTCnVGcKCJ5pcQFaBTyGFW9sJhtdwTW5SUXERmPO0tZFbLONcCzqprpbXN7CeOPSEYGtGrlx5aNiTOffurKgG/a5Ia7/t//WRE/U2bhEsWAAo+fKeG26+Caq/Kk4+beDnUSgIjMwTVP3a+qHxfckIiMAEYA1K9fv0RBZGe72e2s6ckkvc2b4fzzoXFjmDXLnVEYEwXhigJOi9H+mwLdgbrALBE5peAc3ar6IvAiQFpaWonm6962zV1bZInCJK1Fi6BDB6hXD6ZOhTPOcMNfjYkSP4tuZwD1Qh7X9ZaFSgcmq+oBVd0IrMUljugFYUNjTbL6/nu46CJIS8sv4nf22ZYkTNT5mSgWAE1FpJGIVAQGA5MLrDMRdzaBd63GSUDEHeaRsERhko4qvPaaK+I3ZYrrh7AifsZHkdR6AkBEKqnq/kjXV9VsEbkR+ATX//Cyqq4UkQeBhao62XvuHBFZBeQAd6rqrpK9hfAsUZikM3gwvP02nHYajB0LzZoFHZFJcsUmChHpCLwE1ADqi0gb4GpVvam416rqVGBqgWV/CbmvwG3ezRcZGVChAtSu7dcejImB0CJ+vXu7foiRI23KRhMTkXzKngb6ALsAVHUp0MPPoKIpI8NdhGp/TyZhff21m4b0pZfc42HD4MYb7UNtYiaST1o5Vf2uwLIcP4Lxw5Yt1uxkEtSBA67/oU0bV5upatWgIzIpKpI+is1e85N6V1vfhBudlBAyMqBly6CjMKaElixx5b+XLIGBA+Ff/4Ljjgs6KpOiIjmjuB7Xh1Af2AZ09pYlBKvzZBLS99+727vvukJ+liRMgCI5o8hW1cG+R+KDPXvczRKFSQizZ7sifiNHQq9esH49HHFE0FEZE9EZxQIRmSoiw0TEpkA1Jtr27HGd02ecAU89lV/Ez5KEiRPFJgpVbQw8BHQAlovIRBFJiDMMSxQm7n3yiatY+dxzruLrV19ZET8TdyIaX6eq/1PVm4H2wE+4CY3iniUKE9c2b4Y+fdyZw+zZ7mzCRjaZOFRsohCRqiIyRESmAPOBHUBC1AvISxQ2F4WJG6owf767X68efPQRLF5sJThMXIvkjGIFbqTTP1S1iarerqoJMWf2li1Qo4bN12LixNatMGAAdOqUX8TvrLOsiJ+Je5GMejpRVXN9j8QHNjTWxAVVePVVuO02yMqCRx5xdZqMSRBFJgoReVxVbwfeFZHfzAERwQx3gbNEYeLCoEEwYYIb1TR2LJx0UtARGVMi4c4o/uP9W9KZ7eJGRoarxGxMzOXkuAJ+5cpB377Qsydce63VZzIJqchPrap6PW40V9VpoTegeWzCK72cHJsC1QRk9Wp39pBXxO/yy+H66y1JmIQVySf3ykKWXRXtQKJt2zaXLCxRmJg5cAAeegjatoU1a9xICmOSQLg+iotxs9I1EpH3Qp6qBuwu/FXxw66hMDG1eDEMH+5KcFx8MTz9NBxzTNBRGRMV4foo5uPmoKgLPBuyfA+w2M+gosEShYmpbdtg506YOBH69Qs6GmOiqshEoaobgY3AZ7ELJ3q2bHH/2sV2xjezZsHy5XDDDa6I37p1cPjhQUdlTNQV2UchIp97/2aKyA8ht0wR+SF2IZZORgaUL29n/8YHP/3kKrx26+aamPKK+FmSMEkqXGd23nSntYDaIbe8x3EtbwrU8uWDjsQklalT3UxYo0e7C+isiJ9JAeGGx+ZdjV0PKK+qOUAX4Fog7oti2MV2Juo2b3b9DzVqwP/+B48/bvVhTEqIZHjsRNw0qI2BV4CmwFu+RhUFlihMVKjC3Lnufr168Omn7iyiU6dg4zImhiJJFLmqegC4EPiXqo4C4v4r2BKFKbMtW6B/f+jSJb+IX48eULFisHEZE2ORJIpsEbkIGAp84C07zL+Qyu7nn11/oyUKUyqqriZTixbuDOKxx6yIn0lpkVSPvRIYiSszvkFEGgHj/A2rbOwaClMmAwfCe++5UU1jx0KTJkFHZEygik0UqrpCRG4GmohIM2Cdqv7N/9BKzyYsMiUWWsSvf3845xy45hqrz2QMkc1wdwawDngJeBlYKyJxfR6ed7GdnVGYiKxY4ZqW8or4DR1qlV6NCRHJX8KTQG9VPU1VTwXOB/7pb1hlY01PJiK//goPPADt28P69XDUUUFHZExciqSPoqKqrsp7oKqrRSSuh31kZED16jZPvQlj0SJXxG/FCrj0UnjqKagd99eRGhOISBLFVyLyAvCG93gIcV4U0IbGmmLt2gW7d8OUKdCnT9DRGBPXIkkU1wE3A3/wHn8B/Mu3iKLAEoUp1IwZrojfzTe7zupvvoHKlYOOypi4F7aPQkROAXoB76vqBd7tUVXNik14pWOJwhzixx9d53TPnvD88/lF/CxJGBORcNVj/4Qr3zEE+K+IFDbTXdzJyYGtWy1RGM+UKe7CubFj4Y47XN+EFfEzpkTCNT0NAVqr6l4RqQ1MxQ2PjWvbt9sUqMazeTMMGADNmrkJhX73u6AjMiYhhWt62q+qewFUdUcx68YNm7Aoxam6yq6QX8Rv4UJLEsaUQbgv/xNF5D3v9j7QOOTxe2Fed5CI9BKRNSKyTkTuDrPeABFREUkr6RsoyK6hSGHp6XDBBe7iubwift27WxE/Y8ooXNPTgAKPnynJhkWkPG6u7bOBdGCBiEwOvSbDW68acAswryTbL4olihSUmwtjxsCdd0J2NjzxBJx+etBRGZM0ws2ZPa2M2+6Iqwu1AUBExgP9gFUF1vsr8AhwZxn3B+RPgXrssdHYmkkIAwa4PoiePV3COPHEoCMyJqn42e9QB9gc8jidAvNYiEh7oJ6qfhhuQyIyQkQWisjCHTt2hN1pRgYcd5xNgZr0srPdmQS4RDFmDHz2mSUJY3wQWAe1iJQDngBuL25dVX1RVdNUNa12MWUW7BqKFLBsmZtMaMwY9/iyy+Dqq131V2NM1EWcKESkpIPPM3Dzbeep6y3LUw1oBcwUkW+BzsDksnZoW6JIYvv3w333QYcO8N13VpvJmBiJpMx4RxFZDnzjPW4jIpGU8FgANBWRRl4RwcHA5LwnVfVHVa2lqg1VtSEwF7hAVReW5o3ksUSRpBYscFVeH3wQLrkEVq+GCy8MOipjUkIkZxRPA32AXQCquhToUdyLVDUbuBH4BFgNvK2qK0XkQRG5oPQhF23vXletwa6hSEKZmW6O26lT4fXXoWbNoCMyJmVEUhSwnKp+J4e2/+ZEsnFVnYq7ojt02V+KWLd7JNsMxyYsSjLTp7sifrfc4or4rV1r5TeMCUAkZxSbRaQjoCJSXkRuBdb6HFep2DUUSWL3bjcN6ZlnwujR+UX8LEkYE4hIEsX1wG1AfWAbrtP5ej+DKi1LFElg0iRXxO/ll+EPf7AifsbEgWKbnlR1O64jOu5ZokhwmzbBRRdB8+YweTKklbmiizEmCopNFCIyBtCCy1V1hC8RlUFGBlSr5m4mQajC7NlwxhlQv7698461AAAWAElEQVS7aK5zZ6vPZEwciaTp6TNgmnebAxwD7PczqNKyobEJZtMmOP986No1v4hf166WJIyJM5E0Pf0n9LGI/BuY7VtEZWCJIkHk5sILL8Bdd7kziqeftiJ+xsSx0pTwaATEZcm9jAy7hiIhXHgh3HCDK8OxYgXcdJMV5zImjkXSR5FJfh9FOeAHoMi5JYKSm2tToMa17GwoV87dLr4Y+vWD4cOtPpMxCSBsohB3lV0b8ms05arqbzq248GOHe67yBJFHFq6FK680l0bcd11rgSHMSZhhG168pLCVFXN8W5xmSTAhsbGpaws+POf3TDX9HRX/90Yk3Ai6aNYIiLtfI+kjCxRxJn586FdO/jb32DIEFfEr3//oKMyxpRCkU1PIlLBK+zXDjeN6XpgLyC4k432MYoxIpYo4sxPP8G+ffDxx3DuuUFHY4wpg3B9FPOB9oAvlV6jLSPD9ZPaFKgB+vRTWLkSRo2Cs86CNWus/IYxSSBcohAAVV0fo1jKJG8K1AqR1MM10ZWZCbfdBq++Ci1bwsiRLkFYkjAmKYT7Wq0tIrcV9aSqPuFDPKVmF9sF5L333DURO3bAH/8If/mLJQhjkky4RFEeqIp3ZhHvtmyBxo2DjiLFbNoEgwdDq1ZuQqF2cT/mwRhTCuESxVZVfTBmkZRRRoYrE2R8pgqzZkG3bq6I3/Tp0KkTHHZY0JEZY3wSbnhsQpxJgBtck5lpTU++++47OO886N49v4jf6adbkjAmyYVLFGfGLIoysqGxPsvNhWeecR3Vs2fDv/7lyoIbY1JCkU1PqvpDLAMpC0sUPuvfH6ZMcddDjB4NDRoEHZExJoaSYjCpJQofHDjgKrqWK+dqMw0cCEOHWhE/Y1JQacqMxx1LFFH21VfQsaObMwJcorj8cksSxqSopEkUVatC9epBR5Lg9u1z10J07Ajffw/16gUdkTEmDiRN05NNWFRGc+fCsGGwdq0rCf7YY3DUUUFHZYyJA0mRKLZssWanMtu71/VL/Pe/rk6TMcZ4kqbpyRJFKXz8MTz+uLt/5pnw9deWJIwxv5HwiSI3184oSmzXLtfMdN558Npr8OuvbnnFisHGZYyJSwmfKHbudC0mligioAoTJkCLFvDWW272uQULLEEYY8JK+D4KGxpbAps2waWXQuvWbu6INm2CjsgYkwAS/ozCEkUxVF3hPnBXVM+c6UY4WZIwxkTIEkUy27gRzjnHdVTnFfE79VSb3ckYUyJJkShEbArUQ+TkwD//6eaJmDcPnn/eivgZY0ot4X9abtnikoRVug7Rrx98+CH07u3KcNgV1saYMkj4RGHXUHhCi/gNHerqM116qdVnMsaUma9NTyLSS0TWiMg6Ebm7kOdvE5FVIrJMRKaJSInrV1uiABYuhLQ018QEcPHFMGSIJQljTFT4lihEpDzwLHAe0AK4RERaFFhtMZCmqq2BCcA/SrqflE4U+/bBXXe5qUh37LB5IowxvvDzjKIjsE5VN6jqr8B4oF/oCqo6Q1V/8R7OBeqWZAf79sEPP6RoovjySzfE9R//cEX8Vq2CPn2CjsoYk4T87KOoA2wOeZwOdAqz/lXAR4U9ISIjgBEA9evXP7h8yxZvR6mYKPbtc/VLPvvMDX81xhifxEVntohcBqQB3Qp7XlVfBF4ESEtL07zlKXcNxdSpsHIl3Hkn9OwJq1fbcC9jjO/8bHrKAELHZdb1lh1CRM4C7gEuUNX9JdpBqiSKnTvhssvg/PPhzTfzi/hZkjDGxICfiWIB0FREGolIRWAwMDl0BRFpB4zGJYntJd1BXqJI2kmLVGH8eGjeHN5+G+67D+bPtyJ+xpiY8q3pSVWzReRG4BOgPPCyqq4UkQeBhao6GXgUqAq8I24o5yZVvSDSfWzZAkccATVq+PAG4sGmTa4ceJs28NJLcMopQUdkjElBvvZRqOpUYGqBZX8JuV+mWXLyhsYm1eUCqjBtmptAqEEDV6Ppd79zF9MZY0wAErrWU9JdQ7F+vRvBdPbZ+UX8One2JGGMCZQliniQkwNPPOGalhYtgtGjrYifMSZuxMXw2NJQTaIpUPv2hY8+chfMPf881C3RdYfGGOOrhE0UO3e6UaIJmyh+/dXNC1GuHAwf7gr5DR6cZB0uxphkkLBNTwl9DcX8+dChAzz3nHs8aJCr9mpJwhgThxI+USTUNRS//AK33w5dukBmJjRuHHRExhhTrIRtekq4Ok+zZ7trIjZsgGuvhUceSeILQIwxySRhE0XeFKjHHx90JBHKm1hoxgzo3j3oaIwxJmIJnSiOOSbOyx1NmeIK9/3hD9CjhysFXiFhD7kxJkUldB9F3DY77djhpiG94AIYNy6/iJ8lCWNMArJEEU2q8NZbrojfhAnw4IMwb54V8TPGJDRLFNG0aRNccQU0aQKLF8O991qSMMYkvIRMFFlZsGtXnCSK3Fz45BN3v0ED+OILmDMHWrYMNi5jjImShEwUeUNjA7+G4ptv3ExzvXrBrFluWceOVsTPGJNUEjpRBHZGkZ0Njz4KrVvDkiVurggr4meMSVIJOQwn8PIdffq45qZ+/VwZjsBPbYyJTwcOHCA9PZ2srKygQ0kZlStXpm7duhwWxWsHLFFEav9+d9FGuXJw9dVw5ZVw0UVWn8mYMNLT06lWrRoNGzZE7G/Fd6rKrl27SE9Pp1GjRlHbbkI2PWVkwOGHw5FHxmiHc+dC+/bw7LPu8cCBrpCfffCNCSsrK4uaNWtakogREaFmzZpRP4NL2EQRkylQ9+6FUaPg1FNhzx5o2tTnHRqTfCxJxJYfxzthm558b3b64gtXxG/jRhg5Eh5+GKpX93mnxhgTfxL6jMJX2dmuT+Lzz12TkyUJYxLWxIkTERG+/vrrg8tmzpxJnz59Dllv+PDhTJgwAXAd8XfffTdNmzalffv2dOnShY8++qjMsTz88MM0adKEk08+mU/yrsEqYPr06bRv355WrVoxbNgwsrOzD4m7bdu2tGzZkm7dupU5nkgkZKLwbQrUiRPdmQO4In4rV0LXrj7syBgTS+PGjeP0009n3LhxEb/m3nvvZevWraxYsYKvvvqKiRMnsmfPnjLFsWrVKsaPH8/KlSv5+OOPGTlyJDk5OYesk5uby7Bhwxg/fjwrVqygQYMGvPbaawDs3r2bkSNHMnnyZFauXMk777xTpngilXBNT9nZbgBSVEekbtsGN90E77zjOq1vv92V3rAifsZEza23usuOoqltW3jqqfDr/Pzzz8yePZsZM2bQt29fHnjggWK3+8svvzBmzBg2btxIpUqVADj22GMZNGhQmeKdNGkSgwcPplKlSjRq1IgmTZowf/58unTpcnCdXbt2UbFiRU466SQAzj77bB5++GGuuuoq3nrrLS688ELq168PwDHHHFOmeCKVcGcUBw64f6NyRqEK//43tGgBkybB3/7mRjhZfSZjksakSZPo1asXJ510EjVr1mTRokXFvmbdunXUr1+f6hE0OY8aNYq2bdv+5vb3v//9N+tmZGRQr169g4/r1q1LRt54f0+tWrXIzs5m4cKFAEyYMIHNmzcDsHbtWjIzM+nevTsdOnTg9ddfLza+aEi4n8x5Fbujkig2bXLXRKSluaurmzWLwkaNMYUp7pe/X8aNG8ctt9wCwODBgxk3bhwdOnQocnRQSUcNPfnkk2WOseD+x48fz6hRo9i/fz/nnHMO5b2yQNnZ2SxatIhp06axb98+unTpQufOnQ+effgl4RJFmc8o8or4nXeeK+I3Zw60a2f1mYxJQj/88APTp09n+fLliAg5OTmICI8++ig1a9YkMzPzN+vXqlWLJk2asGnTJn766adizypGjRrFjBkzfrN88ODB3H333Ycsq1OnzsGzA3AXJNYp5MusS5cufPHFFwB8+umnrF27FnBnIDVr1qRKlSpUqVKFrl27snTpUt8TBaqaULfjj++goLp/v5bcmjWqZ5yhCqozZ5ZiA8aYkli1alWg+x89erSOGDHikGVdu3bVzz//XLOysrRhw4YHY/z222+1fv36unv3blVVvfPOO3X48OG63/uy2b59u7799ttlimfFihXaunVrzcrK0g0bNmijRo00Ozv7N+tt27ZNVVWzsrK0Z8+eOm3aNFV1x7Nnz5564MAB3bt3r7Zs2VKXL1/+m9cXdtyBhVrK792E7KM45pgSdiNkZ8Mjj7gifsuXwyuv2GgmY1LAuHHj+P3vf3/IsgEDBjBu3DgqVarEG2+8wRVXXEHbtm0ZOHAgY8eOpUaNGgA89NBD1K5dmxYtWtCqVSv69OkTUZ9FOC1btmTQoEG0aNGCXr168eyzzx5sVurduzdbvIqnjz76KM2bN6d169b07duXnj17AtC8eXN69epF69at6dixI1dffTWtWrUqU0yREJdoEkeNGmnauPFCvvqqBC8691z49FO48EJ3TcRxx/kWnzEm3+rVq2nevHnQYaScwo67iCxS1bTSbC8h+ygi6p/IynIXzJUvDyNGuNuAAb7HZ4wxySYhm56KvYZizhw3wDqviN+AAZYkjDGmlBIuUWRnhzmj+PlnuPlmN4lQVhbYKa8xgUu05u1E58fxTrhEAUUkis8/h1at4Jln4MYbYcUKOPvsmMdmjMlXuXJldu3aZckiRtSbj6Jy5cpR3W7C9VFAmDOKI45wVV9POy2m8RhjCle3bl3S09PZsWNH0KGkjLwZ7qIpsRPFe+/B11/Dn/4E3bq5oa924ZwxceOwww6L6kxrJhi+Nj2JSC8RWSMi60Tk7kKeryQi//GenyciDSPZbt0K37tZ5gYMgPffz6/rYUnCGGOizrdEISLlgWeB84AWwCUi0qLAalcBmaraBHgSeKS47dZiF0ee2hw++MCVBP/f/6yInzHG+MjPM4qOwDpV3aCqvwLjgX4F1ukHvObdnwCcKcVU5KrPd0irVrB0Kdx9t7tWwhhjjG/87KOoA2wOeZwOdCpqHVXNFpEfgZrAztCVRGQEMMJ7uF9mz15hlV4BqEWBY5XC7Fjks2ORz45FvpNL+8KE6MxW1ReBFwFEZGFpL0NPNnYs8tmxyGfHIp8di3wisrC0r/Wz6SkDqBfyuK63rNB1RKQCUAPY5WNMxhhjSsjPRLEAaCoijUSkIjAYmFxgncnAMO/+QGC62pU5xhgTV3xrevL6HG4EPgHKAy+r6koReRBXF30y8BLwbxFZB/yASybFedGvmBOQHYt8dizy2bHIZ8ciX6mPRcKVGTfGGBNbCVnryRhjTOxYojDGGBNW3CYKv8p/JKIIjsVtIrJKRJaJyDQRaRBEnLFQ3LEIWW+AiKiIJO3QyEiOhYgM8j4bK0XkrVjHGCsR/I3UF5EZIrLY+zvpHUScfhORl0Vku4isKOJ5EZGnveO0TETaR7Th0k627ecN1/m9HjgRqAgsBVoUWGck8IJ3fzDwn6DjDvBY9ACO8O5fn8rHwluvGjALmAukBR13gJ+LpsBi4Cjv8TFBxx3gsXgRuN673wL4Nui4fToWXYH2wIoinu8NfAQI0BmYF8l24/WMwpfyHwmq2GOhqjNU9Rfv4VzcNSvJKJLPBcBfcXXDsmIZXIxFciyuAZ5V1UwAVd0e4xhjJZJjoUB1734NYEsM44sZVZ2FG0FalH7A6+rMBY4UkeOL2268JorCyn8UnIXikPIfQF75j2QTybEIdRXuF0MyKvZYeKfS9VT1w1gGFoBIPhcnASeJyBwRmSsivWIWXWxFcizuBy4TkXRgKnBTbEKLOyX9PgESpISHiYyIXAakAd2CjiUIIlIOeAIYHnAo8aICrvmpO+4sc5aInKKquwONKhiXAK+q6uMi0gV3/VYrVc0NOrBEEK9nFFb+I18kxwIROQu4B7hAVffHKLZYK+5YVANaATNF5FtcG+zkJO3QjuRzkQ5MVtUDqroRWItLHMkmkmNxFfA2gKp+CVTGFQxMNRF9nxQUr4nCyn/kK/ZYiEg7YDQuSSRrOzQUcyxU9UdVraWqDVW1Ia6/5gJVLXUxtDgWyd/IRNzZBCJSC9cUtSGWQcZIJMdiE3AmgIg0xyWKVJyfdTJwuTf6qTPwo6puLe5Fcdn0pP6V/0g4ER6LR4GqwDtef/4mVb0gsKB9EuGxSAkRHotPgHNEZBWQA9ypqkl31h3hsbgdGCMio3Ad28OT8YeliIzD/Tio5fXH3AccBqCqL+D6Z3oD64BfgCsi2m4SHitjjDFRFK9NT8YYY+KEJQpjjDFhWaIwxhgTliUKY4wxYVmiMMYYE5YlChN3RCRHRJaE3BqGWbdhUZUyS7jPmV710aVeyYuTS7GN60Tkcu/+cBE5IeS5sSLSIspxLhCRthG85lYROaKs+zapyxKFiUf7VLVtyO3bGO13iKq2wRWbfLSkL1bVF1T1de/hcOCEkOeuVtVVUYkyP87niCzOWwFLFKbULFGYhOCdOXwhIl95t1MLWaeliMz3zkKWiUhTb/llIctHi0j5YnY3C2jivfZMbw6D5V6t/0re8r9L/hwgj3nL7heRO0RkIK7m1pvePg/3zgTSvLOOg1/u3pnHM6WM80tCCrqJyPMislDc3BMPeMtuxiWsGSIyw1t2joh86R3Hd0SkajH7MSnOEoWJR4eHNDu97y3bDpytqu2Bi4GnC3nddcA/VbUt7os63SvXcDFwmrc8BxhSzP77AstFpDLwKnCxqp6Cq2RwvYjUBH4PtFTV1sBDoS9W1QnAQtwv/7aqui/k6Xe91+a5GBhfyjh74cp05LlHVdOA1kA3EWmtqk/jSmr3UNUeXimPPwNnecdyIXBbMfsxKS4uS3iYlLfP+7IMdRjwjNcmn4OrW1TQl8A9IlIXeE9VvxGRM4EOwAKvvMnhuKRTmDdFZB/wLa4M9cnARlVd6z3/GnAD8AxurouXROQD4INI35iq7hCRDV6dnW+AZsAcb7slibMirmxL6HEaJCIjcH/Xx+Mm6FlW4LWdveVzvP1UxB03Y4pkicIkilHANqAN7kz4N5MSqepbIjIPOB+YKiLX4mbyek1V/xjBPoaEFhAUkaMLW8mrLdQRV2RuIHAj0LME72U8MAj4GnhfVVXct3bEcQKLcP0T/wIuFJFGwB3A71Q1U0RexRW+K0iA/6rqJSWI16Q4a3oyiaIGsNWbP2AorvjbIUTkRGCD19wyCdcEMw0YKCLHeOscLZHPKb4GaCgiTbzHQ4HPvTb9Gqo6FZfA2hTy2j24sueFeR8309gluKRBSeP0CtrdC3QWkWa42dv2Aj+KyLHAeUXEMhc4Le89iUgVESns7MyYgyxRmETxHDBMRJbimmv2FrLOIGCFiCzBzUvxujfS6M/ApyKyDPgvrlmmWKqahauu+Y6ILAdygRdwX7ofeNubTeFt/K8CL+R1ZhfYbiawGmigqvO9ZSWO0+v7eBxXFXYpbn7sr4G3cM1ZeV4EPhaRGaq6Azcia5y3ny9xx9OYIln1WGOMMWHZGYUxxpiwLFEYY4wJyxKFMcaYsCxRGGOMCcsShTHGmLAsURhjjAnLEoUxxpiw/h+kNcqzG5RBCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC score is 0.96\n"
     ]
    }
   ],
   "source": [
    "plot_AUC_ROC_kNN(X_test , y_test , kNN_pipeline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/sklearn/model_selection/_split.py:2052: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 96.77 %\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.92      0.93       477\n",
      "           1       0.97      0.98      0.98      1473\n",
      "\n",
      "   micro avg       0.97      0.97      0.97      1950\n",
      "   macro avg       0.96      0.95      0.96      1950\n",
      "weighted avg       0.97      0.97      0.97      1950\n",
      "\n",
      "Tuned Model Parameters: {'SVM__C': 10, 'SVM__gamma': 0.1}\n"
     ]
    }
   ],
   "source": [
    "SVM_algorithm(X_test , y_test , X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf_matrix_svm = confusion_matrix(y_test, y_pred_SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(cf_matrix_svm , y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC score is 0.99\n"
     ]
    }
   ],
   "source": [
    "plot_AUC_ROC_SVM(X_test , y_test , y_prob_SVM)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}