Cap09-Exercicio-Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='blue'>Python Fundamentos - Capítulo 9</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Versão da Linguagem Python Usada Neste Jupyter Notebook: 3.7.6\n"
     ]
    }
   ],
   "source": [
    "# Versão da Linguagem Python\n",
    "from platform import python_version\n",
    "print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercício: Análise Exploratória de Dados com Python\n",
    "\n",
    "Neste exercício, você vai realizar uma análise exploratória em um dos mais famosos datasets para Machine Learning, o dataset iris com informações sobre 3 tipos de plantas. Esse dataset é comumente usado em problemas de Machine Learning de classificação, quando nosso objetivo é prever a classe dos dados. No caso deste dataset, prever a categoria de uma planta a partir de medidas da planta (sepal e petal).\n",
    "\n",
    "Em cada célula, você encontra a tarefa a ser realizada. Faça todo o exercício e depois compare com a solução proposta.\n",
    "\n",
    "Dataset (já disponível com o Scikit-Learn): https://archive.ics.uci.edu/ml/datasets/iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mat\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "%matplotlib inline\n",
    "\n",
    "fontsize = 14\n",
    "ticklabelsize = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.18.2'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.3'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.2.1'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Carregando o dataset\n",
    "iris = load_iris()\n",
    "df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "print(len(df))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extração e Transformação de Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imprima os valores numéricos da Variável target (o que queremos prever), \n",
    "# uma de 3 possíveis categorias de plantas: setosa, versicolor ou virginica\n",
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imprima os valores numéricos da Variável target (o que queremos prever), \n",
    "# uma de 3 possíveis categorias de plantas: 0, 1 ou 2\n",
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "  species  \n",
       "0  setosa  \n",
       "1  setosa  \n",
       "2  setosa  \n",
       "3  setosa  \n",
       "4  setosa  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adicione ao dataset uma nova coluna com os nomes das espécies, pois é isso que vamos tentar prever (variável target)\n",
    "df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>species</th>\n",
       "      <th>target</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "  species  target  \n",
       "0  setosa       0  \n",
       "1  setosa       0  \n",
       "2  setosa       0  \n",
       "3  setosa       0  \n",
       "4  setosa       0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inclua no dataset uma coluna com os valores numéricos da variável target\n",
    "df['target'] = iris.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
       "       'petal width (cm)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extraia as features (atributos) do dataset e imprima \n",
    "features = df.columns[:4]\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <td>5.006</td>\n",
       "      <td>5.936</td>\n",
       "      <td>6.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <td>3.428</td>\n",
       "      <td>2.770</td>\n",
       "      <td>2.974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal length (cm)</th>\n",
       "      <td>1.462</td>\n",
       "      <td>4.260</td>\n",
       "      <td>5.552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal width (cm)</th>\n",
       "      <td>0.246</td>\n",
       "      <td>1.326</td>\n",
       "      <td>2.026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "target                 0      1      2\n",
       "sepal length (cm)  5.006  5.936  6.588\n",
       "sepal width (cm)   3.428  2.770  2.974\n",
       "petal length (cm)  1.462  4.260  5.552\n",
       "petal width (cm)   0.246  1.326  2.026"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcule a média de cada feature para as 3 classes\n",
    "df.groupby('target').mean().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploração de Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <td>5.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.6</td>\n",
       "      <td>5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <td>3.5</td>\n",
       "      <td>3</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal length (cm)</th>\n",
       "      <td>1.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal width (cm)</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        0       1       2       3       4       5       6  \\\n",
       "sepal length (cm)     5.1     4.9     4.7     4.6       5     5.4     4.6   \n",
       "sepal width (cm)      3.5       3     3.2     3.1     3.6     3.9     3.4   \n",
       "petal length (cm)     1.4     1.4     1.3     1.5     1.4     1.7     1.4   \n",
       "petal width (cm)      0.2     0.2     0.2     0.2     0.2     0.4     0.3   \n",
       "species            setosa  setosa  setosa  setosa  setosa  setosa  setosa   \n",
       "target                  0       0       0       0       0       0       0   \n",
       "\n",
       "                        7       8       9  \n",
       "sepal length (cm)       5     4.4     4.9  \n",
       "sepal width (cm)      3.4     2.9     3.1  \n",
       "petal length (cm)     1.5     1.4     1.5  \n",
       "petal width (cm)      0.2     0.2     0.1  \n",
       "species            setosa  setosa  setosa  \n",
       "target                  0       0       0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imprima uma Transposta do dataset (transforme linhas e colunas e colunas em linhas)\n",
    "df.head(10).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      " #   Column             Non-Null Count  Dtype   \n",
      "---  ------             --------------  -----   \n",
      " 0   sepal length (cm)  150 non-null    float64 \n",
      " 1   sepal width (cm)   150 non-null    float64 \n",
      " 2   petal length (cm)  150 non-null    float64 \n",
      " 3   petal width (cm)   150 non-null    float64 \n",
      " 4   species            150 non-null    category\n",
      " 5   target             150 non-null    int64   \n",
      "dtypes: category(1), float64(4), int64(1)\n",
      "memory usage: 6.2 KB\n"
     ]
    }
   ],
   "source": [
    "# Utilize a função Info do dataset para obter um resumo sobre o dataset \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "      <td>0.819232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.057333           3.758000   \n",
       "std             0.828066          0.435866           1.765298   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)      target  \n",
       "count        150.000000  150.000000  \n",
       "mean           1.199333    1.000000  \n",
       "std            0.762238    0.819232  \n",
       "min            0.100000    0.000000  \n",
       "25%            0.300000    0.000000  \n",
       "50%            1.300000    1.000000  \n",
       "75%            1.800000    2.000000  \n",
       "max            2.500000    2.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Faça um resumo estatístico do dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal length (cm)    0\n",
       "sepal width (cm)     0\n",
       "petal length (cm)    0\n",
       "petal width (cm)     0\n",
       "species              0\n",
       "target               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifique se existem valores nulos no dataset\n",
    "df.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0    10\n",
       "6.3     9\n",
       "5.1     9\n",
       "6.7     8\n",
       "5.7     8\n",
       "5.5     7\n",
       "5.8     7\n",
       "6.4     7\n",
       "6.0     6\n",
       "4.9     6\n",
       "6.1     6\n",
       "5.4     6\n",
       "5.6     6\n",
       "6.5     5\n",
       "4.8     5\n",
       "7.7     4\n",
       "6.9     4\n",
       "5.2     4\n",
       "6.2     4\n",
       "4.6     4\n",
       "7.2     3\n",
       "6.8     3\n",
       "4.4     3\n",
       "5.9     3\n",
       "6.6     2\n",
       "4.7     2\n",
       "7.6     1\n",
       "7.4     1\n",
       "4.3     1\n",
       "7.9     1\n",
       "7.3     1\n",
       "7.0     1\n",
       "4.5     1\n",
       "5.3     1\n",
       "7.1     1\n",
       "Name: sepal length (cm), dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Faça uma contagem de valores de sepal length\n",
    "df['sepal length (cm)'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crie um Histograma de sepal length\n",
    "exclude = ['Id', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)', 'target']\n",
    "df.loc[:, df.columns.difference(exclude)].hist() \n",
    "plt.figure(figsize=(15,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gráfico de Dispersão dos Atributos, colorido por marcadores da classe alvo')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aou3/SeCT0R2m0wltmp8nPJ4eVPlHJwfXYWH07zLC05DP1BHasDqtI44TgYfd/fTBacyssm3+YF1Ubt85hAu40eqq2h/W3IyhP+4j7QsPla3P5cDlZjaddV1a/5TQhGOY6A7vcYQfv+4qRd5BuHAYzUhPwyrVWt/7y77nCBcA5ep5FwbCtppjZlZxUT2XEOcKAHd/EnhH2d3d/wCuMLOdonH1LqtEOI43KJ4a0+1aZXi147+e7V/veWF94x1Uq34Hj9ny4+XZivmXjx80ln1rfRxHaEZ4nIdOOYjetai2v1XGUk/91zP/xYS745XbblOG3mwd67mmMt5lwD9Z905ipUWwXsdGvfFvR2ii+VZ3X/v0JHqiMJoljLydqzkR+IG7r+0d0ap0CjPaedPdbwVujc6Xrwe+QUistiZsU4CPUr07+MobAJXxjfb7UssvgV9GT7COIyTaV5WNH8v5Q8aZnpBILd8iHITnbuB82hl6twqiF+PWw12EH6l3j1DmTuAgK/tL8GY2l3An5M4RpruH8ENU+YLjWyq+/45wp8Td/d4qn2qJ2oiiH7ALWffy6Kjc/Ql3/w/CSXKXitGV63ACoe31o2XrsAvweLV1GEvsNeJoY/iLtJV1/hjhB/nEiuFvi/4dqa7uJtTx4BMzzGxXQtOMcncCx9jQnnT2AHasNn93X+nuvybctavcpuWOJTQd+Rjhh7b883VgZ1vXU84AY38SWE3l+u5FuDt5d1mZ56vEfWjF9yF3usvcSai3I8qWYYT6ud/d15QXdveiu99DeGE1yRiaX7p7L+Ei5NQRLqrGFE/FdDuX3YnFQu9AxzDyPlVLveeF9Y130M5mtrbuoru3+7Oufv9KOKaqHS8DDN0PmqGNcDFdfvF+MsNfxh6mzvofdf5RovJ3qp/vym3IuQbC+XIb4KUa5/wh3aDXe2yMIf7BHt3WLid6Inv0KHFDuPF0oIWXzOvVVr6syLDeqQaNdt509153/y2hs4CtouaLjxHeVdqhxjZ9ZpT4Rvt9qeX/CNcQJxH2p+sqfrPH+/whY6AnJFKVu99oZl8CPm9muxPuLHQT3kXYm9ArzG11zOoW4H/M7CvA74HXsa7HobHGtMrMvkD4ex1GaMddiuZ5U/QY+duEbiV/F8VvhN44+gkvntea96NmdiVwnpllCE86TqXi7pK732ZmvwFuMLNvEu5QZwkXw//m7sePshpm67oUbWPdH0bcAzjL3WteWJjZXwhtWR+O1udoQlOQWyqKvtZCd5O/JbSP/nfC35MZPPGeT6iDP0bl5hPuPO5FeCm/ZpeQdcZxM/C/I9W5uxfN7IvAf1v4OyzXsK7nmyvd/R8jhPDtaJ1uNrP/Ilxcf4nhj9q/TLjwucHMvkPoqecrhHcvLo3W5T8Jdyj/EE2/PaE51u+o7ZSo7EVe0QudmT1ESFROITRrfBR4t4Vuif8FLBnlx7aW5WXr2xatx3zCcTno18AnzOzjhLbYxxP2q3IvEu7Wv93MniA8VXmIsK/cA/zEzD5DSG7eHU1/TLRuryRs+8sJ74G1EF4CXQH8rWwbOOH9gNNHWJ9PEt7rusPMLiTcEd0dGHD3i+qJp4YfE7b/dRa6Nl1F6CVnFvU9tRqi3vPCBsQ76EXgWjP7HOFi8BxC+/3/juJYGm2nj1l4D++PhDvRHyG86L50rOu2gX5H6F3pYjO7jLCeH2P0J0GDRqv/euf/JeB6M/sl4e78joS77muPyw0810BouvRu4HYz+xbhmO4kNOPa2t3fV++xUcWo8UfLex74etR8K0XoOXEZoz/9+DYhmbgj+j18HNgK2MvdP1JjmpuB95jZo4Tf/NOiadYa7bxpZu8nvHNxA6HJ2OaEpr5/HkzOzewjwJXRE5T/IxyrXYTWBxe7e60EYNTfl1rcvdfMriV0r785oZe6cuN6/pAx8gnwZr0+E/dDODn8H+GxdJ7wfslthB4rsmXlLqV6b0JJQpvUhUAPoQ3n9lT0CEQdvWyVDT8VeIBwMbU0mueOZeNfEQ3riT43ArvVsa6dhBPSKsIF4MWEZGFtbzpRuVQU76NRDEsIL6B+ZJT5nxvNa/CzmvCy5MWEH4jK8kO2KeGR94NRfKsIF0Anlo0/KJrvkYRkrSeK7RtAsmLemxK68ewmXAAtIDxOP7yszO1U6X2ljjjqqvOo7Pui7TgYw3mU9Yo1wrZ8DeGHPkforvXkaDm3V5Q7APgT4eJuBeGi/WVl448gJFIvRfN6hvB0sKPGcucQjoPzRojtSkIPXEnC+0aXRfXgwKWjHC9bU72Xre8Rfhi7CUngrcD2FdO2RfvSYsLFygWEi4nK/ffYaL/rLx9H+NH9YTR9jpBQHVux7j8lvPTcF63TDcCrysq0R/P8Wp11eGu0j6yO6vOosvEjxlN2TPVXDNuS0PRxRRTnn4ADKsrcTvV9+yDKetka43lh1HhrbIfbCfvuKdG2zRGeeOxTUS5BuBB9mnC8zI++l/d2eHoU19w6ljtsX4uGVzuOjo3K7lQ27GOEu9yD23hvon211jlsjPU/6vyjcmdE26Sf8CRpz+j/547lXFOt7iv2669F23yAcL64FXhHvcfGCPUwavyEm0V3EV7sfwb4OKP02lg27RZRbEui+T7O0N/e2yk7Fgi/DVcQurxdQjj3HFm+bRjlvEl4r+O6aDvnCOetH1LW+2XZNr+FcGz1Ep6c/A+wxQjrU+81xaVUP8ceHpVdRpVe46jj/KFPYz4WVYCIbOTM7CDCHav9fOhfgxdpCjN7I+FpwXbuXu39Gqlg4Q/D9rv7YXHHIiISF71DIiIi4+UAQnMtJSMiIlI3vUMiIiLjwt3PiTsGERHZ+KjJloiIiIiIxEZNtkREREREJDZKSEREREREJDZKSEREREREJDaT8qX2bDbrm266adxhiIiIiIgIsGDBggF3z1YbNykTkk033ZTubvU6KSIiIiIyEZjZ4lrj1GRLRERERERio4RERERERERio4RERERERERio4RERERERERio4RERERERERio4RERERERERio4RERERERERio4RERERERERio4RERERERERio4RERERERERio4RERERERERio4RERERERERio4RERERERERio4RERERERERio4RERERERERik4o7ABEREZncvNANhX+AzYDMqzFLxh3SMJ5/AgpPQHJzSO+OmcUdkkwB7h6OjUI3pLbF0jvFHVIsmp6QmFkL8CtgF6APWASc6e5PVpTbGngK+EfZ4Le4+1PNiVREREQ2hPsAvvJT0H8jWAt4ERKdMOO7WGaPuMMDwEsr8OX/Dvn7wLLgeUh2wcwfYKkt4w5PJjEvLsCXvQ+Kz4ClwXN4+hXYzO9hiVlxh9dUcTXZuhjY0d13B64Fflij3Gp336Pso2RERERkI+Grvgr9twIl8F4gB6Ul+LLT8NKyuMMDwFd8BPIPAMUoxjwUn8OXvRP3QtzhySTlXsKXnQ7F+UA+2veKkP8nvvwDMUfXfE1PSNy9391vcHePBv0V2LrZcYiIiEjjeGkN9F0B5KqNhb5rmh3S8CgKT8PA34B8xZgilJZB7o9xhCVTwcBdUFwIFCtG5CH/Dzz/WBxRxWYivNT+UcJTkmrazexvZnafmX3BJmKjUxERERmutAio9YShHy880cxoqis8A5apPb74dNNCkSmm8DRQ47LWslNu34s1ITGzzwLbA5+pMnohsIW77wO8ETgAOKvGfD5uZt2Dn56enobFLCIiInVIbArUejE8C8mtmhlNdcku8IEaIw2SWzQ1HJlCUl1Aqfo4H5hy+15sCYmZnQ0cDxzu7r2V49095+6Lov8vAy4hJCXDuPv57t41+Ono6Ghk6CIiIjIKS3RAy1FAtScQjrUe3+yQhrH0DpDaheF9/BhYG2QPjiMsmQoy/waJ6Qy/FE9CcltIvSKOqGITS0JiZh8HTgLe5O4rapSZY2bp6P9ZQvJyf/OiFBERkQ1h086BzF6EC/5WoAWsDZv5v1hyTszRBTbzu9HTmgwhxiwkZmGzfoyN1JxLZAOYpbBZl0JiNpAl7HsZSHZhsy6ect1O27p3y5u0QLMu4HlgPrA6Gpxz933N7IvAC+7+PTM7Hvgi4W2fFPB74Gx3r/Z23BBdXV3e3d3dmBUQERGRMfH8w5B/MPwdkpaDMWuNO6Qh3B0G7oHik5CYB9kDiO6JijSUewEG/rT275CQeQ1mE+EV7/FnZgvcvavquGYnJM2ghEREREREZOIYKSGZnCmYiIiIiIhsFJSQiIiIiIhIbJSQiIiIiIhIbJSQiIiIiIhIbJSQiIiIiIhIbJSQiIiIiIhIbJSQiIiIiIhIbJSQiIiIiIhIbJSQiIiIiDSQewn3gbjDkIh7cVzqQ/U6fpSQiIiIiDSAl1ZRWvk5/KVX4i/tRmnxm/D+m+IOa8ry4kJKyz+Cv7RbqI8lx+O5u8c+n1IPpZXn4i/tEdXrG/G+6xsQ8dRh7h53DOOuq6vLu7u74w5DREREpij3PL70OCg8DeTLxqSw6V/BWo+NK7QpyUsr8CVvhtJyoFg2JoXNuhTLvLq++XgBX3oCFJ4Eyp+OpGHauSTa3jqOUU8uZrbA3buqjdMTEhEREZHxlrsVCs8yNBkBKOCrv457sdpU0iDeezmUVjM0GQEo4Ku+Uf+McrdD4SmGJiMAeVj9TdwLGxTnVKWERERERGScee7PQK76yNJyKKolR1Plfk/N+ig8VHci4bm7as/HV0PxmfWJbspTQiIiIiIy3qwNSNYY6WAtzYxGrGOEkWlq11XlfNqAVI2RJUD1uj6UkIiIiIiMM2s9CrAqYxKQ2g1LbtbskKY0a30LkK0yJgUth2FWra6qzecIoNr71wapHbFU1VckZBRKSERERETGmaVfAW2nEO6+D17sZsHasOlfjTGyKarlUMj+G0OTkiwkNsE6P1X3bCy9E7SfQajXwcvoDFgrNv1r4xfvFKNetkREREQawN1h4C/RC9VLILMv1nYSlpwTd2hTknsRcrfgvVeB92ItB0PrW7FE59jnlbsL7/0VlBZDZh+s7WQ99RrFSL1sKSEREREREZGGUre/IiIiIiIyISkhERERERGR2CghERERERGR2CghERERERGR2CghERERERGR2CghERERERGR2CghERERERGR2CghERERERGR2CghERERERGR2KTiDkBERERkqvDScrz3csjdAdaJtb0Fsm/CbOR7xJ5/CF/zcyg+C6mdsfZ3YqntKsr8C+/9KRSehOS2WPupWHrnRq7OhOaFZ8P2yD8MyS6s7RQss2djllVaEdXr7WAdWNvxkD10SL16qQf6foP3/w4sg7UeAy1HYJZeV8b7oe8avO+3gGGtR0DrsZhlR1nX5/Den0H+oWhd34Fl9mrIujaCuXvcMYy7rq4u7+7ujjsMERERkbW80I0vPQF8DZCLhmah5Q3Y9PNrJiWlNZfD6v8EHCgC4QLWZn4Xyx4Y5t1/E77i49EUBQbvOdv0r2OtRzVojSYuz92FL39P9C0PJAGDzk+SaD99fJdVXIgvfQuUVjOkXrOvw2b8N2aJkIguPQGKi4aWSe+OzboEswxeWoMvOwkKTw8tk9oWm3UZlmirsa5348vfTdg/8oQGUEnoOItExxnjuq4bwswWuHtXtXFqsiUiIiLSBL7qS+ArWXexSfh//63hiUm1aUrLYPW5hCSjGA3NA3l8xdm453Hvw1d8KipTiMqE//vKz4Y781OIexFfeRaD2ykoAgVY/XW8+NL4Lm/VV6C0nGH1mvsj5G4JZVZfAMUXh5fJPwh9V4Uyay6FwvzhZQrz8d5Lqy/bS9G6DrBuXUvh/z3fxIsLN3j9mkEJiYiIiEiDuQ/AwO2sSyrKDeB9V1efsP9WIFNjpv0wcC/k/kS4O15NIlwYTyX5h6C0ssbINPTfPG6Lci9A7laq12tuXb32X8e6hKGyzJXhv31XEhKL4WXo+031APL/iJKhasZ3XRtJCYmIiIhIo3me2kkDUTOuasP7R5guEcZ7P5iNMO++OoOcJLyP0ESr6shx3h4lqicjg6OjevVqicZgmcF4+muX8RrjvJ/al/OljabulZCIiIiINJgl2iG5fY2xLVj2oOqjMq+h+l2s+6QAACAASURBVJ11wvDMHpDZBzxXo0wOMq8eU6wbvfQrCYlCNUXI7jduizLLQKpWxwFZrOX14b+ZfYBqSWMGBus+cwDV+5tKReOqSO82QnQlyIzfujaSEhIRERGRJrBpn2H4BWcaErOg9bjq06RfDtk3ApW9LGWg/d1YYiaWnAutb69SJgutx2Opl41L/BsLS3RAxwcZ3tQtA5nXYulXju/yOj/N8HpNQWIGtL41KvPxKmWSYK1Y+2mhTMeZhA4Lyi/PE0A6Gldl2Yl26PgQw9c1G5LZ9O7rsUbNp4REREREpAksewA28weQ2iUakoaWN2ObXBkuomtNN+N8aH8v2LQwIDEHOj+NdXx8XZlpX4COj4bkBsBmQseHsWlfatDaTGzWfiY27fOQmBsN6IT2d2Ez/2f8l5XdD5t5CaQGn1akoOUwbJPfYInOUCa9Gzbrl5DeOyqThOzrQ5nknFAmtQ22yRWQ2Z/wNMUgs3/YP1JbjbCu78OmnQOJedGADmg/DZv5PWykpnwTiLr9FREREWky99AV7Wh/f2ToNKFbV7MaL7mXlYH0RnMx2mjuAzRre9RTr+4FIDFKmfBeilmtd2FqTde8dR2rkbr91R9GFBEREWmy8j+GV/80Rs0et8ZQZqoZKYEb/2WNXq9mo19+jzURWTfdxln3arIlIiIiIiKxUUIiIiIiIiKxUUIiIiIiIiKxUUIiIiIiIiKxUUIiIiIiIiKxUUIiIiIiIiKxUUIiIiIiIiKxUUIiIiIiIiKx0R9GFBERkY2CF56Ggb8AWWg5GEvMijukhvHCM9G6Zib9utbDiy9A7o/hS/YgLDk33oBkXJm7xx3DuOvq6vLu7u64wxAREZFx4F7EV34W+q8l/BVyAwag8/Mk2k+OObrxFdb1c9B/NUPX9T9ItJ8Sc3TN5+54z3mw5kdgWXCAHLSfSaLzo3GHJ2NgZgvcvavaODXZEhERkQnN11wC/TcAJaAf6AOKsPq/8IH74g1unPman0L/9Qxf16/iA/fGG1wc+q+HNT8BSuB9hO1RgjU/wPtvjjk4GS9KSERERGRi6/0xkKsyooT3/qTZ0TRW74+ovq5FfM2lTQ4mfr7mh8BAlTED+JofNTscaRAlJCIiIjJhuTuUltQYW4LCs02Np+FGWtfiJFvXehRfGGHcgubFIQ2lhEREREQmLDODxOY1xqYgtWNT42m45Ba1RkBqp6aGMiGktq0xwiC1XVNDkcZRQiIiIiITmnV8gPCCd5Vx7ac3NZZGs/Za62pY+7uaHU7swvZIVxmTwtrf3+xwpEGUkIiIiMjE1vo2aD8DSIG1Ai1gHdiMb2PpneOObny1ngDt72HourZjM87H0rvEHV3TWcvrofNThCQt2h5kofPzWPa18QYn40bd/oqIiMhGwUvLYeDe0P1r5jWYVX9qMhkMXdd9McvGHVKsvNQDA/cABplXY4n2uEOSMRqp218lJCIiIiIi0lD6OyQiIiIiIjIhKSEREREREZHYKCEREREREZHYKCEREREREZHYKCEREREREZHYKCEREREREZHYKCEREREREZHYKCEREREREZHYKCERERGR2LmX8OJCvLRibNOVevHiAtwHGhTZaMtfEeL2UizLbxT3fNiupZ4NnM9gvS4fp8ji4V7Eiy/gpZVxhzIppeIOQERERKY277sGX/1NKC0O3zP7YtO+jKW2rD1NqQdf9SXovw4ogLXibadiHR/FrPGXN154Dl/1ORj4axiQ2BTvOJtE23ENX3YjuTu+5gew5vvgq4EEnn0jNv2LWGLW2ObVdz2++mtQWhS+p/fBpv8XltqmAZE3TmnNr2HN+VBaBhieeW1Yj+TmcYc2aZi7xx3DuOvq6vLu7u64wxAREZFReN/1+MpPAfmyoUmw6dimN2OJ6cOncceXvQPyDwHlT0ay0HocielfbGzMpZX44kPBVwLFsjFpbPrXsdYjG7r8Rir1XAQ932Podk1Dckts9nV1J3vefxO+4iyG1msCrDOq17ElN3Eprfk1rP5Phq5HChKzsNk3Y4n2uELb6JjZAnfvqjZOTbZEREQkFu6Orz6PoRd7AEXwNdD3m+oT5h+A/IMMvWgGyEHfFXhx8fgHW67vyhDfkGQEII+vPo+N9Wavex/0XMzw7ZqHYjfk/lDnfGrVawm8H++9YhyibTz3YngyMmw9ClBaFT2dk/GghERERETi4aug9EKNkTk895fqo/L3UfMSxjKQf3g8oqvJc38FctVHll6InpxshApPMTzJGjSAD9xX33y8D4rP1RiZg9yf1yO4GJQWR820qunHB+5qajiTmRISERERiYe1AMkaIxOQ2KTGdNOpeQnjRajSzGtcJWbWXj5JsNbGLr9RbBq1E5J01eZz1eeTofZryglI1qjXicbaAasxMll7/5QxU0IiIiIisTDLQvYQIF1lbBJrO6H6hC2HAIVqcwzJQnqP8Quy2lLa3kr1RCoN2UPCem2ELLUlpHak+uVhEVrqezfGLAUtR1CzXltr1OsEY4lOyBxA9bo2rPX4Zoc0aSkhERERkdjYtC9Aci7QEg1JAiloOxXL7FN9msQ0bMb5oRyZaGgWaMFmXIRZYy9vLLMPtJ0aLX/wYrUFkpuF9dmI2YzzwTpZVx9pIAmdn8dSVd9Hrj6faZ+B5OaEeiHMgzS0vR0y+49rzI1k0/8LEpuybnukwqf9TCy9W4yRTS7qZUtERERi5d4PfTfgA38Gm4a1Hotldh99usLzeN8VUHweUjtjrW/BmtgcyAcexPuuAV+FZfaH1iMwaxl9wgnOS6ug7xo8fz8k5mJtb8FS2499Pp6D/hvw3J9C71qtR2OZvRoQcWN5qRf6r8MH7obETKz1eCy9a9xhbXRG6mVLCYmIiIiIiDSUuv0VEREREZEJSQmJiIiIiIjERgmJiIiIiIjERgmJiIiIiIjERgmJiIiIiIjERgmJiIiIiIjERgmJiIiIiIjERgmJiIiIiIjERgmJiIiIiIjEJtXsBZpZC/ArYBegD1gEnOnuT1YpeyRwHpAE/gGc7u6rmhiuiIiITDHu/dD7K7zvN+D9kD0Yaz8DS2428nSF+XjPDyB/DyRmYq0nQ+sxmCWbFHlFPKUevPcX0HctUISWQyGzN/T+GgqPQGILrP10rOUNscRXi+fuwNdcAsXnIfVyrP29WOZVcYc1YXhpDd77S+i/GrwALYdgbe/CkpvEHdp6M3dv7gJDQnIwcKO7u5l9CDjB3Q+qKNcBPAUc6O6PmdlFQJ+7f2K0ZXR1dXl3d3cDohcREZHJzD2HLz0ZCo8DuWhoBqwV2+QqLPWy6tMNPIAveydQBArR0CxkX4/NuAAza3zw5fGUevClb4ViN+vWIxXFlgBK0bAMtL+HROf/a2p8tZR6vg89FxLidEKsCWz6V7DWY+MNbgLwUi++7EQoPMuQ/TPRiW1yNZacG2d4IzKzBe7eVW1c05tsuXu/u9/g6zKhvwJbVyl6OHC/uz8Wff8ucFITQhQREZGpqu/qimQEYAC8B199Xs3JfNXnomkKZUNzkPs9DNzdmFhH4L2/qEhGYF1spbJhA7Dme3hxQfOCq8GLi6DnO0CekIxAiLWArzoH9774gpso+n5VkYwADEBpJb76grii2mAT4R2SjwLXVhm+JfBs2fdngHlm1vRmZiIiIjI1eN+1DL3YG1SE3K1Ua1nixRejJKaaAt5/43iGWJ++a6i+HtVkoP+2RkZTn9wfgEz1ce4wcE9Tw5mIau+fBcjd3Oxwxk2sF/dm9llge2CDGi+a2ceBjw9+nz59+gZGJiIiIlOSF0YYWRphuLHurn7luJHm2SjFMZR1wlOJuI0Us4GPZZ0mqZH2z414+8T2hMTMzgaOBw53994qRZ4Dtir7vjWw0H14Tbj7+e7eNfjp6OhoSMwiIiIyuVnrYUC2ypgEZPav/i5IYh4kt6gxxyyWjeGl8ZZDqfm0YZgCZA9sZDT1yfwbtZ/qFCGzTzOjmZhaDqN6vSYnRh2up1gSkuiJxknAm9x9RY1iNwF7mdlO0fcPEnrnEhEREWmM1rdBcg6QLhuYANJYZ/V+dcwMm3YOoeFJecKShfRusVwoWvu7IDGdoetR7cX6LLQci6W2b1JktVlqS2g9ieEJYRo6/h+W6IwjrAnF2k+BxEyG1msSLItNkI4J1kccvWx1Ac8D84HV0eCcu+9rZl8EXnD370Vljwa+QTjCHwZOc/eVoy1DvWyJiIjI+vLScrznu1F3uXnIvBbr+CiW3mHk6QbuxXsuhPxDYB3Q+nas472YVXvi0nheXIT3/Df03wiUIPt6SO8Nfb+G4nxIzMbaz4DWkzCbCK8VE97R6bs8dPtbegmSW2EdZ2Ith8cd2oThxSV4z0XQ/1ugCNkDsY6PYKlt4g5tRCP1stX0hKQZlJCIiIiIiEwcE6rbXxERERERkUFKSEREREREJDZKSEREREREJDZKSEREREREJDZKSEREREREJDZKSEREREREJDZKSEREREREJDZKSEREREREJDZKSEREREREJDapuAMQERGRqcWLi6D/t3hpJZbeHbKvwywZd1gjcs9D7vd4/hEsMRtaj8ASs+IOa6Pn7pC/H8/9CbM0tByKpbaNO6xx4aWVYT8vvoSldoSWN2KWGft8vB/6b8ILT2PJLmg5HEt0NCDi+Ji7xx3DuOvq6vLu7u64wxAREZEK3ncNvvIzQBooAElIbYnN+jmWmBlzdNV58UV82clQXEyIOQ0UsRn/jbUcHHN0Gy/3AXz5h2Dgz9EQAwrQ/l4SnWfFGdoG89yd+PIPEtYpD6QgMROb9Uss1VX/fPKP48tOAe+P5pMGS2Izf4xl9mhM8A1iZgvcverKq8mWiIiINIUXnsVXfhYoAv2Ei/scFJ7BV34+3uBG4Cs+BsUXgRzrYs/jKz6Ml5bFG9xGzHt+CAN3ES6088AAUII1P8b7/xBvcBvAS6uiZCRH2FeK4f+lxfiKD9c/Hy/hy98HvrJsPv3ga/Dl78V9oBHhx0IJiYiIiDSF911FuGNcKQ+5W/HSqmaHNCovLoD8/YTkqVIS+q5rdkiTR9/PCBftlfJ478+bHc346b+B6vt5EQqP4YWn6ptP/u9QWgJUac3kOcjdsQFBTixKSERERKQ5igsJd8JrKK1oWih1Ky6h9iu3A3hpUTOjmVxKK2uM8OiJ1EaqtJjwNKOadLRP1aG4CKzWvmdRsjI5KCERERGRprD0bkBLjZEtkJzb1HjqktoaKNUYmcFSOzcxmEmm5svrKUi/sqmhjKvUTtS+xM5Darv65pPeKTwJqaoQLWdyUEIiIiIizdF6HFiW4ZcfWWg7Y716IGo0S0yH1rcC2YoxSUjMgJZD4ghrUrCODxM6CBg2But4d7PDGT/Z10NyDsOfrGWh5WgsObuu2VhqO8jsB1QeF+mQjKQ3rpfaR6KERERERJrCEp3YJpdBcjsgSbjIT0P7O7GOD8UcXW027XMhmSJJeMKThNQuocekCZhEbSys5VDo/AJYO+GiOw2JOdjMi7HU9nGHt97MUtisX0D6FYRL7WifaTkSm/6lsc1rxoWQPahsPgnI7IvN+iFm1d5T2Tip218RERFpOi88Gd4hSL0cS3TGHU5dvLQcCvMhMRtLbRV3OJOG+wDkHwFLQ2pnzCbP/XIvPAelRZDadoP+bo0XX4Li85DcAkvOG8cIm2ekbn+VkIiIiIiISEPp75CIiIiIiMiEpIRERERERERio4RERERERERio4RERERERERio4RERERERERio4RERERERERio4RERERERERio4RERERERERio4REREQ22NLeXh54cSGL1vTEHYpMIl5cgA88iJdWbeB8loT5FJeE717C84/h+UdwL4xHqBtsvNZ1vZbtjuefwPP/CH81vVa5wtN4/iHc+8L3Um+IufBsWZnnovXYeM4FXuqJ1uP5dcMq1nVj5O544clR63UiSMUdgIiIbLx683k+c9vvuOGJf5FKJMiXShy41TZ865DDmNHSGnd4spHy4kJ8xccg/wDhUqWEt56ETfsMZvVfunipB1/5acjdCqSBAp56JZS6obQcMLAOmHYO1vrmxqzMaDEWX8RXfBzy97FuXd+GTfssZunGL3/gPnzlJ6D4ApAAy+IdZ5NoP3ldmfxjIcbi06EMSTy9ZxSzAwU8uQ1YAgpPMXh56e2nYR1nYTYx73+7F/HVX4feXwBG2D92AM9B8XnWrmvHB7D2MzGzeAMeAx94EF95NhS7CfWawTs+TqL9nXGHVpW5e9wxjLuuri7v7u6OOwwRkUnvvdddzZ+ee5Zcsbh2WDqRYOdN53D1iSdvVD/gMjG4D+CLD4HSIqD86UUWWk8kMf3zdc+rtOxUGLgPGO3ucAqb+SMsu996RLz+3PP4kkOh+CLD1/UtJKaf29jlF57DlxwB5CrGpLHp38Baj8BLy/DFbwRfQ0g+xiIL7e8m0fn/xifgcVZa9XXo/TnD179SBjrPJtF+ehOi2nBefAFffBhhvcrrLI1N/zLWemwscZnZAnfvqjZuYqasIiIy4T2zYjm3P/P0kGQEIF8q8ejiRdz34gsxRSYbtf5bobSUoRfoADnou6zuJk2efwwG/sboyQhAEe+5aIyBjoPc76G4mOrr+mu8tKKhi/fenwKlKmPyeM93ojJXgOcZezICkIM1P8a9fwOibAwv9UDvzxg9GQEYgJ7v4l4cvegE4Gt+TqjXyjrL4z0XxBDR6JSQiIjIenlk8WKyqerNZ1KJBP9ctKjJEclk4IV/AvkaY5NQeLq+GeUfATL1LhUKj9ZZdvx4/p8MT0YGJaEwv7EBDNxHzW1dfBb3UtQsq56L9poLiZoNTTDF5xhTkuUroLSsYeGMq/wITwWLCybk+yRKSEREZL3MbmujUKp2dzX8zM9ua29uQDIpWGJTaicSeUjMqm9GydlUv/tfQ2Jm/WXHiSVmE95tqaYAidmNDSA5j/DuRBXWEd79SMxlw1459vrrrJkSs4CxPPFIQaKjUdGMr+Q8al/it1J7n4uPEhIREVkve2++BbPb2qpezqQTSd6wzbZNj0kmgZajqH6hmIT07ljqZfXNJ7N/dAFZz3tMWazt1PpjHC+tR1JzXVO7YqktG7p4azuZ6slGBlrfHpV52wYsIQWZ12ITMCGx5FxI7019yVYGWt6M2cbRUUeo12SVMRloO3FCvtunhERERNZLwowfHHUc07IttKRSJICWVIrWVJofHHVszeZcIiOx5CbYjG8T7uK2EC5VWiAxB5txfv3zsRQ28/tg7WXzyRIuQJPRv9En+zpoe8c4r0kdMSZmYTMuYPi6zsZmfKfxy8++FtrfQ9ge6ejfTEj8Oj8SyqR3gc5PrhtHIiqbKfueJSR+g/+P/k1ujk3/WsPXY33ZjPMguRlDYl67HmXrltoGm1Z/Zwpxs8w+0PFBhtZZBtK7Yh0fize4GtTLloiIbJDefJ7rH3+M+cuXscW06Ryz405My7bEHZZs5Ly4BPqvw0tLsNTO0HIIZvW+E1I2n1IP9F+PF5/DkltCy5FQfA7vvwUoYtmDwwV4jHeNvbg0WtfFG7Su6738wlPQfyPu/Vhmf8jsN2x7eOH5sB29B0vvjWf2xwbuxPP3YzYdWo8CLJQpLcfSr4TswU3punhDuA9A/6144Z/hSU7L0aHb3/7rcV+NpfeG7Oswq/bEYWLzwtPQfwPufVhmv6he43sWMVIvW0pIRERERESkodTtr4iIiIiITEhKSEREREREJDZKSEREREREJDZKSEREREREJDZKSEREREREJDZKSEREREREJDZKSEREREREJDZKSEREREREJDZKSEREREREJDapuAMQERFZX4VSiZ8+eD8/feh+lvX1sduczfjoq/dj366XxR2axMCLC/DVF0HuNiABrW/G2j+IJWfHHZqIjMDcPe4Yxl1XV5d3d3fHHYaIiDSQu/P+66/lzueeIVcsAmBAMpHgwsOO5LDtd4g3QGkqLy7AlxwD3gsUoqFpSMzAZl+HJWbFGZ7IlGdmC9y9q9o4NdkSEZGN0t9eWMAdzz69NhkBcMJTk3Nuv5ViqRRfcNJ0vvqiimQEIA+llfiaH8cVlojUQQmJiIhslP7wzHxKNZ7yL+/v54llS5sckcQqdxtDk5FBA9B/Y7OjEZExUEIiIiIbpYQZVmOcOyRNP3FTS629AXS5IzKx6QgVEZGN0iHbbg9W/SJ0s/Z2tp+ldwamlJbDgXSVERloObrZ0YjIGCghERGRjdIrN5vL0S/fiWwyuXZY0ox0IsFX33AIViNZkcnJOv4dEjOATNnQDCTnYu2nxhWWiNRBvWyJiMhGq+TO1Y89wk8fvJ+lvb3sMXceZ+79anads1ncoUkMvLgUX3MJ5G4CEtByNNZ+GpaYFndoIlPeSL1sKSEREREREZGGUre/IiIiIiIyIdX1l9rNLAUcBbwJ2BeYB7QCS4HHgTuBK939iQbFKSIiIiIik9CIT0jMrMPMzgEWAL8G9gf+AfwS+B/g5qjox4DHzOwPZnZAA+MVEREREZFJZLQnJE8TnoB8BrjK3VfUKmhmrwZOAq43s8+6+/+MX5giIiIiIjIZjZaQnOjuf6hnRu5+D3CPmf0nsNUGRyYiIiIiIpPeiAlJvclIxTQrgJpPUkRERERERAaply0REREREYlNXb1swdqetj4InAB0AS0VRdzdtxjH2EREREREZJKrOyEBLgDeD/wBuBEYaEhEIiIiIiIyZYwlIXkb8Cl3/1ajghER2RiV3PnTc89y81NPYMCh2+3Aa7fcioRZ3KHJOFvW18tVjz7Cv5YsZssZMzhh592Y19k5pMyqXI5rHnuEf7z0IvM6Ozlhl93YcvqMmCIWmVi88BzedxUUF2Lp3aD1WCzROfqEMqmZu9dX0KwbOMPdf9fYkDZcV1eXd3d3xx2GiEwB+WKR9153DX9d8Dz5YhGAdDLJ/l1b8v0jjyGdTMYcoYyXB15cyClXX0HJnf5CgWwyScmd/z3iGA7eZlsAnli6lBOvvIxcsUh/oUAmkaSE8403HsaxO+0c8xqIxMv7/g9f+WnCK8wDQAtYFtvkMiy1fczRSaOZ2QJ376o2biwvtX8XeNf4hCQiMjn87KEHuHvB8wwUizjgwECxyF3dz3HZww/FHZ6Mk2KpxAd+ey19+Tz9hQIAuWKRfKnEh268jjUDA7g7H7rxOlbncmvLDJSKFEolPnHLTSzuXRPnKojEyotL8JWfAgqsa/XfD74aX/6RGCOTiaDuJlvu/hUz+46ZPQD8nuFd+7q7f2lcoxMRmeB+8Y8HyUVPRsrlikV+/tADnLr7njFEJePt7wtfYEV/P9XaFBhwy/yn2HXTOcxfvpxSlTLpZILrH/8X79pjrwZHKjJB9f8WSAOV58sSFJ/GC0/qKckUNpZeto4DPgBkgF0JKW45B5SQiMiUsjLXX3PcihHGycZleX8fKUswMOxiKvz4rejvY3l/H+lEgmJxeEpSKJZY0d/XhEhFJiYvrWD4peOgFJSWNzMcmWDG0mTrPOB2YAcg4+6tFZ+2hkQoIjKB7TV3cxIMf3k9acar5qkn9MniFXM2o79Y/WKqUCqx59x57DR7NkWv9nwEkgljj7nzGhmiyIRmmd2pfdlZgtSOzQxHJpixJCSbAee5+1Ne75vwIiKT3If33Y9kYnhCkjDjQ/vsG0NE0gibd07jmB13IlvRSUEmmWSveZuz+9x5TMu2cNruew4rk04k2Gr6DA7captmhiwysWQOgNTWhGZb5bLQ9k4sMS2GoGSiGEtCcgehqZaIiEReMWczfnzMW9h6xkyM8D7BNjNm8pNjT2DXOZvFHZ6Mo6++4VBOfsXuZJNJEmYkLcERO+zID486bm2ZT732dbx3r31oTaVJmJEw4+BttuWyt7xN3UDLlGaWxGb9DLKvJ1x+JsBaof3dWOcn4g5PYjaWbn/3AX5C6G3rFmBYYz93XzSu0a0ndfsrIs3m7rzQsxqAzTs6MV18Tlp9+Twvrulhdmsbndls1TK5QoGFPauZ2dLK9JaWJkcoMrF5aWV4ZyQ5D7Pqx5BMPiN1+zuWhKS8YWzVidx9QnS4r4RERERERGTiGCkhGctfaj+DGomIiIiIiIjI+hjL3yG5tIFxiIiIiIjIFFT3S+1m9nIzO7DGuNeZ2Q7jF5aIiIiIiEwFY+ll60LgiBrjDgcu2PBwRERERERkKhlLQrI3oevfav4I7LPh4YiIiIiIyFQyloSkjdovtSeAjg0PR0REREREppKxJCSPAG+tMe4twKMbHo6IiIiIiEwlY+n29wLgJ2ZWBH4ELAC2AN4NnBZ9RERERERE6jaWbn9/ZmbzgC8A7yob1Quc7e4/H+/gRETGU8mdB15cyPK+PnbZdA7zOjtZ3tfHfS++QHs6w96bb0EqMfzB8apcjr8vXEA6kWSfzbcgmxrLvZwN98LqVTyyeBGbtLaxx9x54/ZX4N2dhxcv4qWe1bx8k9lsOX1G1XKPLVlM96qVbDNjJtvN2mS9l/f8ypX8a+li5rR38Io5mzX0r9mv6O/jvoULaUml2GfzLUgnG/d3e3OFAn97YQH5UpFXzduCaVX+enuhVOLeFxawJj/AnnPnMau1rWHxyNThpTWQ/zuQgMzemLXEHVJTeeFJKDwLyZdh6ZfHHY5sgDH9qrr7N8zs+8BrgE2AJcBd7r56LPMxswuBo4GtgD3d/YEqZQ4CbgT+VTZ4P3fvG8uyREQAHlm8iPdffy2L1qwhlUjQX8iz3cxZPLNyBelEgpI7bek03znsCA7Ycuu1033v3nv4zt1/IWlGyZ10MsnX3nAob96h8T9+uUKBT956M7994l+0pFLki0U26+jg+0cey86zN92geT+zYjnvu/5anl2xnHQySX+hwIFbbcMFhx1BR+b/s3ff4XFU9/7H32dmiyTLttyb3HuvYGww3RAINfSWkJAGIAI/JwAAIABJREFUyc3lkoQkXEJIIyThRyolCQnkBgKhY5qxKQaMKa649yp3y0W22u7OnN8fKwtLWskqK41kf17PowczM3vme3bW8nx25pyJALDz0CG+/sqLLN+9m6jrUuolGN+tBw99/iLaZWbWel+FsRi3vvEq72zcQEYoRMzz6N02h79eeAl9c9o1qB+VWWu5/8MP+MuCueXHNSMU5v5zz+OMPv3Sui+AN9at4faZ04l5Ho4xJHyf/544mZsnnFgeuD7Yson/fv1VCuMxHGOI+z43jh7LD085DacRQ5kc2/zCJ+DgvYABLBgH2vwEk/mFoEtrdNbfi933bYgvBBMFW4oNDcO0ewjjdg66PKkHY23TP3zdGHMqsB6YDVxSQyD5vbV2TF3bz83NtXl5eQ2uU0SODQWlpUx59K8cisWqnZnjsLDj8sb1X6JPTjteWrWCH7z5BjHPq7BNyHF47sprGdm5S+MVDdzx1gxeWLmc0iP2b4DW0Sizv/z18uBQV3HP47THHmF3USHeEf8GRFyX03v35eELLsZay+ee+Ccb9u8j4fvl24Qdh1FduvLMFdfUen83vzqNdzaur/A+OsbQMSuL9278GpE0Xr14Ysmn/Py9d6ocs7Dj8Nq1X2zQFZ7Klu3ayaVP/7vC+wPJ9/FXZ57DpUOHsfnAfs55/LEq9URcl+9NOoWvjpuQtnrk+GFL3sHu/zYQr7QmhGn/GCZyYhBlNRk//0qIL6Ni/0MQ6ofp8HKjXn2V+jPGbLXW5qZaV+OgdmPMxHrsLMsYM6ymbay171lrlRhEpEm8tGoFcd8/ahhJsvzfpwsB+PMnH1U5kYTkt/B/mz83rTVWVlBawrMrllUII8nqIOZ5TFtV/3lE3tywjv0lJRXCCGXtvrlhHdsOFvDJ1jw27t9f5WQ77vss2rGDFXt212pfOw4dZOb6tVXeR99aDpSUMnPd2nr3I5UHqjlmAI8uWpDWfT2ycD6pvtSLeR5/nvsRAP9aXOX7tvJtHp7/ScrXixyNLXyIqmEEwMMeeripy2lSNr4c4kup2v8EJDZAfF4QZUkDHW2WrenGmJnGmCvMUW5MNMYMNsb8HNgITE1Tff2NMQuMMXONMbekqU0ROc6szt9DSSJRq23jvs/y3bsA2Hxgf8ptPGtrfUJeX1sLCqjuO76SRII1e/Pr3fbavfl41k+5Luq6bNi/jzV78wk5qSuIhlzW1XL/G/btI1rNFRDP+g3qR2UJ32dH4aGU6+K+z/I0H7Plu3dVCXWHHf7sLNu9q9qAtLe4mMJ4qpNKkaNIrKtmhYXEmiYtpckl1oGp7upwqIb3Rpqzo40h6Qf8CPgbEDLGLAIWkxw7Ugq0BfqQfGhib+AT4IvW2ulpqG0BkGutPWCMyQVeM8bssdY+XXlDY8xtwG2H/79t27Zp2L2IHCty27QpGwOR+sTwSCHHoXfZuIaOWa3YfqjqEDkD9Grk3zOdW2VXe7Kb4Ybo0bpNvdvu3roNruMQ96uGkpjn0b11G0oTHn41+4+XbVPbfVV3Qh5yHHq0qX8/KnONoW00yoHS0pTrelczaL++erfNYe3e/JRX3jpltQKgT9sc5m3NI5HivWwVDpPZxBMkyDHC7QqJaobvuj2atpam5nYDW12Q98Ht3qTlSHrUeIXEWrvPWns70A34BrAWOAP4DvBj4HqgE/AEycHpk9IURrDWFlhrD5T9OQ94EphSzbb3W2tzD/9kZ+sZjSLymS8MGV7L27WSbhiVHLp209jxKb/dDzkOXxk7Pk3VpdYhK4uz+vYnnGLWLx/LJUNqvDO2RucNGJRyNrGQ4zC6azf65rTj1N59aBvNqHKVxjWG3DZtGdu1W6321Tsnh3Hduqfcn2sM5w9I3+QAxhhuHDMu5TFzjOFLo8embV8AXx4zLmW/oq5b/vm4YdQYSHE/e9R1uW7kGNwUrxc5GpN1E5DqKkEE0+rLKZYfQ8Ljy0JH5b87BpwciJwcRFXSQLX6TWitLbbWPmGtvdFaO9Ram2OtzbDWdrfWnmGt/bG1dnE6CzPGdDPGOGV/bg1cACxM5z5E5PjQqVUrHv78xWSGQmSGQkTdECFjaBON4hpDRtnykOPw89PPYkTZYPUbx4zjgkFDKmzjGsNtJ53MyT17N3rdvz77HIZ07ETYcYi6ofL6/3bBJXTMqv+0sVnhMI9dfBltIlEyQiEy3BAR16VX27Y8cN6FQDKcPHbJZXTIzCrbxiXqunTJzuYfF32hToNG/3zehfTJySHiumS4ITJCIVpHIjx68WW0qufA/OrcMmEiZ/TpV+mYOfz41DMYU8sQVVuTevbi+5On4BpDZijZL9cYzh84mC+PGQfA0E6d+eUZZxNynCO2cTi5V2/+56TJaa1HjiOZl0LWVSRP4zLKfhxo9RWInhNsbY3MGINp9wg4XYBo2U8GOB0w7f6BMY03xbc0nqBm2foL8HmgK5APHLTWDjDGPAJMs9ZOM8Z8G7gZSJC8tewZ4Ke2FgVrli0RSaUwFmPGurXkFxcxumtXJnTrwco9u/kwbwutwmGm9h+Q8vkQ6/bm8/7mTYRdl7P79qdLE16FtdYyd9tWFu/cQcesLKb2G5C2k/jSRIKZ69ey49AhBnfsyMk9e1eZhjbueby9cT1bDhygb7t2nN67b72+1fetZc6Wzazcs5su2dlM7defjFA4Lf1IZeWe3czZspnMcJip/QY0KMAdze7CQmaUDdw/tVfvlDN57SsuZsa6NRTG45yU25NhnTQ1qTScTWyG0lmAAxlnYo6j25WsTUDpe+BtALcXRE/HmMb7nSINV9MsW4EEksamQCIiIiIi0nzUe9pfERERERGRxqRAIiIiIiIigVEgERERERGRwCiQiIiIiIhIYOr0RCaTnEvtRKAnyTnmKrDW/l+a6hIRERERkeNArQOJMWY08ALJJ7KnmoDeAgokIiIiIiJSa3W5QvIQUAKcDywHYo1SkYiIiIiIHDfqEkhGAVdba99orGJEREREROT4UpdB7RuB9DweWESkibSEh7+mq8batGOtbRHvSWWVa26JfWhMLfW4iqSb/i60THW5QnIH8GNjzGxr7a7GKkhEJB3e3biB3855n+V7dpMVCvOFocP43uRTaBOtMh9HYJ5fsYw/fPwhWwoOkBPN4Iujx3DLCScRcd06tfP+po38Zs77LNu9i8xQiEuHDuP7k6bQNuOzvs7btpV7Z7/Hwh3biLgu5w8czA9PPpVOrVqlu1tpU1Bawn1zZvP8iuUUJeIM69iJ8wcMYvq6NSzdvYuMUIhLhwzj9skV+3o8Wbh9G/d+8B7ztm0j7DqcN2AQPzz5VLpkZwddmkiTsokt2IO/gdK3AB8bOQnT+nZMeFjQpUktmJpSpDHmtUqLRgHtgMXAvkrrrLX28+ktr35yc3NtXl5e0GWISEBmrFvDt19/hYTvly+LOC59cnJ46erriYbqNMFgo/jL/Ln87sMPiPle+bKo63Jyz948ctGltW7nrfXruPm1aRX76rr0atuWl6++gWgoxCdb87jhhWeIH7FNyHHolNWK16/7Em2i0fR0Ko1KEwkufupxNu7fX+E9qizsuPRs25ZXrrmejFC4CSsM3oLt27jmuf9UOa7tMzOZft2XyMnIDLA6kaZjvZ3YPReAPQQc/n1hgAimw7OY8OAAq5PDjDFbrbW5qdYd7ZatLCDziJ81wCckB7dnVvrJSlfBIiL1Za3l5+/NqnCCDhDzPTYfOMD0dWsCquwzhbEYv/9oTpUT7VLP473NG/l0545atWOt5WfvvVO1r55HXkEBr61ZDcA9s9+tcNIKkPB99hYX8czypQ3oSeN5fe0aNh84UGMYAYj7HtsKCnhl9aomqqz5uHf2eymP64GSEp5aujigqkSani18FGwxn4URSE7+Gsce+kNAVUld1BhIrLWnW2vPqO1PUxUtIlKdnYWH2HqwIOW6Ei/BOxvWN3FFVS3ZtTPl3OkAjjHM3rypVu3sKSpiS8GBlOtKEgne3rieuOexuJqAU+p5zFy/tlb7amqzNq6nxEvUatsSL8HbzeC4NiXfWuZv35pyXannMWP9uiauSCRApW+TevJXH2JzmroaqYdaD2o3xtxljOlezbpuxpi70leWiEj9RN3qb8dyMGSFg7+tJ+q6eNXcLutgyKjlLWU1jTVxgKxwGMcYXFNd/IGsZnqbU2Y4jFNtbKvIlG1/PDEkb8+qTnM9riKNwtR0e6LmY2oJ6jLL1k+AlPd9Ad3L1ouIBKpdZibjunZP+cvNdQwXDR7a5DVVNqpLV9pmpB63EfM9PjdgYK3aaZuRwQnde6Q8cQ+5LhcNHoLrOEztNyBlKIm6Ll8Y2jwHfF48eCiuU7tAEnZdLmkGx7UpGWM4b8CglKEk6rpcNnR4AFWJBMNkXgak+p0ahsyLmrocqYe6BBJD8oa8VLoD+xtejohIw91z1lSyIpEKVxAirstFg4cysUd136s0HddxuP+c8wk7LuGyE0oHQ9hx+N6kU+jRuk2t2/rlmVNpFQkTrdTXzw8czOTcXgD876mnk5ORWWGbqOsyKbcX5w0YlKZepdfEHrlcNGhIhZoPv1fhI07Co67L5/oP5JRevZu8xqD98JRTaZ/iuE7o3oMLBw8JsDKRJpZ1FYSGUDGURMHtisn+VlBVSR0cbZat64Dryv73XOAjoPINyxnAOOB9a+2FjVFkXWmWLRHZcegg/1q8iNmbN9E+M5NrRoxiar8BmBpuX2pq6/ft5dFFC/h05w56tmnDF0eNZWJuzzq3s/PQobK+bqRdZiZXDR/Fuf0r9nVvcRH/XrKYtzaso1U4wuXDRnDBoME13vYTNGstM9ev5cmli9lbXMwpvXqXT/v7/uZN5EQzuGrESM7tPxCnGR3XprS/pJgnlyxmxvq1ZIXDXD50BBcOHtKsj6tIY7A2BsUvYYtfBOKYjPMg8wqMoymwm4uaZtk6WiD5EnBj2f+eBiwEKo8WjQErgN9Ya7c1uNo0UCAREREREWk+agokNY6ctNb+E/hnWSPvALdYa1ekv0QRERERETke1frpYJrWV0RERERE0q3WgeQo0/r6JMeWLLLWvt/gqkRERERE5LhQ60AC/IjkrFyHJzf3+WyWrvjhtowx84DzrbX56SpSRERERESOTXWZhuMkYDNwM9DZWhsCOgO3lC0/BbgA6A38Js11ioiIiIjIMaguV0geAP5grf3L4QXW2j3Aw8aYCPD/rLWTjTH3kLyaIiIiIiIiUqO6XCEZB6yuZt1KYEzZnxcD7RtSlIiIiIiIHB/qEki2A9dUs+66svUAbYG9DSlKRERERESOD3W5Zese4G/GmP7Ai8BuoBNwCcnxIzeVbXcm8Ek6ixQRERERkWNTXZ5D8ndjzA7gTuDestcmgPnABdba18o2/THJWbdERFqcT3fu4N9LPiWv4ABjunbjupGj6d66TYVtVu7ZzeOLF7Fh/z6GduzE9aPG0CenXY3tJnyfN9au4aVVK4h5HucOGMglg4eSGQ7X+Lr6mrstjyeXLGZXYSETuvfgupGj6dSqVaPsqzLP93lj3RpeXLmCUi/Buf0HcumQYUft6+6iQp5csphPtubRqVUrrhkxihN7pHyob4u3p6iIJ5d+ysd5yb5eNWwEHpanly0lv6iIyT17cf7AQcxYt5b3N2+kbTSDK4aN4NTefTDGNHj/a/fm86/Fi1izN5/BHTpyw6gx9GtX8W7rzQf28/jiRSzbvYs+Oe24ZvhIthws4KWVKyhOxDm73wAuGzqcrCOOq7WWWZs28OzypRSUlnJa775cOXwEbaIZDa5ZRI5dxlpb9xcZ45C8OrLbWuunvaoGys3NtXl5eUGXISItzGOLFnDP7Hex1uJZS8R1cYzhX5dezvhuPQB4ceUKbn9zOpAMGRHHxQJ/veBiTuvTN2W7cc/jK9OeZ962rZR6HgAZoRA9WrfhuSuvSfvJ2h8+nsODcz/GsxbfWqKuS8hxefryqxjaqXNa91VZwve5adoLfLJ1S4W+ds9uzXNXXkvbjNR9XblnN1c++xRxz6PU83CMwTWGb4w/kdsmndyoNTe11fl7uOKZJ4kd7isGi00GDWvxgYjjEvc9wq5LzPMwQNh1uXTwMO45a2qDQsnra1dz6/RXAYj7PmEneff2n8+7kKn9BwAwe/MmvvryCwDEPA8Xg4cl5Dgk/OQ/+xmhEF1aZfP8ldfSLjMTay3fnzmdV9asIu55WCDDDdEmGuWFq66jW+vW9a5ZRFo+Y8xWa23Kb5nqMoaknLXWt9bubI5hRESkPrYfPMgv359Fwvfxyr6oiXkeJYkEt05/DWstBaUl/ODN6SR8v/ykLOZ7xH2PW994lVjZCXhlz65Yxrxt28pP0AFKEgm2FBzgjx9/lNZ+rMnP58+ffETc9/HL+lHqeRTFY3x35vS07iuV51YsqxBGINnXvIICfv/xnGpf972Z0ymMxcpf51tL3Pd5aN7HrMrf0+h1N6XvV+4rFkuyz4f/UY35yRP6w5+pw39+YdVy5uRtrve+i+JxvjvjdeK+T7zsM3z4z/8z4zVKEnESvs+t05Of58P7T1ZD+eceksd128EC7vtwNgDvbtrIK2tWEfMObw0lXoK9xcXc/e7b9a5ZRI59dQokxphBxpgfGWMeNMb8o9LP3xurSBGRxvbqmlWEHTflup2Fh1i2exdvrl9HyEn9a7MkkeDjvC0p1/1n2RJKvUSV5THP4/mVy+pfdAovr16Jk+Lbc0vym/ktBw6kdX+VPb1sSYUwcljM93hh5fKUr9l6sIAVu3eT6nq9MYZpq1akucrg7Dh0kKW7dlLfb/MSnsfzK1K/j7Uxa+N6qru24lvL+5s2MXdrHoXxWK3ai/s+L5Udn+dWLCOe4tgnrM9bG9ZRmqj6d0BEBOowhsQYczXwL8ADtgKVf1vV/d4vEZFmoigex6vmoq9rDEXxOIXxeLW/6FzjUBhPPXyuMFb9yV1JPL0naYXxWPk335U5xtT6RLO+DtXU12pOSIticRwDXoo3N+H7NbbZ0hTF4xhjqM/t0gA+cLC0tN77T35GU0cSB0NhPI5jDI5xSP5zf3SHg8bB0tJq/3741hLzPKKhusylIyLHi7pcIbkbeJnkU9r7W2uHVvoZ1jgliog0vpNye9a4flinzkzskZvyG2CAUi/B+G7dU647tXcfIimurBjghB496lxrTU7q0ZOom/pKT1YoXGXgcrqd1rsPkRT7N8CEbqn72icnh+xIJOW6qOsyKbdXOksMVK+2ObSJRuv9+oxQiFN796n360/snpvyah0kb686oUcPxnTtRqyabSozwNiuyc/9aX36klFN4OjXrh2tG9BvETm21SWQ9AQetNYWNFYxIiJBOaF7D8Z27VblZDriutwyYSLZkQiDOnRkar8BVU74o65b4yxWN40dTyQUqnIrVchxuW3SKWntxxl9+9GvXXsilW4/Czsu3518SsqwkE5fGTueqJuqrw7fm5y6r2HX5fuTp5QPrj4s4rr0yWnH2f36N1q9TS3kOPwgRV9r+9r2mZlcOqT+3//1zsnh4sFDUn6GLxs6nB6t29AhK4svjxlXbbCtXNPtJ08B4PKhw2kbzahyW2PIcbjjlNPrXbOIHPvq8hvxY+DY+VdBROQIxhgevfgyrhkxqvxb3k5ZWdw55XS+feJJ5dv9/nOf5ytjx5MdTn6jnxPN4L8nTuau086stu3urdvwwpXXcnLPXuU3y4zq0pUnvnAFo7t0TWs/Qo7Dk5ddxaVDh5WHj27ZrfnVWVO5YdSYtO4rla7ZrXnhqms5pWfvz/rauQuPf+EKxnTtVu3rrh05mnvPOpfu2cmZmMKOw8WDh/Kfy6+qdtxOS3XViFH8+uzP0aNsOumw43DhwMF8fuCg8r72atOWr4+bQN+y6aRdYzi3/0BeuPI6WlVzNam2fn325/jG+BNpXdZOm2iUW06YyD1nTi3f5oennMZtk06hXUYmAFnhMFcPH5mcdrjsyA7v1Jl/XnI5E7onr3y1jkZ54aprmdqvP25ZIO3frj0Pf/5izuzbr0E1i8ixrdbT/hpjhgBPkXzOyAxrbf1vYm1kmvZXRBoi4fsUx+NkRyLVTq/q+T5F8TitIpGUg8irU5pI4FvbaM8fOVLC9ylJJGgVDqfl2RV1VZ++WmspjMfJCIWOuSBSWaq+Hp72+MhjVhiLEXbdtF/d8q2lMBar8TN8eJuscBi3rMbSRALP2grPH6ks5nnEPa/B4UlEjh01Tftbl0BSDLhlPwCVA4m11jbNU7eOQoFERERERKT5qCmQ1GW6i1+jmbRERERERCSNah1IrLV3N2IdIiIiIiJyHKrXDbrGmFxjzGRjTLO4RUtERERERFqmuj6p/SZjzBZgE/A+MLhs+XPGmG83Qn0iIiIiInIMq3UgMcZ8C3gYeBa4mIqPep0NXJXe0kRERERE5FhXl0HttwL3WGt/YoypPPfgSsquloiIiIiIiNRWXW7Z6gW8W826UqB1w8sREREREZHjSV0CSR4wspp1Y4H1DS9HRERERESOJ3W5ZesJ4CfGmDXAjLJl1hgzFvg+cH+6ixMRkWPXoViMWRvXUxiPc1KPnvTOyQm6pCr2FBXx3qYNWODUXn3o1CrYySVjnsd7mzawp6iIEZ27MKJzlyrbJHyfDzZvYvuhgwzq0JGxXbuVP/W9JvlFRby3aSM+lim9etO5VXZjdEFEpIq6BJKfAyOAV4ADZctmAu2A54H70luaiIgcq15evZLbZ07HMQYwlHoJLh48lF+ffS4hp14z0qfdQ/M+5v4PPyDihgBLzPO45YSJ3Dpxcq1O8NNtwfZtfHXaC5QkEjjGEPM9xnbtxt8uvJQ20SgAK/fs5saXnuNASQmOMSR8n4EdOvDoxZfRKav6MPWXeZ9w34ezibghDFDqJfjmhBO57aSTA+mriBxfav1b31obt9Z+ATgDeAB4pOxnqrX2Cmut30g1iojIMWTlnt18d8brlHoexYkExYk4vrW8tmYVD837OOjyAHhz/Vp+/9GHeNZSnIhTnEjgWctf5s/ltTWrm7yegtJSvvjis+wvLaHES1CUiJPwfT7duYPbZ04HoDSR4PoXnmF3YWH5exv3fdbk53PLq9OqbfvtDeu5/6M55X0tSsTxrOVvC+bx8uqVTdVFETmO1flrKGvtu9baO621X7fW/tBa+3ZjFCYiIsemfy1elHJ5qefx94XzsdY2cUVV/XX+XOK+V2V5zPP4y/xPmryeaatW4Kd4X2Kex5sb1rG7qJC3NqyjKB6n8lZx32fRju2s25ufsu2/Lqipr3PTUb6ISI1qvGXLGNO5Lo1Za3c1rBwRETnWrdubT8JPfVG9oLSUkkSCzHC4iauqaNOBA9WuyysoaMJKkrYUHKA0kUi5LuQ4bDt4kM0HDlQb5qJuiC0FBfRv36HKuk3791e7360Hm76vInL8OdoYkh1Q5cuWmlR+PomIiEgFgzp0ZOGO7cRThJKcjAwyQnUZ3tg4+rZrx56iwpT/AAYx+L5PTjuioRAlKUKJ5/vktm5Dn5x2VHxm8WdKvUS1dfdr145dhYdS9rVXm7YNqFpEpHaO9lv/K9QtkIiIiNToi6PH8p9lS6osj7ouXx93QrMYRP3N8SeycPu2KqEp7LjcPOHEJq/nwkFD+NXsdzFU/Ec54rpM7TeADllZnNW3H20zouwp9PCP2CrsOEzo3oO+Oe1Stv3N8Scyb9vWKn2NuC43nzCxEXojIlKRaQ736qZbbm6uzcvLC7oMERGpxox1a7htxuv41uJgKPESXD18JD874+yymbeC99iiBfxq9ruEnOTF/7jv8d2TTuYbAQQSgCW7dvLVaS9QUFqC6ziUJhKc3LM3D5x/Ia0iESB5O9yXpz3PrsJCQo5DacJjTNeu/PWCS2iXmVlt2//36ULumf0ubtmsZ3Hf439OmszNExRIRCQ9jDFbrbW5KdcpkIiISBBKEnE+2LyZwniME7rn0q1166BLqqKgtITZmzcDlsk9e5GTUf1JfVPwfJ8P87aQX1zE8E6dGZBiTIhvLfO2bWX7oYMMbN+BYZ1qNxy0oLSUD7ZswlrLpNxeNQYYEZG6UiAREREREZHA1BRImsfTp0RERERE5LikQCIiIiIiIoFRIBERERERkcAokIiIiIiISGCO9qT2FdT+OSTWWju84SWJiIiIiMjx4mgPRvwYPRhRREREREQaSY2BxFp7YxPVISIiIiIixyGNIRERERERkcAc7ZatCowxLjAVGARkVFptrbW/TVdhIiLSfB0oKQGgbUblfwpaln3FxbiOoU20ZfcjSJ7vs7ekmNaRCBmhcNDliEgLVOtAYozpBrwLDCA5rsSUrTpyjIkCiYjIMWzRju38ZNZbLNm1E4ARnTrz09PPYmy37gFXVjcfbtnMT997m9X5+QCM69qdn59xFkM7dQ64spbDWss/Fi3ggU8+Yn9pCWHH4cJBQ7jrtDNpE40GXZ6ItCDG2tqNWTfGPAH0A64ANgMTgd3Al4BrgfOstesbqc46yc3NtXl5eUGXISJyTFmdv4eLnnqcmOdVWB5xXV686jqGdOwUUGV1s2jHdq569inivl++zAAZoRCvX/clerXNCa64FuSPH3/Ig/M+rvB5iDgugzp04MWrr8cxpoZXi8jxxhiz1Vqbm2pdXcaQnAr8P2Db4XattRuttT8FngL+1LAyRUSkOfvz3I/wjjiJP8zzff78yUcBVFQ/9384m0Slflgg4fv8bcG8YIpqYQpjMR6a90mVcBrzPVbvzWf25k0BVSYiLVFdAkl7YKe11gcOAh2PWPceMCWdhYmISPPycd4WvBRX1T1r+WjrlgAqqp/527ennM8+7vs6ka6lVfl7qO6pAJ7vM3eb7lIQkdqrSyDZBBy+Hr8auOyIdWeSDCkiInKMahWJVLsuu4Z1zU1WuPqB1xr7UDutI1ESfupAEnL4076mAAAgAElEQVRcWkf0PopI7dUlkMwkOcMWwP3Al40xnxpj5gE/BP6Z7uJERKT5uHbEaKKuW2V51HW5ZsSoACqqnyuHj6i2H9e2oH4EaUD79vTOaUuqUSIJ3+OCQYObvCYRabnqEkh+ANwBYK19ErgUWEJygPstwJ1pr05ERJqNG0aNYXSXbhVO5qOuy8guXblx9LgAK6ubWyZMZED7DmS4n000GXVdJuX24rJhIwKsrOUwxvDHz11AVjhS/j6GHIeQ43DXaWfSvXWbgCsUkZak1rNstSSaZUtEpHEkfJ+Z69fy2prVAJw3YBDn9B9AyGlZz9mNeR6vr13NjHVrCDkuFw0awhl9+2lmqDrKLyri6WVLWLhjO91at+bqEaMY2kJmWxORplXTLFt1DiTGmD7ABKAbyRm35ltrNzasxPRSIBERERERaT5qCiR1eTBiFvAgcB1w5M23njHmceAWa21xgyoVEREREZHjSl2usf8euBK4HegNZJT99wfAVWXrRUREREREaq3WV0hIhpE7rLVHBo8twP0mec/tj4FvpLE2ERERERE5xtXlCokFllezbinVPSFJRERERESkGnUJJM8AN1Sz7ovAsw0vR0REREREjid1uWXrQ+Cnxpg5wHPALqAzcDnQA7jLGHPl4Y2ttU+ns1ARERERETn21HraX2OMX4d2rbW26mNwm4im/RURERERaT7SMu0v0DdN9YiIiIiIiAB1CCTW2k2NWYiIiIiIiBx/6nKFBABjzDnAiUAv4BfW2s3GmJOB9dba7bVs44/ARSSfYzLWWruomu1uAn5IcvD92yQfvhiva80iIk1t0Y7tPLJgHqvy99A3px03jR3PxNyeNb4m7nk8t2IZTy9fSlE8xpl9+nPjmLF0bpVdvo3n+7y8eiX/XrKY/aUlnNyzF18dO4Eebdo0uOZ9xcX8a/EiXl+7mrDrctnQYVw1fCQZoXCD2w7ae5s28uii+eQVFDCycxe+Om4Cwzp1rrDNJ1vz+PvC+azft5dBHTrw1bETGNute0AVp8+BkhL+tXgRr61dTcgYLhkyjKtHjCIr3PKPq4gcG+oyhqQD8AJwCskB7Z2AE6y1C4wx/wccsNb+Vy3bOhVYD8wGLkkVSIwxfYEPgHHATuAl4A1r7QNHa19jSEQkSC+uXMHtb07HtxbfWhzAdRzumHI6Xxo9NuVr4p7HjS89x4Lt2yn1EgBEXJesUJgXr76OXm1zsNby7ddf5u0N6yn1vOQ2jkvIdXjmimsY2rFTvWveXVjIRU89zv6S4vK2o67LgPYdePryq8lswSevf/z4Qx6Y+zEJ38MCrjE4xvDg+RdxVr/+ADyxeBE/e+8dPN/HB5yybe496xy+MHR4oPU3RH5RERf/53Hyi4oqHNe+Oe145opraBWJBFyhiBwvahpDUpdpf38H5AJjy/5rjlg3Ezirtg1Za9+z1h4tMVwOTLPW7rDJ1PQwcE0d6hURaXJF8Tj/+/YMEr6PX/aFjw/EfZ9fvj+LfcXFKV/36ppVLNi+rTyMAMQ8j4OxUn7x3iwg+S3/W0eEEYCY71Ecj3Pn2zMbVPfvPvqAvcVFFdou9TzW7t3LU8uWNKjtIG0tKOBPn3xIvCyMAHjWEvd9bn9zOgnfZ39JMT977x3iZWEEwLeWhO9z5ztvUhiLBVV+g/3pkw/ZXVhY5bhu2L+Px5ekvDlBRKTJ1SWQXEDySe2fUvUhiFtI3sKVTr2AI8etbGyEfYiIpNWcLZuqfUps2HF4e+P6lOteXLmiwknjYZ61vLNxPZ7v88rqlcRTbGOBhTu2s78kddipjVfXrCLuV51MsdRL8PyKZfVuN2hvrFtD2Ek96WNhPM7CHduYtXEDISf1P4cG+GBLyx1C+fLqldUcV4/nV1T3rGMRkaZVl0ASBfZXs64NkKhmXaMzxtxmjMk7/HPo0KGgShGR41zM8zEVLiAfyaQMFECFKyOVeWW3fpUe8S1/KqlOPGsrUcNrY9XU3BIkr4ykftccDHHPL+tf6mNmyrZpqWr6TFT3WRQRaWp1CSQLgauqWXcR8HHDy6lgM8lB74f1KVtWhbX2fmtt7uGf7OzsVJuJiDS6k3JziVUTLkq9BFN69Um57tz+A8lwU88zMqFbd8Kuy1l9+1e7Tb+cdnTMzKpXzQBTevfBMVVPyiOuy7n9B9S73aCd2qtP9WHLwJiu3Ti5V+9qA2GplzjqZATN2el9+uKmOq6Oyzkt+LiKyLGlLoHkF8ANxpingUtI3iUwyRjzB+AG4Jdpru054CJjTFdjjAG+CTyV5n2IiKRV+8wsbp4wkYhb8TahqOty7YjR1c6GdfmwEXTObkX4iFuHDBB2XO6YcjoA5w0YRN927Sq0bYCQ43DXaWdiUpx41tZ3TzqFiOtWCCUhx6FNNMqXRo+rd7tBG9qpM+cNGES00vEIOw7fn3wKWeEwPVq34YZRY6psE3Fdvj7+BDpm1T/oBe3WiZOJhkJVjmt2NMJNYycEWJmIyGdqPcsWgDHmEuD/UfEhiZuB71hrp9Whnb8Anwe6AvnAQWvtAGPMIyQHsk8r2+5rJKf9BZgFfLM20/5qli0RCZK1ludWLOPBeR+ztaCALq2y+dr4E7hu5OiUVyEO21dczO8++oCXViXHk0zskct3J53CqC5dy7c5FIvxp08+5OllSylOxBnVuSvfm3wKJ/ZIOXFJnazO38N9c2bz/uaNhByH8wYM4rZJJ9M1u3WD2w5Swvd5dOF8Hl20gPziIvrktOM7J07i84MGl29jreXfSxfzl/mfsPPQIXq0bsPNJ0zk8qHDGxT0moN1e/O578PZvLtpI44xfK7/QG6bdDLdWzd8qmgRkdqqaZatOgWSIxocAHQG9lprVzawvrRTIBERERERaT5qCiR1fjAigLV2LbC2QVWJiIiIiMhxr8YxJMaY/saYC1MsP9cYM88YU2iMWWeMuaXxShQRERERkWPV0Qa1/wS4/cgFxpgRJJ+aPgiYDhQCfzLGVDcDl4iIiIiISEpHCyQnAU9XWvZfgAucZq29DBhNMph8K/3liYiIiIjIsexogaQbsKLSsvOBudbahQA2OSr+78Co9JcnIiIiIiLHsqMFkjhHDHw3xvQCegCzK223i+ST3EVERERERGrtaIFkJXDuEf9/IckHIs6otF1PkqFERERERESk1o427e8fgH8bY3JIBo5vAKuAdyptdz6wOP3liYiIiIjIsazGQGKt/Y8xpgdwK9AemAvcYq31Dm9jjOkCfA64szELFRERERGRY0+9ntTe3OlJ7SIiIiIizUdNT2o/2hgSERERERGRRqNAIiIiIiIigVEgERERERGRwCiQiIiIiIhIYBRIREREREQkMAokIiIiIiISGAUSEREREREJjAKJiIiIiIgERoFEREREREQCo0AiIiIiIiKBUSAREREREZHAKJCIiIiIiEhgFEhERERERCQwCiQiIiIiIhIYBRIREREREQmMAomIiIiIiARGgURERERERAKjQCIiIiIiIoFRIBERERERkcAokIiIiIiISGAUSEREREREJDAKJCIiIiIiEhgFEhERERERCYwCiYiIiIiIBEaBREREREREAqNAIiIiIiIigVEgERERERGRwCiQiIiIiIhIYBRIREREREQkMAokIiIiIiISGAUSEREREREJjAKJiIiIiIgERoFEREREREQCo0AiIiIiIiKBUSAREREREZHAKJCIiIiIiEhgFEhERERERCQwCiQiIiIiIhIYBRIREREREQmMAomIiIiIiARGgURERERERAKjQCIiIiIiIoFRIBERERERkcAokIiIiIiISGAUSEREREREJDAKJCIiIiIiEhgFEhERERERCYwCiYiIiIiIBEaBREREREREAqNAIiIiIiIigVEgERERERGRwCiQiIiIiIhIYBRIREREREQkMAokIiIiIiISmFDQBYjIsclay6ezlvHhy/MwxnDyJScy4pQhGGOCLk1ERESaEWOtDbqGtMvNzbV5eXlBlyFy3ErEE9x92X0smPkpXtzDAm7IZfJFE7jjyVtxXTfoEkVERKQJGWO2WmtzU63TLVsiknbTHnyDhW8uJl6awPct1rckYgk+emU+b/zjnaDLExERkWZEgURE0m7ag28QK4lXWR4rifPSA9MDqEhERESaKwUSEUm7gvyD1a47sLugCSsRERGR5k6BRETSbvAJA1IOXndch6GTBgVQkYiIiDRXCiQiknY33HU5bqjqrxfHMVx7xxcCqEhERESaKwUSEUm7YZMGc/cLt9OpZwcc18FxDF37dOYXr/yIgeP6BV2eiIiINCOa9ldEGo21lrzV2zDG0GNgNz2DRERE5DhV07S/ejCiiDQaYww9B/cIugwRERFpxnTLloiIiIiIBEaBREREREREAqNAIiIiIiIigVEgERERERGRwCiQiIiIiIhIYBRIREREREQkMAokIiIiIiISGAUSEREREREJjB6MKMcd3/dZNXcdxQeLGTShP9k5rYIuSUREROS4FUggMcYMBP4JdAQOADdaa5dV2uZ04HVg1RGLJ1lri5uqTjn2LP1gJb+8+ncc2F2AE3Lw4h6X33YhX7nnWowxQZcnIiIictwJ6grJX4C/WmsfM8ZcDjwGnJBiu1XW2jFNWpkcs3bn5fODc35OrDiWXFD2n+d+/yptOrbmiu9eFFxxIiIiIsepJh9DYozpDEwAHi9b9BzQ0xgzoKlrkePLq3+ZibW2yvJ4aZyn7n0R3/cDqEpERETk+BbEoPaewHZrbQLAJs8QNwO9Umzb3xizwBgz1xhzS1MWKceeVfPWEi+Jp1xXkH+QwgNFTVyRiIiIiDTnQe0LgFxr7QFjTC7wmjFmj7X26cobGmNuA247/P9t27ZtwjKlpejWrwtu2MWLe1XWRTIjZGZnBFCViIiIyPEtiCskW4BuxpgQgEmOJO5F8ipJOWttgbX2QNmf84AngSmpGrTW3m+tzT38k52d3agdkJbpgm+ck3J5OBrmvK+cSSjcnPO5iIiIyLGpyQOJtXYXyasf15ctugzIs9auPXI7Y0w3Y4xT9ufWwAXAwqasVY4t/Ub15jsPfA035BDNihCOhgmFXYZNGsRXf3390RsQERERkbQzqQb5NvpOjRlMcmatDkAB8GVr7RJjzCPANGvtNGPMt4GbgQTJW8ueAX5qa1Fwbm6uzcvLa7T6pWXbt3M/7z37EcUHixl56jCGTRqkKX9FREREGpExZqu1NjfluiACSWNTIBERERERaT5qCiRBjCEREREREREBFEhERERERCRACiQiIiIiIhIYBRIREREREQmMAomIiIiIiARGgURERERERAKjQCIiIiIiIoFRIBERERERkcAokIiIiIiISGBCQRcgtWOt5ZWHZ/Cf37zE7q35dO7Zkat/cAnnf+1sjDFBl1ettQs38MiPnuDTWcsIRUKcdsUkOvXswOuPvM2+Xfvp3r8r1995OWddN6XB+9q7Yx+P/vgpZv1nDolYgjFnDOeme65jwNi+DW572ZxV/P2Of7P8w1VEMyNMveE0vvSzq2jdLrtB7Vpree1vb/LUr19k15Y9dOrRgatuv5gLvnlOsz6u9WGt5fW/v81T977Azs276di9PVd+/2IuvPkcHEffjYiIiByvjLU26BrSLjc31+bl5QVdRlo9cOujvPrXmcRL4uXLwhlhLv32eXztNzcEWFn1Vs9fx62n/JhELMHhz5kxUPkjF46G+OLdV3H1Dy6p974O7jvE10d/j/0795OIe2X7MoQiIf7wwS8YOK5fvdv+9N1l/PCcn5e3CxCKhOjapzMPLfgNGVnRerf9t9v/xQt/fr3CcY1khDn/61P51u+/XO92m6O//+gJnvv9q8RLK/b1czedyX/96asBViYiIiKNzRiz1Vqbm2qdvpZsAXZt2cO0B6ZXOGkFiJfEefZ3r7B3x76AKqvZ337wOPFYnCNDb6r8Gy9N8H93P01hQVG99/XKX2ZSsKegQmiw1hKPxXnkh0/Uu12Ah/7nsQrtAiRiCXZt2cPbT7xf73bzt+/j2d+9UuW4xkriTHtgOrvz8uvddnOzb+d+nvl/0yqEEUj29ZWHZrBr8+6AKhMREZGgKZC0AAvfWkIkI5xyXSQjzKJ3ljVxRUdnrWXxrGVQywtwjmtYPmdVvff3wQsfE6t0Yp8sBBa9s5T6XgksPlTMukUbU66LFcf44MVP6tUuwKfvLK3xuC58a0m9225uPp21jHC0mr5mRljw5rHTVxEREakbBZIWIBwNV3tiby3VnugFzQm5td7W+rZB/QhXc2IP4Ibceo/HcFynxtdGMiP1aheSx626nNScj2t9HE99FRERkbpRIGkBTjxvLImEl3Kd9X0mnDOqiSs6OmMMUy6biFvLUBLJiDD85MH13t/UG05PGQ7ckMuUyybWu91oZpRxU0elHHQdyQg3aDD++HNGY30/5Tov4XHieWPq3XZzM+7skVSXqr14ghPPH9u0BYmIiEizoUDSAmTntOK//nQTobCL4yS/rXdch1DY5daHv0FmdmbAFab21Xuvp3X77ApXL5yQgzGUX3VwQ8l+fO8ftxCO1P9b8qlfPJVB4/tVCCXhjDCt22dz06+uq38ngG//8Stkts4gHP1sUrpIRpgxZ41k0kUT6t1uVutM/vuhryePq5v8q3j4uP7Xn26iVdtWDaq7OcnMzuTWh79Rsa+OIRR2+dYfb2rwbGUiIiLScmmWrRZk2ZxVPHv/y2xZtY3ew3K5/LYLGTpxYNBl1Wj/7gO89MB05rw4l2hWlHNvPJ3OvTsx7cHp7Fi/i/5j+3D5bRcyYEzDp+aNlcaZ8dgsZvzzHUqLYky+5AQu/tbnyOnUtsFt79m2lxf+8Cpzpy8iq20W5990FmddN6XWV4Bqsvyj1Tz3u1fYtDyPnoO7c/ltFzJ8cv2vFjVnKz9Zw7P3v8zGZcm+XvY/FzDi5CFBlyUiIiKNrKZZthRIRERERESkUWnaXxERERERaZYUSEREREREJDAKJCIiIiIiEhgFEhERERERCYwCiYiIiIiIBEaBREREREREAqNAIiIiIiIigVEgERERERGRwCiQiIiIiIhIYEJBFyD15yU85rw0l1lPz8H3fKZcdhKnXn4SoXDLOqzxWJz3n/2I95//GMd1OP2qk+k7sievP/IWG5fl0Wd4Lud/9WzWL9nMrP98kOzrFyYy5fKTCEfCQZd/XFj36UZe+9ub7Ny0myEnDuDsG07j01nLmDNtHpGMMGddO4UTzx+L49TtOw7P8/jo5fm889QHJOIJTr7kRE67cjKRqI6riIjI8cJYa4OuIe1yc3NtXl5e0GU0qngszh3n/ZLlH64mVhIHIJIZoe/IXtz39t1kZEUDrrB2igtL+N4ZP2HjsjxixTEAQmEXL+ETirjESxOEIiES8QShcIhELAEk+9pneE/ue+duMltlBNmFY960h97gwVsfBcCLe4SjyeMQioSIlybAQDgS5oTzxnDXM9/Fdd1atZuIJ7jr4l/z6axlFT7DPQd35/53f0ZW68xG65OIiIg0LWPMVmttbqp1umWrhXrl4ZkVwghArDjGhsWbePb+lwOsrG6e/u1LbFi6pTyMACTiHtba5MkuJEOIpTyMQFlfl27mmfumNXnNx5Pdefk88J1/4MU9vLgHQLw0gbWUHx8sxEvjzJu+iHee/KDWbc94bFaFMALJ47p5xVae/NULae2HiIiINF8KJC3Uq3+dWeFE7rBYSZzXH3krgIrq5/VH3iKeoh+1EW9hfW2JZv1nDqFI7W4BjJXEee2RN2vd9ivVfIbjpXGm/0PHVURE5HihQNJCFR0srnZdcQ3rmpviQyWBvl5qVlRQhJfwar39oX2Ftd628EBRtetKCktr3Y6IiIi0bAokLdSYM0bghqreq++4DqNOHx5ARfUzcspQjGPq9VrHMYw8dViaK5IjjZwyFKeWxyccDTPh3DG1bnv81FGEwlU/w8YYRpwypNbtiIiISMumQNJCXfOjS3HDLlQ6V3RdhxvuuiKYourhSz+9KmWwqg0n5PLFn7ScvrZEY84cQb9RfQhHa75ty3EM4WiIS79zfq3bvuJ7FxGKhDCm4ofYDbl86adX1ateERERaXkUSFqonoN78Lv3fsbQiYPKlw0c15ffvn03/Uf3Ca6wOho4rh+/ffMuBoztW75s6EkDOffG08nITs6elZGdwTk3ns7Qkz7r64Cxffntm3cxcFy/Jq/5eOI4Dr+e+WPOuv7U8rEkbTq25oJvTqX38J7JjQyMPHUYf/zwHjrldqh12936duEPH/yS4acMLl/Wb3Rv7p1xJ0NOHJjWfoiIiEjzpWl/jwGFBwqxFrJzWgVdSoMc2l+IcQyt2mQByWlhD+0vJDunVfmzVQoLirC+bfF9bYlipXGKCopo3T67fGrfgr0HCYVDDZ6it7CgCN/zad0uOx2lioiISDNT07S/CiQiIiIiItKo9BwSERERERFplhRIREREREQkMAokIiIiIiISGAUSEREREREJjAKJiIiIiIgERoFEREREREQCo0AiIiIiIiKBUSAREREREZHAhIIu4Fi0fcNOls9ZTVabTMZPHYUbcln49lL27dzPwHH96DO8J3u25rP43eVEMiOMP2c00cwIi99dzu68fPoM78nAcf3Yt+sAi95eiuM6TDhnFK3aHv3p5NZaVs9fz+YVeXTu1ZGRU4ZSUljK/JmLiZfEGH3GCDp0a1fldaXFpcyfsZjiQyWMOGUIXXp3Ysuqrayau442HVoz7uyR5U9Lr4nneSx6eyl7d+xnwNi+9B3Rq17vYW2tnr+OTcvz6NyzIyNPHYrjVM3YG5ZuZu3CDbTvmsOYM0eUP2X8SHmrt7Hyk7W0bp/NuLNHYoxh/szFHNx7iCEnDiB3UPcqr6nQ1zF96DuyN/nb9/HpO0sJZ0SYcM4oollRlry/gl2b99BraC6DxvejIP8gC99agjGGcVNH1frp5GsWrGfjsi10yu3AqNOGUVocY/6MT4kVxxh12jA69ujAxmVbWLNgPe265DD2zBF4CY95Mz6l+GAJwyYPolvfLlXaTcQTLHhzCQX5Bxl8Qn96Du7Brs27WfL+SjKzMxh/ziiimdGj1metZcn7K9i5aTe9hvRg0IT+GGNq1bfK1i7cwIalm1P2deSpw+iU24FNy7ewev56cjq3ZdxZI/ESHvNnLqaooJhhkwbRrV/VvoqIiEjzo0CSRol4gvu+8iDvPPUBkYwI1vfBMYTDIWIlMZyQQ2lRjA7d27N3+z4iGWGsBd/3yczOoPhgCW7YJVYSo32XHPbu2E8kIww2efJ78/03cuHN51a7/327DnDnBb9i3acbiUTDJOIeWa0zKDpYjOO6GAOxkjiXfud8vnHfF8tPFudMm8u91/8R3/MxjqG0qJSOuR3I37aPSGYE3/PJyIpw9/O3M3LK0Gr3v3r+Ou688F4K9xfihBziJXFGnTacu5//PlmtM9P6Xu/fnezr2kWH+5qgQ7f23PP6HfQc3AOAwoIifvqF+1j83jLCGWH8hE92Tit+8cqPGDiuHwCxkhj3XPsHPnx5Xnlf3VDyvUrEPRzXIVYc46QLxnPHv/+7/MR87cIN/O8Fv+LQvkM4oeQ27btVOq6eT1abTIoKigiFQ8RK4+R0asv+XQcIR5N/9RJxj6//9gYu/a/zq+3rgT0F3HnhvaxZsJ5wNIxXdlyLD5VgHAdjoLQ4Rsce7cuOWbKvkYzkZ8D6FuM4xIpLOfPaKXz37zeXh8uls1fwk0t/S0lRKY5jPmtn614imRGstTiOww//9R0mX3xCtTXmrdnO/57/S3bn5RMKh4iXxuk7she/fPUO2nXJqfVxLcg/yI8vupdVc9eV15+ZnUFJYQnGMRhjUvY1HA3jJY7oa0mM066czO2PfatWQVpERESCY6y1QdeQdrm5uTYvL6/J9/v3O57g+d+/Sqwk3ijth8Iuv5p+J2POGJFy/X+f/L+snreORNyrsZ1IRphv3n8jF37zHPJWb+NrI79LIp446v6jmREe3/ggOZ3aVllXdLCYa3t9k6KCIo78SIWjYU66YDx3PfPdo7ZfF/9z6o9Z+fGaCn11HEO7rjk8vuFBQuEQd1/2Wz55bSHx0s+OhzGQ1SaLJ7c8TGZ2Jn+4+a+88disCtukEs4Ic84XT+PWh79B8f9v787jq6rv/I+/PnfLBiGsIRgISFhk0YDg2ioWFGUc6mhrZcaq1FarduxU7WZbrXa1Puqjddpxa39DW+1UWztI+5NNq2gVCwiIiAsoW9iRPWS5N/nOH+ckXpJ7k5Dc5EB4Px+PPOB+zznf+/1+zsk955Pv95xbUcWMgTdyeH8lmfj9icQi3PvM15k4tSzl8tsvuJs1i98jUdPyPmpJLDvKFV+5lM/94F/Zv/sA/zb4JqoP17TcxmiYR1f9tCHZS1ZbW8s1Q7/E7i17qKutO2Kb0vEn85+Lf9jq9n1tyr28+fe3M9bXy26dxhd+fHW76xIREZH2MbMtzrniVMt0D0mGJOIJZv9iXoclIwC1iTqeuv+ZlMs2vLWZd5euazEZAW+U5Mn7ZgMw56EF0MpZNQ5Y+JtFKZe9+OSrJOIJGl+fx6vjvDJ7CXt37Gvdm7TCpne2eBfojfpaV+c4uLeCJc+uYM/2vSyes6xJouGct69efPJVKiuqmPffL7SYjADEq+LMn/Uihw9WsuipxSRqEhlJRgBq44m0+7X8va2s/vs7GblAB2/fz/7PuSTiCRb+dlGT/ZWWwZz/mp9y0bL5b7Bv5/4jkhHwRn/Wvv4B69/c2Kq32Pr+dt5Y9FZG+zrnl/OI13Tc76SIiIi0nxKSDDm4t4KqQ1Ud+h7OOTa/szXlsq3rthPNjrW6rl2bdwOwYfWmVl8A1lTWsOmd1CNPW9ZuS5uMRaJhtm/Y1eq2tWTruu3EctL3dcvabWzfsItIrF0Lv14AABWgSURBVOm9IgDxqgRb1m5jz7a91CVaTuDq1dXWsWfbXras3Ua8OjMXzeAlSeXvptmv7+/wpu1lUOWhKg7tq2DzO1uoqWx5dAQgUVPL+tWbUi7bsnZb2ntFotlRtqzb3qr36Ii+VlVUc3DPoYzWKSIiIpmlhCRDuvfMIzuv5Rt/28WgeHhRykVFJ/cjfhSjM71P6gVAyahiIrHWzbGP5cQoHt50yg7AgKGFaS8mE/FaCkv6tLptLfH6mv5CumhoIYUlfUlUp042otkRBgztT6/+BYQiqZOWVEIho1dRTwYMLWy4ByQTzGBAaf+Uy4pO7pfxUbfsvCy6FeRRPHxAs4ldskg0QskpKUdZGTC0f9rRonhVnAFDU/etsf5DMt/XrNwsuvdq3UMDREREJBhKSDIkEo3wzzdNJZrhv/A2fo9P3zE95bIhY0soHT+EcLTlC+xYdpTPfO0yAO8m+dbO23GOi649P+WiSVedS9i/cT5ZNCvCWZeeTq/+TZ/s1VYlowYyYmIpkUZ9tZCR1yOXsy49nd5FPTnz0vFNEgczCIfDTLrqXHK65XDhNee3ap9Fs6JM+ex55HbPYdJnzmm48T0TwtEIV371kymXDRxxEqPOHt6kr20Vy44y/eapRKIRplxzfuv3PY7pt1yccsnEi8vI792dUOjIgISjYYaWDebkU0ta9Q7Fw4oY87GRrU6QWxLLjvLPN11ENNZxv5MiIiLSfkpIMmjm96/inOkTCIVDZOdlkZUbI5YdJTc/h1hOjOy8LEJho1dRAaGQ+etkEYlG6NYzj1h2lOy8LMKRED0LCxrqyc7LIhwNc8NPrmb8lFPTvv+9s79GySneiEd2Xhax7CjdeuYRiYYb6gmFQ1zy+clMv9l7WtegkSdx5/98hazcGFm53jrmjwQ09CMnRl6P3GafmJSXn8t9C79Dfp/8hr6GI2FGnjGMr826JeOx/u6fv0rJ6IENfYtlR+k9oBf3P3dXw1OVvv6bLzHyjGGEI/46OTHy++Rz38LvNDz165afz2T85LFH9LU+Vlm5sYaYlX1iDLc8eD0AOd1y+MnCu+iR1NdQOOSNuPj7NTsvi0g0TPfk/RoNU9CvR5P9OvP7Mzhz2vi0fb3rT3cwZGzJEX3t1jPP28/1+8ygV1EB4Uiooa853b3jLrkfZ0+fwMzvzwCgZ78e/HDut8gryG3ot3d8Ju37XC8O33ziy2lHSMKRMD957i76DOxN1O9rJBZh0MiTuPeZrx/Vfv3OU7dx8thBR/a1IPfIvvrHZ0t9PevS0/ncD2Yc1fuLiIhI59NTtjpA+XtbWf3Ku+Tl5zDxknFEomFeX/AGe3fsp3T8EErLhrBj4y5WvrCarJwYZ0wbT1ZujBXPr2Z3+YeUjB7IyDNK2bN9H8sXriIcCTHh4jLye3Vv8b2dc7z92ntsXFNOYUlfyj4xhsqDlSyZu5J4dZyyT4yh38Cm06cqK6pYOncFhw9Wcep5pzBgaH/Wr97Eu0vWkd+7OxMuLiOW1fJfmhPxxEd9HTeE0nFD2hTD1nDO8fY/1rJpTTl9B/ZO+x0ja5d/wPsrN9CzfwGnX3hqysfAblyzmbdfW9vQV4Bl81Zy4MODjDxzGINHD2yyTSKe4PWFq9i7fR9DywYzbPzJ7Ny8m5V/W00sO8rEi8vI6Z7Dyr+tZsfGXZSMKuaUs4azb+d+li14g1Ao1DC60Jq+vrNkHRvf2kyf4t6MmzyGqopqls5dQXVlDWUXjKGwpC/rVq5n3fL19CzswekXnUYiXsvSuSuoOFDJmI+NpHhY0yl/NdVxls1fyYHdBxlxRilDxgxi6/vbWfXS2+R2z2biJePIyctusY11dXWsfOEtdmzYyaBTihl19vA2fQ+Jc453l65jw+rN9D6pF+OnjKX6cA1L566g6nA1p00aTf/B/Xj/jQ2s9b+HZMLU06hN1LJk7koq9h9mzLkjUn53jIiIiASjuadsKSEREREREZEOpcf+ioiIiIjIMUkJiYiIiIiIBEYJiYiIiIiIBEYJiYiIiIiIBEYJiYiIiIiIBEYJiYiIiIiIBEYJiYiIiIiIBEYJiYiIiIiIBEYJyQnKOcehfRUk4omj2i5eE6difwX1X6jpnKPiwGFqquMd0cwTWtXhaioPVWa8XuccFfsriNdon4mIiEjwIkE3QDqXc46/PLSAx7/3R/bu2E80K8rU6yZxw/2fJadbTtrt9u8+wENfmcWLT71KbbyWwpK+fOzyM3ntr6+zZe02QuEQ53xyIrc8+Dn6DOjViT3qetav3sQvb/1/vLHoLXAwYmIpN//sOkadPaLddb/wh1f49TefYMfGXYQjYc779Fnc/LOZFPTtkYGWi4iIiBw9q/9Ld1dSXFzsysvLg27GMel/fvRnfnfvn4gnjWhEsyKUlg3h56/+ADNrsk1NdZwbT7ud7Rt2kqipTVt3OBKmZ2EPfr3mZ+R2T5/cSHrb1u/ghlNvp7qyBlf30e9mNBbhwcU/pHTckDbXvfC3i3jghodJ1Hw0KhaJRuhX0ofHVv2UWHasXW0XERERScfMtjjnilMt05StE0hlRRVPfP/pI5IRgHh1gnUr17Pi+TdTbvfSHxeza/OHzSYjALWJWg7uOcSC37yYqSafcJ66/xkSNYkjkhHwYjvrrifbXG9dXR2PfePxI5IRgEQ8wYdb9rDoqcVtrltERESkPZSQnEDWr9pIXZoRsbpax6pFa1IuW/H8m1RX1rTqPaora1g2f2Wb23iiWzb/DRLxpolfXZ1j1UtvtbneXZs/ZO+OfSmXVVfWsPy5VW2uW0RERKQ9lJCcQHK651CXqEu5LBwJkZNmmlVej1zCkdYdKhYyuhXktbmNJ7rc/PRT3bJzs9tcb3ZeFqSZnRkOh8gryG1z3SIiIiLtoYTkBDJ49EAKB/clxW0iJOK1TPrMOSm3m3z1eaTcKIVINMxF105qRytPbNM+P4VYdrRJeTQrytSZF7S53h598hl73imEwil+5UPGlKvPa3PdIiIiIu2hhOQEYmbc+cSXycrJarjoDUdChKNhbrj/sxSW9E253YgJQ7niPy4lEosQCnmJSTQripkR9esxM6JZEaZ89nzGTR7bOR3qgqZ9YTKjzh5+RFISy4lRPKKIGd/8l3bVfdtjN9GtII9YjnfzeihkRGIR/uXWaYw8Y1i76hYRERFpKz1l6wS0e+se/vrIAt5evJZ+JX249MaLGDFhaIvbrXppDc/+6jn27TxA2QWjOf/Kc1j8zDKWzFtB9555TJ35CU6/8NSUT+qS1qtN1PL3/13C337/MrWJWj5+xVlccNW5GXkK1oEPDzL318+z4m+rKeibzyXXT+a0SaMz0GoRERGR9Jp7ypYSEhERERER6VB67K+IiIiIiByTlJCIiIiIiEhglJCIiIiIiEhglJCIiIiIiEhglJCIiIiIiEhglJCIiIiIiEhglJCIiIiIiEhglJCIiIiIiEhglJCIiIiIiEhgAklIzGyYmb1qZu+Z2VIzG51mvevNbK2ZvW9mj5lZtLPbKiIiIiIiHSeoEZJHgEedc8OB+4BZjVcwsyHA94CPA6VAIXBDJ7ZRREREREQ6WKcnJGbWD5gAPO4XPQ0MNLPSRqt+CpjjnNvunHPAw8CMzmupiIiIiIh0tCBGSAYC25xzCQA/2dgEDGq03iBgY9LrDSnWERERERGR41iXuKndzG4zs/L6n0OHDgXdJBERERERaYUgEpLNQJGZRQDMzPBGPjY1Wm8TUJL0enCKdQBwzj3gnCuu/+nWrVvmWy0iIiIiIhnX6QmJc24nsBy42i+6Aih3zq1rtOrTwHQz6+8nLV8E/tB5LRURERERkY4W1JStG4Ebzew94BvATAAz+5WZTQdwzn0A3A28AqwDduE9nUtERERERLoI8+4p71rMrBovgQlaN0A3tHQOxbpzKM6dR7HuPIp151GsO49i3TkU59br65zLSrWgSyYkxwozK3fOFQfdjhOBYt05FOfOo1h3HsW68yjWnUex7hyKc2Z0iadsiYiIiIjI8UkJiYiIiIiIBEYJScd6IOgGnEAU686hOHcexbrzKNadR7HuPIp151CcM0D3kIiIiIiISGA0QiIiIiIiIoFRQiIiIiIiIoFRQtIBzGyYmb1qZu+Z2VIzGx10m7oCM8s2s9l+XN8ws4VmVuov62dm88xsrZmtNrPzgm5vV2FmM83Mmdll/mvFOsPMLMvMfuHH9E0ze9wv12dJhpnZNDNbbmYr/eP3Wr9cx3U7mNmDZrbB/6woSypPewzr+G6bVLFu7vzoL9fx3Qbpjuuk5UecH/0yxboNlJB0jEeAR51zw4H7gFnBNqdLeRQY4Zw7DXgG+JVf/mPgNefcMGAm8HsziwbUxi7DzAYDXwBeSypWrDPvx4ADhjvnxgJ3+OX6LMkgMzPgceA651wZcCnwiJl1R8d1e/0J+BiwsVF5c8ewju+2SRfrdOdH0PHdVuline78CIp1myghyTAz6wdMwDvpATwNDEz+S4W0jXOuyjn3rPvoSQyvAYP9/18JPOyvtxTYCpzf6Y3sQswshHdC+3egOmmRYp1BZpYHXA98q/7Yds5t12dJh3FAgf//fOBDvONbx3U7OOdecs6VJ5c1dwzr+G67VLFu4fwIOr7bJFWsodnzIyjWbaKEJPMGAtuccwkA/8NhEzAo0FZ1TV8GnjGz3kDUObc9adkGFPP2ug14xTn3en2BYt0hhgJ7gDvNbJmZvWxmk9FnScb5MfwM8Gcz2wj8HbgW6I6O647Q3DGs47tjfRlvlESf2x2jyfkRFOv2iATdAJG2MLM7gVJgMpATcHO6HDMbA1wBaO5rx4sAJcAa59w3zGwcsBD4p2Cb1fWYWQT4NnC5c+4lM5sIzAGazA0XOV41Oj9Khun82DE0QpJ5m4Ei/8RXP2d5EN5ffiQDzOwO4HLgEufcYefch0DCzPonrTYYxbw9Po4Xw7VmtgE4C29+8pUo1pm2CagDngBwzq0A1uMlKfosyawyYIBz7iVomE5RDpyKjuuO0Nz5UOfKDtD4/Aigc2TGpTw/mtlNinXbKSHJMOfcTmA5cLVfdAVQ7pxbF1yrug4zuw2YAVzonNuXtOiPwBf9dSYCJwGLOr+FXYNz7iHnXJFzbrBzbjDefOQbnHMPoVhnlHNuN/A8MBXAzIYAQ4BX0GdJptVfBJ8C4N+vMBR4Fx3XGdfc+VDnysxr5vwIOr4zpoXzIyjWbaJvau8AZjYC72khvYEDwEzn3JuBNqoLMLNivAuKD4CDfnG1c+5MMysEfod3IVcDfMk590IwLe16zOxF4GfOudmKdeaZ2cnAr4E+eKMl9zrnntZnSeaZ2QzgTrw4h4AfOed+r+O6fczsEbxphv3xHhRw0DlX2twxrOO7bVLFGphEmvOjv42O7zZId1w3WudF/POj/1qxbgMlJCIiIiIiEhhN2RIRERERkcAoIRERERERkcAoIRERERERkcAoIRERERERkcAoIRER6YLMLN/MNprZt4Nui4iISHOUkIiIHKPM7Ltm5sxsSYpl3zCz5h6T+CCwFPhBhzUwYGb2sP/FZJmoa4OZPdzM8uv8fdE/3Tpptpvkb3dW+1spItI1KSERETn2TTSzaa1d2cwuA8YD1zo92z1T/j9wNt53EYiISAYpIRERObZV431r+92t3cA5N9s5d6pzrqLjmnV0zCwr6Da0h3Nul3PuNedcPOi2iIh0NUpIRESOffcAZ5jZxelWMLPB/tSgqxqVHzG1K2kK0SVm9nszO2hmW83si/7yz5vZB2a238yeNLP8RvXlm9mDZlZuZtVmttrMrmy0ziwze8fMppjZ62ZWDcz0l11mZsvNrMrMdpnZY2bWs6UAmNkIM3vezCrNbLOZ3ZZinUIz+5WZrfPXW29mD5lZQUv1t+L9j5iylRTva8zsATPbbWY7/f7kpqiihx+XA2a2xcx+ZGbhzmi7iMixLhJ0A0REpHnOuYVmthhvlGRehqr9L+C3wOXAdcBDZjYUGAfcChQBPwfuBf4DwMyiwAJgoF/+AXAZ8Acz2+ecW5BUfyHwKN49LB8AO81sOvBn4H+BbwMlwI+AsWZ2rnOuNlVDzSwbWAjEgc8BVcC3gP5AImnV3sAB4Kt4U6sG++vNAc47+hC1yveAucC/AmPx+rMLuLPReg8Bf8CL9yS/XevxYhRU20VEjglKSEREjg/3APPMbKpzbn4G6pvtnLsbwE92rgCuAYY45w775WcCn8JPSPAuuicCpzvnVvplC83sJLwEJTkhKQA+6Zx7qb7AzH4HrAA+VX9vi5ltx0tSpgF/SdPW64BiYKxz7i1/u5eBjXgX/wA459YADSMnZvYqXjL0spmN8pdn2grn3Bf9/y8ws4nAp2makMxxztWXPWdmU/z1Hg2w7SIixwRN2RIROQ74Scg/OIp7SVrQkDw45w4BW4HF9cmI7z2gKGlq0UXAGmC1mUXqf/BGL8YnT0EC9jdKRroBZcBTjW60nw1UAh9vpq1nAm/XJyN+m3cDi5JXMs+/+9PIKvBGVF72Fw9vpv72aJwcrsEbQTqq9QJqu4jIMUEJiYjI8eMe4GwzuzADde1t9LoG2JeiLARE/df9gDF4F8vJP7/w1ylK2nZHo7oKAAO2Jxf6yclOoLn7SIr8dRpr/B634k0zm4834nMm3hQpgOxm6m+PVHFMdQN/qvWS2xRE20VEjgmasiUicpxwzs31v5PkbuCvjRZX+f/GGpX3ymAT9gBv4U2hSiU5aWj8uOF9fllhcqGZGV6i0/iCPdk24IwU5YWNXl8JPOucuz2p/guaqfdYcjy3XUSkXTRCIiJyfLkHOBeY0qh8J95f3UfVF/gX+5kYTam3ABgC7HDOLUvxU5NuQ39a2Eq8+yaSTQdy+Gh6Uir/AE4xs+S+9QHOb7ReLl4Mkl3TbI+OHcdz20VE2kUjJCIixxHn3LNmtgyY3Ki8zsz+BNxsZuuAcuB6MjtC8ju/zhfN7KfA20B3vGlcg51zN7Sw/XeB2Wb2FPDfwCDgx3gJx7PNbDcL7ybxv5jZt/BGg75N01GV+cAdZnY7sAr4JEf3hKqTzexTKcrT3WyfSe1tu4jIcUsJiYjI8eceUl8k34o3Zet+vC9U/CWwHPhhJt7UOVfj37/yHeAOvCdf7QXexEswWtp+jpldDtyFdzP7AeBp4KvpHvnrb1dlZhfhPap4Ft6TtR4ARgDJ381yL97jc7+Jd0/LPGAGXsLTGheSekSpKEVZprW37SIixy078mEnIiIiIiIinUf3kIiIiIiISGCUkIiIiIiISGCUkIiIiIiISGCUkIiIiIiISGCUkIiIiIiISGCUkIiIiIiISGCUkIiIiIiISGCUkIiIiIiISGCUkIiIiIiISGCUkIiIiIiISGD+D6+Ohdds3ulYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 960x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crie um Gráficos de Dispersão (scatter Plot) da variável sepal length versus número da linha, \n",
    "# colorido por marcadores da variável target\n",
    "plt.figure(figsize=(12, 8), dpi=80)\n",
    "plt.scatter(range(len(df)), df['petal width (cm)'], c=df['target'])\n",
    "plt.xlabel('Número da Linha', fontsize=fontsize)\n",
    "plt.ylabel('Sepal length (cm)', fontsize=fontsize)\n",
    "plt.title('Gráfico de Dispersão dos Atributos, colorido por marcadores da classe alvo', fontsize=fontsize)\n",
    "#plt.title('Scatter plot of features, colored by target labels', fontsize=fontsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'petal width (cm)')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crie um Scatter Plot de 2 Features (atributos)\n",
    "plt.figure(figsize=(12, 8), dpi=80)\n",
    "plt.scatter(df['petal length (cm)'], df['petal width (cm)'], c=df['target'])\n",
    "plt.xlabel('petal length (cm)', fontsize=fontsize)\n",
    "plt.ylabel('petal width (cm)', fontsize=fontsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fae78a279d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fae78a5f9d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fae988b4b90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fae98900710>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fae98935d90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fae58c61450>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fae58c97b50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fae58cdc150>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fae58cdc190>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fae58d11910>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fae71000550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fae690f6bd0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fae69139290>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fae6916e910>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fae691a6f90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fae883d8650>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crie um Scatter Matrix das Features (atributos)\n",
    "attributes = ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
    "pd.plotting.scatter_matrix(df[attributes], figsize=(16, 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fae9867bc10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fae481a1650>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fae71087c90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fae58dcf350>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fae884ae9d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fae28002b90>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crie um Histograma de todas as features\n",
    "df.hist(figsize=(12,12))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}