Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('tasnim.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocesing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "875"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('stopwords.txt',  encoding=\"utf-8\") as stopwords_file:\n",
    "    stopwords = stopwords_file.readlines()\n",
    "len(stopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\User\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1054"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "nltk_stopwords = nltk.corpus.stopwords.words('english')\n",
    "stopwords.extend(nltk_stopwords)\n",
    "len(stopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hazm\n",
    "from hazm import word_tokenize\n",
    "\n",
    "stemmer = hazm.Stemmer()\n",
    "dataset = pd.DataFrame(columns=('title_body', 'category'))\n",
    "for index, row in df.iterrows():\n",
    "    title_body = str(row['title'] )+ ' ' +str( row['body'])\n",
    "    title_body_tokenized = word_tokenize(title_body)\n",
    "    title_body_tokenized_filtered = [w for w in title_body_tokenized if not w in stopwords]\n",
    "    title_body_tokenized_filtered_stemmed = [stemmer.stem(w) for w in title_body_tokenized_filtered]\n",
    "    dataset.loc[index] = {\n",
    "        'title_body': ' '.join(title_body_tokenized_filtered_stemmed),\n",
    "        'category': row['category'].replace('\\n', '')\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### category mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "انواع موضوعات موجود در ستون category:\n",
      "سیاسی\n",
      "اجتماعی\n",
      "ورزشی\n",
      "فرهنگی هنری\n",
      "استان‌ها\n",
      "اقتصادی\n",
      "بین الملل\n",
      "رسانه ها\n"
     ]
    }
   ],
   "source": [
    "unique_categories = df['category'].unique()\n",
    "\n",
    "print(\"انواع موضوعات موجود در ستون category:\")\n",
    "for category in unique_categories:\n",
    "    print(category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_mapping = {\n",
    "    'سیاسی': 1,\n",
    "    'اجتماعی': 2,\n",
    "    'ورزشی': 3,\n",
    "    'فرهنگی هنری': 4,\n",
    "    'استان‌ها': 5,\n",
    "    'بین الملل': 6,\n",
    "    'رسانه ها': 7,\n",
    "}\n",
    "\n",
    "# تبدیل مقادیر ستون category به اعداد\n",
    "dataset['category'] = dataset['category'].map(category_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['category']=df['category']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vectorize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "vectorizer = TfidfVectorizer()\n",
    "dataset['category'] = dataset['category'].fillna(-1)\n",
    "x= vectorizer.fit_transform(dataset['title_body'])\n",
    "y = dataset['category']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
    "\n",
    "def calculate_metrics(y_train, y_test, y_pred_train, y_pred_test):\n",
    "    acc_train = accuracy_score(y_true=y_train, y_pred=y_pred_train)\n",
    "    acc_test = accuracy_score(y_true=y_test, y_pred=y_pred_test)\n",
    "    precision = precision_score(y_true=y_test, y_pred=y_pred_test, average='weighted')\n",
    "    recall = recall_score(y_true=y_test, y_pred=y_pred_test, average='weighted')\n",
    "    \n",
    "    print(f\"accuracy train: {acc_train}   accuracy test: {acc_test}    precision: {precision}    recall: {recall}\")\n",
    "    return acc_train, acc_test, precision, recall\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "multinomial naive bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy train: 0.788305935623585   accuracy test: 0.7766753287660446    precision: 0.7945534585420253    recall: 0.7766753287660446\n"
     ]
    }
   ],
   "source": [
    "model = MultinomialNB()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred_train = model.predict(X_train)\n",
    "y_pred_test = model.predict(X_test)\n",
    "\n",
    "acc_train_NB,acc_test_NB,precision_NB,recall_NB = calculate_metrics(y_train,y_test,y_pred_train,y_pred_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy train: 0.9524165764346885   accuracy test: 0.8985746909205449    precision: 0.8981343951400242    recall: 0.8985746909205449\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "model = SVC(kernel='linear')\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred_train = model.predict(X_train)\n",
    "y_pred_test = model.predict(X_test)\n",
    "\n",
    "acc_train_SVM,acc_test_SVM,precision_SVM,recall_SVM = calculate_metrics(y_train,y_test,y_pred_train,y_pred_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy train: 0.9984053548577616   accuracy test: 0.8541617450192929    precision: 0.8532379182473668    recall: 0.8541617450192929\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model = RandomForestClassifier(n_estimators= 128 , max_depth= 128 , min_samples_split=3)\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred_train = model.predict(X_train)\n",
    "y_pred_test = model.predict(X_test)\n",
    "\n",
    "acc_train_RF,acc_test_RF,precision_RF,recall_RF = calculate_metrics(y_train,y_test,y_pred_train,y_pred_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1- Train Accuracy\n",
    "\n",
    "2- Test Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vw37SqFEjpaSk6K+//sr2euzYsUO///67OnfubG/r3Lmz4uPjtXLlyuvOu2LFChUsWFB9+vSxt7m4uNjPFqfJje9tdl27nEaNGunUqVNKSEhwaH/kkUcczrYHBwdL+vff7Y477kjXnnbszMp+AgC5hdANALmgdOnSGQ6u9Mcff6ht27by8fGRt7e3/Pz87GEvK/cN3nXXXQ7v0wL41aE5M1999ZXq1aunU6dOaf/+/dq/f79q166t5ORkzZ8/397vwIEDqlKlynUHOjpw4IBKlSrl8MM7N5QvXz5d2+nTpzVw4ED5+/urcOHC8vPzs/dL22YnT55UQkKC7r333usuv0iRImrVqpVmzZplb4uKilLp0qX18MMP37C+kiVLauLEiYqNjdWePXv00Ucfyc/PT8OHD9eUKVMkyX7f+/VqSQtXVapUSTetWrVqio+PV2JiokP7tdtm3759sixLlSpVkp+fn8Nr165d9kHQypcvr/DwcH3xxRfy9fVVaGioJkyYkO37VNNqv3Z7tWzZUqmpqemCY2b7QnakPZbt6vCUU/v27dOKFSvSbcO0AQbTtmOaa/8d9u/fL8uyNGzYsHTLSPuj1LXLyMl+nJmvvvpKnp6eqlChgn3/dnd3V7ly5Rz+yJSRv/76SyVLlkw3wN21I9bnxvc2u7K6za7tlxamrx7x/ur2tPlzez8BgOvhnm4AyAVXn51Nc/bsWTVu3Fje3t4aNWqUKlasKHd3d23dulWDBw/O0iPCXF1dM2y3MrlXMs2+ffvsA65VqlQp3fSoqCj7vZS5JbMz3tcOGne1jLZbhw4dFBMTo1deeUW1atWSl5eXUlNT1bx582w9Vi0sLEzz589XTEyMAgMD9c033+i5556Ti0vW/+5ss9lUuXJlVa5cWY8++qgqVaqkqKgo9e7d+6bryaprt01qaqpsNpu+++67DL8XVz++7P3331f37t21ZMkSff/993rhhRc0duxY/fLLL7rzzjuzVc/Vo4tL/14JkJqamu7fJKN/0+zauXOnpPRhMCdSU1PVtGlTvfrqqxlOr1y5ssP7jP4dJOnll19WaGhohsu4tt7s7seZsSxLs2fPVmJiYqajtl+4cMEpj7TLrX//rG6zzPplZX4T+wkAZITQDQCGrF27VqdOndLChQv14IMP2tsPHTpk/LOjoqJUsGBBzZw5M92Pz59//lkfffSRjhw5orvuuksVK1bUxo0bdfny5UwHZ6tYsaJWrlyp06dPZ3q2O+1M1LUjWt/MJbRnzpxRdHS0Ro4cqeHDh9vb9+3b59DPz89P3t7e9lB2Pc2bN5efn5+ioqIUHBysixcvqmvXrlmu6VoVKlRQ0aJFFRsbK+n/BoDauXNnpo9jK1u2rCRpz5496abt3r1bvr6+N3wkWMWKFWVZlsqXL58uGGYkMDBQgYGBGjp0qGJiYtSwYUNNmjTpph8plVb71aNtS9Lly5clpT/TmJtmzpwpSZmG2+yoWLGiLly4kO1H51WoUEHSv48uy83H793MbRrr1q3T33//rVGjRqlatWoO086cOaO+fftq8eLFmd5CUbZsWa1Zsybd49z279+frp+Us+9tZnJyW0puyq39BACuh8vLAcCQtLB79ZmV5ORkffrpp8Y/OyoqSo0aNVLHjh3Vvn17h9crr7wiSfbHZbVr107x8fH65JNP0i0nrfZ27drJsiyNHDky0z7e3t7y9fVNd0/szaxvRttMUroRpV1cXNSmTRt9++239keWZVSTJBUoUECdO3fWvHnzNH36dAUGBtpH5b6ejRs3prt0VpI2bdqkU6dO2S+5ve+++1S+fHmNHz8+3R8c0uooWbKkatWqpRkzZjj02blzp77//nu1bNnyhvU88cQTcnV11ciRI9NtH8uy7I9US0hI0JUrVxymBwYGysXFRUlJSTf8nGuFhITIZrNp7dq1Dp+7YMECSdLjjz9+08vMilmzZumLL75Q/fr19cgjj+Tacjt06KANGzZkeN/z2bNn0227a5UoUUJNmjTRZ599Zv/Dy9Wu/eNEVnl6eqb7/mQm7dLyV155Jd3+3adPH/uVGJkJDQ3V5cuXNXnyZHtbamqqJkyY4NAvN763mbmZ9TUht/cTALgeznQDgCENGjRQ0aJF1a1bN73wwguy2WyaOXNmti8pzaqNGzdq//796t+/f4bTS5curfvuu09RUVEaPHiwwsLC9OWXXyo8PFybNm1So0aNlJiYqB9++EHPPfecWrdurYceekhdu3bVRx99pH379tkv9f7pp5/00EMP2T+rd+/eeuutt9S7d2/VqVNHP/74o/bu3Zvl2r29vfXggw/qnXfe0eXLl1W6dGl9//33GV4dMGbMGH3//fdq3Lix+vbtq2rVqik2Nlbz58/Xzz//7DCoWVhYmD766COtWbMm3SOHMjNz5kxFRUWpbdu2CgoKUqFChbRr1y5NnTpV7u7u9udPu7i4aOLEiWrVqpVq1aqlHj16qGTJktq9e7f++OMPe7h799131aJFC9WvX1+9evWyP3rJx8fH4XnYmalYsaLeeOMNRURE6PDhw2rTpo3uuOMOHTp0SIsWLVLfvn318ssva/Xq1erfv7+efPJJVa5cWVeuXLFf8XD1oHpZ5efnp/r16ysmJkbNmzfX448/rj179uirr76St7f3DQeky4qvv/5aXl5eSk5O1rFjx7Ry5UqtX79eNWvWdBh/IDe88sor+uabb/TYY4+pe/fuCgoKUmJion7//Xd9/fXXOnz4cLpL6a81YcIEPfDAAwoMDFSfPn1UoUIFxcXFacOGDfr777+1ffv2m64rKChIEydO1BtvvKG7775bJUqUyHDcgaSkJC1YsEBNmzZN96z4NI8//rg+/PBDnThxQiVKlEg3vU2bNqpXr55eeukl7d+/X1WrVtU333yj06dPS3I8C53T7+311veHH37QuHHjVKpUKZUvX94+2FleyO39BACuh9ANAIYUL15cS5cu1UsvvaShQ4eqaNGievrpp/XII4/k6uWy10o7w9WqVatM+7Rq1UojRozQjh07VKNGDS1fvlxvvvmmZs2apQULFqh48eL2UJFm2rRpqlGjhqZMmaJXXnlFPj4+qlOnjsMzgYcPH66TJ0/q66+/1rx589SiRQt99913Gf7wz8ysWbM0YMAATZgwQZZlqVmzZvruu+9UqlQph36lS5fWxo0bNWzYMEVFRSkhIUGlS5dWixYt0g0QFRQUpHvuuUe7du1Sly5dslTHM888Iw8PD0VHR2vJkiVKSEiQn5+fmjVrpoiICNWuXdveNzQ0VGvWrNHIkSP1/vvvKzU1VRUrVnQYHTokJEQrVqxQZGSkhg8froIFC6px48Z6++23szz41JAhQ1S5cmV98MEH9qsOypQpo2bNmtnPONesWVOhoaH69ttvdezYMXl4eKhmzZr67rvv7KPAX7hwweFS4kOHDmnbtm32QaRGjx6tM2fO6Msvv5T075nVKlWqaPPmzVqzZo097E2dOjVLz4u/kX79+kmS3N3d5evrq1q1amnq1Kl66qmn5ObmluPlX83Dw0Pr1q3TmDFjNH/+fH355Zfy9vZW5cqVNXLkyCw9n7l69eravHmzRo4cqenTp+vUqVMqUaKEateu7XBbxM0YPny4/vrrL73zzjs6f/68GjdunGHoXrZsmc6ePXvD/fv999/XnDlz9MILL6Sb7urqqmXLlmngwIGaMWOGXFxc1LZtW0VGRqphw4YOYT43vrcZGTdunPr27auhQ4fqn3/+Ubdu3fI0dGdlPwGA3GKzTJ9yAQDgP6B27doqVqyYoqOjnV2K061du1YPPfRQuvZu3bpp+vTp6t69uw4fPqy1a9c6zDNo0CD9+eefuvPOOzVs2DB1794974qGcYsXL1bbtm31888/q2HDhs4uBwBuGYRuAMAtb/Pmzapbt66mT5+ubt26ObscwOn++ecfh5HGU1JS1KxZM23evFnHjx/P1VHoAeB2x+XlAIBb1s6dO7Vlyxa9//77KlmypDp27OjskoD/hAEDBuiff/5R/fr1lZSUpIULFyomJkZjxowhcANALiN0AwBuWV9//bVGjRqlKlWqaPbs2ZkOPAXcbh5++GG9//77Wrp0qS5duqS7775bH3/8caYDMAIAss+pl5f/+OOPevfdd7VlyxbFxsZq0aJFatOmzXXnWbt2rcLDw/XHH3+oTJkyGjp0KPeUAQAAAAD+k5z6nO7ExETVrFkz3XMhM3Po0CE9+uijeuihh7Rt2za9+OKL6t27d4bP2gQAAAAAwNn+MwOp2Wy2G57pHjx4sJYtW6adO3fa2zp16qSzZ89qxYoVeVAlAAAAAABZl6/u6d6wYYNCQkIc2kJDQ/Xiiy9mOk9SUpKSkpLs71NTU3X69GkVL15cNpvNVKkAAAAAgFuYZVk6f/68SpUqJReXzC8iz1eh+/jx4/L393do8/f3V0JCQrpHX6QZO3asRo4cmVclAgAAAABuI0ePHtWdd96Z6fR8FbqzIyIiQuHh4fb3586d01133aWjR4/K29vbiZUBQP7nM9bH2SUAyIZzEeecXQIA5HsJCQkqU6aM7rjjjuv2y1ehOyAgQHFxcQ5tcXFx8vb2zvSZkm5ubnJzc0vX7u3tTegGgJziCVxAvsRvIADIPTe6bdmpo5ffrPr16ys6OtqhbdWqVapfv76TKgIAAAAAIHNODd0XLlzQtm3btG3bNkn/PhJs27ZtOnLkiKR/Lw0PCwuz93/22Wd18OBBvfrqq9q9e7c+/fRTzZs3T4MGDXJG+QAAAAAAXJdTQ/fmzZtVu3Zt1a5dW5IUHh6u2rVra/jw4ZKk2NhYewCXpPLly2vZsmVatWqVatasqffff19ffPGFQkNDnVI/AAAAAADX8595TndeSUhIkI+Pj86dO8f9TACQQ7aRPHoRyI+syNvq5x8AGJHVbJmv7ukG/qsmTJigcuXKyd3dXcHBwdq0aVOmfS9fvqxRo0apYsWKcnd3V82aNbVixQqHPiNGjJDNZnN4Va1a1fRqAAAAAMhlhG4gh+bOnavw8HBFRkZq69atqlmzpkJDQ3XixIkM+w8dOlSfffaZPv74Y/3555969tln1bZtW/32228O/e655x7FxsbaXz///HNerA4AAACAXEToBnJo3Lhx6tOnj3r06KHq1atr0qRJ8vDw0NSpUzPsP3PmTL322mtq2bKlKlSooH79+qlly5Z6//33HfoVKFBAAQEB9pevr29erA4AAACAXEToBnIgOTlZW7ZsUUhIiL3NxcVFISEh2rBhQ4bzJCUlyd3d8eHGhQsXTncme9++fSpVqpQqVKigLl26OAwqCAAAACB/IHQDORAfH6+UlBT5+/s7tPv7++v48eMZzhMaGqpx48Zp3759Sk1N1apVq7Rw4ULFxsba+wQHB2v69OlasWKFJk6cqEOHDqlRo0Y6f/680fUBAAAAkLsI3UAe+/DDD1WpUiVVrVpVhQoVUv/+/dWjRw+5uPzf7tiiRQs9+eSTqlGjhkJDQ7V8+XKdPXtW8+bNc2LlAAAAAG4WoRvIAV9fX7m6uiouLs6hPS4uTgEBARnO4+fnp8WLFysxMVF//fWXdu/eLS8vL1WoUCHTzylSpIgqV66s/fv352r9AAAAAMwidAM5UKhQIQUFBSk6OtrelpqaqujoaNWvX/+687q7u6t06dK6cuWKFixYoNatW2fa98KFCzpw4IBKliyZa7UDAAAAMI/QDeRQeHi4Jk+erBkzZmjXrl3q16+fEhMT1aNHD0lSWFiYIiIi7P03btyohQsX6uDBg/rpp5/UvHlzpaam6tVXX7X3efnll7Vu3TodPnxYMTExatu2rVxdXdW5c+c8Xz8AAAAA2VfA2QUA+V3Hjh118uRJDR8+XMePH1etWrW0YsUK++BqR44ccbhf+9KlSxo6dKgOHjwoLy8vtWzZUjNnzlSRIkXsff7++2917txZp06dkp+fnx544AH98ssv8vPzy+vVAwAAAJADNsuyLGcXkZcSEhLk4+Ojc+fOydvb29nlAEC+Zhtpc3YJALLBirytfv4BgBFZzZZcXg4AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhRwdgHInI3H3wL5ksXjbwEAAPD/caYbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAKebMGGCypUrJ3d3dwUHB2vTpk2Z9m3SpIlsNlu616OPPmrvExcXp+7du6tUqVLy8PBQ8+bNtW/fvrxYFcABoRsAAACAU82dO1fh4eGKjIzU1q1bVbNmTYWGhurEiRMZ9l+4cKFiY2Ptr507d8rV1VVPPvmkJMmyLLVp00YHDx7UkiVL9Ntvv6ls2bIKCQlRYmJiXq4aQOgGAAAA4Fzjxo1Tnz591KNHD1WvXl2TJk2Sh4eHpk6dmmH/YsWKKSAgwP5atWqVPDw87KF73759+uWXXzRx4kTVrVtXVapU0cSJE/XPP/9o9uzZeblqAKEbAAAAgPMkJydry5YtCgkJsbe5uLgoJCREGzZsyNIypkyZok6dOsnT01OSlJSUJElyd3d3WKabm5t+/vnnXKweuDFCNwAAAACniY+PV0pKivz9/R3a/f39dfz48RvOv2nTJu3cuVO9e/e2t1WtWlV33XWXIiIidObMGSUnJ+vtt9/W33//rdjY2FxfB+B6CN0AAAAA8q0pU6YoMDBQ9erVs7cVLFhQCxcu1N69e1WsWDF5eHhozZo1atGihVxciEDIW3zjAAAAADiNr6+vXF1dFRcX59AeFxengICA686bmJioOXPmqFevXummBQUFadu2bTp79qxiY2O1YsUKnTp1ShUqVMjV+oEbIXQDAAAAcJpChQopKChI0dHR9rbU1FRFR0erfv361513/vz5SkpK0tNPP51pHx8fH/n5+Wnfvn3avHmzWrdunWu1A1nh9NB9M8/jk6Tx48erSpUqKly4sMqUKaNBgwbp0qVLeVQtAAAAgNwWHh6uyZMna8aMGdq1a5f69eunxMRE9ejRQ5IUFhamiIiIdPNNmTJFbdq0UfHixdNNmz9/vtauXWt/bFjTpk3Vpk0bNWvWzPj6AFcr4MwPT3se36RJkxQcHKzx48crNDRUe/bsUYkSJdL1nzVrloYMGaKpU6eqQYMG2rt3r7p37y6bzaZx48Y5YQ0AAAAA5FTHjh118uRJDR8+XMePH1etWrW0YsUK++BqR44cSXcv9p49e/Tzzz/r+++/z3CZsbGxCg8PV1xcnEqWLKmwsDANGzbM+LoA17JZlmU568ODg4NVt25dffLJJ5L+vYykTJkyGjBggIYMGZKuf//+/bVr1y6HS09eeuklbdy4MctD/yckJMjHx0fnzp2Tt7d37qyIITabsysAkB3OO6rmPdtIDlRAfmRF3kYHKgAwJKvZ0mmXl2fneXwNGjTQli1b7JegHzx4UMuXL1fLli0z/ZykpCQlJCQ4vAAAAAAAyAtOu7z8es/j2717d4bzPPXUU4qPj9cDDzwgy7J05coVPfvss3rttdcy/ZyxY8dq5MiRuVo7AAAAAABZ4fSB1G7G2rVrNWbMGH366afaunWrFi5cqGXLlmn06NGZzhMREaFz587ZX0ePHs3DigEAAAAAtzOnnenOzvP4hg0bpq5du6p3796SpMDAQCUmJqpv3756/fXXM3zQvZubm9zc3HJ/BQAAAAAAuAGnnenOzvP4Ll68mC5Yu7q6SpKcOB4cAAAAAAAZcuojw8LDw9WtWzfVqVNH9erV0/jx49M9j6906dIaO3asJKlVq1YaN26cateureDgYO3fv1/Dhg1Tq1at7OEbAAAAAID/CqeG7pt9Ht/QoUNls9k0dOhQHTt2TH5+fmrVqpXefPNNZ60CAAAAAACZcupzup2B53QDMO12OqrynG4gf7rtntPNjyog/8kHP6j+88/pBgAAAADgVkfoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIU4P3RMmTFC5cuXk7u6u4OBgbdq06br9z549q+eff14lS5aUm5ubKleurOXLl+dRtQAAAAAAZF0BZ3743LlzFR4erkmTJik4OFjjx49XaGio9uzZoxIlSqTrn5ycrKZNm6pEiRL6+uuvVbp0af31118qUqRI3hcPAAAAAMANODV0jxs3Tn369FGPHj0kSZMmTdKyZcs0depUDRkyJF3/qVOn6vTp04qJiVHBggUlSeXKlcvLkgEAAAAAyDKnXV6enJysLVu2KCQk5P+KcXFRSEiINmzYkOE833zzjerXr6/nn39e/v7+uvfeezVmzBilpKTkVdkAAAAAAGSZ0850x8fHKyUlRf7+/g7t/v7+2r17d4bzHDx4UKtXr1aXLl20fPly7d+/X88995wuX76syMjIDOdJSkpSUlKS/X1CQkLurQQAAAAAANfh9IHUbkZqaqpKlCihzz//XEFBQerYsaNef/11TZo0KdN5xo4dKx8fH/urTJkyeVgxAAAAAOB25rTQ7evrK1dXV8XFxTm0x8XFKSAgIMN5SpYsqcqVK8vV1dXeVq1aNR0/flzJyckZzhMREaFz587ZX0ePHs29lQAAAAAA4DqcFroLFSqkoKAgRUdH29tSU1MVHR2t+vXrZzhPw4YNtX//fqWmptrb9u7dq5IlS6pQoUIZzuPm5iZvb2+HFwAAAAAAecGpl5eHh4dr8uTJmjFjhnbt2qV+/fopMTHRPpp5WFiYIiIi7P379eun06dPa+DAgdq7d6+WLVumMWPG6Pnnn3fWKgAAAAAAkCmnPjKsY8eOOnnypIYPH67jx4+rVq1aWrFihX1wtSNHjsjF5f/+LlCmTBmtXLlSgwYNUo0aNVS6dGkNHDhQgwcPdtYqAAAAAACQKZtlWZazi8hLCQkJ8vHx0blz5/7zl5rbbM6uAEB23E5HVdtIDlRAfmRF3kYHKokfVUB+lA9+UGU1W+ar0csBAAAAAMhPCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwJEehOzk5WXv27NGVK1dyqx4AAAAAAG4Z2QrdFy9eVK9eveTh4aF77rlHR44ckSQNGDBAb731Vq4WCAAAAABAfpWt0B0REaHt27dr7dq1cnd3t7eHhIRo7ty5uVYcAAAAAAD5WYHszLR48WLNnTtX999/v2w2m739nnvu0YEDB3KtOAAAAAAA8rNsnek+efKkSpQoka49MTHRIYQDAAAAAHA7y1borlOnjpYtW2Z/nxa0v/jiC9WvXz93KgMAAAAAIJ/L1uXlY8aMUYsWLfTnn3/qypUr+vDDD/Xnn38qJiZG69aty+0aAQAAAADIl7J1pvuBBx7Q9u3bdeXKFQUGBur7779XiRIltGHDBgUFBeV2jQAAAAAA5Es3fab78uXLeuaZZzRs2DBNnjzZRE0AAAAAANwSbvpMd8GCBbVgwQITtQAAAAAAcEvJ1uXlbdq00eLFi3O5FAAAAAAAbi3ZGkitUqVKGjVqlNavX6+goCB5eno6TH/hhRdypTgAAAAAAPIzm2VZ1s3OVL58+cwXaLPp4MGDOSrKpISEBPn4+OjcuXPy9vZ2djnXxSPPgfzp5o+q+ZdtJAcqID+yIm+jA5XEjyogP8oHP6iymi2zdab70KFD2S4MAAAAAIDbRbbu6b6aZVnKxslyAAAAAABuedkO3V9++aUCAwNVuHBhFS5cWDVq1NDMmTNzszYAAAAAAPK1bF1ePm7cOA0bNkz9+/dXw4YNJUk///yznn32WcXHx2vQoEG5WiQAAAAAAPlRtkL3xx9/rIkTJyosLMze9vjjj+uee+7RiBEjCN0AAAAAACibl5fHxsaqQYMG6dobNGig2NjYHBcFAAAAAMCtIFuh++6779a8efPStc+dO1eVKlXKcVEAAAAAANwKsnV5+ciRI9WxY0f9+OOP9nu6169fr+jo6AzDOAAAAAAAt6Nsnelu166dNm7cKF9fXy1evFiLFy+Wr6+vNm3apLZt2+Z2jQAAAAAA5EvZOtMtSUFBQfrqq69ysxYAAAAAAG4p2TrTvXz5cq1cuTJd+8qVK/Xdd9/luCgAAAAAAG4F2QrdQ4YMUUpKSrp2y7I0ZMiQHBcFAAAAAMCtIFuhe9++fapevXq69qpVq2r//v05LgoAAAAAgFtBtkK3j4+PDh48mK59//798vT0zHFRAAAAAADcCrIVulu3bq0XX3xRBw4csLft379fL730kh5//PFcKw4AAAAAgPwsW6H7nXfekaenp6pWrary5curfPnyqlq1qooXL6733nsvt2sEAAAAACBfytYjw3x8fBQTE6NVq1Zp+/btKly4sGrWrKlGjRrldn0AAAAAAORbN3Wme8OGDVq6dKkkyWazqVmzZipRooTee+89tWvXTn379lVSUpKRQgEAAAAAyG9uKnSPGjVKf/zxh/3977//rj59+qhp06YaMmSIvv32W40dOzbXiwQAAAAAID+6qdC9bds2PfLII/b3c+bMUb169TR58mSFh4fro48+0rx583K9SAAAAAAA8qObCt1nzpyRv7+//f26devUokUL+/u6devq6NGjuVcdAAAAAAD52E2Fbn9/fx06dEiSlJycrK1bt+r++++3Tz9//rwKFiyYuxUCAAAAAJBP3VTobtmypYYMGaKffvpJERER8vDwcBixfMeOHapYsWKuFwkAAAAAQH50U48MGz16tJ544gk1btxYXl5emjFjhgoVKmSfPnXqVDVr1izXiwQAAAAAID+6qdDt6+urH3/8UefOnZOXl5dcXV0dps+fP19eXl65WiAAAAAAAPnVTYXuND4+Phm2FytWLEfFAAAAAABwK7mpe7oBAAAAAEDWEboBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADDkPxG6J0yYoHLlysnd3V3BwcHatGlTluabM2eObDab2rRpY7ZAAAAAAACywemhe+7cuQoPD1dkZKS2bt2qmjVrKjQ0VCdOnLjufIcPH9bLL7+sRo0a5VGlAAAAAADcHKeH7nHjxqlPnz7q0aOHqlevrkmTJsnDw0NTp07NdJ6UlBR16dJFI0eOVIUKFfKwWgAAAAAAss6poTs5OVlbtmxRSEiIvc3FxUUhISHasGFDpvONGjVKJUqUUK9evfKiTAAAAAAAsqWAMz88Pj5eKSkp8vf3d2j39/fX7t27M5zn559/1pQpU7Rt27YsfUZSUpKSkpLs7xMSErJdLwAAAAAAN8Ppl5ffjPPnz6tr166aPHmyfH19szTP2LFj5ePjY3+VKVPGcJUAAAAAAPzLqWe6fX195erqqri4OIf2uLg4BQQEpOt/4MABHT58WK1atbK3paamSpIKFCigPXv2qGLFig7zREREKDw83P4+ISGB4A0AAAAAyBNODd2FChVSUFCQoqOj7Y/9Sk1NVXR0tPr375+uf9WqVfX77787tA0dOlTnz5/Xhx9+mGGYdnNzk5ubm5H6AQAAAAC4HqeGbkkKDw9Xt27dVKdOHdWrV0/jx49XYmKievToIUkKCwtT6dKlNXbsWLm7u+vee+91mL9IkSKSlK4dAAAAAABnc3ro7tixo06ePKnhw4fr+PHjqlWrllasWGEfXO3IkSNycclXt54DAAAAACBJslmWZTm7iLyUkJAgHx8fnTt3Tt7e3s4u57psNmdXACA7bqejqm0kByogP7Iib6MDlcSPKiA/ygc/qLKaLTmFDAAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMCQ/0TonjBhgsqVKyd3d3cFBwdr06ZNmfadPHmyGjVqpKJFi6po0aIKCQm5bn8AAAAAAJzF6aF77ty5Cg8PV2RkpLZu3aqaNWsqNDRUJ06cyLD/2rVr1blzZ61Zs0YbNmxQmTJl1KxZMx07diyPKwcAAAAA4PpslmVZziwgODhYdevW1SeffCJJSk1NVZkyZTRgwAANGTLkhvOnpKSoaNGi+uSTTxQWFnbD/gkJCfLx8dG5c+fk7e2d4/pNstmcXQGA7HDuUTVv2UZyoALyIyvyNjpQSfyoAvKjfPCDKqvZ0qlnupOTk7VlyxaFhITY21xcXBQSEqINGzZkaRkXL17U5cuXVaxYsQynJyUlKSEhweEFAAAAAEBecGrojo+PV0pKivz9/R3a/f39dfz48SwtY/DgwSpVqpRDcL/a2LFj5ePjY3+VKVMmx3UDAAAAAJAVTr+nOyfeeustzZkzR4sWLZK7u3uGfSIiInTu3Dn76+jRo3lcJQAAAADgdlXAmR/u6+srV1dXxcXFObTHxcUpICDguvO+9957euutt/TDDz+oRo0amfZzc3OTm5tbrtQLAAAAAMDNcOqZ7kKFCikoKEjR0dH2ttTUVEVHR6t+/fqZzvfOO+9o9OjRWrFiherUqZMXpQIAAAAAcNOceqZbksLDw9WtWzfVqVNH9erV0/jx45WYmKgePXpIksLCwlS6dGmNHTtWkvT2229r+PDhmjVrlsqVK2e/99vLy0teXl5OWw8AAAAAAK7l9NDdsWNHnTx5UsOHD9fx48dVq1YtrVixwj642pEjR+Ti8n8n5CdOnKjk5GS1b9/eYTmRkZEaMWJEXpYOAAAAAMB1Of053XmN53QDMO12OqrynG4gf+I53QD+8/LBD6p88ZxuAAAAAABuZYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMCQ/0TonjBhgsqVKyd3d3cFBwdr06ZN1+0/f/58Va1aVe7u7goMDNTy5cvzqFIAAAAAALLO6aF77ty5Cg8PV2RkpLZu3aqaNWsqNDRUJ06cyLB/TEyMOnfurF69eum3335TmzZt1KZNG+3cuTOPKwcAAAAA4PpslmVZziwgODhYdevW1SeffCJJSk1NVZkyZTRgwAANGTIkXf+OHTsqMTFRS5cutbfdf//9qlWrliZNmnTDz0tISJCPj4/OnTsnb2/v3FsRA2w2Z1cAIDuce1TNW7aRHKiA/MiKvI0OVBI/qoD8KB/8oMpqtiyQhzWlk5ycrC1btigiIsLe5uLiopCQEG3YsCHDeTZs2KDw8HCHttDQUC1evDjD/klJSUpKSrK/P3funKR/NxAAmHBbHV4uObsAANnB7yAA/3n54DiVdiy90Xlsp4bu+Ph4paSkyN/f36Hd399fu3fvznCe48ePZ9j/+PHjGfYfO3asRo4cma69TJky2awaAK7Px8fZFQDA9fm8xYEKwH9cPvpBdf78eflcp16nhu68EBER4XBmPDU1VadPn1bx4sVl41IjOEFCQoLKlCmjo0eP/udvcQBw++JYBeC/juMUnM2yLJ0/f16lSpW6bj+nhm5fX1+5uroqLi7OoT0uLk4BAQEZzhMQEHBT/d3c3OTm5ubQVqRIkewXDeQSb29v/gcB4D+PYxWA/zqOU3Cm653hTuPU0csLFSqkoKAgRUdH29tSU1MVHR2t+vXrZzhP/fr1HfpL0qpVqzLtDwAAAACAszj98vLw8HB169ZNderUUb169TR+/HglJiaqR48ekqSwsDCVLl1aY8eOlSQNHDhQjRs31vvvv69HH31Uc+bM0ebNm/X55587czUAAAAAAEjH6aG7Y8eOOnnypIYPH67jx4+rVq1aWrFihX2wtCNHjsjF5f9OyDdo0ECzZs3S0KFD9dprr6lSpUpavHix7r33XmetAnBT3NzcFBkZme62BwD4L+FYBeC/juMU8gunP6cbAAAAAIBblVPv6QYAAAAA4FZG6AYAAAAAwBBCNwAAAAAAhhC6AQC4RdhsNi1evNjZZQCAgyZNmujFF190dhmA0xC6gRw6efKk+vXrp7vuuktubm4KCAhQaGio1q1bJ19fX7311lsZzjd69Gj5+/vr8uXLmj59umw2m6pVq5au3/z582Wz2VSuXDnDawIgp7p37y6bzSabzaaCBQuqfPnyevXVV3Xp0iVnl2bU1et99Wv//v1OralNmzZO+3wgv0vbr6/9HbN48WLZbLabWtbChQs1evTo3CwvnWuPQ8WLF1fz5s21Y8cOo58LZAWhG8ihdu3a6bffftOMGTO0d+9effPNN2rSpInOnTunp59+WtOmTUs3j2VZmj59usLCwlSwYEFJkqenp06cOKENGzY49J0yZYruuuuuPFkXADnXvHlzxcbG6uDBg/rggw/02WefKTIy0tllGZe23le/ypcvn61lJScn53J1ALLD3d1db7/9ts6cOZOj5RQrVkx33HFHLlWVuauPQ9HR0SpQoIAee+wx458L3AihG8iBs2fP6qefftLbb7+thx56SGXLllW9evUUERGhxx9/XL169dLevXv1888/O8y3bt06HTx4UL169bK3FShQQE899ZSmTp1qb/v777+1du1aPfXUU3m2TgByJu2KlzJlyqhNmzYKCQnRqlWr7NNPnTqlzp07q3Tp0vLw8FBgYKBmz57tsIwmTZrohRde0KuvvqpixYopICBAI0aMcOizb98+Pfjgg3J3d1f16tUdPiPN77//rocffliFCxdW8eLF1bdvX124cME+Pe1s8JgxY+Tv768iRYpo1KhRunLlil555RUVK1ZMd955Z4Z/PMxsva9+ubq6Svr3mFevXj25ubmpZMmSGjJkiK5cueKwvv3799eLL74oX19fhYaGSpJ27typFi1ayMvLS/7+/uratavi4+Pt83399dcKDAy0r19ISIgSExM1YsQIzZgxQ0uWLLGf9Vq7du0N1wGAo5CQEAUEBGjs2LGZ9snqMS3t8vLXXntNwcHB6ZZTs2ZNjRo1yv7+iy++ULVq1eTu7q6qVavq008/vWG9Vx+HatWqpSFDhujo0aM6efKkvc/gwYNVuXJleXh4qEKFCho2bJguX74sSTp8+LBcXFy0efNmh+WOHz9eZcuWVWpqqqTsH5tw+yJ0Azng5eUlLy8vLV68WElJSemmBwYGqm7dug5BWpKmTZumBg0aqGrVqg7tPXv21Lx583Tx4kVJ0vTp09W8eXP5+/ubWwkAxuzcuVMxMTEqVKiQve3SpUsKCgrSsmXLtHPnTvXt21ddu3bVpk2bHOadMWOGPD09tXHjRr3zzjsaNWqUPVinpqbqiSeeUKFChbRx40ZNmjRJgwcPdpg/MTFRoaGhKlq0qH799VfNnz9fP/zwg/r37+/Qb/Xq1frf//6nH3/8UePGjVNkZKQee+wxFS1aVBs3btSzzz6rZ555Rn///Xe2tsGxY8fUsmVL1a1bV9u3b9fEiRM1ZcoUvfHGG+nWt1ChQlq/fr0mTZqks2fP6uGHH1bt2rW1efNmrVixQnFxcerQoYMkKTY2Vp07d1bPnj21a9curV27Vk888YQsy9LLL7+sDh06OJz1atCgQbbqB25nrq6uGjNmjD7++ONMjwFZPaal6dKlizZt2qQDBw7Y2/744w/t2LHDfpIhKipKw4cP15tvvqldu3ZpzJgxGjZsmGbMmJHl2i9cuKCvvvpKd999t4oXL25vv+OOOzR9+nT9+eef+vDDDzV58mR98MEHkqRy5copJCQk3R8ap02bpu7du8vFxSVHxybcxiwAOfL1119bRYsWtdzd3a0GDRpYERER1vbt2+3TJ02aZHl5eVnnz5+3LMuyEhISLA8PD+uLL76w95k2bZrl4+NjWZZl1apVy5oxY4aVmppqVaxY0VqyZIn1wQcfWGXLls3L1QKQDd26dbNcXV0tT09Py83NzZJkubi4WF9//fV153v00Uetl156yf6+cePG1gMPPODQp27dutbgwYMty7KslStXWgUKFLCOHTtmn/7dd99ZkqxFixZZlmVZn3/+uVW0aFHrwoUL9j7Lli2zXFxcrOPHj9vrLVu2rJWSkmLvU6VKFatRo0b291euXLE8PT2t2bNnZ2m9017t27e3LMuyXnvtNatKlSpWamqqvf+ECRMsLy8v++c2btzYql27tsMyR48ebTVr1syh7ejRo5Yka8+ePdaWLVssSdbhw4czral169aZ1gzg+q7eh+6//36rZ8+elmVZ1qJFi6wbRYiMjmkDBw60v69Zs6Y1atQo+/uIiAgrODjY/r5ixYrWrFmzHJY5evRoq379+tet9+rjkCSrZMmS1pYtW65b67vvvmsFBQXZ38+dO9cqWrSodenSJcuyLGvLli2WzWazDh06ZK8jJ8cm3J440w3kULt27fS///1P33zzjZo3b661a9fqvvvu0/Tp0yVJnTt3VkpKiubNmydJmjt3rlxcXNSxY8cMl9ezZ09NmzZN69atU2Jiolq2bJlXqwIgFzz00EPatm2bNm7cqG7duqlHjx5q166dfXpKSopGjx6twMBAFStWTF5eXlq5cqWOHDnisJwaNWo4vC9ZsqROnDghSdq1a5fKlCmjUqVK2afXr1/fof+uXbtUs2ZNeXp62tsaNmyo1NRU7dmzx952zz33yMXl/34O+Pv7KzAw0P7e1dVVxYsXt3/2jdY77fXRRx/Z66hfv77DwEsNGzbUhQsXHM6cBQUFOSxv+/btWrNmjf2KIi8vL/vVQQcOHFDNmjX1yCOPKDAwUE8++aQmT56c4/tOAWTs7bff1owZM7Rr165007J6TLtaly5dNGvWLEn/jnMze/ZsdenSRdK/V+kcOHBAvXr1ctj/33jjDYez4xm5+ji0adMmhYaGqkWLFvrrr7/sfebOnauGDRsqICBAXl5eGjp0qEOtbdq0kaurqxYtWiTp36sOH3roIfuAthybkB2EbiAXuLu7q2nTpho2bJhiYmLUvXt3+8BJ3t7eat++vf1SpWnTpqlDhw7y8vLKcFldunTRL7/8ohEjRqhr164qUKBAnq0HgJzz9PTU3XffrZo1a2rq1KnauHGjpkyZYp/+7rvv6sMPP9TgwYO1Zs0abdu2TaGhoekGD0sbZDGNzWaz30+YmzL6nOx8dtp6p71Klix5U3Vc/ccB6d9LQ1u1auUQ5Ldt22a/l93V1VWrVq3Sd999p+rVq+vjjz9WlSpVdOjQoZv6XAA39uCDDyo0NFQRERHppmX1mHa1zp07a8+ePdq6datiYmJ09OhR+8mItHEnJk+e7LDv79y5U7/88st167z6OFS3bl198cUXSkxM1OTJkyVJGzZsUJcuXdSyZUstXbpUv/32m15//XWHWgsVKqSwsDBNmzZNycnJmjVrlnr27GmfzrEJ2cGvecCA6tWrOzwrt1evXmrSpImWLl2qmJgYvfvuu5nOW6xYMT3++OOaN2+eJk2alAfVAjDFxcVFr732msLDw/XUU0+pcOHCWr9+vVq3bq2nn35a0r/3Z+/du1fVq1fP8nKrVaumo0ePKjY21h5ur/0xWq1aNU2fPl2JiYn2QLt+/Xq5uLioSpUqubSGWat1wYIFsizLfrZ7/fr1uuOOO3TnnXdmOt99992nBQsWqFy5cpn+8dFms6lhw4Zq2LChhg8frrJly2rRokUKDw9XoUKFlJKSYmSdgNvRW2+9pVq1aqU7fmTnmHbnnXeqcePGioqK0j///KOmTZuqRIkSkv692qZUqVI6ePCg/ex3dtlsNrm4uOiff/6RJMXExKhs2bJ6/fXX7X2uPguepnfv3rr33nv16aef6sqVK3riiSfs03J6bMLtiTPdQA6cOnVKDz/8sL766ivt2LFDhw4d0vz58/XOO++odevW9n4PPvig7r77boWFhalq1ao3HNBn+vTpio+PTzfQGoD858knn5Srq6smTJggSapUqZJWrVqlmJgY7dq1S88884zi4uJuapkhISGqXLmyunXrpu3bt+unn35y+BEp/XvVjLu7u7p166adO3dqzZo1GjBggLp27ZqngzM+99xzOnr0qAYMGKDdu3dryZIlioyMVHh4uMNl7dd6/vnndfr0aXXu3Fm//vqrDhw4oJUrV6pHjx5KSUnRxo0bNWbMGG3evFlHjhzRwoULdfLkSVWrVk3SvwMi7dixQ3v27FF8fLx9dGIA2RMYGKguXbrYbx1Jk91jWpcuXTRnzhzNnz8/XbgeOXKkxo4dq48++kh79+7V77//rmnTpmncuHHXXWZSUpKOHz+u48ePa9euXRowYID9zHRarUeOHNGcOXN04MABffTRR/bLyK9WrVo13X///Ro8eLA6d+6swoUL26fl9NiE2xOhG8gBLy8vBQcH64MPPtCDDz6oe++9V8OGDVOfPn30ySef2PvZbDb17NlTZ86ccbhEKTNpj5gAkP8VKFBA/fv31zvvvKPExEQNHTpU9913n0JDQ9WkSRMFBASoTZs2N7VMFxcXLVq0SP/884/q1aun3r17680333To4+HhoZUrV+r06dOqW7eu2rdvr0ceecTh2JQXSpcureXLl2vTpk2qWbOmnn32WfXq1UtDhw697nylSpXS+vXrlZKSombNmikwMFAvvviiihQpIhcXF3l7e+vHH39Uy5YtVblyZQ0dOlTvv/++WrRoIUnq06ePqlSpojp16sjPz0/r16/Pi9UFbmmjRo1Kd6tJdo9p7du316lTp3Tx4sV0/Xv37q0vvvhC06ZNU2BgoBo3bqzp06erfPny113mihUrVLJkSZUsWVLBwcH2Jzc0adJEkvT4449r0KBB6t+/v2rVqqWYmBgNGzYsw2X16tVLycnJ6X635fTYhNuTzbIYvx4AAAAA0owePVrz58/Xjh07nF0KbgGc6QYAAAAA/TtQ2s6dO/XJJ59owIABzi4HtwhCNwAAAABI6t+/v4KCgtSkSZMs3RIIZAWXlwMAAAAAYAhnugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADDk/wEKadKxyvCgPQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "metrics_acc_train = ['SVM', 'Random Forest', 'Naive Bayes']\n",
    "values_acc_train = [acc_train_SVM, acc_train_RF, acc_train_NB]\n",
    "\n",
    "metrics_acc_test = ['SVM', 'Random Forest', 'Naive Bayes']\n",
    "values_acc_test = [acc_test_SVM, acc_test_RF, acc_test_NB]\n",
    "\n",
    "x_acc_train = range(len(metrics_acc_train))\n",
    "x_acc_test = range(len(metrics_acc_test))\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(x_acc_train, values_acc_train, color=['blue', 'green', 'red'])\n",
    "\n",
    "plt.xticks(x_acc_train, metrics_acc_train)\n",
    "for i, value in enumerate(values_acc_train):\n",
    "    plt.text(i, value + 0.01, str(round(value, 2)), ha='center', va='bottom')\n",
    "\n",
    "plt.title('Train Accuracy Scores for Different Algorithms')\n",
    "plt.ylabel('Score')\n",
    "plt.ylim(0, 1)  \n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(x_acc_test, values_acc_test, color=['purple', 'orange', 'yellow'])\n",
    "\n",
    "plt.xticks(x_acc_test, metrics_acc_test)\n",
    "for i, value in enumerate(values_acc_test):\n",
    "    plt.text(i, value + 0.01, str(round(value, 2)), ha='center', va='bottom')\n",
    "\n",
    "plt.title('Test Accuracy Scores for Different Algorithms')\n",
    "plt.ylabel('Score')\n",
    "plt.ylim(0, 1) \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3- Precision\n",
    "\n",
    "4- Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics_recall = ['SVM', 'Random Forest', 'Naive Bayes']\n",
    "values_recall = [recall_SVM, recall_RF, recall_NB]\n",
    "\n",
    "metrics_precision = ['SVM', 'Random Forest', 'Naive Bayes']\n",
    "values_precision = [precision_SVM, precision_RF, precision_NB]\n",
    "\n",
    "x_recall = range(len(metrics_recall))\n",
    "x_precision = range(len(metrics_precision))\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(x_recall, values_recall, color=['blue', 'green', 'red'])\n",
    "\n",
    "plt.xticks(x_recall, metrics_recall)\n",
    "for i, value in enumerate(values_recall):\n",
    "    plt.text(i, value + 0.01, str(round(value, 2)), ha='center', va='bottom')\n",
    "\n",
    "plt.title('Recall Scores for Different Algorithms')\n",
    "plt.ylabel('Score')\n",
    "plt.ylim(0, 1)  \n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(x_precision, values_precision, color=['purple', 'orange', 'yellow'])\n",
    "\n",
    "plt.xticks(x_precision, metrics_precision)\n",
    "for i, value in enumerate(values_precision):\n",
    "    plt.text(i, value + 0.01, str(round(value, 2)), ha='center', va='bottom')\n",
    "\n",
    "plt.title('Precision Scores for Different Algorithms')\n",
    "plt.ylabel('Score')\n",
    "plt.ylim(0, 1) \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}