part_2/Annual Profit/models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier, GradientBoostingClassifier, HistGradientBoostingClassifier, RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass   fnlwgt   education   education-num   \n",
       "0   39          State-gov    77516   Bachelors              13  \\\n",
       "1   50   Self-emp-not-inc    83311   Bachelors              13   \n",
       "2   38            Private   215646     HS-grad               9   \n",
       "3   53            Private   234721        11th               7   \n",
       "4   28            Private   338409   Bachelors              13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex   \n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male  \\\n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "    capital-gain   capital-loss   hours-per-week  native-country  income  \n",
       "0           2174              0               40   United-States   <=50K  \n",
       "1              0              0               13   United-States   <=50K  \n",
       "2              0              0               40   United-States   <=50K  \n",
       "3              0              0               40   United-States   <=50K  \n",
       "4              0              0               40            Cuba   <=50K  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('adult.data', delimiter=',')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_labels = dataset.iloc[:, [1,3,4,5,6,7,8,9,13]].values\n",
    "X_continious = dataset.iloc[:, [0,2,10,11,12]].values\n",
    "y = dataset.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "le_y = LabelEncoder()\n",
    "y = le_y.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "le_X = LabelEncoder()\n",
    "X_labels = pd.DataFrame(X_labels)\n",
    "X_labels = X_labels.apply(le_X.fit_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>39</td>\n",
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       "      <td>39</td>\n",
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       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
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       "      <td>6</td>\n",
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       "      <td>39</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0   1   2  3   4  5  6  7   8\n",
       "0      7   9  12  4   1  1  4  1  39\n",
       "1      6   9  12  2   4  0  4  1  39\n",
       "2      4  11   8  0   6  1  4  1  39\n",
       "3      4   1   6  2   6  0  2  1  39\n",
       "4      4   9  12  2  10  5  2  0   5\n",
       "...   ..  ..  .. ..  .. .. .. ..  ..\n",
       "32556  4   7  11  2  13  5  4  0  39\n",
       "32557  4  11   8  2   7  0  4  1  39\n",
       "32558  4  11   8  6   1  4  4  0  39\n",
       "32559  4  11   8  4   1  3  4  1  39\n",
       "32560  5  11   8  2   4  5  4  0  39\n",
       "\n",
       "[32561 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_labels = X_labels.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7,  9, 12, ...,  4,  1, 39],\n",
       "       [ 6,  9, 12, ...,  4,  1, 39],\n",
       "       [ 4, 11,  8, ...,  4,  1, 39],\n",
       "       ...,\n",
       "       [ 4, 11,  8, ...,  4,  0, 39],\n",
       "       [ 4, 11,  8, ...,  4,  1, 39],\n",
       "       [ 5, 11,  8, ...,  4,  0, 39]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.c_[X_continious, X_labels]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   39, 77516,  2174,     0,    40,     7,     9,    12,     4,\n",
       "           1,     1,     4,     1,    39], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CART gini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8159066482419776\n"
     ]
    }
   ],
   "source": [
    "criterion = 'gini'\n",
    "\n",
    "classifier = DecisionTreeClassifier(criterion=criterion, random_state=1)\n",
    "classifier.fit(X_train, y_train)\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "print(acc)\n",
    "results.append({\n",
    "    'classifier': 'CART', \n",
    "    'criterion': criterion,\n",
    "    'accuracy_score': acc\n",
    "    })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ID3 C4.5 entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8128358667280823\n"
     ]
    }
   ],
   "source": [
    "criterion = 'entropy'\n",
    "\n",
    "classifier = DecisionTreeClassifier(criterion=criterion, random_state=1)\n",
    "classifier.fit(X_train, y_train)\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "print(acc)\n",
    "results.append({\n",
    "    'classifier': 'C4.5',\n",
    "    'criterion': criterion,\n",
    "    'accuracy_score': acc\n",
    "    })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra Trees Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.855366190695532\n"
     ]
    }
   ],
   "source": [
    "criterions = ['gini', 'entropy', 'log_loss']\n",
    "n_estimators_arr = [10, 20, 50, 100, 150]\n",
    "etc_results = []\n",
    "\n",
    "for criterion in criterions:\n",
    "    for n_estimators in n_estimators_arr:\n",
    "        classifier = ExtraTreesClassifier(criterion=criterion, n_estimators=n_estimators, random_state=1)\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_pred = classifier.predict(X_test)\n",
    "        etc_results.append({\n",
    "            'criterion': criterion,\n",
    "            'n_estimators': n_estimators, \n",
    "            'accuracy_score': accuracy_score(y_test, y_pred)})\n",
    "\n",
    "result = max(etc_results, key=lambda x: x['accuracy_score'])\n",
    "print(result['accuracy_score'])\n",
    "result['classifier'] = 'Extra Trees Classifier'\n",
    "results.append(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8728696453247351\n"
     ]
    }
   ],
   "source": [
    "loss_arr = ['log_loss']\n",
    "learning_rate_arr = [0.1, 0.2, 1]\n",
    "n_estimators_arr = [50, 100, 150] #this param need to change\n",
    "criterion_arr = ['friedman_mse', 'squared_error']\n",
    "max_depth_arr = [2, 3, 4]\n",
    "gb_results = []\n",
    "\n",
    "for loss in loss_arr:\n",
    "    for learning_rate in learning_rate_arr:\n",
    "        for n_estimators in n_estimators_arr:\n",
    "            for criterion in criterion_arr:\n",
    "                for max_depth in max_depth_arr:\n",
    "                    classifier = GradientBoostingClassifier(loss=loss, n_estimators=n_estimators, learning_rate=learning_rate, criterion=criterion, max_depth=max_depth, random_state=1)\n",
    "                    classifier.fit(X_train, y_train)\n",
    "                    y_pred = classifier.predict(X_test)\n",
    "                    gb_results.append({\n",
    "                        'loss': loss,\n",
    "                        'learning_rate': learning_rate,\n",
    "                        'n_estimators': n_estimators,\n",
    "                        'criterion': criterion,\n",
    "                        'max_depth': max_depth,\n",
    "                        'accuracy_score': accuracy_score(y_test, y_pred)\n",
    "                    })\n",
    "\n",
    "result = max(gb_results, key=lambda x: x['accuracy_score'])\n",
    "print(result['accuracy_score'])\n",
    "result['classifier'] = 'Gradient Boosting'\n",
    "results.append(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hist Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8739444188545985\n"
     ]
    }
   ],
   "source": [
    "learning_rate_arr = [0.1, 0.2, 0.5, 1]\n",
    "max_iter_arr = [10, 50, 100, 150]\n",
    "hgb_results = []\n",
    "\n",
    "for learning_rate in learning_rate_arr:\n",
    "    for max_iter in max_iter_arr:\n",
    "        classifier = HistGradientBoostingClassifier(learning_rate=learning_rate, max_iter=max_iter, random_state=1)\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_pred = classifier.predict(X_test)\n",
    "        hgb_results.append({\n",
    "            'learning_rate': learning_rate,\n",
    "            'max_iter': max_iter,\n",
    "            'accuracy_score': accuracy_score(y_test, y_pred)\n",
    "        })\n",
    "\n",
    "result = max(hgb_results, key=lambda x: x['accuracy_score'])\n",
    "print(result['accuracy_score'])\n",
    "result['classifier'] = 'Hist Gradient Boosting'\n",
    "results.append(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest gini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8642714570858283\n"
     ]
    }
   ],
   "source": [
    "n_estimators_arr = [10, 20, 50, 100, 150]\n",
    "criterion_arr = ['gini', 'entropy']\n",
    "rf_results = []\n",
    "\n",
    "for n_estimators in n_estimators_arr:\n",
    "    for criterion in criterion_arr:\n",
    "        classifier = RandomForestClassifier(n_estimators=n_estimators, criterion=criterion, random_state=1)\n",
    "        classifier.fit(X_train, y_train)\n",
    "        y_pred = classifier.predict(X_test)\n",
    "        rf_results.append({\n",
    "            'n_estimators': n_estimators,\n",
    "            'criterion': criterion,\n",
    "            'accuracy_score': accuracy_score(y_test, y_pred)\n",
    "        })\n",
    "\n",
    "result = max(rf_results, key=lambda x: x['accuracy_score'])\n",
    "print(result['accuracy_score'])\n",
    "result['classifier'] = 'Random Forest'\n",
    "results.append(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8687240902809765\n"
     ]
    }
   ],
   "source": [
    "n_estimators_arr = [10, 20, 50, 100, 150]\n",
    "learning_rate_arr = [0.1, 0.2, 0.5, 1]\n",
    "algorithm_arr = ['SAMME', 'SAMME.R']\n",
    "ada_results = []\n",
    "\n",
    "for n_estimators in n_estimators_arr:\n",
    "    for learning_rate in learning_rate_arr:\n",
    "        for algorithm in algorithm_arr:\n",
    "            classifier = AdaBoostClassifier(n_estimators=n_estimators, learning_rate=learning_rate, algorithm=algorithm, random_state=0)\n",
    "            classifier.fit(X_train, y_train)\n",
    "            y_pred = classifier.predict(X_test)\n",
    "            ada_results.append({\n",
    "                'n_estimators': n_estimators,\n",
    "                'learning_rate': learning_rate,\n",
    "                'algorithm': algorithm,\n",
    "                'accuracy_score': accuracy_score(y_test, y_pred)\n",
    "            })\n",
    "\n",
    "result = max(ada_results, key=lambda x: x['accuracy_score'])\n",
    "print(result['accuracy_score'])\n",
    "result['classifier'] = 'Adaboost'\n",
    "results.append(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'classifier': 'CART',\n",
       "  'criterion': 'gini',\n",
       "  'accuracy_score': 0.8159066482419776},\n",
       " {'classifier': 'C4.5',\n",
       "  'criterion': 'entropy',\n",
       "  'accuracy_score': 0.8128358667280823},\n",
       " {'criterion': 'entropy',\n",
       "  'n_estimators': 150,\n",
       "  'accuracy_score': 0.855366190695532,\n",
       "  'classifier': 'Extra Trees Classifier'},\n",
       " {'loss': 'log_loss',\n",
       "  'learning_rate': 0.2,\n",
       "  'n_estimators': 100,\n",
       "  'criterion': 'squared_error',\n",
       "  'max_depth': 4,\n",
       "  'accuracy_score': 0.8728696453247351,\n",
       "  'classifier': 'Gradient Boosting'},\n",
       " {'learning_rate': 0.2,\n",
       "  'max_iter': 50,\n",
       "  'accuracy_score': 0.8739444188545985,\n",
       "  'classifier': 'Hist Gradient Boosting'},\n",
       " {'n_estimators': 150,\n",
       "  'criterion': 'gini',\n",
       "  'accuracy_score': 0.8642714570858283,\n",
       "  'classifier': 'Random Forest'},\n",
       " {'n_estimators': 150,\n",
       "  'learning_rate': 1,\n",
       "  'algorithm': 'SAMME.R',\n",
       "  'accuracy_score': 0.8687240902809765,\n",
       "  'classifier': 'Adaboost'}]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               classifier      criterion  accuracy_score  n_estimators   \n",
      "1                    C4.5        entropy        0.812836           NaN  \\\n",
      "0                    CART           gini        0.815907           NaN   \n",
      "2  Extra Trees Classifier        entropy        0.855366         150.0   \n",
      "5           Random Forest           gini        0.864271         150.0   \n",
      "6                Adaboost            NaN        0.868724         150.0   \n",
      "3       Gradient Boosting  squared_error        0.872870         100.0   \n",
      "4  Hist Gradient Boosting            NaN        0.873944           NaN   \n",
      "\n",
      "       loss  learning_rate  max_depth  max_iter algorithm  \n",
      "1       NaN            NaN        NaN       NaN       NaN  \n",
      "0       NaN            NaN        NaN       NaN       NaN  \n",
      "2       NaN            NaN        NaN       NaN       NaN  \n",
      "5       NaN            NaN        NaN       NaN       NaN  \n",
      "6       NaN            1.0        NaN       NaN   SAMME.R  \n",
      "3  log_loss            0.2        4.0       NaN       NaN  \n",
      "4       NaN            0.2        NaN      50.0       NaN  \n"
     ]
    },
    {
     "data": {
      "image/png": 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4HwoLC13249ixYyQmJlKzZk1nm/j4eJcx4uPjqVatGhZL8fUubm5utG3blrFjx7Jlyxb27t3LihUrLki9UHwJQWhoKOvXr3cus9vtztliERERkYvyeKp/y8nJ4dlnn+XOO+8kMjKSv/76i/Xr13PHHXcAxdeKZmZmsnz5curWrYu3t3epp5iffvppFi5cSJs2bRg6dCg33XQTgYGBbN++nW+//dYZsE4lKiqKgoIC3nnnHTp16uRyk9UJ/fv3JzY2lnHjxtG5c2eWLFly2utTofh60/79++Pv70+HDh3Iy8tjw4YNpKSkuJyiPp2z/QwAHA4HycnJQPFnu3TpUpYsWcIrr7wCFM88d+7cmT59+vD+++/j5+fH888/T/ny5Z2n+5955hkaN27Mq6++yt13382aNWuYNGmS81rdBQsWsHv3bpo3b05gYCCLFi2iqKjI+firSpUqsXbtWvbu3Yuvr2+pM9pn48knn2T06NFERUVRo0YN3nnnHVJSUk75jR0iIiJybbnoM6oWi4Vjx45x//33U61aNbp168bNN9/svCGmWbNmPPbYY9x9992EhISUuAHqBE9PT5YvX87gwYOZNm0aN954I9HR0QwYMIDY2Ngz3i1et25d3nzzTV5//XVq167NzJkzSzx39YYbbuDDDz9k4sSJ1K1bl++++46XXnrptOM+/PDDfPTRR0ybNo2YmBhatGjB9OnTz2lG9Ww/Ayi+JjM8PJzw8HCio6MZP348I0aM4MUXX3S2mTZtGg0bNuTWW2+ladOmOBwOFi1a5DwV36BBA2bPns3nn39O7dq1eeWVVxgxYgS9e/cGICAggHnz5tG6dWuio6N57733mDVrFrVq1QKKbwyzWCzUrFmTkJAQ9u3bd9b7+m+DBw+me/fu3H///TRt2hRfX1/i4uLw9PQ8r/FERETk6mJy/NeLBkUukKKiIqKjo+nWrRuvvvrqWfVJT0/H39+fiAGzMVvP7WYvERERObW9YzpetLFP/P5OS0s77f0mF/3Uv8ip/Pnnn3z33Xe0aNGCvLw8Jk2axJ49e+jR4/R3UIqIiMi14ZLdTCVyMrPZzPTp02ncuDGxsbFs3bqVZcuWER0dfblLExEREQPQjKpcNhERESWePiAiIiJygmZURURERMSQFFRFRERExJAUVEVERETEkBRURURERMSQFFRFRERExJAUVEVERETEkBRURURERMSQFFRFRERExJAUVEVERETEkBRURURERMSQ9BWqclX4bXgcNpvtcpchIiIiF5BmVEVERETEkBRURURERMSQFFRFRERExJAUVEVERETEkBRURURERMSQFFRFRERExJAUVEVERETEkBRURURERMSQFFRFRERExJD0zVRyVag9dAlmq/flLkNEROSqsXdMx8tdgmZURURERMSYFFRFRERExJAUVEVERETEkBRURURERMSQFFRFRERExJAUVEVERETEkBRURURERMSQFFRFRERExJAUVEVERETEkPTNVCIiInLNy/h1AWlr52HPSsGjbCRBbR/FWq76Kdunr/+ajE2LsKcfwexlw7t6LIEtemFy8wDgrykPYk8/XKKfb/2OBLd/HHtOBmk/ziRn78a/x/DHu9oNBNx0H2arj7N9zt5NpK2eQf7RPzG5W/Gt3YaA5vdjMlsu/IdgQAqqIiIick3LSviB4ys+Irh9XzzKVSdjw9ccnv0K5fq8j8UnoGT7P1aSsmo6ZW55Cmv5aAqOH+DYogkABLXpA0B4r7egqMjZJ//onxz+4iV8asQCYM88hj3zOIGtHsQ9uAKF6Yc5vmQy9oxjhHQdUtzn8G4Ozx2Gf9O7Cb51IPaMYxxbMhmKighs/dDF/VAMQqf+RURE5JqWvv4r/OrG4VunHR5lKhAU1xeTu5XMrUtLbZ93IAHP66LxqdkSN/9QvCIb4B3dnPykHc42Fm9/LL6BzlfOznW4BYRjjYgBwCOkEiFdh+AddT3ugeF4VaxLQPP7yd61DkeRHYCshNV4hEQSENsd98ByeFaIIbDVA2RsXEhRXvbF/2AMQEFVRERErlkOewH5yTvxrFjPucxkMuNZqR55B7aV2sdaPpq85F3kHUwEoCA1mZxdG/Cq0uiU28j6YyW+ddphMplOWUtRXhZmD2/naX2HvQCTm7tLG5ObFUdhPvmHdp7Lbl6xFFTPoHfv3nTp0sX5vmXLlgwYMOCy1WNUw4YNo169epe7DBERkXNiz04HR1GJU/wW7wDsWSml9vGp2ZKAG+8leeZg/nyjMwfffxjPCjH4N+1Wavvs7T9TlJuJT+02p6kjjbSfPse3XgfnMq/IBuQd2EbWH6twFNkpzDhKWvys4vaZpdd2tbmigmpycjJPPfUUUVFReHp6EhoaSmxsLFOmTCE7+9JMgc+bN49XX331go55chg+XTuTyeR8BQcH06FDB7Zs2XJB6zkTk8nEV1995bJs0KBBLF++/JLWISIicjnk7ttC2s+zCWr/OOG9JxLSdQg5uzaQ+neIPFnmlu/wqtwQN7/gUtcX5WVzeO5w3IMrEBDbw7ncK7IBgS0f4NiSyewb15WDHz76z6ztaWZmryZXzM1Uu3fvJjY2loCAAEaNGkVMTAxWq5WtW7fywQcfUL58eW677bZS+xYUFODu7l7qunMVFBR0QcY5Xx06dGDatGlAcXB/6aWXuPXWW9m3b99lrcvX1xdfX9/LWoOIiMi5snjbwGTGnpXqstyenYrFJ7DUPqmrZ+BbqzV+deOA4utNiwryOL54Ev7N7sZk+mcesDDtMLl/bnbeIHWyorxsDs9+BbOHF2VvfxGTxTWa2Zp0xa9xF+yZxzF7+mJPO0zqqk9wCwj7D3t95bhiZlSfeOIJ3Nzc2LBhA926dSM6OprKlSvTuXNnFi5cSKdOnZxtTSYTU6ZM4bbbbsPHx4fXXnsNu93OQw89RGRkJF5eXlSvXp2JEye6bMNutzNw4EACAgIIDg7mueeew+FwuLQ5+dR/Xl4egwYNonz58vj4+HD99dezcuVK5/rp06cTEBDAkiVLiI6OxtfXlw4dOpCUlAQUnzL/5JNP+Prrr50zpf/ufzKr1UpYWBhhYWHUq1eP559/nv3793PkyBFnm61bt9K6dWu8vLwIDg7mkUceITMz07m+qKiIESNGcN1112G1WqlXrx6LFy92rs/Pz6dfv36Eh4fj6elJxYoVGT16NACVKlUCoGvXrphMJuf7k0/9n5glHjduHOHh4QQHB9O3b18KCgqcbZKSkujYsSNeXl5ERkbyv//9j0qVKjFhwoRT7r+IiMiFZLK44xEWRe6fm53LHI4icvduxlq+Rql9HAV5JWY0neH0pNyQuXUpFm9/vKo0LjFOUV42h2a/DBY3Qu542floqxI1mky4+QVjdreSlbAKi18IHqFVzmU3r1hXRFA9duwY3333HX379sXHx6fUNidfnDxs2DC6du3K1q1befDBBykqKuK6665jzpw5/PHHH7zyyisMGTKE2bNnO/uMHz+e6dOn8/HHH/Pjjz9y/Phx5s+ff9ra+vXrx5o1a/j888/ZsmULd911Fx06dGDHjn/u/MvOzmbcuHF89tln/PDDD+zbt49BgwYBxafMu3Xr5gyvSUlJNGvW7Kw+l8zMTGbMmEFUVBTBwcWnE7KysoiLiyMwMJD169czZ84cli1bRr9+/Zz9Jk6cyPjx4xk3bhxbtmwhLi6O2267zVnz22+/zTfffMPs2bNJTExk5syZzkC6fv16AKZNm0ZSUpLzfWm+//57du3axffff88nn3zC9OnTmT59unP9/fffz8GDB1m5ciVffvklH3zwAYcPl3zmnIiIyMVka9yFjM1LyNy6nIKj+zm+5F0cBbn4xrQF4OiC8aSsmu5s7xXVhIyNi8j6Y1XxjVR7NpK6egZeUU1cnm/qcBSRuXUZPrXblHjuaVFeNoe+eBlHQR7BNz+FIy8He2YK9swU513/AGlrvyT/yF7yj/xJavws0n6eS1DbR/QcVSPZuXMnDoeD6tVdH7xbpkwZcnNzAejbty+vv/66c12PHj144IEHXNoPHz7c+d+RkZGsWbOG2bNn061b8cXPEyZM4IUXXuD2228H4L333mPJkiWnrGvfvn1MmzaNffv2Ua5cOaA4eC5evJhp06YxatQooPjSg/fee48qVYr/+unXrx8jRowAik+Ze3l5kZeXR1jYmafxFyxY4DzFnpWVRXh4OAsWLMBsLv6b43//+x+5ubl8+umnzlA/adIkOnXqxOuvv05oaCjjxo1j8ODB3HPPPQC8/vrrfP/990yYMIHJkyezb98+qlatyo033ojJZKJixYrO7YeEhAAQEBBwxnoDAwOZNGkSFouFGjVq0LFjR5YvX06fPn3Ytm0by5YtY/369TRqVHy9zUcffUTVqlVPO2ZeXh55eXnO9+np6Wf8zERERE7HJ7o59uw0Un+c8fcD/ytTttsI56n/wvQj8K/T+f7N7gFMpK6egT3zGGYvf7yimhDYvKfLuLl7N2FPP4JvnXYltpl/aCf5ScVPDTj4QR+XdeUfm4qbfygAObt/IW3NbLAX4B4SSdnbXzrl0wWuRldEUD2VdevWUVRUxL333usSXgBn+Pm3yZMn8/HHH7Nv3z5ycnLIz893nq5OS0sjKSmJ66+/3tnezc2NRo0alTj9f8LWrVux2+1Uq1bNZXleXp5zhhPA29vbGVIBwsPDz3vmsFWrVkyZMgWAlJQU3n33XW6++WbWrVtHxYoVSUhIoG7dui4zz7GxsRQVFZGYmIiXlxcHDx4kNjbWZdzY2Fg2by4+7dG7d2/atWtH9erV6dChA7feeivt27c/51pr1aqFxfLPX3zh4eFs3boVgMTERNzc3GjQoIFzfVRUFIGBpV8PdMLo0aNd/uAQERG5EGwNO2Fr2KnUdWE9xri8N5ktBNzYg4Abe5Ta/gSvyAZUHLyg1HWeFeqccp3LtruPOmObq9kVEVSjoqIwmUwkJia6LK9cuTIAXl5eJfqcfInA559/zqBBgxg/fjxNmzbFz8+PN954g7Vr1553XZmZmVgsFn755ReXQAa43Fh08o1cJpPplOH3THx8fIiKinK+/+ijj/D39+fDDz9k5MiR5zXmyRo0aMCePXv49ttvWbZsGd26daNt27bMnTv3nMYpbb+L/vUtHefjhRdeYODAgc736enpRERE/KcxRURExJiuiGtUg4ODadeuHZMmTSIrK+u8xoiPj6dZs2Y88cQT1K9fn6ioKHbt2uVc7+/vT3h4uEtwLSws5JdffjnlmPXr18dut3P48GGioqJcXmdzGv8EDw8P7Hb7mRuWwmQyYTabycnJASA6OprNmze7fE7x8fGYzWaqV6+OzWajXLlyxMfHu4wTHx9PzZo1ne9tNht33303H374IV988QVffvklx48fB4oD6PnWe0L16tUpLCxk48aNzmU7d+4kJeX0z4WzWq3YbDaXl4iIiFydroigCvDuu+9SWFhIo0aN+OKLL0hISCAxMZEZM2awbdu2EjOaJ6tatSobNmxgyZIlbN++nZdffrnEjUBPPfUUY8aM4auvvmLbtm088cQTpKamnnLMatWqce+993L//fczb9489uzZw7p16xg9ejQLFy48632rVKkSW7ZsITExkaNHj7rcGX+yvLw8kpOTSU5OJiEhgSeffJLMzEznUw/uvfdePD096dWrF7/99hvff/89Tz75JD179iQ0tPh6l2effZbXX3+dL774gsTERJ5//nk2bdrEU089BcCbb77JrFmz2LZtG9u3b2fOnDmEhYUREBDgrHf58uUkJyefMVieSo0aNWjbti2PPPII69atY+PGjTzyyCN4eXmd9ls7RERE5NpxRZz6B6hSpQobN25k1KhRvPDCC/z1119YrVZq1qzJoEGDeOKJJ07b/9FHH2Xjxo3cfffdmEwmunfvzhNPPMG3337rbPPMM8+QlJREr169MJvNPPjgg3Tt2pW0tLRTjjtt2jRGjhzJM888w4EDByhTpgw33HADt95661nvW58+fVi5ciWNGjUiMzOT77//npYtW5badvHixYSHhwPg5+dHjRo1mDNnjrO9t7c3S5Ys4amnnqJx48Z4e3tzxx138OabbzrH6N+/P2lpaTzzzDMcPnyYmjVr8s033zhvZPLz82Ps2LHs2LEDi8VC48aNWbRokfOGrfHjxzNw4EA+/PBDypcvz969e896X//t008/5aGHHqJ58+aEhYUxevRofv/9dzw9Pc9rPBEREbm6mBzne7GkyAX2119/ERERwbJly2jT5tRfM/dv6enp+Pv7EzFgNmar90WuUERE5Nqxd0zHizb2id/faWlpp72M74qZUZWrz4oVK8jMzCQmJoakpCSee+45KlWqRPPmzS93aSIiImIACqpy2RQUFDBkyBB2796Nn58fzZo1Y+bMmRfs625FRETkyqagKpdNXFwccXFxl7sMERERMagr5q5/EREREbm2KKiKiIiIiCEpqIqIiIiIISmoioiIiIghKaiKiIiIiCEpqIqIiIiIISmoioiIiIghKaiKiIiIiCEpqIqIiIiIISmoioiIiIgh6StU5arw2/A4bDbb5S5DRERELiDNqIqIiIiIISmoioiIiIghKaiKiIiIiCEpqIqIiIiIISmoioiIiIghKaiKiIiIiCEpqIqIiIiIISmoioiIiIghKaiKiIiIiCHpm6nkqlB76BLMVu/LXYaIiMgVbe+Yjpe7BBeaURURERERQ1JQFRERERFDUlAVEREREUNSUBURERERQ1JQFRERERFDUlAVEREREUNSUBURERERQ1JQFRERERFDUlAVEREREUPSN1OJiIjINSvj1wWkrZ2HPSsFj7KRBLV9FGu56qdsn77+azI2LcKefgSzlw3v6rEEtuiFyc3D2aYw4yipK6eTs/sXHIV5uAWEE3zLAKzhVQEoys8hddV0srf/TFFuBm7+ofg17IRf/VuK+6cd4sB7D5W6/TKdn8enxo0X8BMwNgVVERERuSZlJfzA8RUfEdy+Lx7lqpOx4WsOz36Fcn3ex+ITULL9HytJWTWdMrc8hbV8NAXHD3Bs0QQAgtr0AcCem0nyjOfwrFCHsncNw+ztT2HKQcyevs5xUlZ8RO6fWyjT6Rnc/EPJ2bOR49+9i8U3GO+q12PxK8N1fT9z2XbG5sWkr5uHV+WGF+3zMCIFVREREbkmpa//Cr+6cfjWaQdAUFxfcnatJ3PrUvxvuKtE+7wDCXheF41PzZYAuPmH4h3dnPyD2/8Z8+e5uNnKUKbjAOcy94CwEuP41G6NZ4U6APjV60Dmpm/JS9qOd9XrMZktWHwDXfpkb1+Dd/UbMXt4XYhdv2LoGlURERG55jjsBeQn78SzYj3nMpPJjGeleuQd2FZqH2v5aPKSd5F3MBGAgtRkcnZtwKtKI2ebnJ1r8QirypGvRrP/nXs5OK0/GZsWlxgnZ+c6CjOO4nA4yP1zCwUpB/GKrF/qdvOSd1JweDe+ddr/x72+8mhGVURERK459ux0cBSVOMVv8Q6g4NhfpfbxqdkSe3Y6yTMHAw4osuNb72b8m3ZztilITaZg4yJsjbsQ2rQbeUk7SFn+ASaLO74xbQAIavsYx5a8w4F3e4PZAiYTwR2exDOidqnbzdzyHe7BEXheF30hdv2KohnVq8ywYcOoV6/eOfUxmUx89dVXF6UeERGRq0Xuvi2k/TyboPaPE957IiFdh5CzawOp8bP+aeRwYA2tQmCLXniEVsGvXgd868aRsWmRs0n6L/9H3sFEQu54mfBeEwhs9RDHl75Hzt5NJbZZVJBH1h+rnJcnXGsUVK8Aa9aswWKx0LFjx8tdykWjsCwiIpeSxdsGJjP2rFSX5fbsVCw+gaX2SV09A99arfGrG4dHSCW8qzUjoMX9pP88F4ejqHhc30Dcy1Rw6eceHIE9/QhQHDxTf/iUwNYP4x11PR5lI7E17IRPjZtIXzevxDazE+NxFOThU7vNBdjrK4+C6hVg6tSpPPnkk/zwww8cPHjwcpcjIiJyxTNZ3PEIiyL3z83OZQ5HEbl7N2MtX6PUPo6CPDCZXMcxmU90BsBaviYFx10vHSg4fgA3W9niN0V2KCrEhOs4mMzOMf4tc8t3eEc1weLtfy67d9VQUDW4zMxMvvjiCx5//HE6duzI9OnTXdaPGTOG0NBQ/Pz8eOihh8jNzXVZv379etq1a0eZMmXw9/enRYsW/PrrryW2k5SUxM0334yXlxeVK1dm7ty5Luu3bt1K69at8fLyIjg4mEceeYTMzEzn+qKiIkaMGMF1112H1WqlXr16LF78z8Xj+fn59OvXj/DwcDw9PalYsSKjR48GoFKlSgB07doVk8nkfC8iInIx2Rp3IWPzEjK3Lqfg6H6OL3kXR0EuvjFtATi6YDwpq6Y723tFNSFj4yKy/lhVfCPVno2krp6BV1QTTGbL32N2Ju9gImlrZlOQcpCsP1aSuXkxvg2Kz4qard5YI2qTsvJjcvdtoSA1mcyty8j6fQXe1Zq61FeQcpC8/b/jWzfu0nwgBqSganCzZ8+mRo0aVK9enfvuu4+PP/4Yx99/cc2ePZthw4YxatQoNmzYQHh4OO+++65L/4yMDHr16sWPP/7Izz//TNWqVbnlllvIyMhwaffyyy9zxx13sHnzZu69917uueceEhISAMjKyiIuLo7AwEDWr1/PnDlzWLZsGf369XP2nzhxIuPHj2fcuHFs2bKFuLg4brvtNnbs2AHA22+/zTfffMPs2bNJTExk5syZzkC6fv16AKZNm0ZSUpLzfWny8vJIT093eYmIiJwPn+jmBLZ6kNQfZ3Bw+pPkH95N2W4jnKf+C9OPYM9Mcbb3b3YPtsZdSV09g6SpT3Ds27fxjGxAcFxfZxtreDVCur5I1h+rODi1L6nxnxPYug++tVo524TcNhiPsGoc/b9xJE19grSf5xJwU098693sUl/mlqVY/MrgeYqnAVwLTA5HKfPMYhixsbF069aNp556isLCQsLDw5kzZw4tW7akWbNm1K9fn8mTJzvb33DDDeTm5rJp06ZSxysqKiIgIID//e9/3HrrrUDx9aGPPfYYU6ZMcRmnQYMGvPvuu3z44YcMHjyY/fv34+PjA8CiRYvo1KkTBw8eJDQ0lPLly9O3b1+GDBniHKNJkyY0btyYyZMn079/f37//XeWLVuG6aTTJidqmD9/Pl26dDnt5zFs2DCGDx9eYnnEgNmYrd6n7SsiIiKnt3fMpbkfJj09HX9/f9LS0rDZbKdspxlVA0tMTGTdunV0794dADc3N+6++26mTp0KQEJCAtdff71Ln6ZNXU8bHDp0iD59+lC1alX8/f2x2WxkZmayb9++0/Zr2rSpc0Y1ISGBunXrOkMqFAfooqIiEhMTSU9P5+DBg8TGxrqMERsb6xyjd+/ebNq0ierVq9O/f3++++678/pMXnjhBdLS0pyv/fv3n9c4IiIiYnx6jqqBTZ06lcLCQsqVK+dc5nA4sFqtTJo06azG6NWrF8eOHWPixIlUrFgRq9VK06ZNyc/Pv1hll6pBgwbs2bOHb7/9lmXLltGtWzfatm1b4lrYM7FarVit1otUpYiIiBiJZlQNqrCwkE8//ZTx48ezadMm52vz5s2UK1eOWbNmER0dzdq1a136/fzzzy7v4+Pj6d+/P7fccgu1atXCarVy9OjREts7ud/PP/9MdHTxg4Wjo6PZvHkzWVlZLuOazWaqV6+OzWajXLlyxMfHl9h2zZo1ne9tNht33303H374IV988QVffvklx48fB8Dd3R273X4en5SIiIhcrTSjalALFiwgJSWFhx56CH9/10dS3HHHHUydOpVBgwbRu3dvGjVqRGxsLDNnzuT333+ncuXKzrZVq1bls88+o1GjRqSnp/Pss8/i5VXye4LnzJlDo0aNuPHGG5k5cybr1q1zXmJw7733MnToUHr16sWwYcM4cuQITz75JD179iQ0NBSAZ599lqFDh1KlShXq1avHtGnT2LRpEzNnzgTgzTffJDw8nPr162M2m5kzZw5hYWEEBAQAxXf+L1++nNjYWKxWK4GBpT/DTkRERK4dmlE1qKlTp9K2bdsSIRWKg+qGDRuIjo7m5Zdf5rnnnqNhw4b8+eefPP744yXGSUlJoUGDBvTs2ZP+/ftTtmzZEmMOHz6czz//nDp16vDpp58ya9Ys52yot7c3S5Ys4fjx4zRu3Jg777yTNm3auFx+0L9/fwYOHMgzzzxDTEwMixcv5ptvvqFq1aoA+Pn5MXbsWBo1akTjxo3Zu3cvixYtwmwu/ic4fvx4li5dSkREBPXrX7t3N4qIiMg/dNe/XNFO3DWou/5FRET+O931LyIiIiJyFhRURURERMSQFFRFRERExJAUVEVERETEkBRURURERMSQFFRFRERExJAUVEVERETEkBRURURERMSQFFRFRERExJAUVEVERETEkBRURURERMSQFFRFRERExJAUVEVERETEkNwudwEiF8Jvw+Ow2WyXuwwRERG5gDSjKiIiIiKGpKAqIiIiIoakoCoiIiIihqSgKiIiIiKGpKAqIiIiIoakoCoiIiIihqSgKiIiIiKGpKAqIiIiIoakoCoiIiIihqRvppKrQu2hSzBbvS93GSIiIlekvWM6Xu4SSqUZVRERERExJAVVERERETEkBVURERERMSQFVRERERExJAVVERERETEkBVURERERMSQFVRERERExJAVVERERETEkBVURERERMSR9M5WIiIhcczJ+XUDa2nnYs1LwKBtJUNtHsZarfsr26eu/JmPTIuzpRzB72fCuHktgi16Y3DycbQozjpK6cjo5u3/BUZiHW0A4wbcMwBpetcR4x5ZMInPTYgJb98HWuLNz+eEvR5B/aA/27FQsnr54VqpHQIveuPkFX9gP4AqhoCoiIiLXlKyEHzi+4iOC2/fFo1x1MjZ8zeHZr1Cuz/tYfAJKtv9jJSmrplPmlqewlo+m4PgBji2aAEBQmz4A2HMzSZ7xHJ4V6lD2rmGYvf0pTDmI2dO3xHjZ238i72AiFt+gEus8K9TB/4ZuWHyDKMw4Rur3Uzn61WjCeo67oJ/BlUKn/kVEROSakr7+K/zqxuFbpx0eZSoQFNcXk7uVzK1LS22fdyABz+ui8anZEjf/ULwiG+Ad3Zz8pB3/jPnzXNxsZSjTcQDWctVxDwjDK7IB7oHhLmMVZhzl+NL3KXPrIDCXnC+0Ne6CtXwN3PzL4nldNLYb7iLvYCIOe+GF/RCuEAqqIiIics1w2AvIT96JZ8V6zmUmkxnPSvXIO7Ct1D7W8tHkJe8i72AiAAWpyeTs2oBXlUbONjk71+IRVpUjX41m/zv3cnBafzI2LXbdtqOIowvexHb97XiEVDxjrfacDLL+WIm1fDQmy7V5Evza3GuDMJlMzJ8/ny5dulzuUkRERK4J9ux0cBSVOMVv8Q6g4NhfpfbxqdkSe3Y6yTMHAw4osuNb72b8m3ZztilITaZg4yJsjbsQ2rQbeUk7SFn+ASaLO74xbYDiWVeT2YJfw9tOW2PKymlk/LoAR0EeHuWqU/bOof9pn69k1/SMau/evTGZTJhMJtzd3YmMjOS5554jNzf3cpd2Uf17v//92rlz52WtSYFdRESMKHffFtJ+nk1Q+8cJ7z2RkK5DyNm1gdT4Wf80cjiwhlYhsEUvPEKr4FevA75148jYtAiAvOSdpP/yDcG3DMBkMp12e7YmtxPe+23KdnsVk8nCsQVv4nA4LuYuGtY1P6PaoUMHpk2bRkFBAb/88gu9evXCZDLx+uuvX+7SLqoT+/1vISEh5zVWfn4+Hh4eZ24oIiJymVm8bWAyY89KdVluz07F4hNYap/U1TPwrdUav7pxAHiEVKKoII/jiyfh3+xuTCYzFt9A3MtUcOnnHhxBdmI8AHn7f6coK40DUx74p4GjiJTvp5K+4Wuue/zjf9Xoj8XbH/eg8rgHR3BgSm/yD27DWj76AnwCV5ZrekYVwGq1EhYWRkREBF26dKFt27YsXfrPxdTHjh2je/fulC9fHm9vb2JiYpg1a5bLGC1btqR///4899xzBAUFERYWxrBhw1za7Nixg+bNm+Pp6UnNmjVdtnHC1q1bad26NV5eXgQHB/PII4+QmZnpXH9i1nHUqFGEhoYSEBDAiBEjKCws5NlnnyUoKIjrrruuRAA93X7/+2WxWABYtWoVTZo0wWq1Eh4ezvPPP09h4T8Xcbds2ZJ+/foxYMAAypQpQ1xc8Q/ub7/9xs0334yvry+hoaH07NmTo0ePOvvNnTuXmJgY5/61bduWrKwshg0bxieffMLXX3/tnN1duXLlGfdBRETkXJks7niERZH752bnMoejiNy9m7GWr1FqH0dBHpw0C2oymU90BsBaviYFx10vHSg4fgA3W1kAfGq3IvzBdwh/4G3ny+IbhK3J7YR2G3GaiouKN2MvOJfdvGpc80H133777Td++uknl9nB3NxcGjZsyMKFC/ntt9945JFH6NmzJ+vWrXPp+8knn+Dj48PatWsZO3YsI0aMcIbRoqIibr/9djw8PFi7di3vvfcegwcPdumflZVFXFwcgYGBrF+/njlz5rBs2TL69evn0m7FihUcPHiQH374gTfffJOhQ4dy6623EhgYyNq1a3nsscd49NFH+euv0q+zOZMDBw5wyy230LhxYzZv3syUKVOYOnUqI0eOLLG/Hh4exMfH895775Gamkrr1q2pX78+GzZsYPHixRw6dIhu3Yqv30lKSqJ79+48+OCDJCQksHLlSm6//XYcDgeDBg2iW7dudOjQgaSkJJKSkmjWrFmp9eXl5ZGenu7yEhERORe2xl3I2LyEzK3LKTi6n+NL3sVRkItvTFsAji4YT8qq6c72XlFNyNi4iKw/VhXfSLVnI6mrZ+AV1QST2fL3mJ3JO5hI2prZFKQcJOuPlWRuXoxvg44AWLxseIRUcnlhdsPiE4h78HUA5B1MJP2X/yP/0G4K0w6T8+dmjn7zBm4B4VjLXXuzqaBT/yxYsABfX18KCwvJy8vDbDYzadIk5/ry5cszaNAg5/snn3ySJUuWMHv2bJo0aeJcXqdOHYYOLb7YuWrVqkyaNInly5fTrl07li1bxrZt21iyZAnlypUDYNSoUdx8883O/v/73//Izc3l008/xcfHB4BJkybRqVMnXn/9dUJDQwEICgri7bffxmw2U716dcaOHUt2djZDhgwB4IUXXmDMmDH8+OOP3HPPPWfc7xNuvvlm5syZw7vvvktERASTJk3CZDJRo0YNDh48yODBg3nllVcwm83OfRw7dqyz/8iRI6lfvz6jRo1yLvv444+JiIhg+/btZGZmUlhYyO23307FisV3OsbExDjbenl5kZeXR1hY2GmP1+jRoxk+fPhp24iIiJyOT3Rz7NlppP444+8H/lembLcRzlP/helHwPTPXJ5/s3sAE6mrZ2DPPIbZyx+vqCYENu/pbGMNr0ZI1xdJXfUJqfGzcPMPJbB1H3xrtTrrukzuVrK3ryHtx/9RVJCLxTcIr8gGlOl8NyY39wu2/1eSaz6otmrViilTppCVlcVbb72Fm5sbd9xxh3O93W5n1KhRzJ49mwMHDpCfn09eXh7e3t4u49SpU8flfXh4OIcPHwYgISGBiIgIZ0gFaNq0qUv7hIQE6tat6wypALGxsRQVFZGYmOgMqrVq1XKGRYDQ0FBq167tfG+xWAgODnZu+0z7fcKJ7SYkJNC0aVOXC71jY2PJzMzkr7/+okKF4utvGjZs6DLe5s2b+f77713C7wm7du2iffv2tGnThpiYGOLi4mjfvj133nkngYGlXw90Ki+88AIDBw50vk9PTyciIuKcxhAREbE17IStYadS14X1GOPy3mS2EHBjDwJu7HHaMb2jmuAd1eS0bf7t39elQvG1r2HdR52i9bXpmg+qPj4+REVFAcUzgHXr1mXq1Kk89NBDALzxxhtMnDiRCRMmEBMTg4+PDwMGDCA/P99lHHd31790TCYTRUVFF7ze0rZzPtv+936fj38HaoDMzEzn7O/JwsPDsVgsLF26lJ9++onvvvuOd955hxdffJG1a9cSGRl51tu1Wq1YrdbzrltERESuHLpG9V/MZjNDhgzhpZdeIicnB4D4+Hg6d+7MfffdR926dalcuTLbt28/p3Gjo6PZv38/SUlJzmU///xziTabN28mKyvLuSw+Pt55iv9SiY6OZs2aNS6PwYiPj8fPz4/rrrvulP0aNGjA77//TqVKlYiKinJ5nQi1JpOJ2NhYhg8fzsaNG/Hw8GD+/PkAeHh4YLfbL+7OiYiIyBVFQfUkd911FxaLhcmTJwPF12KemAlMSEjg0Ucf5dChQ+c0Ztu2balWrRq9evVi8+bNrF69mhdffNGlzb333ounpye9evXit99+4/vvv+fJJ5+kZ8+eztP+l8ITTzzB/v37efLJJ9m2bRtff/01Q4cOZeDAgS6XHJysb9++HD9+nO7du7N+/Xp27drFkiVLeOCBB7Db7axdu5ZRo0axYcMG9u3bx7x58zhy5AjR0cUXh1eqVIktW7aQmJjI0aNHKSi4Nu9uFBERkX8oqJ7Ezc2Nfv36MXbsWLKysnjppZdo0KABcXFxtGzZkrCwsHN+ML3ZbGb+/Pnk5OTQpEkTHn74YV577TWXNt7e3ixZsoTjx4/TuHFj7rzzTtq0aeNyY9elUL58eRYtWsS6deuoW7cujz32GA899BAvvfTSafuVK1eO+Ph47HY77du3JyYmhgEDBhAQEIDZbMZms/HDDz9wyy23UK1aNV566SXGjx/vvKGsT58+VK9enUaNGhESEkJ8fPyl2F0RERExMJPjWv2qA7kqpKen4+/vT8SA2Zit3mfuICIiIiXsHdPxkm7vxO/vtLQ0bDbbKdtpRlVEREREDElBVUREREQMSUFVRERERAxJQVVEREREDElBVUREREQMSUFVRERERAxJQVVEREREDElBVUREREQMSUFVRERERAxJQVVEREREDElBVUREREQMSUFVRERERAxJQVVEREREDMntchcgciH8NjwOm812ucsQERGRC0gzqiIiIiJiSAqqIiIiImJICqoiIiIiYkgKqiIiIiJiSAqqIiIiImJICqoiIiIiYkgKqiIiIiJiSAqqIiIiImJICqoiIiIiYkj6Ziq5KtQeugSz1ftylyEiImJ4e8d0vNwlnDXNqIqIiIiIISmoioiIiIghKaiKiIiIiCEpqIqIiIiIISmoioiIiIghKaiKiIiIiCEpqIqIiIiIISmoioiIiIghKaiKiIiIiCHpm6lERETkqpfx6wLS1s7DnpWCR9lIgto+irVc9VO2T1//NRmbFmFPP4LZy4Z39VgCW/TC5OYBQOqPM0mLn+XSxy3oOsr3ec/5Pvl/z5O3/zeXNr71OhAc16/E9uw56SR9/CT2zGNEPPU5Zk/f/7K7Vw0FVREREbmqZSX8wPEVHxHcvi8e5aqTseFrDs9+hXJ93sfiE1Cy/R8rSVk1nTK3PIW1fDQFxw9wbNEEAILa9HG2cy9TgdC7X/uno7nkiWrfunEE3Hif873J3Vpqjce+fRv3spWwZx47v528SunUv4iIiFzV0td/hV/dOHzrtMOjTAWC4vpicreSuXVpqe3zDiTgeV00PjVb4uYfildkA7yjm5OftMO1odmCxTfwn5e3f4mxTG5WlzZmq3eJNhkbF1GUm4mtye0XZH+vJppRFRERkauWw15AfvJO/G+4y7nMZDLjWakeeQe2ldrHWj6azN9XkncwEWu56hSkJpOzawO+tVu7tCtMOchfk+/HZHHHo3wNAlv0ws1W1qVN1h8ryfpjJRafALyimuDf7B7M7p7O9flH95EWP4uw+8dTmJp8Aff86qCgKiIiIlcte3Y6OIpKnOK3eAdQcOyvUvv41GyJPTud5JmDAQcU2fGtdzP+Tbs521jDqxN8y9O4B5XHnnmctPhZJM8cTLkHJztnTX1qtsTNFoLFL5j8w3tIXTmdguMHKNv1RQAchQUc/WYsAa0exM1WVkG1FDr1f5VYuXIlJpOJ1NTUi76tYcOGUa9evRLLQkNDMZlMfPXVV/Tu3ZsuXbpc9FpEREQutNx9W0j7eTZB7R8nvPdEQroOIWfXBlL/dfOUV5VG+NS4EY+ykXhVbkjZu4ZRlJtF1rYfnW386nXAq3JDPEIq4VurFcG3DiRn+xoKUpIASFk1HffgCHxrtbrk+3iluKhBtXfv3phMphKvDh06nPUYLVu2ZMCAARelvpYtW5Za34lXy5YtL8p2z8fGjRu56667CA0NxdPTk6pVq9KnTx+2b99+yWsZNGgQy5cvd75PSEhg+PDhvP/++yQlJXHzzTczceJEpk+ffslrExER+TeLtw1MZuxZqS7L7dmpWHwCS+2TunoGvrVa41c3Do+QSnhXa0ZAi/tJ/3kuDkdRqX3Mnr64B5WnMPXgKWuxhhc/ZaAwpbhN7r4tZCfG8+fY2/hz7G0c+vwlAPa/3YPU1TPPdVevShf91H+HDh2YNm2ayzKrtfQ73s6Xw+HAbrfj5nZuuzNv3jzy8/MB2L9/P02aNGHZsmXUqlULAA8PD5f2BQUFuLu7X5iiz8GCBQu44447iIuLY+bMmVSpUoXDhw8zZ84cXn75Zb744otLWo+vry++vv88NmPXrl0AdO7cGZPJBPz3Y3y+x1REROTfTBZ3PMKiyP1zM97VmgLgcBSRu3czfg1vLbWPoyAP/v595hzH9PfcnsMBppJ9ivJzKExNwuJz6tnR/MO7AbD4BgEQ0mUIjsK8f9Yn7eDYtxMJu/d13ALCz3ofr2YX/dS/1WolLCzM5RUYWPwXzMqVK/Hw8GD16tXO9mPHjqVs2bIcOnSI3r17s2rVKiZOnOic5dy7d6/zNPe3335Lw4YNsVqt/Pjjj+zatYvOnTsTGhqKr68vjRs3ZtmyZaesLSgoyFlTSEgIAMHBwc5lwcHBTJkyhdtuuw0fHx9ee634ERRff/01DRo0wNPTk8qVKzN8+HAKCwud46ampvLwww8TEhKCzWajdevWbN682bl+8+bNtGrVCj8/P2w2Gw0bNmTDhg2l1pidnc0DDzzALbfcwjfffEPbtm2JjIzk+uuvZ9y4cbz//vul9jt27Bjdu3enfPnyeHt7ExMTw6xZrs97mzt3LjExMXh5eREcHEzbtm3JyspyHpsmTZrg4+NDQEAAsbGx/Pnnn4Drqf9hw4bRqVMnAMxmszOonnzqv6ioiNGjRxMZGYmXlxd169Zl7ty5zvWnOqYiIiL/la1xFzI2LyFz63IKju7n+JJ3cRTk4hvTFoCjC8aTsmq6s71XVBMyNi4i649VxTdS7dlI6uoZeEU1wWS2AJCyYiq5+7ZSmHaI3L8SODLvNTCZ8anZAoCClCRS42eRl7yTwrRDZO9Yy7GFb2KNqI1H2UgA3APD8Qip5Hy5BYQWLw+OKPWxWdeiyzpddeK0fs+ePdm8eTO7d+/m5ZdfZs6cOYSGhjJx4kS2b99O7dq1GTFiBAAhISHs3bsXgOeff55x48ZRuXJlAgMD2b9/P7fccguvvfYaVquVTz/9lE6dOpGYmEiFChXOq8Zhw4YxZswYJkyYgJubG6tXr+b+++/n7bff5qabbmLXrl088sgjAAwdOhSAu+66Cy8vL7799lv8/f15//33adOmDdu3bycoKIh7772X+vXrM2XKFCwWC5s2bTrlTO2SJUs4evQozz33XKnrAwICSl2em5tLw4YNGTx4MDabjYULF9KzZ0+qVKlCkyZNSEpKonv37owdO5auXbuSkZHB6tWrcTgcFBYW0qVLF/r06cOsWbPIz89n3bp1zhD6b4MGDaJSpUo88MADJCUlnfJzHD16NDNmzOC9996jatWq/PDDD9x3332EhITQokULZ7uTj+nJ8vLyyMv756/P9PT0U25TREQEwCe6OfbsNFJ/nPH3A/8rU7bbCOep/8L0I2D6Z+7Ov9k9gInU1TOwZx7D7OWPV1QTApv3dLYpzDjK0f97A3tOOhYvf6zX1SSs53jnI6pMFjdy/9xMxoZvKCrIxc1WBu9qzf4eW87WRQ+qCxYscDlNDDBkyBCGDBkCwMiRI1m6dCmPPPIIv/32G7169eK2224DwN/fHw8PD7y9vQkLCysx9ogRI2jXrp3zfVBQEHXr1nW+f/XVV5k/fz7ffPMN/fqV/BaIs9GjRw8eeOAB5/sHH3yQ559/nl69egFQuXJlXn31VZ577jmGDh3Kjz/+yLp16zh8+LDz9Pe4ceP46quvmDt3Lo888gj79u3j2WefpUaNGgBUrVr1lNvfsaP4mW0n2p6t8uXLM2jQIOf7J598kiVLljB79mxnUC0sLOT222+nYsWKAMTExABw/Phx0tLSuPXWW6lSpQoA0dHRpW7H19fXGZZLO0ZQHC5HjRrFsmXLaNq0+LRL5cqV+fHHH3n//fddgurJx/Rko0ePZvjw4Wf5KYiIiBSzNeyErWGnUteF9Rjj8t5kthBwYw8CbuxxyvFCOg8+7fbcbCElxj0Tzwp1qDh4wTn1udpd9KDaqlUrpkyZ4rIsKCjI+d8eHh7MnDmTOnXqULFiRd56662zHrtRo0Yu7zMzMxk2bBgLFy50BrGcnBz27dt33vWfvI3NmzcTHx/vvAwAwG63k5ubS3Z2Nps3byYzM5Pg4GCXfjk5Oc5rOQcOHMjDDz/MZ599Rtu2bbnrrrucgfBkDofjvOq22+2MGjWK2bNnc+DAAfLz88nLy8Pbu/iRGXXr1qVNmzbExMQQFxdH+/btufPOOwkMDCQoKIjevXsTFxdHu3btaNu2Ld26dSM8/Pyul9m5cyfZ2dklAmh+fj7169d3WXby532yF154gYEDBzrfp6enExERcV51iYiIiLFd9KDq4+NDVFTUadv89NNPQPFM3vHjx/Hx8Tnrsf9t0KBBLF26lHHjxhEVFYWXlxd33nmn84ap83HyNjIzMxk+fDi3317y2yM8PT3JzMwkPDyclStXllh/YuZx2LBh9OjRg4ULF/Ltt98ydOhQPv/8c7p27VqiT7Vq1QDYtm2bczbybLzxxhtMnDiRCRMmEBMTg4+PDwMGDHB+FhaLhaVLl/LTTz/x3Xff8c477/Diiy+ydu1aIiMjmTZtGv3792fx4sV88cUXvPTSSyxdupQbbrjhrGs4ITMzE4CFCxdSvnx5l3Un33R1pmNvtVov+M14IiIiYkyX/ZbqXbt28fTTT/Phhx/yxRdf0KtXL5YtW4b57+/L9fDwwG63n9VY8fHx9O7d2xn4MjMzndezXigNGjQgMTHxlOG7QYMGJCcn4+bmRqVKlU45TrVq1ahWrRpPP/003bt3Z9q0aaUG1fbt21OmTBnGjh3L/PnzS6xPTU0t9TrV+Ph4OnfuzH33FX+/cFFREdu3b6dmzZrONiaTidjYWGJjY3nllVeoWLEi8+fPd85Y1q9fn/r16/PCCy/QtGlT/ve//51XUK1ZsyZWq5V9+/a5nOYXEREROZ2LHlTz8vJITnb9pgU3NzfKlCmD3W7nvvvuIy4ujgceeIAOHToQExPD+PHjefbZZwGoVKkSa9euZe/evfj6+rpcNnCyqlWrMm/ePDp16oTJZOLll1+mqKj0552dr1deeYVbb72VChUqcOedd2I2m9m8eTO//fYbI0eOpG3btjRt2pQuXbowduxYqlWrxsGDB1m4cCFdu3alVq1aPPvss9x5551ERkby119/sX79eu64445St+fj48NHH33EXXfdxW233Ub//v2Jiori6NGjzJ49m3379vH555+X+lnMnTuXn376icDAQN58800OHTrkDKpr165l+fLltG/fnrJly7J27VqOHDlCdHQ0e/bs4YMPPuC2226jXLlyJCYmsmPHDu6///7z+sz8/PwYNGgQTz/9NEVFRdx4442kpaURHx+PzWZzXu8rIiIi8m8XPaguXry4xLWN1atXZ9u2bbz22mv8+eefLFhQfOFweHg4H3zwAd27d6d9+/bUrVuXQYMG0atXL2rWrElOTg579uw55bbefPNNHnzwQZo1a0aZMmUYPHjwBb8rPC4ujgULFjBixAhef/113N3dqVGjBg8//DBQPEu5aNEiXnzxRR544AGOHDlCWFgYzZs3JzQ0FIvFwrFjx7j//vs5dOgQZcqU4fbbbz/tDUKdO3fmp59+YvTo0fTo0cN5XWbr1q0ZOXJkqX1eeukldu/eTVxcHN7e3jzyyCN06dKFtLQ0AGw2Gz/88AMTJkwgPT2dihUrMn78eG6++WYOHTrEtm3b+OSTTzh27Bjh4eH07duXRx999Lw/t1dffZWQkBBGjx7N7t27CQgIoEGDBs6b6kREREROZnKc7906IgaQnp6Ov78/EQNmO79bWURERE5t75iOl7sE5+/vtLQ0bDbbKdtd9Af+i4iIiIicDwVVERERETEkBVURERERMSQFVRERERExJAVVERERETEkBVURERERMSQFVRERERExJAVVERERETEkBVURERERMSQFVRERERExJAVVERERETEkBVURERERMSQFVRERERExJLfLXYDIhfDb8DhsNtvlLkNEREQuIM2oioiIiIghKaiKiIiIiCEpqIqIiIiIISmoioiIiIghKaiKiIiIiCEpqIqIiIiIISmoioiIiIghKaiKiIiIiCEpqIqIiIiIIembqeSqUHvoEsxW78tdhoiIyAW1d0zHy13CZaUZVRERERExJAVVERERETEkBVURERERMSQFVRERERExJAVVERERETEkBVURERERMSQFVRERERExJAVVERERETEkBVURERERMSR9M5WIiIgYTsavC0hbOw97VgoeZSMJavso1nLVT9k+ff3XZGxahD39CGYvG97VYwls0QuTmwcAuft/I33tl+Qf2oU98zghXV/Eu1pTlzGOLnyLrN+WuyzzjGxAaLcRzvd5yTtJXTmdvOQdmExmvKs3I7D1w5g9vC7g3ssJCqoiIiJiKFkJP3B8xUcEt++LR7nqZGz4msOzX6Fcn/ex+ASUbP/HSlJWTafMLU9hLR9NwfEDHFs0AYCgNn0AcOTn4l62Mr512nFk/qhTbtszsiFlbhnwzwI3d+d/FmYc4/AXL+Fd4yaC2j1GUX42Kcs/5NjCtwjpOuRC7LqcREFVREREDCV9/Vf41Y3Dt047AILi+pKzaz2ZW5fif8NdJdrnHUjA87pofGq2BMDNPxTv6ObkH9zubONVpRFeVRqdcdsmN3csvoGlrsvZtR7MbgS1fxyTyeysLenjfhSkHMQ9sNy57qqcga5RFREREcNw2AvIT96JZ8V6zmUmkxnPSvXIO7Ct1D7W8tHkJe8i72AiAAWpyeTs2nBWwfRkufu2sv+deznw4aMcWzIZe066S20mi5szpALOSwvy/vrjnLclZ6YZVRERETEMe3Y6OIpKnOK3eAdQcOyvUvv41GyJPTud5JmDAQcU2fGtdzP+Tbud07a9IhvgXa0ZbgGhFKYkkfrDpxyeM5Sw+8ZhMlvwrFCHlBUfkbb2S2yNbqOoII/UldOL6848fh57K2eiGVUpITk5mSeffJLKlStjtVqJiIigU6dOLF/ueoH56NGjsVgsvPHGGyXGmD59OiaTCZPJhNlsJjw8nLvvvpt9+/axd+9e57pTvaZPn36J9lZERK50ufu2kPbzbILaP05474mEdB1Czq4NpMbPOqdxfGq2wLvq9XiEVMK7WlNC7hxKftIOcvdtBcAjpCJlOj5N+vr57Bt/B39Nug+3gDDMPgFgUqS6GDSjKi727t1LbGwsAQEBvPHGG8TExFBQUMCSJUvo27cv27b9c9rl448/5rnnnuPjjz/m2WefLTGWzWYjMTERh8PBnj17eOKJJ7jrrrv46aefSEpKcrYbN24cixcvZtmyZc5l/v7+F3dHRUTEkCzeNjCZsWeluiy3Z6di8Sn92tHU1TPwrdUav7pxAHiEVKKoII/jiyfh3+xul1P158I9IAyzl43C1CSgHlA8e+tTsyX2rBRM7p6AifT1X+EWEHZe25DTU1AVF0888QQmk4l169bh4+PjXF6rVi0efPBB5/tVq1aRk5PDiBEj+PTTT/npp59o1qyZy1gmk4mwsOIf3PDwcB566CH69+9PVlaWczmAr68vbm5uLstEROTaZLK44xEWRe6fm52Pj3I4isjduxm/hreW2sdRkAcmk+s4J8KpwwGmUjqdhcL0oxTlZGDxCSqx7kRoztzyHSY3d7wq1Tu/jchpKaiK0/Hjx1m8eDGvvfaaS0g9ISAgwPnfU6dOpXv37ri7u9O9e3emTp1aIqj+2+HDh5k/fz4WiwWLxXLeNebl5ZGXl+d8n56efprWIiJyJbI17sLRhW/hEVYVa3g10jd8jaMgF9+YtgAcXTAei18wgS16A+AV1YT09V/hUbYyHuWqF19funoGXlFNMJmLf+cU5edQmPLP2bzCtEPkH9qN2csXN1tZivJzSIufhXe1Zlh8AylISSJ15TTcAsPximzg7Jf+y/9hLR+N2cOL3L0bSfl+GgEtemH29L10H9A1REFVnHbu3InD4aBGjRqnbZeens7cuXNZs2YNAPfddx833XQTEydOxNf3nx/UtLQ0fH19cTgcZGdnA9C/f/9SQ/DZGj16NMOHDz/v/iIiYnw+0c2xZ6eR+uOMvx/4X5my3UY4ZzEL04+4XBPq3+wewETq6hnYM49h9vLHK6oJgc17OtvkJ+/g0Kx/nnWasuKj4m3VbkOZjk+DyUz+4T1k/racotwsLL5BeEXWJ+Cm+zD961mq+UnbSfvxfxQV5OAedB1BcX3xrd36In8i1y4FVXFyOBxn1W7WrFlUqVKFunXrAlCvXj0qVqzIF198wUMPPeRs5+fnx6+//kpBQQHffvstM2fO5LXXXvtPNb7wwgsMHDjQ+T49PZ2IiIj/NKaIiBiPrWEnbA07lbourMcYl/cms4WAG3sQcGOPU47nWaEOFQcvOOV6s7uV0LtfPWNdZW595oxt5MJRUBWnqlWrYjKZXG6YKs3UqVP5/fffcXP7559PUVERH3/8sUtQNZvNREVFARAdHc2uXbt4/PHH+eyzz867RqvVitVqPe/+IiIicuXQsxTEKSgoiLi4OCZPnkxWVlaJ9ampqWzdupUNGzawcuVKNm3a5HytXLmSNWvWnDbkPv/883zxxRf8+uuvF3M3RERE5CqhoCouJk+ejN1up0mTJnz55Zfs2LGDhIQE3n77bZo2bcrUqVNp0qQJzZs3p3bt2s5X8+bNady4MVOnTj3l2BEREXTt2pVXXnnlEu6RiIiIXKkUVMVF5cqV+fXXX2nVqhXPPPMMtWvXpl27dixfvpyJEycyY8YM7rjjjlL73nHHHXz66acUFBSccvynn36ahQsXsm7duou1CyIiInKVMDnO9g4aEQNKT0/H39+fiAGzMVu9L3c5IiIiF9TeMR0vdwkXxYnf32lpadhstlO204yqiIiIiBiSgqqIiIiIGJKCqoiIiIgYkoKqiIiIiBiSgqqIiIiIGJKCqoiIiIgYkoKqiIiIiBiSgqqIiIiIGJKCqoiIiIgYkoKqiIiIiBiSgqqIiIiIGJKCqoiIiIgYkoKqiIiIiBiS2+UuQORC+G14HDab7XKXISIiIheQZlRFRERExJAUVEVERETEkBRURURERMSQFFRFRERExJAUVEVERETEkBRURURERMSQFFRFRERExJAUVEVERETEkBRURURERMSQ9M1UclWoPXQJZqv35S5DRETkgtg7puPlLsEQNKMqIiIiIoakoCoiIiIihqSgKiIiIiKGpKAqIiIiIoakoCoiIiIihqSgKiIiIiKGpKAqIiIiIoakoCoiIiIihqSgKiIiIiKGpG+mEhEREUPJ+HUBaWvnYc9KwaNsJEFtH8Varvop26ev/5qMTYuwpx/B7GXDu3osgS16YXLzACB3/2+kr/2S/EO7sGceJ6Tri3hXa+rs77AXkrr6M3J2baAwLRmz1QfPinUJaNEbN79gZ7uC4wdI+f5j8g4k4LAX4BESScBN9+FZsc7F+zCucZpRFREREcPISviB4ys+IiC2O+G9J+JRNpLDs1/BnpVaevs/VpKyajoBsd0p9/AUgm/uT/a21aSs+sTZxpGfi3vZygS1e6zUMRyFeeQn78K/2T2E95pISJchFBw/wJF5r7q0Ozx3OBTZCb3nNcJ7TcC9bCSHvxyOPTPlgu2/uFJQFREREcNIX/8VfnXj8K3TDo8yFQiK64vJ3Urm1qWlts87kIDnddH41GyJm38oXpEN8I5uTn7SDmcbryqNCGzeE+9qzUodw2z1IfSekfhE34R78HVYy9cgqN1j5CfvpDD9MAD27DQKUw5iu+FOPMpG4h5UnsAWvXAU5JF/9M8L/0EIoKAqIiIiBuGwF5CfvBPPivWcy0wmM56V6pF3YFupfazlo8lL3kXewUQAClKTydm1Aa8qjf5TLUV52YAJs9UXALOXDbeg68j6bQVF+bk4iuxkbFqM2TsAj7Co/7QtOTVdoyoiIiKGYM9OB0cRFp8Al+UW7wAKjv1Vah+fmi2xZ6eTPHMw4IAiO771bsa/abfzrsNRmE/qyml412yO2eoNgMlkIvTukRyZP5L9b90FJhMWnwBCuw3H4ul73tuS01NQFRERkStW7r4tpP08m6D2j2MtV53ClIMcX/YhqfGzCIjtfs7jOeyFHPl6DADB7fv+s9zh4PjSKZi9/Qm993VMbh5kbvmOw3NHENbrLdx8gy7YPsk/dOpfzkpycjJPPvkklStXxmq1EhERQadOnVi+fLlLO4fDwc0334zJZOKrr7467Zi9e/fGZDK5vDp06HAR90JERIzM4m0Dk7nEjVP27FQsPoGl9kldPQPfWq3xqxuHR0glvKs1I6DF/aT/PBeHo+ictn8ipBamHabs3a86Z1MBcv/cTM6u9YTcNhjP62piDYsiuP0TmNw9yPpt+WlGlf9CM6pyRnv37iU2NpaAgADeeOMNYmJiKCgoYMmSJfTt25dt2/65bmjChAmYTKazHrtDhw5MmzbN+d5qtV7Q2kVE5MphsrjjERZF7p+bnY+PcjiKyN27Gb+Gt5bax1GQByf93jGZ/p6HczjgLH8lOUNqykFCu4/G4mVzXV+Yd2Lwk4o2F29HLgoFVTmjJ554ApPJxLp16/Dx8XEur1WrFg8++KDz/aZNmxg/fjwbNmwgPDz8rMa2Wq2EhYVd8JpFROTKZGvchaML38IjrCrW8Gqkb/gaR0EuvjFtATi6YDwWv2ACW/QGwCuqCenrv8KjbGU8ylWnMCWJ1NUz8IpqgslsAaAoP4fClCTnNgrTDpF/aDdmL1/cbGWLQ+pXo8k/tIuyd74CRUXOR06ZvXwxWdyxlquB2dOXYwvfwj/2HkxuVjI3L6Ew9dB/vnFLTk1BVU7r+PHjLF68mNdee80lpJ4QEBAAQHZ2Nj169GDy5MnnFDxXrlxJ2bJlCQwMpHXr1owcOZLg4OBTts/LyyMvL8/5Pj09/ex3RkREDM8nujn27DRSf5zx9wP/K1O22wjnqf/C9CPFs5h/8292D2AidfUM7JnHMHv54xXVhMDmPZ1t8pN3cGjWEOf7lBUfFW+rdhvKdHwae+YxcnauBSBpWn+XekK7j8KzQh0s3v6UvWs4qT98yqFZL+IoKsS9TAXK3v4SHmUrX6yP45pncjg0Xy2ntm7dOq6//nrmzZtH165dT9nu0UcfxW6389FHxT/8JpOJ+fPn06VLl1P2+fzzz/H29iYyMpJdu3YxZMgQfH19WbNmDRaLpdQ+w4YNY/jw4SWWRwyY7XItkYiIyJVs75iOl7uEiyo9PR1/f3/S0tKw2WynbKcZVTmts/k75ptvvmHFihVs3LjxnMa+5557nP8dExNDnTp1qFKlCitXrqRNmzal9nnhhRcYOHCg8316ejoRERHntF0RERG5MuiufzmtqlWrYjKZXG6YOtmKFSvYtWsXAQEBuLm54eZW/PfPHXfcQcuWLc96W5UrV6ZMmTLs3LnzlG2sVis2m83lJSIiIlcnBVU5raCgIOLi4pg8eTJZWVkl1qempvL888+zZcsWNm3a5HwBvPXWWy539J/JX3/9xbFjx876RiwRERG5uimoyhlNnjwZu91OkyZN+PLLL9mxYwcJCQm8/fbbNG3alLCwMGrXru3yAqhQoQKRkZHOcWrUqMH8+fMByMzM5Nlnn+Xnn39m7969LF++nM6dOxMVFUVcXNxl2U8RERExFl2jKmdUuXJlfv31V1577TWeeeYZkpKSCAkJoWHDhkyZMuWsx0lMTCQtLQ0Ai8XCli1b+OSTT0hNTaVcuXK0b9+eV199Vc9SFREREUB3/csV7sRdg7rrX0REria667+YTv2LiIiIiCEpqIqIiIiIISmoioiIiIghKaiKiIiIiCEpqIqIiIiIISmoioiIiIghKaiKiIiIiCEpqIqIiIiIISmoioiIiIghKaiKiIiIiCEpqIqIiIiIISmoioiIiIghuV3uAkQuhN+Gx2Gz2S53GSIiInIBaUZVRERERAxJQVVEREREDElBVUREREQMSUFVRERERAxJQVVEREREDElBVUREREQMSUFVRERERAxJQVVEREREDElBVUREREQMSUFVRERERAxJQVVEREREDElBVUREREQMSUFVRERERAxJQVVEREREDElBVUREREQMye1yFyDyXzgcDgDS09MvcyUiIiJytk783j7xe/xUFFTlinbs2DEAIiIiLnMlIiIicq4yMjLw9/c/5XoFVbmiBQUFAbBv377T/kMXY0hPTyciIoL9+/djs9kudzlyBjpeVxYdryvLtX68HA4HGRkZlCtX7rTtFFTlimY2F19m7e/vf03+oF+pbDabjtcVRMfryqLjdWW5lo/X2Uww6WYqERERETEkBVURERERMSQFVbmiWa1Whg4ditVqvdylyFnQ8bqy6HhdWXS8riw6XmfH5DjTcwFERERERC4DzaiKiIiIiCEpqIqIiIiIISmoioiIiIghKaiKiIiIiCEpqIrhTZ48mUqVKuHp6cn111/PunXrTtt+zpw51KhRA09PT2JiYli0aNElqlTg3I7Xhx9+yE033URgYCCBgYG0bdv2jMdXLqxz/fk64fPPP8dkMtGlS5eLW6C4ONfjlZqaSt++fQkPD8dqtVKtWjX9P/ESOtfjNWHCBKpXr46XlxcRERE8/fTT5ObmXqJqDcohYmCff/65w8PDw/Hxxx87fv/9d0efPn0cAQEBjkOHDpXaPj4+3mGxWBxjx451/PHHH46XXnrJ4e7u7ti6deslrvzadK7Hq0ePHo7Jkyc7Nm7c6EhISHD07t3b4e/v7/jrr78uceXXpnM9Xifs2bPHUb58ecdNN93k6Ny586UpVs75eOXl5TkaNWrkuOWWWxw//vijY8+ePY6VK1c6Nm3adIkrvzad6/GaOXOmw2q1OmbOnOnYs2ePY8mSJY7w8HDH008/fYkrNxYFVTG0Jk2aOPr27et8b7fbHeXKlXOMHj261PbdunVzdOzY0WXZ9ddf73j00Ucvap1S7FyP18kKCwsdfn5+jk8++eRilSj/cj7Hq7Cw0NGsWTPHRx995OjVq5eC6iV0rsdrypQpjsqVKzvy8/MvVYnyL+d6vPr27eto3bq1y7KBAwc6YmNjL2qdRqdT/2JY+fn5/PLLL7Rt29a5zGw207ZtW9asWVNqnzVr1ri0B4iLiztle7lwzud4nSw7O5uCggKCgoIuVpnyt/M9XiNGjKBs2bI89NBDl6JM+dv5HK9vvvmGpk2b0rdvX0JDQ6lduzajRo3CbrdfqrKvWedzvJo1a8Yvv/zivDxg9+7dLFq0iFtuueWS1GxUbpe7AJFTOXr0KHa7ndDQUJfloaGhbNu2rdQ+ycnJpbZPTk6+aHVKsfM5XicbPHgw5cqVK/HHhlx453O8fvzxR6ZOncqmTZsuQYXyb+dzvHbv3s2KFSu49957WbRoETt37uSJJ56goKCAoUOHXoqyr1nnc7x69OjB0aNHufHGG3E4HBQWFvLYY48xZMiQS1GyYWlGVUQMYcyYMXz++efMnz8fT0/Py12OnCQjI4OePXvy4YcfUqZMmctdjpyFoqIiypYtywcffEDDhg25++67efHFF3nvvfcud2lSipUrVzJq1Cjeffddfv31V+bNm8fChQt59dVXL3dpl5VmVMWwypQpg8Vi4dChQy7LDx06RFhYWKl9wsLCzqm9XDjnc7xOGDduHGPGjGHZsmXUqVPnYpYpfzvX47Vr1y727t1Lp06dnMuKiooAcHNzIzExkSpVqlzcoq9h5/PzFR4ejru7OxaLxbksOjqa5ORk8vPz8fDwuKg1X8vO53i9/PLL9OzZk4cffhiAmJgYsrKyeOSRR3jxxRcxm6/NucVrc6/liuDh4UHDhg1Zvny5c1lRURHLly+nadOmpfZp2rSpS3uApUuXnrK9XDjnc7wAxo4dy6uvvsrixYtp1KjRpShVOPfjVaNGDbZu3cqmTZucr9tuu41WrVqxadMmIiIiLmX515zz+fmKjY1l586dzj8oALZv3054eLhC6kV2PscrOzu7RBg98UeGw+G4eMUa3eW+m0vkdD7//HOH1Wp1TJ8+3fHHH384HnnkEUdAQIAjOTnZ4XA4HD179nQ8//zzzvbx8fEONzc3x7hx4xwJCQmOoUOH6vFUl9C5Hq8xY8Y4PDw8HHPnznUkJSU5XxkZGZdrF64p53q8Tqa7/i+tcz1e+/btc/j5+Tn69evnSExMdCxYsMBRtmxZx8iRIy/XLlxTzvV4DR061OHn5+eYNWuWY/fu3Y7vvvvOUaVKFUe3bt0u1y4YgoKqGN4777zjqFChgsPDw8PRpEkTx88//+xc16JFC0evXr1c2s+ePdtRrVo1h4eHh6NWrVqOhQsXXuKKr23ncrwqVqzoAEq8hg4deukLv0ad68/XvymoXnrnerx++uknx/XXX++wWq2OypUrO1577TVHYWHhJa762nUux6ugoMAxbNgwR5UqVRyenp6OiIgIxxNPPOFISUm59IUbiMnhuJbnk0VERETEqHSNqoiIiIgYkoKqiIiIiBiSgqqIiIiIGJKCqoiIiIgYkoKqiIiIiBiSgqqIiIiIGJKCqoiIiIgYkoKqiIiIiBiSgqqIiIiIGJKCqoiIiIgYkoKqiIiIiBiSgqqIiIiIGNL/A4J6cE6Z/hzyAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcdefaults()\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "df_sorted = df.sort_values('accuracy_score')\n",
    "\n",
    "ax.barh('classifier', 'accuracy_score', data=df_sorted)\n",
    "for i in range(len(results)):\n",
    "    ax.text(results[i]['accuracy_score'] * 0.9, results[i]['classifier'], round(results[i]['accuracy_score'], 4), ha='center', color='black')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.7 ('thesis_venv': venv)",
   "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": "3.10.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "6442ccbef71e3a52891e24f5e2ea91ce4ad765b6c4cfe5751cd7f175b2f3dc2f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}