end-to-end-heart-disease-classification.ipynb

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   "source": [
    "# Predicting Heart Disease using Machine Learning\n",
    "\n",
    "This notebook looks into using various python based machine learning and data science libraries in an attempt to build a machine learning model capble of predicting whether or not someone has heart disease based on their medical attributes\n",
    "\n",
    "We're going to take the following approach:\n",
    "1. Problem definition\n",
    "2. Data\n",
    "3. Evaluation\n",
    "4. Features\n",
    "5. Modelling\n",
    "6. Experimentation\n",
    "\n",
    "## 1. Problem Definition\n",
    "\n",
    "> Given clinical records of a patient can we predict whether or not they have heart disease ?\n",
    "\n",
    "## 2. Data\n",
    "\n",
    "The original data came from the cleavland data the UCI Machine Learning repository. https://archive.ics.uci.edu/ml/datasets/Heart+Disease\n",
    "\n",
    "There is a also a versio of it available on Kaggle. https://www.kaggle.com/ronitf/heart-disease-uci\n",
    "\n",
    "\n",
    "## 3. Evaluation\n",
    "\n",
    "> If we can get 95% accuracy in predicting whether or not a patient has heart disease during out POC, we will pursue the project.\n",
    "\n",
    "## 4. Features\n",
    "\n",
    "This is where we will get different information about each of the features in our data.\n",
    "\n",
    "**creaete data dictionary**\n",
    "\n",
    "* age \n",
    "* sex \n",
    "* chest pain type (4 values) \n",
    "* resting blood pressure \n",
    "* serum cholestoral in mg/dl \n",
    "* fasting blood sugar > 120 mg/dl\n",
    "* resting electrocardiographic results (values 0,1,2)\n",
    "* maximum heart rate achieved\n",
    "* exercise induced angina \n",
    "* oldpeak = ST depression induced by exercise relative to rest \n",
    "* the slope of the peak exercise ST segment \n",
    "* number of major vessels (0-3) colored by flourosopy \n",
    "* thal: 3 = normal; 6 = fixed defect; 7 = reversable defect\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the tools\n",
    "\n",
    "We're going to use pandas, matplotlib and numpy for our data analysis and manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all the tools we need\n",
    "\n",
    "# Regular EDA(exploratory data analysis) and plotting libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# we want to plot out maps inside the notebook\n",
    "%matplotlib inline \n",
    "\n",
    "# Models from scikit learn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Model Evaluation\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import plot_roc_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"heart-disease.csv\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration (exploratory data analysis or EDA)\n",
    "\n",
    "The goal here is to know more about the data and become a subject matter expert on the dataset we are working with.\n",
    "\n",
    "1. What questions are to trying to solve?\n",
    "2. What kind of data do we have and how do you treat different types of data?\n",
    "3. what are the missing items in data and how do we deal with that?\n",
    "4. What are the outliers and why we should we care about them?\n",
    "5. How can we add change or remove features so that we can get more of them?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
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       "      <th>0</th>\n",
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       "      <td>3.5</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.8</td>\n",
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       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To find the number of classes\n",
    "\n",
    "df[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tI0kNMtwlqUGGuyQ1yHCXpAYZ7pLUoP8B4304fv57Ts0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"target\"].value_counts().plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are they any missing values\n",
    "\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heart Disease Frequency according to Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>sex</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex      0    1\n",
       "target         \n",
       "0       24  114\n",
       "1       72   93"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare target column with sex column\n",
    "pd.crosstab(df.target, df.sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot of crosstab\n",
    "pd.crosstab(df.target, df.sex).plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"]);\n",
    "plt.title(\"Heart Disease Frequency for Sex\")\n",
    "plt.xlabel(\"0 = No Disease, 1 = Disease\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"Female\", \"Male\"])\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age vs Max Heart Rate for Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TEZG6Vs2/gc65YW3HH388n/3sZ3n/+9/PGWecQUdHB7fccgu33HILCxcuBGDbtm1s3LiRE088kcsuu4wbb7wRgCeffJKNGzdy6KGH8uijj/LJT36SU045hbe97W1s27aNu+66izPPPHNwX6+++mpFX894aUA2kp4Xs7WLiIjUiyr9DXz00Udpbm5mv/32Y/369YPtX/ziFznllFP49a9/zTHHHMOtt96Kc44LL7yQD3/4w0O2cfvtt3Prrbdy9913M3nyZE4++WR27NjB1KlTWb16NTfffDPf+c53uO6667j00kvZa6+9BvPYaolyyEbSPjVbu4iISL2owt/AzZs385GPfIRPfOITw8pEPPLIIxx++OF84QtfYPHixWzYsIGlS5dy5ZVXsm3bNgCeeuopnnvuOXp6epg6dSqTJ09mw4YN3HPPPQBs2bKFgYEB3v3ud/N3f/d3/PGPf2TPPffkoIMO4qc/TaZfnXOsXr26Yq9pIoJFyMzsQOCHwP7AALDcOfdtM9sbuBaYCTwO/Llz7kVL3o1vA+8AtgPnOOf+GKp/Y2lasmzo/DlAaytNS5bF6pKIiEhVhPob2Nvby4IFC+jr66OlpYUPfOADfPaznx32vEsvvZT/+q//orm5mTlz5rBs2TJ222031q9fz7HHHgvAHnvswY9//GPe/va3c8UVVzBv3jwOPfRQjjnmGCAZsJ177rmDZTX+4R/+AYCrr76aj370o1x88cX09fXx3ve+l/nz50/odVWClZu7rciGzQ4ADnDO/dHMpgArgXcB5wAvOOe+YWZfBKY6575gZu8APkkyIDsa+LZz7ujR9rF48WK3YsWKIP2HNKnxtpuSEG37VJqWLFP+mIiI5NL69euZPXu29/P1N3Biyh1vM1vpnCtb2yNYhMw59wzwTPr1VjNbD8wA3gmcnD7tB8DtwBfS9h+6ZIR4j5ntZWYHpNuJovnwRTr5RESkIelvYHVVJYfMzGYCC4F7gemFQVb6/37p02YATxb9WHfaVrqt88xshZmt2Lx5c8hui4iIiFRF8AGZme0B/Az4tHPu5dGeWqZt2Hyqc265c26xc27xvvvuW6luioiIiEQTdEBmZq0kg7GrnXM3pM2b0vyyQp5ZoRpcN3Bg0Y93AE+H7J+IiIhILQg2IEvvmvwesN45962ih34BnJ1+fTbw86L2syxxDNATM39MREREpFpCFoY9HvgAsNbMChXYvgR8A7jOzD4IdAGFcrm/JrnD8mGSshfnBuybiIiISM0IFiFzzv23c86cc/OccwvSf792zj3vnFvinDs4/f+F9PnOOfdx59wbnHOHO+fC1bMQERGRqjMzLrjggsHvv/nNb/LVr37V++evuuoq9t13XxYuXMjBBx/M0qVLueuuuwYf//KXv8ytt95ayS5XjSr1i4iISFXstttu3HDDDWzZsmXc2/iLv/gLVq1axcaNG/niF7/IGWecMbjs0te+9jXe8pa3VKq7VaUBmYiIiAzT1bOdmx7ZxA0PPcNNj2yiq2f7hLfZ0tLCeeedxyWXXDLssSeeeIIlS5Ywb948lixZQldX15jbe9Ob3sR5553H8uXLATjnnHO4/vrrgWQ9zDlz5jBv3jw+97nPAclyTe9+97s58sgjOfLII/n9738PwH333cdxxx3HwoULOe6443jooYcAWLduHUcddRQLFixg3rx5bNy4EYAf//jHg+0f/vCH6e/vn/Cx0YBMRCSDEH+kRGpNV892Vm3qoXdnsuxQ784BVm3qqcj5/vGPf5yrr76anp6eIe2f+MQnOOuss1izZg3vf//7Of/88722d8QRR7Bhw4YhbS+88AI33ngj69atY82aNfzt3/4tAJ/61Kf4zGc+wx/+8Ad+9rOf8aEPfQiAWbNmceedd7Jq1Sq+9rWv8aUvfQmAK664gk996lPcf//9rFixgo6ODtavX8+1117L73//e+6//36am5u5+uqrJ3pYgib1i4jUlcIfqf60QmLhjxRAZ/vkiD0Tqax1W7YOnucF/S5pn+i5vueee3LWWWdx2WWX0dbWNth+9913c8MNSYWsD3zgA3z+85/32l65JSD33HNPJk2axIc+9CFOOeUUTj31VABuvfVWHnzwwcHnvfzyy2zdupWenh7OPvtsNm7ciJnRl67heeyxx/L1r3+d7u5uzjjjDA4++GBuu+02Vq5cyZFHHgkk63Put99+w/qQlQZkIuOkdd4aT8g/UiK1pBAZ823P6tOf/jRHHHEE5547ckGFpHrW2FatWjVszciWlhbuu+8+brvtNq655houv/xyfvvb3zIwMMDdd989ZCAI8MlPfpI3velN3HjjjTz++OOcfPLJALzvfe/j6KOP5le/+hVLly7lu9/9Ls45zj777MHFyitFU5Yi49C/diUDv/xpMhgD6HmRgV/+lP61K+N2TIIK/UdKpFa0tZQfHozUntXee+/Nn//5n/O9731vsO24447jmmuuAeDqq6/mz/7sz8bczh133MHy5cv567/+6yHt27Zto6enh3e84x1ceuml3H9/Un3rbW97G5dffvng8wrtPT09zJiRrNZ41VVXDT7+6KOP8vrXv57zzz+f008/nTVr1rBkyRKuv/56nnsuqWv/wgsv8MQTT4zjKAylAZnIOAzcdhOkIe1BfX1Ju9St0H+kRGrF3GlTaC4JUDVb0l4pF1xwwZC7LS+77DK+//3vM2/ePH70ox/x7W9/u+zPXXvttSxYsIBDDjmEv//7v+dnP/vZsAjZ1q1bOfXUU5k3bx4nnXTS4E0El112GStWrGDevHnMmTOHK664AoDPf/7zXHjhhRx//PFDEvSvvfZaDjvsMBYsWMCGDRs466yzmDNnDhdffDFve9vbmDdvHm9961t55pmJ17G3cnOvebF48WK3YoXKlUn19V10wYiPtX7ln6vYE6mm0hwySP5ILZzerilLqXnr168fNnAZTVfPdtZt2UrvzgHaWpqYO22KzvMMyh1vM1vpnFtc7vnKIRMZj/apu6YrS9ulbhX+GOmPlDSCzvbJOrerSAOyBqIk9MppWrIsySErnrZsbaVpybJ4nZKq0B8pEQlBA7IGMZiEXhhApEnogAZl41A4ZhrgiohIJWhA1iBGS0LXIGJ8mg9fpGMnIrninPMuJyHjN578fN0a1CjK5TuN1i4iInVl0qRJPP/88+MaLIg/5xzPP/88kyZNyvRzipA1CiWhSwNS3qTILh0dHXR3d7N58+bYXal7kyZNoqOjI9PPaEDWIJSELo1GeZMiQ7W2tnLQQQfF7oaMQFOWDaL58EU0nXbmrohY+1SaTjtTf5ikbql4r4jkiSJkDURJ6NJQlDcpIjmiCJmI1KeR8iOVNykiNUgRMpEaoiT0ylHepIjkiQZkIjVCSeiVpeK9IpInGpBJ1Sj6MzoV76085U2KSF5oQCZVoeiPByWhi4g0LA3IpCoU/fGQo+K9saOdsfcvIlJpustSqkPRnzE1LVkGra1DG2swCX0w2ll479JoZ//alQ2xfxGREDQgk+pQCYIx5aV4b+yCq7H3LyISgqYspSpUgsBPliT0aNN2saOdsfcvIhKABmRSFSpBUFlRb5KInesWe/8iIgFoQCZVoxIElRPzJonY0c4s+1fyv4jkhQZkInkUcdoudrTTd/8qtSIieaIBmUgeRZ62ix3t9Nm/Sq2ISJ7oLkuRHMpLiYyolPwvIjmiCJmUFTP3pqtnO+u2bKV35wBtLU3MnTaFzvbJVetnHvKOYk8b5oKS/73l4ZwXqXcakMkwMXNvunq2s2pTD/0u+b535wCrNvUADBuUhehnnvKOYk8b1rrYNx/kRZ7OeZF6pilLGSZm4c11W7YODsYK+l3SXipEP1V0tH7kpdBubDrnRWqDImQyXMTcm96dA/7tGfrpPQ0a6LVrSigORRE9KNdOpCYEi5CZ2ZVm9pyZPVDUtsDM7jGz+81shZkdlbabmV1mZg+b2RozOyJUv8RDxGWO2lrKn5Jl2z37WZgGLQzqCtOgXT3bx73NLLT2otQ0LWsmUhNCTlleBby9pO1/Axc55xYAX06/B1gGHJz+Ow/414D9kjHEvINv7rQpNNvQtmZL2kv59jPLNGiI155lSqh/7Ur6Lr2YvosuoO/Si2t20JaXfsrYdMeuSG0INmXpnLvTzGaWNgN7pl+3A0+nX78T+KFzzgH3mNleZnaAc+6ZUP2TkcW8g68wjegzvejbzyzToEFeu+eUUF6Sq/PST/GjO3ZFakO1c8g+DdxsZt8kic4dl7bPAJ4sel532qYBWSQxc2862yePWOZiPNpamsoOvkaaHq04z/ILtVDI1CfXrRb6KZWVl1w75WJKPav2XZYfBT7jnDsQ+AzwvbTdyjzXlWnDzM5L889WbN68OVA3JQ98c7OyTIOGyPfynhKKnFzt/dqVBC4RKBdT6l21I2RnA59Kv/4p8N30627gwKLndbBrOnMI59xyYDnA4sWLyw7apDH4Rmo62ycz0P046/ta6J20B207tjG7dSed7QeMe5sAfb+6HlbeC24ArAkWHU3rKe8Ztk3vKSFrSrZVyqrzucn7teeo4GpeIip56WdMisxKvav2gOxp4CTgduDNwMa0/RfAJ8zsGuBooEf5YzKmDLlZM375U2aUFAjtHyhTk8pzm32/uh5W3L2rwQ3AirvpgxEHZWP+0Sg3GButvdI8X3teCq7mJdctL/2MTpFZqXMhy178BLgbONTMus3sg8BfA/9sZquBvye5oxLg18CjwMPAvwEfC9UvqSOet+tnKnzpWwJg5b3lnzdSu4/Y5Qc895+Xgqt5KXial35GF/v6EAks5F2WfznCQ8N+a6d3V348VF+kPnlHajJ8svbeZoBoVtOSZXStWs2Gw46jd/KetG1/mVkP3EXnwvnj3mbW/ftGvnKRBJ6XiEpe+hlZXiKzIuOlSv2SW965WRlynmLmez3VOZs1rfvTn26jd/d21hy1lOYDptI57q36q7vyB3nJdctLPyOru/NTpIQGZJJrPpGarJ+svaI/i44emkNW3D5O67ZsHRyMFfRbE+u2bK1oGZDR5CLy5SkvEZW89LMW1NP5KVJKAzKpeyE+Wbee8h76wOsuS1+Z1vGUMeUlopKXfopIWJakb+XT4sWL3YoVK2J3Q6Qibnpk04gFbJe9Yfqw9pilElSmQUQkOzNb6ZxbXO6xaheGFZERzN7xPM07h95t17yzj9k7nh/23JhFMlWgU0Sk8jRl2UCyRDUUAam+Gb+5Adc+nQ3zT9h1l+Xq3zGjZxPMnTPkuTGLZKpAZ1yPr3twWJHjmSXnh/jr6tnutXauSGgakDWILMUnVagykp4X6eh5kY6uDV7PzdReSSrTEM3j6x5kNXvQ35YsxdXbNoXVO/tg3YMalI1DV892Vm3qoT/N3OndOcCqTT0AGpRJ1WnKskFkKT6pQpWRZCl8GbNIpgp0RrO+r4X+lqHrova3tLK+T5+tx2Pdlq2Dg7GCfpe0i1SbruJGkSWqETkC4rtGZGyVntZtWrKMgZ9fC/39uxqbm8uWP4hZKqEWyjSEmGbKwzR976Q9vNs1FTc23dkstUQDskaRpfhkxEKVWdeIjCXYtG7pXc8j3AUds1RC7DINIaaZ8jJN39a7ld7Je5ZtL6apOD9tLU0j3tksUm0akDWILFGNqBGQ0daIrNKAzCdCFyKxfeC2m2Cg5I/DwMCI2/Qtkhki8hOzQOdo00ylgw3f156XGxVmPXgPaxa8aci0ZfPOPmY9eA8sPHSwLcsxypNKn8tzp00ZMnAFaLakvZb6KY1BA7IGkSWqETUCEmCNyCy8I3QhpnUDbDMvkZ8sfKeZMr32nNyo0PHwGvjTn4bdiVt6I0hvXz+YDfv53r7+YW15EeJcLgxOKzm1W4/XnFSHBmQNJEtUI1oEJMAakZn4RugyTut6fWIOMFVcC5GfSkcLfKeZMr32vKwn2T6Vjq4Nw+/ELeln245t9LYNj/K07dg2od3HjPyEOpc72ydXNGpYC9ec5JMmyqW2jLQW5ATWiMzEM0LXtGQZtA69222kaV3fQqpZtuktcuQnRBHZudOm0FwS/Ck7zZThtQc59gH49nO/JzeWzUfc78mN49539ILAOYli5lWx4G4AACAASURBVKafUnMUIZOq8fl0HWKNyEw8I3RZpnV9PzE3H76Iga7Hhr72+YsnVrw3cuQnRLTAe5opw2vP8n7GjBL59vO5Aw8ePmVplrSP08BtN9F9wBuGT5dWK/KToyhmLvopNUcDMqmKLHkVrae8p2oJ/MMsOnpoDllxewnvaV3PT8z9a1fC6hW7BoRuAFavoL/zoHEX741eoiJQtMBnminra/d5P2shP8inn1nKY/jqbp/OmqOWDt5Q0Lt7O2uOWgr33cxB496qv+jnsqe89FNqj6YspSryUmy29ZT3wOJjd0XErAkWHzuxCJ1nIdUQxXubD19E02ln7tpX+1SaTjuzerksEYvIhnjteTmP21qbM7X72LDwpLJFaTcsPGnc28wi+rnsKS/9lNqjCJlUR47yKiodofP+xByoeG/MEhWxowUDXY/By0n9LV7uYaDrsYkdi0DncR7KOWSNuoUoTPtU52zWndYxdJsT2mIYMa85yS8NyKQ6Gjivwjs/qW0y9G4fvoG2Mn/EcnI8Y5ZQCVJkOMBxz0s5h7bW5vJ3t5aJuoUoTKtit1LvNCCTqogdKckixNJNlf7EHPt4Zol+RIsWrLyX7s5Zw5PQJ1BkOMRxz3rjg280rdLlHLJE3UIUpq3XYrciBRqQSVXEXm7HV9Slm8pFx0Zoj3k88xKp6D7wkPJJ6DDuJPQgxz3DNGjMmwqyRN1CrBGpdSel3mlAJlWTi7yKmEs3ZZwOi3U88xKp2DD/xPJJ6PNPrMpdgd4yvO+xi476Rt1CrBGpdSel3ulMFikWcemmvBQnzUukondy+QT2kdp9hCiOmul9z8nNMd7FeyNvU6SWKEImUizi0k15mdathUiFTx5VliR0XyHyvTK97zm5mSPETQUhtilSSzQgEymWoTBsCHmY1t1/8m489nJv2fZq8M2jClH6IVS+l+/7HvtmjiwqfVNBqG2K1ApNWYoUCVIYts48u/3VTO2V5luctbN9Mguntw9G7tpamlg4vX1if9AzFLoNUURWRUdF6pciZCIlQizdFHP9w0qLnkOWIUpV6YhK05JlDPz8Wujv39XY3FzVfC/faNrj6x5kfV8LvZP2oG3HNma37mTm3DkT2ncjC1HoVqSYImQigYVIBI9ppFyxquWQRVyOCQDnRv++IGI/H1/3IKvZg962KWBGb9sUVrMHj697MPi+61Gh1EvhQ0eh1EtXzwilakTGQQMykcDysv6hr9h3u8W8G3XgtptgoCQSODBQ9r0M1c+unu3c9MgmbnjoGW56ZFPZQcH6vpayJT/W95WfFOlfu5K+Sy+m76IL6Lv04tx+WAhltFIvIpWiKUuR0HJSqsBX7Lvdot6NmnENUahsP32L8mZZdzJmsdm8iD5NLw1BAzKREhXP98pJqYIssuRm+R7PLDlP0e5GDVS81/cY+RblbduxLZmuLNG2Y9uwttjFZvOQX5m11IvyzWQ8NGUpUiR64c8643s8c5PzdPCsbO0espxzvX39w9rKtc9u3UnzzqGDrOadfcxu3Tn8hyNGcPOSX5llml75ZjJeipCJFMkSLfD9ZN98+CIGuh4bumD5/MU1FwUIwfd4ru9rob+tTM5TbwszJ7D/ikdfNm7I1u4hyznnG/maOXcO7o7b2TDlAHonT6Ft+1ZmbX2GmSedPLwD7VNZ84aFdL1xAc4Mc47Oh+9n3iOrxv2afMWOzvnKMk2fl6XFIB/RyVBqMYqpAZlIMc9oQZa8m/61K2H1il0rALgBWL2C/s6D6v+Xn+fxzJLz5CtIblSIaFKGbc5adceQBdMhiXzNWnUHLDhksK1/7Uo6fv+fdJQUkO3fe8qw177mbX/BE027gyUhIGfGEwcvhDccQvCzM0f5lb7T9HnJN2vk3EHfXMxq05SlSDHPUgVZ7pyst7ssMxlpyamS9rbt5e9WG6ndR5DjHqKUhecxAujo2cS8+26m7ZUecI62V3qYd9/NdPRsGvK8LK+9q3mPwcHYrn1b0h5a7BImAUQvC+OpkX8v1epds4qQiRTxXpomyyf7HEUBKs5zsfZZq+8sH/lZfScccej49t3zIt2ds9gw/wR6J+9J2/aXmbX6d3R0jX96MevSRV5TQhkWtG9asoyOX/506GtobU2q9xfLcM6NUEVtxHZfPlNCeVoKyleQJbtCaODfS7UaxQw2ZDezK83sOTN7oKT9k2b2kJmtM7P/XdR+oZk9nD62NFS/REbjvTRNlk/2bSOEwEdqryeex8k38pNF96xFrDlqKb27tyc3CuzezpqjltI9a/zTMVmWLvJOWM9wLoU4P22EwrYjtfvwTWyvx6WggizZFUIdRid91WoUM2SE7CrgcuCHhQYzexPwTmCec+5VM9svbZ8DvBeYC7wWuNXMDnHOlb+lSCQgn1IFefpkHzNx1/c4eUd+Mtgw/wT6beivuP6WVjbMP4GDyjy/0sfJN2E967nke352rVrNhsOO2xUdfOAuOhfOH/bczice5InXzRk6bekcnU88CLNeO+S5vqVJsiS2P9U5m3WndQyNpI366mrfjK71HFByLlFjg8w8/Q6rtFqNYgYbkDnn7jSzmSXNHwW+4Zx7NX3Oc2n7O4Fr0vbHzOxh4Cjg7lD9E5mITEU/e0e43X2k9gqKnbjre5xCFFHttfK/3sq1+x6nTMfTc0ooxGt/qnM2a1r3pz/NQytEB5sPmDpssDPvnpvgT68Ov8vyj7+FpW8ZfF6hNEnhbtjetims3tkH6x4cNijznRKq1eTqiYh9zfmKWmA5stjFrUdS7RyyQ4ATzOzrwA7gc865PwAzgHuKntedtonULO/ipBELw9ZCWYFYRVyN8nlQVqbN9zhlOp4Z3vcsx8gnkrduy9bBwdjgz1lT+dIL7VPZe8vTPDfjDfRO3pNJvVvZe8vTw/qZpTSJbyHVPJWI8FUL15yvaAWWa0CW4tbVUu0J0xZgKnAM8DfAdWZmlP8dWTaBwczOM7MVZrZi8+bN4XoqUiFRC8PmJHE3RIHQTMnqvscpw/EM8b77HqcsSctPvfWMsrl2T731jKE/m6E0iW8h1VpNrp6QnFxzUnuqHSHrBm5wzjngPjMbAKal7QcWPa8DeLrcBpxzy4HlAIsXL57ojUAiw1S6YGDUqYGcLNsUIqqQabkb3+NkTeXviixToiLE++57nLK89vWT9qG/5Ln9La2sb9lnSOQry3JMvlNCWZck8pXlGq54gdCcXHNSe6o9IPsP4M3A7WZ2CPAaYAvwC+DfzexbJEn9BwP3VblvIsFyWmJNDeQmcTdAVGH2jueTnKeSUhqzd24Dpg95rvdxylCiAgK8757HKUvSsm+UanbrTlbv7Bt+PMstx4TflFCI5Oos13CI6z0315zUnGADMjP7CXAyMM3MuoGvAFcCV6alMP4EnJ1Gy9aZ2XXAg8BO4OO6w1JiqLecltwk7maIPPma8ZsbcO3Th9Uhm9GzCUqS0L2PU4B+ZuIZfcmStOwbpZo5dw6se5D1vX4LwPsIkVyd5RoOcb3n5pqTmhPyLsu/HOGh/zHC878OfD1Uf0R81GNOSy4SdzNGnrxKVPS8SEfPi96FYL2OU8Z+VlqW6Itv0vLcaVNY9cyLQ24CaHYDzJ3WPnz/HTNhy1bYOQBT2mmqQJmASpeIyHINh7rec3HNSc2prbUcRCKr1YKBdS9DkcoQBVdD9DOEEIVUZ3StL1uUd0bX+iHP8y32mkWImzmyXMNtrvx060jtIiFp6SQpq+KJrjlRqwUDJyJLwdNYRWSzRH6yFFz1LY6aqZ8/vxb6izIqmpurmh/kG33xvYYHbrspiSQ+tm5o+wtPD9lPiOm9EDdzZLmGZ63+HWsO+7PhS3Y98N8w633j2r/IeGlAJsPUY7FGX7VaMHC8shSpjFnQMlPejWdie5biqJmULik0gSWGQsl0DXsezyDTewFu5shyDXdsWAnbXxlhvVMNyKS6NCCTYeotsT2rWiwYOF5ZIhCxC1pWutBupuKo+EWUBm67CQZKBiADAyMeo1gRx0zXsOfxDFKiIlCJCO+8tPapdHRtGJ5nqBIVEoESY2SYekxsb1hZIhA5KWjpW3A1y3nsnR+V4RiFyI/yleW1+x7P/SfvVnabI7X7iFk8N9T+RcZLETIZJlSxRokgSwQiJwUtmw9fxEDXY7Dy3uTuRmuC+YuHRZ6ynMfeEaUMxyhmxDHLa/edLn52+6tl9zVSu4/mwxfxZFOb14LlkCGK6XncVaJCaokGZDJMPSa2N6osyfJ5KWjZv3YlrF6xq9SEG4DVK+jvPGjIH9IQxVEzHaOIEccsRXHBb7o4ROS8q2c7q1un0p/+Jeptm8Jqg6ae7eMv4prxuKtEhdQKDchkmHpLbM8qVt4PxF22KS/RAt8ISIjiqJmOUYZoWqXPuSxFcX2FiJwHKeIau3ivyDhpQCZl1VNiexYx7zSshWWbchEtyBAByVQc1TOa5nuMfKNpQc65jEVxfYSInAcp4hq5eK/IeOkjg0iR0aIvoY0WAZAiAYqzdrZPZuH09sFoT1tLEwunt094IOxTxDXIOZeTY5SpiKvvcyMX7xUZL0XIJNcqPr0YMe8na46O72vPcozyUBA4VK6bb6mELMfTK5oW4JwLURQXKh85zxJ1831uqPMjZiqDNAbvAZmZ7e6ceyVkZ0SyCDLVE/FOwyw5Or6vPcsxyktB4BC5biGOp7cA51yworgVliXPz/e5Mc8PkYkYc0BmZscB3wX2ADrNbD7wYefcx0J3TmQ0IcoKxLzTMEu0wPe1ZzlGeSoIXOlctxDH01eIcy5rUdyYskTdfJ+b5fzwiXyFKmGSh2XNpHp8ImSXAEuBXwA451ab2YlBeyXiI8BUT8w7DTPd3er72jMco4YuCBzgePoKcc419HuZgXfkK8D7npdlzaR6vKYsnXNPmllxU/9IzxWpmkDTi76frkN8YvWOFvi+9gzHKMuUaR5yzTIJcDyzeKpzNutO6xh6PEd4rs+xr9fizpW+5rwjXxlLafgWsO0+4A3D19GswWXNpDp8rs4n02lLZ2avMbPPAesD90tkTDGXPYm5LA74v/Ysx2jutCk029C2clOm3ssM5UiI4+kry/H0fa7ve5knQa4538hXhlIavu9Rd/t01hy1lN7d28FsMM+vu3144d68LGsmE+MTIfsI8G1gBtAN3AIof0yiizm9GDunxPe1ZzlGvlOmtZBrVulISYjj6StEcdR6LO4c5JoLEBn1fY82LDxpyEoKAP0trWxYeBIHjbefkms+A7JDnXPvL24ws+OB34fpkoi/aIVMI+eUFNp8XnuWY+QzZRo7PylUPk2I4+kjSHFU6rC4c6DyID43VGS58cL3PeqdtEf555Vpz8uyZjIxPlOW/+LZJtI4AhSfjFmUNossxTxDyMtx8hWkOGo9CnDN+Rbv9X0e+L9Hba3N5Z9Xpj3L/iW/RoyQmdmxwHHAvmb22aKH9gTKn0kiDSLIJ9aMEYBYt8FHX3y+zvJpMhdHfebFISUtmt0Ac6e1V6OrUcWOEvlGRn3fz6zXUS6WNZMJGW3K8jUktcdagOIz5GXgPSE7JVLrguSvZV2MOtJt8NHzk+osnybL8ZzRtZ7+MhX4ZyycX3ZVgXqSl4Kvvu9n9OtIas6IAzLn3B3AHWZ2lXPuiSr2SSQXKv2JNUsEIMst8yFKVMTMT8pynEJEEWOWOxm47aZk0fDH1g1tf+HphoieZCkP4iPUzTm+72fd5fnJhPgk9W83s38C5gKTCo3OuTcH65VIA8oSASjcMl+4S6twyzz33TzkDq28LIeUhe9xChH9iF6gs86ma7MIci438PGU2uMzILsauBY4laQExtnA5pCdEmlUvlE331vma6FERQg+xylE9CN6gc4M07Wxi/f67t834hjkXM7R9Hfs91PC87k1Zx/n3PeAPufcHc65vwKOCdwvERmF7y3zsUtURBUi+hE5ouJbmDZ28V7f/Wcp9hriXI5ZXDqL2O+nVIdPhKzwcfAZMzsFeBroCNclERlLW2tz+aVxSm6Zr9cldLyEiH5k3Galoxq+07VZo0mVzovz3X+WXMgQ53LM4tJZ1GukW4byGZBdbGbtwAUk9cf2BD4TtFciMqpQt9bXkxBlErJsM1T+ns90bZZoUoi8ON/9++ZCQrhzOQ/lJBo60t1AxhyQOef+T/plD/AmADPbPWSnRGR0urV+bCGiH1m2GTOqkSWaFCIvzgA3QnuxLMsHNfK53NCR7gYy6oDMzGYABwBrnHN/MrP9gE8D5wCvDd89kcaSZYpLt8yPLUT0w3ebWaIalZ7azFRANkNenG8/yw3GyrVnWT4oi1hFk0Np5Eh3IxlxeG1mnwbuJ5mmvMfMzgbWA21Afs9skRoVInFXycDx+C6hE+I9mtG1nnn33UzbKz3gHG2v9DDvvpuZ0bV++JM9lyTK0s8QyweFuFEgLzrbJ7Nwevvg8WtraWLh9HZ9IKszo0XIziNZWPwFM+sEHgZOdM7dU52uidQPn8hCiCmu2MndoeSh4KtvVCPre+RzLmUpINu0ZBldZar/dy6cP+5+hshxzHKjQNTSJIH4RsTzcg3LcKMNyHY4514AcM51mdn/1WBMJDvf5O4Qibuxk7tDyEvBV9+cp6xTm143CmSYhnyqczZrWvcfnN4sJNY3HzB1SBX8LP0MkePovf8GLvaal2tYyhttQNZhZpcVfb9f8ffOufPDdUukfvh+sg+RuBs7uTuELKUSsmwz1hI6Wd4j7yhVhvIc67ZsHZJrBtBvTRM+Pyu9fJD3/mug2GusKFVermEpb7Tf9H8DrCz6V/q9iHjw/WQ/d9oUmktuQ5to4m6mbeYkslAoldC7ezuYDUZ0utunj3+jEV/7/pN38273PZeyFDz13WaWfobgey7HLvYaNYctJ9ewlDfa4uI/qGZHROqV7yf7ELf1Z9pmDUQWfGyYf2L5UgnzTxxWKsFbxNf+7PZXvdt9z6Us5Tl8S1Rk6WcIvudy7GKvWaJUFV8OKSfXsJTnUxhWRCYgS+JyiFIWvtsMUUg1hN7J5SOGI7X7iPnas+Rm7T95Nx57ubdseynf8hzeJSpqoDip77kctdirZ5QqROHgvFzDUl6wqnJmdqWZPWdmD5R57HNm5sxsWvq9mdllZvawma0xsyNC9Uuk2vJyy3rz4YtoOu3MXZ+m26fSdNqZNZd70rZjW6Z2HzFfu2+JCAgTpfLdf2nEbKz2huVZRmS0fMDxyss1LOWNGSEzs+Odc78fq62Mq4DLgR+W/OyBwFuBrqLmZcDB6b+jgX9N/xcZVcVD/oHkpYhrHpaRmd26k9U7+4ZMWzbv7GN2684JbTfWa88SQQ1RbHbutCn88dkeirfQxPD9+0bSsu4/dpmGSu/fN0oVKuKYh2tYyvOJkP2LZ9sQzrk7gRfKPHQJ8HmGXsfvBH7oEvcAe5nZAR59kwamoqeNaebcOcxnG229W5Oip71bmc82Zs6dE7tr45Ilghqq2GzpoKrcICtLJC8vRVxD7N83SpXleEpjGDFCZmbHAscB+5rZZ4se2hMoX155DGZ2OvCUc2612ZBA9wzgyaLvu9O2Z8azH2kMMdcKbHRZogohopgz585h5oS2UFt8I6ghis2u27K17ICs9Ln1WMQ11P59olRaDklKjTZl+Rpgj/Q5xWfIy8B7su7IzCYD/xN4W7mHy7SVjYSb2XkkqwjQ2dlZ7inSIGohybgRZSk+GSJxuZGFKDbr+9y6LOIacf+NvFi6lDda2Ys7zOy/gcOdcxdVYF9vAA4CCtGxDuCPZnYUSUTswKLndgBPj9Cv5cBygMWLF4+UviANIEQh1TzJQ/FJRTErr9LFZrM8t+6KuGbYf4jrLS+5pVIdo/7lcs71A3tXYkfOubXOuf2cczOdczNJBmFHOOeeBX4BnJXebXkM0OOc03SljCpEIdW8yEvxSUUx48hybYS4jnyLyMYu4uq7/9i5btIYfOqQrTKzXwA/BV4pNDrnbhjth8zsJ8DJwDQz6wa+4pz73ghP/zXwDpIFzLcD53r0SxpcI4f8o+beZIgqNHoUM5Ys10aI68i3PEfsIq6++4+d6yaNwWdAtjfwPPDmojYHjDogc8795RiPzyz62gEf9+iLyBANG/KPmPuSpfikEpfjyXJtVPo66u3rBxueGtzb1z+sLXaZBq/9x851k4Yw5oDMOadolUitiZh7kyWq0chRzEbWtmMbvW3DB90TKd4bVexcN2kIPoVhJwEfBOYCkwrtzrm/CtgvkbpS6dIPsZdIyRLVaNgoZgObteoO1hy1dFjx3lmr7oAFh4x7u7EKQce+3qQx+CRy/AjYH1gK3EFyB+T413YQaTAhCthqiRSpZR09m5h33820vdKTFO99pYd5991MR8+mcW8zZiFoXW9SDT45ZG90zp1pZu90zv3AzP4duDl0x0TqRajSD7Fzb0RG0rRkGR2//CkdXRt2Nba2JoOacYpdQkXXm4TmMyArxGhfMrPDgGehropkiwSl0g/SaELcPanrSOqdz4BsuZlNBf4XSb2wPYAvB+2VSB1R6QepdSFysyodUdJ1JPVuzDPZOfdd59yLzrk7nHOvT4u7XlGNzonUg0YuYCu1L2ZuVha6jqTe+dxlOR34e+C1zrllZjYHOHaUIq8iUkSlH2Qsse4ehPi5Wb6yXEexlhUTmQifKcurgO+TLAwO8H+BawENyEQ8qfSDjCT2Aux5ys3yuY4GlzkqlKhIlzkCNCiTmuYz+T7NOXcdMADgnNsJDC+3LCIimY0WoaqGkXKw8pqbNdoyRyK1zCdC9oqZ7UOyXBKFxb+D9kokorxMd8Sc5hI/Pu9R7AhVluWtYp9zXvvveZHuzllsmH8CvZP3pG37y8xa/buhJThC7VtkAnwGZJ8lubvyDWb2e2Bf4D1BeyUSSV6mO2JPc8nYfN+j2HcP+uZmxT7nfPffPWsRaw77s8FVAnp3b2fNUUth8u4cFHjfIhPhs5blH83sJOBQwICHnHN9Y/yYSC6NNt1RSwOyvCRiNzLf9yjUAuyPr3uQ9X0t9E7ag7Yd25jdupOZc+eUfa5Pblbsc853/xvmn0C/Df3T1t/Syob5J4x7QBb7tUN+IvcyfiMOyMzsjBEeOsTMcM7dEKhPIvGUW0B4tPZIYk9zydh836MQd+E+vu5BVrMH/W1plKhtCqt39sG6B0cclI0l9jnnu/9eK/9nbaT2Su47lLxE7mViRjtDTyv5+pdF3ztAA7I61rD5Eu1Tyw++CmvY1YjY01wytizvUaXvwl3f1zI4GCvob2llfW/LuJdZiX3O+e7fSBOeS1iZtkrvO5RQkXtF3WrLiGeTc+7cwj/gyeLvnXN/VcU+SpXlpVBkCE1LlkHr0D9ktLYm7TVERTJr3/6Td8vUXkm9k/bI1O4j9jnnu/9yg7HR2iu572ACRO4Ho26FbaRRt/61K8e9TZkY3xjuRM5lyZlayJeIJcQafCGo2Gzte3b7q5naK8mcw9nwmJC58f8qj33O+e4/RDQr62uv+AxDgMh9XvJlG8n4J9WlbsXOl4it0mvwhaJis7Ut5nVUbjA2Wruv2Oecz/5D3STh+9pD3JHZtGTZ0BwymHjkPif5so1ktKT+X7IrMvZ6M/tF8ePOudNDdkziiZ0vIVIPYl5Hba3N5ffd2hx837HFjuSFmGEIErnPSb5sIxktQvbNoq//OXRHpHaE+oQpMpp6u5Ek5nXU6NdwzEheqMhopSP3QaJuMiEjDsicc3dUsyNSO2J/wpTGU4+FN2NeR7qG48nLDENe8mUbiXLIpKzYuSLSWOr1RpKY15GuYT+VjszmKTqZl3zZRqEBmYhE1+g3kkgcISKzik7KeI05IDOzSc65HSVt05xzW8J1S0TqhU8EIi/TPBA/103FPCsnVGRW0UkZD5/fdn8ws2MK35jZu4G7wnVJROqFb5HhmEVUs4hdNFnFPCtLkVmpJT5Tlu8DrjSz24HXAvsAbw7ZKZE8iB0pyQPfCETWIqqxokSxc91CFfMMcS7n4foIFZmN+dqz7FvR1toy5oDMObfWzL4O/AjYCpzonOsO3jORGlaPdwWG4L0gdIZIRcyFlqNHVAIU8wxxLufl+giRgB/ztWfZtxYsrz1jfgwws+8BnwbmAecCvzSzj4fumEgtGy1SIruMFGkobfd9HoweJQotSz+DGKlo5wSKeYY4l/NyfXS2T2bh9PbB96+tpYmF09snNHCK+dqz7DvmdSTl+UxZPgB8yDnngMfSfLJvhe2WSG2LHinJCd8IRKZIRcQlX2KXNGhasoyuVavZcNhx9E7ek7btLzPrgbvoXDh/3NsMcS7n6fqodAJ+zNeead9aOqnm+ExZXlLyfQ/wwWA9EsmBPN0VGJNvCYBMpQIiLvkSu6TBU52zWdO6P/2WnGe9u7ez5qilNB8wlc5xbjPEudzI10fUJbOy7FtLJ9Ucn7IXBwP/AMwBJhXanXOvD9gvkZoWO1KSJ74RCN/nNS1ZxsDPr4X+/l2Nzc1VW/IlZkmDdVu2Dg7GCvqtaUI3FYQ4lxv5+sjLkllaOqn2+ExZfh/4CnAJ8CaSPDIL2SmRWhc7UtLwnBv9+zoVYjosxLncyNdHXpbM0tJJtcdnQNbmnLvNzMw59wTwVTP7HckgTaRhzehazwElv8zQL7PgBm67ie6OQ9gw/4RdeVSrf0fHBEs/5IEB5Yae5T4hZylpECLqFzOSGLucQ16WzNLSSbXFZ0C2w8yagI1m9gngKWC/sN0SqW26ZTye7vbprDlqKf0trcCuPCruu5mDIvcttJHigKXtjXx+NvJrl3zzyTL8NDAZOB9YBHwAODtkp0RqnW4Zj2fDwpMGB2MF/S2tbFh4UqQeVY9v2Y1GPj8b+bXnSf/alfRdejF9F11A36UXa7UJ/O6y/EP65TaS/DER0S3j0fRO2iNTez3xTtpu5POzkV97TiiKWd6IAzIzwIvEvwAAEidJREFU+8VoP+icO320x83sSuBU4Dnn3GFp2z8BpwF/Ah4BznXOvZQ+diFJOY1+4Hzn3M0ZXodIdemW8WjaWpvL39rf2hyhN9XlnbTdyOdnI7/2nAi1BFjejRYhOxZ4EvgJcC/Z76y8Crgc+GFR22+AC51zO83sH4ELgS+Y2RzgvcBckvUybzWzQ5xz/YjUoKy3jOdlbbs8aOSSCuCXtN3IJQ1CvfZ6u46iUhSzrNEGZPsDbwX+kmSB8V8BP3HOrfPZsHPuTjObWdJ2S9G39wDvSb9+J3CNc+5VktUAHgaOAu722ZdItWW5ZTwva9vlRSOXVPDVyCUNQrz2eryOolIUs6wRB2RpdOo/gf80s91IBma3m9nXnHP/UoF9/xVwbfr1DJIBWkF32iZSs3xvGR9tfbnQv8xj7jukmGUF8qKRSxpU+rXX63UUSyNHcEczalJ/OhA7hWQwNhO4DLhhojs1s/8J7ASuLjSVeVrZO7zN7DzgPIDOzvEuFiJSPblZ205EytJ1VFmNHMEdzWhJ/T8ADgNuAi5yzj1QiR2a2dkkyf5L0gXLIYmIHVj0tA7g6XI/75xbDiwHWLx4cWOU55Zcy83adihPRqScRl6bM5RGjuCOZLSz6QPAIcCngLvM7OX031Yze3k8OzOztwNfAE53zm0veugXwHvNbDczOwg4GLhvPPsQqTVzp02huSQGXM217Xz3XciTKfzhKeTJdPVsH/ZckUYS8xqWxjFaDtmEhv5m9hPgZGCamXWTLLV0IbAb8BszA7jHOfcR59w6M7sOeJBkKvPjusNS6kVe1rZTnkzlKeJYH3QjiVSDuRwvyrt48WK3YsWK2N0QqQs3PPTMiI+dcegBVexJfSi9Mw+SqMrC6e36Qy7SoMxspXNucbnHNAEuIoD/sjziZ7SIo4hIKZ/FxUWkATR6wVVfvtOQujNPpLr6167M9Z2bGpCJCKA8GR9ZCoTqzjyR6qmH9TE1IBORQY1ccNXn03WWGx9CRRx1o0D9yHtEp5bUw/qYGpCJSMPz/XSdZRoyRMRRS/jUj3qI6NSUOlgfUwMyaQiKKshofD9dZ52GrHTEUaVJKs/3d0Olo1n1ENGpKXWwPqaSGaTuqeCpjMnz03XsAqG6UaCyfH83DEazCudDGs3qX7ty/Duvg4hOLWlasgxaW4c25mx9TA3IpO6p/ICMaaRP0SXtne2T6ZzSNrj4rgGdU9qqFp1SaZLK8v3dMFo0a9w8zznx03z4IppOO3PX8WufStNpZ+Yq2qgpS6l7iirIWJqWLBuazwNlP1139Wyna2svhb/hDuja2ss+k19TlUGZSpNUlvfvhgDRLN9zTvzlfX1MfaySuqeogozF99N17GhrZ/tkFk5vHzx321qaVPl/Arx/NwSIZtVDREcqSxEyqXuKKogPn0/XirbWF9/fDaGiWSEiOrqBKb80IJO6p4KnUimxi72q7EVl+f5uKAyaar1mmM6PfNOATBpCIxc8FT8+ZQ2yRlsrHa3IUvZCkRI/vr8b8pCfpLIo+aYBmYg0PN8inVmirSGiFb5TpoqUNCZNqeebBmQi0vCyFOn0jaiEiFb4TpkqUtKYYk+py8ToXRIRCVDWIES0wrcwrSIljSl24WKZGEXIREQyLLvim5sVIlrhO2WqSElj6myfzPPb/8TjLye18qpduFgmRgMyEWl4WQrD+uZmhSq34jNlqlIvjSl24WKZGH1cEpGGF6IwbMwiriog25hiFy6WiVGETESEMIVhY5ZbUamXeGKVHFHuYL4pQiYi4qnVsrVL4ylMaxcGQYVp7a6e7cH3rWXi8k3vkoiIJ7PyI6+R2qXxxJw21F2W+aYpSxERT38acJnapfHEnDbUMnH5pgGZiIgnlZOovHpb4in2OaLcwfzSbxEREU+aEqqsmPlWoegckfFShExExFOoKaEQUaI8RJ5CLfHks1B8KJo2lPHSgExEJINKTwmFWAg8L4uLh8i38l0oPiRNG8p4aMpSRCSiEHfl5aVAaIgyDaMtFC9SyzQgExGJKESUKC8FQoPkWwVYKF6kGjQgExGJKESUKC8FQoMs8VRmQfhR20VqRG1dnSIiDWbutCmUlpU1JhYlauQ7/ZqWLIPW1qGNZRaKF6k1SuoXEYnMAFfy/UTk5U6/EDcfFBL3Y91lKTJeGpCJiES0bstWSjO7Bph46Yc83OkXquyFz0LxIrVGU5YiIhHlJQE/hEZ+7SKlNCATEYkoLwn4ITTyaxcppbNeRCSiRk7Ab+TXLlIq2IDMzK40s+fM7IGitr3N7DdmtjH9f2rabmZ2mZk9bGZrzOyIUP0SEaklQUo/5EQjv3aRUiGT+q8CLgd+WNT2ReA259w3zOyL6fdfAJYBB6f/jgb+Nf1fRKTu5SEBP5RGfu0ixYJFyJxzdwIvlDS/E/hB+vUPgHcVtf/QJe4B9jKzA0L1TURERKSWVLvsxXTn3DMAzrlnzGy/tH0G8GTR87rTtmdKN2Bm5wHnAXR2dobtrYiIBNXVs73m66WJVEOtJPWXq4PoyrThnFvunFvsnFu87777Bu6WiIiEUigMWyhzUSgM29WzPXLPRKqv2gOyTYWpyPT/59L2buDAoud1AE9XuW8iIlJFoxWGFWk01R6Q/QI4O/36bODnRe1npXdbHgP0FKY2RUSkPqkwrMguwXLIzOwnwMnANDPrBr4CfAO4zsw+CHQBZ6ZP/zXwDuBhYDtwbqh+iYhIbWhraSo7+FJhWGlEwQZkzrm/HOGhJWWe64CPh+qLiIjUnv0n78ZjL/eWbRdpNFpcXEREvPSvXcnAbTdBz4vQPpWmJcsmtIj3s9tfzdTuS3duSh5pQCYiImPqX7uSgV/+FPr6koaeF5PvYdyDshA5ZIU7Nws3CxTu3AQ0KJOapol6EREZ08BtN+0ajBX09SXt4xRicXHduSl5pQiZiAia5hpTz4vZ2j3MnTZlSDQLJr64uO7clLxShExEGp4KlHpon5qt3UOIxcVDRN1EqkERMhFpeKNNcylKlmhasmxoDhlAaytNS5ZNaLuVXlw8RNRNpBo0IBORhqdprrEVEvcreZdlCIXBnaafJW80IBORhqcCpX6aD19UcwOwcioddROpBv22EZGGN3faFJptaJumuUSkmhQhE5GGp2kuEYlNAzIRETTNJSJxacpSREREJDINyEREREQi04BMREREJDLlkImINDgtGyUSnwZkIiINrLBsVKGyfWHZKECDMpEq0pSliEgDG23ZKBGpHg3IREQamJaNEqkNmrIUEcmRSud7adkokdqgK05EJCcK+V6FAVQh36urZ/u4t6llo0RqgwZkIiI5ESLfq7N9Mguntw9GxNpamlg4vV0J/SJVpilLEZGcCJXvpWWjROJThExEJCdGyutSvpdI/ukqFhHJCeV7idQvTVmKiOREYVpRVfVF6o8GZCIiOaJ8L5H6pClLERERkcg0IBMRERGJTAMyERERkcg0IBMRERGJTAMyERERkcg0IBMRERGJTAMyERERkcg0IBMRERGJTAMyERERkcg0IBMRERGJLMqAzMw+Y2brzOwBM/uJmU0ys4PM7F4z22hm15rZa2L0TURERKTaqj4gM7MZwPnAYufcYUAz8F7gH4FLnHMHAy8CH6x230RERERiiDVl2QK0mVkLMBl4BngzcH36+A+Ad0Xqm4iIiEhVVX1A5px7Cvgm0EUyEOsBVgIvOed2pk/rBmaU+3kzO8/MVpjZis2bN1ejyyIiIiJBxZiynAq8EzgIeC2wO7CszFNduZ93zi13zi12zi3ed999w3VUREREpEpiTFm+BXjMObfZOdcH3AAcB+yVTmECdABPR+ibiIiISNXFGJB1AceY2WQzM2AJ8CDwX8B70uecDfw8Qt9EREREqi5GDtm9JMn7fwTWpn1YDnwB+KyZPQzsA3yv2n0TERERiaFl7KdUnnPuK8BXSpofBY6K0B0RERGRqFSpX0RERCQyDchEREREItOATERERCQyDchEREREItOATERERCQyDchEREREItOATERERCQyDchEREREItOATERERCQyDchEREREItOATERERCQyDchEREREIouyuLiIiEi96urZzrotW+ndOUBbSxNzp02hs31y7G5JjdOATEREpEK6erazalMP/S75vnfnAKs29QBoUCaj0pSliIhIhazbsnVwMFbQ75J2kdFoQCYiIlIhvTsHMrWLFGhAJiIiUiFtLeX/rI7ULlKgM0RERKRC5k6bQrMNbWu2pF1kNErqFxERqZBC4r7uspSsNCATERGpoM72yRqASWaashQRERGJTAMyERERkcg0IBMRERGJTAMyERERkcg0IBMRERGJTAMyERERkcg0IBMRERGJTAMyERERkcg0IBMRERGJTAMyERERkcg0IBMRERGJTAMyERERkcjMORe7D+NmZpuBJ2L3o8g0YEvsTsio9B7VPr1HtU/vUe3Te1SbXuec27fcA7kekNUaM1vhnFscux8yMr1HtU/vUe3Te1T79B7lj6YsRURERCLTgExEREQkMg3IKmt57A7ImPQe1T69R7VP71Ht03uUM8ohExEREYlMETIRERGRyDQgGyczm2Rm95nZajNbZ2YXpe0Hmdm9ZrbRzK41s9fE7msjM7NmM1tlZv8n/V7vT40xs8fNbK2Z3W9mK9K2vc3sN+n79Bszmxq7n43MzPYys+vNbIOZrTezY/Ue1Q4zOzS9fgr/XjazT+s9yhcNyMbvVeDNzrn5wALg7WZ2DPCPwCXOuYOBF4EPRuyjwKeA9UXf6/2pTW9yzi0ouk3/i8Bt6ft0W/q9xPNt4D+dc7OA+STXlN6jGuGceyi9fhYAi4DtwI3oPcoVDcjGySW2pd+2pv8c8Gbg+rT9B8C7InRPADPrAE4Bvpt+b+j9yYt3krw/oPcpKjPbEzgR+B6Ac+5PzrmX0HtUq5YAjzjnnkDvUa5oQDYB6XTY/cBzwG+AR4CXnHM706d0AzNi9U+4FPg8MJB+vw96f2qRA24xs5Vmdl7aNt059wxA+v9+0Xonrwc2A99Pp/+/a2a7o/eoVr0X+En6td6jHNGAbAKcc/1piLgDOAqYXe5p1e2VAJjZqcBzzrmVxc1lnqr3J77jnXNHAMuAj5vZibE7JEO0AEcA/+qcWwi8gqa+alKaE3s68NPYfZHsNCCrgDR8fztwDLCXmbWkD3UAT8fq1//f3t28WlmFYRi/7vyAykAKiaKPgxODLEIhMCdSEQQRVqfBpiCk/oQmTQsHzQIhaOLAQYQVkUgUQRI0sUElaFCDijzUUUIQnNSgp8FadgYOAomz9tnv9Zu8nxseeNibm3et/a6J2w88neQX4D3aUOVb2J+5U1W/9e1F2ryXh4ELSe4A6NuL4yqcvBVgpapO9+MPaAHNHs2fJ4FvqupCP7ZHG4iB7Dol2ZFke9+/EXicNtH1FLDcb3sJ+HhMhdNWVa9V1V1VtUR7hP9FVb2A/ZkrSW5OcsvVfeAJ4CxwgtYfsE9DVdUqcD7Jrn7qMeB77NE8mrE2XAn2aEPxxbDXKcmDtEmSm2jB9nhVvZ5kJ+2JzK3At8CLVfXnuEqV5ADwalU9ZX/mS+/HR/1wM/BuVR1OchtwHLgH+BV4vqouDSpz8pI8RPtzzFbgJ+AQ/XcPezQXktwEnAd2VtXlfs7v0QZiIJMkSRrMIUtJkqTBDGSSJEmDGcgkSZIGM5BJkiQNZiCTJEkazEAmaZKSPJOkktw3uhZJMpBJmqoZ8BXtxcGSNJSBTNLkJNlGW17rZXogS3JDkreTnEtyMsknSZb7tb1JvuwLoH92dTkaSfq/GMgkTdFB4NOq+hG4lGQP8CywBDwAvALsA0iyBTgCLFfVXuAocHhE0ZIW1+b/vkWSFs6Mttg8tKW0ZsAW4P2q+htYTXKqX98F7AY+TwJtubTf17dcSYvOQCZpUvr6fo8Cu5MULWAVa2tqXvMR4FxV7VunEiVNkEOWkqZmGThWVfdW1VJV3Q38DPwBPNfnkt0OHOj3/wDsSPLvEGaS+0cULmlxGcgkTc2Ma5+GfQjcCawAZ4F3gNPA5ar6ixbi3kxyBvgOeGT9ypU0Bamq0TVI0lxIsq2qrvRhza+B/VW1OrouSYvPOWSStOZkku3AVuANw5ik9eITMkmSpMGcQyZJkjSYgUySJGkwA5kkSdJgBjJJkqTBDGSSJEmDGcgkSZIG+wcISuQjbOY6TQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create another figure\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Scatter with positive examples\n",
    "plt.scatter(df.age[df.target==1],\n",
    "            df.thalach[df.target==1],\n",
    "            color=\"salmon\");\n",
    "\n",
    "# Scatter with negative examples\n",
    "plt.scatter(df.age[df.target==0],\n",
    "            df.thalach[df.target==0],\n",
    "            color=\"lightblue\")\n",
    "\n",
    "plt.title(\"Heart Disease in function of Age and Max Heart Rate\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Max Heart Rate\")\n",
    "plt.legend([\"Disease\", \"No Disease\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the distribution of Age with histogram\n",
    "df.age.plot.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heart Disease based on Chest Pain type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "cp             \n",
       "0       104  39\n",
       "1         9  41\n",
       "2        18  69\n",
       "3         7  16"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare target column with chest_pain type column\n",
    "pd.crosstab(df.cp, df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target).plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"])\n",
    "# Add some customization\n",
    "plt.title(\"Heart Disease Frequency for Chest Pain types\")\n",
    "plt.xlabel(\"Chest Pain types\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend([\"No Disease\", \"Disease\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098447</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>0.279351</td>\n",
       "      <td>0.213678</td>\n",
       "      <td>0.121308</td>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.398522</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>-0.168814</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.068001</td>\n",
       "      <td>-0.225439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.098447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.280937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>-0.068653</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.433798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>0.279351</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>-0.144931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.213678</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.085239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>0.121308</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.028046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>0.137230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>-0.398522</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.421741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>-0.436757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.210013</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-0.168814</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.345877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>-0.391724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>0.068001</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.225439</td>\n",
       "      <td>-0.280937</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.144931</td>\n",
       "      <td>-0.085239</td>\n",
       "      <td>-0.028046</td>\n",
       "      <td>0.137230</td>\n",
       "      <td>0.421741</td>\n",
       "      <td>-0.436757</td>\n",
       "      <td>-0.430696</td>\n",
       "      <td>0.345877</td>\n",
       "      <td>-0.391724</td>\n",
       "      <td>-0.344029</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex        cp  trestbps      chol       fbs  \\\n",
       "age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
       "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
       "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
       "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
       "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
       "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
       "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
       "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
       "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
       "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "\n",
       "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
       "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
       "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
       "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
       "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
       "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
       "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
       "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
       "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
       "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "\n",
       "              thal    target  \n",
       "age       0.068001 -0.225439  \n",
       "sex       0.210041 -0.280937  \n",
       "cp       -0.161736  0.433798  \n",
       "trestbps  0.062210 -0.144931  \n",
       "chol      0.098803 -0.085239  \n",
       "fbs      -0.032019 -0.028046  \n",
       "restecg  -0.011981  0.137230  \n",
       "thalach  -0.096439  0.421741  \n",
       "exang     0.206754 -0.436757  \n",
       "oldpeak   0.210244 -0.430696  \n",
       "slope    -0.104764  0.345877  \n",
       "ca        0.151832 -0.391724  \n",
       "thal      1.000000 -0.344029  \n",
       "target   -0.344029  1.000000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a correlation matrix\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QNM3sEQOdJk1qMve6f+GIhrnc+h0RqfniNV7abMDq38Zt3UtQH/1mf8xEAMz/KQCGB3gT5OHMN+XniTd2tifQxZGRa8IZsSacUB83Ontd+SjntWY+v/nfYJC/N23cnFken2h2uUWYL9y/9COKjEZOfPEFzcePR1lZYBNptrT/et/b93skZ4+eoe/dA65BqKtnfqa4ev6jhxM5eyaVfgP1O0XpT/2tbfXfr7u6cLlZ+0H+3rR2c2bF9dR3uUzeWqzG8E376dq3I1bW+vVfs3/uy9TUsABvgtyrbPud7Al0dWTUmnBG/myZbb+5bWeN9/w/6drGE/FY29ni0rjRtc12Ff7Kpr+0pJR5L3/LsLt741tllm7onb2Y98OL3Pf4KP731e91lPT6oLDS7et6ZembIExRSv2r/P9NgP8Dtmialg6glPoeaF2+fBAQXKVDuiqlXDRNq3aOiqZpi4CLIx/tqdc3X/PQiefTaNywstM08vMg6UIGiUnp9O5RebS1UUMPtu3S72LSPT9vI/LXXabXbh1AZmrl0ZvM1Exc/+SUgarcvAxkpmbg5m2gtLSUwrwCHFzq7kLkvWu3cnC9KXvDVgFkV8menWbE+ZLT9Fy8DNXbpBqrncpXVlrKkV2HGP/hs3WW2Zzk/CJ8HSuPuvo62JGaX/NUhm4+bkwIbsKjm6MoLjNtufs18iQ6PZv8EtMpErvOZ9DO04X9qTVPAakrKQVF+FQ5Iu/tYEtaYc38XTzduL9lY57eHV2R/3pg6+5OUXrlLGyR0YiNwXCFZ1RXkp/PkfnzaXzbbbg0b14XEc3auXobe9aZ6r9JmwCMVY7MG1ONf6nvAhzbd4RNS39j0ntP0sC27jfz3y/dzk8rTfmD2wVw4Xxl30y+YMTbp3r+qIMJHI49y+1DX6WkpIyM9Bwem/AfFnw5GQ9PF1JTMvHydiM1JRN3T+c6zw9g626gML1yvRdmZGB3lbVj5+6O8Ujl9TOFGRkY2rS+wjOuvdSComqntHnbX77v3teiMc/suT767qZV29m2ZjcAgW2akJ5cWTsZKUYMXn/9WpjwjQcYO/WOa5bxaiTnVd/2+zjaVZuRuyjMx40JQU2Y9Mcl2/60bPLLT4/bmaT/tt/ew52CKvVfkJ6BncHtkjYGCtIzcPBwp6y0lJL8fGycKq9zTtodib+Osz/rV25n4+o9ALRo24S0C5W1k5aSiftlamfRO9/j19iLkff2Mbv85kGd+O9lriES/xwWG5oppfphGtT00DStI7AfOHKFp1iVt+1U/tXo0sGPXtZu2Md9d/YGoFvnlmRl53E+2ciGLQcZ1LsDBjcnDG5ODOrdgQ1bDv7JT7t2brqlN098/BxPfPwcQT3ac2BjBJqmcSYuAXsne7PX+lxO2+7tOPB7OAAx2w7SrGOrOp0B6jqyT8WNC1p170D0pnA0TSPxcDx2jvY1BkDOHm7YOtiTeDgeTdOI3hROq+7tK5YnHDiCZyMfXL30PQUxNj2bAGcH/J3saGClGBzgzdYqp0UCtDY48UJoS6ZtjyWjsLji8Qt5hXTxdsNagbVSdPF2IyFL39MgDmdm08jJAT8HOxooxYCG3uy8UD1/S1cnnmnXghmRcRiLii/zkyzDOTCQguRkClJTKSspIT0iAveOV3cdXllJCccWLMCrRw88Q/U9hePmW3szdeFzTF34HCE3t2ffBlPfPRWXgIOTg9lrfS4n8fhZVn60gnGvPoKzu0sdpq5095hefPvDv/n2h3/TZ0A7flltyh91MAFnZ4eKU9ouuvPenqzdNJsf189i0ddTCAj0ZsGXkwHo3a8da3+KAGDtTxH06d9Ol9/BtVkg+ReSyU8x1U5yeCSena6udtxDQsiIiaU4N5fi3FwyYmJxDwmp28CXuLTv9m/ozc7kmn13arsWzNx7/fTdAf/qVXHjgs6927NrfSSapnEiJgEHJ/s/vdbnUudPJ5OXk0eLkMC6CXwZsRnZNHF2wN/RtP6HNPFmm7ltf9eWPLuj+rb/vJltf7zO2363Zk3JvZBMXnn9J+2JxKdz9Rlan84dSNxuGqyej9iHZ1Cbiv0CrayMpIh9NLxJv23n0Dt78e7iaby7eBphfdqx9de9aJrG0ehTODrZ425mFm3Zp7+Ql1vAuKdvq/Z40pmUiv/v3xlHwyZedZ7fkuQaIMvOALkBGZqm5Sml2mI67e0zoK9Syh3IxnSq28UrYH8DJgNzAJRSnTRNO1AXwRbPf5LePYLwcnfh+J7/8NoHP2BjY1pV//32d37dtJ+h/TsRs20uefmFPPrspwBkZOby1rxVbP/5dQDe/Oh/ZGTqeyegi1qHBXM0IpYPH3wNG3tb7ph6X8Wyj594lyc+Nt3ee/3nP3Fo816KC4uZc/8sug7rwYD7h9NlaHdWzvmWDx98DQcXR+6ZPk637C1CgzkZGcOnE1/Fxs6WEU+NrVj2xZR3Km5pPfTxe1g7dwklRUU07xpM867BFe1it+7T9eYHF5Vq8O6+E8zr0w5rBavjL3AyK49HQwKIy8hh67l0nurYDIcG1rzdw3SG5/m8QqbtiGPj2VRCfdxYOrQLmmaaAdqWlP4nr3htlWkwL+Yk73YLwQr45WwyCTn5TGgVwJHMHHYmpzOpbSAODax5pUsbAC7kF/FS+R34PurejgAnRxwaWLGifyhzoo4TkarftRDK2prAMWM4MncuWlkZ3j174ujvz9mffsKpaVPcO3UiJyGBo598QmleHsZDh0hcvZoOs2eTHhlJ9tGjlOTkkLpzJwDNJ0zAqYm+dzNq2y2Yw+FxvDP+dWztbLn72TEVyz6c9C5TF5r67trPVnOgvO++cd/LhA3rzpAHhrP2s9UU5Rfy7WtfAmDwcWfCq4/olr9n72B2bo3jzhFvYG9vy8zXR1csu/+uOXz7w7+v+PxxDw3kxWcXs3rVHvwauvPm+/pse5S1NS3Hjibqw4/Qysrw69UTp0b+xP+4GpfApnh16khWfAIxHy+gJDePtIOHSPjpZ8JeewUbZycCRo1k3+tvAdD0lpHYOOt7B9AyDebHnuSdsBCslKnvnsrJZ3x5392VnM7ENoE4WFszq7Op7ybnFzFzn6nvzr2pHU2cHXGwtmJZ/1DeizpOpI59F6B99yCidsfx4n1vYmtnw4TplbU/+6H3ePlz04z+9wt+JnzjPooKivn3XbPpNfImbptgumX5no37CBvQWfeL2Es1mLPftO23UvBz+bZ/YkgAcek5bEtKZ0oH07b/rSrb/md3xLGpfNv/3ZAuaMDu8xls13nbb2VtTfD/jSZizny0sjIa97kZl8b+HP3fz7gFBuDbpSON+/Tk0KKv2PLvWdg4OdLp8Ycqnp9+5Dj2HgYcffS/bhKg881B7N8Vx1N3v4WtvQ2Pzajc7jw37n3eXTyNtGQjqxb/jn9TH6ZP+BCAoXf2ZOCt3Vn/ww6iIo9i3cAaJxcHHn9pzOVeSvxDKL3u7FXjhZWyA34EGmGa+fEGXsF0ytuzwDkgDkjXNG2GUsoL+BgIwjRw26pp2qQ/eRnNIeDGLOL800sBWHHyVwsnqZ17mpvejL48ut7CSWpnQuuhhK3YbukYtRZxTy/6r9th6Ri1tnlETx7YssXSMWrl6759Afjp1C8WTlI7tzU1fW6SsWidhZPUjsF2BAATt/9h2SC1tKhXPwb+cuP23Y3De7Lt/FpLx6i13n4j6fb9jbntD7+7FwBP795k4SS1M7e76XrFA2lrLJykdjp5joJaXbWmP8/WU3Tb+U87Ou+6XCcWmwHSNK0QGH7p40qpSE3TFimlGgCrMM38oGlaKnCvvimFEEIIIYQQ/ySWvgmCOa8opQYB9pgGPz9aOI8QQgghhBD/CNfztTl6ue4GQJqm6XvbLiGEEEIIIUS9cd0NgIQQQgghhBB1ozafLfdPI3NgQgghhBBCiHpDZoCEEEIIIYSoJ+QaIJkBEkIIIYQQQtQjMgASQgghhBBC1BtyCpwQQgghhBD1hJwCJzNAQgghhBBCiHpEZoCEEEIIIYSoJ2QGSGaAhBBCCCGEEPWIzAAJIYQQQghRb8j8h9I0zdIZ6tI/+pcTQgghhBDXDWXpAFfDL/gF3faPz8e+dV2uE5kBEkIIIYQQop6Qa4DqwQBoxclfLR2hVu5pPgwAh4AxFk5SO/mnlwKwMO43CyepnUlBQ1h0eL2lY9TaxLZDiUhZa+kYtRbmPZLnwjdZOkatvNttAMANWz8T2w4F4JPYG7PvPh48BIDRm7daOEntLOvfh4nb/7B0jFpb1KsfH8XcmLUD8FTIEMZv3WLpGLXyVZ++AAz8ZYeFk9TOxuE9ATiWucbCSWqnldsoS0cQf8E/fgAkhBBCCCGEMJEZILkKSgghhBBCCFGPyAyQEEIIIYQQ9YSS+Q9ZA0IIIYQQQoj6Q2aAhBBCCCGEqCfkGiCZARJCCCGEEELUIzIDJIQQQgghRD2h1HX52aS6khkgIYQQQgghRL0hAyAhhBBCCCFEvSGnwAkhhBBCCFFPyE0QZAZICCGEEEIIUY/IDJAQQgghhBD1hHwQqswACSGEEEIIIeoRmQG6DE3TWLfwfxyNiMXGzoY7po3Fv2WTGu02fLWGAxsjKMjJY+aqORWPlxSVsPL9bzl37AyOrk7c88I43H09dcm+cM6jDB/YmZS0LEIHP2e2zfuzxzG0fyfy8ouYOG0BB6ITABh7Vx+mP3k7AG/P/5ElP2zVJXNVmqbxx39XEr83Bhs7W4ZMuR/fFjXX/YXjp1k/71tKiopp1jWEfg/fiVKKXUvXEbVhJ46uzgD0vP8WmoWG6Jp/82crid8bSwM7W4Y9Nfay+X+dt4SSwmKadQ2m/yN3Vtyact+aLRxYuw0rayuahYbQd/xtuub/5qNVHNgVh529LRNfHEOzNo2rtSksKGLezMUkJ6ZhZaXo3DOE0Y+NAqC4qISFr39H/JEzuLg6MfnVB/Bu6KFb/guHYoj6ZgWUaQT060nrW4ZWW15aXMy+TxeTGX8aG2cnwiY/jKO3J3kpaWx8fjbODX0B8GjZjI4T7tMt90X/hPrZ8vlKEvbG0MDOliFP3o+PufwnTrOhvP8Gdg2h70N3Vrs1694fN7J98Y9MXPwWDuV9ua5lxURzdsUytLIyPHv2xm/Y8GrLc44d5eyK5eQnniXwoYm4d+1abXlpfj5xr8zCrVNnmozRv3bSo6I5vnQFmlZGw969CBgxrNpy45GjnFi2gpyziQQ/+jDeoZX5z+/Yxek16wAIGDUCv549dM0OptrZ/vlKTu0z1c7AyffjbaZ2kk+cZtN8U+007RJCr/La2fPdGuIjolBK4eDmwsAn78fJw023/BnR0SQsW45WVoZv7140Gl69frKOHiVh+XJyzybSeuIjeJbXT+7pM5xcsoTS/HyUlRWNRo7AKyxMt9wXhXkZeCKoOVYK1p29wLKTidWW3xXoz4gmvpSWaRiLipkTdZzkgkIA3goNJtjgQnRGFjP2xumeXdM0Fr3/I5E7Te9bT88aTcu21d+3CgqKePuFrzl/NhUrKyu69Q5m/GTT+1by+Qw+nL2U3Ox8yso0xj0xkrCeQbr/HnqRa4BkBuiyjkXEknYuhac/f4nbpozm5/98b7Zd25vaMemjZ2o8vve3XTg4OzD1i5n0uL0fv33xc11HrvDN91u47YG3L7t8aP9OtAj0o12fqUye/hnz3ngIAHc3J2Y8fQd9bp1J71tnMuPpOzC4OekVu0LC3liMSclMWDCLQY+PZtPC5Wbbbfx0OYMeH8OEBbMwJiWTsC+2YlmXW/tz/9zp3D93uq6DH4D4vbFkJKXw4MKZDH7iXn5fsMJsu98XrmDw46N5cOFMMpJSSNhnetM4fegoJ/ZE8cC85xn/nxcJu32AnvE5uDuO82dSeX/Zizz077v56r0fzLYbOaYfc76bzhtfTuNoVDwHd5ny/7FmD04uDnywfAbD7u3LsgVrdMuulZVxaPEyevx7MgPemUXirgiyEpOqtTm9ZSe2To4Mev9VWgwbQMzyVRXLnHy86P/GDPq/McMigx+48esnYV8sxnPJjPtkFgMfG82mT833380LlzPwsTGM+2QWxnPJnKrSf7NTMzh98DAu3u56xUYrK+PM0u9oMfkpgtgM4fAAACAASURBVF5+lYyIcPLPnavWxsbdg6bjJuAe1s3sz0ha/RPOrVvrEbcGrayMY0uW0n7qk4S99grJeyLIvSS/vacHbR4cj+9N1fMX5+RyavUaOs+YTueXpnNq9RqKc3P1jA/A6X2xZCYlM/bjWfSbNJoti8zXztZPl9PvsTGM/XgWmUnJnN5vqp3Otw9k9IcvcO8H0wkMDSFixS+6ZdfKyoj/7juCnppCp1dnkxoeQd4l69/Ww4MWEybg1a36+reytaXlgxPo9Opsgp5+ioTlyynJy9MtO5h2BqeENOeFyBge3LafAQ29aersUK3N8axcHttxkEd2HGDrhTQmtg2sWLYiPpG3Dx3VNXNVkTsPc+5MKotWvsDkF+7mk3dWmm13x9h+LPx+Oh99+wyxBxOI3Gnabi7/4nd6D+zEvG+n8dzr97PgXfPPF/8cMgC6jLjd0XQaGIZSiiZBgeTn5JOdnlmjXZOgQFzMHGE6vCuaToNMG7mQ3h05eeAomqbVeW6AHeGHSTfmXHb5qCFd+W7lNgDC9x/HzdURPx8Dg/t2ZOO2KDIyczFm5rJxWxRD+nbUJXNVJ8KjCOrXDaUUDds0ozA3n5xL1n1OeiZFeQX4t22GUoqgft04sSdK96zmnAiPIri/Kb//FfIXVskf3L8bx/ccAuDgr9vpdudgGtjYAOBocNE1/95t0fQaFopSipbtAsnNyScjNataGzt7W4K7tAKggU0DAls3Jj3FCMC+7dH0Hm46etmtXwdi9h7TrfYzTiTg5OuNk483Vg0a0Kh7KOf3HqzWJmnfQZr06g6Af7cupMYc1i3f1bjR6+dkeBRB/av339xL8uemZ1KUX0DDi/23fzdOhFf2361f/I9eD9wG6PdhfXkJ8dj5eGPnbaod97AwMg8dqNbGzssLh8aNzX6IYN6pUxRnZ+ESFKxX5GqyTsbj4OODQ3l+n26hpO2vXvv2Xl44N2kMl+TPiInBPSQIG2cnbJyccA8JIiM6Rs/4AMSHR9GmfNvv16YZRVeoHb82ptpp068b8eXbflvHyh324oIiXT/sMSc+HntvH+zL179XWBgZB2qufycz9ePg54uDr2nm2dZgwMbFleLsbN2yA7Q1uJCYW0BSfiElmsbmpBRu9qk+c38gPZPCsjIA4ozZeNvbVizbn5ZJXkmprpmr2rM1mgEjuqKUom37puRm55N+yfuWvb0tHUJbAmBj04AWbRuTmmyqL6UgL7cAgNycAjy8XPX9BXSmlJVuX9criydTSjkppdYqpQ4qpaKVUvcqpboqpbYopfYqpdYrpRoqpRoopSKUUv3Kn/eWUuqNusqVlWbEzctQ8b2blxtZqTUHQFd+vunopbW1NXaO9uRl6X9EzRx/Pw/OJqVVfJ94Ph1/Pw/8/dw5ey698vGkdPz99DsCe1FOuhEXr8rXdfY0mN0BdPY0XNLGWPH9wbVb+eapt/ht/hIKcvQ9kpaTlolLldpx8TKQk5ZZs02V/C6elW0yzqVwNvYES559n+UvfsT5Y6f0CV4uIzULT5/KbB4+BjKuUPu52fns3xFDSFfTke+MlEw8yp9v3cAaRyd7cjL1qf2CDCMOHpW14+DhTkGGsXqbdCMOnqY2VtbWNHB0oCjHlC8vJY0/XnqD7a9/QNqRY7pkvtSNXj85aUacPWvRf9NMf6eT4VE4e7jh3az66St1rSjDiK175Q6frcGd4ktq53K0sjISf1hBozvuqqt4f6rIaMSuSu3bubtTaLy6/IUZRuzcL3nuVf7u11JuuhHnKtt+J0+D2QFQ1doxtanMunvJzyx+ZCbHtkbSbfSIug9dzrT+q9SPu4FCY8Zf/jnZ8fFoJSXYe3tfy3h/ysvelpSCoorvUwqK8LK3u2z74Y19CU/5679fXUlLzsTLt7IuPH3cSEu+/PtWTnY+4dti6BRmOpB33yND2fzrXsaNepVXpv6XSc/+q84zC8uy+AAIGAac0zSto6Zp7YBfgfnAXZqmdQW+AN7QNK0EGA8sUEoNLn/e7Et/mFJqolIqUikVuWjRotqnMndA+C8cTTJ3QFnPo1FXoswcVdU0zWw+ixwYN/OiNZKZDWZq1WF4LyYsfJn7P3weJ3dXtn65ykzbumNuNqHmqjVXIKZ/ykrLKMzJ4745z9Bn/O38/O6Xus5QmM1/mbalJaV8/Mo3DL27Nz6NPC/7/L/Sd/6O2r62AuwMrgyZ+wb9Xp9Bu7F3EvnJlxTn51/7kH/iRq+fK2WraGH2d1QUFxYR/sN6uo8ZWUfZruTPc19O6pY/cG3XHlsP/a51q+Fv/Ykt12erpTDbf6+mTWWj7mNvYdxnr9GqTyhRv+h4DavZ7eZfW4dFRiPHP/+CFuPHo6wsv3t2ue3GIH9vWrs5syI+0exyS/gru2ylJaXMeelbbr23N37l71tb1u9n4KgwFq+ZxSsfPsz7ryylrHy2659IYaXb1/XqergJQhTwnlLqHWANkAG0AzaU75BbA0kAmqbFKKW+AX4GemiaVnTpD9M0bRFwceSjrTj561UH2fPzNiJ/3QVAo9YBZKZWHlXKTM3E1fPqp0TdvAxkpmbg5m2gtLSUwrwCHFwcr/r5dSnxfBqNG1bekKGRnwdJFzJITEqnd4/Ki/4aNfRg2y59LmY8sG4r0b/tBMC3VQDZqZVHlnLSjDUuZK16xPhiG+fyNk6Gyr9Tu8E389Mbn9ZldAD2r91K1AZT7fi1DCC7Su1kp5rPn10lf3aV/C6ebrTq0dF0ClHrpigrRX5WDo5udXcq04aV29n8824Amgc1IS25Mlt6shGDl/kLiT9/93v8mngx7J6+FY95+BhITzbi6WOgtKSUvNwCnF31qX0HD3fy0ytrJz89A3tD9ez2Hgby0zJw8HCnrLSUkrx8bJydUEphXX7amKFZU5x8vMhJSsa9edM6z32j18/BdVuJ3lDef1sGkJNWvf86u1fP72Km/zp5uJF5PpWsC2ksmfp2xePfTXuX0e8+i5N73Z6SYuvuTlFG5Qx4kTEDG4PhCs+olHvyBDnHj5O65Q9KCwvRSkuwsrej0b/urKu4Ndi6GyisUvuFGRnYXWV+O3d3jEcqr98ozMjA0Eafa5miftlKbHnt+LQMIKfKtj83zYiT+5W3/ebaALTuHcraNxbSbbQ+g2lbd3cK06vUT4YR26tc/wAl+fkcnj+fgNtvw6VF87qIeEWpBUXVTmnztrclrbDGLhZdPN24r0VjntkTTXGZZU8dXvP9dtb/uAeAVsFNSL1QWRdpyZl4eJt/35r/1vf4N/HitjF9Kh7bsHoPs+c9AkBQh0CKCovJMuZi8ND3FGKhH4sPgDRNO6qU6gqMAN4CNgAxmqZd7hY07QEj4Huts9x0S29uuqU3AEfCY9jz8zba9+3C2cOnsHeyN3utz+W07d6OA7+HExDUjJhtB2nWsdV1MwO0dsM+Jo0bworVO+nWuSVZ2XmcTzayYctBZj93b8WNDwb17sCst5fpkqnTiD50GmHaGJ2MjObguq206d2V80cTsHWyr9i5u8jZww1bB3uSjsTj1zqQuD/C6TTCtBOek55Z0f7EnoN4BjSs8/ydR/ah88iL+WPYv3YrbXt3IeloAnZXyH/uSDwNWwcSuzm84vktb+rA6UNHadK+FemJyZQWl9b5XbAG39mLwXf2AmD/zlg2rNxOj0GdORFzCkdne9zNnA/9/aJ15Ofm8/D0e6o93qVnCNt+iaBVu0DC/zhEcJeWutW+oXlTcs8nk5ucioOHgcTdkXR9/MFqbfw6d+DM9t14tGrOufB9eAW3QSlFYVY2ts5OKCsrcpNTyL2QjJOPly65b/T66TiiDx3L+298ef9t3cvUf+0c7WsM4Jw83LCp2n83h9NxZF+8mvozcfFbFe2+mPgyY977ty53gXNsGkhhcjKFqSnYGNzJiIgg8KGHr+q5gQ89UvH/tJ07yDt1StfBD4Brs0DyLySTn5KKnbuB5PBIgiY+dFXPdQ8JIf5/P1bc+CAjJpZmd+hzClD74X1oP9xUOwmR0UT/spWWvbpy4WgCtperHXt7zh+Jx7d1IEf+CKd9+bbfeC4Zg78PAPERURgaXfPdhMtyDgykIDmZgpRUbN0NpEZE0Orhq6ufspISjnyyAO8ePfAMDa3jpOYdzsymkZMDfg52pBYU0b+hN28cPFKtTUtXJ6a2a8H0iFiMRcUWyVnVqLt7Mepu0/tWxPZY1ny/gz5DOnMk+jSOzvZmr+P5ZsEv5OUUMGVG9fctbz93DkYcY9CobpyJv0BxUQlu7vrcfdIiruNrc/Ri8QGQUsofSNc07VulVA4wEfBWSvXQNG2XUsoGaF0++3MH4An0AdYopbppmlYnJyq3DgvmaEQsHz74Gjb2ttwxtfKOUB8/8S5PfGy6vfT6z3/i0Oa9FBcWM+f+WXQd1oMB9w+ny9DurJzzLR8++BoOLo7cM31cXcQ0a/H8J+ndIwgvdxeO7/kPr33wAzY2pj/1f7/9nV837Wdo/07EbJtLXn4hjz5rmiHJyMzlrXmr2P7z6wC8+dH/yNDp2o2qmnUNIWFvLF9OepUGdjYMmXJ/xbJvn36b++dOB2DApHv5bd63lBQWE9g1iMCupouPty3+iZT4syilcPXxYOBjo3XOH8zJyBg+n/QqNna2DH1ybMWyr59+hwfmPg/AoEn3mG5jXFREsy7BNCvP325Qd9bP/46vnnwL6wbWDH/6fl0Hz516BHFwVxzT7n0TW3sbJr44pmLZi+Pf482vniUt2chPX/+Of1MfXnrwA8A0iOp/S3f6jrqJha99xzP3voGzqyOTX3lAt+xW1tZ0eGA0u+bMRysrI6DPzbg29idu5c8YmgXQsEtHmvbtyb6FX/H7tFnYODsS+oRpJzHtyDEOr1yDsrJCWVnRcfx92DrrfxfEG71+Asv77+LHTP138JOV/XfJ1LcZ+2F5/3303orbYDftEkRgF8vcPOAiZW1N43vv48S8uWhlGp4398TBvxFJq3/CsWlT3Dp2IjchnviFn1Cal0dm1CHOr/mJoJdftWjui5S1NS3Hjibqw4/Qysrw69UTp0b+xP+4GpfApnh16khWfAIxHy+gJDePtIOHSPjpZ8JeewUbZycCRo1k3+umwWfTW0ZiY4Hab9o1hNP7YlnyuKl2BkyurJ3lz7zNvR+Yaqfvo/dW3AY7oEsQAeW1s/vb1RgTk8FK4eLtQd9H79Utu7K2ptl9Y4ibOxdNK8OnZ08cG/lz+qefcG7aFI9OnciJT+DIJ59QkpdHxqFDnPlpNZ1enU1aZCTZx45SkpND8g7TbFjLCRNwCqh5C/C6UqbB/NiTvBMWgpWCX84mcyonn/GtAjiSmcOu5HQmtgnEwdqaWZ3bAJCcX8TM8rtPzr2pHU2cHXGwtmJZ/1DeizpOZKp+15GF9gwicmccj9zxFnb2Njw9s/J9/8mx7zN/yTRSLxhZ/uXvNA704an/+xCAUXf3ZOjt3XnoqVuY/+b3/PjdVpRSPD1r9HVz0FrUDWXpux8ppYYCc4AyoBh4DCgB5gFumAZpc4FVwE5goKZpZ5RSU4CumqZdaWTxl06Bu57c09z0+Q0OAWP+pOX1Kf/0UgAWxv1m4SS1MyloCIsOr7d0jFqb2HYoESlrLR2j1sK8R/Jc+CZLx6iVd7uZbjt9o9bPxLamz036JPbG7LuPBw8BYPRm/T/D7FpY1r8PE7f/YekYtbaoVz8+irkxawfgqZAhjN+6xdIxauWrPqaZsIG/7LBwktrZOLwnAMcy9fvohGupldso0PPWlX9Di65zddv5P7H36etynVh8BkjTtPWAuT2FPmYeqzgpWdO0eXUWSgghhBBCCPGPZPEBkBBCCCGEEEIfcnrf9XEbbCGEEEIIIYTQhQyAhBBCCCGEELpTSg1TSh1RSh1XSk03szxAKbVZKbVfKXVIKXVNPuFYToETQgghhBCinrhePqBUKWUNfAwMBs4CEUqp1ZqmxVZp9hKwQtO0BUqpYGAdEPh3X/v6WANCCCGEEEKI+qQbcFzTtJOaphUBy4DbLmmjARc/1MkNOHctXlhmgIQQQgghhKgnlI4fhKqUmojpMz4vWqRp2qLy/zcCzlRZdha46ZIf8Qrwm1LqScAJGHQtcskASAghhBBCCHHNlQ92Fl1msbnb0V36GUVjgK80TXtfKdUD+EYp1U7TtLK/k0sGQEIIIYQQQtQX189tsM8CTap835iap7g9BAwD0DRtl1LKHvACkv/OC8s1QEIIIYQQQgi9RQCtlFLNlFK2wGhg9SVtTgMDAZRSQYA9kPJ3X1hmgIQQQgghhKgvrpPpD03TSpRSk4H1gDXwhaZpMUqpV4FITdNWA9OAz5RSUzGdHjde07RLT5P7y2QAJIQQQgghhNCdpmnrMN3auupjs6r8Pxboea1fVwZAQgghhBBC1BfXzzVAFnOdTIIJIYQQQgghRN1T1+A0uuvZP/qXE0IIIYQQ140bYmql9c0Ldds/Prpz0nW5TmQGSAghhBBCCFFv/OOvAfry6HpLR6iVCa2HArAw7jcLJ6mdSUFDAHAIGGPhJLWTf3opz4VvsnSMWnu32wAWH7sxax9gXKuhTNz+h6Vj1MqiXv0AeD5io2WD1NI7YQMB6Lp0m4WT1M7eMb0BMBat+5OW1yeD7Qh+T7wxswMMajSCB7ZssXSMWvu6b192Ja+1dIxa6eEzEoAtSTdm/fRtOAKApnNuzPfeU/8eYOkIV0+mP2QVCCGEEEIIIeqPf/wMkBBCCCGEEMJEk7vAyQyQEEIIIYQQov6QAZAQQgghhBCi3pBT4IQQQgghhKgv5Aw4mQESQgghhBBC1B8yAySEEEIIIUR9YSVTQDIDJIQQQgghhKg3ZAZICCGEEEKI+kJugy0zQEIIIYQQQoj6Q2aAhBBCCCGEqC9kAkhmgIQQQgghhBD1h8wACSGEEEIIUV/IXeBkAHQ5mqbx+6KVnNgbi42dLSOfGotfyyY12p0/fpq1c5dQXFRMi67BDJp4J0opfnznS9ITkwEoyM3H3smBB+c9r2v+P/67kvi9MdjY2TJkyv34tqiZ/8Lx06yf9y0lRcU06xpCv4dN+XctXUfUhp04ujoD0PP+W2gWGqJL9oVzHmX4wM6kpGUROvg5s23enz2Oof07kZdfxMRpCzgQnQDA2Lv6MP3J2wF4e/6PLPlhqy6Zq7pwKIaob1ZAmUZAv560vmVoteWlxcXs+3QxmfGnsXF2Imzywzh6e5KXksbG52fj3NAXAI+Wzeg44T7d82uaxoZFKzkRGUsDO1tuedp87ScdP82aD5dQUlRMi9BgBpfX/oWTZ/nl4+WUFJVgZW3FsMfuwb9NU93yp0dFc3zpCjStjIa9exEwYli15WXFxRz+/EuyT53GxsmJ4EmPYO/lRVlJCUe//pachFOgrGg55h4MbdvolvuiCwdjiPrme7Qyjab9bqb1rWbqZ+FijPFnsHVxInTyQzh5e1Ysz0tNZ+Pzr9H2jhG0GjlY7/j0aOjOs12aY60UP544z1dxZ6stv7OlH/e08qdU08gvKeX18OPEZ+UBMCG4Mbc196NU03hv7wl2nTfqml3TND54exU7t8Vhb2/DzNfH0Da4Zu0/NelTUlOyKC0tpVOX5vx7xl1YW1tx9Egi77z6Pfl5RTRs5M7st/8PZ2d7XfN//59VxOyJw9behv97bgwBrWvmX/35Wvb8Fkledh4frnun4vFdv4bz46ercfNyA6Dv7b3pObK7bvmN0dGcWr4crawMn1698B8+vNryrKNHObV8OXmJibR85BE8u3YFIPfMGRKWLKE0Px+srGg0YgSeYWG65b5I0zSWfLSKQ7vjsLWz5eEXxxDYpnGNdj8sWsfO9ZHkZufx6W9vVzyediGDz974jrycAspKy7h70kg69gjWNf/y+auI2m2qn/HTx9DUTP2s+u9adq831c/8X9+ptixy835+/mo9KGjSohEPz/w/veLTN9CDlwe2wloplh1KYkH4KbPtRrT2ZsFt7Rn1dQRRF7Lp6OfCW0PbAqYzw+bujGf9sVTdcgvLkAHQZZzcG0vGuRQe/XQm544ksH7BCsa9P61Gu/WfrGDY5NH4twnk+1cWcnJvHC1Cg7n9+QkVbTZ+vgo7R/3eBAES9sZiTEpmwoJZnD+awKaFyxkz59ka7TZ+upxBj4+hYZtAfnxtAQn7YmnW1TTQ6XJrf0JvH6hrboBvvt/CwsXr+e+Hj5tdPrR/J1oE+tGuz1S6dW7JvDceos9tM3F3c2LG03fQc+QMNGDn2jdYu2Evxsxc3bJrZWUcWryMm5+fgoOHO1tmvY1flw64NmpY0eb0lp3YOjky6P1XObsrgpjlqwib/DAATj5e9H9jhm55zTkRGUv6uRQmLTLV/q+frGD8BzVr/9ePVzB88mgatQ1keZXa3/TlT/QeM5wWocEcj4hh05c/cf/bU3TJrpWVcWzJUjpMexo7d3f2vfYWnp064OTvX9EmadsOGjg6cdNbr5O8J4KTP/yP4EkTSdq6DYDQV1+mKCuLqLnz6fLSCygr/c4U1srKOLh4OT2nT8HBw8Afs97Br2v1+jn1x05snBwZ/MFszu6KJHbZKsKefLhiedSSH/DtqN9OU1VWCqZ3bcHjm6O5kF/IN0M6sSUxvWKAA/BrQgorj58HoE8jD57p0own/4ihmasjQwK8uXvdXrwdbFnQvz3/WhtJmaZf/p3b4jhzKoUf1r5I9KFTvPv6D3zx3dQa7d54bxzOzvZomsb0Z75i428HGDK8C2++vJwp026lS1hLVq/aw7dfbmLSkyN0yx+zJ46UxBRe+eZFEuJOsWzuDzz3Sc387XuE0Pf2Xrzyf2/WWNalX2fufepOPeJWo5WVkfDdd7SdOhVbd3di3nwTQ8eOOFbpu3YeHrSYMIGk336r9lwrW1taTJiAva8vRUYj0a+/jltICA0cHXX9HQ7tjuPC2VTeWfoiJ2JP8fX7PzBr0dM12nXqGcygO3rx/H3V1//qxRvo1r8TA/7Vk8T483zw3Ge8/71+fTl6TxwXzqbw+pIXiY89xZIPf+DFBTXrp2OPEPr/qxczx1bPf+FsCr8s2chz/5mCk4sjWRnZekXHSsFrg9swdsV+zmcXsvr/Qvn9RArH0vKqtXOysWZ8lybsO5dZ8diR1Fxu+TqSUk3Dx8mWX8Z14/fjOyjVdNz46E3uAifXAF3Osd1RtBvQDaUUjdo2ozA3n5z0zGptctIzKcwroFHbZiilaDegG8d2H6rWRtM0Dm/fT3DfrnrG50R4FEH9TPkbtrl8/qK8AvzL8wf168aJPVG65jRnR/hh0o05l10+akhXvltp2lkN338cN1dH/HwMDO7bkY3bosjIzMWYmcvGbVEM6dtRr9gAZJxIwMnXGycfb6waNKBR91DO7z1YrU3SvoM06WU6qurfrQupMYfRrqMN7dE9UbSvUvsFl6v9/AIaB5lqp/2AbhypqH1FYV4BAIV5BTh7uumWPetkPA4+Pjh4m9a/T7dQ0vZXX/9pBw7ie7Np/XuHdiEjzrT+884l4R5kOgpo6+pKAwcHshPMH0GsKxknEnD29cbJxwurBg1o3L1rjfo5v+8QAb0v1k9nUmKOVNTPucgDOHl74VJlwKSnEA8XzuQUkJhbQEmZxm+nU+jX2KNam9yS0or/OzSw5mLp92vswW+nUygu0ziXW8iZnAJCPFz0jM/WzdEMvzXMVNMdA8nOzic1JbNGu4uzOqUlZZQUl6DKdyZOJSTTObQFADf1aM3m3w/VeG5dOrQzmpsGm/I3Cw4kPyefzLSa+ZsFB+KmY7+8Gjnx8dj7+GBf3nc9wsLIOFi99u28vHBs3LjGzpuDry/2vqaZc1uDARtXV0qy9dv5vmj/9mh6DgtFKUXLkEDycvIxpmbVaNcyJBCDl2uNx5WC/PJtZ35uAe5e+v6NDuyIpsdQU/00DzHVj9FM/TQPCcRgpn62rdlFv9t74eRiGni6uuvXfzs1dCUhI48zmQUUl2n8fDiZwS29a7Sb1qs5C8NPUVhSVvFYQUlZxWDHroEV18+7sahL1+0MkFLqAeBZQAMOAaVAARAC+ALPaJq2pq5ePzstExcvQ8X3Lp4GstMycfZwu3wbL1Obqs7EnMDJ4IKHv09dRTUrJ92Ii5d7xffOngZy0qvnz0nPxNnTcEmbylNODq7dStzmcHxbBtBnwr+wd9b3aNrl+Pt5cDYpreL7xPPp+Pt54O/nztlz6ZWPJ6Xj7+du7kfUmYIMIw4ela/p4OFOxon46m3SjTh4mtpYWVvTwNGBohzTLFVeShp/vPQGDewdCLr7FjzbtNIvfLmctExcr6L2XT2rt8kpr/3BE+9g2awFbPziR7QyjXHv1TyCWFeKjEbsqqx/O3d3suKrr//CDCP2HqadcmVtTQMHB0pycnFq0pjU/Qfx6RZGQXoG2adOU5ieAc2b6ZY//5L6sfdwJ+NEwmXbVK0faxsbjq3ZQM/pT3Js7e+6Za7Kx9GOC3mFFd9fyCuinWfNnaC7WzXk/jaNaGBlxaRNpkGCt4MdUWlZVZ5biI+jHaTptyObkpyJr19lXfv4GkhJzsTLu+bO3pRHFxIbdZoevYIYMNh0oKVFy4Zs3RxN3wHt2bj+IMk6n8KXmZqJwacyv8HbgDE18y8Ndg5sO8jxqBP4NPbmrsdvx91Hn21okdGIrUflYNnWYCD3kr57NXLi4ykrKcHOu+bOb13LSMnCo8r6d/c2kJGaaXawY87tE4bx3rSF/L5yO4X5RTw3d1JdRTXLmJKJu3f1/MaUTLODHXMunEkB4J3JH1FWqnHL+KG0uymoTrJeys/ZjqTsym1PUnYhnRtWX+8hPs74u9qx6WQaE8MCqi3r1NCVOcPa0sjVnqnrYv/Zsz8gd4HjOp0BUkqFADOAAZqmdQSeKl8UCPQFRgILlVI1zitTSk1USkUqpSIXLVr0tJcdBgAAIABJREFUN1LULP4aM4bmOsglbeK27iWoj76zP4DZbDXq3WwHN7XqMLwXExa+zP0fPo+Tuytbv1x1zSPWljLTczVNqzgKW/1xPRJVz1HDVUw1K8DO4MqQuW/Q7/UZtBt7J5GffElxfv61D/knzP8OV9Gm3L512xn08L948v/Zu+/wqKqtgcO/nZA6KZMKBEijBwg9gAQQFEFs9+qnggXhqlixgyiKIla8VxFsYMWGIBZAbKBIJ4UACQmElggJIX3S+5zvjxnSgRDJTDDrfZ48MHP2ZNacrLNPWfvs+fQFLr/736x/66sLHOFZNOnv3fjn6xg+EgdPD3YveJmjX6/CvVtXlK2Fu8imxH+GP8/B736k28RxtHO07HDb+nHU19hH+uZwGtf9GM2SfUnc1df/zK+18Abc+Ps1vv0uXnov6zfNp7yikuiIwwA888JkVn+9jak3/Y/i4lLa2dm2YLQNNRZ/Y/3imfQb0YcXvprH3A9n02tQDz571ZLbbvP6ztrKDQaOfvwxwdOmWXTo6mmNr/+mv37XxhhGXhnGm989x2Ov382yBV9hNBrP/cILRGv0uKfpH8BYZSQjJYvHFz3I3fNu57PXV1JcYPl92Gm1P48Cnh3bnRc3HWm07d60fMZ/Esm1n0dz/7BAHCzd9wuLa60VoHHAak3TsgA0Tcsxb4SrNE0zAoeVUseAXsDe2i/UNG0ZcPrMR/vk0K9NftPd67ew79edAHTs7k9BVs3Vu4JsQ50r4GCu+NRuk2XAtVYbY1UViTtjmfZmw3tvWsLen7aw/7cdALTv7k9BVm71ssJsA7p68bt46SnMNtRpc/oz6vQ1V076jr+ENS8tbcnQz0vqqWw6d6y56btTB0/S0nNJTcth1Iiaq02dOnqydecBi8bm5OlBSU7Nei/JycVRX3e9O3rqKcnOxcnTA2NVFZXFJdi56FBKYWtnB4A+KACdrzeFaRl4BLf8BALRP25hrzn3/br7k18v913r5Y6bt5787Hrbh/kqYdzvkYyfYbqHoHf4QH5avKKlw69m76E3VW3MynJzcdDr67Rx8PCgNCcHB08PtKoqKktKaKczrf9uk2+qbrfn5ddwam/Zyq2Tp75O/pTm5OLk4d5oGyevuvmTeySZ1Mg97P/6eyqKS6rzKfiKSy0Wf3pxGe2dHaoft3e2J6uk7Iztf/0rk6eGdAMgo6SMDnVe60BmSXnLBWv2zYptrPnWlPshff1Jr1W1yUg34ON75qv3Dg52jL60D1s27WfYJT0JDG7PkmX3AXA8OYPtW1q+/9n8wza2rzfFH9DTH0NGTfyGTAPuXk2rPgC4uOuq/z/yqhH88EGLDbJowN7Dg/Kcmgp+ucGAXb1t92wqS0pIXLKEztddh2twcEuE2KiN321j87pdAAT16kJOrfWfm2locvUEYMv6CB7/7wwAuvUNpKK8gsK8ohYdSrbp+21s/dGUP4G9/MnNrBu/exOrVwAePu4EhwTSrp0t3h296ODvS0ZqJoG9/M/94r/pVGEZHV1r+o+Org6kF9b0Hy72tvT01vH15IEA+Ojs+ej6UO78Lpa49Joq85GcYkoqqujhravz/D+OzALXOitAmE7WG7sUV/+5C3p5cPBVo/nP4if5z+In6T48lP1/RKJpGqkHk3BwdmxwAuTi6Y69kyOpB5PQNI39f0TSfXi/6uXJexPx6uSLm7dlhhAMmDSa2xbN4bZFc+g6LJQDf5riT0tMwl535vjTEk3xH/gzkq5hpvhr3/NxNGIfXv7WuaegMes3xHDLDaMACBvYjfyCYk5lGNiweR+XjwpF765D767j8lGhbNi87xy/7cLSBwdQdCqDoowsjJWVpO6KpsOg0DptOgwM5cQ20w7zZGQM3iE9UUpRll+AZr7aV5SRSVF6Bjpfb4vEPeTq0dy15EnuWvIkPUaEEteE3Heolftxf0TSY1i/6mXH40xX2ZL3HcLTz3JDUdyCAilJz6Ak07T+MyKj8RpQ9z4wrwGhpO8wrf/M6Bg8evVCKUVVWTlVZaaD9Zz4BJSNTZ3JEyxBHxxAYa38Sdm1u2H+DArl+NbT+bOnOn9GzXucCYteZMKiF+k6YSw9rp1g0ZMfgIScArq4OuKnc6CdjeIKfx82p+TUadOl1qxo4X6eHDdfId6cksMV/j7Y2Sj8dA50cXUkPqflD0BunBLOF6tn8cXqWYwe15ef10aZcnpfMi4uTg2GvxUXl1XfF1RZWcWOrQcIDDKdKOeYh+sZjUY+XraBf990SYvHP+Zf4Tz9wSye/mAW/cP7ErHBFH9SQjJOOqfzGv5W+36h2B376eDfviVCbpRLYCClGRmUZplyPycqCo/+TbuH01hZyeH33sN7xAi8hgxp4Ujruvz6cBZ88gQLPnmCQaP6sf2XaDRN40h8Mk4ujk0e/gbg1d6DhN2mauLJ5HQqyitx1bu0VOgAjP13OPM+msW8j2YxILwvO3815c+xeFP+nM8J3IDwfiTuNcVfYCgk/UQm3rUuVrakfWkFBHk408XdETsbxTW9fNlwpGYmt4LyKga+s43wZTsJX7aTPSfzq09+urg7YmuudHVycyTY05mU/FKLxC2sp7VWgH4HvldKvalpWrZS6vTA4BuVUsuBICAYSGypALoOCeFYdDxLZ7yAnYM9kx6+tXrZxw+9Vj2l9YT7b2L9oi+pLC8neHAIwYNrZmxJ2BJj8ckPTgsa3Ifk3Ql8cu8LtHOw44qHbqte9sUjr3LbojkAjLv3Zn5b/AWVZRUEDu5NoDn+rcvXkJmUglIKN19PLrtvssViX75kJqNG9Mbbw5UjEW+z4I3V2NmZUvXDLzbyyx97mDB2APFbF1FcUsY9T5iqU7l5Rbyy+Hu2rXsRgJff+o5cC84AB6Z7MkKnTmbn60vQjEb8R1+CW2c/Dny7Dn2QPx0H9SdgzEhi3v+UjY/Pw87FmSEP3AlAduJhDn77I8rGBmVjQ/9pt2DvojvHO154XYeEcCQ6nvfuNuX+1Y/U5P6HM1/jriWm3J94/02se9OU+10Hh9B1iCl3Js2czIZl32KsMtLO3o4rZ1oud5StLd1unUzcm2+hGY10CB+JrpMfST+sxTUwAO8B/ek4KpwDH3xMxFPPYKfT0fse0wxqFQX5xL6xGGWjsNfr6XXXfywW92k2traE3nEzOxa+jWY0EjBmhCl/Vq9DHxRAx8GhBIy5hN3vf8qGx57DzsWZoQ/eafE4z6RKg4XRR3n70r7YKsWaY+kcyy/m3n4BJOQUsCU1h5t7+BHWQU+lUaOgvJLndh0C4Fh+MRuOZ7F60mAqNY3Xoo9adAY4gJGjQtix5QA3THoJR0d7nn2xJndv+7/X+WL1LEqKy3li5kdUlFdSZTQyJKx79YnObz/HsPrr7QCMvawf1/wrzKLx9xkWQnzEAZ6/7SXsHe25bXZN/C/f/TpPfzALgO+XriX69xgqyiqYe9PzXDJpOFdNm8if320ldsd+bG1tcXZz5vYnp1gsdmVrS+CUKSQuWoRmNOIzciTOfn6krFmDLiAAjwEDKExO5tC771JVXIwhNpbUtWsJnT+fnOhoCg4dorKwkKwdplEQwdOno+vScArnltR/RG9idx1g9uSXcXC0486natbfs9P/y4JPTKNBVr67jl0bYygvreDR6+cz+uph/Ps/E5n8wLV8snAVv63aDEpx19NTzmsI2t/Vb3gI+yMOMPfWl7B3sGfakzX588KdrzPvI1P+rH5/LZEbYygvq2D2/z1P+FXDuXb6RPqE9SIhOpHn7ngVZWPDDfdeU6eq2JKqNI15Gw/x2f8NwNZGsSruJIezi3hsZBCxpwrYePTM01oP6aTn/uv9qTBqaBo8syGR3JIKi8RtNVIAQrWm2adqU0rdAczCNPnBHvPTucAQmj4JwnkNgWtNpvcwfffH+wd+O0fL1une3lcA4ORvuR3ohVRyfAWzI/+wdhjNtjBsHMsPX5y5D3BH9wnM2PantcNolmXhlwLwZNTv1g2kmV4bapr6fvCKrVaOpHl2TzFVhw3lP1k5kubR209iY+rFGTvA5Z0mMXXzZmuH0WyfjRnDzoz11g6jWUb4XgXA5rSLM3/GdDRNGR/w+sW57/1r1ji4SE4tul/5scUO/g///J9WuU5aawUITdOWA8tPP1ZKfQps1zTNclNKCSGEEEIIIf5RWu0JkBBCCCGEEOLC0uSLUC+eEyBN06ZZOwYhhBBCCCHExe2iOQESQgghhBBC/E0yDXarnQZbCCGEEEIIIS44qQAJIYQQQgjRVkgBSCpAQgghhBBCiLZDKkBCCCGEEEK0FTILnFSAhBBCCCGEEG2HVICEEEIIIYRoK2QWOKkACSGEEEIIIdoOqQAJIYQQQgjRVkgBSCpAQgghhBBCiLZDKkBCCCGEEEK0FTILHErTNGvH0JL+0R9OCCGEEEK0GhfFmUW36z+32PHxke9ub5Xr5B9fARq6apu1Q2iWqJvCAVh28FcrR9I8M3pNAGB25B9WjqR5FoaNw8l/irXDaLaS4yuYunmztcNots/GjOGaDVutHUazrBs/CoDvk3+2ciTN8+/AKwF4N+E3K0fSPPeHXAHAI7suzr5n0fBxTPj14txvAfw6IZw1f12cuQ9wXcCV3LXtT2uH0Swfhl8KwGMRF2fuvzFsHAC/pf5k5Uia54pOk6wdQtNJBUjuARJCCCGEEEK0HXICJIQQQgghhGgz/vFD4IQQQgghhBBmUv6QVSCEEEIIIYRoO6QCJIQQQgghRFshkyBIBUgIIYQQQgjRdkgFSAghhBBCiLZCCkBSARJCCCGEEEK0HVIBEkIIIYQQoo3QbKQEJBUgIYQQQgghRJshFSAhhBBCCCHaCpkFTipAQgghhBBCiLZDKkBCCCGEEEK0FVIAkgqQEEIIIYQQou2QCtAZjOig5/EBwdgoxZqkdJYfTKmz/JYeflwX1IEqTcNQVsELUYc5VVwGwMzQQMI7eqCUIiLdwP/2HLN4/JqmsemDb0nanUA7B3smPnwr7bt2adAu/chxfln8JZVlFQQNDmHs3TegzGNDY37czN71W7GxtSFoSB/GTLvOIrGnx8YT9/kqMGr4XzqSHtdMqLO8qqKCmKXLyUs6jp2LjqEP3oWzjxfFmdn8/uR8XDq2B8CzWxD9p99ikZhre//1e7jysoFkZuczZPzsRtv8b/4dTBg7gOKScmY8/h579ycDcOv/jWbOzH8B8OqSH/hy9RZLhV3NsH8/f61ciWY04hsejt+VV9ZZnn/oEH+tXElxaird7r4br8GDASg6cYLkL7+kqqQEbGzoNGkSXkOHWjz+QV4e3N3TtO1uSD3F6uS6224fvRt39+xKoIuOhXEH2ZGRVb1sWvdAhnp7olDszcllWaJ1tt11731HYuQB7BztuPHxW+jUveG2++sn64nZGEVJYTEvrFlY/fyxuKP8+P73nDp2kilPT6XfqAGWDB9N09j80bck746nnYM9V8y8Dd/G+p6jx9mw+AsqyysIHNyHMXea+p5dX//E/g07cHJzAeCS264haHAfi8SeGRvPgS9XoRk1Oo8ZSderG/Y9scuWk59s6nsG3G/qe1J3RJL084bqdgUnUhk5/yncAhp+7pY0xFvPvb2CsVWKn1PSWZVUN/f7erhxb69ggl10vBx7kG3p2dXL7uwRSJi3BwBfHTvB5lNZWJqmaax99zsORh3AzsGOm564hc6N5P4vn6xn9wZT7r+4tib3t6zeROQvu7CxtcHF3YUbH5+CR3tPi8WfE7efYytWoWlGOowKp8ukiXWW5yUe4ujXqyhKSaXXPXfhM2Rw9bL07Ts5/uNPAPhfPYn2I0dYLO7TMmLjSfjClP9dxoykWyP73n1Ll5OXfBx7Fx0DHzDlP0D+8RTiPvmKytJSlFKMfH4OtvZ2Fotd0zS+fft74iMOYO9ox22zp9ClR8PcWffReiJ/i6a4oJj//fRag+V7Nu/l4/nLmfXeo/j39LdE6NYhs8CdvQKklNIrpe6/EG+klJqmlPKr9ThZKeV9IX73hWajYPagrjy8NZ6bfo3hCn8fgtyc6rRJzC1i6sa93PLbHn5PyeKh0EAAQr1c6e/txpTf9jD51xhCPFwY5ONu8c+QtDuB3LRM/vP+s4x/4GY2vreq0XYb31/F+Psn85/3nyU3LZPkmAMAHI89xNGIOKYufpJpbz/N0H+Ns0jcmtFI7PKvGTHrQca9No/UnVHkp6bVaXN88w7sdc5c/r8X6DpxHPErv69epvP1ZuxLcxn70lyrnPwAfP7NZq6b+uoZl08YO4CugR3oO/pRHpzzAYtfuhMAD3cdcx+5ntHXPsuoa59l7iPXo3fXWSpswLT+k7/6ip4PPUTo/PlkR0VRfPJknTYOnp50nT4d77CwOs/b2NvTdfp0QufPp9fDD/PXypVUFhdbMnxsgHt7deX5PfE8sGM3ozv40EXnXKdNZmkZi+IT2Xwqo87zvdxd6a13Y+bOGB7cuZvubq709bD8tpsYdYCs1Eye+GQu1z98Mz8s+abRdr2H9+GBxY82eF7vo+fGx2+h/9hBLR1qo5JjEjCczOCOd+dx2X2T+WPpykbbbXp/JZfdN4U73p2H4WQGf8UkVC8beM1Ybn1zDre+OcdiJz+a0Uj8Z18z5PEHGfXKPNJ2RVFQr+9J2bIDO50zY15/gcAJ40hcZep7Ol0SRviCuYQvmEv/GdNw8va0+MmPDfBA7648szueu7fFMLajD/66uvutzJIy/hd3iE1pmXWeD/P2oJurjvt27uGhiH38X2AnnG1tLRi9yUFz7s/+ZC43PHIz3y8+c+7PXNIw9/26deahtx/nsaVP0m9Uf9Z/uLalQ66mGY0c/XIFfR6dyeAFz5MZEUVR/b7Ty5Oe/5mG77C6fWdFYRHH1/7IgLlzGPDMHI6v/ZGKoiKLxQ41+R/2xIOMeXUeJxvJ/xObTfk/9r8vEDRxHAfN+15jVRV7l35Kv+m3MOaVeQx/6lFs2lk2fxIiDpCRmsm8z59m8mM3sXLR6kbb9R3RhyfefaTRZaXFpWz+biuBvQNaMlTRSpxrCJweaHACpJRqTmZPA/zO1ag16OPpyonCUlKLyqg0amw4nskYP686bXZn5lFWZQQgLrsAX2cHADTA3sYGO/NPOxtFTmm5pT8CRyPjCBkbhlIKv55BlBWVUJiTV6dNYU4eZcWl+PUKQilFyNgwjkTEArDvl22E3TCednamKzjOeleLxJ17NBldex90vj7YtGtHp+FDOLV7X502aTH76BI+HAC/sEFkxR9E0zSLxNcU2yMPkmMoPOPyq68YzFffbgUgcs8R3N2c6eCrZ/yY/vy+NY7cvCIMeUX8vjWOK8b0t1TYABQmJeHo64ujj2n9ew4dSu6+uuvfwdsb586dG8wi49S+PY7tTdU3e70eOzc3KgsKLBY7QHd3V9KKS0kvKaVS09hyKpNhPnWvAGeUlpFcWEz9jDm97bYzb7u2NgpDueW33YSdcQy6fChKKfx7B1JSVEJ+dl6Ddv69A3HzaniC5tnBi47BfigrXeE7FhlHb3Pf09Hc9xTV63uKcvIoLymlo7nv6T02jKORcVaJ9zTDMVPf42zuezoOG0JGTN3cz4jZRydz39Nh6CCyExr2PSd3ReE33PKVz57urpwsLuVUSRmVmsafaZmM8K2730ovLSOpsBhjvez3d3EmNjcfowZlVUaOFRQxxFwNsqSEHXEMGm/K/YCz5H7AGXK/24Du2DvaA6btIy+z4WtbSsExU9/pZO47fcKGkLOnbv44enuj69Kw78yNj0ffpzd2LjrsdDr0fXqTuz/eYrEDGI4m4+xbk/9+w4eQXi//02P20blW/meZ8z9r/wFcu3TCzb8zAPauLigby95hEbdjP2Hm3AkKCaSksIS8RnInKCQQ90ZyB2D9xz9z+eRxtLNvA4OjlLLcTyt1rgx9FeiqlNqrlIpSSm1SSn0FxAEopW5TSkWaly9VStmafz5VSu1XSsUppR5VSv0fMAT40tz29GWpWebXRyqlupl/56dKqfeVUluVUoeUUlebn+9T671ilVLdW2aVgI+TPenm4WwA6SVl+DjZn7H9dUHt2ZGWC5hOhnZnGvj5mjB+uSaMXacMJBeUtFSoZ1SYnYert776sau3nsJ6nUFhdh6uXrXaeNW0yT2ZSUrCUb584n+sfPotTh3+yyJxl+YacPKs2fE6eXpQmmuo2ybHgJOXqY2NrS3tnJ0oLzRdLSvOzObPZ15i24tvkJ142CIxny+/Dp6kpNUMPUk9lYNfB0/8OniQcjKn5vm0HPw6WPYgpNxgwN6z5oTBXq+nIjf3vH9PYVISxspKHHx8LmR45+Tl4EBWWc22m11WjpeDQ5Nem5hXQFxOHstHD2P56GHsycolpcjy225+Vh56n5q/u7u3vtGDwNaqMNuAi1dN/C5e+kYvvrjU6ntcvPQUZtds5/t+2sIXj7zChiVfUlpomSpiaa4Bx1p9j2NjfU+tNja2trRzcqKisO6V+rSI3XQcPqTlA67Hy9GezNKa3M8qLcPb8cz7rdqOFRQx1NsDBxsb3Oza0d9Tj49j07abCykvu27u6731jR7ENkXUL7voNbT3hQrtnMoMBhxq5Y+9hwdlBsNZXlGjPNeAg0fNax08PCjPbdprL5TS3Jr9Kpwl/2vte+2cTflflJaOAiIWLmbrsy9zdP1vlgwdAENWHh6+NX2K3kdPXlbTc+fE4RRyMw30HWGZirOooZSaqJRKVEodUUrNOUObm5RSCUqpePN5yN92rtPcOUBfTdMGKKUuBdabHycppXoDNwMjNU2rUEq9C9wKxAOdNE3raw5ar2maQSn1IPCEpmnR5ucB8jVNC1NKTQUWAVeb3zcQGAN0BTaZT47uBd7SNO1LpZQ90GgVSik1A5gBsHTpUtCHnPdKaex89Uz1hSv9fejt6cI9m0xXLzu7OBLo6sxVP0YC8Pbovgz0dmNPVv55x/F3NFYRaXgi3sinMrcxVhkpKyzmltcf49Th46xb+Al3LXuu+v6gltJoJacJ76kAB70bVyx6CXtXFwxJfxGxaCnjXn0WOyenc77eklQjGaZpWqPr1uKFrWau/9rKDQaOfvwxwdOnW/wq4Plsu/V1dHKks86Z6VsjAFgwqB99snOJN1h4223syVZ8Fa2hM/cr1S0a7Z9MjfpNDCfsxokoBTu/Ws/WT75n/MxbWyLQ+kGdMaaaNo28rlYTw9EkbB3sce3c6cLG1gSN5n4Tkz8m20BPdxfeHBZKXnkFBwz5VFmjqt5o6px/7sdsjCbl0Anu/e/MCxBUE/2t1fX3+92/7xzJfRZGo5GcQ0cJnz8HW3t7dr26CPdAf7z79LqwIZ5Fc48dwBT/d+/+wG1PWmfYfFtmHlH2DjAeSAGilFJrNU1LqNWmO/AUpvONXKWU74V47/Ot80VqmpZk/v9lwGBzsABOQAawDghWSi3BdMJ0tksBK2r9+2at51dpmmYEDiuljgG9gJ3AXKVUZ+A7TdMavbyvadoyYNnphx+s2naeHxEySspp71xz9au9kwNZJQ2HwoT5ujM9pAv3bIqjwmja+C7t5MX+nAJKKk3D43aeyqWvl6tFToD2rN9C3IadAHTo5k9BVs3Vm4IsAzrPumVfFy89BbWuuhZkG3Axt3H1cqf7iP6mYSw9AlA2ipL8QpzdW3YonJOnByU5NRWHkpxcHPV143b01FOSnYuTpwfGqioqi0uwc9GhlMLWPGRPHxSAztebwrQMPIJb13je1FPZdO5YMzSlUwdP0tJzSU3LYdSImiuWnTp6snXnAYvGZu/hQXlOTRWq3GDATq8/yyvqqiwpIXHJEjpfdx2uwcEtEeJZZZWV4V2r4uPlYE9OrYrQ2Qz39SIxL59S89DW3dm59HR3s8gJ0M61W4n82bTtdu7hjyGzZhvIyzLg5unW4jH8Hft+2sL+DTsAaN/Nn8LsmvgLsw241LuXyrVexacwu6Z/0ulrPmvfKy5h7YtLWzL0ao6eHpTW6ntKc3JxaKTvKc2p1feUlGCnq7lPL21XNH5WqP4AZJWW16naeDs6kF3W9CGcK46lsOKYadKEOaE9SC22TPVzx9qtRPxkyv0uPevmviHLgJvX+eX+4ZhE/ljxG/f+d6ZFhzI5eOgpq5U/5bm5ODSx77T38CAv8VD147LcXNx79rjgMZ6No4cHJdl189+x3nbr6KGntNa+t8K873Xy1OPVqzv2rqaJS3z79yUv+XiLnwBt+WEbO9abcse/pz+5GTV9iiHTgHsTc6esuIy0pFMsfvRtAPJzClj6zEfc8+Kd/9yJEFrPNbUw4IimaccAlFJfA9cBCbXa3A28o2laLoCmaRkNfksznO/l2dq1fgUs1zRtgPmnp6Zpz5sD7A/8CTwAfHiW36c14f8AmqZpXwHXAiXAr0qpFrsrPyGnAH8XJ/x0DrSzUYz392FLraFJAD30Op4a0o3HtyWQW1ZR/Xx6cRmDfNyxVWCrFIN83EnOt8yOZOBVo5m66EmmLnqSbsNDSdgUiaZpnExMwkHnWH1yc5qLpzv2To6cTExC0zQSNkXSNawfAN2GhXI81tQh56RmUFVRVT0rU0vSBwdQdCqDoowsjJWVpO6KpsOg0DptOgwM5cS2XQCcjIzBO6QnSinK8gvQjKaD16KMTIrSM9D5tr55NtZviOGWG0YBEDawG/kFxZzKMLBh8z4uHxWK3l2H3l3H5aNC2bB53zl+24XlEhhIaUYGpVmm9Z8TFYVH/6bdh2SsrOTwe+/hPWIEXkOscxB4OL8AP2dH2js60E4pRnfwITIz59wvxDQ5Ql8Pd2zM225fvTsniiwz/GrEtaN4+L3ZPPzebPpc0o+YjVFomsbxA8k4Ojs1er9Da9J/0ujqSQu6DgvlgLnvSUtMwsHZscHFF52nO3ZOjqSZ+54DmyIJNvc9te8XOrJrH14BHS0b8bwVAAAgAElEQVTyGdyDAihKz6A405T7aRHR+A6s2/f4Dgwl1dz3nIqKwat3z+oqkWY0khYVQ8dh1sn9xPwCOjk70d7JlPuXdvRhV0bTct8GcLUznSwEuTgT5KJjd/b5D31tjkuuHcWj78/m0ffNub/BlPt/HUjGSXd+uZ96JIVv31rFHS/cjYuHZe5bPc01KJDS9AxKzfmTGRmN54Cm9Z0effqQG59ARVERFUVF5MYn4NHHskOx3IPr5v/JXdG0r5f/7QeFklIr/0/ve336hZB/IpWqsnKMVVVkHzyES6eW325H/yucOR/MYs4HswgN70ukOXeSEpJx1Dmd8V6f+pxcnHj1hxeZv2Ie81fMIzAk4J998mNhSqkZSqnoWj8zai3uBJyo9TjF/FxtPYAeSqntSqldSqmJXADnujxSAJypF/kdWKOUelPTtAyllKe5bRFQrmnat0qpo8CnZ/ldN2O6z+hmTBWe025USi0HgoBgIFEpFQwc0zRtsfn/ocAfTfmQ56tKg4UxR1k8ui+2CtYmpXMsv5h7+vhzILeQLSdzeLh/EE7tbHl1hOkKx6niMh7ffoDfU7IY4uvOigmD0DRTBWhrWtN2QhdS0OAQjkXH89G9L2DnYM+EWkNIPnvkNaYuehKAy++9yTQNdnk5QYNCCBpsGjLY9/Lh/LrkKz6d+Qq27Wy58pHbWnz4G5jGFYdOnczO15egGY34j74Et85+HPh2HfogfzoO6k/AmJHEvP8pGx+fh52LM0MeMM2ilp14mIPf/oiysUHZ2NB/2i3Yu1h2FjWA5UtmMmpEb7w9XDkS8TYL3liNnfng4sMvNvLLH3uYMHYA8VsXUVxSxj1PmK5w5+YV8cri79m27kUAXn7rO3LzLDsTkLK1JXDKFBIXLUIzGvEZORJnPz9S1qxBFxCAx4ABFCYnc+jdd6kqLsYQG0vq2rWEzp9PTnQ0BYcOUVlYSNYOUzUgePp0dF0sNxuWUYP3E48yf1BfbJRi48l0jhcVc2vXAA7nFxCZmUN3Nxee7h+Ci107hnp7cmtXfx7YGcOO9Cz6e+p5e/hgNCAmO4eoLMtvuz3DQjgYdYDXp7+InYM9Nz4+pXrZW/ct5OH3TFOr//ThWvZu2k1FWQUv3/ocQycOZ/ztV3Ii8Tifv/ARJQUlHNwVz4bPfuGxDxodVt0iAgf3IXl3Asvve4F2DnaMn3lb9bIvH32VW980xTLunpurp8EOGNSbwEGmvmfbZ2vITEoBpXDz9eSyeydbJG4bW1tCbp9MlLnv6Tz6Elw7+3Hou3W4B/rTflB/Oo8eSeyyT9k8ax52OmcG3H9n9etzEo/g6KnH2dey972dZtTgnQNHeXlwX2wU/Jaazl9FxUzt5s+hvEJ2ZebQw82FeQN749quHcN9PJnazZ8Z2/dga6P4X5jpYLe4spLX4g5htMIIuF5hIRyMPMBr017E3sGeG5+oyf03713Io++bcn/9BzW5/9Itpty/YuqVrP9gLeUlZXyx4BMA9L4eTH/hbovErmxt6XrrZPa/+Raa0Uj78JHoOvmR/MNaXAMD8BrQn4KkZBLeeY/KomJy9sVyfM06Bi94HjsXHf5XX8XeF18BwP+aq7Cz8L7LxtaWvlMnE7lwCZpWk/+J5n1v+0H96TJ6JHuXfsqmJ0z73kHm/LfT6QiaeBnbnjfNfurbvy/tB/SzaPx9hoWQEHGAF257CTtHe26bXdNvvHr368z5YBYAPyxdy+7fY6goq+DZm55nxKThTJp2QY6nLy4WnCSn3sis+poycr0d0B24FOgMbFVK9dU07W/dKKfONXuW+WajUEyVl3RN066utexmTOPybIAKTBWfEuATaqpLT2ma9rNS6gbgZfPyEcABc7tJ5rZTNE07opT6FMjFNGlCe+AxTdN+VEo9Bdxmfp9TwC2app3r6EQb2owhcK1B1E3hACw7+KuVI2meGb1M3x8wO7JFzlFb3MKwcTj5Tzl3w1aq5PgKpm7ebO0wmu2zMWO4ZsNWa4fRLOvGm6p73yf/bOVImuffgabvfXo3wfI3Ml8I94dcAcAjuy7OvmfR8HFM+PXi3G8B/DohnDV/XZy5D3BdwJXcte1Pa4fRLB+GXwrAYxEXZ+6/Mcw0sOe31J+sHEnzXNFpErSmwWVn0XX6Kotd4jj6yU1nXCdKqRHA85qmTTA/fgpA07RXarV5H9iladqn5se/A3M0TYv6O3Gdc4CspmlnvCtM07SVQGNf8tDgCyg0TfsW+LbWU4Hmf+c38vrtmqbVmeTfvDJeaaStEEIIIYQQoilazxehRgHdlVJBQCowGah/3vEDMAX41Pz9oT2Av/0t5ZadokkIIYQQQgjR5mmaVgk8CPyKaWTYKk3T4pVSLyilrjU3+xXIVkolAJuAWZqmZTf+G5uu1X3bk6Zp06wdgxBCCCGEEP9EWqspAIGmaT8BP9V7bl6t/2vAY+afC0YqQEIIIYQQQog2o9VVgIQQQgghhBAtpPXcA2Q1UgESQgghhBBCtBlSARJCCCGEEKKtsMD3OrZ2UgESQgghhBBCtBlSARJCCCGEEKKtkHuApAIkhBBCCCGEaDukAiSEEEIIIURbIeUPWQVCCCGEEEKItkNOgIQQQgghhBBthgyBE0IIIYQQoq2QabBRmqZZO4aW9I/+cEIIIYQQotW4KM4sgh/83mLHx8fe/nerXCf/+ArQ2J+2WzuEZtk0aSQAUZnrrRxJ8wz1uQqA5Yd/tXIkzXNH9wlM3bzZ2mE022djxuDkP8XaYTRbyfEVzN+z0dphNMtzAy8HYGPqT1aOpHku7zQJgFf3bbByJM0zp/94AB7Z9YeVI2meRcPH8UTExRk7wH+HjePlvRdn7gA8PWA8D+3cZO0wmmXxiLEAzNj2p3UDaaZl4ZcC8NXRX6wbSDPd0nWitUNoOpkGW+4BEkIIIYQQQrQd//gKkBBCCCGEEMJEk3uApAIkhBBCCCGEaDukAiSEEEIIIURbIeUPWQVCCCGEEEKItkMqQEIIIYQQQrQVMgucVICEEEIIIYQQbYdUgIQQQgghhGgrZBY4qQAJIYQQQggh2g6pAAkhhBBCCNFWyD1AUgESQgghhBBCtB1SARJCCCGEEKKtkAKQVICEEEIIIYQQbYecAAkhhBBCCCHaDBkCdwZDvfU8GBKMrYL1J9JZcSy1zvIbg/yY1Lk9VZpGXnkFC2OPkF5aBsBrQ0MI0bsSl5vP09EHrBE+mqbx+Vvfs3fnARwc7Znx9BSCenau06astJzFzy4nIzUbGxvFwJF9mHzf1QBUlFfy/otfkZR4Alc3HQ++MBWfjp4Wi33Dsm85Gp1AOwd7rnnkVjp069KgXdqR4/z45pdUllfQdUgI42fcgFKK9GMp/PzOSirLK7GxtWHifTfh1zPAIrEDGPbv56+VK9GMRnzDw/G78so6y/MPHeKvlSspTk2l29134zV4MABFJ06Q/OWXVJWUgI0NnSZNwmvoUIvFDfD+6/dw5WUDyczOZ8j42Y22+d/8O5gwdgDFJeXMePw99u5PBuDW/xvNnJn/AuDVJT/w5eotlgq7jpN749m9fDWa0UjXcSPpc90VdZZXVVSw853PyEk6joOLjpEP34mLrxdlBYVsffNDco7+RdCY4Qz9z81WiV/TNL55+3viIw5g72jH7bOn4N+jYf6v/Wg9Eb9FU1xQzJs/vVb9/M5fIvlh6Vrcvd0BGPOvUYy8arhF44/4ZDUpe+Jp52BP+P234x3cMP6sY8fZ+s7nVJVX0HlgH4ZN/z+UUiTtjGHvNz9hSE3nmpefwLur5bbdzNh4Dny5Cs2o0XnMSLpePaHO8qqKCmKXLSc/+Th2LjoG3H8Xzj5epO6IJOnnDdXtCk6kMnL+U7gFNPzcLSkjNp79X5ji9x8zku7XNIx/79LlGJKPY++iY/ADpviLM7PZNGc+Lh3bA+DRNYjQ6bdYNHYw5U7kp6tJNefOyPtux6uR3Mk+dpxt75pyp9PAPoRNM+VOWWERmxd9TGFmDi4+nox55E4cXJwtFn9mbDwHv1qFZjTSefRIgq+eWGe5saKCuA8+Jc+8/vvfdxdOPt6c3BFBcu38SUllxPNPWzx/cuL2c2TFKjTNSMdR4fhPqhu/IfEQR79eRWFKKiH33IXPkMHVy05t38nxH38CwP/qSXQYOcKisWuaxi9Lv+NwVAJ2Dnb867Fb6djIccPJwydY88aXVJRX0H1oCBPvuR6lFKeOpbL+7VWUl5Shb+/J9bOn4uDsaNHPYEmaTILQOipASqlPlVL/dx7tA5VS+1sqHhvg4T7BzImKZ9qWPVzm50OAi1OdNofzirh3+z7u2raXzaeyuadXYPWylcdSeXnfoZYKr0n27TrAqRNZ/O/rp7lz1o18+t/Vjba7asqlvP7VHF765HEOxSWxb6fphO3PHyPQuTrxxsq5TLx5DF+/96PFYj8anUDOyUzuXfYskx68mV/eXdVou1/eWcWVD07m3mXPknMyk2O7TbH/8ckaRk25kruWPMnoWyfxxydrLBa7ZjSS/NVX9HzoIULnzyc7KorikyfrtHHw9KTr9Ol4h4XVed7G3p6u06cTOn8+vR5+mL9WrqSyuNhisQN8/s1mrpv66hmXTxg7gK6BHeg7+lEenPMBi1+6EwAPdx1zH7me0dc+y6hrn2XuI9ejd9dZKuxqRqOR6I9XMXbOA1z1v2f5a3s0eSlpddoc3bQTexdnrn1rPj2vGsfer34AwNbOjtCbrmbgbddbPO7a4iMOkJmayfOfP80tj93E14sa33b7jejD7HcfaXTZoEsH8vQHs3j6g1kWPfkBSNmTQP6pTG5Y/ByXzJjCzg+/brTdzg9WMvKeKdyw+DnyT2WSujcBAI8ufox74m469O5qybDRjEbiP/uaIY8/yKhX5pG2K4qC1Lq5k7JlB3Y6Z8a8/gKBE8aRuOp7ADpdEkb4grmEL5hL/xnTcPL2tPjBq2Y0EvfZ1wx74kHGvjqPk43Ef2KzKf7L/vsCwRPHcWDl99XLdL7ejHlxLmNenGuVkx+A1L0JFJzK5N9vPceIu6ew66Mz5M6HKxkxYwr/fus5CmrlTtwPG+jYtyfXv/UcHfv2ZP+a3ywWu2Y0cuDzFQx+7EHCX36OtIgoClPr9v0pW7bTztmZ0QsXEHDFZRz6xrT+/S4ZxiULnuGSBc/Qb8Z0nLy9rJI/h79cQb9HZzJ0wfNkRERRVG/f5ejlSc//TKP9sLr7rorCIv5a+yMD585h4DNz+Gvtj1QUFVkyfI5EJ5CTmsnMD5/hmocms/7tbxptt/6dVVz90M3M/PAZclIzOWK+SL3urRVcNv0a7ntvDr0uCWX76t8tGb6wglZxAtTa9NK7crK4lLSSMio1jT/SMhnZvm71Y29OHmVGIwAJhgJ8HO2rl8Vk51FcWWXRmOvbvXU/4ROHoJSiW99AigpLyM3Kr9PGwdGekEHdAWhn147AHp3JyTQAELNtP6OuNFUfwi4NJX73YTRNs0jshyLi6DcuDKUUnXoFUVpUQmFOXp02hTl5lJWU0rl3EEop+o0LI3FXrHmpoqy4FICy4lJcvNwtEjdAYVISjr6+OPr4YNOuHZ5Dh5K7b1+dNg7e3jh37tzgi8ic2rfHsb3pCqy9Xo+dmxuVBQUWix1ge+RBcgyFZ1x+9RWD+erbrQBE7jmCu5szHXz1jB/Tn9+3xpGbV4Qhr4jft8ZxxZj+lgq7WvaRZFw6+ODS3hvbdu0IuGQwKdGxddqkRMcSNHoYAP7DBpIen4imabRzdMC3Vzds7axbGI/dsZ9h44eilCIoJJCSwhLysvMatAsKCcTdgrndVMejY+k22rT9+vYIoryohOLcuvEX5+ZRUVKKb49gUx81Ooy/okx/J33nDrj7tbd43IZjyeja++Dsa9p2Ow4bQkZM3W03I2YfncJNJ5Qdhg4iO+Fgg37x5K4o/IZbtnILkHs0GZ2vDzpz/H7Dh3CqXvynYvbR2Rx/x6GDyGwkfms6ERVLsDl3fJqYO8Gjwzhhzp0T0bF0HWPatruOGcbxqNgG79FS8o4l49zet1b+DCVjT933z9gTS6dwU2Wk/RnyJy0iio7Dhlgs7tPyjyXh5OuLk3nf5Rs2hOw9dfPH0dsbly4N91258fF49OmNnYsOO50Ojz69yd0fb8nwObhrP6GXmfrNzr0CKS0qoaDecUNBTh5lxaV0MR83hF42lIO74gDISskgoK/pokvwwJ4c2L6vwXv8o9goy/20UlY5AVJKTVVKxSql9imlPjc/PVoptUMpdex0NUiZvK6U2q+UilNKWWRMirejPRml5dWPM0vK8XZwOGP7SZ3bE5GZa4nQmiw3Kx8vX331Y09fPblZDQ+iTisqKGHP9nj6DO5hen1mHp7m19u2s8VZ50hhnmWu6BRm5+HmXRO7q5eegnoHgAXZebh51W1TaG4zfsb1/PHJGpZMm8fvH/3A2DuusUjcAOUGA/aeNSfL9no9FbnnnxuFSUkYKytx8PG5kOH9bX4dPElJy65+nHoqB78Onvh18CDlZE7N82k5+HXwsHh8JTkGdF417+vsqac4x3DGNja2ttg5OVFWYNmrlWeTl5WHvta2q/fRYzjLttuYvVv38dJdC/ng+U/IzbBs31ScY0DnXfM30Hk1/BsU5xhwrrX9OjfSxtJKcw04etbE7ejpQWmu4YxtbGxtaefkREVh3dxJi9hNx+GWP4AtzTXg5HXu+J1q576zE+Xm+Iszs9n8zEtsf+kNshMPWy7wWopz622/Z8gdnWdN7ug89RSbP2dJXgHOHqaLAs4e7pTmW+4CUmlubt388dBTWq/vL2tC/pyKiKaDFU6gyw0GHGrF7+DhQZmhadtkWa4BB496r8217PZckGXA3acmL9y83Smo128WZNU9tnDz1lOQZYrTN7AjibtMA4sStu4lP8u6/ZFoeRa/1KmU6gPMBUZqmpallPIE3gA6AuFAL2AtsBq4HhgA9Ae8gSil1FlvLFBKzQBmACxduhQ69zn/GBt5TqPxq2SX+/nQ092FRyLizvt9WlJjV/XOdB5eVVnFO89/zoQbR+HbyeuMr69/1aelNP7eTWhjFvPTNi6/69/0GjmAhK0xrH/rK2556cELHOUZXID1Vm4wcPTjjwmePh1l07qKtKqRLNI0DdXIZ2wtF5YbxtbIttGKLlI1uu2eR4D9RvRhyLhB2Nm3Y+va7Xz26lc8/MYDFzLEs2vs714v/sY3cSv/EZqy3hv9bDX/NRxNwtbBHtfOnS5sbE1yjuDO0sJB78blb76EvasLhqS/iHprKZe+8ix2Tk6NvKLlNN59NuFvYO3cgUbjqp/T59q3WTV//lZ/bb3jhbOq3+802veb2lz3yC38/P63bFnxCz2G9cW2na1FQrSa1vD3sTJrjPUYB6zWNC0LQNO0HHMC/qBpmhFIUEqdHv8QDqzQNK0KSFdKbQaGAmesa2uatgxYdvrhip+2n3eAmaXl+NYa0ubjZE92WXmDdoO83LmtW2ce2bWfCqP1j/Y2fLuNTet2ARDcuwvZGTVXMHIyDOi9Gx8u89HCb+jQxZuJN42pfs7TV09OhgEvXz1VlVUUF5Xi4tZyN5NG/7iFvb/uBMCvu3+dqy8F2QZcPevG7uatJz+7bpvTQ93ifo9k/IwbAOgdPpCfFq9osbjrs/fwoDynphJSbjBgp9ef5RV1VZaUkLhkCZ2vuw7X4OCWCPFvST2VTeeOXtWPO3XwJC09l9S0HEaN6F3zfEdPtu60/AQgTp56irJrrroW5xhw8nCv18aDouxcnL08MFZVUVFSgr2L5e9Xqm3zD9vYvt6U/wE9/THU2nYNmQbcvdya/Ltcat17NfKqEfzwQcvfv3fgl80c+n0HAN5dAyjKqvkbFGUbqq/Kn6bz0lNca/stzjbg5Gnd4XyOnh6U5tTEXZqTi4PevV4bPaU5uTh5mnKnsqQEO13N+k7bFY2fFao/AI4eHpRk143fsX7ue+gpya6Jv6K4BDsXHUopbO3sANAHBaDz9aYoLQN9cMtPQHHw13q5U3v7zW64/Tp76SmqVRUqyqnJLyd3V4pz83D2cKc4Nw9HN9cWj/+0BvmTa8DBQ99oG8cz5M+piCg6DrN89QfA3kNPWa34y3JzcWjivsvBwwNDYs19z2W5ueh79rjgMdYXuW4rMbWOG/Iya/IiPysP13r9ppu3vs6xRX5WzXGDd5f23P7S/QBkp2RwOCqhpcMXVmaNy8uKxq81lNVrU/tfizqYV0AnnRMdnBxopxTjOvqwIz2nTptubjoe69uVudEHMJRXWCPMBsbfEM7Lnz7By58+weBR/dj2SzSapnFkfzLOLo54eDc8iPpm2U+UFJVw20P/qvP8oJF92PpzFACRf8YSMqjbeV2FPl9Drh7NXUue5K4lT9JjRChxf0SiaRqpB5NwcHbEpd7BkYunOw5OjqQeTELTNOL+iKTHsH7Vy47HHQEged8hPP0sN4zMJTCQ0owMSrOyMFZWkhMVhUf/pt0LY6ys5PB77+E9YgReQ6xzEHUu6zfEcMsNowAIG9iN/IJiTmUY2LB5H5ePCkXvrkPvruPyUaFs2Gz5MdReXQMoOJVBYUYWVZWV/LVjN50G96vTpvPgfiRtiQDgeMQe2vfp0aK53RRj/hVePWlB//C+RGyIQtM0khKScdI5nde9PrXvF4rdsZ8O/i1/P03viWO47vWnuO71p/APC+XIFtP2m3EoCXtnpwYnQM4e7tg5OZBxyLT9HtkSif+Q0BaP82zcgwIoSs+gONO07aZFROM7sG5MvgNDSd1mush0KioGr949q3NHMxpJi4qxyv0bAPrguvGf3BVNh3rxtx8USoo5/rSoGLxDTPGX5Regme9pLcrIpCg9A2dfb4vE3WvCGK5d+BTXLnwK/6GhHDPnTuahJOzOlDuODmSac+fYlki6DDV9zi5D+nF0s2nbPro5gi4WzCm3oACK6+RPVMP8GRBK6jbTAXt6VAye9fLnVFQMHayUP25BgZSkZ1Bijj8jMhqvAU3bd3n06UNufAIVRUVUFBWRG5+AR5/zH31zvsKuGcW9b8/m3rdn02tEP2J/N/WbKQeTcdA5Nrhw6urpjoOTAykHk9E0jdjfo+g1vC8ARQbTcEnNaGTL178xZNLIFo/fqmws+NNKWaMC9DvwvVLqTU3Tss1D4M5kC3CPUmo54AmMBmYBLTo3oVGDxfHHWBjWBxvg55QMkgtLmN7dn8S8QnZk5HBvr0Cc2tny/KCeAKSXlPOMeRayt4b3xV/njFM7G1aNHcLrcUeIsvB40gEjerNv5wEev/ll7B3tmPH0lOplT0/7Ly9/+gTZGQbWfLYRvwBfnvnPG4DpJGrsNcMZc/Uw3l/wFY/d/BIubs48+PxUi8XedUgIR6Ljee/uF7BzsOfqR26tXvbhzNe4a8mTAEy8/ybWvfklleXldB0cQtchIQBMmjmZDcu+xVhlpJ29HVfOnGyx2JWtLYFTppC4aBGa0YjPyJE4+/mRsmYNuoAAPAYMoDA5mUPvvktVcTGG2FhS164ldP58cqKjKTh0iMrCQrJ2mK6IBk+fjq6L5WYDWr5kJqNG9Mbbw5UjEW+z4I3V2JknBfjwi4388sceJowdQPzWRRSXlHHPE0sByM0r4pXF37Nt3YsAvPzWd+Ra6J6x2mxsbRky/SY2vfwOmtFI8NgR6Lv4EbvqRzyD/ek8JJSuYy9hxzvLWfvwc9i76Ah/6D/Vr1/z4LNUlJRirKwkJTqWcU8/iHvnjhb9DH2GhRAfcYDnb3sJe0d7bptdk78v3/06T38wC4Dvl64l+vcYKsoqmHvT81wyaThXTZvIn99tJXbHfmxtbXF2c+b2J6ec6a1aROeBfUiJiefbh+Zja2/HqPtvq162ZtYrXPf6UwCMuOtmtr77hWkq4wEhdB5o2n7/itzHro+/oTS/kA2vvo9nYCcmzG35Iaw2traE3D6ZqNeXmKcxvgTXzn4c+m4d7oH+tB/Un86jRxK77FM2z5qHnc6ZAfffWf36nMQjOHrqcfa1zn17Nra29J06mV0Ll6BpRrqY4z/47Tr0Qf50GNQf/9Ej2bP0U35/Yh72Ls4MMsefnXiYxO9+xMbGBmxs6DftFqtURTsN7EPKnni+e3g+7eztGHlfTe6snf0K1y405c7wu25m+7tfUFlhyp1OA0y50/e68Wxe9DGHN+1E5+3BpY/e2ej7tAQbW1t633Yzu/+7GM1opNOoS3Dp5Mfh79biHhSA78D+dBo9krhln7Bl9rPY6Zzpf99d1a/PTTyMo4eH1fJH2drS7dbJxL35FprRSIfwkeg6+ZH0w1pcAwPwHtCf/KRk4t95j8qiYrL3xZK8Zh1DFzyPnYsO/6uvIubFVwAIuOYq7CycP92HhnA4KoEldy7AzsGe6x6tmcnw/QcXcu/bpq91uOqBm/jhzS+pLKug25AQupmPG+L+3E3Uj9sA6D0ylAHjh1k0fmF5yhozwCil7sB0IlMF7DE//aOmaavNyws1TXNRpksjC4ErMVWNXtQ0baVSKtDcvu853kob24whcK3BJvPVh6jM9VaOpHmG+lwFwPLDv1o5kua5o/sEpm7ebO0wmu2zMWNw8rfsge+FVHJ8BfP3bLR2GM3y3MDLAdiY+pOVI2meyztNAuDVfRvO0bJ1mtN/PACP7PrDypE0z6Lh43gi4uKMHeC/w8bx8t6LM3cAnh4wnod2brJ2GM2yeMRYAGZs+9O6gTTTsvBLAfjq6C/WDaSZbuk6EVrFDWnnFvjcLxY7+E+eP7FVrhOrzPeqadpyYPlZlruY/9UwnSjNqrc8GTjXyY8QQgghhBBC1GHdL7wQQgghhBBCWE4r/n4eS2nFtycJIYQQQgghxIUlFSAhhBBCCCHaCqkASQVICCGEEEII0XZIBUgIIYQQQog2QrPyd9+1BlIBEkIIIYQQQrQZcgIkhBucRAMAACAASURBVBBCCCGEaDNkCJwQQgghhBBthZQ/ZBUIIYQQQggh2g6pAAkhhBBCCNFWyCQIUgESQgghhBBCtB1SARJCCCGEEKKtkC9ClQqQEEIIIYQQou1QmqZZO4aW9I/+cEIIIYQQotW4KEorAa//YbHj479mjWuV6+QfPwRu6ubN1g6hWT4bMwaA2ZF/WDmS5lkYNg6AGdv+tG4gzbQs/FKu2bDV2mE027rxo5i/Z6O1w2i25wZejpP/FGuH0Swlx1cA8PmRX60cSfPc3m0CAK/u22DlSJpnTv/xADy7++LM/wWDL+fFi3jbfWbg5Sw7eHHmPsCMXhOYvGmLtcNolq/HjgbgrfjfrBxJ8zzc5woA3tx/cfY9j/Ydb+0QxHn4x58ACSGEEEIIIcxaZU3GsuQeICGEEEIIIUSbIRUgIYQQQggh2ghNZoGTCpAQQgghhBCi7ZAKkBBCCCGEEG2FkgqQVICEEEIIIYQQbYZUgIQQQvw/e/cdV2X5/3H8dbHhMA5TRBRRXGgu0HKi5ra0rbatbGrT0upnZTvb27Tya2XlaGhqw8yZAwEVxYmByFD23nD//jgIHDiUmtxIfJ6Ph486577OOe9z8bkv7vu+7vtGCCFESyHXAMkMkBBCCCGEEKLlkB0gIYQQQgghhO6UUmOVUkeUUrFKqTl/0+46pZSmlAq9EJ8rO0BCCCGEEEK0FErHf38XQylr4ENgHBAMTFVKBVto5wI8COw6369cl+wACSGEEEIIIfTWH4jVNO0vTdNKgW+BSRbavQDMB4ov1AfLDpAQQgghhBAthJWVfv+UUncrpSJq/bu7VpQ2wMlajxOrnqumlOoDtNU0bc2F7AO5C5wQQgghhBDigtM0bSGwsIHFlk6S06oXKmUFvA3cfqFzyQ6QEEIIIYQQLcRF9HdQE4G2tR77A8m1HrsAPYBNyhTaF1itlJqoaVrEv/lg2QFqQPaBA5xYtgytshKfwYPxGzfObHnu0aOcWLaMwqQkgqZPxzMkBICCkyeJX7qUiqIisLKizfjxePbrp3v+09Ex7P9yOVRqtBs2iM5XjjFbXlFWRtQnS8iJS8DW2UC/GXfh5O1JYVoGG2bPw7l1KwA8ggLpNe1GXbNn7j9A7DfL0bRKWg8ZTLvxY82WV5aVcfizxeSdSMDWYCD43uk4eHlRWV7O0S++Ij/+BCgrgqbegLFrF12zA/T1dGd6lw5YKcX6pFOsjE80W97d6Mr0Lh1p72xg/v7DbE9Nr152e6f29PPyQKHYm5nFwiN/6R2f5L0xRC5ZiVZZSccRg+g+abTZ8oqyMnZ8+AWZcQnYOxsY9NCdOPt4UpKXz9a3PyXz+AkCwy6j3x2Tdc++4PV7GHd5H9Iycgkd9YTFNm/Ou40xw3tTWFTK3Y99zN4D8QDcdN1Q5sy8CoBX3/+RpSu36BXbjKZp/PbJd8RGHMTW3o4rH7mJ1kFt67VLOZbA6reXUl5aRlBoMKPvuRalFKeOJ/Lzh8soLy3HytqKsfffQJsuAbrm37V4JYl7YrCxt2Pw/bfg1aF+/vS/Etj64ZdUlJbh36c7l067DqUUcTui2LtiHdlJp7ny5Vl4ddQve8q+GPZ+Yar9wOGD6Daxfu2Hf/wFWXEJ2DkbGPDgnRi8PcmIjSfys68B0DTofu14/Pv11i33GUl7Y4ioWneDRgyih4V198+qddfO2cDQWuvu5rc/JeP4CTqGXUb/Jlh3wVQ7Gxd9R1zkQWzs7Rj70E206li/dk7HJvDLe0spLykjMCSY4dNNtQ8QtWYze9duxcraisDQ7oTdbulygsaRG3OAxOXfolVW4jloCL5jzbcb8o8dJXH5MoqSEml/5924V203nFFRVMSh557BrXcf2k7V9/cumPp/22ffcSLKtO5ePuNmvC30f+rxBP54/yvKS8sI6NudwXea+n/X12uI270fpRSObi5cPvNmDB5uuub/8/OVJETFYGNnx/CZt+BtYexJO57Axg++pLy0jHZ9uzPoDtPYE/7NGuLDo1FWpvzDZ9yMwcOoW/4WajfQSSkVCCQBU4Dq4tc0LQfwOvNYKbUJmPVvd37gIroGSCn1oFLqkFJqqVJqVlNm0Sorif/6a7o8+CA9580jY/duCpOTzdrYe3jQcdo0vPr3N3veys6OjtOm0XPePLo+9BAnli2jvLBQz/holZVEL/mWAY/PYMRrz5C0Yze5SSlmbRI2b8fO4MTIN5+n49gRxCz7oXqZwceL4S89zfCXntZ950errOTY0m+45JGZ9HvhOVJ37aagTt+nbP0TGycDl77yIv6jRvLXyu9Nz2/ZCkDo88/S87GHOL7ctCGgJyvg3q4deW5PDA9sj2SorzdtDU5mbdKKS3gn5gibT6WaPd/VzYVuRldm7ohixo5IOrm60MNdv18eAJWVlUR8vpzhcx5gwptzOfFnBDmJ5rVzfOMO7JydmPjuPLpMGMHer38EwNrWlp43XEGfm6/RNXNtX67YzKRbX21w+ZjhvenY3pceQx9hxpxFvPfSnQC4uxl4+uFrGDpxLkMmzuXph6/B6GbQK7aZ4xEHyUxO4/5Fcxk/czI/f7jcYrufP1rOhJlTuH/RXDKT0zgeeQiADYtXMeTGcUz/YDZhN49nw+JVesYncc9Bck+lce17zzLw7qns+PRbi+12LFrGoHumcu17z5J7Ko2kvQcBcG/rx4hZ0/Ht1lHP2FRWVhK1eDlDnniAMa/PJWF7/dqP27QDW4MT49+eR+dxI4j+xlT7bm39GPnibEa/8hRDZz9A5GffUFlRoXv+8M+XM2LOA1z55lzi/4wgu07+2Kp196p359FtwgiiqtZdK1tbet9wBSFNuO4CxEUeJCsljTsWzGXUA5P5/WPLtf/7guWMun8KdyyYS1ZKGvFRptpPiD7K8V37ufW92dz+wVP0u2qEbtm1ykpOfvM1HWc8RLdnnydrdzhFdX532bp7EHDbNNz79bf4HimrV+HcubMecS1KiDpITkoqN334DMPuncLmhcssttvyyTKG3TeVmz58hpyUVBL2mNbdPlddzpS3n2TyW3NoH9qd3ct/1jN+Vf40pn7wLGH3TWXrQstjz5aFyxh671SmfvAsOSlpnKzK33vS5dzw9lNc/+aTBIT0IHKFvvn1pJR+//6OpmnlwAzgV+AQsFzTtBil1PNKqYmN2QcXzQ4QcD8wHjjW1EHy4+Jw8PHBwdsbKxsbPPr1I2vfPrM29l5eOPn71/vpOrZqhUMr0+yJndGIrasr5Xl5umUHyDoej6GVNwYfU/42l4VyKtI8f0rUPtoOvgwAv/59SY85jKZplt5OV7l/xeHo44NjVd/79A8lY4959oy9+2g10JTdO7QvWYdM2QuTU3Dv1hUAO1dXbBwdyYs/oWv+Tm4upBQWc7qomHJNY8upNC719jBrk1pcQnx+IXV7WwPsrKywsbLC1soKaytFdmmpbtkBMmLjcfb1xrmVF9Y2NgQMDCExItqsTWJENIFDLwWg3aV9OB1zBE3TsHGwx6drENa2TTex/Gf4YTKz8xtcfsXoEL7+zrSjHL4nFjdXJ3x9jIwK68WGrfvJyikgO6eADVv3Mzqsl16xzRzZuZ9LRvRHKYV/10CKC4rIy8wxa5OXmUNJYTH+3QJRSnHJiP4c2WH6OSmlKCk03SinuKAYFx2PwAIkREQTNNSU36dzIKUFRRRmmecvzMqhrKgYn84dUEoRNLQ/J3ab8hv9fXHza6VrZoDM2HicW9XUfrsBISRHmtd+UkQ07YeYat//0j6cPlBV+/Z2WFlbA6ZZln+892sjyIiNx8XXG5da6+7JOuvuyYhoOlatuwGX9uFU1bprexGsuwDHw/cTPNxUO35dAikpKCK/Tu3nV9W+X1dT7QcP70/sLtP33PfLNvpfOwobW1sAnIwuumUvjI/D3scb+6rfXe79+pETvdesjb2XF47+/tWzVWavP3GCsrxcXLrVuwOwbuLC99NlmKn/fbuY1t2COv1fkJlDaVExvl1M/d9lWH/idu0HwM7JsbpdWXGpxe/ZmOJ3R9M5zJS/VWdT/RTUGXsKsnIoKyzGt4tp7Okc1p+48Oj6+UtKaIr1uCXSNG2dpmmdNU3rqGnaS1XPPaNp2moLbYddiNkfuEhOgVNKLQA6AKuBdpjO7/sD03mB8zVNW6SUag0sA1wx5b5P07StjZGnNDsbO4+ajVY7o5GCuLhzfp/8uDgqy8ux9/a+kPH+UXFWNo4e7tWPHT3cyTpunr84MxtHT1MbK2trbJwcKc0vAKAwLYNN//cSNg6OdLv+Sjy7dNIte2l2Nva1stu7u5Nbp+9LsrJxqPr5KGtrbBwdKc8vwNDWn/Q9+/Dp34/izCzyTiRQkpkFHQJ1y+9pb096SUn144ySUjq7nt0v4SM5eezPzGHJ0EtRwNqTySQWFDVSUsuKMrMxeNb0v5OHkfTY+AbbWFlbY+voSEleAQ6uznpGPS9+vh4kpmRUP046lYmfrwd+vu4kJmfWPJ+SiZ+vu6W3aHR5GTm4etecduHqZSQvI8dsRyYvIwcXz/ptAEZPv4avn/mY3z/7ETSN2954RL/wQGFmNgavmr4zeBopzMzGqdZsZmFmNk618jtVtWlKRVnZOHnWHjeNZNat/VptrKytsXVypDSvAHtXZzJi49j9yVcUpmfS//7bqneI9FJYZ901WFh3Tf1+8a67+Rk5uHjV1IWLl5H8jByca9V+fp3ad/E0tQHISk4j8eBxtn21Bhs7G8KmXYVvJ31OoSzNysbOvfZ2g/tZbzdolZUkrVxOwLQ7yTt8qLEi/qOCzGyc66y7BZk5ZqexFWTm4Fyr/01tatbdnUt/4simcOydHJn0/Ex9gldnM8/v7GmkICMbQ62xpyAjG0Ot/M518u9aupqjm8Oxc3Jk4rwH9QneBPTeOb0YXRQzQJqm3YvpoqfhmO720BOYAAwAnlFK+WE6J/BXTdN6A72AvZbeq/bt9hYubOimE/8YyNIbn9NblGZnc/zzz+lw++0oK3272eJMzlnkV4C90ZXR77zEsBefpsdN1xLx0WLKinTcCD+rSShL3w9aDx6EvYc7kS+8zPFvl+MW1BFlrW/f/+3tTP5Ba0cH/A1OTNu6i9u37qKnh5HuRtcLGe+81B8o63+j5jKWKgs/IU3TLP4yaLIJUQsfXC+dpTZVjSLXbWPU9Kt5aMnzjJp+NWve+frCZ/w7lvqtTv9aHKKa+mjrWeRuIDgAnkGBjH19LiNfnM3hVb9RUVp2wSOes2a27lr63VU/X8M/g8qKSkryC7nx9UcZevtV/DR/sY5nNjSc65+kb96Ea49LzA68NgXL2w5n06am0WU3Xclti16g09BQ9v+s83WUFqOdzbZPTZtLb5rILQtfpNPQUA7onV/o6qKYAbJglaZpRUCRUmojpj+UtBv4XCllC/yoaZrFHaA6t9vTtm3efM4fbufuTmlmzdHg0uxsbI1nfyFceVERR95/H/9Jk3Dp0OGcP//fcvRwpygzq/pxUWYWDkbz02AcPIwUZWTh6OFOZUUF5YVF2DobUEphXXX6gDEwAIOPF/kpqbh30Ocomp270TRrU6UkKwv7On1v7+5OcWYm9h7uaBUVlBcVYWMwZQ+ackN1uz0vv4ZjKx9dcp+RXlKCl7199WNPezsya80I/Z3LfDw5kpNLcYXpuqXIjCy6uLkSk53bKFktcfQwUpBR0/+Fmdk41rkOydHDnYKMLJw8TbVTVlSEnXPTXC9zrpJOZeDf2rP6cRtfD1JOZ5GUksmQAd1qnm/twdYd+h2JjVizhT2/7ACgded25KbVHJHMTc/G2dP8Z+DiZSQvo06bqqO00RvCGX3PtQB0G9yHNe9+09jxOfTLZo5u2A6AV8cACtJraqggw3z2B6pmhWrlL8zIxlHnU/XqcvQwUphRe9y0XPuFtWu/sH7tu7bxxdrBjpzEZDx0GjfBNFtbe90tsJDfqSq/4SJad/es3cL+9aba9w1qR156TV3kpWfXu4je2dO89vMyamrfxdONTgN6oZSidecAlJWiKDcfJ7fGPxXOzt2d0qza2w1ZZ73dUPDXcfJjY0nfvImKkhK0inKsHOxpc/W1jRW32v6ft3BwvWnd9QlqR36dddfgXr//82v1v6U2AJ2HhLL2pQX0nzKhkZKbHPh5M4d+N+X3Dgowy5+fkY2TR/2xp6BWfkttADoN7se6lz+mXyPnbyoX04GPpnJRzABZUO/yCE3TtgBDMd0l4kul1K2N9eHO7dtTnJpKcXo6leXlZO7ejXuvs7seoLK8nGMff4zXgAF4hoY2VsS/ZewQQMGpVApSTfmTdkbg27enWRvfPj05uW0nAMnhUXgFdzFdO5CbV33jgILUNApOp2Lw8ar3GY3FNbA9RadTKUozZU8Nj8Czt3nfe/buyentpuxpEVG4d+2KUoqKklIqqnY2MmMOoqysMPj56ZYd4FhuHn5ODrRysMdGKYb6ehOelvnPL8R0c4Qe7m5YKbBWih5GN04W6HsDDc+OAeSdSiU/NZ2K8nJObI+kTcglZm38Qy4hbssuABJ27aFV987NZjp97foobrx2CAD9+wSRm1fIqdRs1m/ex8ghPTG6GTC6GRg5pCfrN+/7h3e7cEKvGMr0D2Yz/YPZdLmsJ/v/CEfTNBIPx+FgcKh3HY+Lhxt2jg4kHo5D0zT2/xFOl8tMPydnDzdO7I8FIH7fUTz8Gv8U3G5jw5j0+pNMev1J2vXvSewWU/7Uo3HYOTnW2wFycnfD1tGe1KOm/LFbwmkX2rOBd9eHR8cA8mvVfsKOSPzq1L5fyCXEbzXVfuKuPfhU1X5+anr1TQ8K0jLIS07F4OVZ7zMa05l1N6/Wutu2Tv62IZdwvGrdPbFrD74XwbrbZ8JQbn1nNre+M5ugy3pycKOpdpKPxGFvcDA7/Q1M9W3n6EDyEVPtHNwYTsf+pu8ZdGlPEqKPApCZlEpFWQWOOp3e5xTQnpLUVErS06gsLydr927cep7ddkP7O6fT45XX6P7yq7S59jo8Lh2gy84PwCXjhjL5rTlMfmsOgf17cmSTqf9PHYnDzsmh3g6owcMNWwcHTlX1/5FN4QRW9X92cs2NfeJ278fYpvGv5esxLozr33yS6998ksD+PTm62ZT/dNXYU3fnzFA19pyuGnuObg6nfb+e9fLHR0TjrkN+0XQu1hmgSUqpVwADMAyYo5QKAJKqrgcyAH2BLxrjw5W1Ne2nTuXIO++gVVbiPWgQTn5+JK5ahSEgAPfevcmPj+foRx9RUVhIdnQ0SatX03PePDIjIsg7epTy/HzSt5uOSnSYNg1D2/q3YmwsVtbW9Lx1Cjtefx+tspJ2Qwfi6u/Hoe9+whjYjtZ9exEQNoioBf/j98eewdbZidAHTHfDyjhyjMPfrUFZWaGsrOh1+426HiFU1tYE3TSF/W+/i1ZZie/gQRja+BH342pc2gfg1bsXrYcM5tCiz9n15P9hazDQ7Z67ACjLyyX6rfdQVgo7o5Gud92hW+4zKjVYcOQ48/r2wEopfk8+TUJBITd1DOBYbh7haZl0cnXmqV7BONva0M/Lg5s6tuOBHVFsP51OLw8jH1wWggZEZWSyO/3sdp4uFCtra0Kn3cDGlz9Eq6ykw/ABGNv6Eb18DR4d2uEf2pOOwwey/cMlrH7oWeycDQx+sKafV82YS1lRMZXl5SRGRDPiqRm4+bfWLf+S92cyZEA3vNxdiN31AS+8tRLbqgu7P/3qd375Yw9jhvcmZus7FBaVcM+sTwDIyinglfd+YNtPLwLw8rvfk5VToFvu2oL6BRMbEcOHdz1ffRvsMxbNeI3pH8wGYNwDN/DT20spKyklKDSYjqGmi6cnPDiF3z75jsrKSmxsbZkwc4qu+f37dCcxKobvHpyHtZ0tQ+6/uXrZqsdfYdLrTwIw4K7JbP3oKypKy2jTOxj/Pqb8J8L3sfPzFRTn5rP+1QV4tG/DmKdnNHpuK2tr+t5+A1teNdV+4LABuPn7cWDFGtw7tKNNSE86DBvIro+WsO6RZ7EzGLhspqn2048c5/Dq37CysQZlRci0ydjrfF2NlbU1/afdwIaqdTeoat3du3wNnh3a0Ta0J0HDB7LtwyX8WLXuDqm17n5fa909GRHN5U/NwKjjugsQGBLMXxExfHavqfbHzKyp/S8efo1b3zHV/sh7bzDdBru0lMC+wQSGmGqnx8jL+PX9r/nfzFewtrFm3MM367aDp6yt8Z98I8ffewetUsNz4CAc/dqQsnoVTgEBuPXqTUF8HHELTNsNOfujObVmFd2efV6XfGcjIKQ7CVEHWXr/89jY2zJiRs26u+zRV5n81hwAwu6ZXH0b7HZ9u9Gur6n/d361muykVLBSuHh7EHaPvrdTb9e3OwlRMXzzwDxs7G0Z9kBN/hWPvcL1b5rGniF3T2bjB6axp22f4Or8u75aRXZyKkqZ8g+5R9+xU0/N5Jhlo1IXw52/AJRS8UAoptvh+QEdMd0Q4cxNEG4DHgfKgHzgVk3T/ukKQ+3W8zgF7mLwRVgYAE+E/9HESc7P/P6m24/evW1T0wY5TwsHD+PK9Y1yjw1d/DRqCPP2/N7UMc7bs31G4thualPHOC9FCaZTzr6M/bWJk5yfW4JMfzPs1X3rmzjJ+ZnTaxQAcyObZ/2/EDKSF5vxuvt/fUay8HDzrH2Au7uOYcrG5nntx7fDhwLwbsxvTZzk/DzU3fR3q94+0DzHnkd6jIJmcuu4Tp9s0W3j/9g9Qy/KPrloZoA0TWtf9b/PNbB8CbBErzxCCCGEEEL816iL9QIYHUkXCCGEEEIIIVoM2QESQgghhBBCtBgXzSlwQgghhBBCiMYlN0GQGSAhhBBCCCFECyIzQEIIIYQQQrQQVjIDJDNAQgghhBBCiJZDZoCEEEIIIYRoIeQaIJkBEkIIIYQQQrQgMgMkhBBCCCFECyEzQDIDJIQQQgghhGhBZAZICCGEEEKIFkLJFJDMAAkhhBBCCCFaDqVpWlNnaEz/6S8nhBBCCCEuGs1iauWSL7bqtn28/9YhF2WfyAyQEEIIIYQQosX4z18DtOrEz00d4bxMChgHwMLDvzZxkvNzd9cxAMzevaGJk5yf1/pdzg/xzbN2AK5uP47fk9Y1dYzzNrLNeL6MbZ61f0uQqfYd201t4iTnpyjhG6B5r7sAi482z/qZ1nlMsx33wTT23/vnxqaOcd4WDBrerGsHYH70+iZOcn6e6DkKgBs3bW7iJOfn62FhTR3hrMklQDIDJIQQQgghhGhB/vMzQEIIIYQQQggTmQGSGSAhhBBCCCFECyI7QEIIIYQQQogWQ06BE0IIIYQQooWQU+BkBkgIIYQQQgjRgsgMkBBCCCGEEC2ElcwAyQyQEEIIIYQQouWQGSAhhBBCCCFaCLkGSGaAhBBCCCGEEC2IzAAJIYQQQgjRQsgMkMwACSGEEEIIIVoQmQESQgghhBCihVByGziZARJCCCGEEEK0HDID1ABN01j90fcc3n0IW3tbbph1I/6d2tZr98vitUSu301RfiEvrp5f/fyWlRsJ/2UnVtZWOLs5c/1jU3Fv5aFr/o2LviMu8iA29naMfegmWnWsn/90bAK/vLeU8pIyAkOCGT79WlTVyaFRazazd+1WrKytCAztTtjtk3TJfnpfDPu/XIFWqREwbCCdJ44xW15RVkbUgiVkx53EzsVA6Iw7MXh7Vi8vTM9kw+wX6HrNeDpNGKVL5to0TeOnj7/nSPghbB1suf6xG2ljoXZ+XbyWqN9NtfP8qpra+Wv/cdYs+IFTfyUz9albuWRIbz3jo2kaKz74gZhdh7BzsOWWJ6bSrnP9/Ks/W8uu3yIozCvk7XWvVT+/45dwfvxkNW5ebgCEXTWEQRMu0zX/b598R2zEQWzt7bjykZtoHVQ/f8qxBFa/vZTy0jKCQoMZfY+p9k8dT+TnD5dRXlqOlbUVY++/gTZdAnTJvuD1exh3eR/SMnIJHfWExTZvzruNMcN7U1hUyt2PfczeA/EA3HTdUObMvAqAV9//kaUrt+iSua7mvP5qmsbvC7/jeKSpdiY8dBO+FmrnVGwCa99ZSllpGR1Dghl5t6l2fnxtMZlJqQAUFxThYHDkjvdm65q/uY77ABn7Yzj29XLQKmk9ZBABE8aaLa8sK+PQp/8j70QCNgYD3e+7C0cvLyrLyzmyZCl58SdAKTrdeAPuXbvolvuM/0L97Fy8kpNRMdjY2zH0gVvw6lA/f/rxBLZ8+CXlpWW07dudy6Zdh1KKuB1RRC1fR3bSaSa+MgvvjvqMmwA5Bw5wcvkyqKzEa/BgfMeOM1ued/QoJ5cvoygpiQ53Tcc9JMRseUVRETHPPYuxd2/aTb1Rt9xNRa4B0nkGSCl1lVIqWM/PPF+Hdx8iPSmNJxY/zbUPT+aH91ZYbNftsu7MfP+Res/7Bfnz4AeP8egns7lkSC/Wfrq6sSObiYs8SFZKGncsmMuoBybz+8fLLbb7fcFyRt0/hTsWzCUrJY34qEMAJEQf5fiu/dz63mxu/+Ap+l01QpfcWmUl+5YsY8ATM7h8/lwSd0aQm5Ri1ubEpu3YGpwY9dY8Oo4dwcFvfzBbvn/pSlr1aroyO1JVO7MWP801D03mx/cbrp0H3qtfO0ZvI9c/diO9hvdt7KgWxew6RFpSGs99+RQ3PnoD376z0mK7SwZ054mPHra4rO+wPjy16HGeWvS4rjs/AMcjDpKZnMb9i+YyfuZkfv7Qcu3//NFyJsycwv2L5pKZnMbxSFPtb1i8iiE3jmP6B7MJu3k8Gxav0i37lys2M+nWVxtcPmZ4bzq296XH0EeYMWcR7710JwDubgaefvgahk6cy5CJc3n64Wswuhn0il2tua+/f0UeJCs5jXs+mcvYBybzawPj5q8fLWfsjCnc88lcspLT+Kuqdq6aPY073pvNHe/NpsvAXnQe0FPPva/ZcAAAIABJREFU+M123AdT7Rz96ht6PTKD/i8+y+lduylISjZrk7L1T2wMTlz26gu0HX05f60w1U7y5m0A9H/hGXrPeojYZd+hVVbqlv2M5l4/iXsOkpuSxvXvP8vge6ayfdG3Ftv9uWgZg+6ZyvXvP0tuShqJew8C4N7Wj8tnTce3W0c9Y6NVVpLwzdd0mvkgwc/NI3P3boqSzWvHzsOD9rdPw6N/f4vvkbx6Fc6dOusRV1wk/tUOkDI5l/e4CmgWO0AHt++n76h+KKUI6NaeooIicjNy6rUL6NYeV0+3es8H9e6EnYMdAO26tScnrf5rG9Px8P0ED++PUgq/LoGUFBSRn2meIT8zh5LCYvy6BqKUInh4f2J3RQOw75dt9L92FDa2tgA4GV10yZ11PB7nVt4YfLywsrHB/7IQTkXuM2tzKiqadkNMG9V+/fuQFnMETdMASI7Yi8HbC5c2rXXJa8nBHfvpO9JUO+3+pnbaNVA7Hr6etO7g12Tn6EZvP8ClVbUfGNyeovwicizkDwxuj5uF/E3tyM79XDLCVPv+XQMpLigir07t51XVvn83U+1fMqI/R3aYal8pRUlhMQDFBcW4eOj3Hf8MP0xmdn6Dy68YHcLX320FIHxPLG6uTvj6GBkV1osNW/eTlVNAdk4BG7buZ3RYL71iV2vu6++xnfvpUVU7bbr+/bjZpmrc7DGiP8d2Rpu10TSNw9v2EBxmfpS5sTXXcR8g9694HH18cPTxxsrGhlaX9iN9r3m/pu2JxnfgAAC8Q/uSdegwmqZRmJyCe3BXAOxcXbFxcjTNBumsudfPid3RBIWZ8vt0DqS0oIjCLPP8hVk5lBUV06pLB5RSBIX150S4Kb/R3xdjm1a6ZgYoiIvDwccHe29T7biH9iN7n/m4Y+/lhZO/f/VMp9nrT5ygLDcX1+BmsXl6QSil37+L1TnvACml2iulDimlPgKigFuUUjuUUlFKqRVKKeeqdq8qpQ4qpaKVUm8opQYCE4HXlVJ7lVIdq/79opSKVEptVUp1rXptK6XUD0qpfVX/BlY9P1cpdVgptV4p9Y1SataF6wpzORk5GL3dqx8bvYwWNwLPxu5fdtK1X7cLFe2s5Gfk4OJlrH7s4mUkv07+/IwcXDxrtfGsaZOVnEbiweMsnfUmy556l1PH9PllUpSVjaNHTb87eLhTVGcArt3GytoaGydHSvMLKC8u4dia9XS9ZrwuWRuSm25eO25eRos7QBernPQcjD41dWH0NpKdfm75927dx0t3zWfRc4vJSs260BH/Vl5GDq7eNfldvYzk1en/vDq1X7vN6OnXsOHzVbx72zNs+PxHht9+pT7Bz4KfrweJKRnVj5NOZeLn64GfrzuJyZk1z6dk4ufrbuktGlVzX3/z6o6bng3UTp2xtW6bkzHHMRhd8PDzadzAdTTXcR+gJDsLh1q1Y+9upCTLfOwozc7GvlbtWDs6UpZfgHNbf9L37KOyooKitHTy4xMoztR33IHmXz+FmdkYPGt+Bk6eRgoys83aFGRmY6hVPwZPI4V12uitLDsbW/eaSwzs3I2UZZ/dz1+rrCRx5Qr8r72useKJi9T5zgB1Ab4ARgF3AiM1TesLRACPKqU8gKuB7pqm9QRe1DRtO7AaeFzTtN6aph0HFgIzNU0LAWYBH1W9/3vAZk3TegF9gRilVChwLdAHuAYItRRMKXW3UipCKRWxcOHC8/x6gGbhvTn3Xdmo3yNIPHqSsOv1O5UAqD6iWlv9PXGLXxKAyopKSvILufH1Rxl6+1X8NH+xxfe84M7mIxqIffj7NQSNHYGNg8OFTnVOLH6Fi/kwSB2Wa+fs818yoDvPf/0MT3/6BF37duaLV7++kPH+maX8Z9OmqlHkum2Mmn41Dy15nlHTr2bNOzrn/xuWxiBN0yz+fPRYXet/6Pm1uXjW37MYNy11bJ02h7ZE0m2ovkfvoRmP+w3Eqhu+oe/nO2Qg9u5GIp9/hdhvluMa1AFl3RT3eGru9VP/uXpjzln8nPRnMdRZvTJt8ybcevTAzkO/a7QvBjIDdP43QTihadpOpdQVmE5p+7PqF7AdsAPIBYqBT5VSa4E1dd+gaqZoILCi1i9v+6r/jgBuBdA0rQLIUUoNBlZpmlZU9fqfLAXTNG0hph0rAG3ViZ/P+kttX72VXet2ANC2Szuy02qOIGSnZ+Pq6XrW7wVwLOoIf3zzG/e+MRMbu8a/38SetVvYv96U3zeoHXnpNUdl8tKzMdQ5lcfZ00heRq02Gdk4V7Vx8XSj04BeKKVo3TkAZaUoys3Hya1xT4lw9DBSVOvIXXFmFo7ubhbbOHq6U1lRQXlhEbbOBrJi40kK38OBb3+grLAIpRTWtrZ0GD2sUTMD7Fi9lfCfTX3v39m8dnLSs3H1OLfa0dvmH7fx51pT/oAu7chOramL7LRs3M6h9p1rXXsyaMIAflxUb/W/4CLWbGHPL6b8rTu3IzetJn9uejbOdU7VMx11rdOmqvajN4Qz+p5rAeg2uA9r3v2mseOftaRTGfi3rrlhQBtfD1JOZ5GUksmQATWzzG1ae7B1xyHd8zXH9Tdy7Rb2/VpVO53qjJu1xsQzXLyM9cbW2qdJVlZUcGRHNLe/3WgnKJj5L4z7APbu7mazNiVZ2dgbjfXalGSaZooqKyqoKCrCxmBAKUWnqTdUt4t8aT5OPvrMnjT3+jn4y2aO/L4dAK+gAAoyan4GhRnZONXJb/A0UlCrfgoysnFyb9pToW2N7pRl1cyAl2ZlY1undhpS8Ndf5B07RtrmzVQUF6NVVGBl74D/Ndc0VlxxkTjfrfKCqv8qYL2maVPrNlBK9QcuB6YAMzDt1NRmBWRrmna2t7hq9P3IgROHMHDiEAAO7Yph+6qt9B7Wl4TDJ3A0OFq8XqMhSbGJfPfucu58+V6c3fU5j7rPhKH0mTAUgL8iYtizdgtdh/Ql5Wg89gaHegOxs4cbdo4OJB+Jo3Xn9hzcGF79+qBLe5IQfZS2l3QiMymVirIKHF2dG/07GDsEkH8qlYLUdBw9jCTujCT0/mlmbXz79iRh6048OnUgOXwPXsFdUEox5JnHqtsc+m4NNg72uuz8AAyYOIQBVbVzeFcM21dvpdewvpw8fAIHp3OrnaYQdtVgwq4aDMCBnTFs/nEbISP6EH/IVPvncq1PTkZOdfvo7Qfwbdf454SHXjGU0CtMtXssPIaINVvoHtaXpCPxOBgc6l3H41JV+4mH42jTpT37/win35Wm1zt7uHFifyzte3Yift9RPPy8Gz3/2Vq7Pop7bxvN8tXb6d8niNy8Qk6lZrN+8z7mPTG5+sYHI4f05JlXLV/A3Jia4/obMmEoIVXjXuzuGKLWbKHb0L4kH4nH3qnhcTPpcBx+Xdpz4I9wQqpqByB+7xE82/jg6qXPKYj/hXEfwCUwgKLTqRSlpWPvbuT0rt10v+dOszZevXtyavsO3II6kBYRhbGrqXYqSkoBDWt7ezJjDqKsrTC08dMld3Ovn+CxYQSPDQMgIfIAh37ZQodBIaQdi8fWybHezo2Tuxu2jvakHo3Du1N7YjeHEzwuTJesDTG0b09xaiol6enYGo1kRewm8M67zuq1tdulb99O4Yl42flpIf7ttMRO4EOlVJCmabFKKSfAH0gGnDRNW6eU2gnEVrXPA1wANE3LVUrFKaWu1zRthTJNA/XUNG0fsAG4D3hHKWUNGIBtwCdKqVeqck8AFv3L/A3q2j+Yw+GHeO32F7Gzt+P6WTX7eG/fO59HFphuUbt20Wr2boykrKSMl258ln5jL2P0reNYu2g1pUUlfPXCYgCMPu5Me356Y8WtJzAkmL8iYvjs3uextbdjzMybqpd98fBr3PqO6daaI++9wXQ71NJSAvsGExhiugiwx8jL+PX9r/nfzFewtrFm3MM3n9NpUOfLytqanrdNZvv8D9AqKwkIG4Crvx+HVv6EMTCA1iE9CQgbSOSC/7H+0WexdXai34w7//mNddSlfzCHdx/i9WkvYmtvx/WP1dTOu/fN56GPTbWz7tOa2nn5JlPtjLplHCePJPDl859RlFfE4Z0xrP/iFx5dNEe3/N0vDSZm1yGeu/kl7BzsuPmJKdXLXp7+Ok8tehyAHz5ZTcSGKMpKynj6hucYOP4yJtw+lk3fbyV6+wGsra1xcnXiltn1jo80qqB+wcRGxPDhXc9X3wb7jEUzXmP6B6baH/fADfz09lLKSkoJCg2mY6ip9ic8OIXfPvmOyspKbGxtmTBzisXPaQxL3p/JkAHd8HJ3IXbXB7zw1kpsbU3D9Kdf/c4vf+xhzPDexGx9h8KiEu6Z9QkAWTkFvPLeD2z76UUAXn73e7JyChr8nMbS3NffjqGmcfOTu021M/6hmtr5/MHXqm9JPOb+G1j7jmnc7BASTIeQmounD26J0v3i9TOa67gPptrpfPNk9r31HlplJa0HD8TQxo+/fliNa/sAvPr0ovXQQRxatJidc+ZiY3Ci+z2mjdfSvFz2vfk+ykphbzQSfNe0f/i0xtHc66dt3+4k7olhxcx52NjZMuSBm6uX/TDrFa5+40kABk6fzJYPv6KitAz/3sH49zHlj9+1jx2fr6A4N5/fXlmAZ/s2jP2/GY2eW1lb027KVI69+w5aZSVegwbh6OdH8upVOAUEYOzVm4L4eI5//BEVhYVkR0eT/NNquj83r9GzXazk76CCOtfze5VS7YE1mqb1qHo8AniNmtPX/g/YDawCHDDN3LyhadoSpdQgTDstJcB1QCXwMdAasAW+1TTteaVUK0ynsXUAKoD7NE3boZR6DpgKnADSgE2apv3dTtA5nQJ3MZkUYLqH/cLDvzZxkvNzd1fT3/6YvXtDEyc5P6/1u5wf4ptn7QBc3X4cvyeta+oY521km/F8Gds8a/+WIFPtO7bTd8fvQilKMJ3y15zXXYDFR5tn/UzrPKbZjvtgGvvv/XNjU8c4bwsGDW/WtQMwP3p9Eyc5P0/0NP3drxs3bW7iJOfn62FhoMPZShfCwO+36XaV6PZrBl+UfXLOM0CapsUDPWo9/gPoZ6FpvZuta5r2J/Vvgz3WQrvTgKW/vvaGpmnPVc00bQHePPvkQgghhBBCtGwX880J9NL4V+ZfWAur/pCqA7BE07Sopg4khBBCCCGEaD6a1Q6Qpmk3NnUGIYQQQgghmivVFHeJv8hIFwghhBBCCCFajGY1AySEEEIIIYQ4f3INkMwACSGEEEIIIVoQmQESQgghhBCihdDr73tdzGQGSAghhBBCCNFiyAyQEEIIIYQQLYRMAMkMkBBCCCGEEKIFkRkgIYQQQgghWgiZAZIZICGEEEIIIUQLIjNAQgghhBBCtBAyAwRK07SmztCY/tNfTgghhBBCXDSaxa7F8HV/6rZ9vHH8oIuyT2QGSAghhBBCiBbC6qLcJdHXf34HKLt0XVNHOC9Gu/EAfHTwtyZOcn7uDx4NQMg3W5s4yfmJnDqk2fY9mPr/1X3rmzrGeZvTa1SzzT+n1ygAZu/e0MRJzs9r/S4HwLHd1CZOcn6KEr4BILNkdRMnOT8e9hM5VdQ8swP4Ok6kw0ebmzrGefvr/jCi0tc2dYzz0tdrAgDfHv+liZOcnykdxwLQ7q2NTZzk/CQ8OrypIzRLSqmxwLuANfCppmmv1ln+KHAXUA6kAXdomnbi336u3ARBCCGEEEIIoSullDXwITAOCAamKqWC6zTbA4RqmtYTWAnMvxCfLTtAQgghhBBCtBBWSr9//6A/EKtp2l+appUC3wKTajfQNG2jpmmFVQ93Av4XpA8uxJsIIYQQQgghRG1KqbuVUhG1/t1da3Eb4GStx4lVzzXkTuDnC5HrP38NkBBCCCGEEMLESul3k2RN0xYCCxtYbGmOyGI4pdTNQCgQdiFyyQ6QEEIIIYQQQm+JQNtaj/2B5LqNlFIjgaeBME3TSi7EB8sOkBBCCCGEEC3ERXQb7N1AJ6VUIJAETAFurN1AKdUH+AQYq2la6oX6YLkGSAghhBBCCKErTdPKgRnAr8AhYLmmaTFKqeeVUhOrmr0OOAMrlFJ7lVIX5O8EyAyQEEIIIYQQLcTFNPuhado6YF2d556p9f8jG+NzL6Y+EEIIIYQQQohGJTNAQgghhBBCtBB63gXuYiUzQEIIIYQQQogWQ2aAhBBCCCGEaCEuorvANRmZARJCCCGEEEK0GDID1ABN03jr1R/YvvUQDg62zH1xKl2D2zbYftbMT0lKzOCbH2YDkJNTwP/N+oLk5Ez8/Dx46Y3bcHVz0is+mqax+bPviI+MwcbejtEzb8anY/38p48nsP69rygvLaN9SHfC7rwWpWoODUT+uIFtS37k7iWv4OjqrEv2Aa3dmdW3A9ZK8ePxU/zvUKLZ8muDfLmhkx8VmkZReQUvhscSl1sIwLRgfyZ18KVC03gj8jg7TmXrkrm2f9v3O79dx4H126v7e+DNVxIY0l3X/LsWryRxjyn/4PtvwatD/fzpfyWw9cMvqSgtw79Pdy6ddh1KKeJ2RLF3xTqyk05z5cuz8OoYoFv2/0L+0/ti2P/lCrRKjYBhA+k8cYzZ8oqyMqIWLCE77iR2LgZCZ9yJwduzenlheiYbZr9A12vG02nCKF2zL3j9HsZd3oe0jFxCRz1hsc2b825jzPDeFBaVcvdjH7P3QDwAN103lDkzrwLg1fd/ZOnKLXrFrqZpGm+/tortWw+bxv0XJtMl2L9eu4fvXURGeh4VFZX06hvIrKeuxtraiqOHk5j/wveUlpZhbW3NrKevpvsl7XTN/978Vezadhh7B1uefH4ynbvVz//4/VX5yyvp2TeQh5805T/j2yWb+Pjttaza+BxGd4Nu+Ye2deeZwUFYWSmWH0xhwZ6TZstv7N6aW3r4UaFBYVkFT206SmxWIbZWipfCOnOJjzOVGjy/LZZdyTm65T5D0zSWvPMDe3ccws7BjvuenkpgF/P+Lyku5Z3/W0JqUgbKShEyuDtT77sCgEN7j/PFuz+ScDyFB+fdwqXDe+me/+dPvufY7oPY2tty1aM34RdUf+z8fcka9m3YTXF+IU9//3r18+Vl5Xz/xlekxJ7E0cXA9U/ehnsrz3qvbwxh7T14blgnrK3g2/0pfLQ7wWK78Z28WXBlD65YGkH06TyGtHNnzpCO2Foryio0XtoSy/aT+m836E1mP86xD5RSRqXU/VX/P0wpteYcX/8/pdR15/Ka8/2sf2v71kOcPJHGyrVPMefZG5j/4soG2278PRpHR3uz5774bAOhl3biu7VPE3ppJ774bENjRzYTH3WQ7ORUbvvoGS6/bwp/fLLMYruNC5Zx+X1Tue2jZ8hOTuVE1MHqZXnpWSTsO4yLt7tesbFSMCekIw9uiuG6dZGMCfAm0NV8x/GX+DQm/xzFjb/sYcmhRB7tGwhAoKsTo9t5c/26SGZuOsCc0KAmmea9EH3f58rh3PT2HG56e46uOz8AiXsOknsqjWvfe5aBd09lx6ffWmy3Y9EyBt0zlWvfe5bcU2kk7TXld2/rx4hZ0/Ht1lHP2NWac36tspJ9S5Yx4IkZXD5/Lok7I8hNSjFrc2LTdmwNTox6ax4dx47g4Lc/mC3fv3QlrXoF6xm72pcrNjPp1lcbXD5meG86tvelx9BHmDFnEe+9dCcA7m4Gnn74GoZOnMuQiXN5+uFrMLrpt+F9xo5thzl5Ip0Va2Yz55nrmP/i9xbbvfTGLXy58lGWfv8Y2Zn5/PFbNAAfvr2WO+8dxRcrHmX6A6P58O21esZn17bDJCaks3T1bGbNvY63XrKc/7n5t/D58kf533ePkZ2Vz6b10dXLUk9lE7HzGK1aG/WKDZjG/nlDOzFt7X7GfLObKzv5EORuPvavPprKuGWRXLE8kk/2nOTpQaZ1dEpwawDGLYvk1p+ieWpgR5riDJ+9Ow5xKjGdt5c9xfQnruezNyxvN1wxdRhvfjOHV//3GEei49i74xAAXq3cuffpqQwa1VfP2NWORRwkIymNBz/9P658cAprPlhhsV2XS3tw9zuP1ns+6tcdODo78tBncxlw9TDWf/5TY0cGTLXz4ojO3PbDPi7/XzgTu7aik0f9A84GW2um9fEnKqVm5zizqIw7foxm9Be7eeSXQ7wzrmnGTqG/c90JNAL3N0aQi82WjQcYN7EfSiku6dWevLwi0tPqH1EqLCzh6y82Me2eUfVeP2FSPwAmTOrH5o37dcl9xl/h++k2vD9KKVp3CaSkoIiCTPP8BZk5lBYV07prIEopug3vz/HwmpxbPv+ewbdOAh1/lXT3cOFkfjFJBcWUV2r8lpDGMH8P89zlFdX/72hjjVZ1M5Nh/h78lpBGWaVGckEJJ/OL6e7holv2My5E3zelhIhogoaa8vt0DqS0oIjCLPP8hVk5lBUV49O5A0opgob258Ru00aU0d8XN79WTREdaN75s47H49zKG4OPF1Y2NvhfFsKpyH1mbU5FRdNuyGUA+PXvQ1rMEbSqlSA5Yi8Gby9c2rTWPTvAn+GHyczOb3D5FaND+Pq7rQCE74nFzdUJXx8jo8J6sWHrfrJyCsjOKWDD1v2MDtP36DfAlo0xjLsyBKUUPXoFkJ9XTHpabr12BmcHACrKKykrq+DMpLlSioKCYgDy84rx8nbVLTvAtk0xjLnClL97T1P+jHPID/DBG6u59+EJKJ13IXr5uHIip4iTucWUVWqsiU1lVKD57EF+Wc3Y72RjVT32B7k78WdSFgAZRWXklZZziY/+Y3/ktgMMGRuKUopOPdpTmFdEVrp5/9s72NE9pBMANrY2BHbxJyPNNOPg3dqDgCA/s7Mw9HR45wF6X27a7mnbtT3FBUXkZdbf7mnbtT0uHm6WXz+yPwDBg3sRt+9o9djUmHr7uhKfXURCjql2fjp8mtEdveq1mzUokAW7Eygpr6x+LiYtn9MFpQAczSjA3toKO+v//gUyVkq/fxerc90BehXoqJTaS9VfZlVKrVRKHVZKLVVVa61S6hml1G6l1AGl1EJlYW1uqI1SKkgp9btSap9SKkopdeYwrMXPaixpqTm08q05AubTykhaav2B4JP313HTbcNwcLAzez4zIw8vb9MA4eXtRlZGwxsFjSE/Ixtnz5qZG2dPI/l1BrL8zBycPY3mbTJMA/Ff4ftx9nDDO7D+6RONycfJntOFJdWPTxeW4l1ndg3g+k6tWXVFKA/2CuT1yOMAeDvac8rstSX4ONV/bWP7t30PsG/dFr56+BXWv7+U4vzCxg9dS2FmNgavmvwGTyOFmdn12jjVyu9koU1Tac75i7KycfSoye7g4U5RnZ232m2srK2xcXKkNL+A8uISjq1ZT9drxuua+Vz4+XqQmJJR/TjpVCZ+vh74+bqTmJxZ83xKJn6++s08n5GWmms27nu3crM47oPpNLjxw+bhZLBn+KiepueemMgHb61l0qgXef+tNdz3kL4/i/TUXHzOMv+s+xYxacQ8nJzsCRtpyv/nphi8vN0I6uKnS97afA12pOTXjN8p+SW0MtQfv2/p4cfGm/oze2AHnt8WC8ChjAJGtffCWoG/iwM9vF3wc9Z/7M9My8XTp6b/PXyMZFo4cHpGQV4RUX/G0COksx7x/lFeejau3jX5Xb3cyE0/+1MJ8zKyca06Y8Ta2hp7JwcKcwsueM66fJ3tSc4rrn6ckl9CKxfzn393b2dau9izIS6j7surje/kTUxqHqUVcovoluBcd4DmAMc1TesNPA70AR4GgoEOwKCqdh9omtZP07QegCNwhYX3aqjNUuBDTdN6AQOBM+d/NPRZZpRSdyulIpRSEQsXLjzHr1fD8lEL832uo4eTSDyZzrDLe5735zQeC/nr7DJa+o5KKcpKSglf+SuXTZ3QSNkaZmmv1tJPYsWxFCatieD9fXHc1aNdw6/V4eiThU+t/9RZ9j3AJWMHc/vHz3LTW7MxuLuydfEP9do2Koulb/4FLHWr3keMG9Sc859NuTZQXoe/X0PQ2BHYODhc6FQXjKU+1jTN4hHvpll1G14v63pnwXR++mMuZaXlRIabNsS/X76Dhx6/klXr/4+HHp/Iy88ub9S4df3duFLXGx9P5/vf51JWVk5UeCzFRaV8+ekG7rh/dGPHtMxCTEs18OWBZIYvDWf+jjgeCDGN/SsOpXCqoIRV14cwd3BHok7lUF6pfwFZ7n/LbSvKK3j/uS8Zc90QWrXR5zqZf2J56Dz7cdHiuKrDbJbl3/3my58ZFsSLm483+B6dPZ14ckhHnvz9yAXPJy5O//YmCOGapiUCVM0KtQe2AcOVUk8AToAHEAPUPRm0Xhul1CagjaZpPwBomlZc9d5/91lmNE1bCJzZ89GyS9ed9ZdZ8c02Vn23A4DgHu04XesC+tTT2Xj7mJ/OsH9fPIcPJnLVmOcpL68kKzOf+6Z9wMeLZ+Dh6UJ6Wg5e3m6kp+Xg7tn4NxDYt24LB9ZvB6BVUDvyM7Kql+VnZOPsbj5l7VJn1iE/IxuDhxs5p9LJPZ3B0kderX7+68fmM2X+LAzujXtKx+nCElrVmrVp5WRHelFJg+1/PZHGk6FBAKQWleBr9lp70opKGy9sLReq7wEMxpo+7jF6IKtf/KQxowNw6JfNHN1gyu/VMYCC9Jr8BRnZONXJb/A0Ulgrf2FGNo4WTonQS3PPf4ajh5GizJrsxZlZONbJfqaNo6c7lRUVlBcWYetsICs2nqTwPRz49gfKCotQSmFta0uH0cN0/hYNSzqVgX/rmo29Nr4epJzOIiklkyEDutU839qDrVXXRTS2ld/+yervdgHQrXtbs3E/7XTO357GZm9vy+Bh3dmyMYb+AzqzbnUkj8yeBMDlo3vyynOWr6G4kH749k/WfG/K36V7W1LPMf+gsO78uSkGDy8XUpIyufOGt02vTc1h+tR3WPDVTDy9Gv9UvlP5pbSuNWvT2tme1MKGx/6fjqXywtBOPM6v4f/cAAAgAElEQVQRKjR48c+ajdsV1/QmPqeoUfOe8dt32/hj9U4AOnRrS0ZqTf9npmbj7mV5XFk0fwW+/l6MnxymS86G7PppK1G/mrZ7/Dq1IzetJn9ueg4unmf/s3f1MpKbloWbl5GKigpKCotxdGn8mz+l5Jfg51Jz4Ke1sz2ptWYTne2s6eJlYNn1vQHwNtjx2aRLuHPVfqJP5+HrbM/CiZfwyC+HOJFTXO/9/4uU/CHUf70DVHt0qgBslFIOwEdAqKZpJ5VSzwFmhyT/ps3fHSqo91n/Mns9108dzPVTBwOwbUsMK7/exuhxfTgQfQJnZ8fqU9rOuHbyIK6dbJqISk7K5LEZi/h48QwAhgzrwdpVu7ntrpGsXbWbocN7XOi49fQaP5Re44cCEBdxgH3rttB5cAinjsZj7+RQvYF9hsHDDVtHB1KOxOHbuT2HNobTa0IYXgF+3L3klep2n9/9LFPfeFyXu8AdzMyjrYsDfgZ7UotKGd3Om6e3mx+RaevswMl80yA12M+DhDzTL7rNiZm8NLALXx1OwtvRjrYuDsRk5jV6ZrhwfQ+m64POtI/duQ/PgMa/nqPb2DC6jTV9/smoAxz6ZQuBg0JIOxaPnZNjvR0IJ3c3bB3tST0ah3en9sRuCa9+fVNo7vnPMHYIIP9UKgWp6Th6GEncGUno/dPM2vj27fn/7N13dFTF38fx92x6I72QQBIgtIQSCL2KgEhR9AERsKEodlBEURRFKWIDFBXFCj+agAoIKr33hB4CGFogkN57snufPzamozHCLpjv6xwOm3tnN5/d3DJzZ+YuMTv34da4IVcOHMYjuClKKbq/+VJJmagf12Jpa3NTNX4A1m08xFOP3MHyNXvo0CaIjMwc4hLS2Lj9KG+/cn/JjQ/6dG/FmzOrvnnF9TZ0eFeGDjcex3fviGLl0t307R9K5LEYHJxsKzUgcnLyycnOx8OzDkVFevbuOkXr4huxeHjW4XD4Odq2b0T4/mjq+1eeh3C93Tu8K/cW59+7I4qffthN7ztDOXk8BgdHW9yryJ+bnY97cf59u07Rqm0DGjWuy+qtU0rK3d9/Bl8uGWeyu8AdS8gg0NmOek62xGfnMyjIixc2lm8EBzrblTRsegW4lzy2tdShgNwiA93quaI3aESnmmbo8B1DunHHEGO94dCek2z4cRdd+rQhOvIi9o62uFbRePxh/q/kZuUy5tVhJsn4Vzre1Z2Od3UH4MyBSPb/spMWPdty+fRFbB1sq5zrcy1NO7bgyKYD1G/egJO7jtKgVWOT9AAdjcukgYsd9evYEpeVz13NvBn7a2TJ+swCPaHzdpf8/MN9oUzfcZZj8ZnUsbHk+3tb8d6uc4Sb4c6Bwnz+aSMiE/i7mYV/NnaSlFKOwFCg4q1QqiyjaVqGUuqyUuoeTdNWKaVsAIt/mPG66No9mD07ohgyYDq2ttZMnja8ZN2DQz9g0cqX//L5j4zuzaQJC1jz83586roy46NHbnTkcgLDQrgQcZIFT7+DpY0VfZ9/sGTd4hdn8sDsVwG4/cn7S27FHNC2OYFtzXsHFL0G74ef5dPbWmChFKvPxXMuI4enWgZwMiWTHbEp3N/Elw4+LhQZNDILinhr3xkAzmXksDEmiZUDwijSNN4LP4sZRkH8689+18LVJJ6/DEpRx8uN3k8Nr/L33Cj12oRw+VAkP459GwtrK7o/U5p/9cvvMviD1wDo/Pj97Px8EfqCQvxCg6nXxpj/4oGj7Pt2BXkZWWyc+QVugX70e/05yV8NOgsLWj1yP3ve/xTNYCCgZ2fq1PMlauUvuDQIoG5YKwJ6diHii+/ZOP4trBztaf/caJNkq44Fc5+ne+fmeLg6Eb3/U6bOWomVlfE08/WiTfy+5TD9eoUSuXMOObn5PDnB2LuZmp7Nu5/8zK5fpgEw4+OfSE2/8XMHKurSvRl7dkZx38CZ2Nha88bU0grqw/fNYuGK8eTlFvDK2O8oKCjCYNAI6xDEvfcZb0rx2ltDmf3eavR6A9bWlrz61j++6em/0ql7M/btimLkXcb8r75dmn/0sFl8s9yY/7Vx31FYWIRBr9GmQxB3D+1k0pxV0WswZWc0C+5qiU4pVpyK44/UHF5oH8jxxEw2X0jmoZa+dK3nSpFBIz2/iAmbTwHgbmfFgkGtMKARn1XA+E2nzPIe2nRuzpG9UbwwbAY2tlY8OWlEybpXH/mQmQsmkJyQxqoFm/AN8GLSo7MAYyPq9rs7cTYqhlmvfVcyN2jF17/z4eKJJsvfuH0wZw6e5OPRU7GyseaeF0eWrJv33Ps8/anx1vYbvlnN8W0RFOYX8tFDb9K2X2d6Pdiftv068dOHi/h49FTsnOwZOtE09R69pjF56xn+N6Q1Fkrxw4mrnEnOYXyXBhyPy2DjuWvP+3kk1I9AFzvGdgxgbEfjVx48+ONRknMLTZLdXG7mmxOYivqncySUUkuAVkAuEK9p2qDi5Z8C4Zqmfa+UmgYMBy4Al4CLmqZNUUp9D6zVNG3lX5RpDHwJeACFwH2APzChqt/1N3H/0RC4m4mLtXHy7OcnN5g5Sc08E2wcRx62dKeZk9RMxIjut+xnD8bPf+bRjeaOUWOvtu57y+Z/tbXxjpATD5r21vfXy3vtewNg5z/ib0renHJjlgKQkr/GzElqxs3mbuJyb83sAD52d9Pw8+3mjlFj557pyaEk096+/Hpp62Gct7vs7O9mTlIzwxvdCYD/rK1mTlIzMeN7gSlvm/svDNu6w2SXh5f36nFTfib/eBiZpmkjr7H8uTKP3wDeqKLMqGqU+QO4vcLic8C2qn6XEEIIIYQQonrki1DlMxBCCCGEEELUItf9RgJCCCGEEEKIm5NO7gInPUBCCCGEEEKI2kN6gIQQQgghhKgl5C5w0gMkhBBCCCGEqEWkB0gIIYQQQohaQno/5DMQQgghhBBC1CLSAySEEEIIIUQtIXOApAdICCGEEEIIUYtID5AQQgghhBC1hHwPkPQACSGEEEIIIWoRaQAJIYQQQgghag0ZAieEEEIIIUQtITdBAKVp/+lxgP/pNyeEEEIIIW4at0TT4vFd20xWP/6622035WciPUBCCCGEEELUEjL/pRY0gMbs2mbuCDUyv9ttAAzfusO8QWpoWa8eAKQV/GrmJDXjYj2AF/ZtMXeMGpvT6fZbPv/kiE3mjlEjU8P6APDdmfVmTlIzjzbpB0BK/hozJ6kZN5u7AbDzH2HmJDWTG7OUdst2mjtGjYUP705U2lpzx6ix5i6DGLVju7lj1Mj3PXoC0Pu33WZOUjOb+3cFIKvw1jx3OVrdbu4I4h/4zzeAhBBCCCGEEEZyG2zpBRNCCCGEEELUItIDJIQQQgghRC0hd4GTHiAhhBBCCCFELSI9QEIIIYQQQtQS0gMkPUBCCCGEEEKIWkR6gIQQQgghhKglpPdDPgMhhBBCCCFELSI9QEIIIYQQQtQS8j1A0gMkhBBCCCGEqEWkB0gIIYQQQohaQu4CJz1AQgghhBBCiFpEGkBCCCGEEEKIWkOGwF1DyvETRC9djqYZqNu9G/4D7iy3Pu30Gc4uW07W5ViCn3wcz3ZhJevidu8lZu2vAPgPGoBP184mzQ6QEXmCy8uXoRkMuHftjs+d/cutz/rjDJeX/0Bu7GUCR4/BNSys3Hp9bi5RU97EObQN9UeMNGV0NE1j1syf2bMzCltbKyZPG0Gz4PqVyo176kuSEjPQ6/WEtm3Iy68PxcJCx5nTsbz3zgpycwqo6+fK2zMfwtHR1mT5E49FErV4OZpBo17PrjQa1K/cen1hIcfmLyDjQgxWjg6EPvM49p7uxO45wPnfNpaUy7wUS9e3X6NOQOX3Lvmv7erRSI4sXIlmMNCgV1ea331HpfwH5i0k9XwM1o4OdB47GgdPd5KjLxDxzRIANA1ChgygXvtQk2Y3/m6NTfN/5GzESaxsrBk47gF8gip/hnHRMaybs5jCgkIahQXTZ8wQlFKseu87UmITAMjLzsXWwY7HPplo0vyz31vNnp2njPvv1PtpGlyvUrkXnvqK5KRM9HoDrds2YMKke43776lY3p/6EwUFhVhYWDDh9XsJaelvkuxffPAk/Xu3ITE5g3Z9X6myzEdvP0K/XqHk5BYw5qV5HDlxAYAHhvbg1efvAWDm3FUsXrnDJJnL6uzjyoS2DdEpxapzcSyIulxu/QNN/Rjc0Ae9ppGaX8g7+88Ql5MPwPOtA+lW1w2AryNj2HgpyeT5NU3j61mriNgThY2tNWMnD6dRs/LbTn5eAe+/tpC42CR0Oh3tuwfz8LODStbv2nSEZV9tQCkIbOzLS1MfNFn+1BMnuLDsBzSDAe/u3fDrX/68m3HmDBd++IHsy7E0GfME7sXn3eyYS5xbvBh9bi5Kp8Nv4AA82rc3We4/tfdw4dnmDdEp+PVyPMvOxZZbPzTQlwH1vdEbNNIKCvngeDQJecbt5912wQS7OHEiNYPXI6JMnh2M288H7y5n985IbG2tmTL9YZoHX/vY8eJznxN7OYnlq94st3zhdxv5+KOf2LTzA1xdHW90bLOQ3g9pAFVJMxj4Y/FSWr30Ajaurhya+i7uoa1w8PUtKWPr7kbTx0Zxef3Gcs8tzMrm4pq1tJ08CRQcemcG7qGtsHJwMGn+S0uXEDTuRaxcXTn97nScW7XGrkx+K1c3Ah55lPiN66t8jatrVuPYpImpIpezZ2cUly4msnLdJE4cu8j701by7ZIXK5Wb/uEjODraomkar47/ns0bjnBH/7bMeOsHxr50N23bB7Hm5/0s+m4LTz0/wCTZNYOByIXL6PDKWGzdXNkzZSZebVrh5Fe3pMzlHXuwcrCn5wfvcGXfQU4v/5k2zz6OX5cO+HXpABgbDxEfzzN54+FWz28wGDj03XJ6vvY8du4ubHrjfXzbtsS5Xmn+89v2YuVgz4DZbxOzJ5xjS1fReexonOv70mfaRHQWFuSmprPhtRn4tm2JzsLCpO/hXMRJUq8k8uSXk7ly+gLr5y3nkY9eqlRu/efLufO54fg2DWTFlC84FxFFo3bB3DPx0ZIym7/5GRt70zX+AfbuOsWli0msWDuRyGMxvD/tJ75ZMrZSuekfPoRD8f47afxCtmw4Rt/+oXw2ex2jn+pL5+7N2LMzis9mr+Pzb582Sfb/rdjOFwvW8/XsZ6pc369XKI0CfWjR40U6tAnik+mj6TF4Mq7ODrz+wv/RdeDraMCeddNZtzGCtPRsk+QG45j+ie0a8ezWE8Tn5rOwbyg7YlM4n5FTUuZUahYrNxwmX29gSFBdxoY2YNKeU3St60ozV0dGrj+ElU7H/N6t2HM1lewivcnyA0TsOcXVS0nMW/kaZ07E8MX7P/LBt+Mqlbvngdto2S6IwsIi3nz2CyL2RBHWpTlXYhL5ccFmZn71HI517ElLyTRZds1g4PySJQS/+CLWrq4cnz4D19atsS9z3rV2c6PRo49yZf2Gcs/VWVsT9Nij2Hl7U5CWxrFp03AJCcHS3t5k+XXA2JCGvHIgksS8Aj7v0pq9CSlczMotKROdkc3Tu4+SbzBwl78PY5oFMu3IaQCWn4/F1kLHoPo+Jstc0e6dkVyKSWDVr29z4th53p26lIVLq774s2XjYezsbSotj7uawv69UfgUXwwQ/13SCKxCxrnz2Hl5Yefpic7SEq8O7Ug+fLRcGVsPDxzr1wNVfiZZamQkriHNsXJ0wMrBAdeQ5qSeiDRlfHIunMfGyxOb4vyu7duTfuxIuTI2Hh7Y1auHUpVnwuVcvEhhZgZOzYNNFbmcHVtP0P/u9iilaNk6kMzMXJIS0yuV+7NXR19koKiwqOS9XLyQQJt2jQDo2LkJWzcdM1n2tHMXcPD2xN7L+NnX7diOhEPlt52EQ0fx69YJAJ/2bUk+eQpNK39Lyiv7DuLbyfRXAG/1/CnRF3D09sTR2wMLS0v8O4dxJaL83z82/BiB3TsCUK9jG+JPnEbTNCxtrEsaO/rCQsA8s0T/2HecFrd3QCmFX7MG5GfnkpVSfvvPSkknPycPv2YNUErR4vYO/LGv/PvUNI1Tuw4T3LN87+6NtmNrJP3vCjPmah1AVmYeSYkZlco5lNl/Cwv1JYdSpRTZ2XkAZGXm4eFZx2TZdx84RUpa1jXXD7ojjCU/7gTgwOFonOvY4+PlQt+erdm88zip6dmkpWezeedx7ujZ2lSxAQhxc+JSZh6x2XkUGTQ2xCTS0698JS4iIZ18vQGAE0kZeNtZA9DQ2Z5DCenoNcjTG/gjLZvOdV1Nmh/gwI4T3NbfuO00bRlAdmYuKUnltx0bW2tatgsCwMrKkkZN65GcYNw/Nqzex4ChXXGsY2w4uLg5mSx71vnz2Hp6YVt83vVo357UI5XrDQ5VnHftfLyx8/YGwNrFBSunOhRmmq7xBtDMxYnY7Dyu5uZTpGlsvZpIF6/y28+RlHTyDcbtJyotE09b65J1h5PTyTFxg7mi7VuPMvDuTsV1h4ZkZeaQWEXdIScnj0ULN/P4k5UvjM56fyXjxv9fxardf45Ome7fzeq6NoCUUg8qpQ4opY4opb5USgUopf5QSnkopXRKqZ1KqTuKy65SSkUopSKVUmPKvEaWUmq6UuqoUmqfUsq7eHmj4p8PKqXeUUpd+yz1LxWkpWHjVnrwt3F1JT8trVrPzU9Nw8a1wnNTq/fc66UgNQ1r19IDl7WLK4XVzKAZDMSuXI7f/w29UfH+VmJCOt4+LiU/e3m7kJhQ+SAGMPbJL7iz52Ts7W25va+xwtEoqC47tp4AYPP6oyTEme7zz0tNw7bMtmPr5kpehc++bBmdhQWWdnYUZpW/Unx1fwR1O7W78YEruNXz56amYe9emt/OzYXclLRrltFZWGBlb0dBpjF/cvR5fn95KhsmTids9HCT9/4AZCan4+RRuv07ubuQmZz+12U8Kpe5FHkWBxcn3Hy9bmzgChITMsrtv57eztfcf1946isG3PY29g429Orbyrjslbv5dNY6BvedxtxZa3l6nGl6b6vD18eNy1eTS36OjUvB18cNXx9XLl9JKV1+NQVfH9M2ILzsbIgvHs4GkJBbgJdd5Svcfxrc0Ic9V1MBOJOWTZe6rthY6HC2tiTMyxnvKq6O32gpiel4eJduO+5ezqRUUYH9U1ZmLgd3RdKqfWMArsQkEhuTyKtPzOWVxz7m0N5TNzzzn4z1hjLnXVcX8tNS//HrZJ4/j1ZUhK2n5/WM97c8bK1JzCso+TkxrwAP22tvA/3reXMg8Z+/vxspIT4N7zL7nZe3K4nxlc//8+b+woOP9MG2TAMOjA0oTy8XmjSrPGRX/PdctwaQUqo5cD/QVdO0UEAP9ATeA74AXgJOapr2Z9/vY5qmhQHtgLFKKffi5Q7APk3TWgM7gCeKl38MfKxpWnvgyl/kGKOUCldKhc+fP79mb+ZffT9UFU82+aWEqjJU75lJ27dRp0VLrN3M1/1bsTfBqOo38MmXT7Fu69sUFBYRvv8PAN54Zzgrl+3i4WEfkZOTh6WVCSuxVWSv1Mv2N28v7ex5LGyscarnd32zVcctn7+KZZXyX3v/cA9qwJ0fTKbPtImcWr0BfUHhdY/496r6G1Qs8vf7eNSOCJr3MG3vD1C9bajYnC+e4JctkyksKCLiQDQAPy3fy7iX72L1xjcY9/LdzHhr+Q2N+0+oKo5DmqZV+f6qPIzdSFV8xNeK0D/Ak+Zujiw8ZZwjtD8ujd1XU/m2T2tmdGnG8aRM9CZ/A//sM9MX6Zk1eREDh3XHx89YfTDoDVy9lMS0ec/w0rQH+Wz6crIyc//mla6Tqrb7f9iLXJCWRvQ339Jo1CiUzvwDdKo+F0MfX0+aODuy/HxslevNpcrDYoU/welTl7gUk8jtfcrP78zNLeCb+b/z1HN33cCENw+lNJP9u1ldzzlAvYEw4GDxycAOSNA0bYpS6j7gKaDsFjdWKXVv8eP6QGMgGSgA1hYvjwD6Fj/uDNxT/HgJ8GFVITRNmw/82fLRwndt+8dvxNrVhfyU0isb+amp2Li4/MUzStm4upJ2+ky557o0Ne1cGmtXVwpSS69GFqSlYlXN/NnnzpIVHU3S9m3o8/PR9EXobG3wu3fIjYoLwIqlu1j9414Aglv4E1+m1yYhPg1Pr2sPg7GxsaLHbSHs2HqCjl2aEtjQm7nzjXMGYi4ksHuH6SZk2rq5kldm28lLScXGxblCGRfyUlKxc3PFoNdTlJtbbo7Y1X3h+Jqh98SY7dbOb+fmQk5yaf7clDTsXJ0rlHElJzkVe3dj/sKcXKwdy8/Rq+Png4WtNemXr+DWMOCG545Yt4Oj643bf93G/mQmlW7/mclpOLqVfw9OHi7lyySl4VSmjEGv5/TeY4yaPeEGJzdauWw3a37cD0DzkPrl9t/E+PS/HMZmY2NFt9tC2LE1kg6dm/DrmghenDgYgN53tOLdKStubPh/IDYumXp13Ut+9vNx42p8KrFXU+jeuXnp8rpu7Nxr2ongCTn55XptvOysSczNr1Sug7cLjwX7M2bLMQoNpZWTb09e4tuTlwCY1rkpl0zUcPh1xS42rDZuO42D65NU5op9ckI6bp7OVT7v83dXULe+B3eP6FGyzN3LhSYt/LG0tMDb1x3fAE+uXkqk8V9MhL9erF1dyU8pc95NTcO6muddgKLcXE7NnYv/PYNxatTwRkT8S0l5BeWGtHnaWpOcX1CpXFt3Z0Y2qsf4/SfKbT/msnzpNn5euRuA4BYBxMeVHv8T4lPx8Cr/Nzh25BxRJ2MYdMfr6PUGUpIzGTNqFi9Pup8rsUmMGDKt+LlpPHDfDBYum4iHR9XboLi1Xc9LDApYoGlaaPG/psWNH3vgz/5ERwCl1G1AH6BzcU/PYeDPmbqFWullBz1muFFDnQaB5MYnkJuYhKGoiIQD4biHVm88t2tICKmRJynMzqYwO5vUyJO4hoTc2MAV2AcEkp+QQH5SIoaiIlIPHsS5VfXyB45+ghbvvkfIjJn4DRmKW8fON7zxA3DfiG4sWvkyi1a+TI/bW/DbmoNomsbxoxdwdLTDo8JJMCcnv2ReUFGRnj07owhsYBzqk5JsHDttMBj4dv5G7h3W5Ybn/5NzgwCy4xPIKd52ru4Px6tNq3JlvNq0InbXPgDiDh7CvXnTkivImsHA1YOHqNvRPA2IWz2/W6MAsuISyEpIQl9URMzeCHzDWpYr4xvWkgs7jRWuy/sP4xXSBKUUWQlJGPTGMezZiclkXknAwcO90u+4EcIG9uCxTyby2CcTadypFSe2HEDTNGJPncfG3rZSA8jRzRlrO1tiT51H0zRObDlA406l7/PCkdO4+3lRx8M0w7CGDu/KwhXjWbhivHH//SXCmOvoRRycbCs1gIz7r3FuR1GRnr27ThFQvP96eNbhcPg5AML3R1Pf38Mk76E61m08xMgh3QHo0CaIjMwc4hLS2Lj9KH26t8LF2QEXZwf6dG/Fxu1H/+bVrq+TKZnUd7LF18EGS53iDn9PdsSmlCvT1MWBSe2DGL8zktT80t5NnQJna+OpNsjZnsbODuyLM83wpgH3dWPOopeYs+glOvZowbbfjNvO6eMXcXC0xc2jcuN58Re/kZ2Vx+gXB5db3rFnC05EnAUgIy2LKzGJePuZZh92DAwkLyGBvOJjZ9LBg7i2rt5511BUxOnP5+HZuTPu7cxz7DyVnomfgx0+djZYKkWvup7sSSi//QTVceDFFo2YHBFFmll6xysbNuI2lv74Okt/fJ3bbm/NujX7iusO53B0tMOzQt3hvuE9Wb91Jms3TOebhRMICPRi/vfjadzEj007PmDthums3TAdL28XFq+Y9J9t/MgcoOvbuNgMrFZKzdY0LUEp5QY4AROAxcBF4CtgEOAMpGqalqOUagZ0qsbr7wOGAD8Aw69j7kqUhQVBDwzn+OyP0QwGfLp1xcHPl/Or1uAUGIBHaGsyzl8g8rN5FGXnkHz0GBdW/0L7qVOwcnTAf9BADk17F4CAuwZi5Wi6O8D9mb/e/SM5+8kcNIOGe5eu2Pn6cXXNauwDAnBuHUr2hfOc/+Jz9Dk5pB8/Rtza1TR/6x2T5ryWrt2D2bMjiiEDpmNra83kaaV/7geHfsCilS+Tm1PAhOe/obCgCL3BQLsOjUsaOht+O8TKZcYrQr16t+SuezqYLLvOwoLgh4Zz8IO5aAYD9Xp0wameL2d++gXnQH+827amXo+uHJv/PdtffhMrB3tCnxld8vyU09HYurlg72Xa8d//pfxtRw1jx8zPjLfBvq0zzvV8ObFiLa4N/fELa0XD27qw//MF/PriW1g7ONDp+ccASDp9llNrNqCztAClI+zR+7GpY/pboDZqF8y58Ei+HPMOVjbWDBj3QMm6b8e+V3JL637PDGPdnMUUFRTQMCyYhmGlNy05ueOQyW9+8KcuxXdvu2/gTGxsrXlj6rCSdQ/fN4uFK8aTl1vAK2O/o6CgCINBI6xDEPfeZzwNvPbWUGa/txq93oC1tSWvvmW6+YgL5j5P987N8XB1Inr/p0ydtRIrK+Np8utFm/h9y2H69QolcucccnLzeXLClwCkpmfz7ic/s+sX49XjGR//RKoJ7wAHoNfgg4izzO3ZAgudYs25eM5l5PBkiwCiUjLZcSWFsaENsLO0YGZXY29VfE4+43eexFIpvuptrKxnFxYxed9p9Ga4uB/WtTkRe6J4asi72NhaMXZy6bH/hQc/Ys6il0iKT2PFd5uoF+jF+IdnAzDwvq70HdyJNp2acmT/aZ67/310FopRz99FHWfTnH+VhQUNRo4gas4cNM2AV9eu2Pv5ErN6NY4BAbiFhpJ1/gKnP/+copwcUo8d49LqNYS+8zbJ4eFk/nGGoqwsEnbvASDo0Udx8DfdXTQNGsw9eY732oegU/Db5QQuZuUyqrE/p9Oz2JuQwpimgdhZWPBmm6aAcW6mecwAACAASURBVJ7Z5EPGns45HVtQ39EeOwsdy3q148Pj0YQnmXb+c7ceLdi98wSD+7+JrZ01U6Y+XLJuxJDpLP3xdZPmETc3da0xnjV6MaXuB17D2LNUCIzHOAeoq6ZpeqXUT8AvGIewrQL8gNOAJzBF07RtSqksTdP+7CkaCgzSNG2UUqoxsAhjT9M6YIymaX83yUAbU4MhcDeD+d1uA2D4VtN/l8T1sKyXcVhCWsGvZk5SMy7WA3hh3xZzx6ixOZ1uv+XzT47YZO4YNTI1rA8A352p+hbzN7tHmxi/9yklf42Zk9SMm83dANj5jzBzkprJjVlKu2U7zR2jxsKHdycqbe3fF7xJNXcZxKgd280do0a+79ETgN6/7TZzkprZ3L8rAFmFt+a5y9HqdjDX7UP/odfDN5vsEsf0dr1vys/kug4v0zTtB4w9NGV1KrP+/8os708V/mz8FD9eCaws/jEW6KRpmqaUGg6EX5fQQgghhBBCiFrjVvoi1DDgU2WcbJAGPGbmPEIIIYQQQtxSdDfx3dlM5ZZpAGmathMw7TfLCSGEEEIIIf5TbpkGkBBCCCGEEOLfuZnvzmYq5v+mLSGEEEIIIYQwEekBEkIIIYQQopaQHiDpARJCCCGEEELUItIAEkIIIYQQQpicUupOpdRppVS0UurVKtbbKKV+KF6/XykVeD1+rzSAhBBCCCGEqCUsTPjvryilLIDPMH43aDAwQikVXKHYaCBV07QgYDbwXg3fdjnSABJCCCGEEEKYWgcgWtO0c5qmFQDLgMEVygwGFhQ/Xgn0Lv5O0H9FboIghBBCCCFELWHKL0JVSo0BxpRZNF/TtPnFj/2AS2XWXQY6VniJkjKaphUppdIBdyDp3+SSBpAQQgghhBDiuitu7My/xuqqenIqts6qU+YfkwaQEEIIIYQQtcRNdBvsy0D9Mj/XA65co8xlpZQl4Ayk/NtfLHOAhBBCCCGEEKZ2EGislGqglLIGhgNrKpRZAzxS/HgosEXTtH/dA6Suw2vczP7Tb04IIYQQQtw0bp6+lb/w/rGNJqsfv9Kq719+JkqpAcAcjDeN+1bTtOlKqXeAcE3T1iilbIH/AW0w9vwM1zTt3L/N9Z8fAtf7t93mjlAjm/t3BWDMrm3mDVJD87vdBsCm2F/NG6SG+vgNoN/6XeaOUWPr+3Vjwv4t5o5RYx92vJ1phzeZO0aNvNGmDwDzT603c5KaGdOsHwBxuRUvwt0afOzuBqDdsp1mTlIz4cO7Y+c/wtwxaiw3Zilwxtwx/oUm3Lbu1qw3bBtorDfc6vWezMLNZk5SM05Wvc0d4ZakadqvwK8Vlr1Z5nEecN/1/r3/+QaQEEIIIYQQwsjiluinurFkDpAQQgghhBCi1pAeICGEEEIIIWqJm+gucGYjPUBCCCGEEEKIWkN6gIQQQgghhKgldEpukiw9QEIIIYQQQohaQ3qAhBBCCCGEqCVkDpD0AAkhhBBCCCFqEWkACSGEEEIIIWoNGQInhBBCCCFELWFh7gA3AekBEkIIIYQQQtQa0gMkhBBCCCFELSE3QZAeICGEEEIIIUQtIj1AQgghhBBC1BLyRajSALqm9h4uPNu8IToFv16OZ9m52HLrhwb6MqC+N3qDRlpBIR8cjyYhLx+Ad9sFE+zixInUDF6PiDJHfFKOnyB66XI0zUDd7t3wH3BnufVpp89wdtlysi7HEvzk43i2CytZF7d7LzFrfwXAf9AAfLp2Nml2TdNY8enPRO6PwtrWiodeGYF/k/qVyq35Zh37N4STk5nD7F/fK1m+9/cDrPpyDc4ezgD0vKc7XQd2Mln+dh4uPNWsIRZK8dvleJafv1xufQvXOjzVrCENHR2YcewUu+KTS9aNbhJIBw9XAJacu8T2uCST5f5TwrFITixajmbQ8O/ZlcZ39Su3Xl9YyJEvF5B2IQZrRwfCnn0ce093chKT2frq2zjW9QbAtVEDWj060uT5Y49EEr5gJZrBQNDtXWkx+I5K+Xd/tpCU88b8PcaNxtHLnfzMLLbP/prksxdp1LMTHR673+TZwbj9b/3qR85HnMTSxpo7xz2Ad6PK2398dAy/f7KYovxCGoQF0+uJIShlHNdwaO12jqzbic5CR4N2IfQcNdik+T95fzX7d53CxtaK1965nybN61Uq9/IzX5GclIm+yECrtg144bV7sbAoHZSwbME25s1ex+qtU3BxdTBJ9s4+rkxo2xCdUqw6F8eCqPL77gNN/Rjc0Ae9ppGaX8g7+88Ql2M87j/fOpBudd0A+Doyho2XTL/vfvHBk/Tv3YbE5Aza9X2lyjIfvf0I/XqFkpNbwJiX5nHkxAUAHhjag1efvweAmXNXsXjlDlPFLqFpGtOnz2f79ghsbW2YOXMcISFBlco99NBrJCSkYmtrDcC3376Du7sLV64kMHHiHDIzs9HrDUyY8Ag9e7YzWf4Oni48F9wQCwXrLsWz5Gz5esN9DXwZWN8bvWasN7x/LJr43HyC6jjwYouG2FtaYtA0FkVfZutV028/t3q9R9M0Pnx3Bbt3RmJra8WU6Q/TLNj/muVffG4esZeTWL5qMgDz5v7C9i1H0el0uLo5MmX6w3h6uZgqvjCxf9UAUkplaZrmWMXy74G1mqat/DevX+E1RwHtNE177nq95rXogLEhDXnlQCSJeQV83qU1exNSuJiVW1ImOiObp3cfJd9g4C5/H8Y0C2TakdMALD8fi62FjkH1fW501CppBgN/LF5Kq5dewMbVlUNT38U9tBUOvr4lZWzd3Wj62Cgur99Y7rmFWdlcXLOWtpMngYJD78zAPbQVVg6mqYAARO6PIjE2kSn/m8SFqIssm7OSVz5/sVK5lp1D6HlPN6Y8NKPSura3teH+cUNMEbccHfBs80a8Fn6CpLwC5nYOZV9CMjHZpdtOYm4+Hx0/w9DA8pXCDh6uBDk58PTew1jpdHzYviUHE1PJ0etNll8zGDi+cBmdXhmLnZsrO9+aiU/bVjj51S0pc2n7Hqwc7On94TvE7jtI1A8/E/bc4wA4eHnQc9rrJstbkcFg4MC3y+nz+vPYu7vw26T3qRfWEpd6pfmjt+7F2tGeez5+m/N7wjm0ZBU9XhiNzsqK0GGDSLt0lbRLV8z2Hs5HnCT1aiKPfTGZq2cusGnech748KVK5TZ9sZy+zwynbtNAfnrnCy4ciqJBWDAxx85wdv9xHv5kIpZWVuSkZZo0//5dp7gck8TiNRM5eTyGWdN/4otFYyuVm/L+Qzg42qJpGm9OWMi2jcfofWcoAAlxaYTv+wPvuqarfOgUTGzXiGe3niA+N5+FfUPZEZvC+YyckjKnUrNYueEw+XoDQ4LqMja0AZP2nKJrXVeauToycv0hrHQ65vduxZ6rqWQXmW7fBfjfiu18sWA9X89+psr1/XqF0ijQhxY9XqRDmyA+mT6aHoMn4+rswOsv/B9dB76OBuxZN511GyNIS882af4dOyK4cOEKGzZ8ydGjp5kyZR4rVnxUZdkPP3yJli0bl1s2b95y+vfvxsiRA4iOjmHMmLfZsuUbU0RHB4wLaciE/cZ6wxfdWrM7vny94Y+MbJ7cZaw33O3vw5PNAnnn8Gny9HpmHPmD2Jw83G2smd+tNQcTU8ky4fZzq9d7AHbvjORSTAI//zqFE8cu8O7UZSxYWvWFgC0bD2Nvb1Nu2UOP9uHp5+8CYNmirXw171cmvWX6i3imYCFzgGQOUFWauTgRm53H1dx8ijSNrVcT6eLlVq7MkZR08g0GAKLSMvEsvhIFcDg5nRwTn/jKyjh3HjsvL+w8PdFZWuLVoR3Jh4+WK2Pr4YFj/Xqgyu8FqZGRuIY0x8rRASsHB1xDmpN6ItKU8Tm25wQd+7ZHKUWD4EBys3JJT06vVK5BcCDO7s4mzfZ3mjo7cSUnj7jibWfb1UQ6e7mXKxOfl8/5rBwMlO+C9ne051hqBgYN8vUGzmVm0664N8hUUs9ewMHLEwcv47bj26kdcYfKbztxh45Sr5uxR61u+7YknjyFpt0c3enJ0Rdw8vHEydsDC0tLArqEcSn8WLkyl8KP0ahHRwACOrYhLvI0mqZhZWuDV7MgLKzM2zF+9sBxgnt1QCmFb9MG5GfnkpVSfvvPSkknPycP32YNUEoR3KsD0fuN7/Po77voMKQvllZWANi7OJk0/65tkfQbFIZSipBWAWRl5pGcmFGpnIOjLQD6IgOFhfpyh6JPP1zDUy8MRGG6s3SImxOXMvOIzc6jyKCxISaRnn7lj/sRCenk643H/RNJGXjbGY/7DZ3tOZSQjl6DPL2BP9Ky6VzXtPsuwO4Dp0hJy7rm+kF3hLHkx50AHDgcjXMde3y8XOjbszWbdx4nNT2btPRsNu88zh09W5sqdonNm/dxzz23o5QiNLQZGRnZJCSkVPv5SkFWlrHBmpmZg1eF8/aN1MzFidic0nrDliuJdPWuUG9ILq03nCxTb7icnUdsTh4AyfkFpBYU4mxtZbLsJflv4XoPwPatxxhwd0eUUrRs3YDMzBySEivXHXJy8li8cAujn+xfbrmjo13J49zc/JIedfHfVO0GkFJqvFLqRPG/FyqsU0qpT5VSJ5VS6wCvMusuKKXeU0odKP4XVLzcUyn1o1LqYPG/rsXLOyil9iilDhf/37SKLAOVUnuVUh41fud/wcPWmsS8gpKfE/MK8LC1uWb5/vW8OZCYeiOi1EhBWho2bqUnXxtXV/LT0qr13PzUNGxcKzw3tXrPvV7Sk9JxKdPt7OLpQlpS5YPYXzmy8yjTH3+fr6Z8R2qC6f427rbWJBYPCQBIysvHo8xJ4q+cy8ymvYcrNjoddawsae3mgudfbHc3Ql5qGnbupX9/WzdX8ir8/cuW0VlYYGVvR0GW8UpxTmIy29+Yzu7ps0g+/YfpghfLSUnDoUx+BzcXclPSKpWxL5vfzo78TNNe6f4rWcnpOHmUbv9OHi5kVbgAkJWcjpN7mTLupWVSryRy+eRZFk/4iB8mfUzcHxdNE7xYUkIGXj6l2Ty9nUlMqHr/nfD0Vwy+/W3s7W3o2acVALu3ReLh6UxQU98qn3OjeNnZEJ9Tuu8m5BbgZXft/W9wQx/2XDUeW86kZdOlris2FjqcrS0J83LG2960+251+Pq4cflq6ZDb2LgUfH3c8PVx5fKV0oZG7NUUfH1M34CLj0/Gx6f0tO7j4058mSHCZU2a9DGDB4/ls8+WlVyAee65kfzyyzZ69BjFmDFTeOONJ02SG8DT1prE3PL1hr86fg+sX3W9oZmzI1Y6xZXiBpGp3Or1HoDE+DR8ymy33t6uJMRXrr/Mm7uWBx/pXTKEsqzPPl7NwN6T+G3dQZ56btANzWtOOmW6fzerajWAlFJhwKNAR6AT8IRSqk2ZIvcCTYGWwBNAlwovkaFpWgfgU2BO8bKPgdmaprUHhgBfFy8/BfTQNK0N8CZQbnyTUupe4FVggKZplQbJKqXGKKXClVLh8+fPr87bq5ZrXeHu4+tJE2dHlp+PrXK9Wfyri/FVPNnEV0Gq+qz/yZWYlp1DeGfJm7z+9Ss0a9uEhTOXXM94f6mqlNXtHDmUnMbBpBRmd2zFa62aEpWWgd7kPStV/T5VrRI2LnXoM3s6Pae9TsjIIRya9x2FublVlDaxSttOVduXaaJUR9Xbf6VSlZ9YXMagN5CflcPID8bTY9Q9/PL+dybtofsn+++H857gp02TKSws4tCBaPJyC/jf15t57Jk7qix/Q1UR8VqfWv8AT5q7ObLwlHGO0P64NHZfTeXbPq2Z0aUZx5MyzbDv/r2qetQ0Tavy72OO+FX9zqqyffjhBH755VMWL55JREQkq1dvBWDduh3ce29vduz4nvnzp/DKK7MwFPdYmIN2jS2or58nTZ0dK82xcbOxYlJoE947+se/O41fJ7dUvYfqHXtOn7rE5ZgEevUJrfI1nh03mHWbZ9B/YHuWL9l+Q3KKm0N1x3p0A37WNC0bQCn1E9C9zPoewFJN0/TAFaXUlgrPX1rm/9nFj/sAwWU2zjpKKSfAGViglGqM8fxTth+4F9AOuEPTtMpjKgBN0+YDf7Z8tB9+213Nt1gqKa+gXNeup601yfkFlcq1dXdmZKN6jN9/gkLDzXC4MrJ2dSE/pfTKTH5qKjYu1RtLb+PqStrpM+We69K0yXXPWNH2VbvYvW4vAAFN/UlLKL1qk5aYhrN7nWq/lqNz6XylrgM7s+qrtdcv6N9IqnDVz8PWpspt51qWnrvM0nPGStWrrZoQm2PaBoStqyu5yaXbTl5KKrau5YcZ2rm6kJucip2bKwa9nsKcXKwcHVBKYVE87MqlQQAOXh5kX03ApWGAyfLbu7mQXSZ/dkoadhXy27u5kpOcioN7cf7cXKwdTTfHrSqH1+3g+Ebj9u8T5E9mUun2n5mUhoNb+ffg6O5CZnKZMslpOBaXcXJ3pnHn1iilqNskAKVT5GZkYe9844bC/bxsN2t/2g9A05D6JMSVZkuMT8fD89r7r42NFV17hrB7WyRuHk5cjU1h9DDjaSIxIZ0nRszhi0XP4+5R/WNATSTk5JfrtfGysyYxN79SuQ7eLjwW7M+YLcfKHfe/PXmJb09eAmBa56ZcyrwJGv8VxMYlU69u6ZBcPx83rsanEns1he6dm5cur+vGzr2mmci+ePE6li9fD0DLlo2JK3Pjl7i45CqHsXl7G9+Do6M9gwb15NixM9xzz+2sXLmBr79+G4A2bZqRn19AamoG7u43fi5ZYl4Bnnbl6w1JeZWP/WHuzjwYVI9xe8vXG+wtLZjZPphvTl/k5F8MY7xRbtV6z/Kl21m10ljPC24RQFxc6fE/Pj4VT6/yx87jR84TdfISd93xBnq9gZTkTMaMms3878vPM75zYHvGPfM5T/5He4Fu5p4ZU6nuELjqfFR/tSdoVTzWAZ01TQst/uenaVomMBXYqmlaC+AuwLbMc88BTsANrZGfSs/Ez8EOHzsbLJWiV11P9lQYh2y8a0sjJkdEkVZQeCPj/GN1GgSSG59AbmIShqIiEg6E4x5avfHcriEhpEaepDA7m8LsbFIjT+IaEnJjAwM97+nGpK9eZtJXL9O6Wwv2bzyIpmmcP3kBOwe7fzTXp+x8oWN7TuDj730jIlfpdEYmfvZ2eBdvO7fV9WRfNcew6wCn4vknDRztaeDoQESyaYcYuDQMIDs+gZzibefKvnB82rQqV8a7bSsu79oHwNWDh/AIbopSivyMTLTiq63ZCYlkxydg73VDRqlek3ujADLjEshMSEJfVMTFPRHUD2tZrkz9sJac3WGsrF/cfxifkCZmH+vdZmAPHp4zkYfnTCSoUytObj2ApmlcOX0eGwfbksbNnxzdnLG2s+XK6fNomsbJrQdo1MH4PoM6tiLmmPEiRkpsAvpCPXZ1Kt2r5rq6d3hXvlk+nm+Wj6d7rxasXxuBpmlEHruIg6Mt7hUaQDk5+SXzgoqK9OzbdQr/Bl40alyX1Vun8MNvk/jht0l4ejnz1dIXbnjjB+BkSib1nWzxdbDBUqe4w9+THbHl992mLg5Mah/E+J2RpOaXHvd1CpytjftukLM9jZ0d2Bd3cw0PAli38RAjhxivXXZoE0RGZg5xCWls3H6UPt1b4eLsgIuzA326t2Lj9qN/82rXxwMPDGT16k9YvfoT+vTpxKpVW9A0jSNHTuHkZF+pAVRUpCeleE5cYWER27YdpHFj40WWunU92bvXmPvs2Uvk5xfi5maaeaKn0zOpV6becLuvJ3viK9cbxrdsxKSD5esNlkoxNawZGy4nsD2u6iF/N9qtWu8ZNqInS36cxJIfJ3Hb7a34dc1+NE3j+NHzODra4eFZ/u8/dHgPft/6Lr9smMbXC1/CP9CrpPETczGhpNz2rccIbGC+GzqIG6+6PUA7gO+VUjMxNobuBR6qsP5JpdRCjPN/egFlxx3dD8ws/n9v8bINwHPABwBKqVBN045g7AH6s191VIUcF4EJwM9Kqfs0Tbshs/MNGsw9eY732oegU/Db5QQuZuUyqrE/p9Oz2JuQwpimgdhZWPBmG+MUpYTcAiYfMl4xm9OxBfUd7bGz0LGsVzs+PB5NeJLp5tEoCwuCHhjO8dkfoxkM+HTrioOfL+dXrcEpMACP0NZknL9A5GfzKMrOIfnoMS6s/oX2U6dg5eiA/6CBHJr2LgABdw3EysRXx0M6BhO5P4opD07H2taaB18ZXrJuxhMfMOmrlwH4+cs1hG8+RGF+Ia8Pm0KXAZ0YOOpOtv20k2N7TmBhYYF9HXsemjjCZNkNGnwWdZYZYS3QKdgQG8/F7BweDvLnTHoW+xJTaFLHkTfbNMfJ0pJOnm48HOTPmN2HsdApPupgbGzkFBXx3vEzmPoCm87CghYPD2ff+3PRNAP1e3TBqZ4vp378BZcG/vi0bY1/j64c/vJ7Nk94E2tHe9o+MxqA5NN/cPqnteh0OtDpaDlqpMl7VnQWFnR4dBibZ3xmvA12r8641PflyPK1uDf0p367VgT16sKuzxawatxbWDs60H3sYyXP/+m5yRTm5mEoKuJS+DF6T3qu3B3kTKFBWDDnwiP55ql3sLKxpt/zD5SsW/jCezw8ZyIAfZ4aZrwNdkEBDdoG0yAsGIAWfTqxfu4Svn/+XSwsLej/woMmbeB16t6MfbuiGHnXTGxsrXn17WEl60YPm8U3y8eTl1vAa+O+o7CwCINeo02HIO4earpb1VdFr8EHEWeZ27MFFjrFmnPxnMvI4ckWAUSlZLLjSgpjQxtgZ2nBzK7G3pL4nHzG7zyJpVJ81dt4kSm7sIjJ+06jN8PF8QVzn6d75+Z4uDoRvf9Tps5aiVXxRZWvF23i9y2H6dcrlMidc8jJzefJCV8CkJqezbuf/MyuX6YBMOPjn0g18R3gAHr2bMf27eH07TsGOzsbZswYV7Ju8OCxrF79CQUFhTz++FsUFuoxGPR07hzKsGHGIZOvvjqaN974lO+/X41Sipkzx5ls29dr8PGJc3zQobTecCErl0eb+HM6LYs9CSk83TwQO0sL3m5rrDfE5xXwengUvXw9aO1WB2crS+6sZ5xCPfNYNNEZpvsb3Or1HoCuPVqwe2ck9/R/C1s7a96aWlpNHTlkBkt+nPSXz587exUXL8SjU4q6vm689uZ/8w5wID1AAKq6Y8OVUuOBP2sKX2uaNufP22Ar4xFmLnA78Of4qUWapq1USl0AvgMGYLzIPULTtOjiGxh8BjTH2BDboWnaU0qpzsACIBHYAjykaVpg2dtgF88/WgzcpWna2b+IrfWuwRC4m8Hm/l0BGLNrm3mD1ND8brcBsCn2V/MGqaE+fgPot36XuWPU2Pp+3Ziwv+JI1FvHhx1vZ9rhTeaOUSNvtOkDwPxT682cpGbGNDN+71Nc7hozJ6kZH7u7AWi3bKeZk9RM+PDu2Pmb7qLN9ZYbs5TSasCtqAm3rbs16w3bBhrrDbd6vSezcLOZk9SMk1VvqN6IKbP7X/R6k12ieSio3035mVT7fq+aps0CZlVY5lj8v4axN+daPtM07e0Kz03C2CNU8ffspfwQt8nFy78Hvi9+fBgIrm52IYQQQgghBFgo88/fMjf5HiAhhBBCCCFErXHDv/FP07TAG/07hBBCCCGEEKI6zPuV50IIIYQQQgiTkeFf8hkIIYQQQgghahHpARJCCCGEEKKWkNtgSw+QEEIIIYQQohaRHiAhhBBCCCFqCekBkh4gIYQQQgghRC0iPUBCCCGEEELUEvJFqNIDJIQQQgghhKhFpAdICCGEEEKIWkLmAEkPkBBCCCGEEKIWkR4gIYQQQgghagnpAQKlaf/piVD/6TcnhBBCCCFuGrdE0+KXmN9MVj++y7//TfmZ/Od7gHbGrTN3hBrp7jMQgI8jN5g5Sc2MC7kDgIe3bzdzkppZ2LMnqy/+Zu4YNTY4oD8zjmw0d4wamxTal/mn1ps7Ro2MadYPgKd2bzVzkpr5omsvABp+fmvuu+ee6QlAVNpaMyepmeYug4Az5o7xLzTBzn+EuUPUWG7MUv4XfWseex4KMh57Vp7/3cxJamZogzsBCP52h5mT1MzJx3qYO0K1SQ+QzAESQgghhBBC1CL/+R4gIYQQQgghhJGF9ABJD5AQQgghhBCi9pAGkBBCCCGEEKLWkCFwQgghhBBC1BI6JTdJlh4gIYQQQgghRK0hPUBCCCGEEELUEtL7IZ+BEEIIIYQQohaRHiAhhBBCCCFqCfkiVOkBEkIIIYQQQtQi0gMkhBBCCCFELSFfhCo9QEIIIYQQQohaRHqAhBBCCCGEqCXke4CkB0gIIYQQQghRi0gP0DVomsbST37m+P4orG2seey1EQQ0qVep3E9f/cre9eHkZOXw2e8zS5Yv+3QVpw9HA1CQV0hGWiZz180waf5d3/zIxUORWNpY0/u5B/FsVL9SuYSzMWyZu4iigkIC2obQbfQQlFLsX7KW8wePo5TCztmJ3s8/iIObs0myp504wcUffkAzGPDq1g3f/v3Lrc84c4aLP/xATmwsQU88gXtYGADZly5xYfFi9Lm5oNPhN2AA7u3bmyRzWZqmsebznzh1MAorGyuGTRhJvcaVP/vfv1tHxMaD5GblMG3N+yXLd6zcyoHf96Gz0OHo7Mh9L43A1dvNpPkPfL+S2MPGbafr0w/h3rBy/uRzMez6/H/oCwrxaxNCh1FDUUqRn5XN9jnfkpWYgqOnGz1fGI2No71J82/96kfOR5zE0saaO8c9gHcV2358dAy/f7KYovxCGoQF0+sJ47YPcGjtdo6s24nOQkeDdiH0HDXYZPmTj0fyx5LloBmo270rAQPvLLfeUFhI1Nffk3kxBksHB0Kefhw7Dw8MRUWcXrCYzAsXQSkajxyGa7OmJssN0KO+K292C0KnUyw/eZUvDl8qt35kSF0eauGLXoOcQj2Ttp0hOjUHK51ies8mtPRyxKDB+3IlpgAAIABJREFUO7ui2X8l3aTZwbjtfD1rFRF7orCxtWbs5OE0alb+uJ+fV8D7ry0kLjYJnU5H++7BPPzsoJL1uzYdYdlXG1AKAhv78tLUB02af/r0+WzfHoGtrQ0zZ44jJCSoUrmHHnqNhIRUbG2tAfj223dwd3fhypUEJk6cQ2ZmNnq9gQkTHqFnz3Ymyf7FB0/Sv3cbEpMzaNf3lSrLfPT2I/TrFUpObgFjXprHkRMXAHhgaA9eff4eAGbOXcXilTtMkrkiTdPY8OWPRIefxMrGmrtefIC6QZWPPVf/iGHN7MUUFRQS1C6YO540Hnvizl7mt89+oKigCJ2FjjufGYZf0wCT5l837ydOHzyJlY0VQ156AL8qzl0bvl/LkU3Gc9dbqz4oWV5UUMTKDxcR+8cl7Os4MPy1R3D1cTdJ9m5+rrzWqREWSrHyTBxfHyt/7Lm/aV1GNPfFoGlkF+mZsvsPzqbl4Otow9r/a8eF9FwAjiZm8PaeaJNkNie5C5yJe4CUUtuUUqY5mv5Lx/dHkXA5iRmLJ/HwhPtYNGtlleVad/l/9u48Lqrq/+P467DvzLBvIiouiAsqouZumZlWtqrtVmr502zxW5pZqS22fMts18r8pubW4la5lfsCuCLiDirIzgz7Pvf3xyAwMCqSXDTO8/Hgocw9M7xn+Nwzc+4599Ke6d+8UOv2URNH8OZ3U3jzuykMuq8PXft2aujIJs4fOEZ2chqPfPEGA54dxbb5y8222/7NcgY8N5pHvniD7OQ0zh88BkCXEbcy6pNpjPx4KkHhoUSt+EOV3IrBQMLSpbR9/nk6zZxJZlQUBRcvmrSxdXOj1ZgxeEREmNxuYWNDqzFj6DRzJu0mT+bc8uWUFRSokru641FxZCSl88rC6dz/wkh+nbfSbLuQnqFM+uzFWrf7BQfw/Ocv89I3r9Kxb2fWf7umoSObSDp0jNyUdO799E16jR3N3u+WmW2359vl9Bo3mns/fZPclHSSDhlrJ+a3Tfh2aMt9n76Jb4e2HF29Uc34xO8/hi45nae+nsHg/xvJ5q9WmG23+esVDJ4wiqe+noEuOZ2EA3EAnD9ykjP7Ynh83qs8+flrdB8xSLXsisHAycU/0fnFiUS8/Sap+6LITzKt/+Qdu7BydKDnnNk0u/1Wzq78FYCL23YCEDH7DcKmTOb08p9RDAbVslsImNmvNWPWxzDkpyjuau1FsNZ04LvmZBpDl+9n+Ir9fHPwAtN7twJgVHtfAIYu38/ja4/w2i2taIz35/27j5N8IYOvVk1jwtQH+fqDn822G/HIAL5YMZWPf3yJuMMJ7N9trJ2L59P5edEW5iyYyGfLXuHpF9UbOANs376fhISLbNz4DbNn/x9vvfXVZdt+9NHLrF49j9Wr5+HurgHgq69WMHRoH3777VM++eQ/zJx5+ftfbz+u3MY9j8+57PYhA8NoFeRDh34vMnHqAua98zQAWldHpr9wH/3unkHfu2cw/YX70Lg6qhXbxJnoY2RdTGfCghncOWkkf3xhvu/548sVDJs0igkLZpB1MZ0z+431s2Xhavo+PJSxn79K/0fvZMvC1WrG52TUMTIupvPS968zYvIo1nxu/r2rXY8OPPvpS7Vuj96wBzsne15eOIPe9w5gw/drGzoyYOx7Xu8VzPiNR7nrl2jubOlJK41p37PubBojftvPfasP8P2RC7wS0bJy24XcIu5bfYD7Vh9oEoMfyUgugbuMQzuP0mtIOEIIWoUGUZBXiD4zp1a7VqFBaNxdrvhYkVsOEnFrl4aKalZ8ZAxtB0QghMCnbQtK8gvJzzI9opqflU1JYRE+bVsghKDtgAji98UAYONgX9mutKik8sh4Q8uLj8fOyws7T08srKxw694d3eHDJm1sPTxwCAiAGpnsvb2x8/YGwEajwdrFhbLcXFVyV3dsdwxdB3dHCEHzkCAK8wvJyax9NLt5SBAu7rVn1YLDWmNTcWQ2MCSI7HR1j4RfiDpCy37G2vFsY6ydAp1phgJdNqWFRXi1aYkQgpb9IrgQdcR4/+gjtOrfA4BW/XtwvuJ2tZyJjKH9QGN+v7YtKM4vJK9G7edlZVNcUIRfO2Pttx8Ywel9xpyH/9xJxP2DsbK2BsBB46xa9pyzCdh7eWHvZax/7x7dyThk+vqlHzyCzy29APAM74ou7jiKolBwMRlt+3YA2Li4YOVgb5wNUklnLxfOZRdyIaeIUoPCutNpDG5hevQ3r7S88v8OVhYoFcvQg7UO7ErSAZBZWEpuSRkdvdR73S+J3H6UAUO7GfvDjs3Jzy0kK8O037e1s6FjuHFWxdrailZtA8hMM9bXxtV7ufOB3ji5GD98adzUfQ5btuxlxIhBCCEIC2tHTk4+aWlZdb6/EJCXZzxolJtbgJeXejPPuyKPk6XPu+z24bd3Y+nPOwCIPHgaVxcHfLw0DO7fmS07YtBl56PPzmfLjhhu799ZrdgmTuyNoeMgY98T0K4FRfmF5Nboe3Ir+p6AEGPf03FQBCf2GPdxIQTFBUUAFOUX4azSqotL4vYcpcutxveuwJAgivLMv3cFXua9K27PUbreZjwwGdq3M2cOnURRGv5ck44ezpzPKSQx19j3/HE2nUGBpn1PfrW+x97assEz3egshHpfN6oGWwInhHAEVgABgCUwu8b20cBrgADWK4ryasXtecA3wEBAB4xSFCVdCNEK+ALwBAqAsYqiHG+o/PqMHNy8NJXfaz016NOzrzrYqSkzJYuM5ExCura+3hGvKD9Lj5OHtvJ7R3cN+VnZJsvY8rOycXLX1Gijr/x+75K1nNgaia2DPffMmqRK7hK9Hhu3qjddG42G/Pj4a36cvPh4DGVl2Hp6Xs94dZKdmY3Gs+q113hoyM7MNvuGcTVRf+6lXfeQ6xnvqgp0ehzdq/I7uGsoyNLjoK3KX5Clx9GtWu24aSjQGWunMDu3sq2D1pWiHHUHoXmZ2Th7VGVz9tCQl5mNU7Xaz8vMxrla7Tu7G9sA6C6mk3jsDDsXr8PKxor+Y0bg01qdZSjFeh12blWvva1WQ85Z0/ov0euxrWhjYWmJpb09pXn5ODULIOPgYbwiwinO0pGXcJ6iLB0uLVuokt3H0YbkvOLK75Pzignzrt1fPtbBj6c6B2BtKXh0tfGDX1xmPoODPFh3Kg1fJzs6eDrj52TLkTR1aycrPRsP76q6cPdyJSs9GzcP8/1+Xm4hUTtjGT6qL2CcAQKYOvYzDOUGRo0dQtde7Ro+eIXU1Ex8fDwqv/fxcSc1NdPsQOa11z7FwsKC22+/hQkTRiKEYOLEh3n66TdYvHgdhYVFLFz4tmrZr8bPx43E5MzK75NSsvDzccPPR0vixapBXlJyFn4+WnMP0eByM7Nx8ayqHxcPDbmZ2SYDmdwafc+lNgC3j72PpW98xebvfgNF4YmPaq8QaEg5mXpcq+f3dCXnGt67jPc3vvaWlpbYOdpRkJOPo6tTg+S9xNvRlpT8qr4nJb+YTp61Dz6MDvHlidAArC0seOrPqgOr/k52/HxPV/JKyph3IIH9qbUPdkv/Pg05A3QHcFFRlM6KonQA/ry0QQjhB7wPDALCgO5CiBEVmx2BA4qidAW2AW9W3D4fmKQoSjdgCvCluR8qhBgnhIgWQkTPnz+/3uHNHrWox0g28q+DdOvfGQtLdSfb6pLffJuqRj0fuYsnFsymdb9wYv5QaU31VTLVRYlez5nvv6flk08iLBphktPsS3/txXNgczSJJy/Q/0H1lmDB5X4FNYvH3D1vjEM95uq6dgldfv8wlBsozivg4Q9fot+TI1j7wUJVjmJeLlbN8Jd7fj59b8FWq2H/rPc4/dMKXIJbItTsd8z8+s29bD8evcjAJZF8sCee/+sWCMDKuGRS8otZ/WA3ZvRpxYGUbMoM6l+l6Fp+zeVl5Xw8YzHDHuqLj7/xaLOh3EDyhQze/moCL7/9KF+8s4K83MIGSltbnfZd4KOPprB27ecsWTKH/ftjWb36bwDWr9/OvffeyvbtPzB//lu88srHGFRcRnkl5vpQRVHMPj+1dte6/ODaXc/l+6f9v+9k8Nh7mbxoFoPH3su6uUuvf8YrqGv9XP4B/uH966muP+GnuGTuWBXFx9FnGd/ZeFArvaCEW1fs4/7VB3g/8iwf9A/BsQnMEFmo+HWjasiLIMQAHwkh3gfWKYqyo9qO0B3YqihKOoAQYgnQD/gNMACXTlhZDPwihHACbgFWVnsMW3M/VFGU+RgHSwDKjpT1dQ7816872bFuLwBBbZuRlVY1G6JL16PxuPYj+JFbDvHIi/dd8/3qI+aP7RzbtBsAr+BA8jJ0ldvyM/U4ak3zO7lryMvUX7ENQJu+4ax/52siRg1roORVbLRaSrKqjuaV6PVYazRXuIepssJCTnz2GQH33INzy5ZXv8N1snvNDvb9vgeAZm0D0adXvfb6DD0u1zhzeOrACf76aSPPfjQJK5uGv1bJ8Q3bOLnFWDserZqTn1mVvyBTj32NunCoMVuYX22GyN7VmQJdNg5aVwp02di5NPwyoIPrtxOzyfj6+wQHkptRlS03Q1/rAh5O7hpyq9V+bqa+cobI2d2V1r06I4TAt01zhIWgMCcPB9eGfx62Wi1FWVWvfbFOj22N+rfVainOMs4UGcrLKS8sxMrRESEErUc/VNlu/zsf4ODl1eCZL0nJK8HXqapb9nWyJa2g+LLt155KY3a/1vyHE5Qr8PauM5XbVt4XVnlSckP7feVONq7eB0Dr9s3ISK2qi8y0bNw8zff7X763Et9mHtw9ul/lbe5eGtp0CMTKyhJvP3f8mnuSfCGd1u0DGyz/kiXrWbFiAwAdO7YmJSWjcltKivnZH29v44DNycmB4cP7c+TISUaMGMSqVRv59tuZAHTp0o7i4hJ0upzKc4QaU1JKJgG+Vcua/H3cSE7VkZScRd9eVbPk/r5u7NgTp1qu6HXbOfinse/xbRNITnpV/eRk6HGqMXvi7GHa9+RkVPU9R7ZEcvv4+wEI6dOFdZ/+1NDx2btmB1EV+QPaBJJdPX96Ns5udX/vcvHQkJ2uw9VTQ3l5OUX5Rdg7N/wFcFLyi/FxrOp7fBxtSSsouWz738+m88YtrWEHlBoUsovLADiWmceF3EKCXOyJzbz8ckzp36HBBmeKopwEumEcCL0nhHij2uZrOSSgYMypVxQlrNrXdV8XNOjePpUXLujStyN7NkSjKApnYhOwd7S75uVvKefTKMgroFVo0PWOalbHof0Y+fFURn48lRYRnTixNRJFUUg5EY+Ng12tD4GObq5Y29mRciIeRVE4sTWSFhEdAdBfTKtsFx8Vg8bfW5Xn4BQURFFaGkUZGRjKysiKikLbuW7ruQ1lZZz66is8evXCPVzda23ccndfXvz6FV78+hVCb+nIgU1RKIrCubgE7B3tr2n5W9LpRH7+dAVPzBqLk1adcwjaDenP3R9M4+4PphHYvRNntxtrJ/1kPNYO9ibL38C4tM3azpb0k8baObs9kmbdjRf6aBbekTPbjB8oz2zbR7Pwhr8ASJdh/Xh87qs8PvdVgnt24tjfxvwXT8Rj62hnsvwNwMnNFRt7Oy5W1P6xvyNpVVH7wT06cf7ISQCyktIoLy3H3qVhl3Bc4tyiOYWpaRSmG+s/dV8UHmGmr59HWCdSdhs/sKRHH0DTri1CCMqLSygvNg44smKPISwtcPT3UyU3wJG0HIJc7QlwtsPaQjA82IvN8ZkmbYJcq84tHNjcvXKQY2dlgb2V8e2oT4CWcoPCaZ06FzC588E+zF38MnMXv0yPfh3Y+sd+Y38Ycw5HJzuzy9+WfP0H+XlFtS5y0KN/B47uNw7kcvR5XDyfjrd/w14F65FHhlVezOC223ry229/oSgKhw4dx9nZodYAqKysnKyK81JKS8vYujWK1hVLPH19Pdmzx7g06MyZCxQXl+Km8nkol7N+0wEevt+41DCiSzA5uQWkpOnZtO0wt/XthMbVEY2rI7f17cSmbYev8mjXT/jwfoz9/FXGfv4qbXt2IuYvY9+TeDweO0e7WufxOFf0PYnHjX1PzF+RtO1p7Huc3Fw5F2M8CT/h8Enc/Bp+CXfPu/sy6ctXmPTlK4T06sjBLcb3rvNxCdg62l3Te1dIzw4c2BwJQOyOw7Ts3FqVGaCjGbk0d7XH38nY9wxt6cnf5037nuYudpX/79/MjXM5xr5Ha2ddeZ5KgLMdzV3sScwtavDMUuNryHOA/IAsRVEWV5zX82S1zfuAT4UQHhjP8xkNfFaxzQJ4AFgGPAzsVBQlRwgRL4R4UFGUlcK4R3VSFKXBermOPUOI2RvHaw+/i42tNWOmjq7cNvPpj3jzuykArPxqLZFbDlBSVMp/HphJn2E9uGeM8bK1+7YcoPugLqpdQKC65t1COX/gGEsmzMLK1ppBE6suxbr8pTmM/HgqAP3Hj6y8DHZg1xACu7YHYO/iNeiT0sBC4OzpRv/xI1XJLSwtCRo9mhNz56IYDHj27o2Dnx+Jq1fj2Lw52rAw8hISOPnll5QXFKA/coSkNWvoNHMmWdHR5J48SVleHhm7jbMZLceMwbFZ7ct4NqR2Ee05HhnH+0++jY2tDQ9OqaqdT579gBe/Nl7idf2CNRz6ez+lxaW88/CbdL+jJ7c/PpT1C9ZQUljM4tkLAdB4aRkza6xq+f27hJJ4MJZfJs/Eysaa3s9V1c6aV97j7g+mAdDzmZHs+nIxZaWl+Ie1xz/MWDsd7hnMtrnfc+rvPTh6aBnw4tOqZQdo0a09Z6Nj+e7ZWVjb2jBk0iOV2/73wvs8PvdVAG579iHjZbBLSmjRtT0tulXkv60nGz5byg+T3sPSypKhLzyq2j5sYWlJm0dHcvjjeSgGA759bsHR34+zv67BJag5Hl0649uvN3ELFrJ36gysHB0IHf8MACW5ORz+72cIC4GtRkP7Z8aokvmScgXe2nGaRXd1xEIIVh5P4ZSugBe6BxGTnsuWhEwe6+hH7wAtZRVHXadsMZ7G6W5vzaLhnTCgkJpXwkubG+z0zivq1juE/bvjePb+97C1s+b5GaMqt73w6H+Zu/hlMlL1rFy4mYAgL156/BMAhj3Ym8H39KRLz7Yc2neCiSM/wMJS8OSku3BR8Ypk/fuHs21bNIMHj8Pe3pZ3351cue2ee55n9ep5lJSU8swzb1JaWo7BUE6vXmE89NDtAEyd+jSvv/45P/ywGiEEc+ZMVq32F302ib69QvDQOnN63+fM/ngV1tbGjyjfLt7Mn38dZMjAMGJ3zKWgsJjxU74BQJedz3vzfmXnWuP5Su9++gu67HxVMtcU3L09p6Nj+eKZWZWXwb5kwcT3Gfu5se8Z+n8PsfaTJZQWlxAc3p5W4ca+Z9jzo9j4zc8YDAasrK0ZNmmU2Z/TUNpGtOdk1DE+fmo21rY23PfSw5XbPpvwAZO+NL53/fntag5vNb53vf/oG4QP6cWtjw2l2x09WfXBYv47Zjb2zg6MmvaEKrnLFXhnz2kWDOmAhRD8eiqF0/oCJnZpTmxGLn9fyOLhEH96+WmMfU9JGa9tPwFAuLcrk7o2p0xRMBgUZu4+RXZJmSq5G1MjfCytFyGEG8ZVYUFAAvCQoii6Gm3CgK8AF6AceEdRFPOXPq5+v4Za2y6EGAJ8iHFJWynwHPARMEVRlGghxMPANIyzQb8rivJKxf3ygE+AO4FsYGTFRRBaYHyCvoA1sExRlFlXiXFNS+BuJH19jMvNPo1V9xLC18vkUOMb6uPbtjVykvr5X//+rD6nzqW/G8I9zYfy7qFNjR2j3l4LG8z84xsaO0a9jGs3BIBnd/3dyEnq5+veAwFo+eXNue+endAfgDj9ukZOUj8hmuHAycaO8Q+0wT5w9NWb3aAKz//Ej6dvzr7nsWBj37Mq/s+rtLwxPdDCePC4/feN83ec/qljT/WDG+Vk2KuITF+v2plyEZ7D6v2aCCE+wDiZMkcIMRXQXrpoWrU2bQBFUZRTFZMv+4EQRVH0Zh6yUoPNACmKsgGo2YsMqLZ9KWD2DD9FUWYAM2rcFo/xwgqSJEmSJEmSJNXDTTFKM7qHqrHDImArYDIAqjjl5tL/Lwoh0jBeMfqKA6Ab+QINkiRJkiRJkiTdpKpfnbnia9w13N1bUZRkgIp/r3hlHyFEBGADnLlSO2jYq8DVi6Io6pxtLEmSJEmSJElNjJrnANW4OrOZLGIz4GNm0/Rr+TlCCF/gR+AJRVGuev3+G24AJEmSJEmSJEnSv5+iKLddbpsQIlUI4asoSnLFACftMu1cgPXA64qi7K3Lz5VL4CRJkiRJkiSpibiJ/hDqGuDS5QSfAFbXbCCEsAF+Bf6nKMrKuj6wHABJkiRJkiRJknSjmQMMFkKcAgZXfI8QIlwI8W1Fm4eAfsCTQohDFV9hV3tguQROkiRJkiRJkpoIIVS7CvY/oihKJnCrmdujgWcq/r8YWHytjy1ngCRJkiRJkiRJajLkDJAkSZIkSZIkNRE30d8BajByBkiSJEmSJEmSpCZDzgBJkiRJkiRJUhOh5t8BulHJGSBJkiRJkiRJkpoMOQMkSZIkSZIkSU2EnACSM0CSJEmSJEmSJDUhQlFujmuB19O/+slJkiRJkiRJN4ybYnLlSNY61T4fd3IbfkO+Jv/6JXARK3c2doR6iXywDwBPbt/WyEnq54d+/QHYk7a+kZPUTy+vYTyzc2tjx6i3b/sM4Pk9fzd2jHqb12sgo/7e3tgx6mXZwH4ALDy5oZGT1M+YNkMAOJBxc+67XT2GATd33zlg/a7GjlFvW4f15sfTN2ftAzwWPAT7wNGNHaNeCs//BMADf92cfeeqQca+U1e8rpGT1I/WdnhjR6gzixtySKIuuQROkiRJkiRJkqQm418/AyRJkiRJkiRJkpGcAJIzQJIkSZIkSZIkNSFyBkiSJEmSJEmSmgj5h1DlDJAkSZIkSZIkSU2InAGSJEmSJEmSpCZCTgDJGSBJkiRJkiRJkpoQOQMkSZIkSZIkSU2EnAGSM0CSJEmSJEmSJDUhcgZIkiRJkiRJkpoICzkFJGeAJEmSJEmSJElqOuQMkCRJkiRJkiQ1EXICSM4ASZIkSZIkSZLUhMgZIEmSJEmSJElqIoRQGjtCo5MDoMvo6a3h5S4tsRCC1WdT+d+JRJPtD7f24+6WPpQbFPTFpcyOPkVKQTEAkzoG0dtXixCCyFQ9/z10VtXsuqNHSVi2HMVgwLtvH/yHDjXZnnPyJAnLl5OfmESbcWNx79YNgPzzFzi7ZAnlhYUICwv8h92JR/fuqmYHUBSFJZ/+ypG9cdjY2vDMa6MJahtQq92q+b+ze0M0+bkFfLNxTuXtmak6FryzlIK8IgzlBh58dhide7VXLX9WzFHO/rQCRTHg07cPze68w2R79omTnFm2gvzEJNqNfwbP8G6V21J37eH8ut8BCBx+J969e6mW+5L0I7EcX7oCxWAgoF9vWg43zW8oLSVmwQ9kJ5zHxsmRzs89g72nBxd37yPhj02V7XITk+j11mu4NG+mav6c2KMkrliGYjDg3rsvPneY1n/eqZMkrlhOYVIiQU+PQ9utm8n28sJC4t56A9ewLjQb/bCa0QFj/W+e/zNn9h/D2taGYZMfwSe49muYcvo86+cuobSklFbd2nPbuPsRQvDb+wvJSkoDoCi/EDtHe56a96pq2RfN/ZVDe+KwsbPhuemjaVFj3y0uKmHu64tIS8pEWAi69Qll9HPDAYg7dIb/ffob588k8/zMx+gxsLMquS+52fvOCE8NE9u3xFLA+gupLD2TZLL9wRZ+DGvmTbmioC8p5YMjp0ktLCbYxZEXO7TEwcoKg6Kw+HQifydnqJ5fURQ2fvMzp6ONtX/Xi4/ga6b2k0+dZ80nSygrKSU4vD23jzfWfsqZRP74YjllJWVYWFpwx4SH8G/bXJXsX384nqG3diE9M4fwwa+YbfPfmU8wZGAYBYUljHv5Kw4dTQDgkQf6MXXSCADmfPYbS1ZtVyVzTbmxR7m4YhkoBrS9++I1xLT+80+d5OLK5RQlJRL49Dhcu1b1nTETxmHn7w+AtdadoAkTVc2uKAofv/8be3bEYWtnw4zZo2jXvvbnhheenU9GRg7l5QbCurZkymv3YWlpwakTF3l/9ioKC4rx8XNj1pxHcHSyU/U5SOqSAyAzLIBXurZi4vajpBWUsOi2MHZczCQ+t7CyzQl9Pk9sPkRxuYH7W/owqVMQ0/eeoKO7M508XHh440EAFgzqRFdPVw6kZ6uSXTEYiF+6lPYvvoiNVkvMO++i7dwZBz+/yjY2bm60GjOGixs2mj5vGxuCnxqDvbc3JXo9R95+G01oKFYODqpkv+TI3jhSEzN4/6fXOHPsHP/77yremP9CrXZhvdtz2319ePXhd01uX7NoExEDwxh0b2+S4lP4+JUF/HelOgMgxWDgzJKf6PDyC9hqtRya/R5uYZ1wrPb627q70fapJ0ncsMnkvqV5+Zxfs46wGa+BgEOz3sUtrBPWjo6qZL+UP+7Hnwj/z2Ts3LTsmfkeXl064eRflT9x+y6sHBzo98FskvdGcXLlr3SeMBa/W3rgd0sPAHIvJHFw3leqD34Ug4ELPy0lePKLWGu1nHjvHVw7dca+2utvrXWj+RNjSN20wexjJK9ZjVObNmpFruXs/mPoLqYz/psZXDyRwIavVvDEf1+u1W7Dlyu4Y+Io/NoGsfKtrzm7P45W4e0Z8eqYyjZbvvsVWwf13sQP7YkjJTGDT5a/xunYc3z30SreXlB73x0+egCh3VpTVlrG289/xaE9cYT1CsHDW8uz00ez/qetqmW+5GbvOy2AyaEtmbIvlvSiEr7u05ldqVmcy6t63zqVk8/4nYcpNhi4O9CH8e2CmHXwBEXl5bx76BRJBUW429owv09notJ15JWVq5Yf4Ez0MbIupjNhwQySTiTwxxcgS5qxAAAgAElEQVQreOqT2rX/x5crGDZpFP7tglj25tec2R9HcHh7tixcTd+HhxIc3p7TUbFsWbiax+c8r0r2H1du4+tFG/j2kwlmtw8ZGEarIB869HuRiC7BzHvnafrdMwOtqyPTX7iP3sOmowC717/D+k370Wfnq5L7EsVg4OKypbR4/kWstFrOzHkHl06dsfOt1ne6uRHw+BgyNtfuOy1sbGg9/U01I5vYs/M4F85lsHLdNGKPnOeDt3/m+6WTa7V756PHcXSyQ1EUpr20iL82Hmbw0C68+9YKJr18F13DW7H2130s/uFvxk8cauYn/TvIc4DkOUBmhbo5k5hXxMX8YsoUhY0X0unn727SZn96NsXlBgBisnLxsrc1blDAxtICawsLrC0tsBKCrKIS1bLnxcdj5+mFnacnFlZWeHTvju7QYZM2dh4eOAYEIITpLmDv4429tzcANhoN1s4ulObmqpb9koM7j9L7jnCEEASHBlGQV4g+I6dWu+DQIDQeLrVuFwIKC4oAKMwvQuvh2uCZL8k9G4+dlxf2Fa+/Z0Q4WQfNvP7NAoxBq9HFxqIJDcHayRFrR0c0oSHojsaqlh0g+2wCDt5eOHgZ8/v26E7awSMmbdIOHsG/j3Fmyrt7VzKPHUdRTKfTk/dF4dsjXLXclxQkxGPr5Yltxeuv7d6d7COHTNrYenhgb6b+AQrOnaM0NwfnEPVmDGs6tTeGDoMiEELg364FxfmF5GWZHkDJy8qmuKAI/3YtEELQYVAEp/aa/p4UReH4zoO07286w9WQ9u88St+Kfbd1hyAKcgvR1dh3be1sCO3WGgAraytatA0gM10PgKevG82D/cz+bhrazd53ttM4k1RQRHKh8X3rr4vp9PZ2M2lzKDObYoPxfeuYPhdPOxsAEvOLSKroMzOLS9CVlOJqY61qfoATe2PoWFH7Ae1aUJRfSG6N2s+tqP2AEGPtdxwUwYk9xtoXQlBc8TyK8otwdlOv798VeZwsfd5ltw+/vRtLf94BQOTB07i6OODjpWFw/85s2RGDLjsffXY+W3bEcHt/dWc+wdh32nh6YlNR/67h3ck5bNp32rgb+86a7103gu1/H+XOu7oZ+8POzcnLLSQjvfbnhkuzOuVlBkpLyytHAucS0ujSrSUAEb3a8PfmGNWyS43jhh0ACSEeF0IcEUIcFkL8KIS4SwixTwhxUAixWQjh3VA/29PehtSK5WwAaQXFeNrbXLb93S282ZOiA4yDof1pen6/K4I/7opgb6qehGozRw2tRK/H1q3qTc9Gq6FYr7vmx8mNj0cpK8PO0/N6xqsTXXoObl6ayu+1nhp0GXWfQRsx5g72bNzPi/fN5OP/LODRF+5tiJhmFev12LppK7+30Wop1uvrdN8SnR5bbdV9bbVaSnR1u+/1UqTTYVctv51WQ5HOtH6KdfrKNhaWlljZ21OaZ3q0MmVfND491V8CVKLTY6OtVv8aLaV1fA0Vg4GkVSvwv++BhopXJ7mZ2Th7VNW/s7uG3MzsK7fxqN3mQuwZHDXOuPl5NWzgarLSc3Cvtu+6eWnIusLsd35uIQd2xdKhW+PNuF1ys/ednnY2pBdWHWxLLyrB0872su2HNfMmMr3282vn6oS1heBixUBCTbmZ2bh4VtWPi5m6zs3MxtndfJvbx97Hlu9X8+kTb7Dl+98Y+ORd6gSvAz8fNxKTMyu/T0rJws/HDT8fLYkXs6puT87Cz0dr7iEaVJlej3W1vtNaq6W0ju9dYFwaffq9tzn9/rtkHzrYEBGvKD0tGy+fqrrw8nYlPc183zP52W8YOuBNHB1tGTTYONhsFezDjq3GA45bNh4hLUXd915JfTfkAEgIEQpMBwYpitIZmAzsBHoqitIFWAaYXWQrhBgnhIgWQkTPnz+/nj/fzI2XOV/sjkBPQrRO/FhxjlCAox1BLg4MXxfJsLWRhHu50sXMLEWDUWoHFdc42Vmi13P6u+9p9eSTCAv1S6TmbAJc2wGnvZsP0HtoBJ/88iYvfTiW+bOXYqg46tng/tF5hWburPaRNnMRatSPud9P9Sb6M/FY2trgHOB/ncPVxZWzXUnGtq24dOiIjZvb1Rs3qDrU/1V+BwBx2/cT0k+92R+4tn23vKycz976kSEP9MW7xgx7o/gX9J01KZfpkAb7e9LW1YllZ03PEXKztea1sDa8f/jUP+vK6svs76AObSoa7f99J4PH3svkRbMYPPZe1s1dev0z1pO5WlIUxexsp7ndu8H9w/fddu+8T/C01wl8aizJK5dTnJ52HcNdndku8TL5P/16POv+epOSkjKiI08BMH3WSFYt28UTIz+hIL8IK2vLBkzb+IRQ7+tGdaOeAzQIWKUoSgaAoihZQoiOwHIhhC9gA8Sbu6OiKPOBSyMf5duVO6/5h6cVlODtUHXkzMvBlnQzy9i6e7kyJqQZz26NodRg3PsG+LtzNDOXworlcbuTdXRwd+agmSVcDcFGq6U4q+poUolOj41Gc4V7mCorLOT4Z58ROOIenFu1bIiIZm3+ZSfb1u4FoEW7ZmSlVR190aXr0bjXfSnD9vX7ePmjcQAEdwiitKSUvOx8XLTO1ze0GbZaDcVZVUdVS3Q6bOv4+ttotWSfOFn5fbFOh2tbdY+M27lpKaqWv0inx1arMdvGzk2LobycssJCk/OUUvZF4dtD/dkfML6GJbpq9a/XYV3H1z//7BnyTp8mY9tWyouLUcrLsLCzxf/e+xsqbqX967dzeMMeAHxbB5KbUVX/uZl6nGos5XH20Ji2ydCbLPcxlJdzYs8RnvxkSgMnh40/7+SvNcZ9t2VIMzKr7btZafrLLkFd8MFKfAI8uHNk/wbPWBc3a995SXpRiclKBU87GzLMvG91c3fl0eAAJu85Wvm+BeBgZcmc7u357sQ5jl1hKdf1Fr1uOwf/rKj9NoHkpFfVT06GHid3M7WfWaNNRe0f2RLJ7eON+2tIny6s+/Snho5fZ0kpmQT4Vg30/X3cSE7VkZScRd9eIVW3+7qxY0+c6vmstFpKq/WdpTodVq51r/9L/ayNpyeObdpQdOECtp4NO/u8atlOVv+8D4CQ0GYmszZpqdl4eF7+c4OtrTV9B4Sy4+9YevRqS1ALb+Z9Mx6A8wnp7N6h/u9AUlfjH6IyT1D7MOhnwOeKonQExgMNdmbvMV0uzZzs8XOwxUoIbm/myY5qU9QAbTSOTOsWzJRdx9AVl1benlJQTFdPVywFWApBV09X4nPUWwLnFBREUVoaRekZGMrKyIiKQtu5buuJDWVlnPjyKzx79cI9XN3zN267rw+zF05h9sIpdO3bkV1/RqMoCqdjE7B3sjN7rs/luHtrObbfeFTnYkIqpSVlOGucGiq6CecWQRSlVr3+6ZHRuIXV7fXXhoaiiz1GaX4+pfn56GKPoQ0NbdjANbi0aE5BahoFFfmT90Xh1aWTSRuvsE4k7TR+YEmNOoBbSNvKo5iKwUBK1AF8GuH8HwCH5kEUp6VRnJGOoawMXVQUrp3q9voHPT2WDu+9T+i7c/C//wHcevRSZfAD0G1YP56a9ypPzXuV1j07cfSvSBRFIel4PLYOdrUGQE5urtjY25F0PB5FUTj6VySte3as3J5w6ATu/l64eDT8Uprb7+/DnEVTmLNoCuH9OrKjYt89dTQBByc7tGb23eXzf6cwr5DHJ49o8Hx1dbP2nZecyM4lwNEeH3vj+9YgP092p5q+bwW7OPJSx1a8FhWHvqTqfctKCGZ3a8fGxDS2pWTWfOgGFT68H2M/f5Wxn79K256diKmo/cTj8dg52tU6j8e5ovYTK2o/5q9I2lbUvpObK+diTgOQcPgkbn7qL+G+nPWbDvDw/X0BiOgSTE5uASlpejZtO8xtfTuhcXVE4+rIbX07sWnb4as82vV3qe8sqeg7s6OjcKlj31men4+h1FhPZXm5FJw5g62vb0PGBeCBUX34ceXL/LjyZfoP6sDva/cb+8PD53BytsPD07TvKSgorjwvqKysnN0742jewjhIy8o0nrNnMBhYOH8T9z6o/hVY1WSh4teN6kadAdoC/CqE+ERRlEwhhBvgClyar3+iIX94uQIfHjzDvH4dsBCwNj6VszkFjAsNJC4rjx3JWTzfqQX2Vpa816sdYBz4TNkVx1+JGYR7ubL09q4owN4UHTuTs678A68jYWlJi4dHEzd3LopiwKt3bxz8/Ti/ejVOzZvjFhZGXnwCJ778krKCAnRHjnBh9RrCZs0kMzqa3FMnKcvLI23XbgCCx4zBMVDdK3l17hXCkb1xvDLqXWztrHl62ujKbTPGfMTshcaj2su/XMvezQcoKSrlxftm0m94D+596g5G/d/dLPxgBRtXbAMheOa10aqdVC0sLWn1yCiOfvKp8VK6fXrj6O9Hwm9rcA5qjntYZ3LjEzj2xVeU5ReQdfgI51evpdvst7B2ciRw+DAOvf0eAIF3DcPaSb0rwIHxnJ6QR0ey/6N5KAYD/n1vwcnfj1O/rMG1RXO8unTGv19vYuYvZPsrM7B2dKDzc89U3l934hR2Wi0OXo3zwUNYWhIw8mHOzJuLYlBwv6U39n7+JK9ZjUPz5rh2DiM/IZ74r7+kvKCA7JgjpKxbTcibsxolrzmtwttzNjqWb8bNwtrWhjsnP1K57fvn36+8pPWQCQ+xfu4SykpKaNmtPS27VV244dj2A6pe/OCSLr1COLQnjhceMu6741+r2nenPvERcxZNITNNz2+LNuPX3IvXxnwMGAdRg+7uyZm483w8bWHluUErv/2Tj5aocwnvm73vLFfg06Nn+TAiFAsBfySmkZBXyJg2gZzQ57E7LYvnQoKwt7JkZte2AKQWlTA9Oo6Bfh50dnPB1dqKOwKMHwjnHDnN6Rx1r0QW3L09p6Nj+eKZWZWXwb5kwcT3Gfu5sRaG/t9DrP1kCaXFJQSHt6dVuLH2hz0/io3f/IzBYMDK2pphk0apln3RZ5Po2ysED60zp/d9zuyPV2FtbfyI9e3izfz510GGDAwjdsdcCgqLGT/lGwB02fm8N+9Xdq59G4B3P/0FncpXgANj/fuNepj4z+aCQUF7S2/s/PxJXbsa+8DmuHQOoyAhnnPfGPvO3JgjpK5bTZs3ZlGUkkzS0sUIIVAUBc8hd5hcPU4Nt/QNYfeOOB4Y9h52dta8Prvqd//Yg//lx5UvU1hYwn+e/56SkjIMBgPdIoIrBzqb/jjIquW7ABhwa0eGj4hQNb+kPmF2Pf8NQAjxBPAfoBw4CPwKfIJxELQX6K4oyoCrPIwSUY8lcDeCyAf7APDk9m2NnKR+fuhnXNayJ219Iyepn15ew3hm59bGjlFv3/YZwPN7/m7sGPU2r9dARv3dOH8L459aNrAfAAtPmr/M9o1uTJshABzIuDn33a4ew4Cbu+8csH5XY8eot63DevPj6Zuz9gEeCx6CfeDoqze8ARWeNy75e+Cvm7PvXDXI2Hfqitc1cpL60doOh5vkCtPn8taq9uG/udNdN+RrcqPOAKEoyiJgUY2bVzdGFkmSJEmSJEmS/h1u2AGQJEmSJEmSJEnX1w05JaOyG/n8JEmSJEmSJEmSpOtKzgBJkiRJkiRJUhNxI/99HrXIGSBJkiRJkiRJkpoMOQMkSZIkSZIkSU2EnACSM0CSJEmSJEmSJDUhcgZIkiRJkiRJkpoICzkFJGeAJEmSJEmSJElqOuQMkCRJkiRJkiQ1EXICSM4ASZIkSZIkSZLUhMgBkCRJkiRJkiRJTYZcAidJkiRJkiRJTYQQSmNHaHRCUf7VL8K/+slJkiRJkiRJN4yb4vSalMI1qn0+9rG/+4Z8TeQMkCRJkiRJkiQ1ETfkiERl//oB0At7/2rsCPUyt+cgAG79Y1cjJ6mfLUN7A7At+fdGTlI//X3v5KV9N2ftAHzcYxDjdm5t7Bj1Nr/PAD6N3djYMeplcujtAHxwZFMjJ6mfVzoNBmDZmT8bOUn9jGp1B3Bz9503a3Yw5l8Vf3PWDsADLe7ggb+2N3aMelk1qB8A9oGjGzlJ/RSe/wmAlMI1jZykfnzs727sCNI1+NcPgCRJkiRJkiRJMhJyCkheBU6SJEmSJEmSpKZDzgBJkiRJkiRJUhMhJ4DkDJAkSZIkSZIkSU2InAGSJEmSJEmSpCZCzn7I10CSJEmSJEmSpCZEzgBJkiRJkiRJUhMhrwInZ4AkSZIkSZIkSWpC5AyQJEmSJEmSJDUZcgpIzgBJkiRJkiRJktRkyBkgSZIkSZIkSWoihJwBkjNAkiRJkiRJkiQ1HXIAJEmSJEmSJEnSDUUI4SaE2CSEOFXxr/YKbV2EEElCiM/r8thyACRJkiRJkiRJTYQQFqp9/UNTgS2KorQGtlR8fzmzgW11fWB5DtBlpB+JJW7JChSDQkD/3rQaPsRke3lpKUfmLyIn4TzWTo6ETXgGB093knZHEv/Hpsp2uReS6D1zGi7Nm6mWvbuHhv8LaYmFgN8TU1l2Nslk+wNBftzZzJtyg4K+pJQPY06TVlQMwHvh7WmvceaoLofp++NUy1ydoigs/+xXYvbGYWNnzZNTR9O8Te3X79dv17N3QzQFuQV89uf7Jtui/z7I2h82gIBmrfx5ZsZjasUn7UgsxxYba6dZ/94E31W7dg5/s4jshPPYODnS5f+MtQOQcz6RmIVLKSsqQghB77emYmljrVp2gKyYo5z+aQWKYsC3bx8C77zDZLv+xEnOLFtBXmIS7cc/g2d4t8ptKbv2cH7d7wAEDr8Tn969VM0OxvrZ+d3PnDsQi5WtDbdOfBTPVrXrJ+3Mef76bDFlJaU07xpKn6fvRwjBvqXriI+KQQiBvaszt056FEc3V1Xz7124igsV+fv932N4tKydP+PMebZ/8SNlJaU06xpKzzEPIIQgfs8BDqz4HX1SKne/NwXPVs1Vy34p/x/f/MKpqGNY21oz4qVH8AuunX/zonUc3hJFUV4B03/5sPL2stIyfvloMcmnL2Dv7MiD055A6+2uSvabve+82fMrisL6r37hREXt3P/yI/i3rl07G39Yx6HNURTmFfDmb9Vqp6SMVR8tJunUBRxcHBk17Qm0PurUDkBu7FEurlgGigFt7754DRlqsj3/1EkurlxOUVIigU+Pw7VrVd8ZM2Ecdv7+AFhr3QmaMFG13ABffzieobd2IT0zh/DBr5ht89+ZTzBkYBgFhSWMe/krDh1NAOCRB/oxddIIAOZ89htLVm1XK7YJRVGY98Fq9u08jq2dNdNmjaRNSECtdv+ZsIDMjFzKywx06tqCF6bdi6Vl1Qf1ZYu28tUn61n991totI5qPgWptnuAARX/XwRsBV6t2UgI0Q3wBv4EwuvywI0yAySE0AghJlT8f4AQYt013v8HIcQDDZMOFIOB2P8tI/zlifR97w2S90aRm5Rs0iZx+26sHR3o/+EsgoYM4sSKXwHwvyWCPrOn02f2dDqPexJ7DzdVBz8WwPOhLZkWHctTOw4yyNeT5k72Jm1O5+Tz3K7DjN11iO2pmYxrF1S5bUV8EnOOnFQtrzlH98WRmpjO20te47GXH2LJJ6vMtuvcK5RpX79Q6/bUxHT+WLKFVz5/npk/TOWhiSMaOnKlS7UTMWUi/ee8wUUztXNhm7F2Bn40ixZ3DOL4cmPtGMrLOfTND3Qc8zD933uDntNexMLKUrXsl/KfWvITHV+cRPfZb5G2L4r8ixdN2ti5u9H2qSfx7hFhcntpXj7n1qyjy/SpdHl9KufWrKM0P1/N+ACcP3CM7OQ0HvniDQY8O4pt85ebbbf9m+UMeG40j3zxBtnJaZw/eAyALiNuZdQn0xj58VSCwkOJWvGHmvFJPHiMnOR0HvzsTfqMH83uBcvMttu1YDm9x4/mwc/eJCc5ncRDxvzaZn7cOmUsPiGt1Ixd6VT0MTKT0nn+29e56/lRrPt8pdl2bXt0YNzcl2rdfmDDHuyd7Jn83Qx63TuATd+vbejIwM3fd97s+QFORh0j42I6L33/OiMmj2LNZWqnXY8OPPtp7dqJ3rAHOyd7Xl44g973DmCDSrUDxr7z4rKltJg4mdZvzCI7KpKiZNO+09rNjYDHx6DpHlHr/hY2NrSe/iatp7+p+uAH4MeV27jn8TmX3T5kYBitgnzo0O9FJk5dwLx3ngZA6+rI9Bfuo9/dM+h79wymv3AfGtfGGTTs23mcxPMZLFnzKlNmPMDH7/xitt1bHzzG9yte4oefX0avy2PrpiOV29JS9ETvPYW3r0at2I1EqPYlhBgnhIiu9jXuGoJ6K4qSDFDxr1etZ2KcZvov8J9reQUaawmcBpjQSD/7qvRnE3D09sTByxMLKyt8e4STduCwSZu0A4fx79MTAJ/uXck8dhxFUUzaXNwbhV/P7qrlBmincSYpv4jkwmLKFIW/k9O5xcvNpM2hrGyKDQYA4vS5eNrZVG47mJlNQVm5qplrOrTrKL2GdEcIQcvQIArzCtFnZtdq1zI0CI177SPzO9btYcCIPjg6OwDgonVu8MyX6M8k4OBVVTt+PcNJrVE7qQcOE1CtdjIqaifjaBzOzfxxCTQesbJxdkJYqLuL5pyNx97LC3tPY36viHAyD5rmt/PwwKlZQK0/Ja2LjUUbGoK1kyPWjo5oQ0PQHY1VMz4A8ZExtB0QgRACn7YtKMkvJD/LtH7ys7IpKSzCp20LhBC0HRBB/L4YAGwcqj40lhaVIFT+k9nnoo4Q3N+Y36uNMX+BzjR/gS6b0sIivNu2RAhBcP8IzkUa38Q1AT5o/L1VzVzd8b1HCbvVuP82axdEUX4huVm1999m7YJwNjOzdnzvUcJuM35AbN+nM/GHT9bqWxvCzd533uz5AeL2HKVLRe0EhgRRlFdIjpm+PzAkCBczfX/cnqN0raid0L6dOXNIndoBKEiIx8bTE5uKvtM1vDs5hw+ZtLFx98A+oHbfeSPYFXmcLH3eZbcPv70bS3/eAUDkwdO4ujjg46VhcP/ObNkRgy47H312Plt2xHB7/85qxTaxc2ssQ4Z3QwhBaKfm5OUWkZmeU6udo5MdAOVlBkpLy01+HZ9/tIZnXxgmr5J2HSmKMl9RlPBqX/OrbxdCbBZCHDXzdU8df8QE4HdFUS5cS67GWgI3B2glhDgElAL5QohVQAdgP/CooiiKEOIN4C7AHtgNjFdU6M2KdHrs3KrOs7Jz06I/E3/ZNhaWlljZ21Oal4+Ns1Nlm+R9++n2wrMNHdeEh50N6UUlld+nF5UQorn8AGBogDeR6To1otWZPj0brWfV0RetpwZ9erbZwY45qRfSAXh/4qcYyhXuenIIHXqENEjWmop0euzd61A77lW1Y+1grJ385FQEsO+DeZTk5uHXM5xWw25XJfclJXo9ttVq31arJSc+/gr3qFKs02OrNb1vsU5/3TNeTX6WHiePqhyO7hrys7JNlrHlZ2Xj5K6p0aYq694lazmxNRJbB3vumTVJneAVCrL0OFarIYeKbA7a6vn1ONbIX5Cl/mttTm6GHpdq+6+Lhys5GdlmBztm75+px8XT+PwtLS2xdbCjICcfR1enq9zzn7nZ+86bPT9ATqYe1+q14+lKTma22cHO5e9fVTt2jurUDkCZXo+1tmrAaa3VUlDHvhPAUFrK6ffeBgsLPIcMxTWsS0PErDc/HzcSkzMrv09KycLPxw0/Hy2JF7Oqbk/Ows/nsuepN6iMtBy8fKrqx9PblfS0bNw9XWq1nfLcAuKOXqBH77b0v60TALu2xuLh6UpwWz/VMjeWG2mApyjKbZfbJoRIFUL4KoqSLITwBdLMNOsF9K1YWeYE2Agh8hRFudL5Qo02AzQVOKMoShjGKasuwAtAe6Al0Lui3eeKonRXFKUDxkHQ8Ks9cPWptvnz51+tuXlmxli1jgKbG4ZVa6I/E4+lrQ3OAf71y3AdXW7MeJufJ21cnVgRn2R2e2NRzLy413IU3lBuIC0xg5fnTmTsG4/xvw+XU5BbeD0jXsFVCuMKDAYDWSfP0OW5p7jl9SmkRB8iI/b49Y13Nf/o8IKZOzfCkU6z9V5z9zXbpqpRz0fu4okFs2ndL5yYP9Rdz24+fl36nxvjDc18tLpnM/+raZzndrP1nTXdbPn/8e++MWvH7OeGut+93TvvEzztdQKfGkvyyuUUp5v7nNd4zH1gVhTF7Our0qSbmZ9b988OH301ll82z6C0tIwDkacpKizhx2+38NQEdQ86Sle1Bnii4v9PAKtrNlAU5RFFUQIVRQkCpgD/u9rgB26ciyBEKoqSCFAxKxQE7AQGCiFeARwANyAWuOKi3oqptUsjH+WFvX9dcxg7Ny1FWVVHxoqydNhqXGu00VCUpcPeTYuhvJyywkKsHavWvSbvjcavZ53Ow7quMopKTJY1eNrZkFlcUqtdV3dXHm4VwEv7jlJqaKTeqpq/f93JjnV7AAhqF4guvepoti5dj6tH7SM4l6P1dKVl+yCsrCzx8HXHJ9CLtKR0gtoFXvfcNdlptRRmmtaOnbZG7Wg1FGVW1U5pQSHWTo7Yu2lwb9e6chbRq3MHshPO4xHarsFzX2Kj1VBcrfaLdTpsNXVbC22r1aI/UXUOQbFOh6Ztm+ue0ZyYP7ZzbNNuALyCA8nLqHoO+Zl6HGv8DpzcNeRl6q/YBqBN33DWv/M1EaOGNVByo2N/buPEZmN+j+Dm5FeroYJMPQ41Zk8c3TXk18jvYCa/Wvat3cGBDcb91691IDnV9t+cjGyc3eu+/7p4aMhJ1+HqoaG8vJzigiLsK5azNqSbte+85GbNv3fNDqL+NNZOQJtAsqvXTno2zm7XVjvZ6TpcPY21U5SvTu0AWGm1lOqqZkJKdTqsXOt+Hol1RT9r4+mJY5s2FF24gK1nrdMdGk1SSiYBvlUXlPD3cSM5VUdSchZ9e1WtsPD3dWPHHvUuovHrsl2s+2UfAG1Dm5GWUlU/6anZeJiZ/bnE1sNIbVEAACAASURBVNaa3v1D2bU1FjcPZ5KTsnj6oU+M903LZuzouXy9eBLu1/D54+ZxYxwwq4M5wAohxNPAeeBBACFEOPCsoijP1PeBb5TLYBdX+385YCWEsAO+BB5QFKUjsACwUyOMa4vm5KemUZCegaGsjOR90Xh16WTSxqtLJ5J27gUgJeoA7iFtK480KAYDyVEH8O2h/gDoeHYu/o72+NjbYiUEA3092Z2WZdIm2MWRFzu0Ysb+OPQlpapnNGfgvX1447v/8MZ3/yGsTwf2bIhCURTOxiZg72hf5+VvAGF9OnLi0CkAcvV5pF5Ix8NXnSsBubY0rZ2Le6PxrlE73l07kVitdjzaG2vHs2N7ci4kUV5cgqG8nMzjJ3Hy91Ul9yUuLYIoTE2jsCJ/WmQ07mF1W8+tDQ1FF3uM0vx8SvPz0cUeQxsa2rCBK3Qc2o+RH09l5MdTaRHRiRNbI1EUhZQT8dg42NW6ipujmyvWdnaknIhHURRObI2kRURHAPQXq468xkfFqHI+Tfs7+nPvR9O496NpNO/eidPbjPnTTsZj7WBfa3DjoHXF2t6WtJPG/Ke3RdK8e6fLPHrD63FXX577/BWe+/wVQnp15NAW4/574XgCdo52dV7+BsaLIxzaHAnAsZ2HadGptSpH8W/WvvOSmzV/z7v7MunLV5j0pbF2DlbUzvm4BGwd7eq8/A0gpGcHDlTUTuyOw7TsrE7tADg0D6I4LY2SjHQMZWVkR0fh0qlufWd5fj6GUuPvoywvl4IzZ7D1Vbfvv5r1mw7w8P19AYjoEkxObgEpaXo2bfv/9u47rqr6j+P46wOyBGUI7pl7ZDjL3JqZOSrTRNs/f5X1a6iNX5aZDbOystKybPyszL21qebeZu5ZiVsRFURAQPj+/jgHuMDFgXCvxOf5ePDg3nu+9973OZz1HeewhVtaNyQo0J+gQH9uad2Qhcu2XOLT8s9dES35atpgvpo2mNbtG/DLgt8xxrBj6wH8A3xzDH9LSEjKuC7owoVU1q7cTeVqpalesxxzlwxn6k8vMfWnlwgrHcgXkwf+Qys/hYcx5pQxpqMxpqb9+7T9+kZnlR9jzARjzGXdRcRdPUBxwKWuTE+v7ESLSADQC3B+O7B85uHpSb37I9gwagwmLY2KbW6mRMXy7J01n8CqlSnT+AYqtmnJ1vETWPb8MLz8ixP+RP+M95/e8ye+IUEULx3mirhZpBkYs/Nv3mlWHw+Bnw5HceBcIg/VrMye2HOsiTrNo7Wr4ufpybBGtQGISkzmlU1Wi82HNzagUkBx/Dw9mNK+Ke9t+5ON0a69tuD6m+qxfd0uXr53BN4+3jz034iMaa/3H8Wwr6wbfcz4bB7rF20iOSmFF3oNp1XXm+jx8G3Ub16HnRv38OqDbyMeHtw9oDsBLrorjYenJw0eiGD9u2MwJnPd2TNzPkHVrHWnUpuWbP58AkueG4ZXQHEa2+uOl78/1W7ryMrh1p14St/QgDLh17skdzrx9KTGvRFsG/0RJi2Nsq1a4l+hPPvnzKNE1SqEht/A2f2R7PhkHBfiEzi1ZSuRc+fT7I3heAX4U7lbVza9ORKAKt274hXg+rsBVWlSn4ObdvL9E69TzMeLDk/elzFt6uC36fOB1TPe9rE+GbfBrty4LpUb1wNg7cR5xByJAg+hRFgIbR/r49L8lRrX5/AfO5j+1GsU8/ai9X8y889+biR3vTcEgJsf6cPyTyaSmpxCxfB6VGxk5Y9ct4U1X0/n/Nlz/DryM0pVrcBtQ113V6mazeqxd8NOPur/Bl4+3tw5qF/GtHFPvsvjY61b7P761Vy2Lf2dlKQU3r9/GI07t6D9fV1o3PkmZr03kY/6v4FfieL0+u+DuX1Vvirs+87Cnh+gdnNr3fngX9a603Nw5roz5ol3eepTa935+cu5bLHXnXfuG0bTzi3oeH8Xmtx2EzPencj7D1vrTsQQ16w7YO07y0f0Y/+YDyHNEHxzS3zLV+DE/Ln4Va5CyRvCSYjcz4HPPyU1IYG4bVs5sWAutYa9zvnjxzgyaSIigjGGsM634VvOtdehfDPmKVq3qEtocAn+XDeWNz6YgZeXdYr45cRF/PzbH3RuH86OFR+SkJjEY899DsCZ2HhGfjyblfPfBOCtj2ZxJtb1d/8EuKl1Hdau3EW/7m/j4+vNi6/dkzGt/z0f8NW0wZxPTGbIM/8jJeUCaamGRs1r0KPXTW7J60758P95Cj1x1R1ScnyxyCSgIZAInDDGdLNfHwtsNMZMEJE3gQggEjgEHDDGDBeRCcACY8ylKkR5GgJ3Lfjwpg4AdPxplZuT5M3iLtZlXMuO/ejmJHnTttztDF5XONcdgA9u7MCjK5e6O0aejW/Vjo92/OruGHnyTH1rDPm7WxdeouS16YWGnQCY8tfPbk6SNxHVrf9bVZj3nYU1O1j5Z+wvnOsOQK9qt9HrN/f8H5urNaNDGwD8Kvd1c5K8STw4GYDjifPcnCRvyvr1gEIytuxsykKXnfyX9Op0TS4Tt10DZIzpl8vrTzo8HgoMdVLmoYJLppRSSiml1D/VNVkncSntA1NKKaWUUkoVGdfKXeCUUkoppZRSBexa+j9A7qI9QEoppZRSSqkiQ3uAlFJKKaWUKiK0B0h7gJRSSimllFJFiFaAlFJKKaWUUkWGDoFTSimllFKqyND+D10CSimllFJKqSJDe4CUUkoppZQqIkT0JgjaA6SUUkoppZQqMrQHSCmllFJKqSJDe4C0B0gppZRSSilVZIgxxt0ZCtI/euaUUkoppdQ1o1B0rSRcWOGy8+PixVpfk8tEe4CUUkoppZRSRcY//hqgzacWuDtCnoSX6gbAvtjCmb9moJW/yqjf3Jwkbw4834Ffj/zo7hh5dmuF25n018/ujpFn/arfxujtC90dI08GNegEQL+ly9ycJG8mtWsLQOUPlrg5Sd4cHNwegHMphXPfE+DVgbiUxe6OkWclvDpS7+vl7o6RZzv/1YYzSYXzuBvsYx13jyfOc3OSvCnr1wMAv8p93ZwkbxIPTnZ3hCug/R+6BJRSSimllFJFxj++B0gppZRSSillkcJxqVKB0h4gpZRSSimlVJGhPUBKKaWUUkoVESLaA6Q9QEoppZRSSqkiQ3uAlFJKKaWUKjK0B0h7gJRSSimllFJFhlaAlFJKKaWUUkWGDoFTSimllFKqiBDt/9AloJRSSimllCo6tAdIKaWUUkqpIkNvgqA9QEoppZRSSqkiQ3uAlFJKKaWUKiL0H6FqBShXxhgmjJ7DH2t24ePrzeNDI7iudsUsZZLOJzP65W85cSQaD08PmrSsR78nugGwcPZqfpm5Cg9PD3z9vHn0v72pWK2sS/OPf38OG1db+QcOi6BGnaz5z59P5u0h33L8cDQeHh40b12Ph5608kcdP8Po1yYTH5dIWprhwf90pVnLui7J3rZqCK92rImnCFO2HmPc+gNOy91eK4xxd1xPt283sO1EHDeULcHIznUAq3P3w9X7+WVftEsyOzLGMHPsbHas24W3rxf3vdCXSrUq5Sg3/6sfWP/rRhLiEnj/x3dyTP9j2Wa+fu0bnh83iMq1K7siOmDl//nzWezbsBMvHy/uHHwv5WrkzH903yHmfvA9Kckp1GxWj9se64mIcPzvI/wwdhrJiUkElQmh5wsP4FPc16X5V309g4ObdlDM25v2T91P2HU585/86yBLxn7HheQUKjeuT8t/9UJEWD95AZHrtyIegl9gCdo/eR/+IUEuyx+7fTuHpk2FtDRCW7Wi7G1dskyP27uXQ9OmknjkCNf9+xGCmzTJMj01MZEdw18lKDycyn37uSw3WNvu8HY18fSAKduO8emGg07L3V4zjM+6N6Db9xvZeiKO1pWDebF1dbw8hZRUw4jlf7L6UIxLs4O17owaOY1VK3bg6+vN8BEPULde7tveoCc/5cjhaKbNGZbl9W//t5CP3p/FohWjCA4OKOjYGYwxvDdyup3fi+EjHqDORfOPs/O/AsC4MfNZ9tsWPDw8CA4JYPiIBwgr7bp1v1WFYIbcVB1PEWbsPc6XWw9lmd6ndjn61i1PmjHEX0hl+Kp9/BWTQPkAHxb0bEpkbCIAW06e5bXVf7osdzpjDB+8M4c1K6zj7itvRFCnXsUc5QYOGE909FlSU9MIb3wdz73UE09PD/btOco7b8wgMSGJsuVDeP3te/EPcO2+8+N357Ju5W58fL0Y8nofatXNmf/5J77gVHQcqRfSaNi4GgOH3IWnZ+aAoinfLGXc6B+Yu2Q4QcH+Lsn+2ajH6NKxESdPnaVppxeclnn/tQfp3D6chMRkHn12HJu3RwJwb682vPjUnQC8PWYO389Y7pLMyr3ybQiciASJyBP59XkX+Z52InJzQX/P5jW7OX44mo+mDeGR//bmq1EznZbr1q8do6e8yDsTBrNnWyR/rNkFQMtbG/PexOd595tn6XFve779eF5BR85i4+rdHD0UzfiZQ3hySG8+fcd5/p73tuOz6S/y0cTB7NwSycbVVv6pXy+idcdwPp74LC+8eR/j3nX+/vzmIfBGp9o8OGMLt3y9jh51S1OzVPEc5fy9PHmocSU2HY3NeG1PdDzdv93I7d9s4MEZW3irUx083dDKsXPdLqKOnGTYdy8RMfgepn44w2m5Bi3q89ynA51OO59wnmWzVlC1bpWCjOrUnxt3cvrISZ76cijdn47gh7HTnZb74ZNpdHu6D099OZTTR07y50Zr3Zn/0WQ6Ptydx8e9SJ2bG7JqxmJXxufgpp3EHjtJ37Gv0vbxvqwYP8VpueXjp9JmQF/6jn2V2GMnOfTHTgDC7+jIPaNfovf7Q6jSpAG/T//JZdlNWhoHJ0+i5lNPU2/4a5zesIHEo0ezlPEOCaHqQw8T0ry50884Om8uATVruSJuFh4Cb3aoxYOzt9Bxwnp61ClDzRDn2+7DjSqy6Vjmtns6MYV/zdnKrd9uYNDPu/iwSz1XRs+wasUODh2MYs6PrzF0eD9GvjE517K/LfwDv+I+OV4/fuw069bsomy5kIKM6lR6/tk/Dufl4fcy8g3n6z5Y+Ytny3//w7cwZfZQJs18idZtr+eLcT8WdOQMHgJDW9TgsV+3033WRm6/LozqQVnXnwV/R3HnnN/pOXcTX289xAvNr8uYdijuPD3nbqLn3E1uqfwArFm5m0MHopm+YAhDhvXm3TedHzdHvPcAE2c8x6RZz3Pm9Dl++3ULAG8Nn8YTA7vy/aznadexARMnLHFlfNat3M3hg9F8P++/PPdKLz4YMctpueHv3s/X0wYzYeazxJw5x9KFWzOmRR2PYePafZQp57qKM8B305dxxwNv5zq9c/twqlctS4M2g3jyxS/4eER/AIID/Xl5YE/a9HiF1j1e4eWBPQkKdE2lzb3EhT/Xpvy8BigIuOwKkFjy8v3tgAKvAG1YsZ02tzVBRKjVoArx5xI5E302SxkfX28aNKkBQDGvYlSrVZHTUdZBvbh/ZqtNUmKyy7sb1y3fTofbrfx1rq9CfFwip7Pl9/X1pmFTK7+XVzGq16lItJ1fBBLizwMQf+48IaElXZI7vFxJIs8kcCj2PClphvm7o+hUIyxHuWdbXcdn6w+QdCEt47XzF9JINQYAn2IeGJckzmnb6u0079QMEaFavaoknksk9lRsjnLV6lUlsFSg08/44eufuCWiA8W8Xd9Ju3vtdhp2tPJXrFOV8/GJxJ3Omj/udCxJCeepVLcaIkLDjs3YvXYbANGHo6jSoDoA1zWqza5VW1yaP3LDVmq1bY6IUKZWNZLiE4k/kzV//JlYUhLOU7b2ddY23rY5+9dbB3Hv4n4Z5VKSknDlDjx+/358S5fGJywMj2LFCG7ajJgtWZefT2goxStWdLpPiT9wgJSzZylZz/UViPCyJYmMSeRgxrZ7glurh+Yo91zLany24WCWbXfHyXOciE8GYO+peHw8PfD2dP2Bc9mSLXTtcRMiwvU3XMe5uAROnsy57SYknGfit4v592O355j2wbszeGZwT9wxwmTZkq3c3uNGO3814uISiM4l//ff/kb/x7L2LgYEZK77iYlJLj1uXR9agoNnEzkcZ60/P/19kg6VS2UpE5+SmvHYz8vTZdku1/Il27m9u3XcbXBDFc7FJRJ98myOcum9OqkX0khJSc3YxRyIjKJRE6tS17xFLZYs2uay7AArl+6gczcrf/2GVTgXd55Tl5HfcTUZ+948Bgzsirj4xHfV+t2cjjmX6/RutzZh0swVAKz/408CSxanbOkgOrW9gcUrtnEmNp6Y2HgWr9jGrW1vcFVs5Ub5WQF6G6guIptFZLSILBaRTSKyTUTuABCRqiKyS0Q+BTYBlUSkv4jsFZGlIvKFiIy1y4aJyEwR2WD/tBSRqsAAYJD9Pa3zMX8WZ07GUqpMZgtGqbBATjs5kKSLj0vk91U7aNC0ZsZrv8xcydO93uL7Txfw0KA7CyqqU6eiYgl1zF86kFNRuec/F5fI+hU7CG9m5e/3SGeW/Pw7D3Z7neGDvmTAc3cVeGaAsgE+HItLynh+LC6JsgFZWynrlw6gfEkffvv7VI73h5crycKHm/PLQ815eeHujAqRK8VExxLsMGwkKCyI2Ojcl312h/Yd5szJGBq0qF8Q8S4pLjqGwLDM/CVDA4nLlj8uOpaSoY5lgoiLtoYsla5ajj1rtwOwc8Vmzka7dihT/OkYAkKDM54HlAoi/lTWDPGnYvAvFZS1zOnMMuu+n8d3jw5l3/KNNIvoWvChbSkxMXgFZ/YceAcHkRJz5rLea9LSODxjOhXv7lVQ8S6qbIAPR+POZzw/di6JMiWybbthAZQr4cPi/Tm33XS31wxjR1Qcyamu33ajTsRQpmzmulO6TDAnT+Rcf8eNmc99D96Cr693lteXLdlCWOkgatXJOWzIFU6eiKGsQ/4yZYKJcpp/Afc92DFHfoBPPppL144v8dMPGxhgD4l2hTL+PhyPz9z3H49PonTxnPn61i3Hz72a8WzT63hrbWZPT4UAX2be0ZhvujSkSRnXNNhldzIqltJlM/crpcsEcjKX4+4zAz6nS7tX8ff3oUMn64S7eo2yrFi6A4DFv24l6rhr953RUWez5A+7SP7nHv+COzq8RvHiPrS9pSEAq5buIDQskBq1y7sk75UoXzaEw8cy9ztHjp+mfNkQypcN5vDR05mvHztNeYdt6J9K8HDZz7UqP5O9CPxljAkHngfuMsY0BtoD70tmU1Jt4FtjTCMgBXgFuAnoBNRx+LyPgNHGmGbA3cCXxphI4DP79XBjzIrsIUTkURHZKCIbx48fn+eZcXbenFtjWOqFVD5+dSK39W5NmQqZLVad727FxzNeot8T3Zg1YVGes+SFs1OHi+UfNXQiPfq0pqydf9kvf9CxWzO+WTCM4aP/zfvDJ5OWlub8AwqYcZgbAV5pX5M3lzgf4rD52Fk6/W89Pb7byBM3VsXH0/Ubn7mSlSebtLQ0Zn06h7sevyOfU12lbPmNkzUsfRO/Y2A/NixYwfinR5GUeB7PYi5uqXW6+C+9/B1bLG+8twf3j3+Tmm2asv0nV44Hd7rlXtY7Ty5bSmCDBniHuH7oFThP6bgpCDCsXQ3eXPZXrp9Rq1RxhrSuzpBFe/I93+W4nE13z+5DHDp4kg63hGd5PTExma/G/8yAJ7sXYMKLc7bvyb7u79l9iMMHo2ifLX+6/zxzBz8sfosuXZsxbdKyAsnpzOX2F0zedYzbZmzgg41/89gN1hDhkwnJdJy2jrvnbuKd9X/zbtu6+Luhh+hKdv0fffYYC357leTkC2xcvw+Al1/vw4wpq3iwz2gS4s9TzMXzcDnrT7r3xj3CrEWvkJJygU3r/+R8YjLffbmYfz1xa0HHzBNnPVLGGKfz54Z2U+UGBTW+RoC3RKQNkAZUAMrY0w4YY9baj5sDy4wxpwFEZDqQPnj9FqCew8pZUkRKXOqLjTHjgfSaj9l8asFlh/5l5koWz1sHQPU6lTjl0HJ26mQswaHOhyuNf2c6ZSuG0rVPG6fTb74lnC9zuYYoPy2YvpJf5lj5a9arRLRj/qhYQsKc5x8zcjrlK4VyR9/M/AvnreO1jx8BoG7DqiQnpXA2Jp6gkEv+Ca7K8XNJlHNoNS5XwocT55Izngd4e1I71J8pEY0ACPP35queDek/ayvbTsRllPvzdAKJKanUCvXP8npBWT5nJat/WANA5dqVOROVuexjTsYQWOryWiSTEpI4tv84Hw8aC8DZ03F8PvQrHnuzf4HeCGH9/BVs+sXKX75mZWJPZuY/Gx1LiWz5S4YGZenZORsdQ4A9nC+0UhnuH2GNhj11OIp9G3YWWO50239axq5FqwEIq1GFc9GZvSbnTsVQPCTruu+frVfIWRmAmq2a8eNb41zWC+QVFEzKmczWyOQzMXgFXd5Y+vi//yZu3z5OLltG6vnzmNRUPHx8qdizZ0HFzeLYuSTKl8gc+lsuwIeoc5kt+unb7tTe1ol3mL83X91xPf3nbmPriTjKBvgwvsf1DPp5Fwdiz+f4/IIybfJSZs9YBUC9BlU4cTxz3Yk6cYbQbDcB2Lr5b3btPEi3W18mNTWN06fiePShD3j+pT4cPRJN37vftN8bw7293+LbKf8lNJdjR/7kX8Ych/zHHfKfOHGGsNJZv3vb5v3s2nmI7rcOdcg/mvETBmUpd1vXZjzzxKc85qJeoOPxSZT1z9z3l/X3ISohOdfyP/59kmE314QVkJJmiE26AMDOU+c4FJdI1ZJ+7DiV+5Co/DJjykrmzrSOu3XrV8rSaxN1IpbQXI67AD4+XrRuV58VS3ZwY4vaVK1Who8/fwyAg5EnWb1iV8GGB2ZPWcWCWVb+2tnynzwRS2hY7scuHx8vWratz6qlOwgJLcGxI6fpf89o671RsTzS90M+m/gUpVw0hP5ijhw/RcVymQ3UFcqGcOzEGY4cO03rFpk3eKpQLoQVawp+ubvftXttjqsUVAXoXiAMaGKMSRGRSCD9yBjvUO5ifwEPoIUxJtHxxYIck9z57lZ0vrsVAJtW7eSXmau4uVMj9u04SHF/X4KdbMRTPv+JhPjzPDbkniyvHzt0knKVrGtX/li9i3KVco6Fz2/dereiW28r/4aVO1kwfRVtbm3Enu0HKR7g6/Q6nu/G/UTCufM8/XLW/GFlg9myYR+3dGvOof0nSEm+QKAL7ma05Vgc1YKLUynQl+NxSXSvU5qnF2SeQMclp9Lok5UZz6f0acSIpX+y7UQclQJ9OXo2iVRjqFDSl+tCinP4rGtOpNrc2Yo2d1rLfvvaHSyfs5ImHRoRuesAvv5+uV7rk51fgB9vz3kz4/lHg8Zy14AeBX4XuObdW9O8uzWidO/6HWyYv4IGbRtzZM8BfPx9KZGtclAiJBAfPx8O746kQu0qbF28geY9rPfHx8ThH1QCk5bG8im/0vT2lgWaHaBBl7Y06NIWgAO/b2f7T8up0aoJUfsi8S7uh39wtgpQcCBefj6c2Luf0jWrsnfZ+oz3xxyNIqh8aQAiN24luEIZXMW/alXOR0WRFB2NV1AQZzZuoFr/f1/Wex3LRa9eTcKBSJdVfgC2HI+jWpAflUr6cvxcEt3rlOHpH3dkTI9LTiV83KqM51N7hzNi+V9sPRFHSZ9iTLirIe+s/JuNRy9/uGh+uKdvO+7p2w6AFcu2MW3yUjp3acr2rfsJCPAjLNsJbO+ItvSOsNaVo0dOMfA/nzB+wmAAFi0flVGu260v893UIQV+F7h7+rblnr5WnpXLtjFt8jI7fyQBAX45TsB7RbShV0Qbh/yfZlR+Dh6IonIVa91ftmQrVV1459Lt0XFUCfSjQoAvUQlJdLkujBeW7s5SpkpJXw7Y+/S2lUI4cNY6PQj29SI2KYU0AxVL+FKlpB+H41yz7+8V0YpeEda+f9XynUyfvIpOXRqxY+tBAkr45qhAJCQkkRCfRGhYSS5cSGX1yl2EN7au+zl9Ko6QUiVIS0vjf+MXclfvFgWe/66IltwVYe2j1yzfxaypq+h4Wzg7tx3EP8CXUk7yJ8YnUcrOv3blbho2rkb1muWYu2R4Rrk+Xd7i80nPuOwucJfyw8JNDHjwVqbNW03zRjU4G5fA8agYFi7bwmsv9Mm48cEtrRsy7O3cbx6i/jnyswIUB6R3DwQCUXblpz2Q262s1gOjRSTYfv/dQPpVf78CTwKjAEQk3Biz2S5X4M0JjW6uyx9rdvFM75F4+3rx+MsRGdNeePB93v3mWU5FxTD7m0WUr1KaFx+2Wj06392Sjj1u4pcZq9i2cS+exTzxL+HHE0P7FnTkLJq2rMvG1bt4pOdIfHy9GPhKZv6n7n2fMd8/S/SJGKb+bxEVq5bmmfut/N16t6TznTfR/5nujHlrOnMmLUdEGDgswiUXxKYaw7BFe/m2VzieHsK0bUfZdyqewS2rsfV4HIv+yv221k0rBPFEz8qkpBmMgaEL93AmMaXAM2dX/8Z67Fy3i9fvG4GXrzf3vZC57N9+ZBQvfvE8AHM+n8fvizeRkpTCK/cMp8XtN3H7Q7e5PG92NZvVY9+GnYzp/wZePt7cMSjzVsqfPfkuA8Zatxjt+p97mDP6ey4kpVCjaT1qNLUuvN+29Hc2LLAqqXVbNiS8040uzV+5cX0ObtrB5P+8RjEfL9r9576MadOfHUnv94cA0PrRPiwZO5HU5BQqNapH5cZW/nUT5xJzNAoRoURYCK0fi3D6PQVBPD2pHNGXfR99iElLI7RlS/zKl+fovLkUr1KFoBvCiY+M5K9xn5KakEDM1q0cnT+P+sNfc1nG3KQawytL9vLd3TfgKcLU7cfYeyqBwTdXY9vxsyx0cs1eugfDK1A1yI+nb6zC0zdah4v7Zm7hlIu331ZtGrBqxXbu6DIMXz9vhr/xQMa0vnePYPLMl12a50q1bNOAVSt2cGeXV/H1wOz5qQAAEFBJREFU8+bVN+7PmNbv7reYNPOli75/zOg5HIg8gYcI5cqHMGSY626jnmpgxJo/+aJzAzxEmL3vOH/GJPBkoyrsiI5jyaHT9KtbgRblg7iQZohNvsBLy62hkk3LBPJU4ypcMIa0NMNrq/cRm3zBZdnT3dy6LqtX7KJX15H4+nox9I3Mfcf9vd/nu+nPkpiYzPNPf01y8gXS0tJo0rxGRkVn4U9/MGOq1UjQruP1dLvT+Z0eC8pNreuwduUu+nV/Gx9fb158LbNhtP89H/DVtMGcT0xmyDP/IyXlAmmphkbNa9Cj100uzenMN2OeonWLuoQGl+DPdWN544MZeHlZp7hfTlzEz7/9Qef24exY8SEJiUk89tznAJyJjWfkx7NZOd9qeHzro1mciY3P9Xv+KVx9k4prkTi9XiGvHyYyCWgIbMC6nscL2Ay0BNJvN7PAGNPA4T2PAs8BR4FdwGljzMsiEgp8AtTFqqgtN8YMEJFawAysoXVPObsOyMEVDYG7loSXsoYd7IstnPlrBlr5q4z6zc1J8ubA8x349YjrbgGb326tcDuT/vrZ3THyrF/12xi9faG7Y+TJoAadAOi31HXXT+SnSe2s3oTKH7j2Frz55eDg9gCcSymc+54Arw7Epbj21vH5qYRXR+p9XXj/j8rOf7XhTFLhPO4G+1jH3eOJrv23G/mlrF8PAPwqu7bBOL8kHpwMhWRs2YW0LS670qmYxw3X5DLJ1yFwxpjLaS5qkO35JGPMeBEpBszG6vnBGBMN9HHyHXuxKllKKaWUUkqpK+Dqf81yLboW7k83XEQ2A9uB/cAcN+dRSimllFJK/UO5/r8sZmOMec7dGZRSSimllFJFg9srQEoppZRSSilXuRYGgLmXLgGllFJKKaVUkaE9QEoppZRSShURehts7QFSSimllFJKFSHaA6SUUkoppVSRoT1A2gOklFJKKaWUKjK0B0gppZRSSqkiQv8RqvYAKaWUUkoppYoQ7QFSSimllFKqyND+D10CSimllFJKqSJDe4CUUkoppZQqIvT/AIEYY9ydoSD9o2dOKaWUUkpdMwpJzWKvC8+Pa12Ty+SfXgEqUCLyqDFmvLtz5JXmd6/CnL8wZwfN726a370Kc/7CnB00v7sV9vwq/+g1QFfnUXcHuEqa370Kc/7CnB00v7tpfvcqzPkLc3bQ/O5W2POrfKIVIKWUUkoppVSRoRUgpZRSSimlVJGhFaCrU9jHkWp+9yrM+QtzdtD87qb53asw5y/M2UHzu1thz6/yid4EQSmllFJKKVVkaA+QUkoppZRSqsjQCpBSSimllFKqyNAKkFKXQUSCROSJfPqsh0SkvMPzSBEJzY/PLigiMkFEel1B+aoisr0gM11mjqdFZJeIfC8iz7k7z9UQkTtFpJ6bM2RsByLSTkQWXOH7r2g9cnjfFX9XUSAi53J5PU/L+RLf9ZCIjM3Pz7zIdy0Vkaau+C6VyV3b95XKz+PxJb6nnYjcXNDfo9xDK0BKXZ4gIMcOV0Q88/BZDwHlL1VI5YsngNuBfe4Okp1YrmQffCfg1goQuWwHSql/hMKyfV9Rzjzsa9O1A7QC9A+lFaCLEJE5IvK7iOwQkUft1/qLyF67heqL9BYxEQkTkZkissH+aene9CAi/iLyg4hsEZHtItJHRJqIyDJ7vn4RkXIiUszO3M5+30gRGeHm+FmIyAMistWel+/slqbPRGSF/ffoVsAR3gaqi8hme1ktEZFJwDY7330ist6e/rmIeNo/E+xlv01EBtmtY02B7+2yfvbnP2+/f72I1LA/0+k8ikh9h+/aKiI183tmsy9v++U2IrJaRP5Ob+WzDyyjHOaxT35nySsR+Qy4DpgHDAJuEJHfRGSfiDxilyknIsvtZbldRFoXcKaqYvVIfQpsAu4XkTUisklEpotIgF3ubRHZaf8N3rNbIXsAo+ys1e2fn+1teYWI1LHfW0ZEZtt/uy3pLZgi8oqI7BaRhSIyWfLWI5axHQCjgAARmWF/7vciIvZ3DbO3k+0iMj799WzLwmkZEakhIovs7JtEpLr9FqffdTWcbLdV7PUjVEQ87OV6q102x/HAfv2ciIyw864VkTL269Xt5xtE5HXJpbfmCrIOtpfVdhEZmG2aiMhYe535ASjtMC1SRN6RnPsXp8csEWlub+d/2L9rO8nS1V5vr7rnWpwcp7JN72vvW7aLyDsOr58TkfftdWSxiITZrzvdLtxFch67uovIOnv5LkpfX64R+bZ9uyqniIy2//6b7PXkDjtj9n1tJbmC8zcRqQoMAAbZ31OgxwblBsYY/cnlBwixf/sB24EKQCQQAngBK4CxdplJQCv7cWVg1zWQ/27gC4fngcBqIMx+3gf42n5cH9gFdAL+ALzdnd8hd31gDxCa/ncBJgA/Y1XiawKHAd8CzFAV2G4/bgfEA9Xs53WB+YCX/fxT4AGgCbDQ4TOC7N9LgaYOr0cCL9uPHwAW2I+dziMwBrjXLuMN+LloeU+3s9QD/nRYxxYCnkAZ4CBQznF5uXndiQRCgeHAFntbDgUOYfXCPeuw7D2BEgWcpyqQBtxk51gO+NvT/gsMs5f3HjLv0pm+3kwAejl81mKgpv34RuA3+/FUYKDDPAViVbo32/NfAqtH7Ll82A5igYr2erGGzH1giMN7vgO6Z5+Hi5RZB9xlP/YFil/su67ib5HbdvtvYAbwPPC5Q/nsx4NS9nPjkP1dYKj9eAHQ1348ADh3FVmbYDW2+AMBwA6gUfpnAj3J3A7LAzEOyzkS5/sXp8csoCRQzH58CzDTfvwQMBa4C+vYF5xP24Sz49RSe50tj7VPCQOKAb8Bdzos9/T94DAyj8VOtwt3/OB8XxpM5rb9b+B9d+Vzkrcq+bR9uzBnMaCk/TgU+BMQHPa19rTyXOH5G9Zx44r3k/pTOH6KoS7maRG5y35cCbgfWGaMOQ0gItOBWvb0W4B6Dg0hJUWkhDEmzpWBs9kGvGe3mi0AzgANgIV2Tk/gGIAxZodYLf3zgRbGmGT3RHaqAzDDGBMNYIw5beefZoxJA/aJyN9AHayTPFdYb4zZbz/uiHWCssHO5QdEYS3L60RkDPAD8OtFPm+yw+/RDq87m8c1wMsiUhGYZYzJ7+FduS3vOXaWnQ6tlq2AycaYVOCEiCwDmgFb8zlTfphrjEkEEkVkCdAc2AB8LSJeWPPnivXngDFmrVg9evWAVfby9cb6254FzgNf2q35Ocbhi9VTdDMw3WGf42P/7oB1oov9d4kVkVZkzj8iMj+f5mW9Meaw/ZmbsU46VgLtReQFrMpLCNYJe/bvzFFGRJYCFYwxs+385+3Pvth35ZXT7dYYM1xEemNVWsIdymc/HtQETgHJZP6NfsdqRAJogTVsEawTrPeuImsrYLYxJh5ARGYBji3SbcjcDo+KyG/Z3u9s/+L0mIVVAflGrJ5lg3WymK49VsXkVmPM2auYH0dZjlPGmBUOmZoBS40xJwFE5HuseZ2DdXI71S43EZh1ie3CHZztS68HpopIOaxtfv/FPsDNrmb7dhUB3hKRNljrRAWsxjiw97X24+Zc4fmbK8Ir99EKUC7EGg52C1ZlIME+MO/BajV0xsMum+iahJdmjNkrIk2wroEYidVCuMMY0yKXt1yP1XJ4LXXJg7WDc/YPq7K/5sp/ahXv8FiAb4wxQ7IXEpEbgM7Af4B7gH/l8nnmMh4DGGPMJBFZB3QFfhGRfxtjsp/wXI3clndStjKOvwsDZ8tyuX3g7Ap8JyKjjDHfFnCO9HVHsHoI+2YvICLNsU7QI4AnsU6kHHkAMcaY8OzvzUVB/Z0c14lUoJiI+GL1pjQ1xhwSkeFYPTmZYXIvc7GcOb7rKrM73W5FpDhWqzdYvS1xuRwP0ucpxRiTvm7lR67csl7KxfZ/zvYpTo9ZdoPNEmPMXfYwoKUOk//GGlZaC9h4GZkuKftxSkQcG4quZL01XPl2UdCc7UvHAB8YY+bZ69VwV4e6Annavl3sXqwewibGmBQRiXTIk/04nZvctoX8zKmuMXoNUO4CgTP2wa4O1pCV4kBbEQkWkWJYXffpfsU6UQFARNy+AxbrTmMJxpiJWK2PNwJhItLCnu4lIvXtxz2BUlitax+LSJCbYjuzGLhHREoBiEiI/XpvscbpV8c6KO8pwAxxWEOHcsvXS0RKp+cT61qCUMDDGDMTeAVofJHP6uPwe43D6znmUUSuA/42xnyMdX1Lw6ucN2fz42x5O7Mc6CPW9U5hWOvP+nzOk1/uEBFfe77aYbX8V8Fq9f8C+IrMv5ErrAVaSuY1GcVFpJbdih1ojPkRGEhmL0TGemO3vu+3eyrSrwG5wS63GHjcft1TREpitdp2t+c/AKvClxcX2w7SpZ98RNvf5eyuUE7L2PN1WETutPP72BWSguB0uwXeAb7HGlb1hV3W2fHgUtaSeYyIuMqsy4E77XXEn8xhaI7TI+y/dzmsnhpHzvYvuR2zAoEj9uOHsn3OAazhdt+mHzuulpPjlOM2uA7rmBsq1g1n+gLL7GkeZK5b/YCVl9gu3MHZvtRx+T7ormC5yK/tu6A55gzE2oeniEh7oEou71nPlZ+/Xc7yUIWU9gDl7mdggIhsxTqxXou103oLa6d8FNiJNUYW4GngE7t8MawD0gBXh87meqyLptOAFKyTogtYFZxArJwfisgJrIsKO9otOmOBj7hGds728LwRwDIRScW6Rgmsv8syrB6rAenDZQoowykRWSXWrZ0TgRMO03aKyFDgV7HuNJOC1eOTCPxPMu8+k97SPAH4TEQSsYbJAPjYvToeWAf5dDnmUayLhO8TkRTgOPB6Ps9rbsvbmdn2PGzBaul8wRhz3G45vtasxxqKWBl4wxhzVEQexLoBRQpwDnvomCsYY06KyEPAZBFJH6YzFOugO9duaRWsGzgATAG+EJGnsU467gXG2euelz19C/AMMF5E+mO12j5ujFkjIvPs6QewWu/T911XkjnX7cChTIyIfIE1tCkSa5jhlZS5H/hcRF7H2pZ6X2nOy5HLdjsYa9hVS2NMqojcLSIPYw1hy348uJSBwEQReRZrvbvi5e2QdZOITCCzceFLY8wfDi3Us7F6CbcBe8msJKRztn/J7Zj1LtYQuMFY19xkz7JHRO7FGmbW3RjzV17ny+bsOPWe/V3HRGQIsARrW/jRGDPXfl88UF9EfsdatumVvNy2C5fLZV86HGvZHcFaj6q5I5sz+bV9F7RsOTcAdURkI9YQ+N25vOeIiFzp+dt8YIZYN1Z4yhizIucnq8Iq/UI8dZlEJMAYc85uQZiNdROB2e7OVdTYJwMLjDEz3J2loBSFeVSu47DvKo51gH/UGLPJ3bn+qezlnGiMMSISgXVDhDvckCMSa7hStKu/uyCJyDljTIC7c6jCQ8/flCPtAbpyw0XkFqxu4F+xLsZUSqlr3Xix/pGqL9a1L1r5KVhNgLFiddPEkPv1f0op19DzN5VBe4CUUkoppZRSRYbeBEEppZRSSilVZGgFSCmllFJKKVVkaAVIKaWUUkopVWRoBUgppZRSSilVZGgFSCmllFJKKVVk/B/Oaa2qqRxeLQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's make our correlation matrix a more visual\n",
    "\n",
    "corr_matrix = df.corr()\n",
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "ax = sns.heatmap(corr_matrix,\n",
    "                 annot=True,\n",
    "                 linewidths=0.5,\n",
    "                 fmt=\".2f\",\n",
    "                 cmap=\"YlGnBu\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into X and y\n",
    "\n",
    "X = df.drop(\"target\", axis=1)\n",
    "y = df[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "0        0   0     1  \n",
       "1        0   0     2  \n",
       "2        2   0     2  \n",
       "3        2   0     2  \n",
       "4        2   0     2  \n",
       "..     ...  ..   ...  \n",
       "298      1   0     3  \n",
       "299      1   0     3  \n",
       "300      1   2     3  \n",
       "301      1   1     3  \n",
       "302      1   1     2  \n",
       "\n",
       "[303 rows x 13 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "# Split the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to experiment with 3 regression models:\n",
    "1. Logistic Regression\n",
    "2. KNieghbours Classifier\n",
    "3. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put models in a dictionary\n",
    "models = {\"Logistic Regression\": LogisticRegression(),\n",
    "          \"KNN\": KNeighborsClassifier(),\n",
    "          \"Random Forest\": RandomForestClassifier()}\n",
    "\n",
    "# Create a function to fit and score models\n",
    "def fit_and_score(models, X_train, X_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Fits and evalutes given machine learning models.\n",
    "    modles: A dictionary of different machine learning models\n",
    "    X_train: training data (no labels)\n",
    "    X_test: testing data (no labels)\n",
    "    y_train: training labels\n",
    "    y_test: testing labels\n",
    "    \"\"\"\n",
    "    # Setup random seed\n",
    "    np.random.seed(42)\n",
    "    # create to dictionary to store model scores\n",
    "    model_scores = {}\n",
    "    # Loop through the models\n",
    "    for name, model in models.items():\n",
    "        # Fit the model with testing data\n",
    "        model.fit(X_train, y_train)\n",
    "        # Evaluate the model and append the score to model_scores\n",
    "        model_scores[name] = model.score(X_test, y_test)\n",
    "    return model_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bharatkammakatla/ML-DS/heart-disease-project/env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:938: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "model_scores = fit_and_score(models, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Comparision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Uht5JjMTUUqP83e3cRvb3FwfSktQGrxSVpEZY6JLUCAtd0k4tya8Nsm8UOIc+JEleDLyf3kn/M+mdm19V9XNd5pJal+Smqjpysn2jYJALizSYC4F3Al8FNnScRQNK8r6tvFxV9YFpC6NtkuR44JXA/knOH/PS3sDj3aTqloU+PA9V1ee6DqFt9m8T7NsDOI3e8hUW+o7rHmAlsIjeQGqTtfQGVyPHKZchSfJBeufp/zXw2Kb9VXVTZ6G0TZLsBbyd3uqhlwJ/WFU/6DaVJpNk16pa33/+M8ABVfX1jmN1whH68GxasGzsGhIF/GIHWbQNkuwDnAWcQm/V0COr6kfdptI2+EKSRfT67Bbg/iT/UFVndZxr2lnoQ1JVL+86g7Zdkj8AXk3v6sLDquqRjiNp282qqoeTnAZ8oqp+O8lIjtA9bXFIksxK8kdJVvYff5hkVte5NKl3AfsBZwP3JHm4/1ib5OGOs2kwM5M8DVgC/G3XYbpkoQ/PRfTejFnSfzwMfKLTRJpUVe1SVT9VVXtV1d5jHntV1d5d59NAzqG31tS3qurGJD8H/EvHmTrhm6JDkuSWqnruZPskaao4Qh+ef09y9KaN/oVG/95hHg1g09RK/+PaMdvrkozkucw7mySHJPliklv724cnObvrXF1whD4kSZ5L7wyJWfSuEn0AeENVfa3TYNom/VMX3wz8OnB5Vb2r40iaRJJ/AN4N/HlVHdHfd2tVPafbZNPPs1yGpKpuARYk2bu/7RtqO5EkPw28A3g98Gng+VW1pttUGtAeVfWVcfelH8m/riz07ZTkdVX1V0nOGrcfgKr6o06CaSBJ9qV3pstr6b2xfURVPdRtKm2jHyZ5Ov37GCd5DfCv3UbqhoW+/fbsf9yr0xR6sr4L3E/vjKR1wJvGjvT8B3mn8BZ61xE8K8n3gW/Tu0hs5DiHrpGW5P30R3YTqKo6ZxrjaBsl2QV4TVVdmmRPYJeqWtt1rq5Y6EOS5Fzgd+id2fJ5YAHwjqr6q06DaauSzK2q1Vt47Ver6m+mO5O2TZJ/rKqXdJ1jR+Bpi8PzS/03Ql8FrAYOoffOu3ZsX0wyb/zOJKcCfzLtafRkfCHJbyY5IMk+mx5dh+qCc+jDs2v/4yuBZVX1wLh33bVjeie9QnhlVf0LQJLfAv4r8NJOk2lQb+x/fMuYfQWM3M1lLPTh+Zsk36Q35fLmJHOARzvOpElU1ZVJHgM+l+S/0FsH/fnAS1xxcedQVfO7zrCjcA59iPprMT9cVRuS7AHsXVX3dp1Lk+tf5bsCuB5YUlX+Y7yTSLIr8BvApnn0a+ldZLS+s1AdsdCHpH9T2s9X1dr+ZcdHAr/jDS52bEnW0vvzPMBTgPX0biG46Z6wLtC1g0vyF/SmPP+yv+u/ARuq6rTuUnXDQh+SJF+vqsP7I73fBz4M/K+qOmqST5W0HZJ8raoWTLZvFHiWy/BsujH0rwAfrarPArt1mEcaFRv6V4oC0F8+dyRv1O6bosPz/SR/DhwLfCjJU/AfTGk6vBu4Jsld9KbKDgJO7TZSN5xyGZL+m6DHAd+oqn/p30HlsKr6+46jSc3rD6CeSa/Qv1lVj03yKU1yBDkkVbUO+AGwaU30xxnRu6ZI0yHJ743ZfElVfb2qvjaqZQ6O0IcmyW8DC4FnVtUhSfYDLquqF3ccTWpSkpuq6sjxz0eZI/ThWQwsAv4NoKruwRUYJU0j3xQdnh9XVSXZtCbznpN9gqTt8rP9+xBkzPPNRnHpYwt9eC7tn+Xy00lOp7e+xMc7ziS17OP85K/gsc9HlnPoQ5TkFcAv0RsxXFVVX+g4kqQRYqEPQZIZ9Ar82K6zSBpdvik6BFW1AViXZFbXWSSNLufQh+dR4BtJvkD/TBeAqnpbd5EkjRILfXj+rv+QNI2S/DTwemAeYzptFAdTzqFL2qkluR74MvANYOOm/VX1l1v8pEZZ6EOS5Bs88e7xDwEr6a2Lvmb6U0nt8yrRn7DQhyTJufSW7Px0f9dJ9E5ffAg4uqp+tatsUsuSvBN4BPhbYPM6LlX1QGehOmKhD0mS68av27JpX5JvVNVhXWWTWpbkLcDvAg/yk7+Sq6q8SbSetKcmOaqqbgBI8gLgqf3XHu8ultS8s4BnVNUPuw7SNQt9eE4DLkqyqcTXAqf113T5/e5iSc27DVjXdYgdgVMuQ9a/uChV9WDXWaRRkORy4OeBa/iPc+gjd9qiI/QhSfKfgN8D9quq45McCvxCVV3YcTSpdSv6j5HnCH1IknwO+ATwnqpakGQmcLNvhkpTL8luwCH9zTuran2XebriWi7Ds29VXUr/woaqepwRvfO4NJ2SvIze7R4vAD4C/HOSl3QaqiNOuQzPvyWZTf+0qSQvpHcOuqSp9YfAL1XVnQBJDgGWAc/rNFUHLPThOQu4Anh6kuuAOcCvdRtJGgm7bipzgKr65yS7dhmoK86hD1F/3vyZ9K4QHdl5PGk6JbmI3l/Gn+rvOgWYWVWndpeqGxb6FOnfveh/VtUrus4itSzJU4C3AEfTG0z9I/CRqnpsq5/YIAt9OyX5ReDPgP3onTr1e8An6f2P9btV9dcdxpM0Qiz07ZTkZuCdwD8Bx9Mr8/dW1XmdBpMat4UVTjerqsOnMc4OwULfTuOX7kzyrap6epeZpFGQ5KD+07f0P46dQ19XVedMf6puWejbKcldwG+O2fXhsdtOuUhTa2srnXaVqSuetrj9/gH41S1sF2ChS1NrzyRHV9WXAJK8CNiz40ydcIQuaaeW5HnARcCs/q4HgTdW1U3dpeqGhS6pCUn2ptdpI3uFtoUuaafWPw/9RGAeY6aRR/FNUefQJe3sPktv3aSvMmY99FHkCH1I+vc1vGTTjS2S/AxwclV9pNtkUtuS3FpVz+k6x47A5XOH5/Sxdymqqh8Bp3eYRxoV1yfxvgM45TJMuyRJ9f/kSTID2K3jTNIoOBp4Q5Jv05tyCVCjeKWohT48VwGXJvkzeuef/w/g891GkkbC8V0H2FE4hz4kSXYBfh04ht4I4e+Bv6gq71okTYMkPwvsvmm7qr7XYZxOWOiSdmpJFtG7a9F+wA+Ag4A7qurnOw3WAadctlOSS6tqyZZWfhvFeTxpmn0AeCHwf6vqiCQvB07uOFMnLPTt9/b+x1d1mkIaXeurak2SXZLsUlXXJPlQ16G64GmL26mq/rX/9M1V9d2xD+DNXWaTRsSDSZ5K705FlyQ5D3i840ydcA59SMavi97f93WnXKSplWRP4N/pDVBPobdI1yVVtabTYB2w0LdTkt+gNxJ/OrBqzEt7AddV1es6CSaNqP41ICdV1SVdZ5luFvp2SjIL+Bng94GlY15aW1UPdJNKal9/dcW3APsDVwBf6G+/G7ilqk7oMF4nLPQhSfJ0YHVVPZbkZcDhwCfHLgcgaXiSfBb4Eb37+R5Db2C1G/D2qrqly2xdsdCHJMktwEJ6S3heRW/E8MyqemWXuaRWJflGVR3Wfz4D+CFwYFWt7TZZdzzLZXg2VtXjwKuBP6mqdwJP6ziT1LL1m570r8j+9iiXOXge+jCtT3Iy8Hp+ck/RXTvMI7VuQZKH+88D/FR/e9PiXHt3F60bFvrwnEpvQa7frapvJ5kP/FXHmaRmVdWMrjPsaJxDl6RGOELfTq7lImlH4Qh9OyV5WlX9a5KDJnq9vwSAJE05C12SGuGUy5AkWcsTp1weAlYC76qqu6Y/laRRYqEPzx8B9wCfpnfa1EnAfwbuBC4CXtZZMkkjwSmXIUlyQ1UdNW7fl6vqhUm+VlULusomaTR4pejwbEyyZNMi+0mWjHnNfzUlTTlH6EOS5OeA84Bf6O/6J+CdwPeB51XVl7rKJmk0WOiS1AinXIYkydwklyf5QZL7kvyfJHO7ziVpdFjow/MJekvm7kdvwf2/6e+TpGnhlMuQJLmlqp472T5JmiqO0Ifnh0lel2RG//E6YORuUiupO47QhyTJgcCf0jvLpYDrgbdV1fc6DSZpZFjoUyjJO6rqT7rOIWk0WOhTKMn3qurArnNIGg3OoU+tdB1A0uiw0KeWf/5ImjautridtrBsLvRvWjvNcSSNMOfQJakRTrlIUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRvx/TdbQLTBB1/AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_scores, index=[\"Accuracy\"])\n",
    "model_compare.T.plot(kind=\"bar\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have got a baseline model....\n",
    "\n",
    "Let's look at the following:\n",
    "* Hyperparameter tuning\n",
    "* Feature importance\n",
    "* confusion matrix\n",
    "* cross validation\n",
    "* Precision\n",
    "* Recall\n",
    "* F1 score\n",
    "* classification matrix\n",
    "* ROC curve\n",
    "* Area Under Curve (AUC)\n",
    "\n",
    "## Hyperparameter tuning (by hand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's tune KNN\n",
    "\n",
    "# train_scores = []\n",
    "test_scores = []\n",
    "\n",
    "# Create a list of different values for n_neighbors\n",
    "neighbors = range(1, 21, 1)\n",
    "\n",
    "# Setup KNN instance\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# Loop through different neighbors\n",
    "for i in neighbors:\n",
    "    # set the parameter\n",
    "    knn.set_params(n_neighbors = i)\n",
    "    # fit the test data\n",
    "    knn.fit(X_train, y_train)\n",
    "    # update the training score\n",
    "    #train_scores.append(knn.score(X_train, y_train))\n",
    "    # update the test score\n",
    "    test_scores.append(knn.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6229508196721312,\n",
       " 0.639344262295082,\n",
       " 0.6557377049180327,\n",
       " 0.6721311475409836,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.7049180327868853,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.7540983606557377,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.6557377049180327]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max KNN score on test data: 75.41%\n"
     ]
    },
    {
     "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": [
    "plt.plot(neighbors, test_scores, label=\"Test scores\")\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xticks(np.arange(1,21,1))\n",
    "plt.legend()\n",
    "\n",
    "print(f\"Max KNN score on test data: {max(test_scores)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning with RandomizedSearchCV\n",
    "\n",
    "we're going to tune:\n",
    "* Logistic Regression\n",
    "* Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a hyperparameter grid for logistic regression\n",
    "log_reg_grid = {\"C\": np.logspace(-4, 4, 20),\n",
    "                \"solver\": [\"liblinear\"]}\n",
    "\n",
    "# Create a hyperparameter grid for random forest\n",
    "rf_grid = {\"n_estimators\": np.arange(10, 1000, 50),\n",
    "           \"max_depth\": [None, 3, 5, 10],\n",
    "           \"min_samples_split\": np.arange(2, 20, 2),\n",
    "           \"min_samples_leaf\": np.arange(1, 20, 2)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'hv got hyperparameter grids for both the models, let's tune them using RandomizedSearchCV..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score=nan,\n",
       "                   estimator=LogisticRegression(C=1.0, class_weight=None,\n",
       "                                                dual=False, fit_intercept=True,\n",
       "                                                intercept_scaling=1,\n",
       "                                                l1_ratio=None, max_iter=100,\n",
       "                                                multi_class='auto', n_jobs=None,\n",
       "                                                penalty='l2', random_state=None,\n",
       "                                                solver='lbfgs', tol=0.0001,\n",
       "                                                verbose=0, warm_start=False),\n",
       "                   iid='deprecated', n_iter=20, n_jobs=None,\n",
       "                   param_distributions={'C':...\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        'solver': ['liblinear']},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=True)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune Logistic Regression\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# setup random hyperparameter search for Logistic Regression\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
    "                                param_distributions=log_reg_grid,\n",
    "                                cv = 5,\n",
    "                                n_iter = 20,\n",
    "                                verbose=True)\n",
    "\n",
    "# Fit random hyperparameter search for Logistic Regression\n",
    "rs_log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'hv tuned LogisticRegression(), let's do the same for RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:   45.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score=nan,\n",
       "                   estimator=RandomForestClassifier(bootstrap=True,\n",
       "                                                    ccp_alpha=0.0,\n",
       "                                                    class_weight=None,\n",
       "                                                    criterion='gini',\n",
       "                                                    max_depth=None,\n",
       "                                                    max_features='auto',\n",
       "                                                    max_leaf_nodes=None,\n",
       "                                                    max_samples=None,\n",
       "                                                    min_impurity_decrease=0.0,\n",
       "                                                    min_impurity_split=None,\n",
       "                                                    min_samples_leaf=1,\n",
       "                                                    min_samples_split=2,\n",
       "                                                    min_weight_fraction_leaf=0.0,\n",
       "                                                    n_estimators=100,\n",
       "                                                    n_jobs...\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        'n_estimators': array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=True)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# setup random hyperparameter search for Random Forest\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(),\n",
    "                           param_distributions=rf_grid,\n",
    "                           cv = 5,\n",
    "                           n_iter = 20,\n",
    "                           verbose = True)\n",
    "\n",
    "# Fit hyperparameter search for RandomForest\n",
    "rs_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the best parameters\n",
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning using GridSearchCV\n",
    "\n",
    "Since our LogisticRegression model provided the best scores so far, we will try to fine tune it using GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
       "                                          fit_intercept=True,\n",
       "                                          intercept_scaling=1, l1_ratio=None,\n",
       "                                          max_iter=100, multi_class='auto',\n",
       "                                          n_jobs=None, penalty='l2',\n",
       "                                          random_state=None, solver='lbfgs',\n",
       "                                          tol=0.0001, verbose=0,\n",
       "                                          warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'C': array([1.00000000e-04, 1.8...\n",
       "       2.04335972e-01, 3.85662042e-01, 7.27895384e-01, 1.37382380e+00,\n",
       "       2.59294380e+00, 4.89390092e+00, 9.23670857e+00, 1.74332882e+01,\n",
       "       3.29034456e+01, 6.21016942e+01, 1.17210230e+02, 2.21221629e+02,\n",
       "       4.17531894e+02, 7.88046282e+02, 1.48735211e+03, 2.80721620e+03,\n",
       "       5.29831691e+03, 1.00000000e+04]),\n",
       "                         'solver': ['liblinear']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=True)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Different hyperparameters for our LogisticRegression model\n",
    "log_reg_grid = {\"C\": np.logspace(-4, 4, 30),\n",
    "                \"solver\": [\"liblinear\"]}\n",
    "\n",
    "# Setup grid hyperparameter search for LogisticRegression\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
    "                          param_grid=log_reg_grid,\n",
    "                          cv=5,\n",
    "                          verbose=True)\n",
    "\n",
    "# Fit grid hyperparameter search to LogisticRegression\n",
    "gs_log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating our tuned machine learning classifier, beyond accuracy\n",
    "\n",
    "* Precision\n",
    "* Recall\n",
    "* F1_score\n",
    "* classification matrix\n",
    "* ROC Curve\n",
    "* AUC Curve\n",
    "\n",
    "... and it would be great if cross validation is used wherever possible.\n",
    "\n",
    "To make comparisions and evaluate our model, first we need to make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds = gs_log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curve and calculate AUC metric\n",
    "plot_roc_curve(gs_log_reg, X_test, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25  4]\n",
      " [ 3 29]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "print(confusion_matrix(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5)\n",
    "\n",
    "def plot_conf_mat(y_test, y_preds):\n",
    "    \"\"\"\n",
    "    Plot a nice looking confusion matrix using Seaborn's heatmap()\n",
    "    \"\"\"\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(3,3))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                     annot=True)\n",
    "    \n",
    "    plt.xlabel(\"True label\")\n",
    "    plt.ylabel(\"Predicted label\")\n",
    "    \n",
    "plot_conf_mat(y_test, y_preds);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'hv got a ROC curve and Confusion matrix, let's get a classification report aswell as cross-validated precision, recall, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.86      0.88        29\n",
      "           1       0.88      0.91      0.89        32\n",
      "\n",
      "    accuracy                           0.89        61\n",
      "   macro avg       0.89      0.88      0.88        61\n",
      "weighted avg       0.89      0.89      0.89        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate evaluation metrics using cross-validation score\n",
    "\n",
    "we're going to calculate accuracy, precision, recall, f1_score of our model using cross-validation and in doing so we will be using `crossval_score`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check best parameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new classifier with best parameters\n",
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "                         solver=\"liblinear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8479781420765027"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validated Accuracy\n",
    "cv_acc = np.mean(cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"accuracy\"))\n",
    "cv_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8215873015873015"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validated Precision\n",
    "cv_pre = np.mean(cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"precision\"))\n",
    "cv_pre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9272727272727274"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validated Recall\n",
    "cv_recall = np.mean(cross_val_score(clf,\n",
    "                         X,\n",
    "                         y,\n",
    "                         cv=5,\n",
    "                         scoring=\"recall\"))\n",
    "cv_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8705403543192143"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validated f1_score\n",
    "cv_f1score = np.mean(cross_val_score(clf,\n",
    "                                     X,\n",
    "                                     y,\n",
    "                                     cv=5,\n",
    "                                     scoring=\"f1\"))\n",
    "cv_f1score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize cross-validated metrics\n",
    "cv_metrics = pd.DataFrame({\"Accuracy\": cv_acc,\n",
    "                           \"Precision\": cv_pre,\n",
    "                           \"Recall\": cv_recall,\n",
    "                           \"F1\": cv_f1score},\n",
    "                           index=[0])\n",
    "\n",
    "cv_metrics.T.plot(kind=\"bar\", legend=False)\n",
    "plt.title(\"Cross Validated Metrics\")\n",
    "plt.xticks(rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "\n",
    "It means which features contributed more to the model and how they contribute ?\n",
    "\n",
    "Finding feature importance is different for each machine learning model. One way to find the feature importance is to search something like `<MODEL NAME> feature importance`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit an instance of LogisticRegresssion\n",
    "clf = LogisticRegression(C=0.20433597178569418,\n",
    "                         solver=\"liblinear\")\n",
    "\n",
    "clf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00316728, -0.86044619,  0.6606706 , -0.01156993, -0.00166374,\n",
       "         0.04386123,  0.31275813,  0.02459361, -0.60413061, -0.56862832,\n",
       "         0.45051624, -0.63609879, -0.67663383]])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check coef_\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.003167276981166473,\n",
       " 'sex': -0.8604461876496617,\n",
       " 'cp': 0.6606705956924419,\n",
       " 'trestbps': -0.011569931456373254,\n",
       " 'chol': -0.0016637425660326452,\n",
       " 'fbs': 0.04386123481563001,\n",
       " 'restecg': 0.3127581278180605,\n",
       " 'thalach': 0.02459361121787892,\n",
       " 'exang': -0.6041306062021752,\n",
       " 'oldpeak': -0.5686283181242949,\n",
       " 'slope': 0.4505162370067001,\n",
       " 'ca': -0.6360987949046014,\n",
       " 'thal': -0.6766338344936489}"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Match co-ef's of features to columns\n",
    "features_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
    "features_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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btmyJo0ePIjk5GZaWlm9ch6SkNCiV4o3Xt7IyQ2Ji6huvX1RyxinqGy9n/PftmL3tOCVpX0panJK0L0WJo6eneOWPalnuOeTfa0hISFArv3nzptryF50+fRo7duyQlGdmZsLAwEBjQiEiIt2QJTnY2tqiatWqOHDggFr5wYMHUb16dVSuXFnymjNnzmDq1Kmqew8AoFQq8fvvv6NBgwYwNDSUo2pERFQEsj3nMGzYMEyaNAlly5ZFq1atcOjQIezfvx9BQUEAgOTkZCQkJKBWrVowNTWFt7c3QkNDMXz4cPj7+8PExARbtmzBv//+i82bN8tVLSIiKgLZhs/w9vbGzJkz8ddff2HYsGE4ceIE5s2bh06dOgEAoqKi0KtXL1y8eBEAULZsWYSGhsLZ2Rlz5syBv78/nj17hg0bNsDFxUWuahERURHIduUAAF9++SW+/PJLjcu8vb3h7e2tVlalShUsWrRIzioQEakxMy8N41KaT3UFdfLIyMxB6tPnxVmtd56syYGI6F1jXMoAXQJ2F+o1ewO7ovj7F73bOGQ3ERFJMDkQEZEEkwMREUkwORARkQSTAxERSTA5EBGRBJMDERFJMDkQEZEEkwMREUkwORARkQSTAxERSTA5EBGRBJMDERFJMDkQEZEEkwMREUkwORARkQQn+yGSAWcbo5KGyYFIBpxtjEoaNisREZEEkwMREUkwORARkQSTAxERSTA5EBGRBJMDERFJMDkQEZEEkwMREUkwORARkQSTAxERSTA5EBGRBJMDERFJMDkQEZEEkwMREUkwORARkQSTAxERSTA5EBGRBJMDERFJMDkQEZEEkwMREUkwORARkQSTAxERSTA5EBGRBJMDERFJMDkQEZEEkwMREUkwORARkQSTAxERSTA5EBGRBJMDERFJMDkQEZEEkwMREUkwORARkYSBnBvbt28fVq5ciVu3bqFKlSoYPHgwunXrVuD66enpWLhwIQ4ePIhnz57B3d0dU6ZMQfXq1eWsFn3AzMxLw7hUwR9zKyszSVlGZg5Snz4vzmoRvfNkSw779+/H2LFj4evrC09PT/zxxx+YMGECjI2N0aFDB42vGT16NM6fP4/x48fDxMQEwcHB8PX1RXh4OMzMpF9aosIyLmWALgG7C/WavYFdkVpM9SF6X8iWHBYtWoSOHTti8uTJAABPT0+kpKRgyZIlGpNDXFwcoqOjsWbNGrRo0QIA4O7uDi8vL2zduhWDBg2Sq2pERFRIstxzuHXrFhISEtCuXTu18vbt2yM+Ph63bt2SvCYmJgYmJiZo1qyZqszS0hINGzbEkSNH5KgWUYljZl4aVlZmkj8AGsutrMxgZl76Ldea3keyXDnEx8cDAOzs7NTKbW1tAQDXr19HtWrVJK+xtbWFvr6+WrmNjQ32798vR7WIShw2k5GuyJIcUlPzPnqmpqZq5SYmJgCAtLQ0yWvS0tIk6+e/RtP6RfWqG5K8GUlEpJksyUEIAQBQKBQay/X0pK1X+cs00bT+65QvL000AJCVnVuo7ejpKTQmjVfJys6FkaF+gcs1be91rykozt7AroV+jZz7U9C2irI/r1LYOhdEl8espMUpzGegqJ9nfm/kPW5yHTNApuSQ37Po5V/86enpastfZGpqitu3b0vK09PTNV5RvE5SUhqUyoITzsusrMyQmCjPxbaVlVmRLvXljC/XtgAUqY1aqRTv7P68zTjv674U9jNdlM9zSfve6Gp/5Hpv9PQUBf6oBmRKDvn3GhISEmBvb68qv3nzptryl1/z999/QwihdsVx8+ZNjeuT7qQ+fa6xjVpXJzoievtk6a1ka2uLqlWr4sCBA2rlBw8eRPXq1VG5cmXJa5o3b46nT5/i6NGjqrLk5GTExcWhadOmclSLiIiKSLbnHIYNG4ZJkyahbNmyaNWqFQ4dOoT9+/cjKCgIQN6JPyEhAbVq1YKpqSkaNmwIDw8PjBkzBmPHjkW5cuWwbNkymJmZ4auvvpKrWkREVASyJQdvb29kZWXhxx9/xI4dO1CtWjXMmzcPnTp1AgBERUVh0qRJ2LRpExo1agQACA4Oxty5czF//nwolUq4ublh8eLFKFu2rFzVIiKiIpB1bKUvv/wSX375pcZl3t7e8Pb2VisrW7Ys5syZgzlz5shZDSIi0hJHZSUiIgkmByIikmByICIiCSYHIiKSYHIgIiIJJgciIpJgciAiIgkmByIikmByICIiCSYHIiKSYHIgIiIJJgciIpJgciAiIgkmByIikmByICIiCSYHIiKSYHIgIiIJJgciIpJgciAiIgkmByIikmByICIiCSYHIiKSYHIgIiIJJgciIpJgciAiIgkmByIikmByICIiCSYHIiKSYHIgIiIJJgciIpJgciAiIgkmByIikmByICIiCSYHIiKSYHIgIiIJJgciIpJgciAiIgkmByIikmByICIiCSYHIiKSYHIgIiIJJgciIpJgciAiIgkmByIikmByICIiCSYHIiKSYHIgIiIJJgciIpJgciAiIgkmByIikmByICIiCSYHIiKSMJBrQ+np6Vi4cCEOHjyIZ8+ewd3dHVOmTEH16tVf+bp+/frh77//lpT//PPPcHJykqt6RERUCLIlh9GjR+P8+fMYP348TExMEBwcDF9fX4SHh8PMzKzA112+fBm+vr749NNP1cpr1qwpV9WIiKiQZEkOcXFxiI6Oxpo1a9CiRQsAgLu7O7y8vLB161YMGjRI4+sePHiAx48fw9PTE66urnJUhYiIZCDLPYeYmBiYmJigWbNmqjJLS0s0bNgQR44cKfB1ly9fBgDY29vLUQ0iIpKJLMkhPj4etra20NfXVyu3sbHB9evXC3zd5cuXYWRkhKVLl6JRo0ZwcnKCn5/fK19DRETF77XNSjk5OQgPDy9weYUKFZCWlgZTU1PJMhMTE6SlpRX42suXLyMrKwvGxsYIDg7GvXv3sHz5cvTu3Ru7d++GlZXVG+4GERHJ6bXJITMzE+PHjy9wuYeHBwwNDQtcrqdX8MXJkCFD0KtXLzRu3FhVVr9+fXTs2BFhYWEYPXr066qnUr68NDm9jpVVwTfKdUHO+LraF8Z5N2PoMs7bjv8+fm/edh2KEuO1ycHExARXrlx55TojR47E7du3JeXp6ekaryjy1a5dW1JWrVo11KxZU3U/4k0lJaVBqRRvvL6VlRkSE1MLFeNV2yoKOePLtS3Gef9iFEeconymCxu/pH1vdLU/cr03enqKV/6oluWeg52dHW7dugUh1E/ON2/ehJ2dncbXCCGwa9cuxMXFSZZlZGTAwsJCjqoREVERyJIcmjdvjqdPn+Lo0aOqsuTkZMTFxaFp06YaX6NQKLBu3TrMnj0bSqVSVX7x4kUkJCTAw8NDjqoREVERyJIcGjZsCA8PD4wZMwY7duxAREQE+vXrBzMzM3z11Veq9a5evYp//vlH9f8RI0bg4sWLGDt2LGJiYrBjxw4MHjwYderUQdeuXeWoGhERFYFsT0gHBwdj7ty5mD9/PpRKJdzc3LB48WKULVtWtc7MmTNx584dHDp0CADQrl07LF++HCEhIRg+fDiMjY3Rtm1bjBkzRtItloiIdEe25FC2bFnMmTMHc+bMKXCd0NBQSdknn3yCTz75RK5qEBGRDDgqKxERSTA5EBGRhGzNSkREhZGRmYO9gYXreJKRmVNMtaGXMTkQ0VuR+vQ5Cnr8S1cPD1LB2KxEREQSTA5ERCTBZiUikijs/QDeCyh5mByISKKg+wG8F/DhYLMSERFJMDkQEZEEm5WIiGRQ0p7bYHIgIpJBSbtPw2YlIiKSYHIgIiIJJgciIpJgciAiIgnekCYieo/o6ul1JgcioveIrnpFsVmJiIgkmByIiEiCyYGIiCSYHIiISILJgYiIJJgciIhIgsmBiIgkmByIiEiCyYGIiCSYHIiISILJgYiIJJgciIhIgsmBiIgkmByIiEiCyYGIiCSYHIiISILJgYiIJJgciIhIgsmBiIgkmByIiEiCyYGIiCSYHIiISMLgbVegJMjIzMHewK6Ffg0R0buKyUEGqU+fI7WAZVZWZkhMLGgpEdG7ic1KREQkweRAREQSTA5ERCTB5EBERBJMDkREJMHkQEREEiWmK6uenkInrykKXcQpSftS0uKUpH0paXFK0r4UNs7r1lUIIYS2FSIiopKFzUpERCTB5EBERBJMDkREJMHkQEREEkwOREQkweRAREQSTA5ERCTB5EBERBJMDkREJMHkQEREEkwOREQkweQgo/v3779yeXR0tI5qUjySk5Nx+fJlKJVKrbbz4MGDQv0Vh3/++QeRkZFIS0srlu3Tm7tx40aByzIyMjBv3jzdVYZUPpiB99LS0rB582bExMQgMTERS5cuxZEjR1C3bl00adJElhgeHh749ttv8emnn0pi//DDD9i1axcuXbokS6yMjAz8+uuvOHnyJFJSUlC+fHk0adIEnTt3hoGB9oPtpqWlYfbs2ahXrx569+6N/fv3Y9y4ccjNzUWNGjWwbt06VKxYsUjbdnBwgELx5qNHanvMHj58iHHjxqFRo0YYOnQowsLC8MMPP0AIAUtLS2zatAm1atXSKoaupaSk4OzZs0hNTdWYrLt06aJ1jFe9TwqFAmXKlIGNjQ18fX3RrVu3Isfx9PTExo0bUaNGDbXyP//8EzNmzMC9e/fwzz//FHn7Lzp9+jRiYmLw8OFDfPPNN7h27Rrq1q2L8uXLy7J9IO/zdvr0aWRnZyP/9KpUKvH8+XPExcVh4cKFRdrutGnT3nhdhUKBWbNmFSlOvhIzZPerPHjwAH369EFiYiKcnZ1x48YNZGVl4cyZMwgKCsKaNWtkSRBNmjRBQEAADh06hG+//Rbm5uaIjo7G9OnTkZKSgtGjR8uwN8CtW7fQt29f3Lt3D7a2tihfvjzOnDmDPXv24Mcff8TGjRthYWGhVYzAwEAcOHAATZs2BQAsXLgQDg4OGDJkCBYvXowFCxYgMDCwSNuePXt2oZKDthYsWIBr165h4MCBUCqVCAkJQdOmTTFu3Dh8//33WLhwIUJCQmSJFRsbW+AyhUIBExMTVKtWDaampkWOERMTg+HDhyMjIwOaftspFApZksPEiROxaNEi2Nraon379rCyssKjR48QGRmJy5cv47PPPkNSUhKmTJkCQ0NDyY+iN2VjYwMfHx9s2LABH3/8MZKTkzF79myEh4fD3t4eixYt0npfsrKyMHbsWERERMDAwAA5OTno1asX1q1bh6tXr2LLli2wsbHROs7BgwcREBCA7Oxs1WdcCKH698sJsDBiYmLeeF1Zvl/iAzBq1CjRsWNH8ejRI5GdnS3s7e3FhQsXRG5urhg4cKDo3bu3bLH27dsnGjVqJFq2bCn8/f2Fg4OD+N///icSEhJkizFo0CDh5eUlrly5olZ+6dIl0aZNGzF+/HitY3h6eopt27YJIYQ4f/68sLe3FwcOHBBCCHHgwAHRqFEjrWPoSpMmTcSePXuEEELExsYKe3t7ERUVJYQQ4vDhw8LNzU22WPb29sLBwUH19/L/HRwcRN26dcWkSZNETk5OkWJ07dpVfPbZZ+Lo0aMiISFB3L59W/Inh1GjRomhQ4cKpVIpWTZmzBgREBAghBBi4cKFwtvbu8hxMjIyxMCBA0Xjxo3FqlWrhIeHh2jQoIFYv369yM3NLfJ2XzRv3jzRoEEDERkZKTIyMlTngFu3bokOHToIf39/WeJ0795d9OzZU1y8eFFMnjxZTJgwQVy9elWsW7dOODo6ir/++kuWOLrwQSQHNzc38dtvvwkhhMjJyVF9MIQQIioqStaTgxBCREZGijp16gh7e3vRvXt3kZqaKuv269evL/bv369x2b59+0TDhg21juHk5CRiY2OFEEIEBweLevXqqfbj77//Fq6urlrHyHf16lUxatQo0aRJE+Ho6Cg8PT3F6NGjxdWrV2XZvrOzs2pfAgMDhZOTk8jIyBBCCBETEyMaNGggSxwh8t57FxcXMWPGDHHixAkRHx8vYmNjxZw5c4Sjo6PYsGGDCAsLEx4eHiI4OLhIMZycnFTJrTi5urqKI0eOaFz2559/qj4DcnwesrOzxciRI4WDg4Po06ePuH//vlbbe1nz5rlPh9IAACAASURBVM1FWFiYEEJ6DggPDxdNmzaVJY6zs7OIiIgQQgixe/du0bVrV9WyefPmiT59+sgS51UyMzNFTEyM1tv5IJqVcnNzUapUqQKXCZluu6SmpmLBggX4+eefUa9ePXTu3BkrVqzAp59+imnTpuGTTz6RJY6ZmRlycnI0LjM0NIShoaHWMapUqYIrV67A3d0df/zxB1xdXVVNIdHR0ahatarWMQDgypUr+Oqrr1C6dGl4eXmhfPnySExMxOHDh3H48GH89NNPsLe31ypG9erVERsbCxcXF/z+++/w8PBQfR727NmD6tWry7AneVavXg0fHx8EBASoyuzs7ODu7g4TExMcPHgQmzdvhkKhwIYNGzBs2LBCx6hUqRIyMjJkq3NBTExMEB8fD09PT8mya9euwdjYGEBek01B36+CnDp1SlLm4+OD27dv4/Llyzh37pzafYAGDRoUsvbqUlJSYGtrq3FZuXLlZOuYoKenh7JlywIAbG1tER8fD6VSCT09PXh6emLnzp2yxLl79y5mzpyJEydOIDs7W1WuVCpV5zNt79V9EMnB3d0dq1evRtOmTVUnzvw2ue3bt2v9wcvXoUMHpKamwt/fHwMHDoSenh46duyI6dOnY8SIEfDy8kJwcLDWcYYOHYoFCxbA1tYWTk5OqvKEhAQsWbIEQ4cO1TrGl19+iblz52Lz5s2Ij49XtfuOGDECf/zxR6Fujr3KwoULUaNGDWzatAllypRRlT979gz9+vXD4sWLsXLlSq1i+Pn5YcKECVi3bh2ePXuG6dOnAwB69OiBixcvFvneiSaXLl3CiBEjNC5zc3PDmjVrAAC1a9d+be+2gvj5+WHJkiWoW7cuqlWrVuS6vk7nzp2xePFiGBkZoV27drC0tERSUhIiIyOxZMkSeHt7Iy0tDWFhYWqfwzfx9ddfa2wXzz+xjRgxAgqFQtVer+2JrlatWggPD0fz5s0ly44cOYKaNWtqtf18dnZ2OH36NBo2bIgaNWogKysLly9fRt26dZGeno6srCxZ4sydOxdxcXH4/PPPcerUKZQuXRqurq6IiYnBv//+i2XLlmkd44NIDmPHjsXXX3+Ndu3aoXHjxlAoFNi4cSOuXr2Ka9euYfPmzbLEqVSpEjZu3KjW88Xa2hohISHYuXMn5syZI0ucAwcO4NmzZ+jZsyeqVasGa2trPHnyBDdu3EBubi42bdqETZs2qdb//fffCx2jb9++KF++PGJjYzF8+HB06tQJAGBkZITvvvsOX3zxhSz7EhcXhwULFqglBgAoU6YMBg4ciClTpmgdo3PnzqhUqRJOnjwJDw8PuLq6AgAaNWqE0aNHq266y6FSpUo4fPgwmjVrJll2+PBhfPTRRwCAxMRElCtX7o23265dO7WT6a1bt9CuXTtUqFBBcuyAor3nLwsICMDjx48xa9YstZ4venp66Nq1K8aNG4eIiAicPXsWP/74Y6G2/eLnUxeGDBmCESNGICUlBa1bt4ZCocCpU6ewZ88ebN68GfPnz5clTq9evTBr1iw8e/YM/v7+aNy4MaZMmYIePXogNDQU9erVkyXO8ePHMXr0aPTp0wdhYWE4dOgQxo0bhzFjxmDAgAGIjIyEl5eXVjE+mK6s169fR3BwMI4fP44nT57A1NQUHh4eGDp0KBwcHGSJkX/5WJCHDx/C2tpa6ziTJk0qVGy5klJxaNq0KWbNmqWxye2PP/7A+PHjNTZBFJVSqURycjLMzc1hZGQk23bz7dixA9OmTUPHjh3Rtm1bWFpaIjk5GZGRkfjtt98wbdo0NG/eHIMGDUKDBg0we/bsN9ruxIkTC9UDRc73PCEhAcePH8fjx49hbW0NNzc31RVLSkoKjI2NC92s9Dbs3bsXgYGBaldslpaW8Pf3R8+ePWWLExoaijt37mDixIlISEiAn58fEhISUKVKFSxfvlzrZlIAcHR0xPr169GwYUMcPXoUY8aMwbFjxwDk/TCYN28eDh06pFWMD+LKAci73JOz+UATPT09KJVK/Pbbb6rnKaZOnYozZ87A0dFRtr70c+bMwZYtWxAXF6dq7omNjcWUKVPwzTffwNvbW5Y4//zzD1avXo24uDg8ffoU5cuXR+PGjTFkyBBZuv0BgKurK9asWQNPT0+1E0xGRgbWrFmD+vXryxInOjoaK1aswMWLF5Gbmwt9fX24uLhg1KhR8PDwkCUGkNdUpaenh+XLl2P//v2q8qpVq2LOnDno1q0bwsPDUbVqVYwdO/aNtzt37txXLhcvdJeUm42NTYHvd377urbOnz+P2NjYAp8N2Lp1q9YxunTpgi5duiA+Ph5PnjyBmZkZatas+cofdEWhr6+Phw8fAsg7dt999x0mTZqEoUOHypIYgLwWiUePHgHIu7eRkpKCxMREWFlZoVy5ckhKStI6xgeRHHTR9xzIuyE9cOBAnDt3DpUrV8bdu3eRnp6OvXv3YtasWQgLC0PdunW1igFA9RBXjx49VGUVK1aEu7s7pk2bBn19fXTt2lWrGEePHsWgQYNQvnx5tGnTBuXLl0dSUhIOHz6M33//HVu2bJHliisgIABffPEFvLy80KZNG1SoUAGPHj3CoUOHkJ6eLkuT3549ezB+/Hi4uLhg5MiRsLS0RGJiIn7//XcMGDAAISEhGtuii+rzzz/H559/joSEBCQnJ+Ojjz5CpUqVVMs//fTTIj8TkG/r1q2IjY1V/TiIi4vDlClTMGTIEHTv3l2rbefLzMzEqlWrEBUVhWfPnmnsuCFH89XWrVsxa9YsjdvX09OT9b25du0a4uLiVA+OlilTBlWqVJFt+5q+m5UqVUKjRo1k+24CeQ8OLl26FJUrV4aLiwsqVqyI9evXY8SIEdi1a5eq+VIrWvd3eg+82Nfc3t5e9Sdn33MhhJg6dapo3ry5uHTpktrzFKmpqaJnz57Cz89Plv1p165dgd0gly1bJjp37qx1DG9vbzFgwACRmZmpVp6RkSH69u0r+vbtq3WMfJcvXxYjRowQTZs2FfXq1RNNmjQRI0eOlDzHUVSdOnUSEyZM0LhsxIgRat0N5bBlyxYxevRo1f9PnDgh2rZtK3799VdZth8aGiocHBzEtGnTVGUJCQli0qRJom7dumLXrl2yxJk2bZpwcHAQvr6+Yvz48WLixImSPzl07NhRDBo0SDx58kTMmzdPTJs2TTx//lwcOHBAuLq6ir1792od4/nz52LEiBGSc0CdOnXEtGnTND7LURS6+G4KIURSUpLo3r278PX1FULkdZutU6eO6ny2ZcsWrWN8EMlBF33PhRCiUaNGqhPAy32pIyIihIeHhyz74+zsLI4ePapx2dGjR4Wzs7MsMaKjozUui4qKkvU5h+Lm5ORU4MNHf/31l3BycpItli5O3Lo6AXl4eIhVq1bJsq1XcXR0VD23ER4eLjp16qRatmLFCtGjRw+tY8yYMUO4urqKsLAw8fDhQ5GTkyMePHggNm7cKFxdXcWyZcu0jiGEbr6bL3rxeZDY2FixZs0acfz4cVm2/UE0K+mi7zmQ105uaWmpcVmpUqVk68ZWuXJlHD9+XOOQHydPnpTlktLW1hb//vsvWrRoIVl2586dIo+rpElxjxNUt25dxMbGauxB9N9//8k6rlJoaCiGDx+u9hmqVq0aZs+ejcqVK2Pt2rVaNyvcv3+/wO7XL3aX1VZWVhacnZ1l2darGBoaqp6ZsLW1xc2bN5GdnQ1DQ0O4ublh/fr1Wsc4cOAAxowZg969e6vKrK2t4evrC6VSifXr12P48OFax9HFd/NFL27P3d0d7u7usm37g0gOuuh7DuT1INi6dStatmwpWfbbb7/Jcr8ByOsut3DhQuTm5uKTTz6BpaUlHj9+jEOHDmHdunUYNWqU1jFmzJiB4cOHQ6FQoHPnzrCyssKTJ08QFRWFJUuWYNq0aWojphb1Q19c4wS92MOpa9eumD17Np4/f4727dujQoUKSElJwZ9//omNGzdi5syZRaq7Jro4cevqBNS8eXMcOXIEjRs3lmV7BXFwcEBUVBQaNWoEOzs7KJVKnD17Fu7u7rKNypudnV3gMyE1a9ZEamqqLHF08d0E8jogbN++HceOHcPTp08l3x2FQoF169ZpFeODSA7F1ff8ZaNGjUL//v3h7e2Nli1bQqFQYP/+/Vi5cqXqwyGHfv364cGDB1i/fj3Wrl2rKtfX14ePjw8GDhyodQwfHx8olUosWLBAbRTJ/A/huHHj1NYv6kNKCxYsgI2NDSZOnIiqVavK1nPk5YeshBDYuHGjWv/6/H0ZPXo0OnToIEtcXZy4dXUC+uyzzzB16lQ8fvwYDRo0UP26f5EcA/z17dsXo0aNQmpqKr7//nt4eXlh/Pjx6NixI3bv3g03NzetY3Tr1g1r1qxB48aN1fZDqVRi69at6Ny5s9YxAN18NwFg8eLFWLVqFSpVqoQqVaoUS0+1D+I5h1f1PQ8PD8f06dOL1Pdck9jYWAQGBuLcuXNQKpVQKBSoW7cuRo0apbGJRhupqak4c+aMqlues7Nzgc1ahVXYx/yL2kPG2dkZy5Yt03i1pY1mzZrB19cXrq6uCA4ORo8ePV7ZFCZXd9YNGzZg4cKF6N+/f4EnbjlOEPPmzUNoaChyc3NVZfknoAkTJmi9fQCv7Y0mx5PL+SIjIxEfHw8/Pz88fvwYAQEBOHXqFJycnDB//ny13l5FsWzZMoSGhsLAwABt2rRRPTj6559/4s6dO+jSpYvquRc5hrsuzu8mkHdV16FDB0ydOlW2bb7sg0gOAPDLL79g+fLluHv3rqqsatWqGD58uKrv+c6dOzF//nyt3sT09HSkpaWhbNmySE5Oxq+//orU1FS0bdtW1vbAkqJ9+/YYM2YM2rdvL+t2nZ2dsWDBArRv3x516tTBtm3bdNJ+DujmxA3knYBOnz6NlJSUYjkB3blz57XryNkNtDi1adPmjddVKBSIjIwsxtpoz8XFBatXr0ajRo2KLcYHkxyAvJnMrl27BoVCgapVq0IIgefPn+PkyZNq/ZKL6uzZs/Dz80OvXr0QEBCAb7/9Ftu3b4eZmRnS09OxbNmyQn1I37akpCRs2LAB586dw8OHD2FhYYFGjRrBx8dHtpPQzz//jB9//BGrVq2SdZwgHx8fnD59GtbW1rh79y6srKwKfCJaoVDgjz/+kC02UPy/HF/l5s2bBQ4y9y7TxUQ8JcWAAQPQuHFjDBo0qNhifBDJ4cqVKxg7diyuXr2qcblCoZBlpqn+/fvj+fPnWLBgASpUqIAmTZrA29sb06dPx/Tp03Hp0iXs2LFD6zi6cP78efTr1w9KpRINGjRA+fLl8ejRI5w8eRLm5uYICwsr8glI0zhBQghZxwl6+PAhQkND8eTJE+zYsQNt2rR55cn5+++/L3SM17l79y4ePnyI2rVrQ6FQoHTp0rJsNzU1FUFBQYiNjVXrAZf/RHFSUpJszT0HDhwo8Mnl06dP4/Dhw1rHyJ+I5+DBgzA0NEROTg5+/vlnLFiwQNaJeIC8nnFnzpxBWloaLC0t4eTkpPXDr7ryYieLq1evYvbs2RgwYADc3Nw0fra0HVD0g7ghPX/+fDx58gQTJkzA4cOHYWRkhNatW+PIkSOIjo6WbRCws2fPIigoCNWqVcMff/yBzMxMVbfFTp06Yc+ePbLE0YX58+ejZs2aWL16tdpN+sTERAwcOBDff/99kXveNGjQQC05vPwhft0YVW/C2tpa1XU5JiYGI0eOlG0Mrdc5dOgQ5s+fj5s3b0KhUGDHjh1YsWIFypYti++++w76+vpabX/27NnYu3cvPD09ER8fj9KlS6N69eo4efIkkpOTtW4vz7d8+XIsW7ZMNUS8oaEhDAwMkJycDD09PVmutoG8m6sxMTFYsWIFmjVrBhcXFwB5CdvPzw9BQUEICgrSOs7KlSsREhKCzMxMVZmRkREGDRokSzfW4vZiJ4v8RL1ixQoAkHS+kOV+kCxPS7zjGjRoIHbs2CGEEOKnn35Sm/ltxIgRYuTIkbLEcXNzE3///bcQQojp06cLDw8P1ZOXERER79Xsac7OzuLQoUMal0VERAgXFxfZYm3evFnjE8W//PKLbDF0JTIyUjg4OIhhw4aJn3/+WfUgZFhYmKhXr55Yvny51jGaNm0qQkJChBBCrFu3TgwePFgIIURaWpro1q2bmD17ttYxhBDCy8tLTJgwQSiVShEUFKSaYfD8+fOiSZMmIjQ0VJY4upiIZ9u2bcLBwUHMmDFDnDx5Uty8eVPExsaKb7/9VtSpU0ds375d6xjF7fjx42p/kZGRkrLjx4+LiIgI1eRm2pB3xKl3VFZWlmpCl+rVq+Py5cuqZd7e3jhz5owscRwdHbFjxw6cOXMGBw4cQKtWraBQKJCUlIQ1a9bA0dFRlji68NFHH6kG9npZdna2bO3nYWFh+O6779Qu7V8cJ2r37t2yxNGVZcuWoXv37ggODka3bt1U5b1798awYcNk2Z+UlBTVgIS1atXChQsXAORNztO/f39ERUVpHQPIe2ajS5cuUCgUqFevHk6fPg0g73P+zTffyNZEqouJeDZu3AgfHx98++23aNCgAWxsbODu7o4ZM2agT58+CA0N1TpGcfPw8FD99e3bFxUqVFAry/8zNTWVpePDB9GsVLlyZdy+fRvu7u6oXr060tLScOfOHVSpUgWlSpVCSkqKLHHGjRuHgQMHIjw8HJaWlhgyZAiAvPkEhBCFHvP+bQoICMCMGTNgaWmpNi58XFwcAgMDMXr0aFni6OKJYl26du2a2pP4L3Jzc9N64iJA/YRpa2uLpKQkPHnyBOXKlUOlSpVke3CsTJkyquY9Gxsb3L59GxkZGTA2NkadOnVw+/ZtWeLoYiKeW7duoVWrVhqXtWrVCtu2bdM6RnGbMGEC7t27ByCv6WjGjBka75fcuHEDFSpU0DreB5EcPvnkEyxcuBAmJiZo27YtatSogSVLlmDw4MHYsGGDbL1k6tWrh4iICFy7dg0ff/yx6ubqd999hwYNGuist0pR1atXT63tMicnB8OHD4eBgQEsLS3x9OlTZGRkwMDAAIGBgbI8AKWroSB0xcLCAjdu3NB4ortx4wYsLCy0jtGkSROsWrUKderUgY2NDcqWLYtdu3ahX79+iIqKkiUGADg5OWH37t1o0qQJ7OzsoK+vj2PHjqFVq1a4fv26bPNh6GIinkqVKuHatWsaJ3b677//ZBt6vDh17NgRGzduVP1fX19fcv9KT08Pbm5u6N+/v9bxPojkMHz4cNy8eRPbt29H27ZtMWnSJAwfPhx79+6Fvr6+athjOZiamqpuqOWTa+7o4vbNN98U25wABdH1WDTFrVOnTliyZAkqVqyoShAKhQKXL1/GihUrZHkSe+TIkejTpw/GjRuHsLAwDB48GHPnzsXq1avx+PHjIo8N9rJBgwbhf//7H1JSUrBy5Up89tlnmDBhApo0aYLo6GjZPtdt27bFggULEBgYqJqg5ocffoClpSWmT5+umoVQGy++L23btlWVHzx4EMHBwbLNgVKcWrVqpbr68fHxwYwZM2Sb3lSTD6Ira76srCzVr51bt27hwoULqFevnmzd5D4kDx48kOXErasninUlMzMTw4YNw19//QUDAwPk5OTA3NwcqampqF+/PtauXauxu25hZWRkID4+XjVe1969e3Hq1Ck4OzvLNp8DkDfh07///otu3bohMzMT33//vSrOxIkTZf/FXVwT8WRmZmLgwIGIjY2FkZGRan6S7Oxs1RzzcnU1Lik+qORAb+5VTxXHxcXBz89PdYNSW7p6oliXYmJicOzYMbUpafM7KLwvdDXtbb6XJ+Jp3Lix7E9gR0VFITY2Fk+fPoW5uTk8PDzQokWL9+p90RUmB1L58ccf8ezZMwBQjUek6erg9OnTuHDhAo4fPy5b7Lf5RLGcdu3ahZYtW2ps909MTMTevXsxYMCAQm932rRpb7yuHGMDAUDPnj2xcOFCjVfWu3btwpw5c2T5DGRkZGD8+PGIiIhQG11UT08PX3zxBWbOnMmT91vwQdxzoDeTk5Oj6k2jUCjw66+/StbR09ODubk5xowZI2tsMzMzeHp6yrrNt2HSpEnYtm2bxuRw6dIlBAUFFSk5xMTEvPG6cp1Ik5KS0LVrV0ycOBG9evUCkHe1MH36dERFRck2fee8efPw559/YurUqWjXrh0sLS2RlJSEAwcOICgoCNbW1kV6SK0wx1mOIa5LGl45kEYODg7Yvn27zgare58NHjxYNTTLnTt3ChzHKSkpCdWqVcPevXt1XcUiefbsGebOnYvt27ejVatWaNmyJYKCgmBkZIRJkyZpPQ92viZNmmDo0KHw8fGRLNuwYQPWr1+P6OjoQm9X0/Ze5X141kGXeOVAGr34oGC+3NxcPH/+/L0Zi0ZXhgwZgp9//hlA3kCCTk5Okiax/Cuu/F/gxSUrKwtxcXEau2wWVpkyZTBr1ix4enpi1KhRiI6ORp06dbBp0yZZPwPFNRHPyyf7/BGTP/roI2RlZWHz5s24f/8+R0wuAJMDaZSbm4uQkBDY2NigS5cuOH78OEaOHImnT5+iadOmCAoKgrm5+duu5jvB1dUVrq6uAPKO29ChQ2UdYfZld+/excyZM3HixAlkZ2erypVKparNXq6B98LDwzFnzhyUKVMGjRo1QmRkJMaOHYsZM2bINlWsLibieXnE5NmzZ2Pbtm0wMzNDWFjYezdisi6wWYk0CgoKwtq1azF58mT07t0bXbp0QVZWFnr37o3169ejRYsWsk6vWdI8e/ZM1WU1IiIC9+7dQ+vWrWVJGiNHjkRMTAy6d++OU6dOoXTp0nB1dUVMTAz+/fdfLFu2TO2p9qL63//+h6NHj6JZs2b4/vvvUbFiRURHR2P69OlITU2Fv78/fH19tY6ji4l4StKIyTqj9ehMVCK1adNGrF27VgghxNWrV4W9vb3YuXOnEEKI3bt3i2bNmr3N6r2zrl27Jtq2bSuCgoKEEEIEBQUJe3t7YW9vL1xcXMTJkye1juHh4aEa9C40NFT0799fCJE3aJ2vr6+YNGmS1jGEyBtIUtOAdKmpqWLixInCwcFBljitW7d+4782bdoUKUb9+vVFVFSUECJv4EgHBwdx5swZIYQQf//9t6wDSZYUbFYijR4+fKh60jsqKgp6enqqaU4rVqwo24TsJU1gYCD09fXh5eWFrKwsbNmyBZ06dcKsWbMwceJEBAUFaX3jMz09Hfb29gCAGjVqIDg4GEDesyFff/015s2bp/V+AMC+fftQsWJF3L9/H8eOHcPDhw/RvXt3JCYmYubMmbLNu53/VHRx0tPTQ6lSpQAAf/75J8zNzVWdLdLS0jTOj/2hY3IgjaytrVWDFR46dAh16tRR3WQ9ffq0bO3NJU1sbCx++OEHODk54a+//kJqaip69eoFU1NTfPnllxgxYoTWMaytrVUj5tra2iIlJQWJiYmwsrJCuXLlkJSUpHUMIO9HQP4Dijk5OVAoFGjWrBkWLVqEBw8eqI3z867LHzHZ2Nj4vR8xWVeYHEijzp07Y86cOdi7dy9OnjyJ6dOnA8gb82br1q2qEWdJXXZ2tmpIiSNHjqB06dJwc3MDkHez2sBA+6+cp6cnli5disqVK8PFxQUVK1bE+vXrMWLECOzatUu28ahWr16N0NBQjB8/Hq1bt1aNSTR8+HCMHDkSQUFBRZ5B7+XZAF+nKLMBvqgkjZisK0wOpJG/vz/KlCmD2NhYBAQE4OuvvwaQN9aOn58fhg4d+pZr+G6qXbs2Dh48CDs7Oxw4cADNmzeHgYEBsrOzsXnzZtSuXVvrGPnjTS1atAgbN27E6NGjMXHiRKxfvx4AVIlcW9u2bcOIESPg6+urNrRJ/fr14e/vjyVLlhR52y/OBqhUKhEeHg4zMzO0bNkSVlZWePLkCWJiYpCcnCxL99/3fcTkt+Jt3/QgKkn++usv4eLiIhwcHISzs7M4d+6cECLvpquzs7M4evSobLHu37+v+ndsbKxYs2aNOH78uGzbd3R0FDExMUII6Qxtf//9t3BycpIlzoIFC8RXX30lnj17plaemZkpBgwYIKZOnSpLHCocXjlQgZRKJX777TfExMQgMTERU6dOxZkzZ+Do6IhatWq97eq9k5o1a4a9e/fi/PnzcHFxUQ0cN2DAADRu3FjW4/Zi85G7u7vsD3LZ2Njgzz//1PhAXVxcnGzPcuzYsQNz586VjIpqZGQEX19fjBkzBt99950ssejNMTmQRqmpqRg4cCDOnTuHypUr4+7du0hPT8fevXsxa9YshIWFqYaLJnXVqlVDtWrVkJOTg8TERFhYWKBPnz6ybV8Ige3bt+PYsWN4+vSp2mB1gHzjBPXt2xfffvstcnJy0KZNGygUCty6dQsnT57EunXrMHbsWK1j5CtoNsb79++rehmRbjE5kEbz58/H3bt3sXPnTtSqVUvVm2PJkiX43//+h8WLF2P16tVvuZbvpgsXLiAoKAgnTpxAbm4uduzYgdDQUFSrVk2WiXgWL16MVatWoVKlSqhSpUqxjVjas2dPPH78GCEhIQgLC4MQAv7+/jA0NMSAAQPQu3dvWeK0adMGCxcuhLW1tdpVyqFDh7Bo0SJZZhykwmNyII0iIiIwYcIEODg4qN2MNDU1hZ+fH6ZMmfIWa/fuOnXqFPr164ePP/4YgwYNwooVKwDkdQsNDg6GhYWF6uZ+Uf3yyy/o06cPpk6dKkeVX2nw4MHo3bs3Tp8+rRpO3cXFRbapSIG8kWyvXr2KAQMGwNjYGBYWFkhOTkZWVhaaNWuGcePGyRaL3hyTA2mUkZFRYA+OUqVKISsrS8c1ej8sXLgQTZs2RUhICHJycrB8+XIAeb2/MjIysHXrVq2TQ2pqqtpUl8XN1NS0WIdTNzc3x/bt2xEdHY24uDg8ffoUFhYWaNy4scbpY0k3oHV/fgAABCxJREFUmBxII0dHR2zduhUtW7aULPvtt994v6EAFy9exNKlSwFI51Vo3bo1fvrpJ61juLm54ezZs2jUqJHW23pXKBQKtTmS6e1jciCNRo0ahf79+8Pb2xstW7aEQqHA/v37sXLlShw+fBhr165921V8J5mYmBT4hPKDBw9gYmJSpO2eOnVK9e8OHTpg9uzZyMjIgJubm8a5jxs0aFCkOLryNma2o8LhqKxUoNjYWAQGBuLcuXNQKpVQKBSoU6cO/P39VeMskbqpU6fiyJEjCAkJgb29PerVq4dff/0VVlZW6NevH1xcXDB79uxCb9fBwUF1JaKpd1I+IQQUCoVsQ3YXl8IMj61QKBAZGVmMtSFNmBxIo1OnTsHJyQmGhobIyMhASkoKTE1Ni/zL90Px5MkT9O3bF//99x8++ugj3Lt3D7Vq1cKdO3dgbW2NrVu3Fulp3BMnTqj9Py0tTeOEO0+fPkV2djY6duxY5H14W/777z+cOHEC6enpsLCwQIMGDVCzZs23Xa0PFpMDaeTp6YmAgAB069btbVflvTJv3jx4eXkhPj4ex44dw5MnT2BqagoPDw94e3urhm3QRp06dbBt2zaNU7geO3YMgwYNwrlz57SOoytCCEybNg2//PKL2lWRQqFAt27dMHv27GLrrksF4z0H0sjAwIDTgRbB9u3b0bx5c/Ts2RM9e/aUbbsTJkzAvXv3AOSdTGfMmKHx/blx4wYqVKggW1xdWLVqFXbt2oWAgAB06dIFFSpUQGJiIvbu3YulS5eiRo0a8PPze9vV/ODoz5gxY8bbrgS9e8qUKYM5c+aompRSUlJw7949tb9KlSq97Wq+c2JiYpCZmYlmzZrJul1DQ0OcO3cOenp6uH37NipWrAhjY2Po6emp/gwMDGBnZ4dJkybJNjKrLkycOBF9+vTBN998A1NTU+jp6cHU1BRubm5QKpXYtWsXfHx83nY1PzhsViKNHBwc1P7/Pt70fBvy5z+oVKkSatWqJfkVL0fPGx8fH8yYMaPEtMe7uLggJCRE4zMNR48exTfffPNeNZOVFGxWIo02bdr02pueJPX777/D2toaubm5uHLlCq5cuaK2XI62c21nknvXVKtWDadPn9aYHE6fPg0rK6u3UCticiCN+vbt+9qbnu9jj5jipospL0uaL774AosWLUKZMmXQqVMnVKhQAY8ePUJ4eDhWrVqFwYMHv+0qfpDYrEQqL970PHHiBOrWrVvgTU8DAwOeCEkWubm5mDx5Mnbv3i1pvvzss88wd+5c6OnpvcUafpiYHEglKipKNS/w33//DScnJ0ly0NPTg7m5Ofr376/xqoKoqK5evYrY2FikpKTA3NwcHh4enDfkLWJyII1K2k1PIiocJgciIpJgQx4REUkwORARkQSTAxERSTA5EBGRBJMDERFJ/B9gtrxPVARGPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize feature importance\n",
    "feature_df = pd.DataFrame(features_dict, index=[0])\n",
    "feature_df.T.plot.bar(title=\"Feature Importance\", legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "sex            \n",
       "0        24  72\n",
       "1       114  93"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"sex\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "cp             \n",
       "0       104  39\n",
       "1         9  41\n",
       "2        18  69\n",
       "3         7  16"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"cp\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.375, 4.555555555555555, 3.8333333333333335, 2.2857142857142856)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "39/104, 41/9, 69/18, 16/7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Experimentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have'nt hit your evaluation metrics yet...ask yourself.....\n",
    "\n",
    "* Could you collect more data?\n",
    "* Could you try a better model? Like CatBoost or XGBoost?\n",
    "* Could you improve the existing the model? (other than what we have done so far)\n",
    "* If your model is good enough(You have hit the evaluation metrics), how would you export it and share it to others?"
   ]
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