Spam_Email_Analysis_With_ML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "03f512fb",
   "metadata": {},
   "source": [
    "# Spam Email Analysis with Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f81a17c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn import svm\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13846c68",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam = pd.read_csv('../Spam-Email-and-Url-Detection-Using-Machine-Learning/data-set/spam.csv') # data set read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "40861b5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5567</th>\n",
       "      <td>spam</td>\n",
       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5568</th>\n",
       "      <td>ham</td>\n",
       "      <td>Will ü b going to esplanade fr home?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>ham</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>ham</td>\n",
       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>ham</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5572 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Category                                            Message\n",
       "0         ham  Go until jurong point, crazy.. Available only ...\n",
       "1         ham                      Ok lar... Joking wif u oni...\n",
       "2        spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3         ham  U dun say so early hor... U c already then say...\n",
       "4         ham  Nah I don't think he goes to usf, he lives aro...\n",
       "...       ...                                                ...\n",
       "5567     spam  This is the 2nd time we have tried 2 contact u...\n",
       "5568      ham               Will ü b going to esplanade fr home?\n",
       "5569      ham  Pity, * was in mood for that. So...any other s...\n",
       "5570      ham  The guy did some bitching but I acted like i'd...\n",
       "5571      ham                         Rofl. Its true to its name\n",
       "\n",
       "[5572 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6c6dafd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ac4b3ecc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5572</td>\n",
       "      <td>5572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>5157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>ham</td>\n",
       "      <td>Sorry, I'll call later</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4825</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Category                 Message\n",
       "count      5572                    5572\n",
       "unique        2                    5157\n",
       "top         ham  Sorry, I'll call later\n",
       "freq       4825                      30"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1bde2f41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category\n",
       "ham     4825\n",
       "spam     747\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam.groupby(spam['Category']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "425b5a1f",
   "metadata": {},
   "source": [
    "We have a total of 5572 data. There are %83 safe and %17 spam."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2cf65089",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam.Category = spam.Category.apply(lambda x: 1 if x == 'spam' else 0) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d671c7c",
   "metadata": {},
   "source": [
    "We changed the spam values in the Category column with 1 and the raw values with 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5504fa55",
   "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>Category</th>\n",
       "      <th>Message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Category                                            Message\n",
       "0         0  Go until jurong point, crazy.. Available only ...\n",
       "1         0                      Ok lar... Joking wif u oni...\n",
       "2         1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3         0  U dun say so early hor... U c already then say...\n",
       "4         0  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6dfe91e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "messages = spam.iloc[:,1] # Messages column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "900b35bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Go until jurong point, crazy.. Available only ...\n",
       "1                        Ok lar... Joking wif u oni...\n",
       "2    Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3    U dun say so early hor... U c already then say...\n",
       "4    Nah I don't think he goes to usf, he lives aro...\n",
       "Name: Message, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "64662414",
   "metadata": {},
   "outputs": [],
   "source": [
    "ifSpam = spam.iloc[:,0] # Spam column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "23df9359",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    0\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ifSpam.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b0dcf375",
   "metadata": {},
   "outputs": [],
   "source": [
    "messages_train, messages_test, ifSpam_train, ifSpam_test = train_test_split(messages, ifSpam, test_size=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1016599b",
   "metadata": {},
   "source": [
    "We will use 75% of our dataset for training and 25% for testing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ae43cab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = CountVectorizer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e55c5969",
   "metadata": {},
   "source": [
    "With CountVectorizer, text is analyzed and word counts are made and these are converted into vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7311d74b",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = cv.fit_transform(messages_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3b5c8372",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_test = cv.transform(messages_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4b83c13",
   "metadata": {},
   "source": [
    "# Learning and Predicts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "68193311",
   "metadata": {},
   "outputs": [],
   "source": [
    "knModel = KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b491c69b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knModel.fit(features, ifSpam_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "63a1489f",
   "metadata": {},
   "outputs": [],
   "source": [
    "knPredict = knModel.predict(features_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3dac1972",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtModel = tree.DecisionTreeClassifier() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4f2e97c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtModel.fit(features, ifSpam_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b14a8d29",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtPredict = dtModel.predict(features_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8e9ad035",
   "metadata": {},
   "outputs": [],
   "source": [
    "svModel = svm.SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6b88ebcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svModel.fit(features,ifSpam_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e5d83155",
   "metadata": {},
   "outputs": [],
   "source": [
    "svPredict = svModel.predict(features_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ed165b53",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfModel = RandomForestClassifier() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8b707454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfModel.fit(features, ifSpam_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "15f42743",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfPredict = rfModel.predict(features_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab6a2465",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f18c557c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix,plot_precision_recall_curve,plot_roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f05e4b5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualization(model):\n",
    "    predict = model.predict(features_test)\n",
    "    plot_confusion_matrix(model,features_test,ifSpam_test)\n",
    "    plot_precision_recall_curve(model,features_test,ifSpam_test)\n",
    "    plot_roc_curve(model,features_test,ifSpam_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86fa0048",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9ad5084",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "13a303b1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mislabeled out of a total of 1393 test entries: 85\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of mislabeled out of a total of %d test entries: %d\" % (features_test.shape[0], \n",
    "                                                                      (ifSpam_test != knPredict).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f5477fff",
   "metadata": {},
   "outputs": [],
   "source": [
    "successRate = 100.0 * f1_score(ifSpam_test, knPredict, average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "aee04454",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Success Rate was calculated as % : 93.89806173725772 with the K-Nearest-Neighbors\n"
     ]
    }
   ],
   "source": [
    "print(\"The Success Rate was calculated as % : \" + str(successRate) + \" with the K-Nearest-Neighbors\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bd2ae6ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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preX0G9MIVPXO4t5AX6AbYH0EcWRfUTEfbioIpnZu3eXk9Hfp0JzzBnViTFoqo3on0ybJcvqNaWy89hH8ATgf2AS8DNynqnt8jMvUUGmZsm57YbCT95OteygtU1oel8DIXslcNaYHWWmpdLOcfmMaPa81gk3ASFXN9zMYUzM7Cg+xZGM+i7Pz+CAnnz1uTn//Tm356Vgnp39wV8vpN8aEizZDWV9V/R+wAujqjjEUZDOUxVYgpz+Q3RPI6e/YuhkTLKffGONRtBrBTcDVRJ6pzGYoq2Oqyhff7nM/+PP5+MtdHHFz+of3cHL6s9JT6XO85fQbY7yLNkPZ1e7DM1W1KHSbiCT5FpUJ2nXgSLCDd0l2Hjv3Hc3pv3REN7LSUxnWvYPl9Btjqs1rH8GHQPmJ7COtMzV0pKSMT7buDn74r/vayelv18Idpz8tlTHpKZzQ1nL6jTG1I1ofwXeATkBzERmEM7wEQBughc+xNQqqylcFB1mcnRccpz80p/+m09MZYzn9xhgfRasRfBeYDnQGHgpZvw/4tU8xNXh7i4r5MMfN6c/OY9uuQwB07dCC8we7Of29kmltOf3GmDoQrY8gcEfxFFX9Zx3F1OCUuuP0B7J7wnP6U7h6jJPa2T2lZaxDNcY0QtGahqap6gtAdxG5qfx2VX0owmGGozn9i7LzWOrm9IsczenPSktlkOX0G2PqgWhNQ4GvqK2q8+QiMgl4BEgAnlbV30XY50LgHpx01DWqekl1zhVrh46U8vGXBcHsnuydTk7/8W2acfpJx5OVnsqpvVPo0NImdjPG1C/RmoaedH/fW9Undmc0mwWcAeQCK0RknqquD9knDfgVMNqd/rJjVc8TK6rK/77ZF8zuWb4lPKf/wswuZKWnkn58K8vpN8bUa1UZa2gmcAh4GzgF+IXbbFSRYUCOqm52n+MlYDKwPmSfHwOzVHU3gKrurPIV1KGC/Yf5ICefRe4NXXluTn/68a24bEQ3xqSnMrxHB5ISLaffGBM/vN5HMFFVbxWR84EtwPeBxUBlBUEnYFvIci4wvNw+6QAishSn+egeVX27/BOJyNU4dzjTtWvX8pt9E8jpDwzctm77XiAkpz89lay0VL7T1u6tM8bEL68FQWC/s4FXVbWwlpo7mgJpwDicFNXFItK//MimqjobmA2QmZmptXHiSFSVLQUHg9k9gZz+pk2EwV3bc/PEdMakpZJhOf3GmAbEa0EwX0T+h9M0dI2IpAJFUY7ZDnQJWe7srguVC3ysqsXAlyKyEadgWOExrhoL5PQ7s3Mdzenvluzk9GelpTLScvqNMQ2Y1xnKbnf7CQpVtVREDuC091dmBZAmIj1wCoCLgfIZQW8AU4G/ikgKTlPR5irEX2WlZcra3D0syXbm5P10m5PT36pZU0b2SubqrF5kpaXQLdly+o0xjYPXzuJEYBqQ5TYJLQKeqOwYVS0RkeuBd3Da/59V1c9FZAawUlXnudsmish6oBS4RVULqn01lTh4pIRfv/YZ732RR+Ghozn914ztRVZ6KoO6tiMxwXL6jTGNj6hGb3IXkaeBROA5d9WlQKmqXuVjbBFlZmbqypUrq3zcmm17mDxrKaf17ch5gzpZTr8xplERkVWqmhlpm9c+gqGqOiBk+b8isqbmodWdQHE3bURXTut7fExjMcaY+sRrW0ipiPQKLIhIT5ymHGOMMXHOa43gFuA9EdmMMxR1N+BHvkXlg0ATmGBpn8YYEypqQeCmihbi3CkcGALiC1U97Gdgxhhj6kalTUMichXwOfBnYDXQXVXXxmMhEOwStwqBMcaEiVYj+Dlwsqrmuf0CLwLzfI/KR1YOGGNMuGidxUdUNQ/AHTyumf8h+cNDlqwxxjRK0WoEnUXk0YqWVfVGf8Lyg9tZbENCG2NMmGgFwS3lllf5FYgxxpjY8DJncYMQaBqy+oAxxoSLljX0lIhkVLCtpYhcISI/9Cc0Y4wxdSFa09As4C4R6Q+sA/KAJJyhotsAz+JkEtV7gb5i6yIwxphw0ZqGVgMXikgrIBM4AWdOgg2q+oX/4dU+u7PYGGPCeZ2PYD/wvr+h+MvSR40xJrJGMwB/cKwhqxAYY0yYRlMQGGOMiaxKBYGItPArEL8FO4tjGoUxxtQ/ngoCERnlTif5P3d5gIg85mtkxhhj6oTXGsGfgO8CBQCqugbI8isoP6hVCYwxJiLPTUOquq3cqricoczSR40xJpzXGcq2icgoQEUkEfgZsMG/sGqfYvmjxhgTidcawU+B64BOwHZgIHCtTzH5ytJHjTEmnNcaQR9VDRtTSERGA0trPySfWIXAGGMi8loj+LPHdfWW9RUbY0xkldYIRGQkMApIFZGbQja1ARL8DMwYY0zdiNY0dBzQyt2vdcj6vcAFfgXlh+B8BNZJYIwxYaKNProIWCQic1T1qzqKyVdWDhhjTDivncUHReQB4GSc+QgAUNXTfInKB5Y+aowxkXntLH4RZ3iJHsC9wBZghU8x+coqBMYYE85rQZCsqs8Axaq6SFWvAOKmNgA2H4ExxlTEa9NQsft7h4icDXwNdPAnJH/YVJXGGBOZ1xrBTBFpC/wSuBl4Gvh5tINEZJKIfCEiOSJyeyX7TRERFZFMj/EYY4ypJV6nqpzvPiwExkPwzuIKiUgCMAs4A8gFVojIPFVdX26/1jhjF31ctdCrRm34UWOMiajSGoGIJIjIVBG5WUQy3HXniMiHwF+iPPcwIEdVN6vqEeAlYHKE/e4Dfg8UVT38qrOmIWOMCRetaegZ4CogGXhURF4AHgT+oKqDohzbCQgdujrXXRckIoOBLqr6r8qeSESuFpGVIrIyLy8vymkjs75iY4yJLFrTUCZwiqqWiUgS8A3QS1ULanpiEWkCPARMj7avqs4GZgNkZmbW6DPdKgTGGBMuWo3giKqWAahqEbC5CoXAdqBLyHJnd11AayADeF9EtgAjgHm+dRhblcAYYyKKViPoKyJr3ccC9HKXBVBVPaWSY1cAaSLSA6cAuBi4JLBRVQuBlMCyiLwP3KyqK6t8FR4E7iy2sYaMMSZctILgpOo+saqWiMj1wDs4I5U+q6qfi8gMYKWqzqvucxtjjKk90Qadq9FAc6q6AFhQbt1dFew7ribnih6L89vqA8YYE87z5PXGGGMapkZTEBydjyC2cRhjTH3juSAQkeYi0sfPYOqCWOOQMcaE8VQQiMj3gNXA2+7yQBGJq85eyx41xpjIvNYI7sEZMmIPgKquxpmbIG4ExhqypiFjjAnntSAodvP+Q9mXbGOMaQC8zkfwuYhcAiSISBpwI/Chf2HVPiu1jDEmMq81ghtw5is+DPwdZzjqn/sUkzHGmDrktUbQV1XvAO7wMxg/WfqoMcZE5rVG8EcR2SAi9wXmJYhXlj5qjDHhPBUEqjoeZ2ayPOBJEflMRO70NbJaZ70ExhgTiecbylT1G1V9FPgpzj0FEccMqq+sacgYYyLzekPZSSJyj4h8BvwZJ2Oos6+RGWOMqRNeO4ufBV4GvquqX/sYj2+CU9dbjcAYY8J4KghUdaTfgRhjjImNSgsCEXlFVS90m4RCe1u9zFBWrxydj8CqBMYYEypajeBn7u9z/A6krljTkDHGhKu0s1hVd7gPr1XVr0J/gGv9D6/2qKWPGmNMRF7TR8+IsO7M2gykrliFwBhjwkXrI7gG55t/TxFZG7KpNbDUz8Bqm1qFwBhjIorWR/B34C3gt8DtIev3qeou36LygaWPGmNMZNEKAlXVLSJyXfkNItIh3goDY4wxx/JSIzgHWIXzpTr0+7QCPX2Kq9ZpsG3IqgTGGBOq0oJAVc9xf8fVtJSVsaYhY4wJ53WsodEi0tJ9PE1EHhKRrv6GZowxpi54TR99HDgoIgOAXwKbgL/5FpWPrEJgjDHhvBYEJeo0sk8G/qKqs3BSSOOGpY8aY0xkXkcf3ScivwIuBcaISBMg0b+wal/gzmKxTgJjjAnjtUZwEc7E9Veo6jc4cxE84FtUxhhj6ozXqSq/AV4E2orIOUCRqj7va2S17Ojoo8YYY0J5zRq6EFgO/AC4EPhYRC7wcNwkEflCRHJE5PYI228SkfUislZE3hWRblW9gKqyliFjjAnntY/gDmCoqu4EEJFUYCHwj4oOEJEEYBbOgHW5wAoRmaeq60N2+xTIVNWD7rhGf8Bphqp11llsjDGRee0jaBIoBFwFHo4dBuSo6mZVPQK8hJN1FKSq76nqQXfxI+pgHmSbmMYYY8J5rRG8LSLvAHPd5YuABVGO6QRsC1nOBYZXsv+VOAPcHUNErgauBujatXr3sVmFwBhjIvM6Z/EtIvJ94FR31WxVfb22ghCRaUAmMLaC888GZgNkZmZW6zM9MNaQ9REYY0y4aPMRpAEPAr2Az4CbVXW7x+feDnQJWe7srit/jtNx+iDGquphj89tjDGmlkRr538WmA9MwRmB9M9VeO4VQJqI9BCR44CLgXmhO4jIIOBJ4NxyfRC1zpqGjDEmsmhNQ61V9Sn38Rci8onXJ1bVEhG5HngHSACeVdXPRWQGsFJV5+HclNYKeNW943erqp5b5auoAmsaMsaYcNEKgiT3W3vg47N56LKqVlowqOoCynUqq+pdIY9Pr3LE1WVVAmOMiShaQbADeChk+ZuQZQVO8yMoP9lYQ8YYEy7axDTj6yoQv6lVCYwxJiKvN5TFPRtryBhjIms0BYExxpjIGk1BEJy63qoExhgTxuvoo+LOVXyXu9xVRIb5G5o/bKwhY4wJ57VG8BgwEpjqLu/DGVk0btjoo8YYE5nXQeeGq+pgEfkUQFV3u3cLxx1rGjLGmHBeawTF7vwCCsH5CMp8i8oHlj5qjDGReS0IHgVeBzqKyG+AD4D7fYvKB5Y+aowxkXkdhvpFEVkFTMD5LD1PVTf4Gpkxxpg64akgEJGuwEHgzdB1qrrVr8BqW7BhyKoExhgTxmtn8b9wPksFSAJ6AF8AJ/sUl28sfdQYY8J5bRrqH7osIoOBa32JyC+WP2qMMRFV685id/jpyuYfrrcsfdQYY8J57SO4KWSxCTAY+NqXiHxi9QFjjInMax9B65DHJTh9Bv+s/XD8ZxUCY4wJF7UgcG8ka62qN9dBPL6xLgJjjIms0j4CEWmqqqXA6DqKxzfqlgQ2Q5kxxoSLViNYjtMfsFpE5gGvAgcCG1X1NR9j84UVA8YYE85rH0ESUIAzR3HgfgIF4qYgsJYhY4yJLFpB0NHNGFrH0QIgIC4/W61lKD4UFxeTm5tLUVFRrEMxJq4kJSXRuXNnEhMTPR8TrSBIAFoRuUUlrgoC6yyOL7m5ubRu3Zru3btbv44xHqkqBQUF5Obm0qNHD8/HRSsIdqjqjJqFVr/YEBPxoaioyAoBY6pIREhOTiYvL69Kx0W7s7jB/BdahSD+WCFgTNVV5/8mWkEwoXqh1D9qExIYY0xElRYEqrqrrgKpK/Yl03jVqlWr4OMFCxaQnp7OV199xT333EOLFi3YuXNnxH0rctZZZ7Fnz55K9xk3bhwrV648Zv2cOXO4/vrrvQdfBQ8++CB9+/Zl4MCBDB06lOeff77SWKpj5cqV3HjjjQAcPnyY008/nYEDB/Lyyy9z1VVXsX79+ho9/8MPPxyMG6CkpITU1FRuv/32sP26d+9Ofn5+cPn999/nnHPOCS6/9dZbZGZm0q9fPwYNGsQvf/nLGsUFsGrVKvr370/v3r258cYbj34pDfHAAw8wcOBABg4cSEZGBgkJCeza5Xz8PvLII2RkZHDyySfz8MMPB4+5+eab+e9//1vj+ADnm3I8/QwZMkSr46nFm7TbbfO18NCRah1v6tb69etjHYK2bNlSVVUXLlyovXr10pycHFVVvfvuu7VLly566623HrNvTY0dO1ZXrFhxzPq//vWvet1111XrOcvKyrS0tDTitscff1wnTpyohYWFqqpaWFioc+bMqTSWmlq2bJlOmDCh2seXlJSELRcXF2v//v21uLg4uG7BggU6atQo7dmzp5aVlQXXd+vWTfPy8oLL7733np599tmqqvrZZ59pz549dcOGDcHzPPbYY9WOM2Do0KG6bNkyLSsr00mTJumCBQsq3X/evHk6fvz4YEwnn3yyHjhwQIuLi3XChAmanZ2tqqpbtmzRM844I+JzRPr/AVZqBZ+r1Rp9NJ5ZhSD+3Pvm51z05LJa/bn3zc89nXvx4sX8+Mc/Zv78+fTq1Su4/oorruDll18OfmsL9cILLzBs2DAGDhzIT37yE0pLS4Hwb6P33Xcfffr04dRTT2Xq1Kk8+OCDweNfffVVhg0bRnp6OkuWLAmu37ZtG+PGjSMtLY177703uP6hhx4iIyODjIyM4DfGLVu20KdPHy677DIyMjLYtm0b06dPJyMjg/79+/OnP/0JgPvvv5/HH3+cNm3aANCmTRsuv/zyY67pmmuuITMzk5NPPpm77747uP7222+nX79+nHLKKdx8883B+DMyMhgwYABZWVnA0W/eO3fuZNq0aaxYsYKBAweyadOmsJrHv//9b0aOHMngwYP5wQ9+wP79+4Ov3W233cbgwYN59dVXw2L773//y+DBg2na9Gjuy9y5c/nZz35G165dWbZsWYR39lh/+MMfuOOOO+jbty8ACQkJXHPNNZ6OrciOHTvYu3cvI0aMQES47LLLeOONNyo9Zu7cuUydOhWADRs2MHz4cFq0aEHTpk0ZO3Ysr73m3L7VrVs3CgoK+Oabb2oUI3i/oSzuWfqoqarDhw9z3nnn8f777wc/HAJatWrFFVdcwSOPPBL2obxhwwZefvllli5dSmJiItdeey0vvvgil112WXCfFStW8M9//pM1a9ZQXFzM4MGDGTJkSHB7SUkJy5cvZ8GCBdx7770sXLgQgOXLl7Nu3TpatGjB0KFDOfvssxER/vrXv/Lxxx+jqgwfPpyxY8fSvn17srOzee655xgxYgSrVq1i+/btrFu3DoA9e/awd+9e9u3bR8+ePaO+Fr/5zW/o0KEDpaWlTJgwgbVr19KpUydef/11/ve//yEiwWavGTNm8M4779CpU6djmsI6duzI008/zYMPPsj8+fPDtuXn5zNz5kwWLlxIy5Yt+f3vf89DDz3EXXfdBUBycjKffPLJMbEtXbo07PUrKipi4cKFPPnkk+zZs4e5c+cyatSoqNe4bt06T01B7733Hr/4xS+OWd+iRQs+/PDDsHXbt2+nc+fOweXOnTuzffv2Cp/74MGDvP322/zlL38BICMjgzvuuIOCggKaN2/OggULyMzMDO4/ePBgli5dypQpU6LGXZlGUxAEWCZK/Ln7e7GZCC8xMZFRo0bxzDPP8Mgjjxyz/cYbb2TgwIHBb8IA7777LqtWrWLo0KEAHDp0iI4dO4Ydt3TpUiZPnkxSUhJJSUl873vfC9v+/e9/H4AhQ4awZcuW4PozzjiD5OTk4D4ffPABIsL5559Py5Ytg+uXLFnCueeeS7du3RgxYgQAPXv2ZPPmzdxwww2cffbZTJw4Mfht24tXXnmF2bNnU1JSwo4dO1i/fj39+vUjKSmJK6+8knPOOSfY1j569GimT5/OhRdeGLwWLz766CPWr1/P6NHO0GZHjhxh5MiRwe0XXXRRxON27NjBSSedFFyeP38+48ePp3nz5kyZMoX77ruPhx9+mISEhIj//1X9TBg/fjyrV6+u0jFevfnmm4wePZoOHToAcNJJJ3HbbbcxceJEWrZsycCBA0lISAju37FjR77+uuYzAvjaNCQik0TkCxHJEZHbI2xvJiIvu9s/FpHufsWilkBqqqhJkya88sorLF++nPvvv/+Y7e3ateOSSy5h1qxZwXWqyuWXX87q1atZvXo1X3zxBffcc0+VztusWTPAaZooKSkJri//gRXtAyxQOAC0b9+eNWvWMG7cOJ544gmuuuoq2rRpQ6tWrdi8eXOlz/Pll1/y4IMP8u6777J27VrOPvtsioqKaNq0KcuXL+eCCy5g/vz5TJo0CYAnnniCmTNnsm3bNoYMGUJBQYGn61ZVzjjjjOBrt379ep555pmI1xOqefPmYXegz507l4ULF9K9e/fg+QOdqsnJyezevTu4765du0hJSQHg5JNPZtWqVVHjfO+994Idu6E/kWodnTp1Ijc3N7icm5tLp06dKnzul156KdgsFHDllVeyatUqFi9eTPv27UlPTw9uKyoqonnz5lFjjsa3gsAdvnoWcCbQD5gqIv3K7XYlsFtVewN/An7vVzyWPWqqo0WLFvzrX//ixRdfDPtQCrjpppt48skngx/YEyZM4B//+Ecwo2jXrl189dVXYceMHj2aN998k6KiIvbv339ME0lF/vOf/7Br1y4OHTrEG2+8wejRoxkzZgxvvPEGBw8e5MCBA7z++uuMGTPmmGPz8/MpKytjypQpzJw5M9jE8qtf/YrrrruOvXv3ArB///6w7BuAvXv30rJlS9q2bcu3337LW2+9Fdy3sLCQs846iz/96U+sWbMGgE2bNjF8+HBmzJhBamoq27Zt83R9I0aMYOnSpeTk5ABw4MABNm7cGPW4k046KXjM3r17WbJkCVu3bmXLli1s2bKFWbNmMXfuXMDJhPrb3/4GQGlpKS+88ALjx48H4JZbbuH+++8PnrOsrIwnnnjimPMFagTlf8o3CwGccMIJtGnTho8++ghV5fnnn2fy5MkRr6OwsJBFixYdsz3wt7R161Zee+01LrnkkuC2jRs3kpGREfU1isbPpqFhQI6qbgYQkZeAyUBonthk4B738T+Av4iIuD3cvrCWIVNVHTp04O233yYrK4vU1NSwbSkpKZx//vnBztd+/foxc+ZMJk6cSFlZGYmJicyaNYtu3boFjxk6dCjnnnsup5xyCscffzz9+/enbdu2UeMYNmwYU6ZMITc3l2nTpgXbiqdPn86wYcMAuOqqqxg0aFBYkxI4bdU/+tGPKCsrA+C3v/0t4HQC79+/n6FDh5KYmEhiYuIx7eQDBgxg0KBB9O3bly5dugSbbvbt28fkyZMpKipCVXnooYcA5wM1OzsbVWXChAkMGDCARYsWRb2+1NRU5syZw9SpUzl8+DAAM2fODPsGHMmZZ57JpZdeCsDrr7/OaaedFqxVAUyePJlbb72Vw4cP83//939cc801DBgwAFVl0qRJTJs2DYBTTjmFhx9+mKlTp3Lw4EFEJCy1tLoee+wxpk+fzqFDhzjzzDM588wzAYKFzE9/+tNg7IEmoFBTpkyhoKAg+LfUrl07wBmPKycnJ6zPoNoqSieq6Q9wAfB0yPKlwF/K7bMO6ByyvAlIifBcVwMrgZVdu3atNPWqIv/+/Bu99oVVeuhISfSdTczVh/RRP+3bt09VVQ8cOKBDhgzRVatWxTii+Hbeeefpxo0bYx1GnXrttdf0zjvvjLitqumjcdFZrKqzgdkAmZmZ1aotnNHveM7od3ytxmVMdV199dWsX7+eoqIiLr/8cgYPHhzrkOLa7373O3bs2EFaWlqsQ6kzJSUltXLDG/jbNLQd6BKy3NldF2mfXBFpCrTFmffAmAbt73//e6xDaFD69OlDnz59Yh1GnfrBD35Qa8/lZ9bQCiBNRHqIyHHAxcC8cvvMAwJ3r1wA/NetwhgT8VZ8Y0zlqvN/41tBoKolwPXAO8AG4BVV/VxEZojIue5uzwDJIpID3AQck2JqGqekpCQKCgqsMDCmCtSdjyApKalKx0m8/aNlZmZqbQ2EZeovm6HMmOqpaIYyEVmlqhFTjOKis9g0PomJiVWaYckYU32NbtA5Y4wx4awgMMaYRs4KAmOMaeTirrNYRPKAr6LuGFkKkB91r4bFrrlxsGtuHGpyzd1UNTXShrgrCGpCRFZW1GveUNk1Nw52zY2DX9dsTUPGGNPIWUFgjDGNXGMrCGbHOoAYsGtuHOyaGwdfrrlR9REYY4w5VmOrERhjjCnHCgJjjGnkGmRBICKTROQLEckRkWNGNBWRZiLysrv9YxHpHoMwa5WHa75JRNaLyFoReVdEukV6nngS7ZpD9psiIioicZ9q6OWaReRC973+XETifuIDD3/bXUXkPRH51P37PisWcdYWEXlWRHaKyLoKtouIPOq+HmtFpOazGlU0dVm8/gAJOFNe9gSOA9YA/crtcy3whPv4YuDlWMddB9c8HmjhPr6mMVyzu19rYDHwEZAZ67jr4H1OAz4F2rvLHWMddx1c82zgGvdxP2BLrOOu4TVnAYOBdRVsPwt4CxBgBPBxTc/ZEGsEw4AcVd2sqkeAl4DJ5faZDDznPv4HMEEkrqe1j3rNqvqeqh50Fz/CmTEunnl5nwHuA34PNITxrL1c84+BWaq6G0BVd9ZxjLXNyzUr0MZ93Bb4ug7jq3WquhjYVckuk4Hn1fER0E5ETqjJORtiQdAJ2BaynOuui7iPOhPoFALJdRKdP7xcc6grcb5RxLOo1+xWmbuo6r/qMjAfeXmf04F0EVkqIh+JyKQ6i84fXq75HmCaiOQCC4Ab6ia0mKnq/3tUNh9BIyMi04BMYGysY/GTiDQBHgKmxziUutYUp3loHE6tb7GI9FfVPbEMymdTgTmq+kcRGQn8TUQyVLUs1oHFi4ZYI9gOdAlZ7uyui7iPiDTFqU4W1El0/vByzYjI6cAdwLmqeriOYvNLtGtuDWQA74vIFpy21Hlx3mHs5X3OBeaparGqfglsxCkY4pWXa74SeAVAVZcBSTiDszVUnv7fq6IhFgQrgDQR6SEix+F0Bs8rt8884HL38QXAf9XthYlTUa9ZRAYBT+IUAvHebgxRrllVC1U1RVW7q2p3nH6Rc1U1nuc59fK3/QZObQARScFpKtpchzHWNi/XvBWYACAiJ+EUBHl1GmXdmgdc5mYPjQAKVXVHTZ6wwTUNqWqJiFwPvIOTcfCsqn4uIjOAlao6D3gGp/qYg9Mpc3HsIq45j9f8ANAKeNXtF9+qqufGLOga8njNDYrHa34HmCgi64FS4BZVjdvarsdr/iXwlIj8AqfjeHo8f7ETkbk4hXmK2+9xN5AIoKpP4PSDnAXkAAeBH9X4nHH8ehljjKkFDbFpyBhjTBVYQWCMMY2cFQTGGNPIWUFgjDGNnBUExhjTyFlB0AiISKmIrA756V7Jvvtr4XxzRORL91yfuHd7VvU5nhaRfu7jX5fb9mFNY3SfJ/C6rBORN0WkXZT9B1ZnZEsROUFE5ruPx4lIoXveDSJydzWe79zAKJwicl7gdXKXZ7g3DtaI+x5eEGWf96tyg5577fM97Bdx9E0ReVBETvN6PuOdFQSNwyFVHRjys6UOznmLqg4Ebse5ka1KVPUqVV3vLv663LZRNQ8POPq6ZODcT3JdlP0H4uRvV9VNwFMhy0vc1yYTZ4ycKg0jrKrzVPV37uJ5OCNuBrbdpaoLqxFjfTIHiDRG0p9x/p5MLbOCoBESkVbizEnwiYh8JiLHjNrpfotdHPKNeYy7fqKILHOPfVVEWkU53WKgt3vsTe5zrRORn7vrWorIv0Rkjbv+Inf9+yKSKSK/A5q7cbzobtvv/n5JRM4OiXmOiFwgIgki8oCIrBBnvPafeHhZluEO3CUiw9xr/FREPhSRPu5drTOAi9xYLnJjf1ZElrv7Rhr9FGAK8Hb5lap6AFgF9HZrGx+58b4uIu3dWG6Uo/NIvOSumy4ifxGRUcC5wANuTL1CXoNJIvJqyGsT/DZe1fdQRO5yX8t1IjJbJGyk3ktD/kaGuft7fV0iqmj0TVX9CkgWke9U5fmMB7EYb9t+6vYH5w7T1e7P6zh3lLdxt6Xg3KEYuLlwv/v7l8Ad7uMEnLF7UnA+2Fu6628D7opwvjnABe7jHwAfA0OAz4CWOHc4fw4MwvmQfCrk2Lbu7/dx5w8IxBSyTyDG84Hn3MfH4YzI2By4GrjTXd8MWAn0iBDn/pDrexWY5C63AZq6j08H/uk+ng78JeT4+4Fp7uN2OOP6tCx3jh7AqpDlccB893EysAU4GVgLjHXXzwAedh9/DTQLnKN8HKGvdeiy+x5vDXmvHgemVfM97BCy/m/A90Leo6fcx1m44+dX9LqUu/ZM4OlK/ma7E2E8fpya1ZRY/081tJ8GN8SEieiQOk0RAIhIInC/iGQBZTjfhI8Hvgk5ZgXwrLvvG6q6WkTG4jRDLHW/FB6H8006kgdE5E6cMV+uxBkL5nV1vgUjIq8BY3C+Kf9RRH6P8yGxpArX9RbwiIg0w2lKWKyqh0RkInBKSBt3W5yB174sd3xzEVntXv8G4D8h+z8nImk4QxYkVnD+icC5InKzu5wEdHWfK+AEjh33ZoyIfIrz2v8OZ6C4dqq6yN3+HE7BBE4B8aKIvIEzjpAn6gzN8DbwPRH5B3A2cCvOqLNe38OA8SJyK9AC6IBTiL/pbpvrnm+xiLQRp5+lotclNL6VwFVeryfETuDEahxnKmEFQeP0QyAVGKKqxeKMzpkUuoP7j52F8wEyR0QeAnYD/1HVqR7OcYuq/iOwICITIu2kqhvdNvKzgJki8q6qzvByEapaJCLvA98FLsKZtAScmZtuUNV3ojzFIVUdKCItcMayuQ54FGcym/dU9XxxOtbfr+B4wfl2+kVl56Dca4vTR3BO8ElE2lZy/Nk437a/B9whIv0r2be8l4DrcZpZVqrqPrdZx+t7iIgkAY/h1M62icg9hF9P+TFqlApeFxE5vgqxVyQJ5zU1tcj6CBqntsBOtxAYDxwzf7E4cxp/q6pPAU/jTJ33ETBaRAJt/i1FJN3jOZcA54lICxFpidOss0RETgQOquoLOAPjReo4LXZrJpG8jDPoVqB2Ac6H+jWBY0Qk3T1nROrM3HYj8Es5Oix5YFjf6SG77sNpIgt4B7gh0GYuzgiv5W3EaeaokKoWArvF7YcBLgUWiTOnQhdVfQ+nCactTrNaqPIxhVqE83r+mKOFZFXfw8CHfr7bl1A+kyjQp3MqziiYhXh7XaorHYg4l6+pPisIGqcXgUwR+Qy4DPhfhH3GAWvcJoyLgEdUNQ/ng3GuiKzFaVLo6+WEqvoJTrvzcpw+g6dV9VOgP7DcbaK5G5gZ4fDZwFpxO4vL+TdOc8dCdaYyBKfgWg98Ik4K4pNEqf26sazFmeTkD8Bv3WsPPe49oF+gsxin5pDoxva5u1z+eQ8AmwIfvJW4HKc5bS1OdtIMnL6LF9z36VPgUT12gpmXgFvcTtle5c5dCswHznR/U9X30D3fUzgfvu/gNBmGKnJfpydwmgDBw+siTiLA05HOKc7om8uAPiKSKyJXuusTcRIP4nko8XrJRh81xmcicj5OM9ydsY4lnrmv42BV/b9Yx9LQWB+BMT5T1ddFJJ7nxK4vmgJ/jHUQDZHVCIwxppGzPgJjjGnkrCAwxphGzgoCY4xp5KwgMMaYRs4KAmOMaeT+H4Kq/im62g50AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualization(knModel)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d517bafc",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7ce69646",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mislabeled out of a total of 1393 test entries: 41\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of mislabeled out of a total of %d test entries: %d\" % (features_test.shape[0], \n",
    "                                                                      (ifSpam_test != rfPredict).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7c328cb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "successRate = 100.0 * f1_score(ifSpam_test, rfPredict, average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a936f762",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Success Rate was calculated as % : 97.05671213208902 with Random Forest\n"
     ]
    }
   ],
   "source": [
    "print(\"The Success Rate was calculated as % : \" + str(successRate) + \" with Random Forest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ed087163",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lTQSbR8QNwKqIeCIifgC4NGBm1gJkrRpalf5+R9IBwNvAZoUJyczMiilrieCXknoAZwBnAn8C/i/fTpL2k/SKpNmSzq5ju29LCkllGeMxM7NGknWoynvTycXASKh+sniDJLUFrgH2AeYDUyRNjoiZNbbrRtJ30fP1C93MzBpDnSUCSW0lHSHpTEkl6bIDJT0D/D7PsXcHZkfEGxGxEpgIHFTLdr8ALgFW1D98MzNrqHxVQzcAxwGbA1dJug24DPhNROySZ9+tgdyuq+eny6pJ2hXYJiLuq+tAko6XVC6pfOHChXlOa2Zm9ZGvaqgMGBwRayV1BN4FvhgRixp6YkltgCuAsfm2jYjxwHiAsrKyaOi5zczsv/KVCFZGxFqAiFgBvFGPJPAWsE3OfO90WZVuQAnwuKQ5wJeByW4wNjMrrnwlggGSpqfTAr6YzguIiBhcx75TgP6S+pEkgO8AR1atjIjFQM+qeUmPA2dGRHm9r8LMzDZavkQwcGMPHBGrJZ0CPEjSU+mNETFD0jigPCImb+yxzcys8eTrdK5BHc1FxP3A/TWWnb+BbUc05FxmZrZxMg9eb2ZmLZMTgZlZK5c5EUjqJGnHQgZjZmbFlykRSBoFVAAPpPOlktzYa2bWAmQtEVxI0mXERwARUUEyNoGZmTVzWRPBqvS+/1x+wtfMrAXIOh7BDElHAm0l9QdOBZ4pXFhmZlYsWUsEPyQZr/hT4M8k3VH/X4FiMjOzIspaIhgQEecC5xYyGDMzK76sJYLLJc2S9IuqcQnMzKxlyJQIImIkychkC4E/SnpZ0nkFjczMzIoi8wNlEfFuRFwFnEDyTEGtfQaZmVnzkvWBsoGSLpT0MnA1yR1DvQsamZmZFUXWxuIbgUnANyLi7QLGY2ZmRZYpEUTEsEIHYmZmTaPORCDpzog4PK0Syn2SOMsIZWZm1gzkKxGclv4+sNCBmJlZ06izsTgi3kknT4qIubk/wEmFD8/MzAot6+2j+9Sy7JuNGYiZmTWNfG0EJ5J8899O0vScVd2ApwsZmJmZFUe+NoI/A/8Efg2cnbN8SUR8ULCozMysaPIlgoiIOZJOrrlC0mZOBmZmzV+WEsGBwFSS20eVsy6A7QoUl5mZFUmdiSAiDkx/e1hKM7MWKmtfQ3tI6pJOHyXpCkl9ChuamZkVQ9bbR/8ALJM0BDgDeB24tWBRmZlZ0WRNBKsjIoCDgN9HxDUkt5CamVkzl7X30SWSzgGOBvaU1AZoX7iwzMysWLKWCMaQDFz/g4h4l2QsgksLFpWZmRVN1qEq3wVuB3pIOhBYERG3FDQyMzMriqx3DR0OvAAcBhwOPC/p0Az77SfpFUmzJZ1dy/rTJc2UNF3SI5K2re8FmJlZw2RtIzgX+FJELACQ1At4GLh7QztIagtcQ9Jh3XxgiqTJETEzZ7NpQFlELEv7NfoNSTWUmZkVSdY2gjZVSSC1KMO+uwOzI+KNiFgJTCS566haRDwWEcvS2efwOMhmZkWXtUTwgKQHgTvS+THA/Xn22RqYlzM/Hxhax/bHknRwtx5JxwPHA/Tp4+fYzMwaU9Yxi8+SdAjwlXTR+Ij4W2MFIekooAz46gbOPx4YD1BWVha1bWNmZhsn33gE/YHLgC8CLwNnRsRbGY/9FrBNznzvdFnNc3ydpA3iqxHxacZjm5lZI8lXz38jcC/wbZIeSK+ux7GnAP0l9ZPUAfgOMDl3A0m7AH8ERtdogzAzsyLJVzXULSKuT6dfkfRi1gNHxGpJpwAPAm2BGyNihqRxQHlETCZ5KK0rcJckgDcjYnS9r8LMzDZavkTQMf3WXjUOQafc+YioMzFExP3UaFSOiPNzpr9e74jNzKxR5UsE7wBX5My/mzMfwNcKEZSZmRVPvoFpRhYrEDMzaxpZHygzM7MWyonAzKyVcyIwM2vlsvY+qnSs4vPT+T6Sdi9saGZmVgxZSwTXAsOAI9L5JSQ9i5qZWTOXtdO5oRGxq6RpABHxYfq0sJmZNXNZSwSr0vEFAqrHI1hbsKjMzKxosiaCq4C/AVtI+hXwL+CigkVlZmZFk7Ub6tslTQX2Jule4uCImFXQyMzMrCgyJQJJfYBlwD25yyLizUIFZmZmxZG1sfg+kvYBAR2BfsArwE4FisvMzIoka9XQzrnzknYFTipIRGZmVlQb9WRx2v10XeMPm5lZM5G1jeD0nNk2wK7A2wWJyMzMiiprG0G3nOnVJG0Gf2n8cMzMrNjyJoL0QbJuEXFmEeIxM7Miq7ONQFK7iFgD7FGkeMzMrMjylQheIGkPqJA0GbgL+KRqZUT8tYCxmZlZEWRtI+gILCIZo7jqeYIAnAjMzJq5fIlgi/SOoUr+mwCqRMGishZh1apVzJ8/nxUrVjR1KGatRseOHenduzft27fPvE++RNAW6Mq6CaCKE4HVaf78+XTr1o2+ffsi1fYnZGaNKSJYtGgR8+fPp1+/fpn3y5cI3omIcQ0LzVqrFStWOAmYFZEkNt98cxYuXFiv/fI9Wez/YGsQJwGz4tqY/7l8iWDvjQvFzMyaizoTQUR8UKxAzAqhbdu2lJaWUlJSwqhRo/joo48a5bgTJkzglFNOaZRj9e3bl5133pnS0lJKS0t55plnGuW4NVVUVHD//fevs+yf//wnZWVlDBo0iF122YUzzjgDgAsvvJDLLrus0c49fPjw6umzzjqLnXbaibPOOovrrruOW265pUHHnjZtGscee+w6yw4++GC+/OUvr7Ns7Nix3H333ess69q1a/X0q6++yv7770///v3ZddddOfzww3nvvfcaFNsHH3zAPvvsQ//+/dlnn3348MMPa93uJz/5CSUlJZSUlDBp0qTq5Y8++ii77rorJSUlHHPMMaxevRqAe++9l/PPP79BseXaqE7nzJqLTp06UVFRQWVlJZttthnXXHNNU4dUq8cee4yKigoqKirW+dCsS9WHQlY1E0FlZSWnnHIKt912GzNnzqS8vJztt9++XsfMKje5jR8/nunTp3PppZdywgkn8L3vfS/zcWq75osuuohTTz21ev6jjz5i6tSpLF68mDfeeCPTcVesWMEBBxzAiSeeyGuvvcaLL77ISSedVO+69pouvvhi9t57b1577TX23ntvLr744vW2ue+++3jxxRepqKjg+eef57LLLuPjjz9m7dq1HHPMMUycOJHKykq23XZbbr75ZgAOOOAA7rnnHpYtW9ag+KpkfY7ArEF+fs8MZr79caMec9AXunPBqOxDYgwbNozp06cD8MILL3DaaaexYsUKOnXqxE033cSOO+7IhAkTmDx5MsuWLeP111/nW9/6Fr/5zW8AuOmmm/j1r3/NpptuypAhQ9hkk00AmDNnDj/4wQ94//336dWrFzfddBN9+vRh7NixdOrUiWnTprFgwQJuvPFGbrnlFp599lmGDh3KhAkTNhhrXcfs2LEj06ZNY4899uDkk0/m5JNPZuHChXTu3Jnrr7+eAQMGcNddd/Hzn/+ctm3b0qNHDx5++GHOP/98li9fzr/+9S/OOecc7rvvPs4991wGDBgAJKWnE088cb1Yrr/+esaPH8/KlSvZfvvtufXWW+ncufN653jyySeZMWMG3//+91m5ciVr167lL3/5C/3796dr164sXbqU0aNHs3TpUnbbbTfOOeccZs2aRdeuXTnzzDN5/fXXa72Wmtd8xRVXVMe2ZMkSpk+fzpAhQ6qX/fWvf2XUqFFsueWWTJw4kZ/+9Kd5/zb+/Oc/M2zYMEaNGlW9bMSIEXn3y+cf//gHjz/+OADHHHMMI0aM4JJLLllnm5kzZ7LXXnvRrl072rVrx+DBg3nggQcYOXIkHTp0YIcddgBgn3324de//jXHHnsskhgxYgT33nsvhx9+eIPjdInAWoU1a9bwyCOPMHr0aAAGDBjAU089xbRp0xg3btw6HxYVFRVMmjSJl19+mUmTJjFv3jzeeecdLrjgAp5++mn+9a9/MXPmzOrtf/jDH3LMMccwffp0vvvd767z7fTDDz/k2Wef5corr2T06NH86Ec/YsaMGbz88stUVFRUbzdy5EhKS0sZOnRo3mPOnz+fZ555hiuuuILjjz+eq6++mqlTp3LZZZdx0knJMCHjxo3jwQcf5KWXXmLy5Ml06NCBcePGMWbMGCoqKhgzZgyVlZXstttueV+7Qw45hClTpvDSSy8xcOBAbrjhhlrPAXDddddx2mmnUVFRQXl5Ob17917nWJMnT64upY0ZM2addRu6lprXnKu8vJySkpJ1lt1xxx0cccQRHHHEEdxxxx15rw/I/FosWbKkugqv5k/u30SV9957j6222gqAz3/+87VWNQ0ZMoQHHniAZcuW8f777/PYY48xb948evbsyerVqykvLwfg7rvvZt68edX7lZWV8dRTT2W6vnxcIrCiqM8398a0fPlySktLeeuttxg4cCD77LMPAIsXL+aYY47htddeQxKrVq2q3mfvvfemR48eAAwaNIi5c+fy/vvvM2LECHr16gXAmDFjePXVVwF49tln+etfk4fsjz76aH784x9XH2vUqFFIYuedd2bLLbdk552TMZ522mkn5syZQ2lpKZBUDfXs2bN6v7qOedhhh9G2bVuWLl3KM888w2GHHVa97tNPPwVgjz32YOzYsRx++OEccsghDXoNKysrOe+88/joo49YunQp3/jGNzZ4jmHDhvGrX/2K+fPnc8ghh9C/f/9M56jrWnKvuaZ33nmn+j2B5IP3tdde4ytf+QqSaN++PZWVlZSUlNR6N01977Dp1q3bOgm8PiTVer59992XKVOmMHz4cHr16sWwYcNo27Ytkpg4cSI/+tGP+PTTT9l3333XeQ222GIL3n67cUYDKGiJQNJ+kl6RNFvS2bWs30TSpHT985L6FjIea32qvn3OnTuXiKhuI/jZz37GyJEjqays5J577lnn6eeqKh9IqkvqWxefq+pYbdq0Wee4bdq02ejjdunSBYC1a9ey6aabVrctVFRUMGvWLCD5Zv7LX/6SefPmsdtuu7Fo0aL1jrPTTjsxderUvOcbO3Ysv//973n55Ze54IILql+r2s5x5JFHVn/r33///Xn00UczXVNd15J7zTV16tRpnffuzjvv5MMPP6Rfv3707duXOXPmVJcKNt9883Uaaz/44IPq5Jv1tahviWDLLbfknXfeAZKktcUWW9R63HPPPZeKigoeeughIqK6OmjYsGE89dRTvPDCC+y1117Vy4Hqas3GULBEkHZffQ3wTWAQcISkQTU2Oxb4MCK2B64ELsGsADp37sxVV13F5ZdfzurVq1m8eDFbb701QJ119VWGDh3KE088waJFi1i1ahV33XVX9brhw4czceJEAG6//Xb23HPPBseb5Zjdu3enX79+1bFEBC+99BIAr7/+OkOHDmXcuHH06tWLefPm0a1bN5YsWVK9/1lnncVFF11UXbJZu3Yt11133XrnWbJkCVtttRWrVq3i9ttvr15e2zneeOMNtttuO0499VQOOuig6jaZfOq6lroMHDiQ2bNnV8/fcccdPPDAA8yZM4c5c+YwderU6tdxxIgRTJo0iZUrVwLJ+z5y5EgAjjzySJ555hnuu+++6mM9+eSTVFZWrnO+qhJBbT+DBtX8eIPRo0dXN/DefPPNHHTQQetts2bNmupEPX36dKZPn86+++4LwIIFC4CkdHTJJZdwwgknVO/36quvrlcttrEKWSLYHZgdEW9ExEpgIlDzVTgIuDmdvhvYW34CyQpkl112YfDgwdxxxx38+Mc/5pxzzmGXXXbJ9M18q6224sILL2TYsGHsscceDBw4sHrd1VdfzU033cTgwYO59dZb+d3vftfgWLMe8/bbb+eGG25gyJAh7LTTTvzjH/8Akg/5nXfemZKSEoYPH86QIUMYOXIkM2fOpLS0lEmTJjF48GB++9vfcsQRRzBw4EBKSkpqvcvmF7/4BUOHDmWPPfaoblje0DnuvPNOSkpKKC0tpbKysl53BG3oWuoyYMAAFi9ezJIlS5gzZw5z585d57bRfv360aNHD55//nkOPPBA9txzT3bbbTdKS0t5+umnqxtuO3XqxL333svVV19N//79GTRoENdee+061U4b4+yzz+ahhx6if//+PPzww5x9dlIxUl5eznHHHQckfXLtueeeDBo0iOOPP57bbruNdu2SWvtLL72UgQMHMnjwYEaNGsXXvva16mM/9thjHHDAAQ2Kr4oiCtNlkKRDgf0i4rh0/mhgaESckrNNZbrN/HT+9XSb92sc63jgeIA+ffrsNnfu3HrH8/9mvMvfK97iisNL6dh+/bpGa3yzZs1a5wPTrBCuvPJKunXrVv3B2hq89957HHnkkTzyyCO1rq/tf0/S1Igoq237ZnHXUESMj4iyiCjb2Ay9706f59rv7uYkYNbCnHjiieu0v7QGb775JpdffnmjHa+Qdw29BWyTM987XVbbNvMltQN6kIx7YGaWSceOHTn66KObOoyi+tKXvtSoxytkiWAK0F9SP0kdgO8Ak2tsMxk4Jp0+FHg0ClVXZU3Cb6dZcW3M/1zBEkFErAZOAR4EZgF3RsQMSeMkjU43uwHYXNJs4HRgvVtMrfnq2LEjixYtcjIwK5Kq8Qg6duxYr/0K1lhcKGVlZVH1pJ19tnmEMrPi29AIZXU1FvvJYiuY9u3b12uUJDNrGs3iriEzMyscJwIzs1bOicDMrJVrdo3FkhYC9X+0ONETeD/vVi2Lr7l18DW3Dg255m0jotYncptdImgISeUbajVvqXzNrYOvuXUo1DW7asjMrJVzIjAza+VaWyIY39QBNAFfc+vga24dCnLNraqNwMzM1tfaSgRmZlaDE4GZWSvXIhOBpP0kvSJptqT1ejSVtImkSen65yX1bYIwG1WGaz5d0kxJ0yU9ImnbpoizMeW75pztvi0pJDX7Ww2zXLOkw9P3eoakPxc7xsaW4W+7j6THJE1L/773b4o4G4ukGyUtSEdwrG29JF2Vvh7TJe3a4JNGRIv6AdoCrwPbAR2Al4BBNbY5Cbgunf4OMKmp4y7CNY8EOqfTJ7aGa0636wY8CTwHlDV13EV4n/sD04DPpfNbNHXcRbjm8cCJ6fQgYE5Tx93Aa94L2BWo3MD6/YF/AgK+DDzf0HO2xBLB7sDsiHgjIlYCE4GDamxzEHBzOn03sLckFTHGxpb3miPisYhYls4+RzJiXHOW5X0G+AVwCdAS+sLOcs3/A1wTER8CRMSCIsfY2LJccwDd0+kewNtFjK/RRcSTwAd1bHIQcEskngM2lbRVQ87ZEhPB1sC8nPn56bJat4lkAJ3FwOZFia4wslxzrmNJvlE0Z3mvOS0ybxMR9xUzsALK8j7vAOwg6WlJz0nar2jRFUaWa74QOErSfOB+4IfFCa3J1Pf/PS+PR9DKSDoKKAO+2tSxFJKkNsAVwNgmDqXY2pFUD40gKfU9KWnniPioKYMqsCOACRFxuaRhwK2SSiJibVMH1ly0xBLBW8A2OfO902W1biOpHUlxclFRoiuMLNeMpK8D5wKjI+LTIsVWKPmuuRtQAjwuaQ5JXerkZt5gnOV9ng9MjohVEfEf4FWSxNBcZbnmY4E7ASLiWaAjSedsLVWm//f6aImJYArQX1I/SR1IGoMn19hmMnBMOn0o8GikrTDNVN5rlrQL8EeSJNDc640hzzVHxOKI6BkRfSOiL0m7yOiIaM7jnGb52/47SWkAST1JqoreKGKMjS3LNb8J7A0gaSBJIlhY1CiLazLwvfTuoS8DiyPinYYcsMVVDUXEakmnAA+S3HFwY0TMkDQOKI+IycANJMXH2SSNMt9puogbLuM1Xwp0Be5K28XfjIjRTRZ0A2W85hYl4zU/COwraSawBjgrIpptaTfjNZ8BXC/pRyQNx2Ob8xc7SXeQJPOeabvHBUB7gIi4jqQdZH9gNrAM+H6Dz9mMXy8zM2sELbFqyMzM6sGJwMyslXMiMDNr5ZwIzMxaOScCM7NWzomgFZC0RlJFzk/fOrZd2gjnmyDpP+m5Xkyf9qzvMf4kaVA6/dMa655paIzpcapel0pJ90jaNM/2pRvTs6WkrSTdm06PkLQ4Pe8sSRdsxPFGV/XCKengqtcpnR+XPjjYIOl7eGiebR6vzwN66bXfm2G7WnvflHSZpK9lPZ9l50TQOiyPiNKcnzlFOOdZEVEKnE3yIFu9RMRxETEznf1pjXXDGx4e8N/XpYTkeZKT82xfSnL/dn2dDlyfM/9U+tqUkfSRU69uhCNickRcnM4eTNLjZtW68yPi4Y2I8bNkAlBbH0lXk/w9WSNzImiFJHVVMibBi5JelrRer53pt9gnc74x75ku31fSs+m+d0nqmud0TwLbp/uenh6rUtL/pcu6SLpP0kvp8jHp8scllUm6GOiUxnF7um5p+nuipANyYp4g6VBJbSVdKmmKkv7a/zfDy/IsacddknZPr3GapGck7Zg+1ToOGJPGMiaN/UZJL6Tb1tb7KcC3gQdqLoyIT4CpwPZpaeO5NN6/SfpcGsup+u84EhPTZWMl/V7ScGA0cGka0xdzXoP9JN2V89pUfxuv73so6fz0tayUNF5ap6feo3P+RnZPt8/6utRqQ71vRsRcYHNJn6/P8SyDpuhv2z/F/SF5wrQi/fkbyRPl3dN1PUmeUKx6uHBp+vsM4Nx0ui1J3z09ST7Yu6TLfwKcX8v5JgCHptOHAc8DuwEvA11InnCeAexC8iF5fc6+PdLfj5OOH1AVU842VTF+C7g5ne5A0iNjJ+B44Lx0+SZAOdCvljiX5lzfXcB+6Xx3oF06/XXgL+n0WOD3OftfBByVTm9K0q9Plxrn6AdMzZkfAdybTm8OzAF2AqYDX02XjwN+m06/DWxSdY6aceS+1rnz6Xv8Zs579QfgqI18DzfLWX4rMCrnPbo+nd6LtP/8Db0uNa69DPhTHX+zfamlP36SktW3m/p/qqX9tLguJqxWyyOpigBAUnvgIkl7AWtJvglvCbybs88U4MZ0279HRIWkr5JUQzydfinsQPJNujaXSjqPpM+XY0n6gvlbJN+CkfRXYE+Sb8qXS7qE5EPiqXpc1z+B30nahKQq4cmIWC5pX2BwTh13D5KO1/5TY/9OkirS658FPJSz/c2S+pN0WdB+A+ffFxgt6cx0viPQJz1Wla1Yv9+bPSVNI3ntLybpKG7TiHgiXX8zSWKCJEHcLunvJP0IZRJJ1wwPAKMk3Q0cAPyYpNfZrO9hlZGSfgx0BjYjSeL3pOvuSM/3pKTuStpZNvS65MZXDhyX9XpyLAC+sBH7WR2cCFqn7wK9gN0iYpWS3jk75m6Q/mPvRfIBMkHSFcCHwEMRcUSGc5wVEXdXzUjau7aNIuLVtI58f+CXkh6JiHFZLiIiVkh6HPgGMIZk0BJIRm76YUQ8mOcQyyOiVFJnkr5sTgauIhnM5rGI+JaShvXHN7C/SL6dvlLXOajx2pK0ERxYfRCpRx37H0DybXsUcK6knevYtqaJwCkk1SzlEbEkrdbJ+h4iqSNwLUnpbJ6kC1n3emr2URNs4HWRtGU9Yt+QjiSvqTUitxG0Tj2ABWkSGAmsN36xkjGN34uI64E/kQyd9xywh6SqOv8uknbIeM6ngIMldZbUhaRa5ylJXwCWRcRtJB3j1dZwuiotmdRmEkmnW1WlC0g+1E+s2kfSDuk5axXJyG2nAmfov92SV3XrOzZn0yUkVWRVHgR+WFVnrqSH15peJanm2KCIWAx8qLQdBjgaeELJmArbRMRjJFU4PUiq1XLVjCnXEySv5//w3yRZ3/ew6kP//bQtoeadRFVtOl8h6QVzMdlel421A1DrWL628ZwIWqfbgTJJLwPfA/5dyzYjgJfSKowxwO8iYiHJB+MdkqaTVCkMyHLCiHiRpN75BZI2gz9FxDRgZ+CFtIrmAuCXtew+HpiutLG4hv9HUt3xcCRDGUKSuGYCLyq5BfGP5Cn9prFMJxnk5DfAr9Nrz93vMWBQVWMxScmhfRrbjHS+5nE/AV6v+uCtwzEk1WnTSe5OGkfSdnFb+j5NA66K9QeYmQiclTbKfrHGudcA9wLfTH9T3/cwPd/1JB++D5JUGeZakb5O15FUAUKG10XJjQB/qu2cSnrffBbYUdJ8Scemy9uT3HjQnLsS/0xy76NmBSbpWyTVcOc1dSzNWfo67hoRP2vqWFoatxGYFVhE/E1Scx4T+7OiHXB5UwfRErlEYGbWyrmNwMyslXMiMDNr5ZwIzMxaOScCM7NWzonAzKyV+/+8KUlxVaqGtAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualization(rfModel)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4154b82b",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8f89dd32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mislabeled out of a total of 1393 test entries: 45\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of mislabeled out of a total of %d test entries: %d\" % (features_test.shape[0], \n",
    "                                                                      (ifSpam_test != dtPredict).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b1798875",
   "metadata": {},
   "outputs": [],
   "source": [
    "successRate = 100.0 * f1_score(ifSpam_test, dtPredict, average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9f057b53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Success Rate was calculated as % : 96.76956209619526 with Decision Tree\n"
     ]
    }
   ],
   "source": [
    "print(\"The Success Rate was calculated as % : \" + str(successRate) + \" with Decision Tree\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "04d3215a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualization(dtModel)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bc92c8e",
   "metadata": {},
   "source": [
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bc83b358",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mislabeled out of a total of 1393 test entries: 28\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of mislabeled out of a total of %d test entries: %d\" % (features_test.shape[0], \n",
    "                                                                      (ifSpam_test != svPredict).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5c2f7197",
   "metadata": {},
   "outputs": [],
   "source": [
    "successRate = 100.0 * f1_score(ifSpam_test, svPredict, average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "81494f90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Success Rate was calculated as % : 97.98994974874373 with Support Vector Machine\n"
     ]
    }
   ],
   "source": [
    "print(\"The Success Rate was calculated as % : \" + str(successRate) + \" with Support Vector Machine\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d6d18817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualization(svModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0197bb1a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}