Credit_Approval_Data_Set.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oiR-0Keu12_D"
   },
   "source": [
    "# Credit Approval Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "--G3LPzB7pmR",
    "outputId": "a617e9e8-e4c6-4ff1-f674-732674b01e8a"
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "#from google.colab import drive\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#drive.mount('/content/gdrive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a2XXjHeClgo8"
   },
   "source": [
    "# Descrição dos Dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XecdjZ-WsGz-"
   },
   "source": [
    "Fonte dos dados: https://archive.ics.uci.edu/ml/datasets/Credit+Approval\n",
    "\n",
    "Os dados trabalhados dizem a respeito de pedidos de cartões de crédito.  Todos os nomes de atributos e valores foram alterados para símbolos sem sentido para proteger a confidencialidade dos dados.\n",
    "  \n",
    "Este conjunto de dados possui uma boa mistura de atributos: contínuos, nominais com pequenos números de valores, e nominais com maior número de valores. Existem também alguns dados nulos.\n",
    "\n",
    "O problema a ser tratado com esse dataset é um problema de classificação, a variável resposta é a A16, um pedido de cartão pode ser classificado como + (aprovado) ou como - (reprovado)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m6AyR6SJxFe5"
   },
   "source": [
    "Informações sobre as variáveis\n",
    "\n",
    "```\n",
    "    A1:\tb, a.\n",
    "    A2:\tcontinuous.\n",
    "    A3:\tcontinuous.\n",
    "    A4:\tu, y, l, t.\n",
    "    A5:\tg, p, gg.\n",
    "    A6:\tc, d, cc, i, j, k, m, r, q, w, x, e, aa, ff.\n",
    "    A7:\tv, h, bb, j, n, z, dd, ff, o.\n",
    "    A8:\tcontinuous.\n",
    "    A9:\tt, f.\n",
    "    A10:\tt, f.\n",
    "    A11:\tcontinuous.\n",
    "    A12:\tt, f.\n",
    "    A13:\tg, p, s.\n",
    "    A14:\tcontinuous.\n",
    "    A15:\tcontinuous.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ao procurar online sobre o significado das variáveis encontra-se a seguinte resposta:\n",
    "\n",
    "\n",
    "```\n",
    "'data.frame':   689 obs. of  16 variables:\n",
    " $ A1 Male          : num  1 1 0 0 0 0 1 0 0 0 ...\n",
    " $ A2 Age           : chr  \"58.67\" \"24.50\" \"27.83\" \"20.17\" ...\n",
    " $ A3 Debt          : num  4.46 0.5 1.54 5.62 4 ...\n",
    " $ A4 Married       : chr  \"u\" \"u\" \"u\" \"u\" ...\n",
    " $ A5 BankCustomer  : chr  \"g\" \"g\" \"g\" \"g\" ...\n",
    " $ A6 EducationLevel: chr  \"q\" \"q\" \"w\" \"w\" ...\n",
    " $ A7 Ethnicity     : chr  \"h\" \"h\" \"v\" \"v\" ...\n",
    " $ A8 YearsEmployed : num  3.04 1.5 3.75 1.71 2.5 ...\n",
    " $ A9 PriorDefault  : num  1 1 1 1 1 1 1 1 1 0 ...\n",
    " $ A10 Employed      : num  1 0 1 0 0 0 0 0 0 0 ...\n",
    " $ A11 CreditScore   : num  6 0 5 0 0 0 0 0 0 0 ...\n",
    " $ A12 DriversLicense: chr  \"f\" \"f\" \"t\" \"f\" ...\n",
    " $ A13 Citizen       : chr  \"g\" \"g\" \"g\" \"s\" ...\n",
    " $ A14 ZipCode       : chr  \"00043\" \"00280\" \"00100\" \"00120\" ...\n",
    " $ A15 Income        : num  560 824 3 0 0 ...\n",
    " $ A16 Approved      : chr  \"+\" \"+\" \"+\" \"+\" ...\n",
    " \n",
    " ```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iZuMg44ATjKn"
   },
   "source": [
    "## Importando e Explorando os Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 241
    },
    "id": "id_BCQps8Euj",
    "outputId": "4f294165-90fc-41ed-c70a-9937d5f29913"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(690, 15)\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a1</th>\n",
       "      <th>a2</th>\n",
       "      <th>a3</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
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       "      <td>g</td>\n",
       "      <td>00202</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>3.04</td>\n",
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       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00043</td>\n",
       "      <td>560</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>24.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>1.50</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00280</td>\n",
       "      <td>824</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>3.75</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>00100</td>\n",
       "      <td>3</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.71</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>00120</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      a1     a2 a3 a4 a5 a6    a7 a8 a9  a10 a11 a12    a13  a14 a15\n",
       "b  30.83  0.000  u  g  w  v  1.25  t  t    1   f   g  00202    0  + \n",
       "a  58.67  4.460  u  g  q  h  3.04  t  t    6   f   g  00043  560   +\n",
       "a  24.50  0.500  u  g  q  h  1.50  t  f    0   f   g  00280  824   +\n",
       "b  27.83  1.540  u  g  w  v  3.75  t  t    5   t   g  00100    3   +\n",
       "b  20.17  5.625  u  g  w  v  1.71  t  f    0   f   s  00120    0   +"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/crx.data', sep = \",\")\n",
    "\n",
    "# Visualização dos dados \n",
    "print(data.shape)\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "21iRWborYHgA"
   },
   "source": [
    "Ao visualizar os dados nota-se que a variável A1 (como descrita no arquivo crx.names) não está representando duas classes: \n",
    "    A1:\tb, a.\n",
    "\n",
    "E sim uma variável contínua, que deveria ser a A2, os valores de a, b acabaram indo para o index, iremos corrigir isso e renomear as variáveis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
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    "id": "JIT7XbimXoK0",
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       "      <th></th>\n",
       "      <th>A1</th>\n",
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       "      <td>00043</td>\n",
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       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>24.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>1.50</td>\n",
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       "      <td>0</td>\n",
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       "      <td>g</td>\n",
       "      <td>00280</td>\n",
       "      <td>824</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>3.75</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>00100</td>\n",
       "      <td>3</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.71</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>00120</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  A1     a1     a2 a3 a4 a5 a6    a7 a8 a9  a10 a11 a12    a13  a14 a15\n",
       "0  b  30.83  0.000  u  g  w  v  1.25  t  t    1   f   g  00202    0  + \n",
       "1  a  58.67  4.460  u  g  q  h  3.04  t  t    6   f   g  00043  560   +\n",
       "2  a  24.50  0.500  u  g  q  h  1.50  t  f    0   f   g  00280  824   +\n",
       "3  b  27.83  1.540  u  g  w  v  3.75  t  t    5   t   g  00100    3   +\n",
       "4  b  20.17  5.625  u  g  w  v  1.71  t  f    0   f   s  00120    0   +"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.rename_axis('A1').reset_index()\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora iremos renomear todas as colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "bYJ3AqqyYuvM",
    "outputId": "3c7e23fe-7d76-4025-c37f-62c7b5e19109"
   },
   "outputs": [
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       "      <td>1.540</td>\n",
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       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>3.75</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>00100</td>\n",
       "      <td>3</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.71</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>00120</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  A1     A2     A3 A4 A5 A6 A7    A8 A9 A10  A11 A12 A13    A14  A15 A16\n",
       "0  b  30.83  0.000  u  g  w  v  1.25  t   t    1   f   g  00202    0  + \n",
       "1  a  58.67  4.460  u  g  q  h  3.04  t   t    6   f   g  00043  560   +\n",
       "2  a  24.50  0.500  u  g  q  h  1.50  t   f    0   f   g  00280  824   +\n",
       "3  b  27.83  1.540  u  g  w  v  3.75  t   t    5   t   g  00100    3   +\n",
       "4  b  20.17  5.625  u  g  w  v  1.71  t   f    0   f   s  00120    0   +"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'A1' : 'A1',\t'a1':'A2',\t'a2':'A3',\t'a3':'A4',\t'a4':'A5',\t'a5':'A6',\t'a6':'A7',\t'a7':'A8',\t'a8':'A9',\t'a9':'A10',\t'a10':'A11',\t'a11':'A12',\t'a12':'A13',\t'a13':'A14',\t'a14':'A15',\t'a15': 'A16'}\n",
    "\n",
    "data.rename(columns = d, inplace = True)\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "hXn2YsaF8Duu"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(690, 16)\n",
      "(690, 16)\n"
     ]
    }
   ],
   "source": [
    "# Droppando duplicatas caso existam\n",
    "print(data.shape)\n",
    "data.drop_duplicates(inplace= True)\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zusp4_pwTqE2"
   },
   "source": [
    "### Média, desvio padrão, min, max e quartis de todas as variáveis numéricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "id": "O-dE_cpOMbRk",
    "outputId": "9458cb6a-2d20-4fdf-f3f5-7e142b1d15c5"
   },
   "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>A3</th>\n",
       "      <th>A8</th>\n",
       "      <th>A11</th>\n",
       "      <th>A15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.00000</td>\n",
       "      <td>690.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.758725</td>\n",
       "      <td>2.223406</td>\n",
       "      <td>2.40000</td>\n",
       "      <td>1017.385507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.978163</td>\n",
       "      <td>3.346513</td>\n",
       "      <td>4.86294</td>\n",
       "      <td>5210.102598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.207500</td>\n",
       "      <td>2.625000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>395.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>67.00000</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A3          A8        A11            A15\n",
       "count  690.000000  690.000000  690.00000     690.000000\n",
       "mean     4.758725    2.223406    2.40000    1017.385507\n",
       "std      4.978163    3.346513    4.86294    5210.102598\n",
       "min      0.000000    0.000000    0.00000       0.000000\n",
       "25%      1.000000    0.165000    0.00000       0.000000\n",
       "50%      2.750000    1.000000    0.00000       5.000000\n",
       "75%      7.207500    2.625000    3.00000     395.500000\n",
       "max     28.000000   28.500000   67.00000  100000.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tam7ARzvtHQr"
   },
   "source": [
    "Nem todas as variáveis descritas como contínuas na descrição do dataset foram lidas como contínuas pelo python, isso será corrigido. No dataset os valores nulos estão sendo representados por ?, portanto iremos substitui-los pelo valor nulo do numpy para ficar mais fácil trabalhar com eles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "id": "v4A5HVWVxuEn",
    "outputId": "1ebf24b2-5e4c-4e8e-8463-2dd8e5358b44"
   },
   "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>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A8</th>\n",
       "      <th>A11</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>666.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>666.00</td>\n",
       "      <td>666.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>31.562500</td>\n",
       "      <td>4.798078</td>\n",
       "      <td>2.222320</td>\n",
       "      <td>2.459459</td>\n",
       "      <td>inf</td>\n",
       "      <td>998.584084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.921875</td>\n",
       "      <td>5.005309</td>\n",
       "      <td>3.347599</td>\n",
       "      <td>4.929794</td>\n",
       "      <td>inf</td>\n",
       "      <td>5202.975198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>13.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22.601562</td>\n",
       "      <td>1.010000</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>75.25</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.500000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>160.00</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>7.207500</td>\n",
       "      <td>2.585000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>271.00</td>\n",
       "      <td>399.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.250000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A2          A3          A8         A11      A14            A15\n",
       "count  666.000000  666.000000  666.000000  666.000000   666.00     666.000000\n",
       "mean    31.562500    4.798078    2.222320    2.459459      inf     998.584084\n",
       "std     11.921875    5.005309    3.347599    4.929794      inf    5202.975198\n",
       "min     13.750000    0.000000    0.000000    0.000000     0.00       0.000000\n",
       "25%     22.601562    1.010000    0.165000    0.000000    75.25       0.000000\n",
       "50%     28.500000    2.750000    1.000000    0.000000   160.00       5.000000\n",
       "75%     38.250000    7.207500    2.585000    3.000000   271.00     399.000000\n",
       "max     80.250000   28.000000   28.500000   67.000000  2000.00  100000.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.replace('?', np.nan)\n",
    "\n",
    "data[\"A2\"] = data[\"A2\"].astype(np.float16)\n",
    "a14_before = data[\"A14\"]\n",
    "data[\"A14\"] = data[\"A14\"].astype(np.float16)\n",
    "data_sem_nulos = data.dropna(subset=['A2', 'A3', 'A8', 'A11', 'A14', 'A15'])\n",
    "data_sem_nulos.describe() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WLkFX6Xp0Mr4"
   },
   "source": [
    "É interessante notar que o desvio padrão de muitas das variáveis chega a ser maior que sua média."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xYnErnh7UHuB"
   },
   "source": [
    "### Visualizando a distribuição das classes na variável resposta\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "I1s4j-BWT0uk",
    "outputId": "8ce2faaa-c54f-47bb-cca6-6bbae0e25b64"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import PercentFormatter\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "plt.hist(data[\"A16\"])\n",
    "plt.gca().yaxis.set_major_formatter(PercentFormatter(690))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EQXUmqC3V4ja"
   },
   "source": [
    "Notamos que existem algumas instâncias onde a classe \"+\" está com espaços vazios na string, fazendo com que o histograma indique que \"+\" e \"+ \" são duas classes diferentes, o que não é o caso, portanto iremos realizar um trim nessa variável."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "5QX3V-ugUmOg",
    "outputId": "1fa30458-77bc-47f1-af6e-e0ff2042ed73"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def strp(a):\n",
    "  return a.strip()\n",
    "\n",
    "data[\"A16\"] = data[\"A16\"].apply(strp)\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "plt.hist(data[\"A16\"])\n",
    "plt.gca().yaxis.set_major_formatter(PercentFormatter(690))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2dnLm4LSuLDK",
    "outputId": "d6b1408c-c01e-4f17-fc0e-6494d917e7d6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-    0.555072\n",
       "+    0.444928\n",
       "Name: A16, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.A16.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L4uJOmgDuQW1"
   },
   "source": [
    "Assim vemos que os dados estão distribuidos quase igualmente entre as duas classes, sendo os pedidos aprovados detentores de 44,5% do total e os reprovados de 55,5%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-xeMz78YaGyT"
   },
   "source": [
    "## Dados Nulos\n",
    "\n",
    "Nesse gráfico o que está preenchido com preto são dados não nulos, além disso há uma barra no lado direito deste diagrama. Este é um gráfico de linha para a integridade dos dados de cada linha.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "id": "2lcK3sljWkLR",
    "outputId": "774d18e8-15b3-42d1-e946-5bbf1c6d1822"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import missingno as msno\n",
    "msno.matrix(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cjA25VeNcH3f"
   },
   "source": [
    "Dessa forma podemos visualizar que existem poucos dados faltantes nesse conjunto de dados, desse modo pode ser interessante apenas retirar-los pois não haveria uma perda grande de informação, ou retirar apenas aqueles que estão em variáveis categóricas e preencher com a média da coluna aqueles que se encontram em variáveis numéricas. Para essa análise preferimos retirar apenas aqueles que estão em variáveis categóricas e preencher com a média da coluna aqueles que se encontram em variáveis numéricas.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 207
    },
    "id": "FVJmlKZY2Gtd",
    "outputId": "3ff29b5a-1d45-4cb3-a3ec-618767718f39"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:87: RuntimeWarning: overflow encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n",
      "/home/dell/.local/lib/python3.8/site-packages/numpy/core/_methods.py:47: RuntimeWarning: overflow encountered in reduce\n",
      "  return umr_sum(a, axis, dtype, out, keepdims, initial, where)\n",
      "/home/dell/.local/lib/python3.8/site-packages/pandas/core/series.py:4433: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().fillna(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data_cont = data[['A2', 'A3', 'A8', 'A11', 'A14', 'A15']]\n",
    "data_cont.fillna(data_cont.mean(), inplace = True)\n",
    "msno.matrix(data_cont)\n",
    "\n",
    "data[\"A2\"] = data_cont[\"A2\"]\n",
    "data[\"A3\"] = data_cont[\"A3\"]\n",
    "data[\"A8\"] = data_cont[\"A8\"]\n",
    "data[\"A11\"] = data_cont[\"A11\"]\n",
    "data[\"A14\"] = data_cont[\"A14\"]\n",
    "data[\"A15\"] = data_cont[\"A15\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "id": "Zat0I9Wc_jCl",
    "outputId": "368834fb-9627-4e76-8dc0-2bfc13da9780"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kX/rSl/Lcc8/lpz/9aR599NGMHDkyK1asyG9/+9tUV1fn5JNPbt0rvMWaNWvym9/8Jl27ds0+++zzkf+Nyy+/vPXPf/zjH5Mk1113XXr06JEk2X///bPDDju0dckAAAAAAHRibQ7gAwYMyHe/+93cdNNNmTdvXhYsWJCePXtm4sSJ+cxnPpPtttvuPV8zZ86crF27NpMnT27TxS/vu+++93xs7ty5rX/ecccdBXAAAAAAANqkqvmjzi0HAAAAAIBNUJsuggkAAAAAAJsaARwAAAAAgCIJ4AAAAAAAFEkABwAAAACgSAI4AAAAAABFEsABAAAAACiSAA4AAAAAQJEEcAAAAAAAiiSAAwAAAABQpP8HyuPZePJKhmwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = data.dropna()\n",
    "msno.matrix(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ESE8L8uWa1ef"
   },
   "source": [
    "## Visualizando a relação das variáveis categóricas com a variável resposta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "id": "6KWYAWS-d3kK",
    "outputId": "fcf93791-ada1-4735-c472-51c836fc0575"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0f593e6040>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 404.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 404.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"A1\", kind=\"count\", hue=\"A16\", data=data)\n",
    "sns.catplot(x=\"A4\", kind=\"count\", hue=\"A16\", data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "id": "thF4an9aeE9A",
    "outputId": "1dad6ac8-3269-40e3-9024-90866c040730"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0f59587610>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 404.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 404.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"A5\", kind=\"count\", hue=\"A16\", data=data)\n",
    "sns.catplot(x=\"A6\", kind=\"count\", hue=\"A16\", data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "id": "JwsyniHPht_f",
    "outputId": "00a16b70-6ddf-4f59-b1fe-6ab2f2053b6c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0f595f5220>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 404.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 404.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"A7\", kind=\"count\", hue=\"A16\", data=data)\n",
    "sns.catplot(x=\"A9\", kind=\"count\", hue=\"A16\", data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "id": "3IDc_SdRh4Au",
    "outputId": "66519f4b-148d-4803-fa44-1c25ec241312"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0f593cc5e0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 404.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 404.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"A10\", kind=\"count\", hue=\"A16\", data=data)\n",
    "sns.catplot(x=\"A12\", kind=\"count\", hue=\"A16\", data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "id": "7BZqP8FEh_-1",
    "outputId": "748d5bde-77c7-4def-d8ef-ec7b2a88a611"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0f59825d00>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WQ0ODEhMTv3OvIABA73fxxRfrq6++0uHDh+X3+5WZmSlJWrJkiSZOnHjS7cOOT1JSkpYsWaI9e/aosbFRSUlJysjICOv+bo888oi2bt2q+Ph4FRcXS5Kef/55vfLKK4qLi5MkTZ8+XRdddJEk6aWXXlJFRYXcbrdmzZoV+qYAAN3DW2+9Jenrcz5lZWUqKyvr1Pad+gtRt9t9SrfUzs/P1+WXX/6dd0OdNGmSpkyZ0m7Zvn37VF1drYceekhNTU26//779fDDD3e7m5gCAE6dye0Jzj//fNXX14e1bk1NjUaPHq0+ffpo4MCBOuuss7Rnz56Ivo8EAPQmFpdGR5qj98bZuHGjqqqqlJ6erhtuuEGxsbHy+/3t3iPc4/HI7/c7OCUA4Pvk5+crPz+/09s5Fp8JEybommuukSStX79ezzzzjAoKCjr1HD6fTz6fT5JUVFSk5OTksLar69yoOIlwX3cA+IZj8UlISAh9PH78eC1ZskTS13s6jY2Noc99cyufjni9Xnm93tDjhoaGrhkWP4jXHbDTW95MzrGz+E1NTaGP3377bQ0ZMkSSlJ2drerqah0/flz19fWqq6tTRkaGU2MCALqAyZ7P8uXL9eGHH+rQoUO65ZZbNG3aNH3wwQf69NNP5XK5lJKSorlz50qShgwZotzcXN15551yu92aPXs2V7oBQC/jCvai++PU1taGtV5d4ZwuniQyFo2c7fQIYSm7MdfpEYDTBofdAAA4RbwNKQD0cBP+Z0NEn+//Fl0d0efrCHs+AABzxAcAYI74AADMER8AgDniAwDotNLSUmVmZiozMzPsP3M5EVe7AQA67bbbbtNtt912ytsTHwDo4SwujY40DrsBAMwRHwCAOeIDADBHfAAA5ogPAMAc8QEAmCM+AABzxAcAYI74AADMER8AgDniAwAwR3wAAOaIDwDAHPEBAJgjPgAAc8QHAGCO+AAAzBEfAIA54gMAMEd8AADmiA8AwBzxAQCYIz4AAHPEBwBgjvgAAMwRHwCAOeIDADBHfAAA5ogPAMBctMUXeeSRR7R161bFx8eruLhYknT48GGVlJTowIEDSklJ0YIFCxQbG6tgMKg1a9Zo27Zt6tevnwoKCpSenm4xJgDAiMmeT35+vu6+++52y8rLyzVq1CitWLFCo0aNUnl5uSRp27Zt2r9/v1asWKG5c+fqySeftBgRAGDIJD7nn3++YmNj2y2rqalRXl6eJCkvL081NTWSpC1btmjs2LFyuVwaNmyYjhw5oqamJosxAQBGTA67daS5uVmJiYmSpISEBDU3N0uS/H6/kpOTQ+slJSXJ7/eH1j2Rz+eTz+eTJBUVFbXb7ofU/djh0U64rzsAfMOx+JzI5XLJ5XJ1ejuv1yuv1xt63NDQEMmxECZed8BOamqq0yNEhGNXu8XHx4cOpzU1NSkuLk6S5PF42v0ya2xslMfjcWRGAEDXcCw+2dnZqqyslCRVVlYqJycntLyqqkrBYFC7d+9WTExMh4fcAAA9l8lht+XLl+vDDz/UoUOHdMstt2jatGmaOnWqSkpKVFFREbrUWpIuvPBCbd26VfPnz1ffvn1VUFBgMSIAwJArGAwGnR4iUmpra8Nar65wThdPEhmLRs52eoSwlN2Y6/QIwGmDcz4AAJwi4gMAMEd8AADmiA8AwBzxAQCYIz4AAHPEBwBgjvgAAMwRHwCAOeIDADBHfAAA5ogPAMAc8QEAmCM+AABzxAcAYI74AADMER8AgDniAwAwR3wAAOaIDwDAHPEBAJgjPgAAc8QHAGCO+AAAzBEfAIA54gMAMEd8AADmiA8AwBzxAQCYIz4AAHPEBwBgjvgAAMwRHwCAOeIDADBHfAAA5ogPAMAc8QEAmIt2eoDbbrtN/fv3l9vtVlRUlIqKinT48GGVlJTowIEDSklJ0YIFCxQbG+v0qACACHE8PpK0ePFixcXFhR6Xl5dr1KhRmjp1qsrLy1VeXq7rr7/ewQkBAJHULQ+71dTUKC8vT5KUl5enmpoahycCAERSt9jzefDBByVJv/zlL+X1etXc3KzExERJUkJCgpqbmzvczufzyefzSZKKioqUnJwc1teri8DM+K9wX3cA+Ibj8bn//vvl8XjU3NysBx54QKmpqe0+73K55HK5OtzW6/XK6/WGHjc0NHTprOgYrztg59u/I3sqxw+7eTweSVJ8fLxycnK0Z88excfHq6mpSZLU1NTU7nwQAKDnczQ+x44d05dffhn6+L333tPQoUOVnZ2tyspKSVJlZaVycnKcHBMAEGGOHnZrbm7WsmXLJEltbW0aM2aMMjMzde6556qkpEQVFRWhS60BAL2Ho/EZNGiQli5d+p3lZ555pv7whz84MBEAwILj53wAAKcf4gMAMEd8AADmiA8AwJzjf2QK9GQzn37D6RFOquzGXKdHAL6DPR8AgDniAwAwR3wAAOaIDwDAHPEBAJgjPgAAc8QHAGCO+AAAzBEfAIA54gMAMEd8AADmiA8AwBzxAQCYIz4AAHPEBwBgjvgAAMwRHwCAOeIDADBHfAAA5ogPAMAc8QEAmCM+AABz0U4PAHSkrnCO0yOEZ+RspycAeiT2fAAA5ogPAMAc8QEAmCM+AABzxAcAYI74AADMER8AgDniAwAwR3wAAOaIDwDAXLe+vc727du1Zs0aBQIBjR8/XlOnTnV6JABABHTbPZ9AIKDVq1fr7rvvVklJiV5//XXt27fP6bEAABHQbfd89uzZo7POOkuDBg2SJI0ePVo1NTVKS0tzeDIAPeXGr4t6yI1fy27MdXoEc902Pn6/X0lJSaHHSUlJ+sc//tFuHZ/PJ5/PJ0kqKipSampqWM+d+uz/Rm7QLvR/Tg/gIH5G3Rs/H/xY3fawWzi8Xq+KiopUVFTk9ChdYuHChU6PgJPgZ9S98fPpvrptfDwejxobG0OPGxsb5fF4HJwIABAp3TY+5557rurq6lRfX6/W1lZVV1crOzvb6bEAABHQbc/5REVF6aabbtKDDz6oQCCgyy67TEOGDHF6LFNer9fpEXAS/Iy6N34+3ZcrGAwGnR4CAHB66baH3QAAvRfxAQCYIz4AAHPEBwBgrtte7XY6e/HFF/Xaa68pLi5OSUlJSk9P15QpU5weC/9x7NgxlZSUyO/3KxAI6Oqrr9bo0aOdHgsnqK+v1x//+Eelp6frk08+UVpamm6//Xb169fP6dHwH8Snm9mzZ4/eeustLV26VG1tbbrrrruUnp7u9Fg4wfbt25WYmKhFixZJko4ePerwROhIbW2tbrnlFg0fPlyPPPKINm7cyH/iuhEOu3Uzu3btUk5Ojvr27asBAwYoKyvL6ZHwLUOHDtWOHTu0du1affTRR4qJiXF6JHQgKSlJw4cPlySNHTtWO3fudHginIj4AJ2UmpqqJUuWaOjQoXruuef04osvOj0SOuByuX7wMZxFfLqZn/3sZ3rnnXfU0tKiY8eOaevWrU6PhG/x+/3q27evxo4dqylTpmjv3r1Oj4QONDQ0aPfu3ZKkzZs3h/aC0D1wzqebycjIUFZWlgoLCxUfH68hQ4ZwWKeb+eyzz7R27Vq5XC5FR0drzpye8d42p5vU1FT9/e9/16OPPqrBgwdrwoQJTo+EExCfbmjKlCmaNm2avvrqKy1evJgLDrqZzMxMZWZmOj0GTiIqKkrz5893egx8D+LTDT3++OPat2+fjh8/rry8POIDoNfhxqIAAHNccAAAMEd8AADmiA8AwBzxAQCY42o34Hvce++9+uc//6knnnhCffr0kSS9//772rBhg/bu3avY2FiVlpa22+a+++7TZ599ptbWVg0cOFDTpk1TTk6OE+MD3Rp7PkAH6uvr9dFHH0mStmzZElrev39/XXbZZZoxY0aH282cOVNPPPGEnn76ac2dO1crV65UU1OTycxAT0J8gA5UVVVp2LBhys/PV2VlZWh5RkaGxo4dq4EDB3a43U9+8hNFRUVJ+vpeYm1tbWpsbDSZGehJOOwGdKCyslKTJ0/Weeedp3vuuUdffPGFEhISwtq2qKhIO3bs0PHjx/Xzn/+cPxIGOkB8gG/ZuXOnGhoalJubq7i4OA0aNEibN2/W5MmTw9p+4cKFam1t1Y4dO/T555/L7eYAA/Bt/KsAvmXTpk264IILFBcXJ0kaM2ZMu0Nv4YiOjtaFF16od999t905IwBfY88HOEFLS4veeOMNBQIB3XzzzZKk1tZWHTlyRJ9++ql++tOfdur5AoGA9u/f3wWTAj0b8QFO8Pbbb8vtdqu4uFjR0f/951FSUqKqqioNHTpUra2tamtrUzAYVEtLi9xut6Kjo/X555+rvr5eI0aMkNvtVnV1tT788ENdd911Dn5HQPdEfIATVFZW6rLLLlNycnK75RMnTtSaNWuUmZmpBx54ILT8+uuv1/nnn697771XwWBQL7zwgkpKSuR2u3X22WdrwYIFXHAAdIC7WgMAzHHBAQDAHPEBAJgjPgAAc8QHAGCO+AAAzBEfAIA54gMAMEd8AADm/h/R+emoHnNG9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 404.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"A13\", kind=\"count\", hue=\"A16\", data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DOz5SkPsox40"
   },
   "source": [
    "Pelos gráficos é possível notar uma relação de possível correlação entre a A9 e A16 e entre a A10 e A16, tornando A9 e A10 variáveis interessantes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GCFLnAbvpB8E"
   },
   "source": [
    "## Plotando as variáveis contínuas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 275
    },
    "id": "qZdRzHqQodwK",
    "outputId": "a2620304-7b90-4888-9f6c-8a0bf18534b9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/.local/lib/python3.8/site-packages/seaborn/distributions.py:306: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/home/dell/.local/lib/python3.8/site-packages/seaborn/distributions.py:306: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/home/dell/.local/lib/python3.8/site-packages/numpy/lib/function_base.py:3961: RuntimeWarning: invalid value encountered in subtract\n",
      "  diff_b_a = subtract(b, a)\n",
      "/home/dell/.local/lib/python3.8/site-packages/numpy/lib/function_base.py:3961: RuntimeWarning: invalid value encountered in subtract\n",
      "  diff_b_a = subtract(b, a)\n",
      "/home/dell/.local/lib/python3.8/site-packages/numpy/lib/function_base.py:3961: RuntimeWarning: invalid value encountered in subtract\n",
      "  diff_b_a = subtract(b, a)\n",
      "/home/dell/.local/lib/python3.8/site-packages/numpy/lib/function_base.py:3961: RuntimeWarning: invalid value encountered in subtract\n",
      "  diff_b_a = subtract(b, a)\n",
      "/home/dell/.local/lib/python3.8/site-packages/numpy/lib/function_base.py:3961: RuntimeWarning: invalid value encountered in subtract\n",
      "  diff_b_a = subtract(b, a)\n",
      "/home/dell/.local/lib/python3.8/site-packages/numpy/core/function_base.py:151: RuntimeWarning: invalid value encountered in multiply\n",
      "  y *= step\n",
      "/home/dell/.local/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1389: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "/home/dell/.local/lib/python3.8/site-packages/numpy/lib/function_base.py:3961: RuntimeWarning: invalid value encountered in subtract\n",
      "  diff_b_a = subtract(b, a)\n",
      "/home/dell/.local/lib/python3.8/site-packages/numpy/core/function_base.py:151: RuntimeWarning: invalid value encountered in multiply\n",
      "  y *= step\n",
      "/home/dell/.local/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1389: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n",
      "/home/dell/.local/lib/python3.8/site-packages/numpy/lib/function_base.py:3961: RuntimeWarning: invalid value encountered in subtract\n",
      "  diff_b_a = subtract(b, a)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1128.3x1080 with 27 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "variaveis_pertinentes = data[[\"A16\", \"A2\", \"A3\", \"A8\", \"A11\",\"A14\", \"A15\"]].copy()\n",
    "variaveis_pertinentes = pd.get_dummies(variaveis_pertinentes, columns=[\"A16\"])\n",
    "variaveis_pertinentes.drop([\"A16_-\"], axis=1, inplace=True) # Manter ambas as variáveis seria redundante\n",
    "sns.set(style=\"ticks\", color_codes=True)\n",
    "plt.style.use('ggplot')\n",
    "# Plotando a distribuição em pares das variáveis pertinentes e seus histogramas\n",
    "plt.figure(figsize=(8,8))\n",
    "sns.pairplot(variaveis_pertinentes, hue=\"A16_+\", corner=True, kind=\"reg\", diag_kind=\"kde\", plot_kws={'scatter_kws': {'alpha': 0.2}})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z9nXrzXYvVBY"
   },
   "source": [
    "As variáveis contínuas, possuem uma distribuição bastante concentrada nos valores mais à esquerda do gráfico. As A3 e A2 são as que tem ainda uma distribuição mais \"uniforme\". É possível ver que as classes se misturam bastante em todas combinações de variáveis e é difícil fazer sua distinção."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IL9wFHIRpOeR"
   },
   "source": [
    "## Visualizando a Matriz de Correlação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "QTm8pF3kog-n",
    "outputId": "8d1444f5-42f5-46a2-ce6f-55128f09b906"
   },
   "outputs": [
    {
     "data": {
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       "            <tr>\n",
       "                        <th id=\"T_22105_level0_row1\" class=\"row_heading level0 row1\" >A3</th>\n",
       "                        <td id=\"T_22105_row1_col0\" class=\"data row1 col0\" >0.213260692</td>\n",
       "                        <td id=\"T_22105_row1_col1\" class=\"data row1 col1\" >1.000000000</td>\n",
       "                        <td id=\"T_22105_row1_col2\" class=\"data row1 col2\" >0.291674473</td>\n",
       "                        <td id=\"T_22105_row1_col3\" class=\"data row1 col3\" >0.266505652</td>\n",
       "                        <td id=\"T_22105_row1_col4\" class=\"data row1 col4\" >-0.224197224</td>\n",
       "                        <td id=\"T_22105_row1_col5\" class=\"data row1 col5\" >0.121717980</td>\n",
       "                        <td id=\"T_22105_row1_col6\" class=\"data row1 col6\" >0.205815974</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_22105_level0_row2\" class=\"row_heading level0 row2\" >A8</th>\n",
       "                        <td id=\"T_22105_row2_col0\" class=\"data row2 col0\" >0.411986588</td>\n",
       "                        <td id=\"T_22105_row2_col1\" class=\"data row2 col1\" >0.291674473</td>\n",
       "                        <td id=\"T_22105_row2_col2\" class=\"data row2 col2\" >1.000000000</td>\n",
       "                        <td id=\"T_22105_row2_col3\" class=\"data row2 col3\" >0.318445396</td>\n",
       "                        <td id=\"T_22105_row2_col4\" class=\"data row2 col4\" >-0.073289648</td>\n",
       "                        <td id=\"T_22105_row2_col5\" class=\"data row2 col5\" >0.049433093</td>\n",
       "                        <td id=\"T_22105_row2_col6\" class=\"data row2 col6\" >0.324797935</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_22105_level0_row3\" class=\"row_heading level0 row3\" >A11</th>\n",
       "                        <td id=\"T_22105_row3_col0\" class=\"data row3 col0\" >0.196309299</td>\n",
       "                        <td id=\"T_22105_row3_col1\" class=\"data row3 col1\" >0.266505652</td>\n",
       "                        <td id=\"T_22105_row3_col2\" class=\"data row3 col2\" >0.318445396</td>\n",
       "                        <td id=\"T_22105_row3_col3\" class=\"data row3 col3\" >1.000000000</td>\n",
       "                        <td id=\"T_22105_row3_col4\" class=\"data row3 col4\" >-0.118362561</td>\n",
       "                        <td id=\"T_22105_row3_col5\" class=\"data row3 col5\" >0.061737704</td>\n",
       "                        <td id=\"T_22105_row3_col6\" class=\"data row3 col6\" >0.409645374</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_22105_level0_row4\" class=\"row_heading level0 row4\" >A14</th>\n",
       "                        <td id=\"T_22105_row4_col0\" class=\"data row4 col0\" >-0.082654238</td>\n",
       "                        <td id=\"T_22105_row4_col1\" class=\"data row4 col1\" >-0.224197224</td>\n",
       "                        <td id=\"T_22105_row4_col2\" class=\"data row4 col2\" >-0.073289648</td>\n",
       "                        <td id=\"T_22105_row4_col3\" class=\"data row4 col3\" >-0.118362561</td>\n",
       "                        <td id=\"T_22105_row4_col4\" class=\"data row4 col4\" >1.000000000</td>\n",
       "                        <td id=\"T_22105_row4_col5\" class=\"data row4 col5\" >0.070575781</td>\n",
       "                        <td id=\"T_22105_row4_col6\" class=\"data row4 col6\" >-0.094302093</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_22105_level0_row5\" class=\"row_heading level0 row5\" >A15</th>\n",
       "                        <td id=\"T_22105_row5_col0\" class=\"data row5 col0\" >0.020680641</td>\n",
       "                        <td id=\"T_22105_row5_col1\" class=\"data row5 col1\" >0.121717980</td>\n",
       "                        <td id=\"T_22105_row5_col2\" class=\"data row5 col2\" >0.049433093</td>\n",
       "                        <td id=\"T_22105_row5_col3\" class=\"data row5 col3\" >0.061737704</td>\n",
       "                        <td id=\"T_22105_row5_col4\" class=\"data row5 col4\" >0.070575781</td>\n",
       "                        <td id=\"T_22105_row5_col5\" class=\"data row5 col5\" >1.000000000</td>\n",
       "                        <td id=\"T_22105_row5_col6\" class=\"data row5 col6\" >0.177212567</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_22105_level0_row6\" class=\"row_heading level0 row6\" >A16_+</th>\n",
       "                        <td id=\"T_22105_row6_col0\" class=\"data row6 col0\" >0.175263720</td>\n",
       "                        <td id=\"T_22105_row6_col1\" class=\"data row6 col1\" >0.205815974</td>\n",
       "                        <td id=\"T_22105_row6_col2\" class=\"data row6 col2\" >0.324797935</td>\n",
       "                        <td id=\"T_22105_row6_col3\" class=\"data row6 col3\" >0.409645374</td>\n",
       "                        <td id=\"T_22105_row6_col4\" class=\"data row6 col4\" >-0.094302093</td>\n",
       "                        <td id=\"T_22105_row6_col5\" class=\"data row6 col5\" >0.177212567</td>\n",
       "                        <td id=\"T_22105_row6_col6\" class=\"data row6 col6\" >1.000000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f0f5984d6d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando a matriz de correlação\n",
    "corr = variaveis_pertinentes.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm').set_precision(9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lDfxg7rNvjDk"
   },
   "source": [
    "A8 e A11 são as variáveis com maior correlação com a variável resposta A16_+, no entanto suas correlações não chegam a ser fortes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "u3o8vIReX7bI",
    "outputId": "a08301a8-f7b5-4132-dafd-776e1051fefc"
   },
   "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>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "      <th>A16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b</td>\n",
       "      <td>30.828125</td>\n",
       "      <td>0.000</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.25</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>202.0</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>58.656250</td>\n",
       "      <td>4.460</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>3.04</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>43.0</td>\n",
       "      <td>560</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>1.50</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>280.0</td>\n",
       "      <td>824</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>27.828125</td>\n",
       "      <td>1.540</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>3.75</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>20.171875</td>\n",
       "      <td>5.625</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.71</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  A1         A2     A3 A4 A5 A6 A7    A8 A9 A10  A11 A12 A13    A14  A15 A16\n",
       "0  b  30.828125  0.000  u  g  w  v  1.25  t   t    1   f   g  202.0    0   +\n",
       "1  a  58.656250  4.460  u  g  q  h  3.04  t   t    6   f   g   43.0  560   +\n",
       "2  a  24.500000  0.500  u  g  q  h  1.50  t   f    0   f   g  280.0  824   +\n",
       "3  b  27.828125  1.540  u  g  w  v  3.75  t   t    5   t   g  100.0    3   +\n",
       "4  b  20.171875  5.625  u  g  w  v  1.71  t   f    0   f   s  120.0    0   +"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lij08srTYv5p"
   },
   "source": [
    "# Preparação dos dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "diAR03eroafc"
   },
   "source": [
    "## O problema da variável A14\n",
    "\n",
    "Ao ler a documentação dos dados é possível notar que a variável A14 foi descrita como sendo contínua, no entanto ela aparenta ser uma variável de endereço postal, portanto existe perda de informação ao transformá-la em contínua, ela seria categórica e deve ser tratada de outra maneira, portanto voltará ao seu estado inicial.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KmHSzS4ZofZY",
    "outputId": "5a89179a-bc99-4946-ae39-6991af727150"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0      131\n",
       "120.0     35\n",
       "200.0     34\n",
       "160.0     33\n",
       "100.0     30\n",
       "        ... \n",
       "93.0       1\n",
       "17.0       1\n",
       "121.0      1\n",
       "52.0       1\n",
       "395.0      1\n",
       "Name: A14, Length: 167, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.A14.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Bl_pah3DpR0P",
    "outputId": "1bcf2317-b0f0-4ed3-95d7-8d1b83f49701"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "00000    132\n",
       "00120     35\n",
       "00200     35\n",
       "00160     34\n",
       "00080     30\n",
       "        ... \n",
       "00288      1\n",
       "00204      1\n",
       "00170      1\n",
       "00395      1\n",
       "00230      1\n",
       "Name: A14, Length: 170, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a14_before.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lZIEGrmBsNr5",
    "outputId": "6bfde60e-6f15-465c-ee77-bd2295cd6bcd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       677\n",
       "unique      170\n",
       "top       00000\n",
       "freq        132\n",
       "Name: A14, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a14_before.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7yTEcrfasl_Y"
   },
   "source": [
    "Em sua forma atual, com 170 valores categóricos únicos nessa coluna de \"código postal\", um modelo de aprendizagem de máquina não pode extrair nenhuma das informações úteis contidas em cada código postal para avaliar uma relação com o preço. \n",
    "\n",
    "Dentro de cada uma destas categorias existe uma seleção única de fatores imobiliários significativos como parques, escolas, cafés, lojas, mercearias, acesso a trânsito e estradas principais, além disso também podem ser extraídas informações sobre renda média da área, todos esses novos fatores poderiam ser úteis para um modelo. \n",
    "\n",
    "Existem diversas formas de fazer com que as informações de código postal sejam úteis, pode ser possível extrair a latitude e longitude das mesmas e realizar um cruzamento com bases externas populacionais (como as do IBGE), podemos também agrupar os códigos postais de uma mesma área e criar classes com eles ou podemos realizar o one hot enconding (que será tratado adiante) da mesma forma que em qualquer outra variável categórica.\n",
    "\n",
    "Como os dados estão anonimizados é difícil extrair muitas informações ou realizar agrupamentos com a variável de código postal, dada essa limitação poderiamos apenas realizar o procedimento de one hot encoding nela, assim como em outras variáveis categóricas, no entanto optamos por exclui-la do dataset para que seja mais fácil enxergar depois quais são as variáveis mais importantes para a classificação, dado que seriam criadas muitas colunas com o one hot enconding desse dado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "JFxqvvLFrC2l"
   },
   "outputs": [],
   "source": [
    "data.A14 = a14_before\n",
    "data.drop([\"A14\"], inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FNF1NWVujJCz"
   },
   "source": [
    "## Convertendo dados categóricos em dados numéricos\n",
    "\n",
    "### Por que converter?\n",
    "\n",
    "Dados categóricos são variáveis que contêm valores de rótulos ao invés de valores numéricos, seu número de valores possíveis é muitas vezes limitado a um conjunto fixo.\n",
    "\n",
    "As variáveis \"A1\", \"A4\", \"A5\", \"A6\", \"A7\", \"A9\", \"A10\", \"A12\", \"A13\", \"A14\" do nosso conjunto de dados são categóricas.\n",
    "\n",
    "Alguns algoritmos podem funcionar diretamente com dados categóricos, por exemplo, uma árvore de decisão pode ser aprendida diretamente com dados categóricos sem necessidade de transformação de dados (dependendo de uma implementação específica). No entanto muitos algoritmos de aprendizagem de máquina (regressões por exemplo) não podem operar diretamente com dados nominais, eles exigem que todas as variáveis de entrada e de saída sejam numéricas.\n",
    "\n",
    "Dessa maneira é interessante que, para não perdermos as informações contidas nos dados categóricos, eles sejam convertidos para uma forma numérica. \n",
    "\n",
    "\n",
    "### Pode ser feito de duas maneiras:\n",
    "\n",
    "1. Integer Encoding\n",
    "2. One-Hot Encoding\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XfkIPLm02GwX"
   },
   "source": [
    "\n",
    "### 1. Integer Encoding\n",
    "\n",
    "Para cada valor único de uma categoria é atribuído um valor inteiro.\n",
    "\n",
    "Em nossa base por exemplo, na coluna A16, podemos atribuir 1 a \"+\" (aprovado) e \"-\" (reprovado) seria 0. Isto é chamado de codificação de etiqueta ou Integer Encoding e é facilmente reversível. Para algumas variáveis, como é o caso da nossa variável resposta isto já é suficiente.\n",
    "\n",
    "Nesse caso os valores inteiros têm uma relação de ordenação natural entre si e alguns algoritmos de aprendizagem da máquina são capazes de entender e aproveitar esta relação. Portanto é interessante manter o 1 como aprovado e 0 como reprovado pois aprovado é uma classe \"superior\" à reprovado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "OlztPPzmlA2P"
   },
   "outputs": [],
   "source": [
    "data[\"A16\"] = data[\"A16\"].replace('+', 1)\n",
    "data[\"A16\"] = data[\"A16\"].replace('-', 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nas seguintes variáveis, dada a informação que temos sobre o que significam e dado que temos 2 classes para cada (t ou f, ou seja true ou false) podemos também realizar o integer encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"A9\"] = data[\"A9\"].replace('t', 1)\n",
    "data[\"A9\"] = data[\"A9\"].replace('f', 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"A10\"] = data[\"A10\"].replace('t', 1)\n",
    "data[\"A10\"] = data[\"A10\"].replace('f', 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"A12\"] = data[\"A12\"].replace('t', 1)\n",
    "data[\"A12\"] = data[\"A12\"].replace('f', 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hqddvw572z35"
   },
   "source": [
    "\n",
    "### 2. One-Hot Encoding\n",
    "\n",
    "Para variáveis categóricas onde não existe relação ordinal, a integer encoding não é suficiente. Ao usar esse tipo de codificação permitiríamos que qualquer modelo utilizado assumisse uma ordenação natural entre as categorias, o que pode resultar em um desempenho ruim ou resultados inesperados com previsões \"no meio do caminho\" entre categorias.\n",
    "\n",
    "Dessa forma podemos usar o one-hot encoding e criar uma variável binária para cada classe da variável categórica. Por exemplo a variável A4 possui três valores, u, y e l, quando a classe for u será colocado 1 na variável nova a4_u e 0 nas demais. \n",
    "\n",
    "\n",
    "\n",
    "|  a4_u | a4_y  | a4_l  |\n",
    "|---|---|---|\n",
    "|  1 | 0  |  0 |   \n",
    "|  0 |  1 |  0 |   \n",
    "|  0 |  0 |  1 |   \n",
    "\n",
    "Também é interessante remover colunas redundantes dos nossos dados, por exemplo, com as informações das variáveis u e y já podemos inferir o valor da l, portanto ela não é necessária na nossa base de dados e ao final da transformação da coluna A4 em dados numéricos as colunas finais poderiam ser apenas: \n",
    "\n",
    "|  a4_u | a4_y  | \n",
    "|---|---|\n",
    "|  1 | 0  |    \n",
    "|  0 |  1 |   \n",
    "|  0 |  0 |   \n",
    "\n",
    "sem haver perda alguma de informação.\n",
    "\n",
    "Não há informação o suficiente sobre as variáveis restantes para presumir que existe alguma relação ordinal entre as classes, portanto iremos assumir que para o restante das variáveis categóricas da nossa base é mais interessante utilizar o one-hot encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "xNIiwBiivv-5"
   },
   "outputs": [],
   "source": [
    "data_dummified = pd.get_dummies(data, columns = [\"A1\", \"A4\", \"A5\", \"A6\", \"A7\", \"A13\"], drop_first= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i5b-ZU6QoA2b"
   },
   "source": [
    "### Observando a matriz de correlação agora com as transformações nas variáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "GVlfu6uY1WuM",
    "outputId": "3ddcc48a-6930-4f6d-9d4c-7ada92225616"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4Yalc1O4xh8MBq9WKxsZG+Hw+AEBiYiJuvvlm/OAHoWsS7t69G++99x6efPJJPPnkk3jvvfewe/fuaB0eERERUdRFrUdLVVUsW7YMmzdvhtPphM1mQ0pKClJSUvD555+H5Ldt2wa73Q6TqffMKbvdjm3btmH27NnROkQiIiIS0OlH5hitgwcPYv369XC73Rg/fjzuuOMOTJ06tU+mqqoKra2twb8//fRTlJWV4dvf/jZee+01vPHGG5g4cSIA4Pzzz++zLGA4olJotba2wu12IzMzEx0dHVBVFTabbdDrtLe3Y86cOcG/U1NT8dFHH/Wb9Xg88Hhkp0UTERHR2PTcc8/hu9/9Lux2OxoaGvDss8/ioYce6pO54447gr+3tLTg3//933HRRRcF2+x2O26++eaIH1tUCq26ujrY7XYoioKsrCxUV1fD5XJFbEmd2tpa1NTUhLRX/fz+iGyfiIiIQo3Esw6PHz+OAwcOYNWqVQCARYsWobq6Gp2dnUhM7H9esrq6OixatAhGo2zuu+GIeKHl8/ngcDhgNBrR0NAAAPD7/aivr0d+fv6A10tNTcWRI0eCf7e3t2PSpEn9ZvPy8pCTkxN6QfunYR07ERERjQwDfXtlNpthNpuDfx89ehQpKSnQ/c9AfZ1Oh4kTJ6K9vb3fQsvn86GxsTFYmH1p27Zt2LlzJ5KTk1FYWAir1RqR2xHxQsvpdMJisaCioiLY1tzcjKqqqkELLZvNhl//+te46qre2c4bGhrwox/9qN/s1+/kLx1moUVERBQ1sTzrcKBvrwoKClBYWDjs7f71r39FamoqZsyYEWy78sorkZ+fD4PBgJ07d+IXv/gFfvnLX2LChAnD3s+XIl5oqaqK7OzsPm1WqxWapmH37t146qmn0N3djZ6eHtx6660oLCzE4sWLMWfOHHznO9/BPffcA6D3u1IOhCciIvpmGujbq693tEyaNAkulwuBQAA6nQ6BQADHjh1Dampqv9tVVRWXX355n7bk5OTg7xdeeCEmTZqEzz77LCJ1SMQLrRUrVvTbXllZCQDYuHHjgNctLCwMq0olIiKi6InlGK2Bvr36uqSkJMyYMQMOhwN2ux0OhwPp6en9fm149OhR7NmzB3fffXef9q+OI29pacGRI0dgsVgicju4BA8RERGNarfccgvWr1+P3//+9zCbzcEzDNeuXYvCwkJkZGQAAN555x1cfPHFGD9+fJ/r/+53v8OBAweg0+lgMBhwxx139OnlCgcLLSIiIhqSkXjWIQBMmzYNa9asCWl/8MEH+/w90Fjxr079EGljqtCKO3V0yFnDyU7RtnVdwnm74uJF8UVrr5JtH4DjwddF+Vn5d58+9BUBnV6UV4S3eZzSLcp7x8kGJcYZvKK8DoGobl8f6JFt33tClB8fL3uOKpomynu1caK8WX9SlPdresQpp2TXUWTP0R6YRPluk+w5p4dPlO/Snf5rka/ymWTHL+WD7FR36XuE9DUmJd2+X5Mdv/Q1PJzbmxwnew0AstclxR4XLSKiEUFaZBERjQZjqkeLiIiIooeLSsvxHiMiIiKKkqj1aLndbpSWliI3NxclJSUAek+ZfOGFF3DgwAHMnz8f9957bzA/2GVERER05o3UwfAjWdR6tBwOB6xWKxobG+Hz9Q4QTUxMxM0334wf/OAHIfnBLiMiIiIajaJWaKmqivz8fKSlpcHpdAIAUlJSMHPmzH4XcRzsMiIiIjrzFJ0uZj9jRVS+OmxtbYXb7UZmZiY6OjqgqipsNlvEtj/QQpNxEdsDERERUfiiUmjV1dXBbrdDURRkZWWhurq6z/T24RpoocnnH/jXiGyfiIiI+qFwjJZUxAstn88Hh8MBo9GIhoYGAIDf70d9ff2AM7JKDbTQJD77ICLbJyIiIoqEiBdaTqcTFosFFRUVwbbm5mZUVVVFrNAaaKHJzs8isnkiIiLqB886lIt4oaWqKrKzs/u0Wa1WaJqG3bt346mnnkJ3dzd6enpw6623orCwEIsXL8bhw4fxs5/9rN/LiIiIiEajiBdaK1as6Le9srISALBx48Z+L58yZcqAlxEREdGZN5bOBowV3mNEREREUaJomqad6YOIlLbmXUPOntJPEG075dTnorxPL1tR/aQpSZQHgIAiq5P3nP9/RPnZe2pFefHK9ppflO82JIjymiYbSxDvd4vyp/TjRfkA9KK8DrL7R3r8nXrZWcAn/fGi/Fk4KMoDgFufLMrHa6HTvAxG+hyVPufie2SPQY/wfSKgyJ5DPTCJ8sd9iaL8FP0XorxfkX2JYgx0i/JefXQn+fFrsuPXKz7xPvQB2XVmnGcV7yMch+5dGrN9TX3ydzHbVzSxR4uIRgRpkUVENBpEba1DIiIiGls4RkuO9xgRERFRlLDQIiIiIoqSqH116Ha7UVpaitzcXJSUlAAAWlpa8MILL+DAgQOYP38+7r333mD+rbfewuuvvw5N06AoCq699lrY7fZoHR4REREJccJSuagVWg6HA1arFY2NjSguLobBYEBiYiJuvvlmtLS0YOfOnX3yU6dOxc9//nOMHz8eR48exf33349Zs2ZhypQp0TpEIiIioqiKWqGlqiqWLVuGzZs3w+l0wmazISUlBSkpKfj889CpEubMmRP8fdKkSUhOTobL5eq30PJ4PPB4ZKd1ExERUXjYoyUXlUKrtbUVbrcbmZmZ6OjogKqqsNlsQ77+hx9+iJMnT+Lcc8/t9/La2lrU1NSEtP/Hwz8b9jETERERRVpUCq26ujrY7XYoioKsrCxUV1fD5XIhJeX0EyS2tbWhqqoKd999N0ym/ifby8vLQ05OTki7t+NwuIdOREREA+H0DmIRL7R8Ph8cDgeMRiMaGhoAAH6/H/X19cjPzx/0uocOHcLatWuxfPlyzJo1a8Cc2WyG2WwOaW9joUVEREQjSMQLLafTCYvFgoqKimBbc3MzqqqqBi20vvjiCzzyyCMoKSnB/PnzI31YREREFCZF4RgtqYgXWqqqIjs7u0+b1WqFpmnYvXs3nnrqKXR3d6Onpwe33norCgsLsXjxYmzatAknTpzAq6++ildffRUAUFRUhHnz5kX6EImIiIhiIuKF1ooVK/ptr6ysBABs3Lix38vvueeeSB8KERERRRCX4JHjPUZEREQUJWNqUWmfrv+zFPsz8dRB0bZPjksW5Y3+blE+rsctygNAQKcX5WfvqRXld8/KE+UXNa0X5duT+5++YyCaJhsb4NXGifJT1edF+eNX3C7KGxSfKD8ucEqUP6GfKMon9xwR5ZMU2f9lJwyy41GgQYEmuk6XkiDK6/Wyx0B6PEaf7DFzG5JFeb3wOaTT/KL8BIPsfagHQ3/PBQBN+L+9Xyf7iLK8+awo3yp8DSdAdv9Mfus3ojwAtFxxh/g6scR5tOTYo0VEI4K0qCEiGg3GVI8WERERRRHHaInxHiMiIiKKkqj1aLndbpSWliI3NxclJSUAgJaWFrzwwgs4cOAA5s+fj3vvvTeYr6+vx4svvojJkycDAKZMmYKysrJoHR4REREJcYyWXNQKLYfDAavVisbGRhQXF8NgMCAxMRE333wzWlpasHPnzpDrzJ07t0/xRURERDSaRe2rQ1VVkZ+fj7S0NDidTgBASkoKZs6cCaPRGK3dEhERUZQoii5mP2NFVHq0Wltb4Xa7kZmZiY6ODqiqCpvNdtrr7d69G2VlZUhISMD3vvc9LFiwoN+cx+OBx+OJ9GETERERRVRUCq26ujrY7XYoioKsrCxUV1fD5XIhJSVlwOssWLAAl156KUwmEw4cOIA1a9bgoYcewvTp00OytbW1qKmpCWn/xZqHI3o7iIiIiMIR8ULL5/PB4XDAaDSioaEBAOD3+1FfXz/ootKJiYnB39PT03H++edj//79/RZaeXl5yMnJCWk/2dkR9vETERHRADgYXizihZbT6YTFYkFFRUWwrbm5GVVVVYMWWl/t8Tpy5Aj27duH66+/vt+s2WyG2WwOaW9hoUVEREQjSMQLLVVVkZ2d3afNarVC0zTs3r0bTz31FLq7u9HT04Nbb70VhYWFWLx4MV5//XX87W9/g17fu6zMTTfdhPT09EgfHhEREQ0TF5WWi3ihtWLFin7bKysrAQAbN27s9/KlS5di6dKlkT4cIiIiojOGS/AQERHRkHDCUjn2ARIRERFFyZjq0ZrY+emQs7qAT7Tt8Qf3iPJf1GwR5adkf1uUBwAlLl6UPzzvalF+UdN6Ud6RdbsoP3PPm6L8WcebRfkd404/d9tXJV1+oyjf3p0kyk+P+4cob+5yifJH4yaL8uN1spf/Jz2yMZMJ8IryADDeIJsf76Rf9hqYoHeL8inuob+nAMCJhCmifHxAdjxJnZ+J8v9IvkCUP9w1UZSf1+UQ5b9IniXK6yF7n/4i90eivAJNlDf5ukR56fEAgDcwwif0HkMTicYK7zEiGhGkRRYR0Wgwpnq0iIiIKHo4RkuOPVpEREREURK1Hi23243S0lLk5uaipKQEANDS0oIXXngBBw4cwPz583Hvvff2uc6uXbvw8ssvw+vtHdtx1113YcaMGdE6RCIiIpLgPFpiUSu0HA4HrFYrGhsbUVxcDIPBgMTERNx8881oaWnBzp07++RdLhc2bNiAlStXwmKxwOv1wueTDYQkIiIiGkmiVpqqqor8/HykpaXB6XQCAFJSUjBz5kwYjaFnVWzduhV2ux0WiwUAYDKZkJCQEK3DIyIiIiFFUWL2M1ZEpUertbUVbrcbmZmZ6OjogKqqsNkGP9W+ra0NkydPxurVq+HxeDBnzhwsXbq036LM4/HA4wk9Q2lcxG4BERERUfiiUmjV1dXBbrdDURRkZWWhurq6z6LR/QkEAti7dy9WrVoFk8mEdevWYcuWLSgoKAjJ1tbWoqamJqT9uZ8uj+jtICIioq/gGC2xiBdaPp8PDocDRqMRDQ0NAAC/34/6+nrk5+cPeL3U1FRkZGQEvy602WzB639dXl4ecnJyQi9o2xnaRkRERHSGRLzQcjqdsFgsqKioCLY1Nzejqqpq0EJr0aJFeOWVV7BkyRLo9Xrs2LEDaWlp/WbNZjPMZnNI+/G28I+fiIiIKFIiXmipqors7Ow+bVarFZqmYffu3XjqqafQ3d2Nnp4e3HrrrSgsLMTixYtx/vnnY968eSgrK4NOp0N6ejquu+66SB8eERERDRMnLJWLeKG1YsWKftsrKysBABs3bhzwukuWLMGSJUsifUhEREREZwSX4CEiIqKh4aLSYmOq0Do+YdqQsyd1E0TbnmieLMp3P3CFKP8ZgG5NNkHFOKVblE/wd4ry7cnnivIz97wpyu+b9c+ivPejd0T58/wfi/JufbJs+5ps+0pPQJQ/ES97zk3xHxLlvfo4Ud5slD3fJunaRXlowKGes0VX+Zb+U1E+7tQJUf6/8R1R/nzhcyKuxy3KtyefJ8rr4Bfl4w09ovzB5Dmi/AnfeFF+qiIbeHtSnyjKJ/ccEeW7jLLjV6BBH5Ddp1P9n4nyQKowT7E2pgqt0UxaZBGNNdIii2ikkxZZowLHaImx0CIiIqJR7eDBg1i/fj3cbjfGjx+PO+64A1OnTu2Tee211/DGG29g4sSJAIDzzz8fP/7xjwEA3d3dePrpp/HJJ59Ar9ejuLgYF198cUSOjYUWERERDYkyQsdoPffcc/jud78Lu92OhoYGPPvss3jooYdCcna7HTfffHNI+x//+EfEx8ejsrIShw4dws9+9jNUVlYiLk42xKI/I/MeIyIiom80j8eDw4cPh/x8fQm+48eP48CBA1i0aBGA3nk5Dxw4gM7OoY9L3rZtG/75n3vHDU+dOhUZGRn44IMPInI72KNFREREQxPDMVoDLbdXUFCAwsLC4N9Hjx5FSkoKdP+zPJBOp8PEiRPR3t6OxMS+J0hs27YNO3fuRHJyMgoLC2G1WgEA7e3tmDz5f09ASk1NxdGjRyNyO8IutNxuN0pLS5Gbm4uSkhIAQEtLC1544QUcOHAA8+fPx7333nva7RQWFuKll16KSDcdERERjW4DLbfX38owQ3HllVciPz8fBoMBO3fuxC9+8Qv88pe/xIQJslkIpMIutBwOB6xWKxobG1FcXAyDwYDExETcfPPNaGlpwc6dkV9/0OPxhHQdEhERUXQpMVxUOmGA5fa+btKkSXC5XAgEAtDpdAgEAjh27BhSU/tOfZGcnBz8/cILL8SkSZPw2WefYfbs2UhNTcWRI0eCPWDt7e2YM0c2fclAwi60VFXFsmXLsHnzZjidTthsNqSkpCAlJQWff/65aFt/+MMf4HQ64fV6cdNNN+GSSy7pNzdQd+ITj6we1m0gIiKi0SkpKQkzZsyAw+GA3W6Hw+FAenp6yNeGLpcLKSkpAHq/eTty5AgsFgsA4JJLLsGbb76JjIwMHDp0CB9//DHuvvvuiBxfWIVWa2sr3G43MjMz0dHRAVVVYbPZhr09nU6Hxx9/HAcPHkR5eTkuuOACJCUlheQG6k7sOh6Z71OJiIioH8rInEfrlltuwfr16/H73/8eZrMZd9xxBwBg7dq1KCwsREZGBn73u9/hwIED0Ol0MBgMuOOOO4K9XNdeey2efvpp3HnnndDpdFi+fDni4+MjcmxhFVp1dXWw2+1QFAVZWVmorq7uUzFKLV68GABgsViQnp6Offv24dvf/nZIzjxAd+KnLLSIiIi+caZNm4Y1a9aEtD/44IPB378svvoTFxeHe+65JyrHNuxCy+fzweFwwGg0oqGhAQDg9/tRX1+P/Pz8iB0gERERjRAxHKM1Vgy70HI6nbBYLKioqAi2NTc3o6qqatiFlqqquP7663Ho0CG0tLRg5syZwz08IiIiojNu2IWWqqrIzs7u02a1WqFpGnbv3o2nnnoK3d3d6Onpwa233orCwsLgV4MD8fv9uP/++9Hd3Y1bbrml3/FZRERERKPFsAutFStW9NteWVkJANi4caNoe6+99hoA9JmEjIiIiEaQEToYfiQbUzPDx/ecGHLWqOsWbTug6EX5VE+rKK/3yY4HALzjZJOsdcZPPn3oKzRN9oI663izKO/96B1RvvWCy0T5W696VpSv/A+LKP+j+w6I8tW/ShflDegR5X1Goyiv0wKivEknOx6pqcZ/4ARkvdgdSD196CvM8bIJkeefel+Ud+mmifK+cSZRXhN+yAU02fuWUfgYmyB730o0aKJ8t5Igykvfs3r040R5neYX5U/pxovyAODXyV7HNPLFtNCqqalBU1NTSHt5eTm/JiT6hpMWWUQUe7GcsHSsiGmhVVBQgIKCgljukoiIiOiMGVNfHRIREVEUKezRkuI9RkRERBQlESm03G43ioqK8Otf/zrkss7OTtxyyy148sknI7ErIiIiOlN0Sux+xoiIFFoOhwNWqxWNjY3w+Xx9Lnv++ecxf/78SOyGiIiIaFSJSKGlqiry8/ORlpYGp9MZbH/33XeRlJSE2bNnn3YbH374IX76058O+PdXeTweHD58OOSHiIiIokdRdDH7GSvCHgzf2toKt9uNzMxMdHR0QFVV2Gw2uFwu/OlPf8Lq1avx3nvvReJYg2pra1FTUxPSvv7n90V0P0REREThCLvQqqurg91uh6IoyMrKQnV1NVwuF5555hksW7YMcXGyCQKHIi8vDzk5OaEXtLdEfF9ERET0P8bQ2KlYCavQ8vl8cDgcMBqNaGhoANC7XmF9fT2am5uDy/B0dXXB6/Vi7dq1ePDBB/vdll6vh6b976zBPT0Dz1BsNpthNptD2o+w0CIiIqIRJKxCy+l0wmKxoKKiItjW3NyMqqqqPmcg1tfX4/3338e999474LamTJmCL774Am63G2azGQ6HI5xDIyIiokgbQ2OnYiWsQktVVWRnZ/dps1qt0DQNu3fvHtIg+C+lpKTgX/7lX/DTn/40OIC+ra0tnMMjIiIiOqPCKrRWrFjRb3tlZWWfv3NycvofU/U1XKKHiIhoBBMubE6cGZ6IiIgoamK+1uGzzz6Lffv29WnT6/V49NFHw952m/HcIWf1CIi2rSja6UNfcQrponyPQS/KA0CcwSvKJ2nHRXmvNk6U3zHOJsqf5/9YlL/1qmdF+QdfXy7KJ/zqrahu36T9UZT3KrIzdvXwnT70FT4YRflJ/i9EebchWZQfh25M6HGJrqMJx4t4DEmi/CfGoQ9/AIBUtIvyfshe9wpk70MmrUuUn+CX3f8enez+jA+4Rfkevew9SOqUbnxUtz8cPTCd6UOgCIt5obV8uezDiYi+GaRFFhGdATp+ESbFe4yIiIgoSmLeo0VERESjFKd3EOM9RkRERBQlESm03G43ioqK+kxS+qXOzk7ccsstePLJJyOxKyIiIjpTdErsfsaIiBRaDocDVqsVjY2N8Pn6nvn0/PPPY/78+ZHYDREREdGoEpFCS1VV5OfnIy0tDU6nM9j+7rvvBmd5P53nn38ef/jDH4J/HzhwAHfffXef9Q+/5PF4cPjw4ZAfIiIiiiJFF7ufMSLswfCtra1wu93IzMxER0cHVFWFzWaDy+XCn/70J6xevRrvvffeabdz1VVX4bHHHsM111wDRVHw+uuv47vf/S6Ufmahra2tRU1NTUj7g488He7NISIiIoqYsAuturo62O12KIqCrKwsVFdXw+Vy4ZlnnsGyZcsQFze0SRenT5+Os846C9u3b8fMmTPxt7/9DT/4wQ/6zebl5fW7pM/nsvk4iYiISIJL8IiFVWj5fD44HA4YjUY0NDQAAPx+P+rr69Hc3IyNGzcCALq6uuD1erF27Vo8+OCDA27v//yf/4OtW7eira0NWVlZSEhI6DdnNpthNptD2j8/LpuVmYiIiCiawiq0nE4nLBYLKioqgm3Nzc2oqqrqcwZifX093n//fdx7772Dbm/+/Pl46aWXcODAgQEXrCYiIqIzhDPDi4V1j6mqiuzs7D5tVqsVmqZh9+7d8oPR6XDZZZdhypQpSEtLC+fQiIiIiM64sHq0Bup1qqys7PN3Tk5Ov2Oq+vPhhx/i6quvDuewiIiIKBo4RktsxPQBfvzxx7jzzjuRkJCArKysM304RERERGFTtP4mqoqiZ599Fvv27evTptfr8eijj4a97Q/3HxpydqL/iGjbfp2s869TmSjK65SAKA8AOsiuM97fIconq/+fKN9x+Y2ivEefJMsH+j85YiAJulOi/P5ZuaL8eXveEuWlJnZ/IcofHzdZlFekz5+eDlH+hDFFlAfkx6RF+X9FDbL/3vWK7/ShrwhoelFeSnp/mntkp253GieJ8nrI7h+pqXUviPItl98uyo9TukR56fEAwIns60X5s2fFdkLwrj8/G7N9xV29PGb7iqaYLyq9fPnYuOOIKLKkRQER0WgwYr46JCIiIhprYt6jRURERKMUp3cQ4z1GREREFCVD6tFyu90oLS1Fbm4uSkpKAPROQvriiy9i8uTeAbhTpkxBWVlZ9I6UiIiIzixO7yA2pELL4XDAarWisbERxcXFMBh6rzZ37tzTzvYeDR6PBx6PJ+b7JSIiIpIYUqGlqiqWLVuGzZs3w+l0wmazDWtnr7/+Ov785z/DbDZj/vz52Lp1K1544YXTXvZ1tbW1qKmpCWl/aM26YR0XERERDYHCEUdSpy20Wltb4Xa7kZmZiY6ODqiqGiy0du/ejbKyMiQkJOB73/seFixYMOh2/t//+394/PHHkZiY2GctxMEu609eXl6/M80f6fSf7uYQERERxcxpS9O6ujrY7XYoioKsrCzs27cPLpcLCxYswIYNG/D444/jhz/8ITZs2IC2trYBt/Phhx9i/vz5SExMBABcfvnlQ7qsP2azGVOmTAn5ISIioihSlNj9jBGD9mj5fD44HA4YjUY0NDQAAPx+P+rr65Gfnx/Mpaen4/zzz8f+/fsxffr06B4xERER0SgxaKHldDphsVhQUVERbGtubkZVVRVycnKQktK7xMaRI0ewb98+XH/9wEsHzJ49G3/4wx/Q2dmJxMREvPPOO0O6jIiIiEYIzqMlNmihpaoqsrOz+7RZrVZomobKykocP34cen3vWl033XQT0tPTB9zWjBkzcO2116K8vBzx8fGYO3cuEhISTnsZERER0Wg1aKG1YsWKftsrKyuHtbPLL78cV199NQDgtddew8yZM4d0GREREZ152hgaOxUrMV2CZ9OmTdi7dy98Ph/OOuusPgtMD3bZUHk145Cz7bqzRdtWFE2UP+WLE+V7AnpRHgDiDF5RXq/3ifLHr5CtbN/enSTKn6d9LMr/6L4DovyDr8ueQ+fteUuU3z8rV5Sf89EfRfmOcbITPPSK7PENaLLnXECR5TXI3pA16DGhxyW7jvBUc49B9hw97ksU5VMN7aK89D6Svg/pA7LnhJQesu2bAl2ifI9+nCjftrhUlDcIj98v/MiUHg8AKJA9xrJPMjoTIl5oPfvss9i3b1+fNr1ej0cffRQ//vGPB7zeYJcR0dgnLbKI6AzgPFpiES+0htMTRURERDQWxfSrQyIiIhrF2KMlxnuMiIiIKErC7tFyu90oLS1Fbm4uSkpKAAD19fV48cUXMXnyZADAlClTUFZWFu6uiIiIiEaVsAsth8MBq9WKxsZGFBcXw2Do3eTcuXNx7733hn2ARERENDJwege5sAstVVWxbNkybN68GU6nM7jgtMThw4fx4IMP4rLLLsPOnTuhaRp+/OMf44ILLug37/F44PF4+rmE34QSERHRyBFWodXa2gq3243MzEx0dHRAVdVgobV7926UlZUhISEB3/ve97BgwYJBt3XixAmkpaXh5ptvxocffoh169ahsrISRmPo3Fi1tbWoqakJaX/wkafDuTlEREQ0GA6GFwur0Kqrq4PdboeiKMjKykJ1dTVcLhcWLFiASy+9FCaTCQcOHMCaNWvw0EMPDbrgtMFgCC73M2fOHJhMJhw8eBBpaWkh2by8POTk5IS0f348nFtDREREFFnDLrR8Ph8cDgeMRiMaGhoAAH6/H/X19cjPzw/m0tPTcf7552P//v2DFloSZrMZZrM5pP3z47JZmYmIiEiAY7TEhl1oOZ1OWCwWVFRUBNuam5tRVVWFnJwcpKSkAACOHDmCffv24frrrx90e18Wbna7HR999BG8Xi+mTZs23MMjIiKib4iDBw9i/fr1cLvdGD9+PO644w5MnTq1T6ampgbbtm2DTqeDXq/HTTfdhHnz5gEA1q9fj7///e+YMGECAMBms/XpNArHsAstVVWDX/V9yWq1QtM0VFZW4vjx49Dre9dGu+mmm5Cenj7o9iZMmICWlhb84Q9/gKZpuPvuu4NnMBIREdEIoBuZY7See+45fPe734XdbkdDQwOeffZZPPTQQ30y5513Hq655hqMGzcOLS0t+PnPf45nn30WJpMJALBkyRJcddVVET+2YVcyK1as6Le9srJy2Adz8803D/u6RERENHYMNMPA14cPHT9+HAcOHMCqVasAAIsWLUJ1dTU6OzuRmPi/C8N/2XsFAGlpadA0DSdOnMCkSZOidyPAJXiIiIhoiGI5j9ZAMwwUFBSgsLAw+PfRo0eRkpIC3f/0tul0OkycOBHt7e19Cq2veuedd3D22Wf3KbL+9Kc/4c0338RZZ52FpUuXRmxceUwLrWeffRb79u3r06bX6/Hoo4/ihRdeCHv7egSGnDXqekTbHocuUT7eeFKU18MvygOAPiC7DV0IPYFgMAbFJ8pPj/uHKK/0DP3xAoDqXw3+9fPXmbQ/ivLdojQw5yPZ9j+84BpR/qxd74nyOkV2f0rzR7VkUV7zyt6QjyIZicb+5scbmMeXIMrrfLLb3O0PnV5mMF7DOFH+qDdZlDfpZK9Js0H2PnRIO1uUn4yjonyXTvgepMne46RSug+J8kdMsg/e8Zr8VPhunew5PZYNNMNAfyfDSezevRuvvvpqsAcM6B3ilJycDJ1Oh3feeQdr1qxBVVVVsHgLR0wLreXLl8dyd0Q0ikiLLCI6A2I4j9ZAMwx83aRJk+ByuRAIBKDT6RAIBHDs2DGkpqaGZJubm1FZWYmysjJYLJZg+5cn8AHAZZddht/85jc4evRocCnBcIzMUW1EREREQ5CUlIQZM2bA4XAA6F0aMD09PeRrw/379+OXv/wl7rnnHpx77rl9LnO5XMHft2/fDp1O16f4CgfHaBEREdGQaCN0ZvhbbrkF69evx+9//3uYzWbccccdAIC1a9eisLAQGRkZeOGFF+D1evHss88Gr3fnnXfinHPOwfr169HR0QGdTof4+Hjcf//9wZkTwqVomqZFZEsjwM59h4ecjfYYrYCwszAmY7SE4yOkDJAdT3zPCVH+hFH234VJkz1m3Uq8KD9OOyXKj/YxWl1+kyivabIxWsP56lA8RivKt3nyONfpQ18x0sZodXgniPKTTbIxWgHIPrik7yk9kD1eY2GM1syM0NVTosn93h9itq/xl1wbs31FU0R6tNxuN0pLS5Gbm4uSkpJg+65du/Dyyy/D6/UCAO666y7MmDEjErskIiKiWOPM8GIRKbQcDgesVisaGxtRXFwMg8EAl8uFDRs2YOXKlbBYLPB6vfD5ZP+NEREREY1mESm0VFXFsmXLsHnzZjidTthsNmzduhV2uz04qt9kMgVnXx1Ic3Mzfvvb36Krq/crn2XLluGiiy4KyQ00iRkRERHRSBJ2odXa2gq3243MzEx0dHRAVVXYbDa0tbVh8uTJWL16NTweD+bMmYOlS5fCaOx/Xhq3243HH38c9913H84//3wEAgGcPNn/+IKBJjErf6Qq3JtDREREAxipg+FHsrALrbq6OtjtdiiKgqysLFRXVwfns9i7dy9WrVoFk8mEdevWYcuWLSgoKOh3O83NzZg+fTrOP/98AL0zu44fP77f7ECTmP1DPu6QiIiIKGrCKrR8Ph8cDgeMRiMaGhoAAH6/H/X19UhNTUVGRgYSEnrPoLDZbMFMuAaaxOwfx4d+1iEREREJcTC8WFiFltPphMViQUVFRbCtubkZVVVVuP322/HKK69gyZIl0Ov12LFjB9LSBj4N1Wq1oq2tDc3NzbBarcGvDgfq1SIiIiIa6cIqtFRVRXZ2dp82q9UKTdPg9/sxb948lJWVQafTIT09Hdddd92A2xo/fjzuu+8+/OY3v0F3dzcURUFxcTEuvPDCcA6RiIiIIoVjtMQ4YekQccLS0+OEpYPjhKWD44Slp8cJSwfHCUuj78TfXo/ZviZ8+6qY7SuauAQPERERDYnGMVpiMS+0ampq0NTUFNJeXl6OpKSksLYdpxt6D4a0x8nkl/VeGP3donyPfpwoDwBxXlmPkDc+TpQfF5DdZnOX7L/5E/GyVdGl/916Fdntndj9hSjfMW6KKC/tofoi8xJRPv0jVZRXFFlntl7YGyQVgA4mxSu6ToJB9hxVILvNBmEPkg6y+2iCUdbjJN2+tKc8yegW5VNOfi7KH0mQ9b740P90QAOR3j+ucVNFeen92a3IeqcA+WcTjXwxL7QKCgoGnOKBiL65pEUWEZ0BHKMlxnuMiIiIKEo4RouIiIiGRAPHaEmxR4uIiIgoSkQ9Wm63G6WlpcjNzUVJSUmwfdeuXXj55Zfh9faOsbjrrrswY8aMiB4oERERnVlc61BOVGg5HA5YrVY0NjaiuLgYBoMBLpcLGzZswMqVK2GxWOD1euHzyc7UkfJ4PPB45HPuEBEREcWSqNBSVRXLli3D5s2b4XQ6YbPZsHXrVtjtdlgsFgCAyWSCyTT4pHHNzc347W9/i66u3ukYli1bhosuughtbW148cUX0dHRAU3TcM011/S7eHRtbS1qampC2h9e+4Tk5hAREZEEe7TEhlxotba2wu12IzMzEx0dHVBVFTabDW1tbZg8eTJWr14Nj8eDOXPmYOnSpTAa+5//xO124/HHH8d9992H888/P7imod/vx+OPP44bb7wRNpsNAHDiRP/zROXl5fVbgHWckM0ETkRERBRNQy5N6+rqYLfboSgKsrKysG/fPrhcLgQCAezduxdlZWVYs2YN2tvbsWXLlgG309zcjOnTp+P888/vPQCdDuPHj8fBgwfh9/uDRRYATJjQ/3IQZrMZU6ZMCfkhIiIiGkmG1KPl8/ngcDhgNBrR0NAAAPD7/aivr0dqaioyMjKQkNA7A67NZgtmiIiIaOzgEjxyQ+rRcjqdsFgs2LhxI9avX4/169ejvLwc9fX1WLRoEXbt2gWfzwdN07Bjxw6kpQ28zILVakVbWxuam5sBAIFAAG63GxaLBXq9Hn/5y1+C2YG+OiQiIiIaDYbUo6WqKrKzs/u0Wa1WaJoGv9+PefPmoaysDDqdDunp6bjuuusG3Nb48eNx33334Te/+Q26u7uhKAqKi4tx4YUXoqysDNXV1aipqYFOp8M111wDu90e3i0kIiKiiOD0DnKKpmmyVVZHsOaPPx1yVrpw53h/hyg/EheV7og/W5QfaYtK+xXZQgbSBWmTuw+L8tJFpTt9/Y85HMhIW1Tap0V3IYnhrHXYo8keY+mi0j3C22zWyRaJ7tJkC59LF002KrKF2KWP8dmnPhHlpYtKSx8vaT7aCzhLHy9AfkznZ3xLvI9wuHa+G7N9pVyYffrQKDCmluCRvMiMwjd1rz5elHcpsg/h7sDgU2L0Z3y8bC6xRL+wENJPFOWPxskKpyn+Q6K8b4AzWQeih2w+t+PjZMevV2Tb1ymyN11p4XTggstF+Yw9b4vy0g8N6VIdPZpRXGxF+4NYL3zMpKJ9/FLSx+yY2SLKK8L/66N9e6X/QHv0SdE5kK9I9sr+4QNiW2iBY7TEolZo1dTUoKmpKaS9vLwcSUnRf7IS0egynB4tIqKRLmqFVkFBAQoKCqK1eSIiIooxjtGS4z1GREREFCVjaowWERERRY90HB+xR4uIiIgoasLu0XK73SgtLUVubi5KSkqC7bt27cLLL78Mr7d3gOtdd92FGTNmDHm7hYWFeOmllxAXJzv9mYiIiKKDY7Tkwi60HA4HrFYrGhsbUVxcDIPBAJfLhQ0bNmDlypWwWCzwer3w+WSnwg/G4/HA45FNbUBEREQUa2EXWqqqYtmyZdi8eTOcTidsNhu2bt0Ku90Oi6V3jhWTyQSTafB5opqamvDKK6/AaDQiKytr0GxtbS1qampC2h9Z+/jwbwgRERENjvNoiYVVaLW2tsLtdiMzMxMdHR1QVRU2mw1tbW2YPHkyVq9eDY/Hgzlz5mDp0qUwDjDhZEdHB5555hk8/PDDsFgs2LJly6D7zcvLQ05OTkj78ROymcyJiIiIoimsL1vr6upgt9uhKAqysrKwb98+uFwuBAIB7N27F2VlZVizZg3a29sHLZ7279+P9PT0YA9Ybm7uoPs1m82YMmVKyA8RERFFjwZdzH7GimH3aPl8PjgcDhiNRjQ0NAAA/H4/6uvrkZqaioyMDCQkJAAAbDZbMENERET0TTHsktHpdMJisWDjxo1Yv3491q9fj/LyctTX12PRokXYtWsXfD4fNE3Djh07kJY28GKiM2fOREtLCw4d6l377u23ZWuwEREREY1Ew+7RUlUV2dl9V9a2Wq3QNA1+vx/z5s1DWVkZdDod0tPTcd111w24raSkJCxfvhyPPfYYTCbTaQfDExERUexpHAwvpmiacDn1EWzfx61DzuoUv2jb0lXn3YEJonx3YPCzMvsz3iCb4iLR7xLlT+gnivJdgXGi/JTAIVH+lHG8KC8V0PSivPQ5dNwnW0w9Ttctyh+44HJRPmOPrOfYL7x/pDNID2dR6R6t/xNsBqJA9jr2Q3ab45QuUb5bk71mdAiI8gZFNq2O9P6M08lOQPJrsv/tpY+XVIK/U5T36GWv4eEcf6K3XZQ/64KLxfsIxxcfvR+zfcX6tkXLmFqCJ9XdMuSsppO9gfr1skLIYJR9aAT0suMB5MVfpz5FlE/uOSLKj9fJnk5evWwyWp0m+5DxQfohLNu+vDCTbV9RZI+vtHD6eNYVonz6R6ooL9WjGWFUekTXkX6QSe9T6WtMKtrHP73+eVH+wGX/V5Tv0WTvi9JCUUr6Gv4scI4of8EffibKH7nuJ6I8ALjHyf7BPUu8h/BwwlK5mBZaNTU1aGpqCmkvLy9HUpLsPwUiGlukRRYR0WgQ00KroKAABQUFsdwlERERRQgXlZZjHyARERFRlIypMVpEREQUPRyjJRd2oeV2u1FaWorc3FyUlJQE23ft2oWXX34ZXm/voPC77roLM2bMCHd3RERERKNG2IWWw+GA1WpFY2MjiouLYTAY4HK5sGHDBqxcuRIWiwVerxc+n+w0YyIiIhpZOI+WXNiFlqqqWLZsGTZv3gyn0wmbzYatW7fCbrcH1y40mUwwmQY/DXjfvn343e9+h5MnTwIAbrjhBixYsKDfrMfjgccTOoeU7GR+IiIiougKq9BqbW2F2+1GZmYmOjo6oKoqbDYb2traMHnyZKxevRoejwdz5szB0qVLYTT2Xwp5PB4899xzePDBBzFx4kQcO3YMDz74IJ588kmYzeaQfG1tLWpqakLan1l5ezg3h4iIiAbBsw7lwiq06urqYLfboSgKsrKyUF1dDZfLhUAggL1792LVqlUwmUxYt24dtmzZMuDUDnv37sXhw4exZs2aYJuiKPjHP/6BjIyMkHxeXh5ycnJCN3Too3BuDhEREVFEDbvQ8vl8cDgcMBqNaGhoAAD4/X7U19cjNTUVGRkZSEhIAADYbLZgZiBpaWlYvXr1kPZtNpv77ek6xkKLiIgoanjWodyw7zGn0wmLxYKNGzdi/fr1WL9+PcrLy1FfX49FixZh165d8Pl80DQNO3bsQFpa2oDbslqtOHToEHbt2hVs279/P8bQMoxERET0DTTsHi1VVZGdnd2nzWq1QtM0+P1+zJs3D2VlZdDpdEhPT8d111034LbGjx+P+++/Hy+//DJ+85vfwOfzYcqUKXjggQeg8AwHIiKiEYFjtOSGXWitWLGi3/bKykoAwOzZs7FkyZIhb++8887Dz3/+8+EeDhEREdGIwy9biYiIiKIkpkvw1NTUoKmpKaS9vLwcSUlJYW9f5+8ZctYdnyLa9lHlLFG+syd0sP5gpsZ9IcoDgFcbJ8qf9MeL8knCQY+f9KSL8mZjtyhv0g398QWASX7ZfarT/KJ8QNGL8ke1ZFFerwREeR1k+fSPVFH+wAWXi/LTdjtE+R7NgDid7Dnh02RvYdL7qEe4fZPijer2DZA9Rz/LuUWUP3zVVaJ8/B8HP8np6yYaO0V5qQk9HaK88+gMUX7idfeJ8kk97aI8ABwzThFfJ5Y4GF4upoVWQUHBgFM8ENE3m7TIIiIaDbioNBEREQ0JB8PLsQ+QiIiIKErYo0VERERDwjFacmEVWm63G6WlpcjNzUVJSUmwfdeuXXj55Zfh9fYODL3rrrswY8aMAbfz17/+Fa+88gqMRiN+8pOfoK2trc/fXy5OTURERPR1Bw8exPr16+F2uzF+/HjccccdmDp1ap9MIBBAdXU1duzYAQBYsmQJrrjiitNeFq6wCi2HwwGr1YrGxkYUFxfDYDDA5XJhw4YNWLlyJSwWC7xeL3w+36Dbeeutt1BYWAibzQYAePHFF/v8/XUejwcejyekXXYOHhEREUmM1DFazz33HL773e/CbrejoaEBzz77LB566KE+mXfffRdffPEF1q1bB7fbjfvvvx9z587FlClTBr0sXGEVWqqqYtmyZdi8eTOcTidsNhu2bt0Ku90e7IUymUwwmUwDbuPFF1/ERx99hIMHD+KNN95AWlpan7+/fkcBQG1tLWpqakLan/vp8nBuDhEREY0yx48fx4EDB7Bq1SoAwKJFi1BdXY3Ozk4kJiYGc9u2bcMVV1wBnU6HxMRELFy4EO+99x6uvfbaQS8L17ALrdbWVrjdbmRmZqKjowOqqsJms6GtrQ2TJ0/G6tWr4fF4MGfOHCxduhRGo7Hf7fzwhz9ES0sLrrnmGlx88cUAEPL31+Xl5SEnJyf0gradw705REREdBpaDJfFG+jbK7PZDLP5f+eqPHr0KFJSUqDT9Y4f0+l0mDhxItrb2/sUWu3t7UhNTQ3+nZqaivb29tNeFq5hF1p1dXWw2+1QFAVZWVmorq6Gy+VCIBDA3r17sWrVKphMJqxbtw5btmyJ6PxZX7+Tv3S8LWK7ICIiojNooG+vCgoKUFhYeAaOaHiGVWj5fD44HA4YjUY0NPTODOz3+1FfX4/U1FRkZGQgISEBAGCz2YIZIiIiGr00LXY9WgN9e/X1jpZJkyYFO3p0Oh0CgQCOHTvWp4cK+N9eqvPOOw9Aby/W5MmTT3tZuIZ1nqbT6YTFYsHGjRuxfv16rF+/HuXl5aivr8eiRYuwa9cu+Hw+aJqGHTt2IC0tLSIHS0RERN8MZrMZU6ZMCfn5eqGVlJSEGTNmwOHoXfbL4XAgPT29z9eGQG/Hz9tvv41AIIDOzk44nU5ccsklp70sXMPq0VJVFdnZ2X3arFYrNE2D3+/HvHnzUFZWBp1Oh/T0dFx33XUROVgiIiI6c7QROs/5LbfcgvXr1+P3v/89zGYz7rjjDgDA2rVrUVhYiIyMDNjtduzbtw933303gN6vIL88q3Cwy8KlaJqmRWRLI8Dx/35ryFn3eNki0d/ERaWnarJBb/t9GaL8N21R6U81Wc+u2dAlyksXTFYge+lHe1Hp4ax16A0MfEZzf3TChbqliz6bdSdF+ZMB2WvSoMieo0ZF9po5eNW/iPLRXlRa+hxN7DkqyqtH54nyC86WvSfGYlHpCzKmifcRjn0ft8ZsXzMzxsa3YWNqZviOxG8NOXtKkRVCcZB96PUYZHetqydFlAcAs172pn4WDoryJwwTRfkEeEX5SbrInNExELchWZSXvqlL55PRvNEd2xDt+W2khdPnsxeJ95Gx523xdSTEj1mUx6OIjyfKj/HEPw/9n1UASNAdF+WjffzS7VuST0XpSHqdMMrf18cJP2tibaTOozWSxazQqqmpQVNTU0h7eXk5kpKSYnUYRDRCRbvIIiI6E2JWaBUUFER0igciIiKKLfZoyY3MUW1EREREYwALLSIiIqIoGVOD4YmIiCh6+NWhXMR7tNxuN4qKivDrX/+6T/uuXbvw05/+FPfccw/uuecetLS0DLqdsrIyeL2ys9iIiIiIRpKI92g5HA5YrVY0NjaiuLgYBoMBLpcLGzZswMqVK2GxWOD1euHz+QbdzuOPPz7gZQMtNElERETRwx4tuYgXWqqqYtmyZdi8eTOcTidsNhu2bt0Ku90Oi8UCADCZTDCZBp9osLCwEC+99BLi4uJCLhtoocnH11RE5kYQERERRUBEC63W1la43W5kZmaio6MDqqrCZrOhra0NkydPxurVq+HxeDBnzhwsXboURqNxWPsZaKHJU53HwrwFRERENJBYLio9VkR0jFZdXR3sdjsURUFWVhb27dsXXFF77969KCsrw5o1a9De3o4tW7YMez8DLTRJRERENJJErEfL5/PB4XDAaDSioaF3/Su/34/6+nqkpqYiIyMDCQkJAHpXyf4yQ0RERKMDx2jJRazQcjqdsFgsqKj433FSzc3NqKqqwu23345XXnkFS5YsgV6vx44dO5CWNjYWiyQiIiIaSMQKLVVVkZ2d3afNarVC0zT4/X7MmzcPZWVl0Ol0SE9Px3XXXRepXRMREVEMsEdLLmKF1ooVK/ptr6ysBADMnj0bS5YsGfL2XnvttUgcFhEREdEZM6Zmhvfphn4Wo0HrEW37lJYgyo/XR3+eLyNkE7q69cmivA4BUX68QXabD3nPlm3feEqUT+05JMp7jEmi/Pie46J8j/AsW4My+FxzX6dAE+Wl9IpflM/Y87Z4Hx/PuiKq+5DeRwa97DHQQXYfxeu6RHnp8Utfw5NwRJTv1CaK8sZVJaK87uENorz0NWxWukV56f2pCPMA4MU48XViiT1acme00KqpqUFTU1NIe3l5OZKSZC8YIhrdpEUWEdFocEYLrYKCAhQUFJzJQyAiIqIh4jxachFf65CIiIiIeo2pMVpEREQUPQGO0RKLeI+W2+1GUVERfv3rX/dp37VrF37605/innvuwT333IOWlpZBt1NWVgavVzbYm4iIiGgkiXiPlsPhgNVqRWNjI4qLi2EwGOByubBhwwasXLkSFosFXq8XPt/gZ/M8/vjjkT40IiIiopiKeI+WqqrIz89HWloanE4nAGDr1q2w2+2wWCwAAJPJFFyOZyCFhYXo6ur/1GePx4PDhw+H/BAREVH0aFBi9jNWRLRHq7W1FW63G5mZmejo6ICqqrDZbGhra8PkyZOxevVqeDwezJkzB0uXLoVROK/Ql2pra1FTUxPSvvbRR8O9CUREREQRE9FCq66uDna7HYqiICsrC9XV1XC5XAgEAti7dy9WrVoFk8mEdevWYcuWLcOe2iEvLw85OTkh7Sfc7jBvAREREQ2E0zvIRazQ8vl8cDgcMBqNaGhoAAD4/X7U19cjNTUVGRkZwa8LbTZbMDMcZrMZZrM5pJ2FFhEREY0kESu0nE4nLBYLKioqgm3Nzc2oqqrC7bffjldeeQVLliyBXq/Hjh07kJaWFqldExERUQyMpbFTsRKxQktVVWRnZ/dps1qt0DQNfr8f8+bNQ1lZGXQ6HdLT03HddddFatdEREREI1LECq0VK1b0215ZWQkAmD17NpYsWTKkbR0/fhxGoxHjxo3sxTWJiIi+SThGS27EzQx/4MABPPnkkygoKICi8AElIiKi0euMFlo1NTVoamoKaX/kkUeQlJQU1X33wCTK6xAQ5bsD0e+N8yt6Ud6ME6J8lzL4XGdfd9IfL8p/S/+pKN+BVFFeU2TTxGnCaeWk2/f4ZPdnguGUKK9Ai2rep0X37eJbu9+FSSdbDeLjWVeI8mkfvSPK9whvs0E3+ETMX9clfJ8wKH5R3qTI7k+3Tva+a9Zk7yndDz8nygei/Jy24DNR/gQmivLDmagyISC7T2ONY7TkzmihVVBQMOwpHohobJEWWUREo8GI++qQiIiIRiaO0ZKL+BI8RERERNSLPVpEREQ0JLLRygREqEfL7XajqKgIv/71r/u079q1Cz/96U9xzz334J577kFLS8ug2xlsIWkiIiKi0SYiPVoOhwNWqxWNjY0oLi6GwWCAy+XChg0bsHLlSlgsFni9Xvh8sjNyiIiIaOTgGC25iPRoqaqK/Px8pKWlwel0AgC2bt0Ku90Oi8UCADCZTMG1Dk8nEAjgxRdfxK9+9Sv09PSEXO7xeHD48OGQHyIiIqKRJOwerdbWVrjdbmRmZqKjowOqqsJms6GtrQ2TJ0/G6tWr4fF4MGfOHCxduhRGo3HQ7Xm9Xqxfvx5TpkzB3Xff3e+kpbW1taipqQlpX/voo+HeHCIiIqKICbvQqqurg91uh6IoyMrKQnV1NVwuFwKBAPbu3YtVq1bBZDJh3bp12LJly2nnzVqzZg0uvfRSXHvttQNm8vLykJOTE9J+wu0O9+YQERHRADhhqVxYhZbP54PD4YDRaERDQwMAwO/3o76+HqmpqcjIyAh+XWiz2YKZwcyePRs7duzAd7/73QHXOjSbzTCbzSHtLLSIiIhoJAlrjJbT6YTFYsHGjRuxfv16rF+/HuXl5aivr8eiRYuwa9cu+Hw+aJqGHTt2IC0t7bTbLCwsxNy5c/HII4/g5MmT4RweERERRZCmKTH7GSvC6tFSVRXZ2dl92qxWKzRNg9/vx7x581BWVgadTof09HRcd911Q9rukiVLYDKZUFFRgZUrV2L8+PHhHCYRERHRGaFomiZbhXME+/iTT4ac9WqyxVwDmqzzz6/JFnweDoMimy4j2otKe/yy/BQcEuU7dLJFpVP8sjNRPQbhgrq+46J8m3b6Ht2v+qYtKj2ctQ5H2qLS43UeUd4TkL1mor2otB+y9614TXZ7uxXZwvPRfk5LX8MnDNJFpeXTe44LyL7JOWfmBeJ9hMOxW/aYh2PR7NAhQqPRmJoZ3ujvHnL2pCJ7ACdA9oL06+V3rU6TvSh7YBLl9f7QqTIGzetlhdwEvWyMXNwpWeFnjo8T5aWFk5R0+zqf7PGN9oeMosjy0g8N6aDZHs0oLgykhVPrBZeJ8t/a/a4oL6VXovuckJIW0x2arPBIUKL7z4NUnLdTlHcbkkV5DYr4NqQc3SfKI8aFFsnFvNCqqalBU1NTSHt5eTmSkqL7wTiSSYssorFGWmQRjXTRLhTPhMDYu0lRF/NCq6Cg4LRTPBARERGNBWPqq0MiIiKKHs6jJReRJXiIiIiIKBR7tIiIiGhIxtL8VrEyrELL7XajtLQUubm5KCkpCbbv2rULL7/8Mrze3kGtd911F2bMmBGRAyUiIiIabYZVaDkcDlitVjQ2NqK4uBgGgwEulwsbNmzAypUrYbFY4PV64fPJpgcYKo/HA48ndnN5EBERETB2Zt6MnWEVWqqqYtmyZdi8eTOcTidsNhu2bt0Ku90Oi8UCADCZTDCZBp/nqbCwEDfeeCOcTidOnDiB0tJS/P3vf8f27dvh9/vxb//2b5g+fXrI9Wpra1FTUxPS/sQjq4dzc4iIiIiiQlxotba2wu12IzMzEx0dHVBVFTabDW1tbZg8eTJWr14Nj8eDOXPmYOnSpTAajYNuLyEhAWvXrsVf/vIX/OIXv8BPfvITLF26FFu2bMF//dd/4a677gq5Tl5eHnJyckLau44fld4cIiIiGqIAzzoUE591WFdXB7vdDkVRkJWVhX379sHlciEQCGDv3r0oKyvDmjVr0N7eji1btpx2e5deeikAID09HYqi4OKLLwYAnHvuufjiiy/6vY7ZbMaUKVNCfoiIiIhGElGPls/ng8PhgNFoRENDAwDA7/ejvr4eqampyMjIQEJC79pdNpstmBnMl18v6nS6Pr1fOp0Ofr9sXS8iIiKikURUaDmdTlgsFlRUVATbmpubUVVVhdtvvx2vvPIKlixZAr1ejx07diAtTbaILhEREY1cnN5BTlRoqaqK7OzsPm1WqxWapsHv92PevHkoKyuDTqdDeno6rrvuuogeLBEREdFoomja2DlZ89N9Hw05e1xJEW17Ao6L8n6d7DyD4Swq3YPBz+r8ugS/bKX6Lr1ZlNeEQ/4mnjooyp+InyzK+zD4iRixdtyXKMrH67tEeekCtooiy/s1vSgvXapjOItK92iyx7j1gstE+W/tfleUj9edEuW7tDhRXgfZ+4RR6RHlu7Vxorxfk73mE4T3T7QXZZ508jNRvj3hHFF+OMc/9cgOUX78JdeK9xGON3d0x2xf/3yR7Pk4Uo2pmeFPKMlDziaiQ7RtnSYbL5bcKSsiAjrZhxgAdJsmiPInTbIPeumbRIr7U1H+v/EdUX7+qfdF+U+Ms0X5JIOsEJUWTt1+WVFg0MnmodMrsg9hRfg/Vo8me7uQfsXQA4O4uJQek7Rw+mx29ulDXzFzz5uivDcge04YFdlzwiDM9wRk92f6qV2i/BfjM0T5eE02X2K3Ei/KH084W5QPCP+ZlBbGANCSKntfzBTv4Zupu7sbTz/9ND755BPo9XoUFxcHT777KqfTiZqaGvh8PmiahssvvxzXXHMNAKC+vh4vvvgiJk/u/ad/ypQpKCsrO+2+o15o1dTUoKmpKaS9vLwcSUlJ0d49EY0S0iKLiGJvtC4q/cc//hHx8fGorKzEoUOH8LOf/QyVlZWIi+vbq5ycnIwHHngAKSkpOHnyJB544AGcd955uOCCCwAAc+fOxb333ivad9QLrYKCAhQUFER7N0RERET92rZtG26//XYAwNSpU5GRkYEPPvgANputT27mzJnB3xMSEjBt2jQcOXIkWGgNx5j66pCIiIiiJxDDUd0DLbdnNpthNsvGELe3twe/8gOA1NRUHD06+CTnn3/+Ofbt24fly5cH23bv3o2ysjIkJCTge9/7HhYsWHDafbPQIiIiohFnoOX2CgoKUFhY2KftgQceQHt7e7/bee6558T7PnbsGH7xi1/gxz/+MVJSek+eW7BgAS699FKYTCYcOHAAa9aswUMPPdTvUoFfFXah5Xa7UVpaitzcXJSUlATbd+3ahZdffhleb++ZRHfddRdmzJgR7u6IiIjoDInlPFoDLbfXX2/WY489Nui2UlNTceTIESQm9p7E1N7ejjlz5vSbPX78OCoqKvC9732vz1eLX14X6F3N5vzzz8f+/fujX2g5HA5YrVY0NjaiuLgYBoMBLpcLGzZswMqVK2GxWOD1euHzyc5+ISIiom+u4XxFOJBLLrkEb775JjIyMnDo0CF8/PHHuPvuu0NyJ06cwMMPP4yrrroKixcv7nOZy+UK9m4dOXIE+/btw/XXX3/afYddaKmqimXLlmHz5s1wOp2w2WzYunUr7HY7LBYLgN5ldr5camcgTU1NeOWVV2A0GpGVlYXXXnsNL730UsgZAcDA39sSERFR9IzWmTevvfZaPP3007jzzjuh0+mwfPlyxMf3Tgfy6quvYuLEibjyyiuxefNmHDx4EG+++SbefLN3uparr74al19+OV5//XX87W9/g17fOx3TTTfdhPT09NPuO6xCq7W1FW63G5mZmejo6ICqqrDZbGhra8PkyZOxevVqeDwezJkzB0uXLu2zluFXdXR04JlnnsHDDz8Mi8Vy2sWoB/re9qE168K5OURERDQGxcXF4Z577un3shtuuCH4e3FxMYqLi/vNLV26FEuXLhXvO6xCq66uDna7HYqiICsrC9XV1XC5XAgEAti7dy9WrVoFk8mEdevWYcuWLQNO87B//36kp6cHe8Byc3OxadOmAfc70Pe2Rzq5CDUREVG0BEbpPFpn0rALLZ/PB4fDAaPRiIaGBgCA3+9HfX09UlNTkZGRgYSEBACAzWYLZiJhoO9tj3Qeitg+iIiIiMIlW0/gK5xOJywWCzZu3Ij169dj/fr1KC8vR319PRYtWoRdu3YFp7DfsWMH0tLSBtzWzJkz0dLSgkOHegult99+e7iHRURERFGiabH7GSuG3aOlqiqys/uuA2a1WqFpGvx+P+bNm4eysjLodDqkp6fjuuuuG3BbSUlJWL58OR577DGYTCZkZWUN97CIiIiIRoxhF1orVqzot72yshIAMHv2bCxZsmTI28vKyupTYL366qvDPTQiIiKiEYEzwxMREdGQxHLC0rFC0bTYfRNaU1ODpqamkPby8nIkJSWFvf39Hx8YclaHgGjbhoBXlPfr+p/KYiA+yPIAoIdsEtg4n2zuMaPvlCh/clyyKK/TZGeJdulkE9cZ0CPKBxTZkEWdJnsOeTFOtn3hczTatBicbaSD7DmhDX+Y6RC3L7vN+2b9syg/c8+borwC2du1InwOBaAX5U1alyjvU2Tvc5P/6z9E+fbr/02UPxmQvafEKydFeUWRf7z6NVn/hzXjHPE+wvGHv8Xu7P5rvy17Po5UMe3RKigoGHCKByL6ZpMWWUQUe7FcVHqsiO6/g0RERETfYByjRUREREMylqZdiBX2aBERERFFSUR7tNxuN0pLS5Gbm4uSkpJg+65du/Dyyy/D6+0dUH7XXXdhxowZkdw1ERERRVksTooZayJaaDkcDlitVjQ2NqK4uBgGgwEulwsbNmzAypUrYbFY4PV64fMN/Ww5v98fXCmbiIiIaDSJaKGlqiqWLVuGzZs3w+l0wmazYevWrbDb7cEFo00mE0wm06DbKSwsREFBAT744ANcdNFFuPHGG/tc7vF44PHIpiogIiKi8PCsQ7mIFVqtra1wu93IzMxER0cHVFWFzWZDW1sbJk+ejNWrV8Pj8WDOnDlYunQpjMbB51MxmUxYu3Ztv5fV1taipqYmpH3N2scicluIiIiIIiFihVZdXR3sdjsURUFWVhaqq6vhcrkQCASwd+9erFq1CiaTCevWrcOWLVtOO59WTk7OgJfl5eX1e3nnCfZyERERRQvPOpSLSKHl8/ngcDhgNBrR0NAAoHdsVX19PVJTU5GRkYGEhAQAgM1mC2YGExcXN+BlZrMZZnPojL6dJ4Y+MzwRERFRtEWk0HI6nbBYLKioqAi2NTc3o6qqCrfffjteeeUVLFmyBHq9Hjt27EBaWlokdktEREQxxB4tuYgUWqqqIjs7u0+b1WqFpmnw+/2YN28eysrKoNPpkJ6ejuuuuy4SuyUiIiIa0SJSaK1YsaLf9srKSgDA7NmzsWTJkiFv77XXXovEYREREVEEBTTOoyU1ppbg6cHg00Z81Th0wS+4+QZ44dUNPG7s6/TwQ9ECQ85DAeICssH8PbpxotsAAD36cUPOGn2ncCxu6pDz47RT0GmyhYHjetxDznaNM8OAHtH2/Rj6HGwKNAS0oed1CIi2DwBHvclDzk42udClDf05F6d0oVsb+uM7TumGVxv6a8ao9OBkIH7I+QTdKXgCCUPOm3UnERAuVqGHX7wPyX06TunGCf940THN3POmKL9v1j8POWvd84bo+OOVk6L3CAWa6P40KV04gaShHw9OwofBzzj/ui/yy4ac1cMneg0DQFrnziFnDyedJzp+I7zi46Gx54wVWjU1NWhqagppLy8vR1LS0F+4wyUtUCRFFgBZkQV5kQXIb4OkyAIgKrIARLXIAhDVIguA+A0xmkUWANEHKgBRkQVAVGQBEBVZAEQf2ADERdZw9iG9T0dSkQXIj1/6HiG9PyVFFoCoFlmA/DUsKbIA+fGzyCLgDBZaBQUFp53igYiIiEYODoaX46LSRERERFEypsZoERERUfSwR0uOPVpEREREUTLsHi23243S0lLk5uaipKQk2L5r1y68/PLL8Hq9AIC77roLM2bMCPtAiYiI6MziotJywy60HA4HrFYrGhsbUVxcDIPBAJfLhQ0bNmDlypWwWCzwer3w+XyRPF4AgMfjgcfDdQ2JiIhoZBt2oaWqKpYtW4bNmzfD6XTCZrNh69atsNvtsFgsAACTyQSTaeBTyN988020trbixz/+Mfbv348VK1ZgzZo1OO+88/D8889jxowZyM3NDblebW0tampqQtpXr/3lcG8OERERnYbGCUvFhlVotba2wu12IzMzEx0dHVBVFTabDW1tbZg8eTJWr14Nj8eDOXPmYOnSpTAa+597ZO7cuaitrQUA/P3vf4fVasWuXbtw3nnn4e9//zv+5V/+pd/r5eXlIScnJ6T96AnZPEtERERE0TSswfB1dXWw2+1QFAVZWVnYt28fXC4XAoEA9u7di7KyMqxZswbt7e3YsmXLgNs5++yz4fV6cfToUezatQs33XQT/v73v6O9vR0+nw9nn312v9czm82YMmVKyA8RERFFj6bF7mesEPdo+Xw+OBwOGI1GNDQ0AAD8fj/q6+uRmpqKjIwMJCT0zi5ss9mCmYFkZmbi/fffR0dHB+bMmYPq6mr893//N+bMmTOMm0NEREQ0cogLLafTCYvFgoqKimBbc3MzqqqqcPvtt+OVV17BkiVLoNfrsWPHDqSlpQ26vczMTLz66qu46KKLAADnn38+tmzZghtvvFF6aERERBRFPOtQTvzVoaqqyM7O7tNmtVqhaRr8fj/mzZuHsrIy3HffffD7/bjuuusG3V5mZiaOHDmCuXPn9vk7MzNTemhEREREI4qiaWPnm9CPPv58yFkDZNNOKJAtEm3QZAPz9Zp8GowunVmUN2ldony3IltEOD4gWyRauqi0e1yKKC9d9FkT/t8hfU4c9qaK8hOMJ0V5BbKXsjTfo8k6wDXIzk6K18menwDQFZAtpK1XZI+ZNyBbRHiCXvacli4qfc5Hgw/F+DqT4hXlpQuHGxTZQvJGRfa+qBO+xqTP6bOON4vyB5MuEOX1kN0/gPx1Y804R7yPcPxajd2+Si6P3b6iaUwtwTMOQ3+jHueXfYhJxXd1iPI9xgTxPnyDTJ3Rn4AiKzz0iqz4S+r8TJRvTz5PlNcU2RuQ9E1X+i+HosiuYNLJ7s9of8hIj98g/NCQfmD0aEZxYSD9oJfeR0bha0C6fWnh9OkFdlF+5p43RXnp/Tnj2Pui/KEU2TcV0vtTqjXxQlF+HLqjdCT/S/oPIo18MSm0ampq0NTUFNJeXl6OpKSkWBwCEY1w0iKLiGJv7HwHFjsxKbQKCgpQUFAQi10RERERjRhcVJqIiIgoSsbUGC0iIiKKHk7vIBeRQsvtdqO0tBS5ubkoKSkJtu/atQsvv/wyvN7esRd33XUXZsyYEYldEhEREY14ESm0HA4HrFYrGhsbUVxcDIPBAJfLhQ0bNmDlypWwWCzwer3w+eRTGBAREdHIwMHwchEZo6WqKvLz85GWlgan0wkA2Lp1K+x2OywWCwDAZDIFl+bpz+eff47/+3//L44cOQIA+M///E/86le/6jfr8Xhw+PDhkB8iIiKikSTsHq3W1la43W5kZmaio6MDqqrCZrOhra0NkydPxurVq+HxeDBnzhwsXboURmP/EwBOmzYNN910E371q1+hsLAQDocDa9eu7TdbW1uLmpqakPZHB8gTERFR+AKy6f0IESi06urqYLfboSgKsrKyUF1dDZfLhUAggL1792LVqlUwmUxYt24dtmzZMug0D3a7HX//+9/x+OOP49///d8H7AHLy8tDTk5OSLv7xIlwbw4RERFRxIRVaPl8PjgcDhiNRjQ09M5w7Pf7UV9fj9TUVGRkZASLJZvNFswMtr22tjaYzWZ0dHQMmDObzTCbQ5efYaFFREQUPRyjJRfWGC2n0wmLxYKNGzdi/fr1WL9+PcrLy1FfX49FixZh165d8Pl80DQNO3bsQFpa2qDb++1vf4v09HSUl5fjueeew9GjR8M5PCIiIqIzKqxCS1VVZGdn92mzWq3QNA1+vx/z5s1DWVkZ7rvvPvj9flx33XUDbuuvf/0rdu/ejR/+8If41re+he9///tYt24d/H75opxEREQUeZoWu5+xQtG0sXNzPvn44yFnx8Ki0idN0V0nMqDI6vDJx/aJ8tFeVFoqoMkWc9UJF+A91jNRlI/XD32RdCD6i0r7hfePdFHp4ax12KP1f3LNQKT3kfQ2x+tOifJdWpwoH+1Fpbu1caL8aF9UWnr/j1Nki0oP5/h9whE9F2RME+8jHBtej92+/u9VsdtXNHFmeCIiIhoSzgwvF/NCq6amBk1NTSHt5eXlSEoKr4fmlCboFZL9o4puyP7zOWGW9V7oMPLOmdVpsh6bfyRfINs+ZNuX9jiZNFmPkLTHTB+QTcBrNsh6UfXC+0dqev3zovxnObdE6Uj+l/R1MJxeMAmDInuMlSgfv7SHat+sfxblz9vzlijflnKRKG9EdB8vqZ6A7CMwXh/db0KAkflZQOGJeaFVUFAw6BQPRPTNxA8YopEvtqONojtcJFYiMjM8EREREYXiGC0iIiIakrFz+lzssEeLiIiIKEqG1KPldrtRWlqK3NxclJSUAAA2bdqE7du3BzMHDx5EUVERrr766iHvvLCwEC+99BLi4kIHmg92GREREdFoMKRCy+FwwGq1orGxEcXFxTAYDCgqKkJRUREAoLOzE7fddhsuvfTSqB4sERERnTlcVFpuSIWWqqpYtmwZNm/eDKfTCZvN1ufyhoYGzJ07F8nJyYNup6mpCa+88gqMRiOysrKGfNnXeTweeDyeoRw6ERER0Rlz2kKrtbUVbrcbmZmZ6OjogKqqIYWWqqq44YYbBt1OR0cHnnnmGTz88MOwWCzYsmXLkC7rT21tLWpqakLaH1qz7nQ3h4iIiIaJg+HlTlto1dXVwW63Q1EUZGVlobq6Gi6XCykpKQCA/fv3o7OzExdffPGg29m/fz/S09NhsVgAALm5udi0adNpL+tPXl4ecnJyQtqPdHJdRCIiIho5Bi20fD4fHA4HjEYjGhoaAAB+vx/19fXIz88H0FuIZWdnQ68XTrUeBrPZDLPZHNJ+pPNQzI6BiIjom2a0LsHT3d2Np59+Gp988gn0ej2Ki4v77SD68MMPsXbtWkydOhUAYDQasWbNmuDlNTU1eOeddwAAl1122ZAmYB+00HI6nbBYLKioqAi2NTc3o6qqCvn5+fB6vdi2bRsefvjh0+5o5syZ2LBhAw4dOoSpU6fi7bffHtJlREREROH44x//iPj4eFRWVuLQoUP42c9+hsrKyn5nNpg+fToeffTRkPbdu3fjvffew5NPPgkAWLFiBWbPno3Zs2cPuu9BCy1VVZGdnd2nzWq1QtM07N69G0ePHsW0adMwffr0097IpKQkLF++HI899hhMJlOfAe+DXUZEREQjQyzHaA104ttA32oNZtu2bbj99tsBAFOnTkVGRgY++OCDkDHnp9uG3W6HyWQCANjtdmzbti28QmvFihX9tldWVgZ//3ohNpisrKw+RdT1118/pMuIiIjom2WgE98KCgpQWFgo2lZ7ezsmT54c/Ds1NRVHjx7tN3vo0CE88MAD0Ov1uPLKK4Njwtvb2zFnzpw+2/joo49Ou28uwUNERERDosVwkNZAJ77115v1wAMPoL29vd/tPPfcc0PeZ3p6OjZs2ICEhAQcPnwYFRUVSElJwYUXXjjkbXxdxAutmpoaNDU1hbSXl5cjKSkp0rvrY9qJ01eWXzo+YZpo2z7hXXWse7wob4k7LMoDgA9GUf6ET3ZMEwxuUf5w10RRPt7QI8obdbL8BL9LlB8nSssd0s4W5ZOMsvtfE650f+Cy/yvKH77qKlF+4p/fEuUBYBKOiPJunew9xafJXsc9AVl+vF42v9/JQLwob1BkZ1aft0f2GOyflSvKp+x0yvKm46K8AtmHujS/53CKKH/x1JOivA6c3TMckq8IH3vssUEvT01NxZEjR5CYmAggtHfqSwkJCcHfp0yZgoULF2Lv3r248MILg9v4Unt7OyZNmnTaY4t4oVVQUDCkUfhERF8lLbKIKPZG61mHl1xyCd58801kZGTg0KFD+Pjjj3H33XeH5I4dO4bk5GQoigK3240dO3bgxhtvBADYbDb8+te/xlX/809nQ0MDfvSjH5123/zqkIiIiMa0a6+9Fk8//TTuvPNO6HQ6LF++HPHxvT3Kr776KiZOnIgrr7wSTU1NeOONN6DX6xEIBHDZZZdh4cKFAIA5c+bgO9/5Du655x4AvYPhTzcQHmChRUREREM0WmeGj4uLCxZIX/fVlW2uuuqqYI9VfwoLC8UD8XWiNBERERENWVg9Wm63G6WlpcjNzUVJSQkAYNOmTdi+fXswc/DgQRQVFeHqq68ecDt//etfgwtK/+QnP0FbW1ufv79cmoeIiIjOnMBoHaR1BoVVaDkcDlitVjQ2NqK4uBgGgwFFRUUoKioCAHR2duK2227DpZdeOuh23nrrLRQWFgYnDnvxxRf7/P11A01iZgrnxhARERFFWFiFlqqqWLZsGTZv3gyn0xlSGDU0NGDu3LlITk4ecBsvvvgiPvroIxw8eBBvvPEG0tLS+vz90EMPhVxnoEnMnn3w1nBuDhEREVFEDbvQam1thdvtRmZmJjo6OqCqakihpapqn0Fm/fnhD3+IlpYWXHPNNcEFHr/+99cNNIkZPt81rNtCREREpzdaB8OfScMutOrq6mC326EoCrKyslBdXQ2Xy4WUlN4J4Pbv34/Ozs4Bi6VwDDSJWcfnEd8VERER0bANq9Dy+XxwOBwwGo1oaGgAAPj9ftTX1yM/Px9AbyGWnZ0NvV4fuaMlIiKiM4Y9WnLDKrScTicsFgsqKiqCbc3NzaiqqkJ+fj68Xi+2bduGhx9+OGIHSkRERDTaDKvQUlUV2dnZfdqsVis0TcPu3btx9OhRTJs2DdOnT4/IQRIREdGZF2CXltiwCq0VK1b0215ZWRn8/euF2GB+/vOfD/o3ERER0Wg0ppbg6Zgw9B40Q8Ar2va3PB+K8mmaX5T36CeL8gAQ0MnGv8XrQ+ceG0yPcGayeV0OUf5gcujK6YMxoVuU9+iSRHm/8OWgh0+Un4yjonzKSdnZHcfMsol9ezTZ4xv/xwZRPkF3XJQ/hfHo0Yyi65i1E6J8hzZRlE8/JTuT+bh5qijfrYwT5Wcce1+Ub0u5SJRP2ekU5V0XLhTlkz5SRXmdEhDlFch6Wz5sln0OzDtbOOZYkcUBiF8DsabJHhJCDAutmpoaNDU1hbSXl5cjKUn2gUhEY89I/4AhIhqOmBVaBQUFKCgoiNXuiIiIKMI0jtES46LSRERERFEypsZoERERUfQEOEZLjD1aRERERFESdo+W2+1GaWkpcnNzUVJSAgDYtGkTtm/fHswcPHgQRUVFuPrqqwfcTmFhIV566SXExcWFe0hEREQUBRyjJRd2oeVwOGC1WtHY2Iji4mIYDAYUFRWhqKgIANDZ2YnbbrsNl156adgH+yWPxwOPRzZVAREREVGshV1oqaqKZcuWYfPmzXA6nbDZbH0ub2howNy5c5GcnHzabf35z3+G0+nEiRMnsGzZMlxyySX95mpra1FTUxPS/os1XPKHiIgoWgLs0BILq9BqbW2F2+1GZmYmOjo6oKpqSKGlqipuuOGGIW0vISEBa9euxZ49e/DLX/5ywEIrLy8POTk5Ie0nOzukN4GIiIgoasIaDF9XVwe73Q5FUZCVlYV9+/bB5XIFL9+/fz86Oztx8cUXD2l7X369aLVacezYMXi9/c/aazabMWXKlJAfIiIiopFk2D1aPp8PDocDRqMRDQ29S3P4/X7U19cjPz8fQG8hlp2dDb1+aMsWmEy9S4LodL31X4DnkRIREY0YGr87FBt2oeV0OmGxWFBRURFsa25uRlVVFfLz8+H1erFt2zY8/DDHTREREdE307ALLVVVkZ2d3afNarVC0zTs3r0bR48exbRp0zB9+tAXeiYiIqKRi7M7yA270FqxYkW/7ZWVlcHfv16IDea1114b9G8iIiKi0WZMLcHjU4xDznbpE0TbPj5hkiivV/yi/HDoIBvDZkCPKK8Jz5X4InmWKH/CN16UTzTI/pWKD7hFea9ONlmuKdAlynfpzKL8kYQ0UV4R/qspff5MNHaK8hoUUd6g+KCU3yK6TvfDz4nyCcopUf6L8RmivPQ1ZhTmD6VkCrff/wlFA0kxHRflkz5SRfkDF1wuyp+/53VRXmrJP8meD0ZF9ngpwtcYAJiUbvF1YinAMVpiMS20ampq0NTUFNJeXl6OpKSkWB4KEY0w0iKLiGg0iGmhVVBQgIKCgljukoiIiCKES/DIcVFpIiIioigZU2O0iIiIKHo0Tm8pFvFCy+12o7S0FLm5uSgpKQEAbNq0Cdu3bw9mDh48iKKiIlx99dUDbqesrAyPPPJIcBJTIiIiotEm4oWWw+GA1WpFY2MjiouLYTAYUFRUhKKiIgBAZ2cnbrvttuByOwN5/PHHI31oREREFIYAx2iJRXyMlqqqyM/PR1paGpxOZ8jlDQ0NmDt3LpKTkwfdTmFhIbq6+j993uPx4PDhwyE/RERERCNJRHu0Wltb4Xa7kZmZiY6ODqiqCpvN1iejqipuuOGGsPZTW1uLmpqakPY1ax8La7tEREQ0MJ51KBfRQquurg52ux2KoiArKwvV1dVwuVxISUkBAOzfvx+dnZ24+OKLw9pPXl4ecnJyQto7T3jC2i4RERFRJEWs0PL5fHA4HDAajWhoaAAA+P1+1NfXIz8/H0BvIZadnQ29Xh/WvsxmM8zm0Fm2O08cCGu7RERENDDODC8XsULL6XTCYrGgoqIi2Nbc3Iyqqirk5+fD6/Vi27ZtePjhhyO1SyIiIqIRLWKFlqqqIYtIW61WaJqG3bt34+jRo5g2bRqmT58eqV0SERFRDHGIllzECq0VK1b0215ZWRn8/euF2ECOHz8Oo9GIcePGReTYiIiIiM6EETcz/IEDB/Dkk0+ioKAAiqKc6cMhIiIiGrYzWmjV1NSgqakppP2RRx5BUlKSeHtebeg9YON0/c/RNRADZEVfD2Qz2o+D7HiGwxjoFuX9OtnTQw+fKD9VaRPlu5UEUb5HL+wRFXaJS7dv0HpEeR+MorwivQEjzcPPQge/6CoB4W2W3kfxmuxM5uT/VyXKf5FfJspH+zGWbl+nyNZjOX/P66L83llXRXX7Rp3sNakg+uvPSD87Yk3jYHixM1poFRQUoKCg4EweAhGNENIii4hoNBhxXx0SERHRyMQleOQivgQPEREREfVijxYRERENCcdoyYVdaLndbpSWliI3NxclJSUAgE2bNmH79u3BzMGDB1FUVISrr7463N0RERERjRphF1oOhwNWqxWNjY0oLi6GwWBAUVERioqKAACdnZ247bbbcOmll4Z9sF/yeDzweLiuIRERUSyxR0su7EJLVVUsW7YMmzdvhtPphM1m63N5Q0MD5s6di+Tk5EG3U1hYiBtvvBFOpxMnTpzAsmXLcMkll/Sbra2tRU1NTUj7z9f8arg3g4iIiCjiwiq0Wltb4Xa7kZmZiY6ODqiqGlJoqaqKG264YUjbS0hIwNq1a7Fnzx788pe/HLDQysvLQ05OTkh7e6dsHiciIiIaOnZoyYV11mFdXR3sdjsURUFWVhb27dsHl8sVvHz//v3o7OzExRdfPKTtffn1otVqxbFjx+D1evvNmc1mTJkyJeSHiIiIaCQZdo+Wz+eDw+GA0WhEQ0MDAMDv96O+vh75+fkAegux7Oxs6PX6IW3TZOqdEVen663/AoHoz8JLREREQ8MxWnLDLrScTicsFgsqKiqCbc3NzaiqqkJ+fj68Xi+2bduGhx9+OCIHSkRERDTaDLvQUlUV2dnZfdqsVis0TcPu3btx9OhRTJs2DdOnTw/7IImIiOjM0zgzvNiwC60VK1b0215ZWRn8/euF2GBee+21Qf8mIiIiGm3G1MzwccqpIWfjvSdE2+40ThLlA5rsPINuJU6UBwCdcCV5Ta+I8pY3nxXlv8j9kSh/Up8oymua7Pilptc9I8q3LS6N0pH0kj6+Uopw+xN6OkR5DfLHy2NMEuUVRPe/624lXpRvv/7fRHllhPUOSO/PaN//5+95XZTfO+sqUX7GR/WivNTUuhfE1zm4+JYoHEnkBDhGSyymhVZNTQ2amppC2svLy5GUJHuDJaKxRVpkERGNBjEttAoKClBQUBDLXRIRERGdMWPqq0MiIiKKHg6GlwtrwlIiIiIiGhh7tIiIiGhIRuuEpd3d3Xj66afxySefQK/Xo7i4uN9Va/785z9DVdXg34cPH8bixYvxgx/8AB9++CHWrl2LqVOnAgCMRiPWrFlz2n1HpNByu90oLS1Fbm4uSkpKAACbNm3C9u3bg5mDBw+iqKgIV199dSR2SURERDQkf/zjHxEfH4/KykocOnQIP/vZz1BZWYm4uL5n/F999dXBOsXn8+HWW2/FokWLgpdPnz4djz76qGjfESm0HA4HrFYrGhsbUVxcDIPBgKKiIhQVFQEAOjs7cdtttwXXMgyXx+OBx+OJyLaIiIhoaGLZozXQZ73ZbIbZbBZta9u2bbj99tsBAFOnTkVGRgY++OAD2Gy2Aa/z/vvvY+LEicjIyJAd+NdEpNBSVRXLli3D5s2b4XQ6Qw68oaEBc+fORXJy8qDb2b59O373u98hEAggMTERy5cvx9lnnx2Sq62tRU1NTUj7o2vXhnU7iIiIaGQY6LO+oKAAhYWFom21t7dj8uTJwb9TU1Nx9OjRQa+jqipycnL6tB06dAgPPPAA9Ho9rrzyypDL+xN2odXa2gq3243MzEx0dHRAVdWQQktVVdxwww2Dbuf48eOorKzE6tWrMX36dNTV1eGpp57q9/vPvLy8fm+c+4RsElIiIiIaukAMzzoc6LO+v96sBx54AO3t7f1u57nnnhPv+9ixY9i1axduu+22YFt6ejo2bNiAhIQEHD58GBUVFUhJScGFF1446LbCLrTq6upgt9uhKAqysrJQXV0Nl8uFlJQUAMD+/fvR2dnZ76Czr9q3bx9mzJgRXBsxJycHzz//PE6dOoX4+L6zMw/UbchCi4iIaGyQfEX42GOPDXp5amoqjhw5gsTE3hVJ2tvbMWfOnAHz77zzDubPnx/MA0BCQkLw9ylTpmDhwoXYu3fvaQutsKZ38Pl8cDgcUFUVt99+O/7t3/4Nfr8f9fX1wUxdXR2ys7Oh1+vD2RURERGdYVpAi9lPJF1yySV48803AfR+/ffxxx9j3rx5A+ZVVcXll1/ep+3YsWPBecTcbjd27NiBGTNmnHbfYfVoOZ1OWCwWVFRUBNuam5tRVVWF/Px8eL1ebNu2DQ8//PBpt2W1WrFhwwZ8/vnnmDZtGt555x2kp6eH9GYRERERSVx77bV4+umnceedd0Kn02H58uXB+uLVV1/FxIkTceWVVwIA9uzZg66urpBCrKmpCW+88Qb0ej0CgQAuu+wyLFy48LT7DqvQUlUV2dnZfdqsVis0TcPu3btx9OhRTJs2Lfh14GASExNx55134qmnnoLf7w/+TURERCPDaJ0ZPi4uDvfcc0+/l319DPmsWbPwzDPPhOSuuuoqXHWVbOFyIMxCa8WKFf22V1ZWBn//eiE2mHnz5g3alUdEREQ0mijaaC1P+/HWzu4hZyfFywbOd/lMonyS0S3K6xW/KA8Afk027m2crkuU7w7EnT70FYoieyol9xwR5Xv040T5U7rxorxPk/3fYVB8onxK9yFR3jVuqig/3t8hyn8WOEeUP3A08fShr7AknxLlAcBsGPprGAAs+EyUj/N2ivLHE0KnlxmMW5sgyqd17hTlWxMHH3T7dT0B2XN6z+EUUf7DZq8ov+SfZM8Jo65Hlhe+JlsuyBHlp+xqEuUnGo6J8gAQ75d9dkyzzhXvIxzLVh6M2b5efsQSs31FU8yX4KmpqUFTU+iTtby8HElJSbE+HCIaIaRFFhHRaBDzQqugoAAFBQWx3i0RERGFabSudXgmhTW9AxERERENjIUWERERUZRE5KtDt9uN0tJS5ObmoqSkBACwadMmbN++PZg5ePAgioqKgqtiExER0egyhs6fi5mIFFoOhwNWqxWNjY0oLi6GwWBAUVERioqKAACdnZ247bbbcOmll0Zid0RERESjQkS+OlRVFfn5+UhLS4PT6Qy5vKGhAXPnzkVycvKA2/j973+PF198Mfj3iRMn8K//+q/o6gqdksDj8eDw4cMhP0RERBQ9WiAQs5+xIuwerdbWVrjdbmRmZqKjowOqqsJms/XJqKoaMvPq11122WVYsWIFiouLodfr4XA4cPHFFyMuLnQup9raWtTU1IS0Ly//bXg3hoiIiCiCwi606urqYLfboSgKsrKyUF1dDZfLhZSU3onv9u/fj87OTlx88cWDbic1NRXf+ta38MEHH+Db3/426uvr8YMf/KDfbF5eHnJyckLad/4j3FtDREREAwlwegexsAotn88Hh8MBo9GIhoYGAIDf70d9fT3y8/MB9BZi2dnZ0OtPP4v5ZZddhvr6ekyZMgUnT57EBRdc0G/ObDbDbDaHXvAPTnhIREREI0dYhZbT6YTFYkFFRUWwrbm5GVVVVcjPz4fX68W2bdvw8MMPD2l7WVlZ+M1vfoM//vGPyMnJgaIo4RweERERRRDPOpQLazC8qqohi0ZbrVZomobdu3ejqakJ06ZNw/Tp04e0vXHjxmHhwoVoaGjAZZddFs6hEREREZ1xYfVorVixot/2ysrK4O9fL8RO59Zbb8Wtt94azmERERFRFHAJHjnODE9EREQUJTFfVLqmpgZNTU0h7eXl5UhKSgpr2zZ37ZCzAW+CaNuacLyYx3yWcPvymlcf6BHlj5smi/IJcIvyJl/onGeD6TKOF+V1ml+UlxqnyI7fL3z5HDEN7Sv0L+khu70evez1c8EffibKT7zuPlF+OHSQzZ1zAhNFebchWZQPCP8XjVdOivIHk/o/4Wcg4yA74SdeLzuei6fK8vPOPv1JTl9lVGTvWYrw+SA1ZVfoZ9FgDmdmifIT97wuygOAW58svk4ssUdLLuaFVkFBAQoKCmK9WyIa4aRFFhHRaBDzQouIiIhGp4DGf4ikOEaLiIiIKErYo0VERERDwjFachErtNxuN0pLS5Gbm4uSkhIAwKZNm7B9+/Zg5uDBgygqKsLVV18dqd0SERERjVgRK7QcDgesVisaGxtRXFwMg8GAoqIiFBUVAQA6Oztx22234dJLL43ULomIiIhGtIgVWqqqYtmyZdi8eTOcTidsNlufyxsaGjB37lwkJycPuI3//u//xiuvvBL8u62tDffccw8WLlzYJ+fxeODxeEKu38/qh0RERBQh/OpQLiKFVmtrK9xuNzIzM9HR0QFVVUMKLVVVccMNNwy6nQULFmDBggUAgLfeegv19fW46KKLQnK1tbWoqakJaf/1T24M41YQERERRVZECq26ujrY7XYoioKsrCxUV1fD5XIhJSUFALB//350dnbi4osvHtL2tm/fjtraWqxevRomkynk8ry8POTk5IRecb8jnJtBREREg+Ci0nJhF1o+nw8OhwNGoxENDQ0AAL/fj/r6euTn5wPoLcSys7Oh159+FuGWlhY899xzWLlyJRITE/vNmM1mmM2hXxR69odxQ4iIiIgiLOxCy+l0wmKxoKKiItjW3NyMqqoq5Ofnw+v1Ytu2bXj44YdPuy2Xy4Unn3wSd955JywWS7iHRkRERBEUCHDCUqmwCy1VVZGdnd2nzWq1QtM07N69G0ePHsW0adMwffrp13l7++230dnZiRdeeCHY9oMf/ACZmZnhHiYRERFRzIVdaK1YsaLf9srKyuDvXy/EBvL9738f3//+98M9JCIiIooCnnUoN6Zmhm87a+HpQ/8jseeoaNsnjCmifI9mFOUBQKfIumSli/AalB5RfvJbvxHlv8j9kShvCHhF+VO68aK81NS6F04f+oq2xaWi/HjtuCjfrSSI8lJHrvuJKJ/U0y7KS18zAKAIn9PRXkNM+hpTFNmHkF7zi/LRJl7YW5HGo/u1k/Q1jMX/KopP3PO6bPsA9s66SpS37nlDvA8a2c5IoVVTU4OmpqaQ9vLyciQlJZ2BIzrzpEUW0VgT7Q9holiTFlmjgcZFpcXOSKFVUFCAgoKCM7FrIiIiopgZU18dEhERUfRwjJZctIc4EBEREX1jsUeLiIiIhoQ9WnJDKrTcbjdKS0uRm5uLkpISAL0zuL/wwgs4cOAA5s+fj3vvvTeY3759OzZt2gSgd5b4hQsX4sYbb4SiCE9RISIiIhrFhlRoORwOWK1WNDY2ori4GAaDAYmJibj55pvR0tKCnTt39snPmjULjz32GHQ6HXw+H1atWoWZM2fi29/+dkQO2uPxwOPxRGRbRERENDQBnnUoNqRCS1VVLFu2DJs3b4bT6YTNZkNKSgpSUlLw+eefh+Tj4uKCv/f09MDn8522N6uwsBAFBQVwOp3wer246aabcMkll/Sbra2tRU1NTUh7xdonh3JziIiIiGLitIVWa2sr3G43MjMz0dHRAVVVYbPZTrvhjz/+GBs2bMChQ4dw5ZVXYsGCBae9jk6nw+OPP46DBw+ivLwcF1xwQb/zauXl5SEnJyek/diJ7tPug4iIiChWTnvWYV1dHex2OxRFQVZWFvbt2weXy3XaDWdkZOCJJ57Ahg0b8Mknn+Cjjz467XUWL14MALBYLEhPT8e+ffv6zZnNZkyZMiXkh4iIiKJHC2gx+xkrBu3R8vl8cDgcMBqNaGhoANA7uL2+vh75+flD2kFiYiLmz5+P9957D7Nnzw7/iImIiIhGiUELLafTCYvFgoqKimBbc3MzqqqqBi20Dh48iLPPPhs6nQ5dXV344IMPhrSwtKqquP7663Ho0CG0tLRg5syZgptCRERE0aQFOBheatBCS1XVkALJarVC0zTs3r0bTz31FLq7u9HT04Nbb70VhYWFWLx4Mf72t7+hvr4eer0egUAACxcuDH4tOBi/34/7778f3d3duOWWW76x6x4SERHR2DBoobVixYp+2ysrKwEAGzdu7Pfya6+9Ftdee634YK699loUFhaKr0dERETRN5bGTsXKmJoZXoehd2ke1J0j2nYSjovyPVq8KH/EkyjKA0By3ClRfrL+sCjfcsUdorw3YBTlp/o/E+X9Otn2e2AS5U9kXy/KK5C94XTrEkT5gHCFrGSv7PF1j5soyh8zyk44GYcuUR4AvBgnyicETojyKUf7P8FmIC2p3xHlZc84OT/0orzkPXE4ejTZa9KkyM4Ml76GDy6+RZRP8HeK8m59sihv3fOGKA8AzbOuFOVn9uwV74NiK6aFVk1NDZqamkLay8vL8dprr8XyUIhohJEWWUQUexonLBWLaaFVUFCAgoKCWO6SiIiI6IwZU18dEhERUfQEOEZLTDYIhIiIiIiGTNSj5Xa7UVpaitzcXJSUlAAAWlpa8MILL+DAgQOYP38+7r333mB++/bt2LRpE4DeqRsWLlyIG2+88bTrHhIREdHIw3m05ESFlsPhgNVqRWNjI4qLi2EwGJCYmIibb74ZLS0t2LlzZ5/8rFmz8Nhjj0Gn08Hn82HVqlWYOXMmvv3tb0f0RhARERGNRKJCS1VVLFu2DJs3b4bT6YTNZkNKSgpSUlLw+eefh+Tj4uKCv/f09MDn8w3am9Xd3Y3169fjs88+g8FgwNSpU3HPPfeE5DweDzwej+TQiYiIKEycR0tuyIVWa2sr3G43MjMz0dHRAVVVYbPZTnu9jz/+GBs2bMChQ4dw5ZVXYsGCBQNmd+zYgVOnTuGXv/wlgN6vKvtTW1uLmpqakPZH1j4+xFtDREREFH1DLrTq6upgt9uhKAqysrJQXV0Nl8uFlJSUQa+XkZGBJ554Ap2dnXjyySfx0UcfDbi4dFpaGtra2vD8889jzpw5AxZleXl5yMnJCWk/fkI2gScRERENHefRkhtSoeXz+eBwOGA0GtHQ0ACgd3B7fX39oItLf1ViYiLmz5+P9957b8BC66yzzsJ//Md/4O9//zu2b9+OV155BU888QRMpr6zA5vNZpjN5pDrHz/ROqRjISIiIoqFIRVaTqcTFosFFRUVwbbm5mZUVVUNWmgdPHgQZ599NnQ6Hbq6uvDBBx+ELFL9VUePHsX48ePxne98BxdddBFKS0vhdrtP22tGRERENBINqdBSVTWkQLJardA0Dbt378ZTTz2F7u5u9PT04NZbb0VhYSEWL16Mv/3tb6ivr4der0cgEMDChQuxePHiAffz6aef4ne/+x0AIBAIYMmSJSyyiIiIRggOhpdTNE0bM/favo+H/tVhp3+CaNtJetmi0u7AeFH+yMmRt6i0B7L7yCtcYFa6qPQpo+x4pAvSJvYcFeU7jZNEeeki1CNtUWmvJluLULqo9HDWOhxxi0orXlFearQvKm1W+j/BacDtC1/D0tdYtBeVHs79L11UOi/Gi0pnf+/dmO3r3S0DfwM2mnAJHiIiIhoSTlg6DNoZ8J//+Z/afffdF/LT0dER8X253W7t1Vdf1dxuN/PMi/Mj8ZiYZz6W+ZF4TMzTaHJG1josKCjA448/HvKTlJQU8X15PB7U1NQMeYJT5pkf6cfEPPOxzI/EY2KeRhMuKk1EREQUJSy0iIiIiKKEhRYRERFRlLDQIiIiIoqSMV9omc1mFBQU9LtkD/PMj4R9MM/8SM6PxGNinkaTMTVhKREREdFIMuZ7tIiIiIjOFBZaRERERFHCQouIiIgoSlhoEdGQHTp06EwfAkUZH2OiyGKhRSNGZ2cn3n//fbz//vvo7Ow804cj5vP58Omnn+LTTz+F3+8fNLtz506cPHky+LfH48GuXbvCPobu7u5BfwZz8uRJfPLJJ4NmfvWrXwEA/v3f/33Ix9TT0zPosUnyXq93wP34fL6QtkguWSK5bwOBAF577TXR9t9++20cPnw4rGMM9LPgr/Q5MZzH+Kt8Pl9UHt+hbv/rurq60NXVddrcUPb9VX/7299CnnO7d+8+7fWG+z7n8/nQ0dEhOkYaGQxn+gAirb29HUePHsW5554Lo9EYbN+5cycuvPDCkPz7778PRVGwYMEC7NmzB3/5y19wzjnn4IorrgjJdnV14Y033sCkSZPwT//0T/jTn/6EXbt24ZxzzkF+fj7i4uJCrrN//368++67OHLkCPR6PaZPn47vfve7SE5O7vf49+zZg23btqG9vR0AkJqaCpvNhgsuuEB0P6iqissvvzykXXr/fNXBgwexf/9+nHPOOZgxY0bI5Z9++immTJmCuLg4+Hw+bNmyBfv370daWhry8/NhMpkG3HZTUxOeffZZnHvuudA0DRs2bMDy5cvxne98p9/8gw8+2G/72rVr+23/yU9+gquuugqXXXYZ4uPjB72dAPCv//qvUBSlT1tCQgKsViuWLVsW8vjt2bMH69atC95Gr9eLn/z/7Z19UFTX+ce/IIJWKkbTqsEEjAqtSNEdXzPKqomDtqlJHZMJLw1ppzHVEV8gLkHXl9YIKEo0DaKIFooGLWJaSW1iiLu8xKGSCEYJ02ARmoSqmCUgCCwv5/cHc+9vX+7d3XN373Il5zPDDHv3e88995y75zz3nOc8Z9MmBAcHC6Z/8uRJ7N27l/88cuRI5OXlmR0zpbS0FCqVCr6+vgCA9vZ2VFVVYdGiRWa6l19+2eZ9nTlzRvD41atXcezYMXh6eiIjIwP/+c9/UFBQgDfeeMNMZzQaUVFRgebmZly9etUqHZVKZXVMq9Vi7969onmzzJM9vbe3N1avXo3nnnvO7Pjhw4exYcMG/nNnZyeSk5OxZ88ewXSamppQWFiIO3fumBnGYs8QTdl6enqiqqoKL774os1zTLl16xaKiorQ29uLGTNm8H9ibcWhQ4fw2muv8e2OwWDAoUOH8Ic//EFyvgFpdQwAV65cwYkTJ9DS0mIzfan162j6HLdv38af/vQnNDQ0AAAmT56MuLg4jB8/XlC/du1azJkzB4sXL8ZPfvITQY0p6enpmDJlChITE/nfZW5uruhvGKBv5w4ePIg1a9bAy8sLW7ZsQVtbG371q19h5cqVdvPHUA5DytAqKytDTk4OHnnkETx48ACbNm1CUFAQAODUqVNWhsTp06fx+eefo6+vDzdu3EB9fT1mzpyJsrIyfPvtt1aN5JEjR9Df34+amhp8+umn6OnpQXh4OKqrq5GVlWXWyANAUVERSktLMX36dDQ1NSEkJAT3799HYmIiNm7ciOnTp5vpCwsLUVFRgfDwcN6wunfvHk6cOIF58+Zh9erVDpfFX//6VytDi7Z89u/fj9dffx3AQEd85MgRBAcHIz8/H5GRkQgPDzfTHzp0CKmpqQCAd999F/fu3UN4eDiuXbuG7OxsrFu3TjS/p0+fxu7du/HYY48BGJi+2Ldvn2gD9Otf/5r/32g04pNPPsEjjzwimv7GjRvx4YcforCwEHPnzkVERASeeOIJUX1ERAQePHiAJUuWgBCC0tJSeHp6wsfHB0ePHkViYqKZ/vjx44iLi+PrtLa2FtnZ2UhLSxNMnxBiZsh5enoKjkZwFBUVmZW3r68vioqKrAwtrtMpLCzE8OHD8cwzz4AQgo8//lhwxIejoKAAKSkpSE5OBgBMmTIFd+7csdJFRUWhuLgYra2tKCoqsvpeqBPmOh6xDpGjra0No0ePtqtvbW2FVqvF0qVL8cMf/pA/7ufnh5MnTyImJgZGoxGpqalYuHCh6PXS09MRHh6OJUuWwNPT/uD+iy++KFimq1atEtSrVCqcP38earXa7CXMx8dHUP+73/0OAPDtt9/i6tWryM/Px71790TL4bHHHkNSUhI2btyIlpYWHDt2DC+88IKVjvaZkFLHAJCXl4fNmzdj2rRpNstTav06mj7HsWPH8PTTT/PtoF6vR1ZWFrZv3y6oP3ToEMrLy5GTk4POzk6o1Wqo1WqMGzdOUD9p0iQsXrwYO3bsgEajwYQJE2AvWhJtO9fU1IQf/OAHqKioQEhICGJjY7Ft2zZmaD1kDClD6/z580hLS8PYsWNRU1ODgwcP4rXXXkNYWJjgD6CyshJpaWno7u7GmjVrkJmZCV9fXyxfvhxardbK0Prqq69w4MAB9Pb2Ys2aNcjKyoKXlxfmz5+PLVu2WKV/6dIlpKamwsfHB21tbXj77beh1WqxbNkyZGZm8kYJR0lJCfbv32818hMREYGEhAQrQys9PV20LNrb250uH9NpjPPnz2Pr1q0IDAxEc3Mz0tLSrAwtAPwoWU1NDZKTkzFs2DDMnz8fGo1GNK/ceVzjAwATJ060OQJmaaSGhYVhx44dovrJkyfj97//PTo6OqDT6ZCSkoIf//jH+PnPf4558+ZZ6aurq3mjAwACAwORlJSElJQUxMfH282TvRHIkSNHoq6uDtOmTQMA1NXViXbAYtgyzK5cuWL2Zr1y5UokJiaKGgUArEZOTEc8OSZNmoSkpCTk5uYiNjaWKr/22LNnj83RAA4/Pz/Ex8fjzTffNNO//PLLeOutt/D++++juroaKpUKK1asEE1n2LBhVB0WbZmePXsWwMBLjCliBkZ9fT2uX7+O69evo6WlBWFhYQgNDRXNzwsvvIDg4GBs374do0aNglarxaRJk5zOv9Q69vX1FR3BpUGsfmnTb2trw9KlS/nPS5YswYULF0T1XNu/fPly/Pe//8X777+P9evXIz8/X1Dv4eGBp59+GuPGjcOePXsQFxdnNQpuCW07x420fvHFF1CpVPDx8bF7DYbyGFKGFgCMHTsWABASEoKtW7ciJSUFv/3tbwUfTi8vL3h6emLkyJGYMGECP/w7YsQIu29MpoaJmHbYsGF85zlq1Ci0trYCAAICAng/Bcs0HXlT46iqqkJsbCy8vKyrsaamRvAcmvIxPdbZ2clPF/7oRz8STNvb2xu3b9/GhAkTMGrUKPT09GDYsGHo6+sT9VnifCxmz56Nc+fOYenSpSCEQKfTYc6cOeI3b0FnZ6dD/gt1dXWoqamBt7c3wsLC8NFHH+Hy5cvYvHmzma6jowPt7e38M3H//n3ep0qovENDQ1FWVsaPMJWXlyMsLEw0HzExMUhLS8Pjjz8OAPj666+RkJAgqh8zZgz+9a9/8UZhRUUF/Pz8RPVGo5GvC2BgGsWWD8qIESPw3Xff8XVeU1MjGIX64MGD2Lt3LxobG0XTkgpN7OTJkyfzelM/nVdffRXJyckICQnB8uXL0d3dLWrAhoWFoaqqCrNmzXLomrRl6ugIHkdSUhKCgoIQGRlp9SIhBDfatWDBAnz11Vf44IMP8Morrwg+nzT5p61jrvznzp2Lixcv4qmnnjIz0mlfIADz+uWgTd/T0xNNTU28YdPU1GS3fe3v70dVVRX0ej1qa2uhVqtFtVz+Zs6ciYSEBKSnp+P+/fs20xdr54xGIwghVvfi7++P5ORkfPPNN4iKiqL2I2MogyFnaHV0dPAdxKRJk6DVapGcnCw4wtPf389P4axdu5Y/TggRNAwCAgLw1ltvobu7G2FhYcjIyMC8efNQXV2NiRMnWukDAwNx9OhRhIWFoaKigp/3NxqNgkP2arUaSUlJUKvVePTRRwEMNKYlJSWCP/jAwEAEBgZi6tSpVt+JNfI05XP37l1+1MxgMKCnp4dv4ITKJzo6Grt374ZarUZAQAB2794NlUqFGzduiDZYln4alvkWmy419dEihODOnTv45S9/KagFBkbkiouLMX78eKxYsQKzZs2Ch4cHVq1ahbi4OCv9ihUrsGXLFr4TrqqqwnPPPYeuri7Bt+qSkhL84x//wJEjRwAMOK76+vpCp9PBw8MD2dnZZvqgoCCkp6fjyy+/5D9zRh1g3QnHxsYiLS0NJ0+eBDBgxNsaJXzppZewbds2PPnkkwAG/H/WrFkjqo+OjkZKSgru3r2LXbt24X//+5/V9Cgg3X/HEWjf1Dm9kK9PfX09P+0l9lv42c9+hn379sHDwwPDhw/n2wLLuuKgLVN7WI7g7dmzBzdu3EBhYSH+/Oc/Izg4GKGhoYIjrgCwfft2REVFYdGiRejt7UVeXh62bt2Kffv22c0/IQQNDQ2C+aetY8vyP378uNlnewanGJbPw+nTp6nSj4yMxI4dO/gXxMbGRqxfv170erm5ubh8+TIef/xxqNVqxMXF2RxtioyM5P8PDAzErl27cOnSJf6Y5W8YGJi+FcpzQUGB4PH169ejuroagYGBGDFiBAwGA6Kjo21eg6E8htQWPB9//DEmTpxo9TZ49+5dHDlyxGpqqbq6Gj/96U+t3iJu376N8vJyq07eaDSiuLgYALBs2TJ8/vnn+OijjzB+/HjMnz/farqoq6sL7733HhobG/Hkk0/i+eefh7e3N7q6unDr1i3B6aXa2lpBZ3ihN9yGhgaMGTNG0Fm2ubnZauRJrHzu3LmDU6dOWU2J6fV6s8+zZ8+Gr68vDAYDPvzwQ7OGhsNgMODixYv4+uuv0d/fj0cffRQLFy7kfcGkYtmgmK7u8fT0xPjx4236aB0/fhzLly+Hv7+/1Xf19fV852lKY2Mjf53p06cjICBANP3m5mab+RcbBRQjMTHRahqtv78fTU1NAAb8c+y9nbe2tqKurg7AgCFnWn5CDfSDBw/w73//G4QQBAcHm41ocfrKykoUFxejtrYWU6ZMsbrmzp07qe7TFKF7dqXe8p7j4uIQHR2NyZMnm5WlrbqiLVNbaDQaQaPIYDDgs88+w9/+9jebPlqmozUclZWV/EiwUH5s5d80DTnqmLZ8nK1f7hh3v9OmTbNZX+fOnUN4eDj/kuvu/LvrGoxBgAxhDAYDOXfuHNmwYQOJi4uTRV9YWChb+oz/R6PRUOnfeOMNWfW00Ka/ZcsWs8/Xrl0jHR0d/Of29nZy/fp1yfmhLU9LfU5Ojk19a2srdZ4s79nVest7cHWdO1um2dnZZNOmTWTdunXk8OHDpLS0lBgMBqfz09XVZfNPDFfXMW35OFu/g62nzb+7rsFwP0Nu6rCvrw+VlZXQ6XSoq6tDX18ftm3bJjqiojS9GGLhGoaq3hJCOfBqL46VPT1teAdX58fy2rThIOxBW56WentO0o46tpsitvLKVXrLe5gzZ47LfIqE0qclICAAv/jFL3gfKktoRzu4/EgN+eHqOqYtH2frd7D1UpzW3XENhvsZUoZWTk4OPvnkEwQEBGDx4sVISEjA5s2bRY0apeltIRSuYSjrLZHqvyNVTxvewdX5sYRQhoNwdX5o9aYdhqPx1bipelq91HvgDAy5fIrsYdmpPvPMMzb1tIYNlx+pIT/sIdUo+OCDD/DUU0/ZNRqdrd/B1kuBGU5DkyFlaBUXFyMoKAjPP/88ZsyYAcD2g6s0PW24hoddr2SkhHdwJZadmCvCQbgT0+ecNr6aM/HYaKBdFehq5B6xsURKyA9bSDUK8vLycOrUKYSGhmLp0qVQqVRUq60fFpytL6Vcg+E8Q8rQOnr0KMrLy3Hy5Em0t7cjPDzc5pSN0vS04Roedj0N7p4WoA3v4Or8WHbCtOEgXJ0fZxt02vhqUuOx2YL2HqROjblrRM7R/HDQhqdwNVx+/P39odVqUVpaitOnTyMrK4sPHCu0WIU2faXoaQ1pd12D4X6GlKE1atQoREREICIiAo2NjdDpdOjp6cHOnTuxcOFCLFu2TNF62nAND7ueBtoGhRv5kaqnDe/gaPpSO2HacBD2cKc/FG18NSnx2OS+B5r03TUi52h+OFwdnkJq+Xh4eGD06NF49tln8eyzz+LmzZvQ6XTQarXw9/fHm2++KSk/cj/TnF6uqU/LPDmyPZyUazAGATk97ZVAT08PuXz5MklOTla8/tatW6SlpUXwvC+++GLI6e1x6dIlh3QpKSlU6Tqqb2xsJBcuXCAXLlwgDQ0NDp3zzTffkJKSEnLr1i3B7+Pj44nRaCSEEJKbm0sOHDhALl++TDIzM0lGRoZD1xCCW63U2NhIOjs7CSEDz9rZs2dJamoqyc/PJ93d3Vbn0eodpaCggP//+vXrZN26deTMmTMkJyeHbN26lZw9e5bs2rWL/P3vf7c6l1Yv1z1ILdP4+HizNHp7ewkhhPT395PXX39dcn64FWbO3O93331HKisrSWVlpdWqQdpVhFwd//Of/6Q6V2ylXHd3NykrK7M6Tpu+3PqoqCgSExND9u7dSyorK0lfX59D53366afks88+I4QQUltbS06cOEGKi4sFtfn5+SQpKYloNBqSm5tLdu7cSd577z2yc+dOcubMGYfzylAGQyqO1lCjpaUFer0eer0ehBC8/fbbQ1pvydq1a5GZmWl2TMgPrLq6GjNnzgQAK/8pWr2jcP5aYvtB3rx5U3A/yISEBBw4cADAQAwcblqMEAKNRiO6N6I9uJhMCQkJSE1NxfDhw/GXv/wF9+7dw4IFC3Dt2jX09/dbjabQ6qVuHC4WX62vr08wnhyNnvYeHIWLUUSbPrf/4IQJE/DHP/4RGo2GLy+NRmPTl9EWZ8+exerVq2W/X0dHbDiio6Ph6enpsM/Vu+++i6ioKMHvamtrreqXNn259RqNhp/61Ov1aGtrszv1abqvbkhICL+vbnV1NaZPn2613VtCQoLg9nBdXV3QarXYv3+/aP4YymNITR0OBZQWbkJuPa0DfW1tLVQqlVljXFNTIxqRnFbvKNwUFu1+kHJNi5n678jpDyV1Wmzs2LF46aWXAAwY4DqdDhkZGaIGOK1ebp8umvRt7ZCwePFiK72U6WQ575fWWZ3W58rSyLL3Qkabvtx6KVOftPvqOrM9HEN5MENLQSgt3IQ7wlnQOtAfOHAA2dnZqK+vR3R0NHx8fFBQUCDYgUnROwpn2NjaD1JosJi2E6bFHf5QUjp5OQ12uX26aNOfMWMGdu/ejYsXL6K5uRl+fn5obW1FZGSkoJ7WeJXbWHeH4UFTv7Tpy623/F1PnToVU6dORWxsLK5cuSJYtrSGE+32cAxlwwwtBaG0cBNy6wF6B/rRo0cjPj4eZWVl2LVrF6Kjo21eg1ZPi639IIXiXNF2wo7CNf60hhytXkonL7fBLvcIkhTjWM4RObmNdbkND9r6pU1fbj3ndmCJt7c3xo0bJ/gdreEUHR0No9EIHx8fs+3Bbt++bXOja4YyYYaWglBauAm59cBABHaxKOsbNmwQPW/RokUICQlBVlYWOjs7AQj7d0jV24NrnE2jZ6tUKnR3d2P48OEwGAzw8/MTPJe2E3YEbvSF1pCj1Uvp5OU22OUeQZJiHMs5Iie3sS634UFbv7Tpy62nnfoE6A0nsTxNnDgRK1euFPyOoVyYM7xC4cJBlJeXw9/fXzAcxFDSc9A60BsMBuj1epSUlMiiF+PYsWN49dVXJeWfphOW6nxumh+dTufw/Tqip3Vs7+joQHl5OXQ6HW+A63Q6q4UOUvW09+DsggR76VuO2MydOxebN29GRkaGYHo3btxAZmYm1Go1Ojs78eWXX/LG66xZs+x2rLR1LAbnbE/rrC6UH1u/AVfU72Av4nHV1mpScHY7M4b7YYaWwunt7UVlZSX0ej2SkpKGpF5pDvqmNDU14ebNm3jiiSd4/ytn0qfthKWsMHNn+dB28nIZ7DT3IGVVIE36MTExCAoKwqpVq/gRm/Xr1+Odd94RvU9a45UmP7SrCC2R0/CQo37l1tP+hu1BazgJrcZmKBtmaDEGFdpGS249bbgG2vRpO2Ha0Re5ywdwzdu8Kw12uUeQaNOXe0SONj+04QsA9xserqxfufVSDGlbOBrGhqOqqgp5eXmSrsUYHJiPFmNQUZqDPm24Btr0af3YaP135C4fV22U7uXlhQULFmDBggVO6+X26aJNn3ZHCIDOsKHND+0qQto6lrIIxhJX1q/ceim+qLRhbOTczozhfpihxRhUlOagTxuugTZ92k6Y1vlc7vJxRafqaqR0fDQLEqSkzxEQEIBXXnkFMTEx/IiNZR3TGjZSnmmaVYTuMDxoUFobIcWQpjWc5NzOjOF+2NQhQzEowUH/N7/5DUJDQwEMOP4ePnyYX3pvOo3nivxw2JtGo/XfkZofR/TOTovJjVw+P7Tp0+DMVJQj+eF2DbDEaDTiypUrWLhwodlxZ+pYjvJxJn13LeKx9xvevn07YmNjBQ0noanDhoYGjBkzRnBFtjOrpRmDAzO0GIpjMB309Xq92efZs2fD19cXBoMB77zzDnbs2OHy/NAgZYWZXOUpd6fqDK70+aFNnxZXGK+28uPMKkK5DA9nUcIiHhqcNZyc3c6MMbgwQ4vBsIESGrjBXEruCHJ3qq7G1c7MrsQdxquUZ/phq2Olo/QQEgzXwgwtBsMCJTVwrl5KzlD+9CfgesNGSc/095XBDCHBGFyYMzyDYYKrVtW5CiU6nz/sSHFmdje0qzJtobRn+vvIYKzkZCgHZmgxGCYorYGTe0XX9x1HVgU+7Cjtmf4+orSVnAz3wqYOGQwTlDytpGTnc4ZyUfIz/X1BySs5GfLDDC0GQwSlNnDMMZkhFaU+098nlLqSkyEfzNBiMOzAGjjGUIM904MPq4PvD8zQYjAYDAaDwZAJ2zuLMhgMBoPBYDAkwwwtBoPBYDAYDJlghhaDwWAwGAyGTDBDi8FgMBgMBkMmmKHFYDAYDAaDIRP/B7vy/85AwcP7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizando a matriz de correlação\n",
    "corr = data_dummified.corr()\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(corr, cmap= 'coolwarm')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hS36rqnt08mP"
   },
   "source": [
    "A matriz possui bastante colunas agora, o que torna sua visualização difícil, portanto iremos buscar na coluna A16 quais seriam as variáveis com maior correlação."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Hv1sDj6QziYu",
    "outputId": "1b78dbd6-1a91-4165-e55c-6ed92ec90dde"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A6_c     0.004803\n",
       "A13_p    0.005818\n",
       "A7_o     0.005818\n",
       "A7_n     0.008240\n",
       "A6_m     0.012833\n",
       "A7_j     0.015928\n",
       "A1_b     0.019472\n",
       "A7_dd    0.021734\n",
       "A6_r     0.029596\n",
       "A6_j     0.036389\n",
       "A12      0.041799\n",
       "A6_w     0.049668\n",
       "A7_v     0.050240\n",
       "A6_e     0.052775\n",
       "A5_gg    0.060803\n",
       "A7_z     0.066922\n",
       "A6_d     0.093011\n",
       "A13_s    0.104792\n",
       "A6_k     0.106776\n",
       "A6_i     0.127269\n",
       "A6_cc    0.140731\n",
       "A6_q     0.151529\n",
       "A2       0.175264\n",
       "A4_u     0.175350\n",
       "A15      0.177213\n",
       "A4_y     0.183900\n",
       "A5_p     0.183900\n",
       "A6_ff    0.185547\n",
       "A7_ff    0.187976\n",
       "A7_h     0.191465\n",
       "A6_x     0.194659\n",
       "A3       0.205816\n",
       "A8       0.324798\n",
       "A11      0.409645\n",
       "A10      0.456158\n",
       "A9       0.732671\n",
       "A16      1.000000\n",
       "Name: A16, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr[\"A16\"].abs().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GL78Y0wV3bQJ"
   },
   "source": [
    "Dessa maneira vemos que A10 e A9 seriam as variáveis que possuem maior correlação com A16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 321
    },
    "id": "5JNkhTFL2Q2b",
    "outputId": "5e0c21c1-cf75-453b-a5d9-3046d6127391"
   },
   "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>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A15</th>\n",
       "      <th>A16</th>\n",
       "      <th>A1_b</th>\n",
       "      <th>...</th>\n",
       "      <th>A7_dd</th>\n",
       "      <th>A7_ff</th>\n",
       "      <th>A7_h</th>\n",
       "      <th>A7_j</th>\n",
       "      <th>A7_n</th>\n",
       "      <th>A7_o</th>\n",
       "      <th>A7_v</th>\n",
       "      <th>A7_z</th>\n",
       "      <th>A13_p</th>\n",
       "      <th>A13_s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.828125</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58.656250</td>\n",
       "      <td>4.460</td>\n",
       "      <td>3.04</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.500000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>824</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27.828125</td>\n",
       "      <td>1.540</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.171875</td>\n",
       "      <td>5.625</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          A2     A3    A8  A9  A10  A11  A12  A15  A16  A1_b  ...  A7_dd  \\\n",
       "0  30.828125  0.000  1.25   1    1    1    0    0    1     1  ...      0   \n",
       "1  58.656250  4.460  3.04   1    1    6    0  560    1     0  ...      0   \n",
       "2  24.500000  0.500  1.50   1    0    0    0  824    1     0  ...      0   \n",
       "3  27.828125  1.540  3.75   1    1    5    1    3    1     1  ...      0   \n",
       "4  20.171875  5.625  1.71   1    0    0    0    0    1     1  ...      0   \n",
       "\n",
       "   A7_ff  A7_h  A7_j  A7_n  A7_o  A7_v  A7_z  A13_p  A13_s  \n",
       "0      0     0     0     0     0     1     0      0      0  \n",
       "1      0     1     0     0     0     0     0      0      0  \n",
       "2      0     1     0     0     0     0     0      0      0  \n",
       "3      0     0     0     0     0     1     0      0      0  \n",
       "4      0     0     0     0     0     1     0      0      1  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dummified.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pag0TLZevsb9"
   },
   "source": [
    "## Normalizando dados contínuos\n",
    "\n",
    "A normalização é uma técnica frequentemente aplicada como parte da preparação de dados para a aprendizagem de máquina. O objetivo da normalização é mudar os valores das colunas numéricas do conjunto de dados para uma escala comum, sem distorcer as diferenças nas faixas de valores. Para a aprendizagem de máquina nem todo conjunto de dados requer normalização, ela só é necessária quando as colunas possuem faixas de valores diferentes.\n",
    "\n",
    "Por exemplo, considerando o nosso conjunto de dados, a variável A2 e a A3, A2 varia de aproximandamente 13 até 76, com média de 31.46 e desvio padrão de 11.70. Enquanto A3 varia de 0 até 28, com média de 4.83 e desvio padrão de 5.0006. Portanto podemos afirmar que estas duas características estão em faixas diferentes, quando fazemos mais análises, como a regressão linear multivariada, por exemplo, é possível. que A2 influenciará o resultado mais devido a seu maior valor. Mas isto não significa necessariamente que seja mais importante como um preditor.\n",
    "\n",
    "Uma observação importante é que a normalização deve ser feita após a divisão dos dados entre o conjunto de treinamento e o conjunto de testes, normalizando separadamente ambos os conjuntos.\n",
    "\n",
    "Devemos fazer isso porque o conjunto de teste desempenha o papel de dados novos e invisíveis, portanto não deve ser acessível na fase de treinamento. Usar qualquer informação (média, máximo, mínimo, desvio padrão) proveniente do conjunto de teste antes ou durante o treinamento é um viés potencial na avaliação do desempenho.\n",
    "\n",
    "## Conjunto de treino e conjunto de testes\n",
    "\n",
    "A divisão do treino/teste é uma técnica para avaliar o desempenho de um algoritmo de aprendizagem de máquinas. O procedimento envolve pegar um conjunto de dados e dividi-lo em dois subconjuntos. O primeiro subconjunto é usado para se ajustar ao modelo e é chamado de conjunto de dados de treinamento. O segundo subconjunto é utilizado para realizar previsões, apenas levando em consideração a variável resposta como elemento de entrada, depois que são feitas as previsões elas são comparadas com os valores esperados. Este segundo conjunto de dados é referido como o conjunto de dados de teste.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "qszQwHsM3p_j"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "X = data_dummified.drop(\"A16\", axis=1)\n",
    "y = data_dummified[\"A16\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=123)\n",
    "\n",
    "## Normalizando os dados\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2ZASWHCf-yXj",
    "outputId": "2221f722-ba04-4572-e1c9-95585c4e858e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.14869323, 0.08928571, 0.15789474, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.42197412, 0.23214286, 0.01754386, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.01065719, 0.27232143, 0.00438596, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.12585638, 0.00607143, 0.00298246, ..., 0.        , 0.        ,\n",
       "        1.        ],\n",
       "       [0.18117229, 0.46428571, 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.12433393, 0.10714286, 0.04526316, ..., 0.        , 0.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9FSSX5zy-1qo",
    "outputId": "33b2e72f-edf2-4eae-dab8-409b5e4d49a4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.09870591, 0.08339286, 0.01754386, ..., 0.        , 0.        ,\n",
       "        1.        ],\n",
       "       [0.13651358, 0.41071429, 0.10526316, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.42755646, 0.055     , 0.12280702, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.23141335, 0.04464286, 0.06140351, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.4818574 , 0.25      , 0.05701754, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.34483634, 0.02678571, 0.02052632, ..., 0.        , 0.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Funções Auxiliares"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stratified K-Fold\n",
    "\n",
    "Realiza um K-Fold estratificado e normaliza os dados nesse processo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "def KFolds_norm(df_all, model):\n",
    "    \n",
    "  acc_list = []\n",
    "  conf_matrix_list = []\n",
    "  f1_list = []\n",
    "  class_repo_list = []\n",
    "  estimator_list = []\n",
    "    \n",
    "  df = shuffle(df_all)\n",
    "  X = df.drop(\"A16\", axis=1)\n",
    "  y = df[\"A16\"]\n",
    "\n",
    "  X = X.values\n",
    "  y = y.values\n",
    "\n",
    "\n",
    "  i = 0\n",
    "  kf = StratifiedKFold(n_splits=10,random_state=11, shuffle=True)\n",
    "  kf.get_n_splits(X,y)\n",
    "\n",
    "  print(kf)\n",
    "    \n",
    "  for train_index, test_index in kf.split(X,y):\n",
    "    scaler = MinMaxScaler()\n",
    "    \n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "    \n",
    "\n",
    "    scaler.fit(X_train)\n",
    "\n",
    "    X_train = scaler.transform(X_train)\n",
    "    X_test = scaler.transform(X_test)\n",
    "    \n",
    "    i = i + 1\n",
    "    \n",
    "  \n",
    "    print(\"--------------------------------------------------------\")\n",
    "    print(\"Fold número: \", i)\n",
    "    print(\"Tamanho do X_train: {}\".format(X_train.shape))\n",
    "\n",
    "    modelo = model\n",
    "    modelo.fit(X_train,y_train)\n",
    "\n",
    "    y_pred = modelo.predict(X_test)\n",
    "    acc = accuracy_score(y_test, y_pred)\n",
    "    class_repo = classification_report(y_test, y_pred)\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "    conf_matrix = confusion_matrix(y_test, y_pred,labels=[0,1])\n",
    "    print(\"Acurácia: \", acc)\n",
    "    print(\"f1-score: \", f1)\n",
    "    \n",
    "    acc_list.append(acc)\n",
    "    conf_matrix_list.append(conf_matrix)\n",
    "    f1_list.append(f1)\n",
    "    class_repo_list.append(class_repo)\n",
    "    estimator_list.append(modelo)\n",
    "  \n",
    "  print(\"Terminado\")\n",
    "\n",
    "    \n",
    "\n",
    "  return acc_list, conf_matrix_list, f1_list, class_repo_list, estimator_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot Resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def boxplot_results(acc_list, f1_list):\n",
    "  results_kfold = pd.DataFrame()\n",
    "  results_kfold[\"mean\"] = acc_list\n",
    "  results_kfold[\"method\"] = \"Accuracy\"\n",
    "\n",
    "  results_kfold1 = pd.DataFrame()\n",
    "  results_kfold1[\"mean\"] = f1_list\n",
    "  results_kfold1[\"method\"] = \"f1\"\n",
    "\n",
    "  results_kfold = pd.concat([results_kfold,results_kfold1])\n",
    "  results_kfold.head()\n",
    "\n",
    "  fig, ax = plt.subplots(figsize=(12,10)) \n",
    "  plt.ylim(0.70, 0.95)\n",
    "  ax = sns.boxplot(x=\"method\", y=\"mean\", hue=\"method\", data=results_kfold, palette=\"Set1\")\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matriz de confusão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conf_matrix(conf_matrix, list_scores):\n",
    "  index_best = list_scores.index(max(list_scores))\n",
    "  conf_matrix_best = conf_matrix[index_best]\n",
    "\n",
    "  fig, ax = plt.subplots(figsize=(15,10)) \n",
    "  sns.set(font_scale=1.4) # for label size\n",
    "  sns.heatmap(conf_matrix_best, annot=True, annot_kws={\"size\": 10}) # font size\n",
    "\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GridSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grid_search(model, param_grid, n_folds, X, y):\n",
    "  scoring = {'PRECISION': 'precision', 'F1': 'f1'}\n",
    "  #use gridsearch to test all values for n_neighbors\n",
    "  gscv = GridSearchCV(model, param_grid, cv=n_folds, scoring=scoring, refit='F1')\n",
    "  #fit model to data\n",
    "  gscv.fit(X, y)\n",
    "  return gscv.best_params_, gscv.best_score_, gscv.cv_results_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Métricas de avaliação"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wviaJHPC92qP"
   },
   "source": [
    "### Recall\n",
    "\n",
    "Segundo o Google Developers para Machine Learning [https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall] , entendemos que o recall responde a seguinte pergunta: qual proporção de positivos foi identificados corretamente? Em outras palavras, quão bom meu modelo é para prever positivos. Ele é definido como a razão entre verdadeiros positivos sobre a soma de verdadeiros positivos com negativos falsos.\n",
    "\n",
    "### Precision\n",
    "\n",
    "Ainda usando o material do Google Developers, precisão é definida como a resposta para a seguinte pergunta: Qual a proporção de identificações positivas foi realmente correta? É a razão entre os verdadeiros positivos sobre verdadeiros positivos + falsos positivos.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T_ie4Phc8VsV"
   },
   "source": [
    "### Matriz de Confusão\n",
    "\n",
    "A matriz de confusão é tabela que mostra as frequências de classificação para cada classe do modelo. \n",
    "\n",
    "- Verdadeiro positivo (true positive — TP): ocorre quando no conjunto real, a classe que estamos buscando foi prevista corretamente. \n",
    "- Falso positivo (false positive — FP): ocorre quando no conjunto real, a classe que estamos buscando prever foi prevista incorretamente.\n",
    "- Verdadeiro negativo (true negative — TN): ocorre quando no conjunto real, a classe que não estamos buscando prever foi prevista corretamente. \n",
    "- Falso negativo (false negative — FN): ocorre quando no conjunto real, a classe que não estamos buscando prever foi prevista incorretamente. \n",
    "\n",
    "No nosso problema estamos procurando prever a classe de cartões aprovados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A acurácia é uma métrica para avaliar os modelos de classificação. A acurácia (accuracy score) é a fração das previsões que nosso modelo acertou:\n",
    "\n",
    "Número de predições corretas / número de predições em total\n",
    "\n",
    "Para a classificação binária, a precisão também pode ser calculada em termos de positivos e negativos da seguinte forma: \n",
    "\n",
    "(TP + TN) / (TP + TN + FP + FN)\n",
    "\n",
    "A acurácia por si só não conta a história completa quando se trabalha com um conjunto de dados desequilibrado, onde há uma disparidade significativa entre o número de etiquetas positivas e negativas. No entanto como nosso conjunto de dados é relativamente equilibrado é seguro utilizar a acurácia em conjunto com outros métodos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### f1 score\n",
    "\n",
    "O f-measure ou f1 score é a média harmônica do precision e recall. Na maioria das situações, você terá um trade-off entre precision e recall. Se seu classificador for otimizado para aumentar um e desfavorecer o outro, a média harmônica diminui rapidamente, no entanto, é maior quando tanto o precision quanto o recall tem valores parecidos.\n",
    "\n",
    "F-measure tem um significado intuitivo. Ela lhe diz quão preciso é seu classificador (quantas instâncias ele classifica corretamente), bem como quão robusto ele é (ele não perde um número significativo de instâncias).\n",
    "\n",
    "Com alta precisão, mas baixo recall, seu classificador é extremamente preciso, mas perde um número significativo de instâncias que são difíceis de classificar e isso não seria muito útil. Portanto o f-measure será a métrica principal que iremos utilizar para comparar os algoritmos de classificação.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6AbrSGiQds0S"
   },
   "source": [
    "# Algoritmos de Classificação"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5e7ewAAetDCQ"
   },
   "source": [
    "## KNN\n",
    "\n",
    "O algoritmo KNN assume que coisas similares existem em grande proximidade. Em outras palavras, coisas semelhantes estão próximas uma da outra, ele depende desta suposição ser verdadeira o suficiente para que seja útil. O KNN captura a idéia de semelhança (às vezes chamada distância, proximidade ou proximidade) a partir do cálculo da distância entre pontos, a distância em linha reta (também chamada de distância Euclidiana) é uma escolha popular e familiar para calcular a proximidade entre pontos no KNN e, assim, realizar uma tarefa de classificação em diferentes grupos.\n",
    "\n",
    "\n",
    "Abaixo iremos aplicar o algoritmo KNN diretamente nos dados que foram separados em treino e teste na missão anterior.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "EC-EFXvddwIY"
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "739dflYWC5vZ"
   },
   "source": [
    "### Escolhendo o valor certo para k (número de vizinhos)\n",
    "\n",
    "Para selecionar o K adequado para nossos dados, executamos o algoritmo KNN várias vezes com diferentes valores de K e escolhemos o K que reduz o número de erros que encontramos, mantendo a capacidade do algoritmo de fazer previsões com precisão quando são fornecidos dados que não foram vistos antes.\n",
    "\n",
    "1. À medida que diminuimos o valor de K para 1, nossas previsões se tornam menos estáveis. Basta pensar por um minuto, imagine K=1 e temos um ponto de consulta cercado por vários vermelhos e um verde, mas o verde é o único vizinho mais próximo. Razoavelmente, pensaríamos que o ponto de interrogação é provavelmente vermelho, mas porque K=1, KNN prevê incorretamente que o ponto de interrogação é verde.\n",
    "\n",
    "2. Inversamente, à medida que aumentamos o valor de K, nossas previsões se tornam mais estáveis devido à votação por maioria / média e, portanto, mais propensas a fazer previsões mais precisas (até um certo ponto). Eventualmente, começamos a testemunhar um número crescente de erros. É neste ponto que sabemos que elevamos demais o valor de K.\n",
    "\n",
    "Iremos variar k entre 3 e 25 para o nosso problema."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    371\n",
       "1    300\n",
       "Name: A16, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dummified.A16.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "X = data_dummified.drop(\"A16\", axis=1)\n",
    "y = data_dummified[\"A16\"]\n",
    "\n",
    "X = X.values\n",
    "y = y.values\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "best_params, best_score, results = grid_search(knn, {'n_neighbors': range(3,25)},10, X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6536050898992076"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Melhores parâmetros: {'n_neighbors': 16}\n"
     ]
    }
   ],
   "source": [
    "knn_best = best_params\n",
    "print(\"Melhores parâmetros: {}\".format(knn_best))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y5YABks_wCNc"
   },
   "source": [
    "### Usando k-fold e normalizando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "id": "79UrQlmiuSWM",
    "outputId": "531025a5-32d3-432b-9977-a43c07f3e2ae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StratifiedKFold(n_splits=10, random_state=11, shuffle=True)\n",
      "--------------------------------------------------------\n",
      "Fold número:  1\n",
      "Tamanho do X_train: (603, 36)\n",
      "Acurácia:  0.8676470588235294\n",
      "f1-score:  0.8421052631578948\n",
      "--------------------------------------------------------\n",
      "Fold número:  2\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.835820895522388\n",
      "f1-score:  0.8135593220338982\n",
      "--------------------------------------------------------\n",
      "Fold número:  3\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.835820895522388\n",
      "f1-score:  0.8070175438596491\n",
      "--------------------------------------------------------\n",
      "Fold número:  4\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8208955223880597\n",
      "f1-score:  0.793103448275862\n",
      "--------------------------------------------------------\n",
      "Fold número:  5\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8059701492537313\n",
      "f1-score:  0.7636363636363636\n",
      "--------------------------------------------------------\n",
      "Fold número:  6\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.9104477611940298\n",
      "f1-score:  0.8928571428571429\n",
      "--------------------------------------------------------\n",
      "Fold número:  7\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8208955223880597\n",
      "f1-score:  0.7857142857142856\n",
      "--------------------------------------------------------\n",
      "Fold número:  8\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8507462686567164\n",
      "f1-score:  0.8148148148148148\n",
      "--------------------------------------------------------\n",
      "Fold número:  9\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.9104477611940298\n",
      "f1-score:  0.896551724137931\n",
      "--------------------------------------------------------\n",
      "Fold número:  10\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8656716417910447\n",
      "f1-score:  0.8421052631578948\n",
      "Terminado\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(**knn_best)\n",
    "acc_list, conf_matrix_list, f1_list, class_repo_list, modelos = KFolds_norm(data_dummified, knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9104477611940298\n",
      "0.896551724137931\n"
     ]
    }
   ],
   "source": [
    "print(max(acc_list))\n",
    "print(max(f1_list))\n",
    "\n",
    "dict_results = {}\n",
    "dict_results['knn_acc'] = max(acc_list)\n",
    "dict_results['knn_f1'] = max(f1_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nosso f1 é de 0.8965"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot_results(acc_list, f1_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pelo gráfico de boxplot podemos observar que a variância dos scores é baixa no cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_matrix(conf_matrix_list, f1_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pela matriz de confusão podemos observar que o algoritmo classifica bem o problema, tendo poucos falsos positivos e também poucos falsos negativos (no entanto possuindo dois falso negativos a mais do que os falsos positivos)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.95      0.92        37\n",
      "           1       0.93      0.87      0.90        30\n",
      "\n",
      "    accuracy                           0.91        67\n",
      "   macro avg       0.91      0.91      0.91        67\n",
      "weighted avg       0.91      0.91      0.91        67\n",
      "\n"
     ]
    }
   ],
   "source": [
    "index_best = f1_list.index(max(f1_list))\n",
    "class_repo_list_best = class_repo_list[index_best]\n",
    "\n",
    "print(class_repo_list_best)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5SA9H-_ZtK5P"
   },
   "source": [
    "## SVM\n",
    "\n",
    "As máquinas vetoriais de suporte (SVMs) são um conjunto de métodos de aprendizagem supervisionada usados para classificação, regressão e detecção de anomaliaz.\n",
    "\n",
    "As vantagens das máquinas vetoriais de suporte são:\n",
    "\n",
    "- Eficazes em espaços de alta dimensão.\n",
    "\n",
    "- Ainda eficazes nos casos em que o número de dimensões é maior do que o número de amostras.\n",
    "\n",
    "- Utiliza um subconjunto de pontos de treinamento na função de decisão (chamados vetores de suporte), portanto, também é eficiente em termos de memória.\n",
    "\n",
    "- Versátil: diferentes funções do Kernel podem ser especificadas para a função de decisão. São fornecidos núcleos comuns, mas também é possível especificar núcleos personalizados.\n",
    "\n",
    "As desvantagens das máquinas vetoriais de suporte incluem:\n",
    "\n",
    "- Se o número de características for muito maior do que o número de amostras, evitar o excesso de ajustes na escolha das funções do Kernel e o termo de regularização é crucial.\n",
    "\n",
    "- As SVMs não fornecem diretamente estimativas de probabilidade, estas são calculadas usando uma dispendiosa validação cruzada de cinco vezes (ver Pontuação e probabilidades, abaixo).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V2SnL93vGjZu"
   },
   "source": [
    "Abaixo iremos instanciar um modelo SVM para usar nos nossos dados de Credit Approval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 509
    },
    "id": "UuCEsgKftKSE",
    "outputId": "ea8d9ac0-60f0-42dc-b349-babe863d237b"
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import preprocessing\n",
    "\n",
    "X = data_dummified.drop(\"A16\", axis=1)\n",
    "y = data_dummified[\"A16\"]\n",
    "\n",
    "X = X.values\n",
    "X = preprocessing.scale(X) \n",
    "\n",
    "y = y.values\n",
    "\n",
    "svm = SVC()\n",
    "grid_params = {'C':[1,10],'gamma':[1,0.1], 'kernel':['linear','rbf']}\n",
    "\n",
    "best_params, best_score, results = grid_search(svm, grid_params,10, X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8707532407325074"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Melhores parâmetros: {'C': 1, 'gamma': 1, 'kernel': 'linear'}\n"
     ]
    }
   ],
   "source": [
    "svm_best = best_params\n",
    "print(\"Melhores parâmetros: {}\".format(svm_best))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Usando K-Fold e normalizando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StratifiedKFold(n_splits=10, random_state=11, shuffle=True)\n",
      "--------------------------------------------------------\n",
      "Fold número:  1\n",
      "Tamanho do X_train: (603, 36)\n",
      "Acurácia:  0.9117647058823529\n",
      "f1-score:  0.9032258064516129\n",
      "--------------------------------------------------------\n",
      "Fold número:  2\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.9253731343283582\n",
      "f1-score:  0.923076923076923\n",
      "--------------------------------------------------------\n",
      "Fold número:  3\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8059701492537313\n",
      "f1-score:  0.7868852459016393\n",
      "--------------------------------------------------------\n",
      "Fold número:  4\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8955223880597015\n",
      "f1-score:  0.8955223880597014\n",
      "--------------------------------------------------------\n",
      "Fold número:  5\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.9104477611940298\n",
      "f1-score:  0.9032258064516129\n",
      "--------------------------------------------------------\n",
      "Fold número:  6\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.7910447761194029\n",
      "f1-score:  0.8\n",
      "--------------------------------------------------------\n",
      "Fold número:  7\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.835820895522388\n",
      "f1-score:  0.835820895522388\n",
      "--------------------------------------------------------\n",
      "Fold número:  8\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.835820895522388\n",
      "f1-score:  0.8450704225352113\n",
      "--------------------------------------------------------\n",
      "Fold número:  9\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8208955223880597\n",
      "f1-score:  0.8235294117647058\n",
      "--------------------------------------------------------\n",
      "Fold número:  10\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.835820895522388\n",
      "f1-score:  0.8253968253968254\n",
      "Terminado\n"
     ]
    }
   ],
   "source": [
    "svm = SVC(**svm_best)\n",
    "acc_list, conf_matrix_list, f1_list, class_repo_list, modelos = KFolds_norm(data_dummified, svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9253731343283582\n",
      "0.923076923076923\n"
     ]
    }
   ],
   "source": [
    "print(max(acc_list))\n",
    "print(max(f1_list))\n",
    "\n",
    "dict_results['svm_acc'] = max(acc_list)\n",
    "dict_results['svm_f1'] = max(f1_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O score f1 da svm é ligeiramente maior do que o do algoritmo knn, sendo 0.923076"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot_results(acc_list, f1_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observando seus boxplots também podemos afirmar que a variância do cv é maior no svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_matrix(conf_matrix_list, f1_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pela matriz de confusão podemos observar que o algoritmo classifica bem o problema, tendo nenhum falso positivos e também poucos falsos negativos. Assim possuindo o mesmo número de falsos negativos que o modelo de knn e menos falsos positivos que o mesmo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.86      0.93        37\n",
      "           1       0.86      1.00      0.92        30\n",
      "\n",
      "    accuracy                           0.93        67\n",
      "   macro avg       0.93      0.93      0.93        67\n",
      "weighted avg       0.94      0.93      0.93        67\n",
      "\n"
     ]
    }
   ],
   "source": [
    "index_best = f1_list.index(max(f1_list))\n",
    "class_repo_list_best = class_repo_list[index_best]\n",
    "\n",
    "print(class_repo_list_best)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Florestas aleatórias ou florestas de decisão aleatória são um método de aprendizagem em conjunto para classificação, regressão e outras tarefas que operam através da construção de uma multiplicidade de árvores de decisão em tempo de treinamento e produzindo a classe que é o modo das classes (classificação) ou previsão de média/ média (regressão) das árvores individuais. Florestas de decisão aleatória corrigem o hábito das árvores de decisão de se ajustarem em excesso ao seu conjunto de treinamento. Florestas aleatórias geralmente superam as árvores de decisão, mas sua precisão é menor do que as árvores de decisão de gradiente. Entretanto, as características dos dados podem afetar seu desempenho."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "X = data_dummified.drop(\"A16\", axis=1)\n",
    "y = data_dummified[\"A16\"]\n",
    "\n",
    "X = X.values\n",
    "#X = preprocessing.scale(X) \n",
    "\n",
    "y = y.values\n",
    "\n",
    "n_estimators = [5, 10, 25, 50]\n",
    "# Number of features to consider at every split\n",
    "crit = ['gini', 'entropy']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [8,9,10]\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2,5]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1,2]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]# Create the random grid\n",
    "grid_params = {'n_estimators': n_estimators,\n",
    "               'criterion': crit,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "\n",
    "# Create a based model\n",
    "rf = RandomForestClassifier()# Instantiate the grid search model\n",
    "\n",
    "best_params, best_score, results = grid_search(rf, grid_params,10, X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8584518720819219"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Melhores parâmetros: {'bootstrap': True, 'criterion': 'entropy', 'max_depth': 8, 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "rf_best = best_params\n",
    "print(\"Melhores parâmetros: {}\".format(rf_best))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Usando K-Fold e normalizando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StratifiedKFold(n_splits=10, random_state=11, shuffle=True)\n",
      "--------------------------------------------------------\n",
      "Fold número:  1\n",
      "Tamanho do X_train: (603, 36)\n",
      "Acurácia:  0.8970588235294118\n",
      "f1-score:  0.888888888888889\n",
      "--------------------------------------------------------\n",
      "Fold número:  2\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.835820895522388\n",
      "f1-score:  0.8\n",
      "--------------------------------------------------------\n",
      "Fold número:  3\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8507462686567164\n",
      "f1-score:  0.8333333333333334\n",
      "--------------------------------------------------------\n",
      "Fold número:  4\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8656716417910447\n",
      "f1-score:  0.8421052631578948\n",
      "--------------------------------------------------------\n",
      "Fold número:  5\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8805970149253731\n",
      "f1-score:  0.8620689655172413\n",
      "--------------------------------------------------------\n",
      "Fold número:  6\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8805970149253731\n",
      "f1-score:  0.8666666666666667\n",
      "--------------------------------------------------------\n",
      "Fold número:  7\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8805970149253731\n",
      "f1-score:  0.8571428571428571\n",
      "--------------------------------------------------------\n",
      "Fold número:  8\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8507462686567164\n",
      "f1-score:  0.84375\n",
      "--------------------------------------------------------\n",
      "Fold número:  9\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8805970149253731\n",
      "f1-score:  0.8620689655172413\n",
      "--------------------------------------------------------\n",
      "Fold número:  10\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8955223880597015\n",
      "f1-score:  0.8923076923076922\n",
      "Terminado\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(**rf_best)\n",
    "acc_list, conf_matrix_list, f1_list, class_repo_list, modelos = KFolds_norm(data_dummified, rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8970588235294118\n",
      "0.8923076923076922\n"
     ]
    }
   ],
   "source": [
    "print(max(acc_list))\n",
    "print(max(f1_list))\n",
    "\n",
    "dict_results['rf_acc'] = max(acc_list)\n",
    "dict_results['rf_f1'] = max(f1_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A random forest não obteve um score f1 melhor do que o modelo de svm nem do que o de knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot_results(acc_list, f1_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sua variância no cv no entanto é relativamente baixa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_matrix(conf_matrix_list, f1_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No caso da random forest ele se assemelha ao svm, pois quase não possui falsos negativos, mas possui falsos positivos em pequeno número."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.84      0.90        37\n",
      "           1       0.83      0.97      0.89        30\n",
      "\n",
      "    accuracy                           0.90        67\n",
      "   macro avg       0.90      0.90      0.90        67\n",
      "weighted avg       0.91      0.90      0.90        67\n",
      "\n"
     ]
    }
   ],
   "source": [
    "index_best = f1_list.index(max(f1_list))\n",
    "class_repo_list_best = class_repo_list[index_best]\n",
    "\n",
    "print(class_repo_list_best)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree\n",
    "\n",
    "As árvores de decisão (DTs) são um método de aprendizagem supervisionado não paramétrico utilizado para classificação e regressão. O objetivo é criar um modelo que preveja o valor de uma variável alvo através do aprendizado de regras de decisão simples inferidas a partir das características dos dados. Uma árvore pode ser vista como uma aproximação constante por partes.\n",
    "\n",
    "Algumas vantagens das árvores de decisão são:\n",
    "\n",
    "- Simples de entender e de interpretar. As árvores podem ser visualizadas.\n",
    "\n",
    "- Requer pouca preparação de dados. Outras técnicas muitas vezes requerem normalização dos dados, variáveis fictícias precisam ser criadas e valores em branco precisam ser removidos. Observe, entretanto, que este módulo não suporta valores ausentes.\n",
    "\n",
    "- O custo de usar a árvore (isto é, prever dados) é logarítmico no número de pontos de dados usados para treinar a árvore.\n",
    "\n",
    "- Utiliza um modelo de caixa branca. Se uma determinada situação é observável em um modelo, a explicação da condição é facilmente explicada pela lógica booleana. Em contraste, em um modelo de caixa preta (por exemplo, em uma rede neural artificial), os resultados podem ser mais difíceis de interpretar.\n",
    "\n",
    "- É possível validar um modelo usando testes estatísticos. Isso torna possível contabilizar a confiabilidade do modelo.\n",
    "\n",
    "- Funciona bem mesmo que suas suposições sejam de alguma forma violadas pelo modelo verdadeiro a partir do qual os dados foram gerados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "\n",
    "X = data_dummified.drop(\"A16\", axis=1)\n",
    "y = data_dummified[\"A16\"]\n",
    "\n",
    "X = X.values\n",
    "\n",
    "#X = preprocessing.scale(X) \n",
    "\n",
    "y = y.values\n",
    "\n",
    "\n",
    "param_grid = { 'criterion':['gini','entropy'],'max_depth': np.arange(3, 15)}\n",
    "dtree = DecisionTreeClassifier()\n",
    "best_params, best_score, results = grid_search(dtree, param_grid, 10, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8514729557631217"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Melhores parâmetros: {'criterion': 'entropy', 'max_depth': 5}\n"
     ]
    }
   ],
   "source": [
    "dtree_best = best_params\n",
    "print(\"Melhores parâmetros: {}\".format(dtree_best))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Usando K-Fold e Normalizando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StratifiedKFold(n_splits=10, random_state=11, shuffle=True)\n",
      "--------------------------------------------------------\n",
      "Fold número:  1\n",
      "Tamanho do X_train: (603, 36)\n",
      "Acurácia:  0.7647058823529411\n",
      "f1-score:  0.7333333333333333\n",
      "--------------------------------------------------------\n",
      "Fold número:  2\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.835820895522388\n",
      "f1-score:  0.819672131147541\n",
      "--------------------------------------------------------\n",
      "Fold número:  3\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8507462686567164\n",
      "f1-score:  0.8484848484848485\n",
      "--------------------------------------------------------\n",
      "Fold número:  4\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.835820895522388\n",
      "f1-score:  0.8253968253968254\n",
      "--------------------------------------------------------\n",
      "Fold número:  5\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.9253731343283582\n",
      "f1-score:  0.9180327868852459\n",
      "--------------------------------------------------------\n",
      "Fold número:  6\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8656716417910447\n",
      "f1-score:  0.847457627118644\n",
      "--------------------------------------------------------\n",
      "Fold número:  7\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8208955223880597\n",
      "f1-score:  0.8064516129032259\n",
      "--------------------------------------------------------\n",
      "Fold número:  8\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.9701492537313433\n",
      "f1-score:  0.967741935483871\n",
      "--------------------------------------------------------\n",
      "Fold número:  9\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8656716417910447\n",
      "f1-score:  0.8421052631578948\n",
      "--------------------------------------------------------\n",
      "Fold número:  10\n",
      "Tamanho do X_train: (604, 36)\n",
      "Acurácia:  0.8955223880597015\n",
      "f1-score:  0.8852459016393444\n",
      "Terminado\n"
     ]
    }
   ],
   "source": [
    "dtree = DecisionTreeClassifier(**dtree_best)\n",
    "acc_list, conf_matrix_list, f1_list, class_repo_list, modelos = KFolds_norm(data_dummified, dtree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9701492537313433\n",
      "0.967741935483871\n"
     ]
    }
   ],
   "source": [
    "print(max(acc_list))\n",
    "print(max(f1_list))\n",
    "\n",
    "dict_results['dtree_acc'] = max(acc_list)\n",
    "dict_results['dtree_f1'] = max(f1_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O resultado do score f1 da árvore de decisão foi melhor do que os dos modelos anteriores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot_results(acc_list, f1_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sua variância no cv é menor do que a da svm porém um pouco maior do que a da random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_matrix(conf_matrix_list, f1_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No caso da árvore de decisão houve uma diminuição nos falsos negativos para zero e uma siminuição também nos falsos positivos em relação ao modelo de random forest, esse modelo  também se mostra mais equilibrado entre os falsos positivos e falsos negativos ainda mantendo um score baixo para os mesmos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.95      0.97        37\n",
      "           1       0.94      1.00      0.97        30\n",
      "\n",
      "    accuracy                           0.97        67\n",
      "   macro avg       0.97      0.97      0.97        67\n",
      "weighted avg       0.97      0.97      0.97        67\n",
      "\n"
     ]
    }
   ],
   "source": [
    "index_best = f1_list.index(max(f1_list))\n",
    "class_repo_list_best = class_repo_list[index_best]\n",
    "\n",
    "print(class_repo_list_best)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "\n",
    "\n",
    "Com a árvore de decisão iremos avaliar a importância das features selecionadas para o modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_best = f1_list.index(max(f1_list))\n",
    "final_dtree = modelos[index_best]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = data_dummified.drop(\"A16\", axis=1)\n",
    "y = data_dummified[\"A16\"]\n",
    "\n",
    "importances = final_dtree.feature_importances_\n",
    "# Sort feature importances in descending order\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Rearrange feature names so they match the sorted feature importances\n",
    "names = [X.columns[i] for i in indices]\n",
    "\n",
    "plt.figure(figsize=(15, 6), dpi=80)\n",
    "# Barplot: Add bars\n",
    "plt.bar(range(X.shape[1]), importances[indices])\n",
    "# Add feature names as x-axis labels\n",
    "plt.xticks(range(X.shape[1]), names, rotation=20, fontsize = 8)\n",
    "# Create plot title\n",
    "plt.title(\"Feature Importance\")\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aqui vemos que apenas as colunas \n",
    "\n",
    "```\n",
    "A9, A15, A3, A8, A10, A5_gg, A6_k, A6_x, A2, A11 e A1_b\n",
    "\n",
    "```\n",
    "\n",
    "São realmente relevantes para o modelo, sendo a coluna A9 (PriorDefault) a mais relevante dentre todas. É interessante notar que todas as variáveis contínuas foram incluidas como relevantes. \n",
    "\n",
    "A variável A15 (Income), a A3 (Debt), a A8 (YearsEmployed) e a A10 (Employed) também se destacam, são variáveis interessantes pois realmente são o que se espera que um modelo de avaliação de crédito leve em consideração."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Melhor modelo?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "knn_f1 : 0.896551724137931\n",
      "svm_f1 : 0.923076923076923\n",
      "rf_f1 : 0.8923076923076922\n",
      "dtree_f1 : 0.967741935483871\n"
     ]
    }
   ],
   "source": [
    "for key, value in dict_results.items():\n",
    "    if key.split('_')[1] == \"f1\":\n",
    "        print(\"{} : {}\".format(key,value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os modelos, em geral, possuem valores de f1 bastante semelhantes, no entanto caso fosse necessário trabalhar apenas com um e tivessemos que escolher pelo f1 a decision tree se destaca. \n",
    "\n",
    "Porém, por eles serem tão semelhantes, é interessante olhar também para a matriz de confusão para fazer a escolha de qual modelo efetivamente usar. Para todos os casos (menor falso positivo ou negativo, ou maior equilibrio entre os dois mas ainda os minimizando) no entanto a árvore de decisão segue sendo a melhor escolha, dado que possui 0 falsos negativos e 2 falsos positivos.\n",
    "\n",
    "Por exemplo, no entanto, se não a levarmos em consideração e levantarmos a hipótese de que queremos um modelo cujo ponto forte é não ter nenhum falso negativo podemos escolher o svm (entendendo que todo erro que tivermos provavelmente seria um falso positivo). \n",
    "\n",
    "Para esse problema em questão seria mais interessante minimizarmos os falsos positivos, pois são pessoas cujo crédito não deveria ser aprovado mas é, portanto fazendo com que provavelmente se perca mais dinheiro com esses falsos positivos do que com falsos negativos, assim, nesse cenário (supondo que o modelo de árvore de decisão não exista) o melhor modelo seria o de knn.\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "xYnErnh7UHuB",
    "XfkIPLm02GwX",
    "hqddvw572z35",
    "i5b-ZU6QoA2b"
   ],
   "name": "Credit Approval Data Set",
   "provenance": []
  },
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}