Replicando_Projeto_ML_PrograMaria_IA.ipynb

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  "metadata": {
    "colab": {
      "name": "Replicando_Projeto_ML-PrograMaria_IA.ipynb",
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      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP1pi/HWzs9DOPo/5B/Xv9b",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Irisrangel/Sprint_PrograMaria_IA/blob/master/Replicando_Projeto_ML_PrograMaria_IA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "afg0sQu4Hb0K",
        "colab_type": "text"
      },
      "source": [
        "Primeiro, vamos realizar os imports necessários para nosso projeto."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rs9UUrHaF36X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from plotnine import *\n",
        "import numpy as np\n",
        "\n",
        "# Importando o pacote para salvar o modelo\n",
        "import pickle"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MnPwcz9LLt7h",
        "colab_type": "text"
      },
      "source": [
        "Agora vamos ler e visualizar os dados:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OUEoC1_6Hmua",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Carregando os dados no dataframe\n",
        "df = pd.read_csv('movie_metadata.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_B0Sz5LSOnCK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "bdc7e206-f74e-4a84-c278-e3e519f93476"
      },
      "source": [
        "# Visualizando o dataframe criado; utilizando o método head que exibe as primeiras 5 linhas por padrão\n",
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>color</th>\n",
              "      <th>director_name</th>\n",
              "      <th>num_critic_for_reviews</th>\n",
              "      <th>duration</th>\n",
              "      <th>director_facebook_likes</th>\n",
              "      <th>actor_3_facebook_likes</th>\n",
              "      <th>actor_2_name</th>\n",
              "      <th>actor_1_facebook_likes</th>\n",
              "      <th>gross</th>\n",
              "      <th>genres</th>\n",
              "      <th>actor_1_name</th>\n",
              "      <th>movie_title</th>\n",
              "      <th>num_voted_users</th>\n",
              "      <th>cast_total_facebook_likes</th>\n",
              "      <th>actor_3_name</th>\n",
              "      <th>facenumber_in_poster</th>\n",
              "      <th>plot_keywords</th>\n",
              "      <th>movie_imdb_link</th>\n",
              "      <th>num_user_for_reviews</th>\n",
              "      <th>language</th>\n",
              "      <th>country</th>\n",
              "      <th>content_rating</th>\n",
              "      <th>budget</th>\n",
              "      <th>title_year</th>\n",
              "      <th>actor_2_facebook_likes</th>\n",
              "      <th>imdb_score</th>\n",
              "      <th>aspect_ratio</th>\n",
              "      <th>movie_facebook_likes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Color</td>\n",
              "      <td>James Cameron</td>\n",
              "      <td>723.0</td>\n",
              "      <td>178.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>855.0</td>\n",
              "      <td>Joel David Moore</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>760505847.0</td>\n",
              "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
              "      <td>CCH Pounder</td>\n",
              "      <td>Avatar</td>\n",
              "      <td>886204</td>\n",
              "      <td>4834</td>\n",
              "      <td>Wes Studi</td>\n",
              "      <td>0.0</td>\n",
              "      <td>avatar|future|marine|native|paraplegic</td>\n",
              "      <td>http://www.imdb.com/title/tt0499549/?ref_=fn_t...</td>\n",
              "      <td>3054.0</td>\n",
              "      <td>English</td>\n",
              "      <td>USA</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>237000000.0</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>936.0</td>\n",
              "      <td>7.9</td>\n",
              "      <td>1.78</td>\n",
              "      <td>33000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Color</td>\n",
              "      <td>Gore Verbinski</td>\n",
              "      <td>302.0</td>\n",
              "      <td>169.0</td>\n",
              "      <td>563.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>Orlando Bloom</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>309404152.0</td>\n",
              "      <td>Action|Adventure|Fantasy</td>\n",
              "      <td>Johnny Depp</td>\n",
              "      <td>Pirates of the Caribbean: At World's End</td>\n",
              "      <td>471220</td>\n",
              "      <td>48350</td>\n",
              "      <td>Jack Davenport</td>\n",
              "      <td>0.0</td>\n",
              "      <td>goddess|marriage ceremony|marriage proposal|pi...</td>\n",
              "      <td>http://www.imdb.com/title/tt0449088/?ref_=fn_t...</td>\n",
              "      <td>1238.0</td>\n",
              "      <td>English</td>\n",
              "      <td>USA</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>300000000.0</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>7.1</td>\n",
              "      <td>2.35</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Color</td>\n",
              "      <td>Sam Mendes</td>\n",
              "      <td>602.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>161.0</td>\n",
              "      <td>Rory Kinnear</td>\n",
              "      <td>11000.0</td>\n",
              "      <td>200074175.0</td>\n",
              "      <td>Action|Adventure|Thriller</td>\n",
              "      <td>Christoph Waltz</td>\n",
              "      <td>Spectre</td>\n",
              "      <td>275868</td>\n",
              "      <td>11700</td>\n",
              "      <td>Stephanie Sigman</td>\n",
              "      <td>1.0</td>\n",
              "      <td>bomb|espionage|sequel|spy|terrorist</td>\n",
              "      <td>http://www.imdb.com/title/tt2379713/?ref_=fn_t...</td>\n",
              "      <td>994.0</td>\n",
              "      <td>English</td>\n",
              "      <td>UK</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>245000000.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>393.0</td>\n",
              "      <td>6.8</td>\n",
              "      <td>2.35</td>\n",
              "      <td>85000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Color</td>\n",
              "      <td>Christopher Nolan</td>\n",
              "      <td>813.0</td>\n",
              "      <td>164.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>23000.0</td>\n",
              "      <td>Christian Bale</td>\n",
              "      <td>27000.0</td>\n",
              "      <td>448130642.0</td>\n",
              "      <td>Action|Thriller</td>\n",
              "      <td>Tom Hardy</td>\n",
              "      <td>The Dark Knight Rises</td>\n",
              "      <td>1144337</td>\n",
              "      <td>106759</td>\n",
              "      <td>Joseph Gordon-Levitt</td>\n",
              "      <td>0.0</td>\n",
              "      <td>deception|imprisonment|lawlessness|police offi...</td>\n",
              "      <td>http://www.imdb.com/title/tt1345836/?ref_=fn_t...</td>\n",
              "      <td>2701.0</td>\n",
              "      <td>English</td>\n",
              "      <td>USA</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>250000000.0</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>23000.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>2.35</td>\n",
              "      <td>164000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Doug Walker</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>131.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Rob Walker</td>\n",
              "      <td>131.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>Doug Walker</td>\n",
              "      <td>Star Wars: Episode VII - The Force Awakens    ...</td>\n",
              "      <td>8</td>\n",
              "      <td>143</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>http://www.imdb.com/title/tt5289954/?ref_=fn_t...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.0</td>\n",
              "      <td>7.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   color      director_name  ...  aspect_ratio  movie_facebook_likes\n",
              "0  Color      James Cameron  ...          1.78                 33000\n",
              "1  Color     Gore Verbinski  ...          2.35                     0\n",
              "2  Color         Sam Mendes  ...          2.35                 85000\n",
              "3  Color  Christopher Nolan  ...          2.35                164000\n",
              "4    NaN        Doug Walker  ...           NaN                     0\n",
              "\n",
              "[5 rows x 28 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJnnwSFPPck-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ab9f8200-29bd-4716-e78b-3ed9ae85dfa4"
      },
      "source": [
        "# O metodo shape exibe o tamanho do nosso dataframe, todas de linhas e colunas\n",
        "df.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5043, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z9lvSPCLP1RA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "f746eb3f-8d52-4d62-d465-d87ab1e635de"
      },
      "source": [
        "# Metodo dtypes exibe o tipo de dado de cada coluna\n",
        "df.dtypes"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "color                         object\n",
              "director_name                 object\n",
              "num_critic_for_reviews       float64\n",
              "duration                     float64\n",
              "director_facebook_likes      float64\n",
              "actor_3_facebook_likes       float64\n",
              "actor_2_name                  object\n",
              "actor_1_facebook_likes       float64\n",
              "gross                        float64\n",
              "genres                        object\n",
              "actor_1_name                  object\n",
              "movie_title                   object\n",
              "num_voted_users                int64\n",
              "cast_total_facebook_likes      int64\n",
              "actor_3_name                  object\n",
              "facenumber_in_poster         float64\n",
              "plot_keywords                 object\n",
              "movie_imdb_link               object\n",
              "num_user_for_reviews         float64\n",
              "language                      object\n",
              "country                       object\n",
              "content_rating                object\n",
              "budget                       float64\n",
              "title_year                   float64\n",
              "actor_2_facebook_likes       float64\n",
              "imdb_score                   float64\n",
              "aspect_ratio                 float64\n",
              "movie_facebook_likes           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ChZ5uWK8P4t8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "4a382e63-ec50-40af-e98e-674788a74ac8"
      },
      "source": [
        "# colocando o nome das colunas em uma lista\n",
        "list(df.columns)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['color',\n",
              " 'director_name',\n",
              " 'num_critic_for_reviews',\n",
              " 'duration',\n",
              " 'director_facebook_likes',\n",
              " 'actor_3_facebook_likes',\n",
              " 'actor_2_name',\n",
              " 'actor_1_facebook_likes',\n",
              " 'gross',\n",
              " 'genres',\n",
              " 'actor_1_name',\n",
              " 'movie_title',\n",
              " 'num_voted_users',\n",
              " 'cast_total_facebook_likes',\n",
              " 'actor_3_name',\n",
              " 'facenumber_in_poster',\n",
              " 'plot_keywords',\n",
              " 'movie_imdb_link',\n",
              " 'num_user_for_reviews',\n",
              " 'language',\n",
              " 'country',\n",
              " 'content_rating',\n",
              " 'budget',\n",
              " 'title_year',\n",
              " 'actor_2_facebook_likes',\n",
              " 'imdb_score',\n",
              " 'aspect_ratio',\n",
              " 'movie_facebook_likes']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lsLIIFPCQx4M",
        "colab_type": "text"
      },
      "source": [
        "A partir desse ponto, vamos fazer a Análise Exploratória:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "55tNUhjHQXdS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "6996dfef-b967-4eb0-8c37-9eb024c97ccd"
      },
      "source": [
        "# olhando a primeira coluna que se refere aos filmes que sao coloridos ou P/B\n",
        "df['color'].value_counts()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Color               4815\n",
              " Black and White     209\n",
              "Name: color, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WkVM8PG8RBCw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Tirando essa coluna 'color' que nao ira interferir no modelo\n",
        "df.drop('color', axis=1, inplace=True)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ngoY5_b_SgdK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "4f922dd2-33bc-4fbf-b68b-b9020b6e3e94"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>director_name</th>\n",
              "      <th>num_critic_for_reviews</th>\n",
              "      <th>duration</th>\n",
              "      <th>director_facebook_likes</th>\n",
              "      <th>actor_3_facebook_likes</th>\n",
              "      <th>actor_2_name</th>\n",
              "      <th>actor_1_facebook_likes</th>\n",
              "      <th>gross</th>\n",
              "      <th>genres</th>\n",
              "      <th>actor_1_name</th>\n",
              "      <th>movie_title</th>\n",
              "      <th>num_voted_users</th>\n",
              "      <th>cast_total_facebook_likes</th>\n",
              "      <th>actor_3_name</th>\n",
              "      <th>facenumber_in_poster</th>\n",
              "      <th>plot_keywords</th>\n",
              "      <th>movie_imdb_link</th>\n",
              "      <th>num_user_for_reviews</th>\n",
              "      <th>language</th>\n",
              "      <th>country</th>\n",
              "      <th>content_rating</th>\n",
              "      <th>budget</th>\n",
              "      <th>title_year</th>\n",
              "      <th>actor_2_facebook_likes</th>\n",
              "      <th>imdb_score</th>\n",
              "      <th>aspect_ratio</th>\n",
              "      <th>movie_facebook_likes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>James Cameron</td>\n",
              "      <td>723.0</td>\n",
              "      <td>178.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>855.0</td>\n",
              "      <td>Joel David Moore</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>760505847.0</td>\n",
              "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
              "      <td>CCH Pounder</td>\n",
              "      <td>Avatar</td>\n",
              "      <td>886204</td>\n",
              "      <td>4834</td>\n",
              "      <td>Wes Studi</td>\n",
              "      <td>0.0</td>\n",
              "      <td>avatar|future|marine|native|paraplegic</td>\n",
              "      <td>http://www.imdb.com/title/tt0499549/?ref_=fn_t...</td>\n",
              "      <td>3054.0</td>\n",
              "      <td>English</td>\n",
              "      <td>USA</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>237000000.0</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>936.0</td>\n",
              "      <td>7.9</td>\n",
              "      <td>1.78</td>\n",
              "      <td>33000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Gore Verbinski</td>\n",
              "      <td>302.0</td>\n",
              "      <td>169.0</td>\n",
              "      <td>563.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>Orlando Bloom</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>309404152.0</td>\n",
              "      <td>Action|Adventure|Fantasy</td>\n",
              "      <td>Johnny Depp</td>\n",
              "      <td>Pirates of the Caribbean: At World's End</td>\n",
              "      <td>471220</td>\n",
              "      <td>48350</td>\n",
              "      <td>Jack Davenport</td>\n",
              "      <td>0.0</td>\n",
              "      <td>goddess|marriage ceremony|marriage proposal|pi...</td>\n",
              "      <td>http://www.imdb.com/title/tt0449088/?ref_=fn_t...</td>\n",
              "      <td>1238.0</td>\n",
              "      <td>English</td>\n",
              "      <td>USA</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>300000000.0</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>7.1</td>\n",
              "      <td>2.35</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sam Mendes</td>\n",
              "      <td>602.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>161.0</td>\n",
              "      <td>Rory Kinnear</td>\n",
              "      <td>11000.0</td>\n",
              "      <td>200074175.0</td>\n",
              "      <td>Action|Adventure|Thriller</td>\n",
              "      <td>Christoph Waltz</td>\n",
              "      <td>Spectre</td>\n",
              "      <td>275868</td>\n",
              "      <td>11700</td>\n",
              "      <td>Stephanie Sigman</td>\n",
              "      <td>1.0</td>\n",
              "      <td>bomb|espionage|sequel|spy|terrorist</td>\n",
              "      <td>http://www.imdb.com/title/tt2379713/?ref_=fn_t...</td>\n",
              "      <td>994.0</td>\n",
              "      <td>English</td>\n",
              "      <td>UK</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>245000000.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>393.0</td>\n",
              "      <td>6.8</td>\n",
              "      <td>2.35</td>\n",
              "      <td>85000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Christopher Nolan</td>\n",
              "      <td>813.0</td>\n",
              "      <td>164.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>23000.0</td>\n",
              "      <td>Christian Bale</td>\n",
              "      <td>27000.0</td>\n",
              "      <td>448130642.0</td>\n",
              "      <td>Action|Thriller</td>\n",
              "      <td>Tom Hardy</td>\n",
              "      <td>The Dark Knight Rises</td>\n",
              "      <td>1144337</td>\n",
              "      <td>106759</td>\n",
              "      <td>Joseph Gordon-Levitt</td>\n",
              "      <td>0.0</td>\n",
              "      <td>deception|imprisonment|lawlessness|police offi...</td>\n",
              "      <td>http://www.imdb.com/title/tt1345836/?ref_=fn_t...</td>\n",
              "      <td>2701.0</td>\n",
              "      <td>English</td>\n",
              "      <td>USA</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>250000000.0</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>23000.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>2.35</td>\n",
              "      <td>164000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Doug Walker</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>131.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Rob Walker</td>\n",
              "      <td>131.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>Doug Walker</td>\n",
              "      <td>Star Wars: Episode VII - The Force Awakens    ...</td>\n",
              "      <td>8</td>\n",
              "      <td>143</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>http://www.imdb.com/title/tt5289954/?ref_=fn_t...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.0</td>\n",
              "      <td>7.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       director_name  ...  movie_facebook_likes\n",
              "0      James Cameron  ...                 33000\n",
              "1     Gore Verbinski  ...                     0\n",
              "2         Sam Mendes  ...                 85000\n",
              "3  Christopher Nolan  ...                164000\n",
              "4        Doug Walker  ...                     0\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "elV7vwztSlvQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "6a71f00c-931c-49a7-ebe8-b9ac20c5720c"
      },
      "source": [
        "list(df.columns)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['director_name',\n",
              " 'num_critic_for_reviews',\n",
              " 'duration',\n",
              " 'director_facebook_likes',\n",
              " 'actor_3_facebook_likes',\n",
              " 'actor_2_name',\n",
              " 'actor_1_facebook_likes',\n",
              " 'gross',\n",
              " 'genres',\n",
              " 'actor_1_name',\n",
              " 'movie_title',\n",
              " 'num_voted_users',\n",
              " 'cast_total_facebook_likes',\n",
              " 'actor_3_name',\n",
              " 'facenumber_in_poster',\n",
              " 'plot_keywords',\n",
              " 'movie_imdb_link',\n",
              " 'num_user_for_reviews',\n",
              " 'language',\n",
              " 'country',\n",
              " 'content_rating',\n",
              " 'budget',\n",
              " 'title_year',\n",
              " 'actor_2_facebook_likes',\n",
              " 'imdb_score',\n",
              " 'aspect_ratio',\n",
              " 'movie_facebook_likes']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eQUCl__USwZH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "5e3336d7-d4a7-45f3-c376-c473ee8aac96"
      },
      "source": [
        "# Verificando se existem valores faltantes nos dados\n",
        "df.isna().any()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "director_name                 True\n",
              "num_critic_for_reviews        True\n",
              "duration                      True\n",
              "director_facebook_likes       True\n",
              "actor_3_facebook_likes        True\n",
              "actor_2_name                  True\n",
              "actor_1_facebook_likes        True\n",
              "gross                         True\n",
              "genres                       False\n",
              "actor_1_name                  True\n",
              "movie_title                  False\n",
              "num_voted_users              False\n",
              "cast_total_facebook_likes    False\n",
              "actor_3_name                  True\n",
              "facenumber_in_poster          True\n",
              "plot_keywords                 True\n",
              "movie_imdb_link              False\n",
              "num_user_for_reviews          True\n",
              "language                      True\n",
              "country                       True\n",
              "content_rating                True\n",
              "budget                        True\n",
              "title_year                    True\n",
              "actor_2_facebook_likes        True\n",
              "imdb_score                   False\n",
              "aspect_ratio                  True\n",
              "movie_facebook_likes         False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gfYDsB23S6s3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "932db2c5-f45a-4e35-d909-04f78970a475"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "director_name                104\n",
              "num_critic_for_reviews        50\n",
              "duration                      15\n",
              "director_facebook_likes      104\n",
              "actor_3_facebook_likes        23\n",
              "actor_2_name                  13\n",
              "actor_1_facebook_likes         7\n",
              "gross                        884\n",
              "genres                         0\n",
              "actor_1_name                   7\n",
              "movie_title                    0\n",
              "num_voted_users                0\n",
              "cast_total_facebook_likes      0\n",
              "actor_3_name                  23\n",
              "facenumber_in_poster          13\n",
              "plot_keywords                153\n",
              "movie_imdb_link                0\n",
              "num_user_for_reviews          21\n",
              "language                      12\n",
              "country                        5\n",
              "content_rating               303\n",
              "budget                       492\n",
              "title_year                   108\n",
              "actor_2_facebook_likes        13\n",
              "imdb_score                     0\n",
              "aspect_ratio                 329\n",
              "movie_facebook_likes           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p-bLEomOTGif",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Retirando as linhas com valores nulos menos 'gross', 'content_rating', 'budget' e 'aspect_ratio' pois nos interessam\n",
        "df.dropna(axis=0, subset=['director_name', 'num_critic_for_reviews', 'duration', \n",
        "                          'director_facebook_likes', 'actor_3_facebook_likes', \n",
        "                          'actor_2_name', 'actor_1_facebook_likes', 'actor_1_name', \n",
        "                          'actor_3_name', 'facenumber_in_poster', 'plot_keywords', \n",
        "                          'num_user_for_reviews', 'language', 'country', 'title_year', \n",
        "                          'actor_2_facebook_likes'],inplace=True)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mBQhqrpkU3NK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ff3a108b-606a-4ee5-cf6a-1e49f0ff3331"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4737, 27)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KyPct7XrU4zS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "9d23e058-cf1f-476f-d4c0-cf65ccaaa9e8"
      },
      "source": [
        "# Analisando a classificação indicativa do filme ('content_rating') -> R -> Livre\n",
        "df['content_rating'].value_counts()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "R            2082\n",
              "PG-13        1436\n",
              "PG            681\n",
              "G             109\n",
              "Not Rated     100\n",
              "Unrated        58\n",
              "Approved       55\n",
              "X              13\n",
              "Passed          9\n",
              "NC-17           7\n",
              "GP              6\n",
              "M               5\n",
              "TV-14           3\n",
              "TV-G            3\n",
              "TV-PG           1\n",
              "Name: content_rating, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bC_lG0RXWJcm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Preenchendo os valores faltantes dos outros filmes com a indicação livre\n",
        "df['content_rating'].fillna('R', inplace = True)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMbdXW0IWULQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "ae89f26f-16e9-4b2e-fc90-ff21abf31963"
      },
      "source": [
        "df['content_rating'].value_counts()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "R            2251\n",
              "PG-13        1436\n",
              "PG            681\n",
              "G             109\n",
              "Not Rated     100\n",
              "Unrated        58\n",
              "Approved       55\n",
              "X              13\n",
              "Passed          9\n",
              "NC-17           7\n",
              "GP              6\n",
              "M               5\n",
              "TV-14           3\n",
              "TV-G            3\n",
              "TV-PG           1\n",
              "Name: content_rating, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0d3vE-GGWsGR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "0393e80b-fe46-4c10-bd0e-932a96c741f4"
      },
      "source": [
        "# Analisando os valores de tamanho de tela ('aspect_ratio')\n",
        "df['aspect_ratio'].value_counts()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.35     2311\n",
              "1.85     1884\n",
              "1.37       97\n",
              "1.78       80\n",
              "1.66       63\n",
              "1.33       34\n",
              "2.20       15\n",
              "2.39       14\n",
              "16.00       8\n",
              "2.00        4\n",
              "1.75        3\n",
              "2.40        3\n",
              "2.76        3\n",
              "2.55        2\n",
              "1.50        2\n",
              "2.24        1\n",
              "1.20        1\n",
              "1.18        1\n",
              "1.44        1\n",
              "1.77        1\n",
              "Name: aspect_ratio, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OtXpjMXCW56i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Substituindo os valores faltantes dos tamanhos de tela pela mediana dos valores\n",
        "df['aspect_ratio'].fillna(df['aspect_ratio'].median(), inplace=True)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8FNJak33XBA1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "69107634-e1ae-4a48-aca2-bee15f079b3e"
      },
      "source": [
        "df['aspect_ratio'].value_counts()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.35     2520\n",
              "1.85     1884\n",
              "1.37       97\n",
              "1.78       80\n",
              "1.66       63\n",
              "1.33       34\n",
              "2.20       15\n",
              "2.39       14\n",
              "16.00       8\n",
              "2.00        4\n",
              "1.75        3\n",
              "2.40        3\n",
              "2.76        3\n",
              "2.55        2\n",
              "1.50        2\n",
              "2.24        1\n",
              "1.20        1\n",
              "1.18        1\n",
              "1.44        1\n",
              "1.77        1\n",
              "Name: aspect_ratio, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JsABjd7rXOvJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "2c352924-ee89-4888-b3fe-33b612cb4e62"
      },
      "source": [
        "# Analisando a coluna de orçamentos dos filmes ('budget'):\n",
        "df['budget'].value_counts()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20000000.0     172\n",
              "30000000.0     141\n",
              "15000000.0     140\n",
              "25000000.0     140\n",
              "40000000.0     130\n",
              "              ... \n",
              "27220000.0       1\n",
              "133000000.0      1\n",
              "4300000.0        1\n",
              "46000.0          1\n",
              "218.0            1\n",
              "Name: budget, Length: 424, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQBanf8RXIL_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Substituindo os valores faltantes dos orçamentos dos filmes pela mediana dos valores\n",
        "df['budget'].fillna(df['budget'].median(), inplace=True)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PMzt1vhwXh5r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "f8fa6275-6b63-4d72-e7e8-385e6916b20c"
      },
      "source": [
        "df['budget'].value_counts()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20000000.0     524\n",
              "30000000.0     141\n",
              "15000000.0     140\n",
              "25000000.0     140\n",
              "10000000.0     130\n",
              "              ... \n",
              "27220000.0       1\n",
              "133000000.0      1\n",
              "4300000.0        1\n",
              "46000.0          1\n",
              "218.0            1\n",
              "Name: budget, Length: 424, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o5UXC8p-Xncf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "90da3e82-6210-468f-fd98-db6e8daf308f"
      },
      "source": [
        "# Analisando os valores dos faturamentos dos filmes ('gross')\n",
        "df['gross'].value_counts()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "47000000.0     3\n",
              "177343675.0    3\n",
              "5773519.0      3\n",
              "144512310.0    3\n",
              "8000000.0      3\n",
              "              ..\n",
              "10965209.0     1\n",
              "32416109.0     1\n",
              "56443482.0     1\n",
              "160762022.0    1\n",
              "162.0          1\n",
              "Name: gross, Length: 3972, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r52koJzDXkVY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Substituindo os valores faltantes dos faturamentos dos filmes pela mediana dos valores\n",
        "df['gross'].fillna(df['gross'].median(), inplace=True)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_yfg1LUaX4GZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "0329230a-5f6e-45bd-c8d5-e987983ae18e"
      },
      "source": [
        "df['gross'].value_counts()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26005908.0     645\n",
              "218051260.0      3\n",
              "34964818.0       3\n",
              "144512310.0      3\n",
              "47000000.0       3\n",
              "              ... \n",
              "10965209.0       1\n",
              "32416109.0       1\n",
              "56443482.0       1\n",
              "160762022.0      1\n",
              "162.0            1\n",
              "Name: gross, Length: 3972, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jSJIaIC9X69S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "459182ff-44ed-4129-bade-b36cc9bb38a7"
      },
      "source": [
        "# Confirmando que todas as colunas estao preenchidas:\n",
        "df.isna().sum()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "director_name                0\n",
              "num_critic_for_reviews       0\n",
              "duration                     0\n",
              "director_facebook_likes      0\n",
              "actor_3_facebook_likes       0\n",
              "actor_2_name                 0\n",
              "actor_1_facebook_likes       0\n",
              "gross                        0\n",
              "genres                       0\n",
              "actor_1_name                 0\n",
              "movie_title                  0\n",
              "num_voted_users              0\n",
              "cast_total_facebook_likes    0\n",
              "actor_3_name                 0\n",
              "facenumber_in_poster         0\n",
              "plot_keywords                0\n",
              "movie_imdb_link              0\n",
              "num_user_for_reviews         0\n",
              "language                     0\n",
              "country                      0\n",
              "content_rating               0\n",
              "budget                       0\n",
              "title_year                   0\n",
              "actor_2_facebook_likes       0\n",
              "imdb_score                   0\n",
              "aspect_ratio                 0\n",
              "movie_facebook_likes         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWrqLVkXYGhY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0f1049fa-471b-436d-d039-aaee050079ba"
      },
      "source": [
        "# Verificando se existe dados duplicados a fim de evitar vieses:\n",
        "df.duplicated().sum()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "42"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dgkcCSMrYV5g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Removendo as duplicatas\n",
        "df.drop_duplicates(inplace=True)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p6pF6Cj1Ycs8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9e87b846-a183-4493-d301-929175d7bde8"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4695, 27)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MiC-hoXyYf9S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 728
        },
        "outputId": "5175210d-c0ab-4954-8b69-bcf1ed105728"
      },
      "source": [
        "# Analisando quais os valores da coluna 'language':\n",
        "df['language'].value_counts()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "English       4405\n",
              "French          69\n",
              "Spanish         35\n",
              "Hindi           25\n",
              "Mandarin        24\n",
              "German          18\n",
              "Japanese        16\n",
              "Russian         11\n",
              "Italian         10\n",
              "Cantonese       10\n",
              "Korean           8\n",
              "Portuguese       8\n",
              "Danish           5\n",
              "Hebrew           4\n",
              "Norwegian        4\n",
              "Swedish          4\n",
              "Dutch            4\n",
              "Persian          4\n",
              "Thai             3\n",
              "Arabic           3\n",
              "Dari             2\n",
              "None             2\n",
              "Zulu             2\n",
              "Aboriginal       2\n",
              "Indonesian       2\n",
              "Dzongkha         1\n",
              "Bosnian          1\n",
              "Filipino         1\n",
              "Kazakh           1\n",
              "Mongolian        1\n",
              "Hungarian        1\n",
              "Greek            1\n",
              "Romanian         1\n",
              "Polish           1\n",
              "Icelandic        1\n",
              "Maya             1\n",
              "Aramaic          1\n",
              "Vietnamese       1\n",
              "Telugu           1\n",
              "Czech            1\n",
              "Name: language, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENSPvxELYy_Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Retirando essa coluna pois nao eh interessante para nosso modelo\n",
        "df.drop('language', axis=1, inplace=True)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7pqP47RY8rg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "389937d2-6d14-4702-97c4-735994b3c218"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4695, 26)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWlTwJwuZDAH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5d1a76b6-dcb2-423c-d25b-47a66f956028"
      },
      "source": [
        "# Analisando os valores da coluna de onde os filmes foram produgidos ('country')\n",
        "df['country'].value_counts()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "USA               3568\n",
              "UK                 420\n",
              "France             149\n",
              "Canada             107\n",
              "Germany             96\n",
              "Australia           53\n",
              "Spain               32\n",
              "India               27\n",
              "China               24\n",
              "Japan               21\n",
              "Italy               20\n",
              "Hong Kong           16\n",
              "New Zealand         14\n",
              "South Korea         12\n",
              "Ireland             11\n",
              "Russia              11\n",
              "Mexico              11\n",
              "Denmark             11\n",
              "Brazil               8\n",
              "South Africa         8\n",
              "Norway               7\n",
              "Netherlands          5\n",
              "Sweden               5\n",
              "Thailand             4\n",
              "Iran                 4\n",
              "Argentina            4\n",
              "Czech Republic       3\n",
              "West Germany         3\n",
              "Belgium              3\n",
              "Israel               3\n",
              "Switzerland          3\n",
              "Taiwan               2\n",
              "Iceland              2\n",
              "Poland               2\n",
              "Romania              2\n",
              "Greece               2\n",
              "Hungary              2\n",
              "Official site        1\n",
              "Soviet Union         1\n",
              "Georgia              1\n",
              "Bulgaria             1\n",
              "Cambodia             1\n",
              "Bahamas              1\n",
              "Aruba                1\n",
              "Peru                 1\n",
              "Chile                1\n",
              "Finland              1\n",
              "Kyrgyzstan           1\n",
              "Slovakia             1\n",
              "Libya                1\n",
              "Turkey               1\n",
              "New Line             1\n",
              "Indonesia            1\n",
              "Nigeria              1\n",
              "Colombia             1\n",
              "Philippines          1\n",
              "Afghanistan          1\n",
              "Name: country, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U4qDVP8tZU-s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Retirando essa coluna pois nao eh interessante para nosso modelo\n",
        "df.drop('country', axis=1, inplace=True)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nogfgRgvZgWr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "89bbb266-0e69-4d93-fab9-711e2e52d3eb"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4695, 25)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XnoKw924Zl1z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Criando uma nova coluna na tabela a partir de duas ja existentes para otimizar os dados\n",
        "df['Profit'] = df['budget'].sub(df['gross'], axis=0)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rz3WKQQ3Z9BS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "bc04fed7-bde9-4b0c-dd65-8e3953e56321"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4695, 26)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sa05XvBVZ_pa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "341e5316-587c-4a40-c30d-ff3961e78e0b"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>director_name</th>\n",
              "      <th>num_critic_for_reviews</th>\n",
              "      <th>duration</th>\n",
              "      <th>director_facebook_likes</th>\n",
              "      <th>actor_3_facebook_likes</th>\n",
              "      <th>actor_2_name</th>\n",
              "      <th>actor_1_facebook_likes</th>\n",
              "      <th>gross</th>\n",
              "      <th>genres</th>\n",
              "      <th>actor_1_name</th>\n",
              "      <th>movie_title</th>\n",
              "      <th>num_voted_users</th>\n",
              "      <th>cast_total_facebook_likes</th>\n",
              "      <th>actor_3_name</th>\n",
              "      <th>facenumber_in_poster</th>\n",
              "      <th>plot_keywords</th>\n",
              "      <th>movie_imdb_link</th>\n",
              "      <th>num_user_for_reviews</th>\n",
              "      <th>content_rating</th>\n",
              "      <th>budget</th>\n",
              "      <th>title_year</th>\n",
              "      <th>actor_2_facebook_likes</th>\n",
              "      <th>imdb_score</th>\n",
              "      <th>aspect_ratio</th>\n",
              "      <th>movie_facebook_likes</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>James Cameron</td>\n",
              "      <td>723.0</td>\n",
              "      <td>178.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>855.0</td>\n",
              "      <td>Joel David Moore</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>760505847.0</td>\n",
              "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
              "      <td>CCH Pounder</td>\n",
              "      <td>Avatar</td>\n",
              "      <td>886204</td>\n",
              "      <td>4834</td>\n",
              "      <td>Wes Studi</td>\n",
              "      <td>0.0</td>\n",
              "      <td>avatar|future|marine|native|paraplegic</td>\n",
              "      <td>http://www.imdb.com/title/tt0499549/?ref_=fn_t...</td>\n",
              "      <td>3054.0</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>237000000.0</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>936.0</td>\n",
              "      <td>7.9</td>\n",
              "      <td>1.78</td>\n",
              "      <td>33000</td>\n",
              "      <td>-523505847.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Gore Verbinski</td>\n",
              "      <td>302.0</td>\n",
              "      <td>169.0</td>\n",
              "      <td>563.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>Orlando Bloom</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>309404152.0</td>\n",
              "      <td>Action|Adventure|Fantasy</td>\n",
              "      <td>Johnny Depp</td>\n",
              "      <td>Pirates of the Caribbean: At World's End</td>\n",
              "      <td>471220</td>\n",
              "      <td>48350</td>\n",
              "      <td>Jack Davenport</td>\n",
              "      <td>0.0</td>\n",
              "      <td>goddess|marriage ceremony|marriage proposal|pi...</td>\n",
              "      <td>http://www.imdb.com/title/tt0449088/?ref_=fn_t...</td>\n",
              "      <td>1238.0</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>300000000.0</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>7.1</td>\n",
              "      <td>2.35</td>\n",
              "      <td>0</td>\n",
              "      <td>-9404152.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sam Mendes</td>\n",
              "      <td>602.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>161.0</td>\n",
              "      <td>Rory Kinnear</td>\n",
              "      <td>11000.0</td>\n",
              "      <td>200074175.0</td>\n",
              "      <td>Action|Adventure|Thriller</td>\n",
              "      <td>Christoph Waltz</td>\n",
              "      <td>Spectre</td>\n",
              "      <td>275868</td>\n",
              "      <td>11700</td>\n",
              "      <td>Stephanie Sigman</td>\n",
              "      <td>1.0</td>\n",
              "      <td>bomb|espionage|sequel|spy|terrorist</td>\n",
              "      <td>http://www.imdb.com/title/tt2379713/?ref_=fn_t...</td>\n",
              "      <td>994.0</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>245000000.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>393.0</td>\n",
              "      <td>6.8</td>\n",
              "      <td>2.35</td>\n",
              "      <td>85000</td>\n",
              "      <td>44925825.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Christopher Nolan</td>\n",
              "      <td>813.0</td>\n",
              "      <td>164.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>23000.0</td>\n",
              "      <td>Christian Bale</td>\n",
              "      <td>27000.0</td>\n",
              "      <td>448130642.0</td>\n",
              "      <td>Action|Thriller</td>\n",
              "      <td>Tom Hardy</td>\n",
              "      <td>The Dark Knight Rises</td>\n",
              "      <td>1144337</td>\n",
              "      <td>106759</td>\n",
              "      <td>Joseph Gordon-Levitt</td>\n",
              "      <td>0.0</td>\n",
              "      <td>deception|imprisonment|lawlessness|police offi...</td>\n",
              "      <td>http://www.imdb.com/title/tt1345836/?ref_=fn_t...</td>\n",
              "      <td>2701.0</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>250000000.0</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>23000.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>2.35</td>\n",
              "      <td>164000</td>\n",
              "      <td>-198130642.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Andrew Stanton</td>\n",
              "      <td>462.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>475.0</td>\n",
              "      <td>530.0</td>\n",
              "      <td>Samantha Morton</td>\n",
              "      <td>640.0</td>\n",
              "      <td>73058679.0</td>\n",
              "      <td>Action|Adventure|Sci-Fi</td>\n",
              "      <td>Daryl Sabara</td>\n",
              "      <td>John Carter</td>\n",
              "      <td>212204</td>\n",
              "      <td>1873</td>\n",
              "      <td>Polly Walker</td>\n",
              "      <td>1.0</td>\n",
              "      <td>alien|american civil war|male nipple|mars|prin...</td>\n",
              "      <td>http://www.imdb.com/title/tt0401729/?ref_=fn_t...</td>\n",
              "      <td>738.0</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>263700000.0</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>632.0</td>\n",
              "      <td>6.6</td>\n",
              "      <td>2.35</td>\n",
              "      <td>24000</td>\n",
              "      <td>190641321.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       director_name  num_critic_for_reviews  ...  movie_facebook_likes       Profit\n",
              "0      James Cameron                   723.0  ...                 33000 -523505847.0\n",
              "1     Gore Verbinski                   302.0  ...                     0   -9404152.0\n",
              "2         Sam Mendes                   602.0  ...                 85000   44925825.0\n",
              "3  Christopher Nolan                   813.0  ...                164000 -198130642.0\n",
              "5     Andrew Stanton                   462.0  ...                 24000  190641321.0\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2mBoBKRiaHxR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Criando uma nova coluna a relacao em porcentagem\n",
        "df['Profit_Percentage'] = (df['Profit']/df['gross'])*100"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wds8NfqwalNn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "4ce5184d-8ba4-43b7-e4f4-a0ae0f3a31e5"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>director_name</th>\n",
              "      <th>num_critic_for_reviews</th>\n",
              "      <th>duration</th>\n",
              "      <th>director_facebook_likes</th>\n",
              "      <th>actor_3_facebook_likes</th>\n",
              "      <th>actor_2_name</th>\n",
              "      <th>actor_1_facebook_likes</th>\n",
              "      <th>gross</th>\n",
              "      <th>genres</th>\n",
              "      <th>actor_1_name</th>\n",
              "      <th>movie_title</th>\n",
              "      <th>num_voted_users</th>\n",
              "      <th>cast_total_facebook_likes</th>\n",
              "      <th>actor_3_name</th>\n",
              "      <th>facenumber_in_poster</th>\n",
              "      <th>plot_keywords</th>\n",
              "      <th>movie_imdb_link</th>\n",
              "      <th>num_user_for_reviews</th>\n",
              "      <th>content_rating</th>\n",
              "      <th>budget</th>\n",
              "      <th>title_year</th>\n",
              "      <th>actor_2_facebook_likes</th>\n",
              "      <th>imdb_score</th>\n",
              "      <th>aspect_ratio</th>\n",
              "      <th>movie_facebook_likes</th>\n",
              "      <th>Profit</th>\n",
              "      <th>Profit_Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>James Cameron</td>\n",
              "      <td>723.0</td>\n",
              "      <td>178.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>855.0</td>\n",
              "      <td>Joel David Moore</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>760505847.0</td>\n",
              "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
              "      <td>CCH Pounder</td>\n",
              "      <td>Avatar</td>\n",
              "      <td>886204</td>\n",
              "      <td>4834</td>\n",
              "      <td>Wes Studi</td>\n",
              "      <td>0.0</td>\n",
              "      <td>avatar|future|marine|native|paraplegic</td>\n",
              "      <td>http://www.imdb.com/title/tt0499549/?ref_=fn_t...</td>\n",
              "      <td>3054.0</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>237000000.0</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>936.0</td>\n",
              "      <td>7.9</td>\n",
              "      <td>1.78</td>\n",
              "      <td>33000</td>\n",
              "      <td>-523505847.0</td>\n",
              "      <td>-68.836532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Gore Verbinski</td>\n",
              "      <td>302.0</td>\n",
              "      <td>169.0</td>\n",
              "      <td>563.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>Orlando Bloom</td>\n",
              "      <td>40000.0</td>\n",
              "      <td>309404152.0</td>\n",
              "      <td>Action|Adventure|Fantasy</td>\n",
              "      <td>Johnny Depp</td>\n",
              "      <td>Pirates of the Caribbean: At World's End</td>\n",
              "      <td>471220</td>\n",
              "      <td>48350</td>\n",
              "      <td>Jack Davenport</td>\n",
              "      <td>0.0</td>\n",
              "      <td>goddess|marriage ceremony|marriage proposal|pi...</td>\n",
              "      <td>http://www.imdb.com/title/tt0449088/?ref_=fn_t...</td>\n",
              "      <td>1238.0</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>300000000.0</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>7.1</td>\n",
              "      <td>2.35</td>\n",
              "      <td>0</td>\n",
              "      <td>-9404152.0</td>\n",
              "      <td>-3.039439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sam Mendes</td>\n",
              "      <td>602.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>161.0</td>\n",
              "      <td>Rory Kinnear</td>\n",
              "      <td>11000.0</td>\n",
              "      <td>200074175.0</td>\n",
              "      <td>Action|Adventure|Thriller</td>\n",
              "      <td>Christoph Waltz</td>\n",
              "      <td>Spectre</td>\n",
              "      <td>275868</td>\n",
              "      <td>11700</td>\n",
              "      <td>Stephanie Sigman</td>\n",
              "      <td>1.0</td>\n",
              "      <td>bomb|espionage|sequel|spy|terrorist</td>\n",
              "      <td>http://www.imdb.com/title/tt2379713/?ref_=fn_t...</td>\n",
              "      <td>994.0</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>245000000.0</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>393.0</td>\n",
              "      <td>6.8</td>\n",
              "      <td>2.35</td>\n",
              "      <td>85000</td>\n",
              "      <td>44925825.0</td>\n",
              "      <td>22.454585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Christopher Nolan</td>\n",
              "      <td>813.0</td>\n",
              "      <td>164.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>23000.0</td>\n",
              "      <td>Christian Bale</td>\n",
              "      <td>27000.0</td>\n",
              "      <td>448130642.0</td>\n",
              "      <td>Action|Thriller</td>\n",
              "      <td>Tom Hardy</td>\n",
              "      <td>The Dark Knight Rises</td>\n",
              "      <td>1144337</td>\n",
              "      <td>106759</td>\n",
              "      <td>Joseph Gordon-Levitt</td>\n",
              "      <td>0.0</td>\n",
              "      <td>deception|imprisonment|lawlessness|police offi...</td>\n",
              "      <td>http://www.imdb.com/title/tt1345836/?ref_=fn_t...</td>\n",
              "      <td>2701.0</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>250000000.0</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>23000.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>2.35</td>\n",
              "      <td>164000</td>\n",
              "      <td>-198130642.0</td>\n",
              "      <td>-44.212697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Andrew Stanton</td>\n",
              "      <td>462.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>475.0</td>\n",
              "      <td>530.0</td>\n",
              "      <td>Samantha Morton</td>\n",
              "      <td>640.0</td>\n",
              "      <td>73058679.0</td>\n",
              "      <td>Action|Adventure|Sci-Fi</td>\n",
              "      <td>Daryl Sabara</td>\n",
              "      <td>John Carter</td>\n",
              "      <td>212204</td>\n",
              "      <td>1873</td>\n",
              "      <td>Polly Walker</td>\n",
              "      <td>1.0</td>\n",
              "      <td>alien|american civil war|male nipple|mars|prin...</td>\n",
              "      <td>http://www.imdb.com/title/tt0401729/?ref_=fn_t...</td>\n",
              "      <td>738.0</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>263700000.0</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>632.0</td>\n",
              "      <td>6.6</td>\n",
              "      <td>2.35</td>\n",
              "      <td>24000</td>\n",
              "      <td>190641321.0</td>\n",
              "      <td>260.942743</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       director_name  num_critic_for_reviews  ...       Profit  Profit_Percentage\n",
              "0      James Cameron                   723.0  ... -523505847.0         -68.836532\n",
              "1     Gore Verbinski                   302.0  ...   -9404152.0          -3.039439\n",
              "2         Sam Mendes                   602.0  ...   44925825.0          22.454585\n",
              "3  Christopher Nolan                   813.0  ... -198130642.0         -44.212697\n",
              "5     Andrew Stanton                   462.0  ...  190641321.0         260.942743\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "16A7FoRBaqG5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Salvando tudo o que foi feito até agora\n",
        "df.to_csv('dados_imdb_pt1.csv', index=False)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Im4W-IbZa9Os",
        "colab_type": "text"
      },
      "source": [
        "Agora vamos fazer algumas visualizações:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K33slKJHa8Nz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "outputId": "4b6a2db7-4382-484a-d683-8480f9502d4a"
      },
      "source": [
        "# Criando gráfico de correlaciona lucro e nota do IMDB  // ggplot - plotnine\n",
        "ggplot(aes(x='imdb_score', y='Profit'), data=df) +\\\n",
        "    geom_line() +\\\n",
        "    stat_smooth(colour='blue', span=1)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363265266825875)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BgYTjyF6cTHv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "outputId": "df904179-8f68-4ba9-e61c-9c78eb217fe9"
      },
      "source": [
        "# Criando gráfico que correlaciona likes no facebook do filme e nota do IMDB\n",
        "(ggplot(df)+\\\n",
        "    aes(x='imdb_score', y='movie_facebook_likes') +\\\n",
        "    geom_line() +\\\n",
        "    labs(title='Nota no IMDB vs likes no facebook do filme', x='Nota no IMDB', y='Likes no facebook'))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363265269502653)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-PyTgzzcmr5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "245ca999-6c76-4b00-86f7-8d35df8f24f9"
      },
      "source": [
        "# Criando gráfico dos 20 filmes com melhor nota com relação aos atores principais\n",
        "plt.figure(figsize=(10,8))\n",
        "\n",
        "df= df.sort_values(by ='imdb_score' , ascending=False)\n",
        "df2=df.head(20)\n",
        "ax=sns.pointplot(df2['actor_1_name'], df2['imdb_score'], hue=df2['movie_title'])\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ydfR6PR4eHmg0Gp544gkmTpyIp6dnuX3a2dkxadIknJ2d8ff3L1W3U6dOqKpqfrGud+/e2NnZ8dBDDwEwd+5c84tjvXr1QqvVljuOVqvF1dUVBwcHRo8ejbe3d6Xvu3fv3oSGhvL0009z4cKFCusuWrSIt99+m6eeeorRo0fzxBNP4OLiwogRI8jOzsbNzY3AwEC0Wi2DBg0qdb+Veb4Vady4MXFxcTg7O/Pdd9+xaNEioPjlvSlTpphfvivRrl07li1bRt++fdFqtbi7u/Pss89WaUxx/1Fu03kYVsvDw0MteRtVCCHE3ZGamkr37t1vWa9kV4ryDleSfeRFiSZNmphXqIUoYelnjaIoelVVPSzVlxxjIYQQVqvkcKXa3sdYCCEskcBYCCGEVZPDlURlyGqxqA3yq7YQQgghhBBIYCyEEEIIIQQggbEQQgghhBCABMZCCCGEEEIAEhgLIYR4AF28eBGdTodOp6Nt27a0b98enU6HnZ0djo6ONep7586deHl54eDggE6nIzAwkJMnT1a6vdFoxNnZ2fz5hRdeQKPRsHLlynLbREVFkZKSYv7s5+eHbE0qRNXJrhRCCCGsmmoqwpR8kKKkONTsLJSmdti6eGHj5Fnt7dpatGhhPn44ODiYJk2aMGfOHIxGI0OGDKn2XA8fPszMmTOJjo42750aHR2N0Wis8AS88vz+++/Ex8dz7NixCutFRUUxZMiQGgf1AIWFhdSpI+GBeDDJirEQQgirpZqKKNz6OYW7vkI9a4TsLNSzRgp3fUXh1s9QTUW1PmZRURGTJk3CycmJAQMGmE9Ly8jIYODAgbi7u+Pj40NaWlqZtsuXL2fBggWlDhQICAgwn15nMBjo2bMnGo2GoUOHcunSJaD4xDytVotWq+X99983tx0wYABnzpxBp9Oxf/9+1qxZg6enJ1qtluHDh5Obm0tsbCzR0dHMnTsXnU5HRkYGAJs2bcLLy4uuXbuyf/9+873NnTsXT09PNBoNH330EQAxMTH4+PgQEBBQK8G1EPcqCYyFEEJYLVPyQUzpSZbL0pMwJetrfcz09HSmT59OcnIydnZ2bN68GYDJkycTFhaGXq8nNDSUadOmlWmbnJyMm5tbuX2PHTuW5cuXk5iYiIuLC4sXLwZg/PjxhIWFkZCQUKp+dHQ0nTt3xmAw4OPjw7Bhw4iPjychIYHu3buzdu1aevXqRUBAACtWrMBgMNC5c2egeOU3Li6OVatWmcdZu3YtzZs3Jz4+nvj4eNasWcPx48cBOHToEO+88w5Hjx6t+UMU4h4lfysRQghhtYqS4iouP/wLti5etTqmvb09Op0OAHd3d4xGIzk5OcTGxjJy5EhzvWvXrlXYz8WLF+nXrx+5ublMnjyZSZMmkZWVRZ8+fQAYN24cI0eOJCsri6ysLPOq8ksvvcSOHTss9nn48GFef/11srKyyMnJwd/fv9zxhw0bVuoeAHbv3k1iYiKRkZEAXL58mfT0dOrVq4eXlxf29vaVeEJC3L8kMBZCCGG11OysisuvVFxeHfXr1zd/bWtrS15eHiaTCTs7O3NecnmcnJw4dOgQWq3WnMccGhpaa6eyBQUFERUVhVarJTw8nJiYmFveh62tLYWFhQCoqkpYWFiZgDomJobGjRvXyhyFuJdJKoUQQgirpTS1q7i8WcXltaVZs2bY29uzadMmoDjAvDntAWDevHmEhISQmppqvpabmwtA8+bNeeihh8z5vp9//jl9+vTBzs4OOzs7Dhw4AEBERES588jOzqZdu3YUFBSUqte0aVOys7NveR/+/v58+OGHFBQUAHD06FGuXr16y3ZCPCgkMBZCCGG1bpUmYevc4w7NpDhgXbt2LVqtFicnJ7Zs2VKmjouLC++88w5jx46lW7dueHt7k5qayujRowFYt24dc+fORaPRYDAYWLRoEQCffvop06dPR6fToapquXN488036dGjB97e3jg4OJivjxo1ihUrVuDq6mp++c6SiRMn4ujoiJubG87Ozrzyyivm1WQhBCgV/Qd4P/Lw8FBlb0chhLi7UlNTS+3cUB7VZKJw62cWX8Cz6eJCnWfGVnvLNiHE/c/SzxpFUfSqqnpYqi85xkIIIayWYmNDnWdewpSsp+jwL6hXslCa2WHr3AMbJw8JioUQtUoCYyGEEFZNsbHF1sWr1nefEEKIm8mv2kIIIYQQQiCBsRBCCCGEEIAExkIIIYQQQgASGAshhBBCCAFIYCyEEOIBdPHiRXQ6HTqdjrZt29K+fXt0Oh12dnY4OjrWqO+dO3fi5eWFg4MDOp2OwMBATp48Wen2RqMRZ2dn8+cXXngBjUbDypUry20TFRVFSkqK+bOfnx+3a2tSPz8/unXrhlarxdvbmyNHjlS6bVZWFh988EG55U2aNAHKPgMh7hQJjIUQQlg11VTItdStXPl6ElmfPcuVrydxLXUrqqmo2n2WHNdsMBiYMmUKs2fPNn+2qcEWcIcPH2bmzJmsW7eOtLQ0DAYDY8aMwWg0Vqu/33//nfj4eBITE5k9e3a59W4OjGuiMgd+REREkJCQwLhx45g7d26l+71VYCzE3SaBsRBCCKulmgq5uvt1cveFUPR7EmpOJkW/J5G7L4Sruxeimmr/1LaioiImTZqEk5MTAwYMIC8vD4CMjAwGDhyIu7s7Pj4+pKWllWm7fPlyFixYUOpAgYCAAHx9fQEwGAz07NkTjUbD0KFDuXTpEgB6vR6tVotWq+X99983tx0wYABnzpxBp9Oxf/9+1qxZg6enJ1qtluHDh5Obm0tsbCzR0dHMnTsXnU5nPvlu06ZNeHl50bVrV/Mx1EVFRcydOxdPT080Gg0fffQRADExMfj4+BAQEFClFXNfX1+OHTuG0WjEx8cHNzc33NzciI2Ntdjv/PnzycjIQKfTVTqgFuJOksBYCCGE1bp+ZAcFv8VYLCv4LYbrR3bW+pjp6elMnz6d5ORk7Ozs2Lx5MwCTJ08mLCwMvV5PaGgo06ZNK9M2OTkZNze3cvseO3Ysy5cvJzExERcXFxYvXgzA+PHjCQsLIyEhoVT96OhoOnfujMFgwMfHh2HDhhEfH09CQgLdu3dn7dq19OrVi4CAAFasWIHBYKBz585A8QptXFwcq1atMo+zdu1amjdvTnx8PPHx8axZs4bjx48DcOjQId555x2OHj0KgE6nu+Wz2rp1Ky4uLrRu3Zpvv/2WQ4cOsXHjRmbNmmWuc2O/y5YtM9/PihUrbtm/EHeaHPAhhBDCal1Ljb5lef3uQ2p1THt7e3NQ6O7ujtFoJCcnh9jYWEaOHPm/sa9dq7Cfixcv0q9fP3Jzc5k8eTKTJk0iKyuLPn36ADBu3DhGjhxJVlYWWVlZ5lXll156iR07dljs8/Dhw7z++utkZWWRk5ODv79/ueMPGzas1D0A7N69m8TERCIjIwG4fPky6enp1KtXDy8vL+zt7c3tDQZDuX2PGTOGhg0b0qlTJ8LCwigoKGDGjBkYDAZsbW3NwTVQpl8hrJkExkIIIayWKeePW5Rn1vqY9evXN39ta2tLXl4eJpMJOzu7CoNFACcnJw4dOoRWqzXnMYeGhpKTk1MrcwsKCiIqKgqtVkt4eDgxMTG3vA9bW1tz3rCqqoSFhZUJqGNiYmjcuHGl5xEREYGHh4f5c3BwMG3atCEhIQGTyUSDBg3MZVXpV4i7TVIphBBCWC2bJq1vUd7mjsyjWbNm2Nvbs2nTJqA4wLw57QFg3rx5hISEkJqaar6Wm5sLQPPmzXnooYfM+b6ff/45ffr0wc7ODjs7Ow4cOAAUB53lyc7Opl27dhQUFJSq17RpU7Kzs295H/7+/nz44YcUFBQAcPToUa5evXrLdrdy+fJl2rVrh42NDZ9//jlFRZZfjKzsPIW4WyQwFkIIYbXqdw+oUXltioiIYO3atWi1WpycnNiyZUuZOi4uLrzzzjuMHTuWbt264e3tTWpqKqNHjwZg3bp1zJ07F41Gg8FgYNGiRQB8+umnTJ8+HZ1Oh6qq5c7hzTffpEePHnh7e+Pg4GC+PmrUKFasWIGrq6v55TtLJk6ciKOjI25ubjg7O/PKK6+UuwtFZXKMS0ybNo1169ah1WpJS0srd5W4RYsWeHt74+zsLC/fCaukVPQf4P3Iw8NDvV17OwohhKic1NTUUjs3lEc1FXF190KLL+DVfdyPxgNCUGxsa3+CQoj7gqWfNYqi6FVV9bBUX3KMhRBCWC3FxpbGA97i+pGdXEuNxpSTiU2TNtTvHkC9boMkKBZC1CoJjIUQQlg1xaYO9bsPqfXdJ4QQ4maSYyyEEEIIIQQSGAshhBBCCAFIYCyEEEIIIQQggbEQQgghhBCABMZCCCEeQBcvXkSn06HT6Wjbti3t27dHp9NhZ2eHo6NjtfsNDw+nVatWuLq60qVLF/z9/YmNja1yP8HBwYSGhla6Tn5+Pv379yc4OLjCNosWLWLPnj3VGjsrK4sPPvig4okLcY+TXSmEEEJYNdVUSNZvO7h0LJqCq39Qt3FrHvpLAHaPD672dm0lxzVDcSDYpEkT5syZg9FoZMiQmu1+ERgYyHvvvQfAvn37GDZsGPv27avUvs1AuQdulOf69esMHz4cd3f3WwbGS5YsqVLfNyoJjKdNm1btPoSwdrJiLIQQwmqppkJO7X+dMz+FkHs+iYLcTHLPJ3HmpxBO7V+IaqpaEFkZRUVFTJo0CScnJwYMGEBeXh4AGRkZDBw4EHd3d3x8fEhLS7tlX3379mXy5Ml8/PHHAKxZswZPT0+0Wi3Dhw83HxcdFBTElClT6NGjB/PmzSvVx5o1axg0aJB5HjcqLCwkMDCQLl26sGzZMgCMRiPdu3e3eA9BQUFERkYCsH37dhwcHHB3d2fWrFmlfiFISUnBz8+Pxx9/nHfffReA+fPnk5GRgU6nk1PrxH1LAmMhhBBWK+u3HVw5GWOx7MrJGLJ+21nrY6anpzN9+nSSk5Oxs7Nj8+bNAEyePJmwsDD0ej2hoaGVXjl1c3MzB9HDhg0jPj6ehIQEunfvztq1a831Tp8+TWxsLG+//bb52nvvvce2bduIioqiYcOGZfr+17/+Rb169Vi1alWl7qFEfn4+r7zyCjt27ECv13P+/PlS5WlpaezatYu4uDgWL15MQUEBy5Yto3PnzhgMBlasWFGpexfiXiOpFEIIIazWpWPRtyx/6C+1e/CHvb09Op0OAHd3d4xGIzk5OcTGxjJy5EhzvWvXrlWqP1VVzV8fPnyY119/naysLHJycvD39zeXjRw5Elvb/6WGfPbZZ3Ts2JGoqCjq1q1rse/evXsTGxvL0aNH6dq1a4X3cKO0tDQef/xx7O3tAXjhhRfMq9oATz/9NPXr16d+/fq0bt2azMzMSt2rEPc6CYyFEEJYrYKrf9yivPYDtvr165u/trW1JS8vD5PJhJ2dnTkvuSp+/fVXc35xUFAQUVFRaLVawsPDiYmJMddr3LhxqXYuLi4YDAZOnz5tDmBv5uvry7hx4xg0aBAHDhygXbt25d5DVdzcvqp5z0LcqySVQgghhNWq27j1Lcrb3JF5NGvWDHt7ezZt2gQUrwInJCTcst3333/Pxx9/zKRJkwDIzs6mXbt2FBQUEBERUWFbV1dXPvroIwICAjh79my59YYPH86cOXMYOHAgWVlZlbqfbt268dtvv5lXkjdu3HjLNk2bNiU7O7tS/Qtxr5LAWAghhNV66C8BNSqvTREREaxduxatVouTkxNbtmyxWG/jxo3odDq6du3K0qVL2bx5s3nF+M0336RHjx54e3vj4OBwyzF79+5NaGgoTz/9NBcuXCi33tSpUxk6dCgBAQHk5+ffst+GDRvywQcfmF8mbNq0Kc2bN6+wTYsWLfD29sbZ2VlevhP3LeXG3KcHgYeHh3rw4MG7PQ0hhHigpaamVmr7MtVUxKn9Cy2+gNfsUT86+oRUe8u2B11OTg5NmjRBVVWmT59Oly5dmD179t2elhC1ytLPGkVR9KqqeliqLznGQgghrJZiY0tHn7fI+m3nf/cxzqRu4zb/3cd4kATFNbBmzRrWrVvH9evXcXV15ZVXXrnbUxLirpMVYyGEEHdcZVeMhRCiJqq6Yiw5xkIIIYQQQiCBsYGP2IgAACAASURBVBBCCCGEEIAExkIIIYQQQgASGAshhBBCCAFIYCyEEOIBNWHCBFq3bo2zs3Op63PnzsXBwQGNRsPQoUMrPDRj1apVNGjQgMuXL9/u6ZYRHh6Ooijs2bPHfC0qKgpFUYiMjLzj8xHifiCBsRBCCKtmMhVy9ng0B7+bxIFtARz8bhJnj0ejqkU16jcoKIidO3eWud6/f38OHz5MYmIiXbt25Z///Ge5faxfvx5PT0++/vrrGs3lRlU5ftnFxYUNGzaUmo9Wq621uQjxoJHA+B6mmoooSvqF61+Gce2jN7n+ZRhFSb+gmkx3e2pCCFErTKZCDv+8kNSDIVy+mMi1vEwuX0wk9WAIST8twGSqfBB5M19fXx5++OEy1wcMGECdOsXb/Pfs2ZPTp09bbJ+RkUFOTg5vvfUW69evN18PDw9n2LBhDBw4kC5dujBv3jxz2dq1a+natSteXl5MmjSJGTNmAMVB+pQpU+jRowfz5s2jS5cunD9//r/PwMRf/vIX8+cb+fj4EBcXR0FBATk5ORw7dgydTmcu1+v19OnTB3d3d/z9/Tl37hwAfn5+vPbaa3h5edG1a1f2799f1ccnxH3JqgNjRVFeVRTlsKIoyYqi/NVC+RhFURIVRUlSFCVWUZQH5tdk1VRE4dbPKdz1FepZI2RnoZ41UrjrKwq3foZqqtlKihBCWIPfT2zn/JkYi2Xnz8Tw+4kdt3X8Tz75hEGDBlks27BhA6NGjcLHx4cjR46QmZlpLjMYDGzcuJGkpCQ2btzIqVOnOHv2LG+++SY///wzP/74I2lpaaX6O336NLGxsbz99tu8+OKLREREALBnzx60Wi2tWrUqMwdFUXjqqafYtWsXW7ZsISDgf0dkFxQUMHPmTCIjI9Hr9UyYMIGFCxeaywsLC4mLi2PVqlUsXry4Rs9JiPuF1QbGiqI4A5MAL0ALDFEU5S83VTsO9FFV1QV4E/j4zs7y7jElH8SUnmS5LD0JU7L+Ds9ICCFq39njW29RHn3bxg4JCaFOnTqMGTPGYvn69esZNWoUNjY2DB8+nE2bNpnL+vXrR/PmzWnQoAGOjo6cOHGCuLg4+vTpw8MPP0zdunUZOXJkqf5GjhyJrW3xSX4TJkzgs88+A4qD8/Hjx5c7z1GjRrFhwwY2bNjACy+8YL5+5MgRDh8+TP/+/dHpdLz11lulVr+HDRsGgLu7O0ajsWoPR4j7lDUfCd0d+EVV1VwARVG+B4YB/yqpoKpq7A31fwY63NEZ3kVFSXEVlx/+BVsXrzs0GyGEuD3yczNrVF5d4eHhbNu2jb1796IoSpnypKQk0tPT6d+/PwDXr1/H3t7enBpRv359c11bW9tK5Q03btzY/HXHjh1p06YN3333HXFxcebVY0u8vLxISkqiUaNGdO3a1XxdVVWcnJz46aefLLYrmWNl5yfEg8BqV4yBw4CPoigtFEVpBAwGOlZQ/2XA4t/UFEWZrCjKQUVRDlrK0boXqdnlvyUNoF6puFwIIe4FDRq1qVF5dezcuZN//etfREdH06hRI4t11q9fT3BwMEajEaPRyNmzZzl79iwnTpwot19PT0++//57Ll26RGFhIZs3b65wHhMnTuTFF18stZJcnmXLlrF06dJS17p168b58+fNgXFBQQHJyckV9iPEg85qA2NVVVOB5cBuYCdgACwmziqK0pfiwPi1cvr6WFVVD1VVPSzlaN2LlKZ2FZc3q7hcCCHuBY/YP3OL8oAKyyvywgsv8MQTT3DkyBE6dOjA2rVrAZgxYwbZ2dnmFIQpU6aUabthwwaGDh1a6trQoUNL7RBxs/bt27NgwQK8vLzw9vamU6dONG/evNz6AQEB5OTkVJhGUWLQoEH07du31LV69eoRGRnJa6+9hlarRafTERsbW04PQggARVXVuz2HSlEUZSlwWlXVD266rgG+AQapqnr0Vv14eHioBw8evE2zvHOKkn6hcNdX5ZbX8Q+UVAohhNVKTU2le/fut6ynqkUk/bTA4gt4rdr74fLEUhSl4tVUa5KTk0OTJk0oLCxk6NChTJgwoUyAXeLgwYPMnj1bdowQogYs/axRFEWvqqqHpfrWnGOMoiitVVX9Q1GURynOL+55U/mjwNfAS5UJiu8nNk6e2PyWavEFPJsuLtg4Wfx+CyHEPUVRbHHuGcLvJ3Zw9ng0+bmZNGjUhkfsA2jXafA9FRQDBAcHs2fPHvLz8xkwYADPPfecxXrLli3jww8/rDC3WAhR+6x6xVhRlP1AC6AA+JuqqnsVRZkCoKrqakVR/g0MB0qSugrL+w2gxP2yYgzFW7aZkvUUHf4F9UoWSjM7bJ17YOPkgWJjtVkyQghR6RVjIYSoiftqxVhVVR8L11bf8PVEYOIdnZQVUWxssXXxkpQJIYQQQohaIMuKQgghhBBCIIGxEEIIIYQQgATGQgghhBBCABIYCyGEeEDZ2tqi0+lwdnZm5MiR5ObmVljfz8+Pqry8HRwcTPv27dHpdOh0OubPn8/q1avNRz1bYjQa+fLLL82fDx48yKxZsyo9phCiZqz65TshhBDCZCrk+OntHDuxldz8TBo1aMNfHnsG+45PY1OD7doaNmyIwWAAYMyYMaxevZq//e1vtTVtAGbPns2cOXMqXb8kMB49ejQAHh4eeHjI9ptC3CmyYiyEEMJqmUyFHNAv5GdDCBcuJZKbl8mFS4n8bAjhwMEFmEyFtTKOj48Px44dIyYmhiFDhpivz5gxg/Dw8DL1mzRpwuzZs3FycqJfv36cP3++UuMEBwcTGhoKwLFjx3jqqafQarW4ubmRkZHB/Pnz2b9/PzqdjpUrV5aaz59//slzzz2HRqOhZ8+eJCYmmvucMGECfn5+PP7447z77rs1fBpCPLgkMBZCCGG1jp/ezqlzMRbLTp2L4fjpHTUeo7CwkB07duDi4lLpNlevXsXDw4Pk5GT69OnD4sWLLdZbuXKlOZVi165dpcrGjBnD9OnTSUhIIDY2lnbt2rFs2TJ8fHwwGAzMnj27VP033ngDV1dXEhMTWbp0KWPHjjWXpaWlsWvXLuLi4li8eDEFBQVVeAJCiBISGAshhLBax05srbA842R0tfvOy8tDp9Ph4eHBo48+yssvv1zptjY2NgQGBgLw4osvcuDAAYv1Zs+ejcFgwGAw4O/vb76enZ3NmTNnzMdBN2jQgEaNGlU45oEDB3jppZcAePLJJ7l48SJXrlwB4Omnn6Z+/fq0bNmS1q1bk5mZWel7EUL8j+QYCyGEsFq5+RUHeFfzqh8A3phjXKJOnTqYTCbz5/z8/Er1pShKtedRG+rXr2/+2tbWlsLC2kkxEeJBIyvGQgghrFajBm0qLG/csOLyqnrsscdISUnh2rVrZGVlsXfvXov1TCYTkZGRAHz55Zf07t27SuM0bdqUDh06EBUVBcC1a9fIzc2ladOmZGdnW2zj4+NDREQEADExMbRs2ZJmzZpVaVwhRMUkMBZCCGG1/vLYMxWWd340oFbH69ixI88//zzOzs48//zzuLq6WqzXuHFj4uLicHZ25rvvvmPRokVVHuvzzz/n3XffRaPR0KtXL37//Xc0Gg22trZotVpWrlxZqn5wcDB6vR6NRsP8+fNZt25dte5RCFE+RVXVuz2HO8rDw0Otyj6UQgghal9qairdu3e/ZT2TWsSBgwssvoDXsZ0fvT2W1mjLtupq0qQJOTk5d3xcIUTVWPpZoyiKXlVVi/sgSo6xEEIIq2Wj2NLbPYTjp3eQcTKaq3mZNG7Yhs6PBmDfcfBdCYqFEPcvCYyFEEJYNRubOnR+9Bk6P1pxWsWdJKvFQtyfJMdYCCGEEEIIJDAWQgghhBACkMBYCCGEEEIIQAJjIYQQQgghAAmMhRBCPKBCQkJwcnJCo9Gg0+n45Zdfyq0bHh7OjBkzLJYNHjyYrKysSo9rNBpxdnYGig/qGDJkSJXmbTQa8fPzq3T9Tp06ceHChVLXoqOjWbZsWbltDAYD27dvr9K8hLgfyK4UQgghrJrJVEjiue38emYrV/IzadagDa7tn0HzyNPV3q7tp59+Ytu2bRw6dIj69etz4cIFrl+/Xq2+7mQAWVtHPQcEBBAQUP7hKAaDgYMHDzJ48OBaGU+Ie4WsGAshhLBaJlMhmxMXsjU5hNNZiVzJz+R0ViJbk0PYnLAAk6l6geK5c+do2bIl9evXB6Bly5Y88sgjAMTHx9OrVy+0Wi1eXl7mI5rPnj3LwIED6dKlC/PmzTP3VbIiazQa6d69O5MmTcLJyYkBAwaQl5cHgF6vR6vVotVqef/99y3O6erVq0yYMAEvLy9cXV3ZsmULULxaHRAQwJNPPkm/fv2wtbXl4YcfBiA5ORkvLy90Oh0ajYb09PRK3f+NK+CbNm3C2dkZrVaLr68v169fZ9GiRWzcuBGdTsfGjRur+niFuGdJYCyEEMJqJZ7bTtofMRbL0v6IIfHcjmr1O2DAAE6dOkXXrl2ZNm0a33//PQDXr18nMDCQd955h4SEBPbs2UPDhg2B4lXUjRs3kpSUxMaNGzl16lSZftPT05k+fTrJycnY2dmxefNmAMaPH09YWBgJCQnlzikkJIQnn3ySuLg49u3bx9y5c7l69SoAhw4dIjIyku+//56OHTvy9ddfA7B69WpeffVV8wpvhw4dqvwslixZwq5du0hISCA6Opp69eqxZMkSAgMDMRgMBAYGVrlPIe5VEhgLIYSwWr+e2VphueFMdLX6bdKkCXq9no8//phWrVoRGBhIeHg4R44coV27dnh6egLQrFkz6tQpzjrs168fzZs3p0GDBjg6OnLixIky/drb26PT6QBwd3fHaDSSlZVFVlYWvr6+ALz00ksW57R7926WLVuGTqfDz8+P/Px8Tp48CUD//v3Nq8Q3euKJJ1i6dCnLly/nxIkT5iC+Kry9vQkKCmLNmjUUFRVVub0Q9xMJjIUQQlitK/mZFZZfvkV5RWxtbfHz82Px4sW899575tXd8pSkXZS0tZTvW5k65VFVlc2bN2MwGDAYDJw8eZLu3bsD0LhxY4ttRo8eTXR0NA0bNmTw4MF89913lR6vxOrVq3nrrbc4deoU7u7uXLx4scp9CHG/kMBYCCGE1WrWoE2F5c1vUV6eI0eOlMrHNRgMPPbYY3Tr1o1z584RHx8PQHZ2do1feLOzs8POzo4DBw4AEBERYbGev78/YWFhqKoKwK+//nrLvn/77Tcef/xxZs2axbPPPktiYmKV55eRkUGPHj1YsmQJrVq14tSpUzRt2tScWy3Eg0QCYyGEEFbLtf0zFZbr2pe/s0JFcnJyGDduHI6Ojmg0GlJSUggODqZevXps3LiRmTNnotVq6d+/P/n5+dUa40affvop06dPR6fTmQPfm/3jH/+goKAAjUaDk5MT//jHP27Z71dffYWzszM6nY7Dhw8zduxYi/U0Gg0dOnSgQ4cO/O1vfytVNnfuXFxcXHB2dja/dNi3b19SUlLk5TvxwFHK+w/0fuXh4aEePHjwbk9DCCEeaKmpqeY0gYqY1CI2Jyyw+AKeQ2s/hmuXVnvLNiHE/c/SzxpFUfSqqnpYqi/7GAshhLBaNootwzUhJJ7bgeFMNJfzM2neoA269gFoHhksQbEQolZJYCyEEMKq2djUQdf+GXS3SKsQQoiakhxjIYQQQgghkMBYCCGEEEIIQAJjIYQQQgghAAmMhRBCCCGEACQwFkII8QC6ePEiOp0OnU5H27Ztad++PTqdDjs7OxwdHavdb3h4ODNmzKiVOQYHBxMaGlrp+ufPn6dHjx64urqyf//+UmWrVq0iNzfX/LlJkybVnldMTAzNmzdHp9Ph4ODAnDlzzGXR0dEsW7as2n1XVlJSkvn79/DDD5uP4n7qqaeIiYlhyJAh1e47ODjY/O/B0dGR9evXV1g/KyuLDz74oNrj1cQLL7yARqNh5cqVpa5HRUWRkpJi/uzn50dNtqq98d/L9u3b6dq1KydOnGD16tV89tln1e7XGsmuFEIIIaxaoVrE9sxv2Zq5g8xr52lTvxXPtBnE0236Y1vN7dpatGiBwWAAigOhJk2aMGfOHIxGY42CqupSVRVVVbGxqf561d69e3FxceHf//53mbJVq1bx4osv0qhRo5pM08zHx4dt27aRl5eHq6srQ4cOxdvbm4CAAAICqnfoSlW4uLiYv39BQUEMGTKEESNGAMWBe03Nnj2bOXPmkJ6ejru7OyNGjKBu3boW65YExtOmTavSGEVFRdjaVn+7wd9//534+HiOHTtWpiwqKoohQ4bU6Jc8S/bu3cusWbPYtWsXjz32GFOmTKnV/q2BrBgLIYSwWoVqEQtT3yIk/f+ReCWFzGvnSbySQkj6/2NB6lsUqkW1PmZRURGTJk3CycmJAQMGkJeXBxQfnTxw4EDc3d3x8fEhLS2t0n2+/fbbODs74+zszKpVqwAwGo1069aNsWPH4uzszKlTpwgJCaFr16707t2bI0eOWOzLaDTy5JNPotFo6NevHydPnsRgMDBv3jy2bNmCTqczzxng3Xff5ezZs/Tt25e+ffuary9cuBCtVkvPnj3JzMwEiledhw8fjqenJ56envz4448V3lfDhg3R6XScOXMGKL1iHhQUxKxZs+jVqxePP/44kZGRAJhMJqZNm4aDgwP9+/dn8ODB5rL58+ebTyO8cSW6qnJychgxYgQODg6MGTPGfNqgXq+nT58+uLu74+/vz7lz5yrsp0uXLjRq1IhLly4BsGLFCjw9PdFoNLzxxhvmOWdkZKDT6Zg7d26ZFesZM2YQHh4OQKdOnXjttddwc3Nj06ZNdOrUiTfeeAM3NzdcXFws/pvKz89n/PjxuLi44Orqyr59+wAYMGAAZ86cQafTlfoLQWxsLNHR0cydOxedTkdGRgYAmzZtwsvLi65du5rrFxUVMXfuXPM9ffTRR+U+ix9++IFJkyaxbds2OnfuDJT+q4afnx+vvfZamTFyc3N5/vnncXR0ZOjQofTo0aNGq9e3mwTGQgghrNb2zG+JuXjAYlnMxQPsyPy21sdMT09n+vTpJCcnY2dnx+bNmwGYPHkyYWFh6PV6QkNDK71CqNfr+fTTT/nll1/4+eefWbNmDb/++qt5rGnTppGcnMyFCxfYsGEDBoOB7du3Ex8fb7G/mTNnMm7cOBITExkzZgyzZs1Cp9OxZMkSAgMDMRgMNGzY0Fx/1qxZPPLII+zbt88cVF29epWePXuSkJCAr68va9asAeDVV19l9uzZxMfHs3nzZiZOnFjhvV26dIn09HR8fX0tlp87d44DBw6wbds25s+fD8DXX3+N0WgkJSWFzz//nJ9++gkoTm/55ptvSE5OJjExkddffx0oTs9YtGhRpZ51iV9//ZVVq1aRkpLCb7/9xo8//khBQQEzZ84kMjISvV7PhAkTWLhwYYX9HDp0iC5dutC6dWt2795Neno6cXFxGAwG9Ho9P/zwA8uWLaNz584YDAZWrFhxy7m1aNGCQ4cOMWrUKABatmzJoUOHmDp1qsXUmffffx9FUUhKSmL9+vWMGzeO/Px8oqOjzeP6+PiY6/fq1YuAgABWrFiBwWAwB7GFhYXExcWxatUqFi9eDMDatWtp3rw58fHxxMfHs2bNGo4fP15mDteuXeO5554jKioKBweHcu/N0hgffPABDz30ECkpKbz55pvo9fpbPqO7SVIphBBCWK2tmTsqLI/O3MkzbQfW6pgl+aoA7u7uGI1GcnJyiI2NZeTIkeZ6165dq1R/Bw4cYOjQoTRu3BiAYcOGsX//fgICAnjsscfo2bMnAPv372fo0KHmdIfyUhJ++uknvv76awBeeukl5s2bV+V7rFevnnlV093dnW+/Lf4FY8+ePaVyU69cuUJOTk6ZnOT9+/ej1WpJT0/nr3/9K23btrU4znPPPYeNjQ2Ojo7mVekDBw4wcuRIbGxsaNu2rXkVu3nz5jRo0ICXX36ZIUOGmOdXnfQMLy8vOnToAIBOp8NoNGJnZ8fhw4fp378/ULxa2q5dO4vtV65cyaeffsrRo0fZunUrALt372b37t24uroCxavS6enpPProo1WaW2BgYKnPw4YNA4q/DyXf1xsdOHCAmTNnAuDg4MBjjz3G0aNHadasWZXGvXEco9FovqfExETziv3ly5dJT0/H3t6+VNu6devSq1cv1q5dyzvvvFOlMQ4cOMCrr74KgLOzMxqNpkrzvtMkMBZCCGG1Mq+dv0X5H7U+Zv369c1f29rakpeXh8lkws7OzpzXWltKguU7rW7duiiKAhTfY2FhIVCc5vDzzz/ToEGDCtuX5BgfP36cnj178vzzz5t/mbjRjc+yJJ2hPHXq1CEuLo69e/cSGRnJe++9x3fffVfVWyszbsn9qaqKk5OTeYW6IiU5xtHR0bz88stkZGSgqip///vfeeWVV0rVLQkAb7wPk8lk/pyfn1+q/Obveclcb/w+3A6WxlFVlbCwMPz9/Stsa2Njw1dffUW/fv1YunQpCxYsqPQY9xpJpRBCCGG12tRvdYvy1ndkHs2aNcPe3p5NmzYBxQFFQkJCpdr6+PgQFRVFbm4uV69e5Ztvvin1p+8Svr6+REVFkZeXR3Z2tnml8ma9evViw4YNAERERFjs62ZNmzYlOzv7lvUGDBhAWFiY+fOtfhGwt7dn/vz5LF++/JZ9l/D29mbz5s2YTCYyMzPNL8vl5ORw+fJlBg8ezMqVKyv9fCurW7dunD9/3hwYFxQUkJycXGGbgIAAPDw8WLduHf7+/nzyySfk5OQAcObMGf74448yz/axxx4jJSWFa9eukZWVxd69e2s0bx8fHyIiIgA4evQoJ0+epFu3bhW2qez329/fnw8//JCCggJz/1evXrVYt1GjRvznP/8hIiKCtWvXVnr+3t7efPXVVwCkpKSQlJRU6bZ3g6wYCyGEsFrPtBlE4pWUcssD2tRuGkVFIiIimDp1Km+99RYFBQWMGjUKrVZbpl54eDhRUVHmzz///DNBQUF4eXkBMHHiRFxdXcusNLq5uREYGIhWq6V169Z4enpanEdYWBjjx49nxYoVtGrVik8//fSWc588eTIDBw405xqX591332X69OloNBoKCwvx9fVl9erVFfY9ZcoUQkNDy9xPeYYPH87evXtxdHSkY8eOuLm50bx5c7Kzs3n22WfJz89HVVXefvttoDjH+ODBgyxZsqRS/ZenXr16REZGMmvWLC5fvkxhYSF//etfcXJyqrDdokWLGD16NKmpqaSmpvLEE08AxVuYffHFF3Tu3Blvb2+cnZ0ZNGgQK1as4Pnnn8fZ2Rl7e3tz6kV1TZs2jalTp+Li4kKdOnUIDw8vtSJuyahRo5g0aRLvvvuuOU3CkokTJ2I0GnFzc0NVVVq1alXq3+7NHn74YXbu3Imvry+tWlX8S+uN8x83bhyOjo44ODjg5ORE8+bNK9X2blBu9aeN+42Hh4dqzW9DCiHEgyA1NZXu3bvfsl6RWsSC1LcsvoDn16I3S7u/Xu0t28TdU5K3fPHiRby8vPjxxx/LzVMW97aioiIKCgpo0KABGRkZPPXUUxw5coR69erdkfEt/axRFEWvqqqHpfqyYiyEEMJq2Sq2hHR/nR2Z3xKduZPMa3/Qpn5rAtoMZHAN9jEWd9eQIUPIysri+vXr/OMf/5Cg+D6Wm5tL3759KSgoQFVVPvjggzsWFFeHBMZCCCGsWh3FlmfaDqz13SfE3VMbh3CIe0PTpk2tet/im8nLd0IIIYQQQiCBsRBCCCGEEIAExkIIIYQQQgASGAshhBBCCAFIYCyEEOIBdPHiRXQ6HTqdjrZt29K+fXt0Oh12dnY4OjrWqO+dO3fi5eWFg4MDOp2OwMBATp48WeM5G41GnJ2dK6yTlZXFBx98YP4cExNjPlq5OoKDg83PxsHBgalTp5Y61a0ybj5OurZY6re27z8oKKjMPsCVuR8/P7976oUz8T8SGAshhLBqhSYTW88lMPnQZzz303tMPvQZW88lUKRWLUC7UYsWLTAYDBgMBqZMmcLs2bPNn21sqv8/jYcPH2bmzJmsW7eOtLQ0DAYDY8aMqfThFzV1c2BYG0qeTcmpZd9//32t9l+bbsf9iweLBMZCCCGsVqHJxD9SvmHpkf+QdOU0mdeukHTlNEuP/IfXk7+hsIqrl5VRVFTEpEmTcHJyYsCAAeTl5QGQkZHBwIEDcXd3x8fHh7S0tDJtly9fzoIFC0odKBAQEICvry9QfMRyz5490Wg0DB06lEuXLlV4Xa/Xo9Vq0Wq1vP/++7ec+/z588nIyECn0zF37lyg+DCNESNG4ODgwJgxYyg52Euv19OnTx/c3d3x9/fn3LlzFfZ9/fp18vPzeeihhwBYs2YNnp6eaLVahg8fTm5uLgDHjx/niSeewMXFhddff73c/p577jnc3d1xcnLi448/Nl9v0qQJCxcuRKvV0rNnTzIzMyvd7+28/5uZTCamTZuGg4MD/fv3Z/DgwWVWlz/55BP++te/mj+vWbOG2bNnV2kccWdJYCyEEMJq7chMIubCEYtlMReOsDMzqdbHTE9PZ/r06SQnJ2NnZ8fmzZuB4mOVw8LC0Ov1hIaGMm3atDJtk5OTcXNzK7fvsWPHsnz5chITE3FxcWHx4sUVXh8/fjxhYWEkJCRUau7Lli2jc+fOGAwGVqxYAcCvv/5/9u48rqpqffz4Z4OIKAomOJQaYAoyHhBwQBA1h9TMMTW9Sv7M1NS0G2XXBistvfoth7qZ3pQyKwrnITVNVFCTwYMCDpiSY+AQCggKnP37A9lX5DCIeMR83q8XLzl7rb3W2sctPqzz7LUOMn/+fJKTkzl58iTR0dHk5eUxadIkIiIiiIuLY/To0UyfPt1om59++ik6nY4mTZrQqlUrdDodAAMGDCAmJoaEhARat27NV199BcCrr77K+PHjOXz4ME2aNCl1h3VskAAAIABJREFUrMuWLSMuLo7Y2FgWLlzI5cuXAcjOzqZdu3YkJCQQFBTE0qVLK9zu/bj+0qxevZrU1FSSk5NZsWIF+/btK1Hn+eefZ8OGDeTl5QGwfPlyRo8efVf9CNOSwFgIIUS1teFC2QFheeWV4ejoqAV/bdq0ITU1laysLPbu3cvgwYPR6XS8/PLL5c4wFuUxt2rVinnz5nH16lUyMjLo1KkTAKNGjWL37t2lHs/IyCAjI0Obbf7HP/5Rqevx9/enadOmmJmZodPpSE1N5dixYyQmJtKtWzd0Oh0zZ87k7NmzRs8vSqVIT08nOzubH374AShMGwkMDMTDw4OVK1eSlJQEQHR0NMOGDSt3zAsXLtRmhc+cOUNKSgoANWvW1PKCi97/u2m3Kq9fUZRSj0VFRTF48GDMzMxo3LgxnTt3LlHX2tqaLl26sHHjRo4ePUpeXh4eHh4VHrswPdn5TgghRLWVfuNameVp5ZRXhqWlpfa9ubk5OTk5GAwGbG1t0ev1ZZ7r5uZGfHw8Xl5eWh7zvHnzyMrKqvJxVtSd15Ofn4+qqri5uRmd5SyNhYUFPXv2ZPfu3QwdOpSQkBDWrl2Ll5cXYWFhxXazMxZQ3i4yMpLt27ezb98+ateuTXBwMLm5uVo/RecXjbei7RpzL9ffoEEDLa0F4MqVK9jZ2d1V/2PGjOGjjz7CxcWFF1988e4GL0xOZoyFEEJUWw0t65VZ3qic8qpSr149HB0d+emnnwBQVdVoesMbb7zBrFmzOHLkiHasKPfWxsaG+vXrs2fPHgBWrFhBp06dSj1ua2uLra0tUVFRAKxcuVJr89y5c3Tt2rVE/3Xr1iUzM7Pc63F2dubixYtaYJiXl6fN+JZGVVWio6Np0aIFAJmZmTRp0oS8vLxiYwsICNBmlW8/frurV69Sv359ateuzdGjR9m/f3+5Y65Iu1V9/cHBwYSHh3Pz5k0AwsLCtJnhgIAAVq1ahcFgIC0trdRtrtu2bcuZM2f47rvvtBlvUX1JYCyEEKLaeraJ1z2VV6WVK1fy1Vdf4eXlhZubG+vWrStRx8PDgwULFjBy5EicnZ0JCAjgyJEjvPDCCwB8/fXXhIaG4unpiV6v59133y3z+PLly3nllVfQ6XTaQ2MAFy5coEaNkh/6NmjQgICAANzd3bWHz4ypWbMmERERvPnmm3h5eaHT6di7d6/RukU5xu7u7hQUFGi51R9++CFt27YlICAAFxcXrf6CBQv4/PPP8fDw4Ny5c0bb7NmzJ/n5+bRu3Zpp06bRrl27Usd6N+1W9fX36dOHwMBA2rRpg06nIzo6mjlz5gAwcOBAmjZtiqurKyNGjMDHxwcbGxuj/T3//PMEBARoDy6K6ku5/R/ao8DX11eVtQWFEOLBOnLkSLGVG0pToBp4O2mN0Qfwgu2cmenWH3Pl0Zvj+eyzz2jevDl9+/Z90EN5pGVlZWFtbc3ly5fx9/cnOjqaxo0bl6jXp08fpk6danSWX9xfxn7WKIoSp6qqr7H6kmMshBCi2jJXzPjQtT9b0g6z4UICaTeu0ciyHs828eKZxh6PZFAMMHHixAc9BEFhwJuRkcHNmzd55513SgTFGRkZ+Pv74+XlJUHxQ0ICYyGEENVaDTMz+jTxoo8J0yaEqIjS8oqL2Nracvz4cdMMRlSJR/NXbSGEEEIIIe4ggbEQQgghhBBU88BYUZRXFUVJVBQlSVGUKUbKXRRF2acoyg1FUV5/EGMUQgghhBB/D9U2x1hRFHfgJcAfuAlsURRlo6qqJ26rdgWYDPR7AEMUQgghhBB/I9V5xrg18JuqqtdVVc0HdgEDbq+gqmq6qqoxQN6DGKAQQoiHl7m5OTqdTvtKTU2lQ4cO5Z7n4ODApUuXShyPjIwsdS1ggJ9//hlfX19cXV3x9vbmn//8JwAzZsxg3rx5ZfYZFhYmK1EIYQLVdsYYSARmKYrSAMgBegGyALEQQjxi8g0Gtvz5OxsvpJCem03DWnXo06QlzzRpcU/LtVlZWZXY4rmswLY8kZGRWFtbGw2uExMTmThxIps2bcLFxYWCggKWLFlS6b6EEPdHtZ0xVlX1CDAH2AZsAfRAQWXaUhRlrKIosYqixF68eLEKRymEEOJ+yjcYeDdpFx8fjebw1XTSbmRz+Go6Hx+N5p3EXeQbDFXan7W1NQAGg4EJEybg4uJCt27d6NWrFxEREVq9RYsW4ePjg4eHB0ePHiU1NZXFixdru8QVbe9c5N///jfTp0/XdogzNzdn/PjxJfoPDg6maBOqS5cu4eDgoJWdOXOG4OBgWrZsyfvvv1+l1y2EKFRtA2MAVVW/UlW1jaqqQcBfQKUWA1RVdYmqqr6qqvra29tX7SCFEELcN1v+/J1dF/8wWrbr4h9s/fP3Sredk5OjpVH079+/WNnq1atJTU0lOTmZFStWsG/fvmLldnZ2xMfHM378eObNm4eDgwPjxo1j6tSp6PV6AgMDi9VPTEykTZs2lR4rwIEDB1i1ahWHDh3ip59+QnZxFaLqVevAWFGUhrf+bE5hfvF3D3ZEQgghTGnjhZR7Ki9LUSqFXq9nzZo1xcqioqIYPHgwZmZmNG7cmM6dOxcrHzCg8JGXNm3akJqaWukx3I1u3brRoEEDrKysGDBgAFFRUSbpV4hHSXXOMQZYdSvHOA94RVXVDEVRxgGoqrpYUZTGFOYd1wMMt5Z0c1VV9dqDG7IQQoiqkp6bXWZ5Wjnl94ulpSVQmBKRn59fbn03Nzfi4uLw8ip7974aNWpguJUekpubW6xMUZQyXwsh7l21njFWVTVQVVVXVVW9VFXdcevYYlVVF9/6/k9VVZuqqlpPVVXbW99LUCyEEH8TDWvVKbO8UTnllRUQEMCqVaswGAykpaWVu/UvQN26dcnMzDRaFhoaykcffaRtD2wwGFi8eHGJeg4ODsTFxQEUy2kG+OWXX7hy5Qo5OTmsXbuWgICAu7wqIUR5qnVgLIQQ4tHWp0nLeyqvrIEDB9K0aVNcXV0ZMWIEPj4+2NjYlHnOs88+y5o1a4w+fOfp6cn8+fMZNmwYrVu3xt3dnZMnT5Zo4/XXX+eLL77A29u7xJJw/v7+DBw4EE9PTwYOHIivr++9X6gQohhFVdUHPQaT8vX1VeWBBSGEeLCOHDlC69aty61XoBp4J3GX0QfwOtk/yYfune5pybayZGVlYW1tzeXLl/H39yc6OprGjRvfl76EEPeHsZ81iqLEqapq9DfL6p5jLIQQ4hFmrpjxgVsntt5axzgtN5tGt9Yx7nmP6xiXp0+fPmRkZHDz5k3eeecdCYqFeARIYCyEEKJaq2FmRu/HW9L78fuTNlGaiuQVCyH+XiTHWAghhBBCCCQwFkIIIYQQApDAWAghhBBCCEACYyGEEEIIIQAJjIUQQjyirK2tTdbXRx99VOx1hw4dTNa3EKLiJDAWQghRreUbDGw8d5rxMVEM2PML42Oi2HjuNAUP0Tr8dwbGe/fufUAjEUKURQJjIYQQ1Va+wcB7h+OYfSSBw1f/Iv1GLoev/sXsIwm8eyiWfIPhnvuIjIwkODiYQYMG4eLiwvDhwyna/ComJoYOHTrg5eWFv78/mZmZFBQUEBoaip+fH56ennz55ZdaO0FBQfTu3RtnZ2fGjRuHwWBg2rRp5OTkoNPpGD58OPC/2WpVVQkNDcXd3R0PDw/Cw8PLHZMQ4v6RdYyFEEJUW1sunGXXxT+Nlu26+Cdb/zxL78eb33M/Bw8eJCkpiccff5yAgACio6Px9/dnyJAhhIeH4+fnx7Vr17CysuKrr77CxsaGmJgYbty4QUBAAN27dwfgwIEDJCcn8+STT9KzZ09Wr17N7Nmz+eyzz9Dr9SX6Xb16NXq9noSEBC5duoSfnx9BQUGljqljx473fK1CiNLJjLEQQohqa9P502WWbzxXdnlF+fv707RpU8zMzNDpdKSmpnLs2DGaNGmCn58fAPXq1aNGjRps27aNb775Bp1OR9u2bbl8+TIpKSlaO05OTpibmzNs2DCioqLK7DcqKophw4Zhbm5Oo0aN6NSpEzExMaWOSQhxf8mMsRBCiGorLTfnnsorytLSUvve3Nyc/Pz8UuuqqsqiRYvo0aNHseORkZEoilLs2J2v79eYhBBVQ2aMhRBCVFuNalndU/m9cHZ25sKFC9oMbmZmJvn5+fTo0YMvvviCvLw8AI4fP052djZQmEpx6tQpDAYD4eHhWuqDhYWFVv92gYGBhIeHU1BQwMWLF9m9ezf+/v737ZqEEGWTGWMhhBDVVu/Hm3P46l+llvd54t7zi0tTs2ZNwsPDmTRpEjk5OVhZWbF9+3bGjBlDamoqPj4+qKqKvb09a9euBcDPz4+JEydy4sQJOnfuTP/+/QEYO3Ysnp6e+Pj4sHLlSq2P/v37s2/fPry8vFAUhX//+980btyYo0eP3rfrEkKUTnnUnnL19fVVY2NjH/QwhBDikXbkyBFat25dbr0CVeXdQ7FGH8DrZN+YDzx9Mb+HdIWqFBkZybx589i4ceODHooQ4hZjP2sURYlTVdXXWH2ZMRZCCFFtmSsK73u0YeufZ9l47jRpuTk0qmVFnyea07NJs2oTFAsh/h4kMBZCCFGt1TAzo/fjzatkWbb7KTg4mODg4Ac9DCHEPZCH74QQQgghhEACYyGEEEIIIQAJjIUQQgghhAAkMBZCCCGEEAKQwFgIIcQjatasWbi5ueHp6YlOp+O3334DYP78+Vy/fr3S7WZkZNCgQQOKlkPdt28fiqJw9uxZAK5evcpjjz2GwWC494swIiwsDHt7e3Q6nfaVnJxc5jm9evUiIyPjnvqNjIykT58+Fa7v5OTEsWPHih2bMmUKc+bMqdD5y5cv166vZs2aeHh4oNPpmDZtGjNmzGDevHl3Nf7bBQcH4+zsrLU/aNCgSrd1u3fffZft27dX+vz9+/fTtm1bdDodrVu3ZsaMGUDhe793795Sz1u/fj2zZ88GICQkhIiIiEqP4U4hISE4Ojqi0+lwcXHh/fffr1Q7d3v/3C+yKoUQQohqLd+gsvVCGhvPpZGee5OGtWrS54lG9Hy8UaWXa9u3bx8bN24kPj4eS0tLLl26xM2bN4HCwHjEiBHUrl27wu0VFBRgbm4OgK2tLU2aNOHIkSO4urqyd+9evL292bt3L88//zz79+/H398fM7Py56by8/OpUePu/6seMmQIn332WYXrb968+a77uFdDhw7lhx9+4L333gPAYDAQERFBdHR0hc5/8cUXefHFFwFwcHBg586d2NnZAWgB471YuXIlvr5Gl7qttA8++MDo8dvvn7KMGjWKH3/8ES8vLwoKCrRfLCIjI7G2tqZDhw4lzsnPz6dv37707dv33gZfhrlz5zJo0CByc3NxdXVl5MiRODo63rf+7ieZMRZCCFFt5RtUZhw6yuykEyRmZJKee4PEjExmJ53gvYSj5Bsqt0nVhQsXsLOzw9LSEgA7Ozsef/xxFi5cyPnz5+ncuTOdO3cGYPz48fj6+uLm5qYFcVAYjL355pv4+Pjw008/FWu/Q4cO2gze3r17mTp1arHXAQEBpKamEhgYiI+PDz4+Plp5ZGQkgYGB9O3bF1dXV7Kzs+nduzdeXl64u7sTHh5eqWuOjIwkKCiI3r174+zszLhx47RZawcHBy5dulRqXzt27MDb2xsPDw9Gjx7NjRs3ANiyZQsuLi74+PiwevVqra/s7GxGjx6Nv78/3t7erFu3rsR4hg0bVuxadu/ezZNPPsmTTz5Zqeu7U3JyMsHBwTg5ObFw4ULt+Lfffou/vz86nY6XX36ZgoKCCrcZEhLC+PHjadeuHU5OTkRGRjJ69Ghat25NSEiIVs/a2pqpU6fi5uZG165duXjxonZ+0WztnffPtm3baN++PT4+PgwePJisrKwS/aenp9OkSRMAzM3NcXV1JTU1lcWLF/Ppp5+i0+nYs2cPISEhjBs3jrZt2/LGG28QFhbGxIkTS7T3zjvvEBISQkFBAXPnzsXPzw9PT0/tPr/bey83NxeAOnXqAIW/CPj5+eHu7s7YsWO1T1FOnDjB008/jZeXFz4+Pvz+++/F2omJicHb27vEcVOQwFgIIUS1tfVCGrvSLxst25V+ma0X0ivVbvfu3Tlz5gytWrViwoQJ7Nq1C4DJkyfz+OOPs3PnTnbu3AkUplzExsZy6NAhdu3axaFDh7R2GjRoQHx8PEOHDi3WfkBAgBbonjx5ksGDB1O06+revXvp0KEDDRs25JdffiE+Pp7w8HAmT56snR8fH8+CBQs4fvw4W7Zs4fHHHychIYHExER69uwJFH4sv379eqPXFx4eXiyVIicnB4ADBw6waNEikpOT+f3334sFs4DRvnJzcwkJCSE8PJzDhw+Tn5/PF198QW5uLi+99BIbNmwgLi6OP//83+6Es2bNokuXLhw4cICdO3cSGhpKdnZ2sb48PDwwMzMjISEBgB9++IFhw4ZV5K+vQo4ePcrWrVs5cOAA77//Pnl5eRw5coTw8HCio6PR6/WYm5sX26L7dsOHD9fev9DQUO34X3/9xb59+/j000/p27cvU6dOJSkpicOHD6PX64HCgNLX15ekpCQ6depUanpB0f3z9NNPM3PmTLZv3058fDy+vr588sknJepPnToVZ2dn+vfvz5dffklubi4ODg6MGzeOqVOnotfrCQwMBODs2bPs3bvXaDsAoaGhXLx4keXLl7Njxw5SUlI4cOAAer2euLg4du/eXeq9Z6wtnU5H06ZNGTp0KA0bNgRg4sSJxMTEkJiYSE5OjrYr5PDhw3nllVdISEhg7969WrAPhf8+xo0bx7p162jRooXR/u4nCYyFEEJUWxvPpZVZvqmc8tJYW1sTFxfHkiVLsLe3Z8iQIYSFhRmt++OPP+Lj44O3tzdJSUnF8nWHDBli9JyiGeNTp07h4OBArVq1UFWVrKws4uLiaNu2LXl5ebz00kt4eHgwePDgYu36+/trH0V7eHjwyy+/8Oabb7Jnzx5sbGyAwtm40j4eHzJkCHq9XvuysrLS2nVycsLc3Jxhw4YRFRVV7DxjfR07dgxHR0datWoFFH6cv3v3bo4ePYqjoyMtW7ZEURRGjBihtbNt2zZmz56NTqcjODiY3NxcTp8+XWKcw4YN44cffiA/P5+1a9cyePBgo9dTGb1798bS0hI7OzsaNmxIWloaO3bsIC4uDj8/P3Q6HTt27ODkyZNGz1+5cqX2/s2dO1c7/uyzz6IoCh4eHjRq1EgL8N3c3EhNTQXAzMxMuzdGjBhR4n0uUlRn//79JCcnExAQgE6n4+uvv+aPP/4oUf/dd98lNjaW7t27891335UaqAIMHjy41PSMDz/8kKtXr7J48WIURWHbtm1s27YNb29vfHx8OHr0KCkpKaXee3eaO3cuer2eP//8kx07dmi/FO7cuZO2bdvi4eHBr7/+SlJSEpmZmZw7d47+/fsDUKtWLS1t6ciRI4wdO5YNGzbQvPmD2dBHcoyFEEJUW+m5N8ssT8u9Uem2zc3Ntd3qPDw8+Prrr4t9HA5w6tQp5s2bR0xMDPXr1yckJET7uBj+95HxnVq2bElGRgYbNmygffv2ALRp04bly5fj4OCAtbU1M2bMoFGjRiQkJGAwGKhVq5bRdlu1akV8fDybN2/m7bffpmvXrrz77ruVumbljpzsO18b6+u55567635UVWXVqlU4OzuXWW/o0KF0796dTp064enpSaNGjUrUmT59Ops2bQLQZmQroihNBgr/rvPz81FVlVGjRvHxxx9XuJ3S2jUzMyvWh5mZGfn5+UbPufN9LlL096yqKt26deP7778vt/8WLVowfvx4XnrpJezt7bl82fgnKqXdmwB+fn7ExcVx5coVHnvsMVRV5a233uLll18uUfdu7j1ra2uCg4OJiorCx8eHCRMmEBsbS7NmzZgxY0axfzvGNGnShNzcXA4ePMjjjz9eZt37RWaMhRBCVFsNa9Uss7xRLcsyy0tz7NgxUlJStNd6vV7Lba1bty6ZmZkAXLt2jTp16mBjY0NaWho///xzhfto164dCxYs0ALj9u3bM3/+fAICAoDC1SmaNGmCmZkZK1asKDXX9fz589SuXZsRI0YQGhpKfHx8pa4ZClMpTp06hcFgIDw8nI4dO5bbl7OzM6mpqZw4cQKAFStW0KlTJ1xcXEhNTdXyQG8P6nr06MGiRYu0nNKDBw8aHU+LFi2ws7Nj2rRppaZRzJo1S5u5vVddu3YlIiKC9PTCFJwrV64YnZm9V0UPEgJ89913Jd7nO7Vr147o6GjtPc7Ozub48eMl6m3atEl7T1NSUjA3N8fW1rbYPVsRPXv2ZNq0afTu3ZvMzEx69OjBsmXLtLzmc+fOkZ6eftf3Xn5+Pr/99hstWrTQgmA7OzuysrK096Nu3bo0bdqUtWvXAnDjxg1tFRhbW1s2bdrEW2+9RWRkZIWvpyrJjLEQQohqq88TjUjMKP0//N5PlJxhrIisrCwmTZpERkYGNWrU4KmnnmLJkiUAjB07lp49e2q5xt7e3ri4uNCsWTMtqK2IgIAANm/erK1s0L59e06ePKmtHDBhwgQGDhzIN998Q8+ePUud4Tt8+DChoaGYmZlhYWHBF198ARR+rO7r62s0nSI8PLzYx/f/+c9/gMKZwokTJ3LixAk6d+6sfZxdVl+1atVi+fLlDB48mPz8fPz8/Bg3bhyWlpYsWbKE3r17U7t2bQIDA7Xg7J133mHKlCl4enpiMBhwdHTU8kvvNGzYMKZNm8aAAQMq/N5WlqurKzNnzqR79+4YDAYsLCz4/PPPjT7wN3z4cC0Fxc7O7q6WWatTpw4HDhxg5syZNGzYsNyH1uzt7QkLC2PYsGHag40zZ87U0leKrFixgqlTp1K7dm1q1KjBypUrMTc359lnn2XQoEGsW7eORYsWVWiMgwcPJjMzk759+7J582ZeeOEF7Zc4a2trvv32W06cOGH03rtTaGgoM2fO5ObNm3Tt2pUBAwagKAovvfQS7u7uNG7cGD8/v2LX8fLLL/Puu+9iYWFR7OHVRo0asXHjRp555hmWLVtG27ZtK3Q9VUUp+s3jUeHr66sWPQAhhBDiwThy5AitW7cut16BqvJewlGjD+B1atiA971cKr1k26MmMjKSefPmlRqgiqpjbW1tdFUJYXrGftYoihKnqqrRtfhkxlgIIUS1Za4ozPB0YeuFdDadSyMt9waNalnS+4lG9Hy8oQTFQogqJYGxEEKIaq2GmULvJxpVOm1CFCp60FDcfzJb/PCSh++EEEIIIYRAAmMhhBBCCCEACYyFEEIIIYQAJDAWQgghhBACkMBYCCHEI+jy5cvodDp0Oh2NGzfmiSeeQKfTYWtri6ura6XbDQsLY+LEiVUyxhkzZjBv3rwK17948SJt27bF29ubPXv2FCubP3++tokCFC4nVlmRkZHY2Nig0+lwcXHh9ddf18rWr1/P7NmzK912RR0+fFj7+3vsscdwdHREp9Px9NNPExkZSZ8+fSrd9owZM7T7QafTMW3atFLrpqam4u7uDnDP/Vakjzu9++67d7W+cmlu3LjB008/jU6nK7HmclhYGOfPn9deOzg4cOnSpUr1k5qaipWVFTqdDldXV0aOHEleXh4AsbGxTJ48ufIXUUVMtiqFoihPAi1VVd2uKIoVUENV1Ypv0yKEEOKRlG9Q2Xr+KpvOZpCem0fDWhb0bmpLzydsKr1cW4MGDbSd1GbMmIG1tTWvv/46qamp9yW4KY+qqqiqiplZ5eerduzYgYeHB//9739LlM2fP58RI0ZQu3btexmmJjAwkI0bN5KTk4O3tzf9+/cnICCAvn37Gt1wpKp5eHhof38hISH06dOHQYMGAVTJjmlTp04tFvBXVx988EGVtFO0M6Gx3QXDwsJwd3evsi2aW7RogV6vp6CggG7duvHjjz8yfPhwfH19tc1wHiSTzBgrivISEAF8eetQU2CtKfoWQgjx8Mo3qMxIOMecxAskZuSQnptPYkYOcxIv8J7+HPmGqt+kqqCggJdeegk3Nze6d+9OTk4OAL///js9e/akTZs2BAYGcvTo0Qq3+cknn+Du7o67uzvz588HCmfPnJ2dGTlyJO7u7pw5c4ZZs2bRqlUrOnbsyLFjx4y2lZqaSpcuXfD09KRr166cPn0avV7PG2+8wbp169DpdNqYARYuXMj58+fp3LkznTt31o5Pnz4dLy8v2rVrR1paGlA46zxw4ED8/Pzw8/MjOjq6zOsqmv07d+4cUHzGPCQkhMmTJ9OhQwecnJy0LYENBgMTJkzAxcWFbt260atXL61s2rRpuLq64unpeU+BaVZWFoMGDcLFxYXhw4dr2yjHxcXRqVMn2rRpQ48ePbhw4UKF2isoKCA0NBQ/Pz88PT358ssvy6x/5coV+vXrh6enJ+3atePQoUNAYUCfkZGBqqo0aNCAb775BoCRI0fyyy+/kJSUhL+/PzqdDk9PT23b8tLuyZCQEO29c3Bw4I033sDDwwN/f39te+nyxpWens6IESOIiYlBp9NpW3wDREREEBsby/Dhw4vdV4sWLcLHxwcPDw/t30F2djajR4/G398fb29v1q1bV+Z7ZG5ujr+/v3bv3D7jPmPGDEaPHk1wcDBOTk4sXLhQO+/DDz/E2dmZjh07MmzYsLv6VKUiTJVK8QoQAFwDUFU1BWhoor6FEEI8pLaev8ruNOMfLu5Oy2Tb+atV3mdKSgqvvPIKSUlJ2NrasmrVKqBwq+hFixYRFxfHvHnzmDBhQoXai4uLY/ny5fz222/s37+fpUuXajN0KSkpTJgwgaSkJC5dusQPP/yAXq9n8+bNxMTEGG1v0qRJjBo1ikOHDjF8+HAmT56MTqfjgw8+YMiQIej1em0rY4DJkydr21vv3LkTKAxi2rVrR0JCAkFBQSxduhSAV199lalTpxJWRLTjAAAgAElEQVQTE8OqVasYM2ZMmdf2119/kZKSQlBQkNHyCxcuEBUVxcaNG7WUhNWrV5OamkpycjIrVqxg3759QGF6y5o1a0hKSuLQoUO8/fbbQGF6xrvvvluh97rIwYMHmT9/PsnJyZw8eZLo6Gjy8vKYNGkSERERxMXFMXr0aKZPn270/E8//VRLpdi6dStfffUVNjY2xMTEEBMTw9KlSzl16lSp/b/33nt4e3tz6NAhPvroI0aOHAkUbhMeHR1NUlISTk5OWsrLvn376NChA4sXL+bVV19Fr9cTGxtL06ZNgdLvyTvZ2Nhw+PBhJk6cyJQpUyo0roYNG/Lf//6XwMBA9Ho9LVq00OoPGjQIX19fVq5cWey+srOzIz4+nvHjx2uB6axZs+jSpQsHDhxg586dhIaGkp2dXep7lJuby2+//UbPnj2Nlh89epStW7dy4MAB3n//ffLy8rT7MiEhgZ9//pn7sZOxqVIpbqiqelO59ZGXoig1gEdrL2ohhBB3bdPZjHLLezW1rdI+i/JVAdq0aUNqaipZWVns3buXwYMHa/Vu3LhRofaioqLo378/derUAWDAgAHs2bOHvn378uSTT9KuXTsA9uzZQ//+/bV0h9JSEvbt28fq1asB+Mc//sEbb7xx19dYs2ZNbXauTZs2/PLLLwBs376d5ORkrd61a9fIysoqkZO8Z88evLy8SElJYcqUKTRu3NhoP/369cPMzAxXV1dtVjoqKorBgwdjZmZG48aNtVlsGxsbatWqxf/7f/+PPn36aOOrTHqGv7+/FlTqdDpSU1OxtbUlMTGRbt26AYWzsE2aNDF6/p2pFIMGDeLQoUPa7OzVq1dJSUmhVatWRs+PiorSgtcuXbpw+fJlrl27RmBgILt37+bJJ59k/PjxLFmyhHPnzlG/fn3q1KlD+/btmTVrFmfPnmXAgAG0bNkSMH5PGjNs2DDtz6lTp1Z4XHdrwIAB2liK7sVt27axfv16LVDOzc3l9OnTJbZj/v3339HpdJw6dYrevXvj6elptI/evXtjaWmJpaUlDRs2JC0tjejoaJ577jlq1apFrVq1ePbZZ+967OUxVWC8S1GUfwFWiqJ0AyYAG0zUtxBCiIdUem5emeVp5ZRXhqWlpfa9ubk5OTk5GAwGbG1tjeZg3ouiYNnULCwsKJqsMjc3Jz8/HyhMc9i/fz+1atUq8/yiHONTp07Rrl07nn/+eS1wu93t72VROkNpatSowYEDB9ixYwcRERF89tln/Prrr3d7aSX6Lbo+VVVxc3PTZqjvhqqqLFq0iB49ehQ7XlqAWpqgoCA+//xzTp8+zaxZs1izZg0REREEBgYC8MILL9C2bVs2bdpEr169+PLLL3FycjJ6Txqj3JZzr9zH7dKLxnP7vaOqKqtWrcLZ2bnMc4tyjC9dukRAQADr1683+ouPsb9DUzBVKsWbwEXgMPAysBl420R9CyGEeEg1rGVRZnmjcsqrSr169XB0dOSnn34CCoOAhISECp0bGBjI2rVruX79OtnZ2axZs0YLhG4XFBTE2rVrycnJITMzkw0bjM8fdejQgR9++AGAlStXGm3rTnXr1iUzs/zn3bt3786iRYu01+X9IuDo6Mi0adOYM2dOuW0XCQgIYNWqVRgMBtLS0rSH5bKysrh69Sq9evXi008/rfD7W1HOzs5cvHhRC4zz8vJISkqq0Lk9evTgiy++0FZQOH78eJlpAoGBgaxcuRIozJ21s7OjXr16NGvWjEuXLpGSkoKTkxMdO3Zk3rx5WirKyZMncXJyYvLkyTz33HNabnJFFa0oER4eTvv27Ss8rrJU9N7p0aMHixYt0n4BKkoXKo2dnR2zZ8/m448/LrftIgEBAWzYsIHc3FyysrLYuHFjhc+tqPs+Y6woijmQpKqqC7D0fvcnhBDi76N3U1sSM4zPjhWVm8rKlSsZP348M2fOJC8vj6FDh+Ll5VWiXlhYGGvX/u/58v379xMSEoK/vz8AY8aMwdvbu8Rso4+PD0OGDMHLy4uGDRvi5+dndByLFi3ixRdfZO7cudjb27N8+fJyxz527Fh69uyp5RqXZuHChbzyyit4enqSn59PUFAQixcvLrPtcePGMW/evArPng4cOJAdO3bg6upKs2bN8PHxwcbGhszMTJ577jlyc3NRVZVPPvkEKMwxjo2NvecVGGrWrElERASTJ0/m6tWr5OfnM2XKFNzc3Mo9d8yYMaSmpuLj44Oqqtjb2xf7O75T0cNjnp6e1K5dm6+//lora9u2LQUFBUBhoPrWW2/RsWNHAH788UdWrFiBhYUFjRs35l//+tddpTr89ddfeHp6Ymlpyffff39X4ypNSEgI48aNw8rKqszZ9nfeeYcpU6bg6emJwWDA0dGx3MC1X79+zJgxo8TygqXx8/Ojb9++eHp60qhRIzw8PLCxsanQuRWllPfRRpV0oijrgEmqqp6+752Vw9fXV70fydpCCCEq7siRIyVyD40pUFXe058z+gBeUKO6vK97otJLtokHpyhv+fLly/j7+xMdHV1qnrKoGAcHB2JjY7Gzs3vQQ7mviu6d69evExQUxJIlS/Dx8Sm1vrGfNYqixKmqanRtOFPlGNcHkhRFOQBonz2oqnr/FzsUQgjx0DJXFGZ4PcG2W+sYp+Xm0ejWOsY97mEdY/Fg9enTh4yMDG7evMk777wjQbGosLFjx5KcnExubi6jRo0qMyiuDFMFxu+YqB8hhBB/MzXMFHo1ta3y1SfEg1MVm3CI4u72QcCH1XfffXdf2zdJYKyq6i5FURoBRQlTB1RVTTdF30IIIYQQQlSEqXa+ex44AAwGngd+UxRlkCn6FkIIIYQQoiJMlUoxHfArmiVWFMUe2E7hNtFCCCGEEEI8cKZax9jsjtSJyybsWwghhBBCiHKZKjjdoijKVkVRQhRFCQE2AT+bqG8hhBCiBHNzc3Q6nfZlqoeXIiMj2bt3b6nlW7Zswd/fHxcXF3Q6HUOGDOH06Qe+2qkQjwRTPXwXqijKAKDjrUNLVFVdY4q+hRBCPNwKDCpbz+ax+UweF3MM2FuZ0auZBT2aWdzTcm1WVlaV2uI5Pz+fGjVqlPq6PJGRkVhbW9OhQ4cSZYmJiUyaNIn169dra6+uX7+e1NRUmjdvftdjFULcHZMExoqiOAKbVVVdfeu1laIoDqqqppqifyGEEA+nAoPK+/E57PkzXzuWnltA0l8F7E/P5z0fK8zNqm4tY71ez7hx47h+/TotWrRg2bJl1K9fn+DgYHQ6HVFRUQwbNowNGzYUex0cHMxrr71GVlYWdnZ2hIWF0aRJExYuXMjixYupUaMGrq6uzJ49m8WLF2Nubs63337LokWLim3pPGfOHP71r38V25Cgb9//LfkfHBzMvHnz8PX15dKlS/j6+pKamqrttpednU1KSgqvv/46N2/eZMWKFVhaWrJ582Yee+wxgoOD8fLyYteuXeTn57Ns2TJtRz4hhOlSKX4CDLe9Lrh1TAghhCjV1rN5xYLi2+35M59t5/Iq3XZOTo6WRtG/f38ARo4cyZw5czh06BAeHh68//77Wv2bN28SGxvLP//5z2KvJ0+ezKRJk4iIiCAuLo7Ro0czffp0AGbPns3Bgwc5dOgQixcvxsHBgXHjxjF16lT0en2xoBggKSmp0hsWJCYmsnr1amJiYpg+fTq1a9fm4MGDtG/fnm+++Uard/36dfR6Pf/5z38YPXp0pfoS4u/KVKtS1FBV9WbRC1VVbyqKUtNEfQshhHhIbT5TduC7+XQezzSr3H8nd6ZSXL16lYyMDDp16gTAqFGjGDx4sFY+ZMiQYucXvT527BiJiYl069YNgIKCApo0aQKAp6cnw4cPp1+/fvTr1++uxnf58mW6du3K9evXGTt2LK+//nqZ9Tt37kzdunWpW7cuNjY2PPvsswB4eHhw6NAhrd6wYcMACAoK4tq1a2RkZGBrK5unCAGmmzG+qCiK9lmQoijPAZdM1LcQQoiH1MUcQ5nl6eWUV6U6deoYfa2qKm5ubuj1evR6PYcPH2bbtm0AbNq0iVdeeYX4+Hj8/PzIzzc++13Ezc2N+Ph4ABo0aIBer2fs2LFkZWUBUKNGDQyGwmvOzc0tdq6lpaX2vZmZmfbazMysWL/KHXnZd74W4lFmqsB4HPAvRVFOK4pyBngTeNlEfQshhHhI2VuV/d9Uw3LK74aNjQ3169dnz549AKxYsUKbPS6Ls7MzFy9eZN++fQDk5eWRlJSEwWDgzJkzdO7cmTlz5nD16lWysrKoW7cumZmZRtt64403mDVrFkeOHNGOXb9+XfvewcGBuLg4ACIiKrcVQHh4OABRUVHY2NhgY2NTqXaE+Dsy1aoUvwPtFEWxvvU6yxT9CiGEeLj1amZB0l8FpZc3t6jS/r7++mvt4TsnJyeWL19e7jk1a9YkIiKCyZMnc/XqVfLz85kyZQqtWrVixIgRXL16FVVVmTx5Mra2tjz77LMMGjSIdevWlXj4zsPDgwULFjBy5EiuXbuGnZ0dzZs313KdX3/9dZ5//nmWLFlC7969K3WNtWrVwtvbm7y8PJYtW1apNoT4u1JUVb3/nSjKq8ByIBNYCvgA01RV3XbfO7+Dr6+vGhsba+pu//ZUQz43j/3MjSPrMWSlY2bdEMvWfanp3AvFzPyBjCfj5M/8dWI9ednpWNRpSP2n+mLr9GDGI4Qo7siRI8VWXihNgaryflyO0QfwAhvX4L02Vve0ZNuj5vZVLYR4FBj7WaMoSpyqqkb/EZjq4bvRqqouUBSlB9AA+AewAigzML4VUL8EKMBSVVXn31GuAAuAXsB1IERV1fj7MH5RBtWQT/a2t8k7GakdK8hK4/qfh8n7I5o63WeimJnqViscz5k9b3Pt9P/Gk3c9jesXD5N5LppmgaYdjxCi8swVhfd8rNh2Lo/Np/NIzzHQ0MqMXs0t6N703tYxFkKIO5kqOij6ydUL+EZV1SSlnGx/RVHcKQyK/YGbFO6et1FV1RO3VXsGaHnrqy3wxa0/hQndPPZzsaD4dnknI7l5bAuWrfuYbDwZJ38uFhTf7trpSDJObqH+U6YbjxDi3pibKTzTrGalV58Q/xMZGfmghyBEtWaqh+/iFEXZRmFgvFVRlLoUX9fYmNbAb6qqXldVNR/YBQy4o85zFAbaqqqq+wFbRVGaVPXgRdluHFl/T+VV7a8TZfdXXrkQQgghHk2mCoz/HzAN8FNV9TpQE3ixqFBRFDcj5yQCgYqiNFAUpTaFQXWzO+o8AZy57fXZW8eKURRlrKIosYqixF68ePHerkSUYMhKL6c8zUQjKZSXXfZ48rJNOx4hhBBCPBxMEhirqmpQVTVeVdWMW68vq6p66LYqK4yccwSYQ2Ee8hZAT+GOeZXpf4mqqr6qqvra29tXpglRBjPrhuWUNzLRSApZ1Cl7PBZ1TDseIYQQQjwcTDVjXB6j+caqqn6lqmobVVWDgL+A43dUOUfxWeSmt44JE7Js3feeyqta/afK7q+8ciGEEEI8mqpLYGx0zThFURre+rM5hfnF391RZT0wUinUDriqquqF+zpSUUJN515YOAUbLbNwCqam8zOmHA62Tr2o19z4eOo1D8bWybTjEUJUT3/++SdDhw6lRYsWtGnThl69enH8+HHOnz/PoEGDANDr9WzevLlK+rtx4wZPP/00Op1O22Tjdp988gkuLi54eHjg5eXFa6+9Rl5e2Vti3y4sLIyJEydWyViFeFRV9zWrVimK0gDIA15RVTVDUZRxAKqqLgY2U5h7fILC5dpeLLUlcd8oZubU6T6Tm8e23FrHOA0z60a31jF+xuTrBitm5jQLnEnGyS231jFOw6JOo1vrGJt+PEKIe2MwqBxOLSDhZAGZOSp1rRS8nMzxcDDHzKxyy7Wpqkr//v0ZNWoUP/zwAwAJCQmkpaXRqlUrbVc5vV5PbGwsvXr1uufrOHjwoNbmnRYvXsy2bdvYv38/tra23Lx5k08++YScnBwsLKp2ExMhROlMssFHuYNQlP2qqrYzRV+ywYcQQjx4Fd3gw2BQWbvvJsfOllzIyLmpGf3a16xUcPzrr78yY8YMdu/eXaIsNTWVPn36EB8fz1NPPUVOTg5PPPEEb731Fm+//TZ79+7F3t4eg8FAq1at2LdvH7c/v3LlyhVGjx7NyZMnqV27NkuWLKFx48Z06NCBixcv4ujoyKpVq2jRooV2TrNmzdi9ezeOjo5Gx/v999/z0UcfoaoqvXv3Zs6cOQAsX76cjz/+GFtbW7y8vLC0tOSzzz676/dDiL+ru93gw2SpFIqiDFAU5RNFUf5PUZT+t5eZKigWQgjxcDmcWmA0KAY4dtZAYmqlnskmMTGRNm3alFmnZs2afPDBBwwZMgS9Xs+QIUMYMWIEK1euBGD79u14eXlx50Pd7733Ht7e3hw6dIiPPvqIkSNH0rBhQ/773/8SGBiIXq8vFhRfu3aNrKysUoPi8+fP8+abb/Lrr7+i1+uJiYlh7dq1XLhwgffee4/o6GiioqJITk6u1HshhPgfkwTGiqL8BxgHHKZwGbaXFUX53BR9CyGEeHglnCw78E04VbnAuLJGjx7NN998A8CyZct48cWSGXxRUVH84x//AKBLly5cvnyZa9euVbiPrVu3otPpcHBwYO/evcTExBAcHIy9vT01atRg+PDh7N69m99++007XrNmTYYMGVI1FynEI8xUM8ZdgB6qqi5XVXU5hXnBXUzUtxBCiIdUZk7Z6X7XrlcuHdDNzY24uLi7Pq9Zs2Y0atSIX3/9lQMHDvDMM/f+MG+9evWwtrbm1KlTAPTo0QO9Xo+7uzs3b9685/aFEBVnqsD4BND8ttfNbh0TQgghSlXXquz84Xq1K/fwXZcuXbhx4wZLlizRjh06dIg9e/YU779uXTIzM4sdGzNmDCNGjGDw4MGYm5d8mDcwMFBLt4iMjMTOzo569eqVOZ633nqL8ePHk5GRARQ+HJibmwuAv78/u3bt4tKlSxQUFPD999/TqVMn2rZty65du7h8+TJ5eXn89NNPd/9GCCGKua+BsaIoGxRFWQ/UBY4oihKpKMpO4MitY0IIIUSpvJzKXkXGy7Fyq8woisKaNWvYvn07LVq0wM3NjbfeeovGjRsXq9e5c2eSk5OLLbHWt29fsrKyjKZRAMyYMYO4uDg8PT2ZNm0aX3/9dbnjGT9+PF27dqVt27Z4enoSEBCAt7c33t7eNGnShNmzZ9O5c2e8vLxo06YNzz33HE2aNGHGjBm0b9+egICACj3MKIQo231dlUJRlE5llauquuu+dV4KWZVCCCEevAe9KsW9iI2NZerUqSVml4UQ1c/drkpxX9cxfhCBrxBCiL8PMzOFfu1rkphaQMKpAq5dV6lXW8HL0Rz3e1jHuLJmz57NF198oaVKCCH+Xu5rYKwoSial7GoHoKpq2UlXQgghHnlmZgqeTjXwdHrwe1JNmzaNadOmPehhCCHuk/s9Y1wXQFGUD4ELwApAAYYDTe5n30IIIYQQQtwNU61K0VdV1f+oqpqpquo1VVW/AJ4zUd9CCCGEEEKUy1SBcbaiKMMVRTFXFMVMUZThQLaJ+hZCCCGEEKJcpgqMXwCeB9JufQ2+dUwIIYQQQohqwSSBsaqqqaqqPqeqqp2qqvaqqvZTVTXVFH0LIYQQxpw9e5bnnnuOli1b0qJFC1599dUq22kuJCQER0dHdDodOp2OhQsXAuDg4MClS5eqpA8o3Ahk5syZtGzZklatWtG5c2eSkpKqrP3bhYWFcf78ee31vV7L8ePH6dWrFy1btsTHx4fnn3+etLS0u25nxowZzJs3r9LjKOLg4FDhusHBwfj6/m+1r9jYWIKDgyvV70cffVSp88T9YZLAWFEUR0VRPlEUZbWiKOuLvkzRtxBCiIebwaDyx/F89my4wbYfctmz4QZ/HM9HNVR+HX5VVRkwYAD9+vUjJSWF48ePk5WVxfTp06ts3HPnzkWv16PX65k8eXKVtXu7zz//nL1795KQkMDx48d566236Nu3r7Zr3r0oKCgo9vrOwPhe5Obm0rt3b8aPH09KSgrx8fFMmDCBixcvVkn7ppCens7PP/98z+1IYFy9mCqVYi2QCiwC/u+2LyGEEKJUBoNK7M6b6PfkcSXdQE62ypV0A/o9ecTsvImhksHxr7/+Sq1atbTd68zNzfn0009ZtmwZ169fJywsjAEDBtCzZ09atmzJG2+8oZ27bds22rdvj4+PD4MHDyYrK6tSY/j222/x9/dHp9Px8ssvU1BQwE8//cRrr70GwIIFC3BycgLg5MmTBAQElGhjzpw5fPbZZ9SuXRuA7t2706FDB1auXMnixYsJDQ3V6oaFhTFx4sRS+wawtrbmn//8J15eXuzbt087NyIigtjYWIYPH45OpyMnJweARYsW4ePjg4eHB0ePHgUgOzub0aNH4+/vj7e3N+vWrSsx7u+++4727dvz7LPPaseCg4Nxd3cvNk6APn36EBkZCcCWLVvw8fHBy8uLrl27lmh36dKlPPPMM+Tk5PDJJ5/g7u6Ou7s78+fP1+qUdtze3h6ACxcuEBQUhE6nw93dvdSNXEJDQ5k1a1aJ4wUFBYSGhuLn54enpydffvllqe1OmzaNnJwcdDodw4cPN9qPMC1TBca5qqouVFV1p6qqu4q+TNS3EEKIh9SZEwVcSC256x3AhVQDZ04UGC0rT1JSEm3atCl2rF69ejRv3pwTJ04AoNfrCQ8P5/Dhw4SHh3PmzBkuXbrEzJkz2b59O/Hx8fj6+vLJJ58Y7SM0NFRLpTh8+HCxsiNHjhAeHk50dDR6vR5zc3NWrlxJYGCgFojt2bOHBg0acO7cOfbs2UNQUFCxNq5du0Z2drYWPBfx9fUlKSmJgQMHsmbNGu14eHg4Q4cOLbVvKAxq27ZtS0JCAh07dtTOHTRoEL6+vqxcuRK9Xo+VlRUAdnZ2xMfHM378eC2dYdasWXTp0oUDBw6wc+dOQkNDyc4u/rx9YmJiife/PBcvXuSll15i1apVJCQk8NNPPxUr/+yzz9i4cSNr164lOTmZ5cuX89tvv7F//36WLl3KwYMHiYuLM3ocICYmBigM2nv06IFerychIQGdTmd0PO3bt6dmzZrs3Lmz2PGvvvoKGxsbYmJiiImJYenSpZw6dcpou7Nnz8bKygq9Xi+bxlQTplotfYGiKO8B24AbRQdVVY03Uf9CCCEeQqePlR34nj5ewJOt7s9/ZV27dsXGxgYAV1dX/vjjDzIyMkhOTtZmb2/evEn79u2Nnj937lwGDRpktGzHjh3ExcXh5+cHQE5ODg0bNqRx48ZkZWWRmZnJmTNneOGFF9i9ezd79uxhwIABdzV+e3t7nJyc2L9/Py1btuTo0aMEBATw+eefG+0bCmfOBw4cWOE+isbUpk0bVq9eDRTOqK9fv14LlHNzczl9+nSFtgAvy/79+wkKCsLR0RGAxx57TCv75ptvaNasGWvXrsXCwoKoqCj69+9PnTp1tHHu2bMHVVWNHvf29tba8vPzY/To0eTl5dGvX79SA2OAt99+m5kzZzJnzhzt2LZt2zh06BAREREAXL16lZSUlLtqVzw4pgqMPYB/AF2Aol/91VuvhRBCCKNysstOlcjJqlwqhaurqxa4FLl27RqnT5/mqaeeIj4+HktLS63M3Nyc/Px8VFWlW7dufP/995Xqt4iqqowaNYqPP/64RFmHDh1Yvnw5zs7OBAYGsmzZMvbt28f//V/xDMR69epRp04dTp48WWzWOC4ujk6dOgEwdOhQfvzxR1xcXOjfvz+KopTZd61atTA3N6/wdRS9R0XvT9G1rVq1Cmdn51LPc3NzY9cu4x8c16hRA4Phf58SVCRf2sPDA71ez9mzZ7XAubKCgoLYvXs3mzZtIiQkhNdee42RI0cardulSxfefvtt9u/frx1TVZVFixbRo0ePEvUr2q54cEyVSjEYcFJVtZOqqp1vfUlQLIQQokxWdZSyy63LLi9N165duX79Ot988w1QmBf6z3/+k5CQEC1f15h27doRHR2tpVtkZ2dz/PjxSvUfERFBeno6AFeuXOGPP/4AIDAwkHnz5hEUFIS3tzc7d+7E0tJSm72+XWhoKJMnT9Zyfrdv305UVBQvvFC4Imr//v1Zt24d33//PUOHDi2377LUrVuXzMzMcuv16NGDRYsWoaqFv7QUpSrc7oUXXmDv3r1s2rRJO7Z7924SExNxcHBAr9djMBg4c+YMBw4cAArf+927d3Pq1Clt3EW8vb358ssv6du3L+fPnycwMJC1a9dy/fp1srOzWbNmDYGBgaUev90ff/xBo0aNeOmllxgzZgzx8WV/uP3222/z73//u9j1f/HFF+Tl5QGFq29kZ2eX2q6FhYVWVzx4ppoxTgRsgXQT9SeEEOJvoLmzOVfSjecYAzRvVfHZzdspisKaNWuYMGECH374IQaDgV69epW7QoC9vT1hYWEMGzaMGzcKMwNnzpxJq1at7qp/V1dXZs6cSffu3TEYDFhYWPD555/z5JNPEhgYyJkzZwgKCsLc3JxmzZrh4uJitJ1Jkybx119/4eHhgbm5OY0bN2bdunVaDnD9+vVp3bo1ycnJ+Pv7l9t3WUJCQhg3bhxWVlbFHsy70zvvvMOUKVPw9PTEYDDg6OjIxo0bi9WxsrJi48aNTJkyhSlTpmBhYYGnpycLFizAzc0NR0dHXF1dad26NT4+PkDhe79kyRIGDBiAwWCgYcOG/PLLL1qbHTt2ZN68efTu3ZtffvmFkJAQ7ZrHjBmjpUuUdrxIZGQkc+fOxcLCAmtra+2Xp9L06tVLe3CvqM3U1FR8fHxQVRV7e3vWrl1bartjx47F09MTHx8fyTOuBpSi3+juayeKEgl4AjEUzzHue987v4Ovr68aGxtr6m6FEEL8f/buPD6KIv//+KuSAOE+FMYm+uAAACAASURBVMIll3KHHCTcBhRFEBAEdcETvF1EXL8r6oq6eO2uKx7r8RPPxXVRVjkUBc9VFxBdBQ23gEdADgMqVyCBZKZ+f1TPOITJBCEzmZX38/HIIzNTPd2fqe7p/nR1dU2INWvWHFafU+u3fPbBgbA34DVplUC3U6tiEo6s1VhEfv3C7WuMMUuttdnhpo9Vi/EfY7QcERH5FTEJhuxTq/LdVz42rvNRWGCpXsvQol0iLU5KVFIsIhUqJomxhmYTEZEjlZBgaNkuKWqjT4iIBER1L2OMWWStPdkYswc3CkWwCLDW2jrRXL6IiIiIyOGKamJsrT3Z+187mssRERERETlasRquTUREREQkrikxFhERERFBibGIiByjNm3axPDhw2nbti1t2rRh/PjxwbGJ48nEiRPp3LkzEydOPOj1Dz/8kMWLFwefjx079pBf8/slWrVqdciPXWRkZJCamnrE8yzLq6++yurVq8udburUqeWOIyxSkZQYi4hIXLN+y08rS/h6xn6+fKaIr2fs56eVJVj/kY/Db61l5MiRnH322axfv57169dTWFjITTfdVIGRV4ynnnqK5cuXc//99x/0eunEuCLs2bOH7777DnDjv0bL4SbG11xzjX42WWJKibGIiMQt67dsnHeAze8Ws2+rn+I9ln1b/Wx+t5iN8w4ccXL8/vvvk5yczKWXXgpAYmIiDz30EP/4xz8oKChg2rRpjB8/Pjj90KFD+fDDDwF455136NWrF127duW8886joKAAgKVLl9KvXz+ysrIYOHAgW7duBeCUU07h5ptvpnv37rRr146FCxce+jmtZeLEiaSmptKlSxf+9a9/ATBs2DAKCgrIysoKvgaQl5fH1KlTeeihh8jIyAjOc8GCBfTu3Zs2bdoc1Hp8//33061bN9LS0vjjH8v+aYHf/OY3weW89NJLnH/++cGyoqIiLr30Urp06RL8qWogYl3VqlWLSZMmkZ6eTs+ePcnPz2fx4sXMnTuXiRMnkpGRwddff83TTz9Nt27dSE9P55xzzmHfvn0ATJ48mSlTpkSsx1WrVtG9e3cyMjJIS0tj/fr1ZX4+kfIoMRYRkbi1Y7WP3V+F/0no3V/52bHGd0TzXbVqFVlZWQe9VqdOHVq1asVXX31V5vt++OEH7rnnHt577z0+//xzsrOzefDBBykuLua6665j5syZLF26lMsuu4xJkyYF31dSUsKnn37Kww8/zJ133nnIfGfPnk1ubi7Lli3jvffeY+LEiWzdupW5c+dSvXp1cnNzGTVqVHD6Vq1acc0113DDDTeQm5sb7AKxdetWFi1axBtvvMEtt9wCuER+/fr1fPrpp+Tm5rJ06VIWLFgQ9vOdc845zJ49G4DXX3+ds846K1j2+OOPY4xhxYoVvPTSS4wZM4aioqKI9bx371569uzJsmXL6Nu3L08//TS9e/dm2LBh3H///eTm5nLiiScycuRIPvvsM5YtW0bHjh159tlnw84vXD1OnTqV66+/ntzcXJYsWULz5s0jxiQSiUZLFxGRuLVjZeTEd8dKHw06x+5Q9sknn7B69Wr69OkDwIEDB+jVqxdr165l5cqVDBgwAACfz0eTJk2C7xs5ciQAWVlZ5OXlHTLfRYsWcf7555OYmEhKSgr9+vXjs88+Y9iwYb8ovrPPPpuEhAQ6depEfn4+4BLjd955h8zMTAAKCgpYv349ffv2PeT9xx13HPXr12fGjBl07NiRGjVqHBTjddddB0CHDh1o2bIl69atixhP1apVGTp0aPCzv/vuu2GnW7lyJbfddhs7d+6koKCAgQMHhp0uXD326tWLe++9l02bNjFy5Ejatm0bMSaRSJQYi4hI3CouiNxVonjPkXWl6NSp0yE3qu3evZvvv/+e9u3bs3LlSvz+n1uqAy2j1loGDBjASy+9dNB7V6xYQefOnfn444/DLq9atWqA67JRUlJyRDEfjsByArEG/v/hD3/g6quvPqx5jBo1imuvvZZp06Yd1vRJSUlh6wqgSpUqGON+tjvSZx87diyvvvoq6enpTJs2LdgVo7Rw9XjBBRfQo0cP5s2bx+DBg3nyySfp37//YcUuUpq6UoiISNyqUstELq8dubwsp512Gvv27QuOeODz+fj973/P+PHjqV69Oq1atSI3Nxe/3893333Hp59+CkDPnj356KOPgt0t9u7dy7p162jfvj3bt28PJsbFxcWsWrXqsOPJycnhX//6Fz6fj+3bt7NgwQK6d+8e8T21a9dmz5495c574MCBPPfcc8G+0Js3b2bbtm1lTj9ixAhuuummQ1ptc3JymD59OgDr1q1j48aNtG/fvsy6+iWx79mzhyZNmlBcXBxcxuH65ptvaNOmDRMmTGD48OEsX778F71fJJQSYxERiVv1UxOPqrwsxhjmzJnDzJkzadu2LccddxwJCQnBfsF9+vShdevWdOrUiQkTJtC1a1cAGjZsyLRp0zj//PNJS0ujV69efPnll1StWpWZM2dy8803k56eTkZGxi8aMWLEiBGkpaWRnp5O//79+etf/0rjxo0jvuess85izpw5B918F84ZZ5zBBRdcQK9evejSpQvnnntuxIS6du3a3HzzzVStWvWg18eNG4ff76dLly6MGjWKadOmUa1atTLrKpLRo0dz//33k5mZyddff83dd99Njx496NOnDx06dCj3/aFefvllUlNTycjIYOXKlRrFQo6KCVxqOVZkZ2fbJUuWVHYYIiLHtDVr1tCxY8dypwuMShHuBrw6JyXQYkhVTMKRtRqHWrx4Meeffz5z5sw5rMRORP43hNvXGGOWWmuzw02vPsYiIhK3TIKhxZCq7FjjY8dKH8V7LFVqG+qnJlK/Y2KFJMUAvXv3ZsOGDRUyLxH536XEWERE4ppJMDTonBTT0SdE5NikPsYiIiIiIigxFhEREREBlBiLiIiIiABKjEVEREREACXGIiJyDPrxxx/JyMggIyODxo0b06xZMzIyMqhXrx6dOnU64vlOmzaN8ePHV0iMkydPZsqUKYc9/fbt2+nRoweZmZmHjGv88MMPs2/fvuDzWrVqHXFcH374IXXr1iUjI4MOHTpw4403Bsvmzp3LX/7ylyOe9+FasWJFcP01aNCA1q1bk5GRwemnn86HH34Y/BnqX+ree+8NzjcxMTH4+JFHHjmqeHv06EFGRgYtWrSgYcOGwfmG+3lwqVy6xVdEROKa9Vl8X5TgW1KM3WUxdQ2J2VVI7Jp0xMO1HXfcceTm5gIuAa1VqxY33ngjeXl5R5xUHQ1rLdZaEhKOvL3q3//+N126dOGZZ545pOzhhx/moosuokaNGkcTZlBOTg5vvPEGhYWFZGZmMmLECPr06cOwYcMYNmxYhSwjki5dugTX39ixYxk6dCjnnnsuQJk/J304Jk2aFPyRl1q1agWXcbT++9//Au7EacmSJTz22GMVMt+ylJSUkJSkFO9IqMVYRETilvVZDswoonj2fvwb/dhdFv9GP8Wz93PgpSKsr+J/pMrn83HllVfSuXNnzjjjDAoLCwH4+uuvGTRoEFlZWeTk5PDll18e9jwffPBBUlNTSU1N5eGHHwYgLy+P9u3bc8kll5Camsp3333HvffeS7t27Tj55JNZu3Zt2Hnl5eXRv39/0tLSOO2009i4cSO5ubncdNNNvPbaa2RkZARjBnjkkUfYsmULp556Kqeeemrw9UmTJpGenk7Pnj3Jz88HXKvzOeecQ7du3ejWrRsfffRRxM9VvXp1MjIy2Lx5M3Bwi/nYsWOZMGECvXv3pk2bNsycORMAv9/PuHHj6NChAwMGDGDw4MHBsltuuYVOnTqRlpZ2UEv0L1VQUMC5555Lhw4duPDCCwn8mNnSpUvp168fWVlZDBw4kK1bt5Y7r2uvvZa5c+cC7hcKL7vsMgCee+65YBIdbv2WJzc3l549e5KWlsaIESPYsWMH27ZtIysrC4Bly5ZhjGHjxo0AnHjiiezbt6/MdTR58mQuvvhi+vTpw8UXX8yqVavo3r07GRkZpKWlsX79+l9Qg8cuJcYiIhK3fF+U4F/lC1vmX+XDl1tS4ctcv3491157LatWraJevXrMmjULgKuuuopHH32UpUuXMmXKFMaNG3dY81u6dCl///vf+e9//8snn3zC008/zRdffBFc1rhx41i1ahU//PADM2bMIDc3l/nz5/PZZ5+Fnd91113HmDFjWL58ORdeeCETJkwgIyODu+66i1GjRpGbm0v16tWD00+YMIGmTZvywQcf8MEHHwCwd+9eevbsybJly+jbty9PP/00ANdffz033HADn332GbNmzeKKK66I+Nl27NjB+vXr6du3b9jyrVu3smjRIt544w1uueUWAGbPnk1eXh6rV6/mhRde4OOPPwZc95Y5c+awatUqli9fzm233Qa47hl33HHHYdV1wBdffMHDDz/M6tWr+eabb/joo48oLi7muuuuY+bMmSxdupTLLrssmNhGkpOTE+yasnnzZlavXg3AwoUL6du3b8T1G8kll1zCfffdx/Lly+nSpQt33nknjRo1oqioiN27d7Nw4UKys7NZuHAhGzZsoFGjRtSoUSPiOlq9ejXvvfceL730ElOnTuX6668nNzeXJUuW0Lx5819Uh8cqtbOLiEjc8i0pLrc8KatKhS4z0F8VICsri7y8PAoKCli8eDHnnXdecLr9+/cf1vwWLVrEiBEjqFmzJgAjR45k4cKFDBs2jJYtW9KzZ0/AJVojRowIdncoq0vCxx9/zOzZswG4+OKLuemmm37xZ6xatWqwy0hWVhbvvvsuAO+9914w8QPYvXs3BQUFh/RJXrhwIenp6axfv57f/e53NG7cOOxyzj77bBISEujUqVOwVXrRokWcd955JCQk0Lhx42Ardt26dUlOTubyyy9n6NChwfiOpHtG9+7dg4lgoC9vvXr1WLlyJQMGDADclYEmTZqUO6+cnJxgkt2pUyd27NjB1q1b+fjjj3nkkUd47rnnwq7fzMzMMue5a9cudu7cSb9+/QAYM2ZMcNvq3bs3H330EQsWLODWW2/lrbfewlpLTk4OUPY6CtRV4KSoV69e3HvvvWzatImRI0fStm3bX1SHxyolxiIiErfsrshdJezOiu9KUa1ateDjxMRECgsL8fv91KtXr8L6nAYEkqlYq1KlCsa4/tmJiYmUlLiWd7/fzyeffEJycnLE9wf6GH/77bf07NmT3/zmN8GTiVChdRnozlCWpKQkPv30U/79738zc+ZMHnvsMd5///1f+tEOWW7g81lr6dy5c7CF+nA1a9aMnTt38tZbb9G3b19++uknXn75ZWrVqkXt2rWPKL5I+vbtG2wlHj58OPfddx/GGIYMGQJEXkeh29MFF1xAjx49mDdvHoMHD+bJJ5+kf//+FR7vr426UoiISNwydSPfXGfqHdnNd79UnTp1aN26Na+88grgkrxly5Yd1ntzcnJ49dVX2bdvH3v37mXOnDnB1r9Qffv25dVXX6WwsJA9e/bw+uuvh51f7969mTFjBgDTp08PO6/SateuzZ49e8qd7owzzuDRRx8NPi/vRKB169bccsst3HfffeXOO6BPnz7MmjULv99Pfn5+8Ga5goICdu3axeDBg3nooYcOu34PV/v27dm+fXswMS4uLmbVqlWH9d6ePXvy8MMP07dvX3JycpgyZUqw3g93/YaqW7cu9evXD3bReOGFF4Ktxzk5Ofzzn/+kbdu2JCQk0KBBA+bPn8/JJ58MHP46+uabb2jTpg0TJkxg+PDhLF++/LA+67FOibGIiMStxOzI3STKK69I06dP59lnnyU9PZ3OnTvz2muvhZ1u2rRpNG/ePPjXqFEjxo4dS/fu3enRowdXXHFF2MvsXbt2ZdSoUaSnp3PmmWfSrVu3sPN/9NFH+fvf/05aWhovvPACf/vb38qN/aqrrmLQoEEH3XwXziOPPMKSJUtIS0ujU6dOTJ06tdx5X3PNNSxYsOCwhx4755xzaN68OZ06deKiiy6ia9eu1K1blz179jB06FDS0tI4+eSTefDBB4Ej62McTtWqVZk5cyY333wz6enpZGRksHjx4sN6b05ODiUlJZx00kl07dqVn376KZj8du3a9bDWb2nPP/88EydOJC0tjdzc3OBnbNWqFdbaYL/tk08+mXr16lG/fn3g8NfRyy+/TGpqKhkZGaxcuZJLLrnksD7rsc6Ud2nj1yY7O9suWbKkssMQETmmrVmzho4dO5Y7nfVbDrxUFPYGvITOiVQ9P/mIh2yTyhPot/zjjz/SvXt3PvroozL7KYscjXD7GmPMUmttdrjp1cdYRETilkkwVB2djC/XG8d4p8XU88YxzjzycYylcg0dOpSdO3dy4MABbr/9diXFEjeUGIuISFwziYakrCoVPvqEVJ6j+REOkWhSH2MREREREZQYi4iIiIgASoxFRERERAAlxiIiIiIigBJjERE5RuXn53PBBRfQpk0bsrKy6NWrF3PmzKmQeZ9yyikEhgZ95ZVX6NixY8QxhPPy8khNTQXcjWmBn0MWkdjSqBQiIhLXrN/iW7YD3xc7sLuKMXWrkJhZn8T0+kc8XJu1lrPPPpsxY8bw4osvArBhwwbmzp1bkaED8Oyzz/L0008Hf7lMROKXWoxFRCRuWb+leOZGSuZuxn63D3YXY7/bR8nczRTP3Ij1H9mPVL3//vtUrVqVa665Jvhay5Ytue666wAoKiri0ksvpUuXLmRmZvLBBx9EfL2wsJDRo0fTsWNHRowYQWFhIQB33XUXixYt4vLLL2fixIn4fD4mTpxIt27dSEtL48knn4wY56effkqvXr3IzMykd+/erF27FoBVq1bRvXt3MjIySEtLY/369UdUDyJyMLUYi4hI3PIt24F/ze6wZf41u/Et20FSZoNfPN9Vq1bRtWvXMssff/xxjDGsWLGCL7/8kjPOOIN169aV+foTTzxBjRo1WLNmDcuXLw/O+4477uD9999nypQpZGdn89RTT1G3bl0+++wz9u/fT58+fTjjjDMwJnzLd4cOHVi4cCFJSUm899573HrrrcyaNYupU6dy/fXXc+GFF3LgwAF8vkN/GVBEfrm4ToyNMTcAVwAWWAFcaq0tCilvCTwHNAR+Ai6y1m6qjFhFIvH7S/h+w3y2fPs6RfvySa6RQtPWZ9Gk1RCMSayUeL7dNJ+vNrzOvqJ8aiSncFLLs2h9whASYhyP31/C8q3z+WLz6+wuyqdOcgqZzc4irWnsYwEosT7m57/L6/lvkr9/OynVGnJWypkMSRlAYmXE4/fzZv4KXt+6jG37d9OoWh3OapLO4MZdSDS//ot+vi92lFt+JIlxaddeey2LFi2iatWqfPbZZyxatCjYetyhQwdatmzJunXrynx9wYIFTJgwAYC0tDTS0tLCLuedd95h+fLlzJw5E4Bdu3axfv162rVrF3b6Xbt2MWbMGNavX48xhuLiYgB69erFvffey6ZNmxg5ciRt27Y96joQkThOjI0xzYAJQCdrbaEx5mVgNDAtZLIpwD+stc8bY/oDfwYujnmwIhH4/SWs/GQS2zd/GHxtf2E+u35czg9bPyK1570kJMTuq+j3l7Bo6SS+2/pzPPsK8/lhx3I253/EyVmxi8fvL2HW8kl8ue3nWHYX5bNp53LWb/+Ic9JiWzcl1sekNffw4Y+Lgq/l79/O8t2r+ein/3Jvx9tIimFyXOL3c/vqOXz4w9qQeHazYvcmFv/4FXd3GkFSwq87Oba7io+qvCydO3dm1qxZweePP/44P/zwA9nZ2Uc0v8NlreXRRx9l4MCBB72el5cXdvrbb7+dU089lTlz5pCXl8cpp5wCwAUXXECPHj2YN28egwcP5sknn6R///5RjV3kWBDve9QkoLoxJgmoAWwpVd4JeN97/AEwPIaxiRyW7zfMPygpDrV984d8v+HNmMbz7ab5ByXFob7b+iHfbopdPMu3zj8oKQ715bYPWb41tnUzP//dg5LiUB/+uIg389+NaTxv5q84KCk+KJ4f1vJW/oqYxlMZTN3IPwNdXnlZ+vfvT1FREU888UTwtX379gUf5+TkMH36dADWrVvHxo0bad++fZmv9+3bN3gT38qVK1m+fHnY5Q4cOJAnnngi2PK7bt069u7dW2acu3btolmzZgBMmzYt+Po333xDmzZtmDBhAsOHDy9zeSLyy8RtYmyt3YxrEd4IbAV2WWvfKTXZMmCk93gEUNsYc1zsohQp35ZvXy+nvOLvgo/kqw2R4/l6Y+zi+WJz5FhyN8e2bl7Pj5yIz81/K0aROK9vXXZU5b8GiZn1j6q8LMYYXn31Vf7zn//QunVrunfvzpgxY7jvvvsAGDduHH6/ny5dujBq1CimTZtGtWrVynz9t7/9LQUFBXTs2JE77riDrKyssMu94oor6NSpE127diU1NZWrr76akpKSMuO86aab+MMf/kBmZuZB07388sukpqaSkZHBypUrueSSS46oHkTkYMbaI7ujN9qMMfWBWcAoYCfwCjDTWvvPkGmaAo8BrYEFwDlAqrV2Z6l5XQVcBdCiRYusDRs2xOQziAAsemMY+wvzyyyvVj2Fk4fGLgGc8+4w9kWIp0b1FEYMiE08f1swjN1FZcdSJzmF6/vGrm6GfXoB+fu3l1meUq0hc7u/GLN4zv74MfL3h7/xzMVTh1d7jY9ZPBVpzZo1dOzYsdzpAqNShLsBL6FjHaqc2+KIh2wTkV+/cPsaY8xSa23YflNx22IMnA58a63dbq0tBmYDvUMnsNZusdaOtNZmApO813aWnpG19ilrbba1Nrthw4axiF0kKLlGylGVV7QayZGXV7N67OKpU04sdcspr2gp1SLvH1KqNYpRJE6janUilqeUU/5rYBIMVc5tQdKwZpgTakCdKpgTapA0rJmSYhGpcPGcGG8Eehpjahg3js1pwJrQCYwxxxsTvC37D7gRKkTiStPWZ5VTPixGkTgntYwcz4ktYhdPZrPIsWQ0i23dnJVyZsTyYSmDYhSJc1aT9KMq/7UwCYakzAZUu+xEkm/oQLXLTiQps4GSYhGpcHGbGFtr/wvMBD7HDdWWADxljLnLGBM4Wp4CrDXGrANSgHsrI1aRSJq0GkLDZqeELWvY7BSatBoc03hanzCEE5qEj+eEJqfQ+oTYxZPWdAgdGoWPpUOjU0hrGtu6GZIygFOOC//rZKccdzKDUwbENJ7BjbtwyvHtw8dzfHvObNwlpvGIiPzaxW0f42jJzs62gd+vF4kVN47xm2z5dm7IOMbDaNJqcCWOY/wmX2+cy97CfGpWT+HEFsNofcLgShrH+E1yN89lV1E+dZNTyGg2jLSmsY8F3JBtb+a/y9z8t8jfv42Uao0YljKIwZU4jvFb3jjG+ft3k+KNY3zm//g4xofbx1hE5Gj80j7GSoxFRCTmlBiLSCz8mm6+ExERERGJGSXGIiJyzPnxxx/JyMggIyODxo0b06xZMzIyMqhXrx6dOnU64vlOmzYNYwzvvfde8LVXX30VY0zwZ6AjvXfLltK/Y/WzO+6446D5ikjFi9ufhBYREQGwfj++5VvwLduE3V2EqZNMYnpzEtOaHfHIFMcddxy5ubkATJ48mVq1anHjjTeSl5fH0KFDjyreLl26MGPGDE4//XQAXnrpJdLTyx9BZNq0aaSmptK0adNDynw+H3fddddRxSUi5VOLsYiIxC3r91M8exkl81ZiN+2E3UXYTTspmbeS4tm5WL+/wpfp8/m48sor6dy5M2eccQaFhYUAfP311wwaNIisrCxycnL48ssvw74/JyeHTz/9lOLiYgoKCvjqq6/IyMgIlt91111069aN1NRUrrrqKqy1zJw5kyVLlnDhhReSkZFBYWEhrVq14uabb6Zr16688sorjB07lpkzZ7Jr1y7at2/P2rXu58LPP/98nn766QqvB5FjkRJjERGJW77lW/CvDf/riP61+fhWlN314EitX7+ea6+9llWrVlGvXj1mzZoFwFVXXcWjjz7K0qVLmTJlCuPGjQv7fmMMp59+Om+//TavvfYaw4YdPB73+PHj+eyzz1i5ciWFhYW88cYbnHvuuWRnZzN9+nRyc3OpXr064Fq2P//8c0aPHh18f926dXnssccYO3YsM2bMYMeOHVx55ZUVXg8ixyJ1pRARkbjlW7YpcnnuJpLSm1foMlu3bh1s4c3KyiIvL4+CggIWL17MeeedF5xu//79Zc5j9OjRPPLII+zatYsHHniAP/3pT8GyDz74gL/+9a/s27ePn376ic6dO3PWWeF/7GbUqFFhXx8wYACvvPIK1157LcuWLTuSjykiYSgxFhGRuGV3Fx1V+ZGoVq1a8HFiYiKFhYX4/X7q1asX7Jdcnu7du7NixQpq1KhBu3btgq8XFRUxbtw4lixZwgknnMDkyZMpKir7M9SsWTPs636/nzVr1lCjRg127NhB8+YVe3IgcqxSVwoREYlbpk7yUZVXlDp16tC6dWteeeUVAKy15bbU/uUvfzmopRgIJsHHH388BQUFB41UUbt2bfbs2XNY8Tz00EN07NiRF198kUsvvZTi4uJf8nFEpAxKjEVEJG4lltNNIjEjdi2l06dP59lnnyU9PZ3OnTvz2muvRZz+zDPP5NRTTz3otXr16nHllVeSmprKwIED6datW7Bs7NixXHPNNcGb78qydu1annnmGR544AFycnLo27cv99xzz9F9OBEB9Mt3IiJSCQ73l++s31I8OzfsDXgJ7VOoMjLjiIdsE5Ffv1/6y3fqYywiInHLJBiqjEzHt2ILvtyQcYwzmpPY5cjHMRYRCUeJsYiIxDWTkEBSevMKH31CRKQ09TEWEZFKcax15ROR2DqSfYwSYxERibnk5GR+/PFHJcciEhXWWn788UeSk3/ZyDXqSiEiIjHXvHlzNm3axPbt2ys7FBH5lUpOTv7FY3wrfzeJuwAAIABJREFUMRYRkZirUqUKrVu3ruwwREQOoq4UIiIiIiIoMRYRERERAZQYi4iIiIgASoxFRERERAAlxiIiIiIigBJjERERERFAibGIiIiICKDEWEREREQEUGIsIiIiIgIoMRYRERERAZQYi4iIiIgASoxFRERERAAlxiIiIiIigBJjERERERFAibGIiIiICKDEWEREREQEUGIsIiIiIgIoMRYRERERAZQYi4iIiIgASoxFRERERAAlxiIiIiIigBJjERERERFAibGIiIiICKDEWEREREQEUGIsIiIiIgIoMRYRERERAZQYi4iIiIgASoxFRERERAAlxiIiIiIigBJjERERERFAibGIiIiICKDEWEREREQEUGIsIiIiIgIoMRYRERERAZQYi4iIiIgASoxFRERERAAlxiIiIiIigBJjERERERFAibGIiIiICKDEWEREREQEUGIsIiIiIgIoMRYRERERAZQYi4iIiIgASoxFRERERABIquwARETkf0OJ389b33/NG1vXs61oL42SazK0SVvObHIiiSb27Swlfj9vbd3EvC0byS8qJCW5OkOatuDMpieQaEwlxGN5e2s+b2zOZ1vRARolV2VosxQGNU2pvHi27GLepp1sKyqmUXIVhjSvx6BmdWMej89veXtTMfO/K2Z7oZ+G1RMYfEIVBp5QpVLqRqQsxlpb2TGUyRhzA3AFYIEVwKXW2qKQ8hbA80A9IBG4xVo7P9I8s7Oz7ZIlS6IXtIjIr1CJ388dq/7Df7ZvOKSsX8OW3NW5H0kJsUuOS/x+/rhiKf/Z/n2YeBpzZ5esGMdjmbz8S/6z7cdD42l0HJPTOpCUELsEsMRvmbxsMwvy9xxS1jelNpPTm8UsHp/fcufnhSz8vuSQspzGSfyxa3USY1g3IsaYpdba7HBlcduVwhjTDJgAZFtrU3GJ7+hSk90GvGytzfTK/l9soxQROTa89f3XYZNigP9s38Db338d23i2bgqbFLt4vuft7zfFNJ63t+aHTYoB/rPtR97eui228WzZFTYpBliQv4d3tuyKXSybisMmxQALvy/hnc3FMYtFpDxxmxh7koDqxpgkoAawpVS5Bep4j+uGKRcRkQrwxtb1R1Ve0eZt2Rix/I3Nkcsr2hub8yOWzyunvKLN27TzqMor0vzvIie+8zcqMZb4EbeJsbV2MzAF2AhsBXZZa98pNdlk4CJjzCZgPnBduHkZY64yxiwxxizZvn17FKMWEfl12la0N2J5fjnlFS2/qPCoyivatqIDEcvzi/bHKBJnW1HkZDO/nPKKtL3QH7F8WznlIrEUt4mxMaY+MBxoDTQFahpjLio12fnANGttc2Aw8IIxh94BYq19ylqbba3NbtiwYbRDFxH51WmUXDNieUo55RUtJbn6UZVXtEbJVSOWpyRXi1EkTqPkKhHLU8opr0gNq0dONRqVUy4SS/G8NZ4OfGut3W6tLQZmA71LTXM58DKAtfZjIBk4PqZRiogcA4Y2aXtU5RVtSNMWEcuHNotcXtGGNkuJWD6knPKKNqR5vaMqr0iDT4ichA9uEbskXaQ88ZwYbwR6GmNqGGMMcBqwJsw0pwEYYzriEmP1lRARqWBnNjmRfg1bhi3r17Alg5qcGNt4mp5Av4aNy4inMYOanBDTeAY1TaFfo+PCx9PoOAY1bRTbeJrVpW9K7bBlfVNqM7BZ3ZjFMvCEKuQ0Dj86bE7jJM5orsRY4ke8D9d2JzAKKAG+wA3dNglYYq2da4zpBDwN1MLdiHdTmH7IB9FwbSIiR6bE7+dtbxzj/KK9pHjjGA+qxHGM3/5+E29s/nkc46HNWjCoSWWOY7yNeZvzyS/aT0pyNYY0S2FQ00aVFs873jjG+UXFpHjjGA+spHGM39lczPyNxWwr9NOoegKDW1ThjOYax1hiL9JwbXGdGEeDEmMRERGRY9f/5DjGIiIiIiKxpMRYRERERAQlxiIiIiIigBJjERERERFAibGIiIiICKDEWEREREQEUGIsIiIiIgIoMRYRERERAZQYi4iIiIgASoxFRERERAAlxiIiIiIigBJjERERERFAibGIiIiICKDEWEREREQEUGIsIiIiIgIoMRYRERERAZQYi4iIiIgASoxFRERERAAlxiIiIiIigBJjERERERFAibGIiIiICKDEWEREREQEUGIsIiIiIgIoMRYRERERAZQYi4iIiIgASoxFRERERAAlxiIiIiIigBJjERERERFAibGIiIiICKDEWEREREQEUGIsIiIiIgIoMRYRERERAZQYi4iIiIgASoxFRERERAAlxiIiIiIigBJjERERERFAibGIiIiICKDEWEREREQEUGIsIiIiIgIoMRYRERERAZQYi4iIiIgASoxFRERERAAlxiIiIiIigBJjERERERFAibGIiIiICKDEWEREREQEUGIsIiIiIgIoMRYRERERAZQYi4iIiIgASoxFRERERAAlxiIiIiIigBJjERERERFAibGIiIiICKDEWEREREQEUGIsIiIiIgIoMRYRERERAZQYi4iIiIgASoxFRERERAAlxiIiIiIigBJjERERERFAibGIiIiICABJlR1AJMaYG4ArAAusAC611haFlD8EnOo9rQE0stbWi3mggvVbfMt24PtiB3ZXMaZuFRIz65OYXh+TYCo7PBGRY471WXxflOBbUozdZTF1DYnZVUjsmnTM75f9fsuKPB/LvvGxp9BSu7ohvU0iXVolklAJdeP3W777ysfGtT4K91qq1zS0aJ9Ii5MSK2VdWb9lx2ofO1b6KC6wVKllqJ+aSP1OsY/H+v34lm/Bt2wTdncRpk4yienNSUxrFpVYjLW2wmdaEYwxzYBFQCdrbaEx5mVgvrV2WhnTXwdkWmsvizTf7Oxsu2TJkgqP91hm/ZbimRvxr9l9SFlCxzpUObfFMb8TFhGJJeuzHJhRhH+V75CyhM6JVB2djEk8NvfLfr/l1Y8PsHaT/5Cy9s0TOLtX1Zgmx36/ZckHB9iad2g8TVolkH1qbOOxfsvGeQfY/dWh8dQ5KYEWQ6rG7Jhu/X6KZy/Dvzb/kLKE9ilUGZmOSfjlnR+MMUuttdnhyuK9K0USUN0Yk4RrEd4SYdrzgZdiEpUcxLdsR9ikGMC/Zje+ZTtiHJGIyLHN90VJ2KQYwL/Khy+3JMYRxY8Veb6wSTHA2k1+VuaFr7do+e4rX9ikGGBrnp/vvoptPDtW+8ImxQC7v/KzY03s4vEt3xI2KQbwr83HtyJSWnhk4jYxttZuBqYAG4GtwC5r7TvhpjXGtARaA+/HLkIJ8H0ROfEtr1xERCqWb0nxUZX/mi37JnJit+zb2CaiG9dGXt7GdTFOjFdGXl555RXJt2xT5PLcyOVHIm4TY2NMfWA4LuFtCtQ0xlxUxuSjgZnW2rBryxhzlTFmiTFmyfbt26MT8DHM7oq8gy2vXEREKpbdFbmbpN0Zn90oY2FPYeTPvntfbOumcG/k5RUWxDae4nKWV7wndvHY3UVHVX4k4jYxBk4HvrXWbrfWFgOzgd5lTDuaCN0orLVPWWuzrbXZDRs2jEKoxzZTt8pRlYuISMUydSP3ATX1js3+xQC1q0f+7HVqxLZuqteMvLzqtWIbT5VyllelduziMXWSj6r8SMRzYrwR6GmMqWGMMcBpwJrSExljOgD1gY9jHJ94EjPrH1W5iIhUrMTsyA0S5ZX/mqW3SYxc3jpyeUVr0T7y8lq0i2089VMjL6+88oqUmN48cnlG5PIjEbeJsbX2v8BM4HPcUG0JwFPGmLuMMcNCJh0NzLDxOrzGMSAxvT4JHeuELUvoWIfEdCXGIiKxlNg1iYTO4ROYhM6JJGbG9WitUdWlVSLtm4dPf9o3TyC1VYwT45MSadIqfDxNWiXQ4qQYJ8adEqlzUvh46pyUQP2OMUyM05qR0D4lbFlC+xQSuzSr8GXG7XBt0aLh2qJD4xiLiMQX67P4cr1xjHdaTD1vHONMjWPs91tW5vlY9q2P3fssdWoY0lsnklrZ4xiv81FYYKley9CiXSWPY7zGG8d4j6VKbW8c446VNI7xii34ckPGMc5oTmKXIx/HONJwbUqMRUREROSY8b88jrGIiIiISEwoMRYRERERQYmxiIiIiAigxFhEREREBFBiLCIiIiICKDEWEREREQGUGIuIiIiIAEqMRUREREQAJcYiIiIiIoASYxERERERQImxiIiIiAigxFhEREREBFBiLCIiIiICKDEWEREREQGUGIuIiIiIAGCstZUdQ0wZY7YDGypgVscDP1TAfCpKPMUTT7GA4ilPPMUTT7GA4okknmIBxVOeeIonnmIBxVOeeIqnomJpaa1tGK7gmEuMK4oxZom1Nruy4wiIp3jiKRZQPOWJp3jiKRZQPJHEUyygeMoTT/HEUyygeMoTT/HEIhZ1pRARERERQYmxiIiIiAigxPhoPFXZAZQST/HEUyygeMoTT/HEUyygeCKJp1hA8ZQnnuKJp1hA8ZQnnuKJeizqYywiIiIiglqMRUREREQAJcYiIiIiIoASY5FfLWOMifT8WBeoj3ipF2NMP2PMrZUdB4AxpnrIYx0nSjHGpBhjmlR2HPHKGJNY2THEq9Lfp3jY/8RDDPFEO7ww4u1AEE/xxNuXOs7qJm4OBsaYBOvdQGCMSTPG1LGVeENBPNUNHFw/QNVKDeZnm4DrjDHnQuV9t4wxVYAJxphsY0x74PbKiKNUTAkhjyt7n2OA+4A/GmOOq8xYvHji8bvlM8bUMMakV3Yslbn80owxidZav/e4C0Bl7pcDrLXWGFMNKvf7FS/bclxtNJUtsEFYa/3eQeEyY8zpoWWVEZMXT0NjTFtjTO3KiMOLJfRL3baydzreDthvjKlpjGlcybEY72BQxRjT2RhzfGXGE7KeJgFTgJaBslhvy4EDpff4T8aYC4wx3WMZQ2kh9XM78A9jzABjTM3KiCXwPbLWfg1MBf5qjOntHawqY10VAx8BnwKvA6/FMoYwMQX2gbW9+kiqxFiSvETmCqAFMCqQUFRSPKHfrTHGmFPjYd9jjOkAfAwMqqxkp9Tx6sR4aOEPWVePAjd6x/VKP9EzxlQF3jPGDKiM/Y4XR+CEqrEx5kZjTK1YxxCgxDhESAvbqcC/gHbAi8aY87yNJeb15S33DNwB6hHgAWPMebGOw4sl8KV+Efh/wIvApcaYmB2oAl/YkINlT+DfuGTibmNM01jFEhqTt54aA0uAh4DHvO0opq0Wxphexphk7/FlwMnAMGvtikDiF+sdn7eeqhljHgS64r5XdxtjYv5LSqEHaWPM5UAa8AHwf8BZxpg6sY4p5OD9Iu7nTtcAs40xLWK5rowxGYFYgHrAHKCOtTbXK495y3rId6svMA94GnjQGFM31rEAWGtLvIeX4o6f9wOXxHIfWCqewAnDAiADmAD8yRjTIpZxmINb9OsDzwN/sdbeB1hjTINYHz9DjlcP4o5XM40xF8cyhoDAZzfGJBhj7geqA5dZa7cDNb2ymO2TQ5dlnQPALOBU74Qi5q3YgQZJXK6TbK0tiHUMAUqMSzHGDAcuAcZba28BxgLPGGPSvBUX07Nf70B9E/B7L653gDHGmF4xjCGQjCYYY/4P2GutHeDF0hG4KlaxAO1DwuoM3ANchGvVugqI+aVN78DdA7gc1zo7DJesTzLGtPe2m6h/14wx/YCG1toi76WawPdAjjHmj8B8Y8zTgZijHY8XU4IxpiFuWzlgrR0E/BmYC9xjYtjS7yVZgRaJcUBrYJK1diowAxgI9KmkBPB0oJm19jpr7RBgOjDLGFMt2usqZNscbox5yhjzHDDSWnsOLkH/DMBae8AYUyOasYTElGaMqeJ9t9oCT+C2myeBYuClWMQREk9o4vc73L74bNwJ1VjglFjG48URSG5+A8yy1t4ANAa2WGs3xvhYFWhUao/LKxYCjYwxk3EnWNOAc2MVTEgi+jeggbV2IG5f+JAxZkgM4wi9Cn0C0AV3pWEhMNCrn/eMMSNjuE8OJr7GmCHGmKpefS0BjgucUMQoltInA1cB/89ae48xpqkxpn9lNHYd84lxmJ1HI6At0N4YU8NaOx/4I27jrRvtjSZMPHVxyd4y7+zyPeBzIDOacXixBC7xWmNMBjAYSAeWeZO8DKwAOngtglE94zXGtAP+bYw5xWvZ8uHq42TgZmCM1zIa9Va/MInuubiTlw1eYvoyLjm+z9tu/KXnUYGxtDbGjAIWWWvnGmMmG2P6AP8AmgOjgbXAb4FO0W5NCq0ba63f226/wh0Iqltr9+MSmyXA87FqSfK241bAbKAbMBT4i1f2PLAKuNAri5oyvicFwDfGuyxvrf09UAuYGeVYGgG/82J6EhgBpFprL/PiGAfsMca8YIzpCjxnjGkQ5Zh6Atm47zdAIrDUWvsmsBTXULDX2+ajzvzcZes4L0n/CfjAWltkrX0St44eN8ZkxmAfGPrdCiRSPwInG2OWADOttZO940jbaMbixTPZ279ZY8wA4EFgD7AeSAXygDtwXXMaxSCeQP0H6ukfwLVeg0A+btt51tuWoy4kAW0PvADUxp3k/RHoj9svPwFcEYsTci8pDrSkdwJuAR4AbgUWA1nGmGHRjiMklkD9BNbXNlzjxDzgT7iGpiuNMdVj2aJ+TCfG5uc+LXWMMSOMMcdba5/Gtda0xV2awlr7MPA2MDIW8XiPhxljaltrv8P11brPi2UnUAjUj3IsySGXeNO95W/CdTE52RjTwbvUsRA4CagS7TNea+063I1A9xtjWgJ7gdOAK4FLrLVvGWNScd0qotZf1Bzcd62PMaaqtXYi8ApwjRfrLuBZYCdwWbRi8fQEBgFnec8b4BKcusAZ1trLrbUzcCdTxcCuaAXitcoG6uZ8Y8zVxph0a+3lwHfAXwGstT/hfsFoAe6yYtQZYzKBycD71tpLcXVWx2u1AXeAWIlL4qMVgwk5GIww7j6G83Anm52AwSEHgNeBBiZKN3h5B6NduBa9erirP9cCdY0xI0ImPQ2XpD8ITPfWXdRYaz+x1j4HDDHGnIlr6etnjBninWiVABtwCVjUeUlxa9yl5iG4RL2ucVdCsNY+BFTBffej1qIeSNC9x6ONMRcbY5oDO3Df+RnW2ge8yafjunhELZnw5n0irjUYoAT40rss/wxwlbV2GrAFOBOIdiyBk9/2wBJjTANr7VLcsby5tXact13tAW6NZiIaiMe4K2Y9cCd0s621i6y1HwLdrLU3WmtfwjVe5OHqL2oCSbFxN0X+B3gM6IfropmJS0Q34E5ootoF0NsPBnKdx4DJ3j5nGq6x66/W2rG4E5mTgKJYdu845n/5zmsJfQrXCpsEfGit/acx5l7cDvAV78sV7TiSrLUlxt048TLuMvgPwMO4A8NEL75puOTiLmvt7CjFcjHuC7IQ13LzFjDVWjvJGHMiMAr3hZqEa4UEuNr+3P+uouMJHhC853fjksGBuJbilsCbuBaBx3CXFf8cjVhCYjC4g0813AnDBty6+gSYZ62905uuQbQSCWPMWUBTa+2Txpjf4hKr54EvcX0ft+JaZvOAR73yC72TrajyWmhaA+twJ3FLvRgWcfABPJoxBFtHvOdtcDv/YuBWa+13xpg04O/AM9baJ6IdU0gsgcvxzwLX4/ql5+KS88W4g9Mm4DprbWEUln8DbnvY6i37YWCttfZPxvWNfxbXnSLXuBsBF3sn6lFLRgMnDV7CORDXMGFx3+l2uP7FV+FOiB/FrcO3ohVPSFwtcFcW9lprr/ROVJ7DbdMfe7H1Au621q6NQTyPAK2AL4DhwHjgdFyClQg0xXWnuDQGsdTG7XPW4fqAt7DWTg8pH4BrMZ5hrX08BvFcDHQAxgB51tqTjTEpwOO4E8C2uAaDh621G6IUw0H7He+1eUCKtTbbe14NdzL6D9zJ6YXW3fQajXhCT8ZTcYnwMtw2dL53hRPvCsxooJ21tnM0YikVV3VcPrMO1/C4AOhnrV3olXfFrbdXrLUPRjueg1hrj9k/3I5kBtAD1/fnG1xf1XNxZ/4vAr/DDecUOIkwFRxD75DHLYFXca2f4C5vTAFOxSUXU4C/4W6mikZ9BD5jF9zZfTvv+cO4g3WgvAHwB9xG+2gU148JedwEqB/yfDrwlPf4KtyBajYwOkqxNMb1vwo8vx3Xv7AGXou+93oLXAvJlaXenxCFmC7CJXUDcQn67biEuLW3Lf0dd9BsjDtwV+i2G2Fd1cPtzALPO+KuNPTCJXw/Ae2jFUvp+sa1OpzrbddNve/1mMD2hOsrenc04ykVWyLu5LeX97wJrpX6NOAEXOva76O4/MdxJ7t9cd1Zfoc7AX4BuNyb5mrcqBRvAO8CSTGqm2a4LkiDcAfuP+Fa+esDF3gx/hsYHs31EyamKbjGk07ea528feAMXEJ4fIzqJxt4zHt8N/CW97iGF9PFwKiyPksFLD/0e1XF+38C7oR3D66BYqK3bzoHd+xqF6O66Ys7hnfCtWS/g+taAu5myRlefDVKf5YKjCFwjEzwtt1xQKb32pd4xwnveXXg3FjUjbe8qrgT3vHe88/Dbbe4HGhkDOJpDtzm7ZP/GdiuvbK2uGPG+bGqn4Niq4yFVtZfmB1eDX7uMrEC1xI6Dngf6IPrE1UryjE9h5dweTuRLcBt3vOa3s7vfiAtynHUBO7E3ciRhOt/+SrQ1St/G9cpPvQ9VWK03s4GVuNaiZ73XquNO2DfHjJdnSgtvwou6f5zYBm4m/6uwLVC3O+9Vsv73wvoG6VYknAnTB1xycLl3jaUCTTEdXm5E5ec9sDd5JYZxXVjQg4GKbgTg+NxLZE9vNdr4FrcrvGet4zRdlMTmI9LQm/Htep39+rlBbwT4BjEkVD6Oe7E7tSQuhuN66sa1QTU+8wbgWzveXvclanGuNbHf+KdeOMSjVFRjif0hKoL7jLq1JDXeuFa0X8P1AxsT+HeH4V4fofrBtUBdxC/C3e1rkFgWqBqFOumLt4xK2Q7acvPV16eCZm2d5j3RzMpvhJ3pWGM97wrrgV9Fq5L1z24fWaDKNZP6e9Vb1xLcHBd4l1t9Z5XLeu9FRxXA1xjya24Y8ZC3MlVE2Az8NtoLbuMeAzupPvF0G0D76TKe96fn08YXgLOjmI8Pb19zYm44+cXwP+FlE/w/teLZT2F/h1TfYztz31abjfGDAWaWGsDNwk8Z639Fy4xLQQyrLXbrLUF0ehrY7zhfay7yeVMY8zN1toPcAeAHONuMNuL65h/PO6mt6jxluXDHYi6WWvfwF1uucS4u2nPBbobNy5u4D1RufQTyhgzEtdlYyBuZ3OxMeZB6y7pjgMu9/poYq3dHYXlJ3mfcxxu5z/cK1qDO2FZZF3/YnD9pEZbaz+21i6o6P593nq4FHdJ+VtcS83zXixX41oE/g7U8eJdirsU/0VFxhHKerxuHW/hWod+wCXCNxljGltr9+GS48BNW1HvyuHpCOy21v7GWns3biirmbj6WoxrlYz62MXW9VFNN8Y8boy5A7feFuO+64FROWoBm22UuiOFWIbbp4w27qaptbgkq4q19jXcQfx8Y8xp1toF3j4xKkypYaGstStwl1Vbe9s61tqPcclyOu7gjrc9Bd5TIX0BjTH1jDGtvW25qjHmFVyidSKupbEVrgUyAbg+cHnauv60Fc64EXf+jNsfh37OIlyStcxae4U37STgL8aY+qH7HFvBN4rbn/s3X4XrBvQ57n6Pi621n+O6TLQDvrLW3matHWmj140scENkHWNMM+9z/wCca9wYyoE6+ycw0bhRHw6EvrcCYym9n88AXrbW/gnXwPaWtXaztXYr7gT4Tm97i2af6+AN1t52+m/c/RR9vdd8uJtqU40xrwEXWGv3GXdD3h5c42BFxVI6f+oC3GHduO2bga9xw+lhjJmKu8+ihnX3U1WOysrIY/mHd3aIO0uagrsU9wTuzLs9rkVyF+7GiqVEufmeQ1uuM4F9wOne8/G4s+323vPmUYylK3Ci97g67rLlQ1691MJddr0Ll0CkAv8F6kYpFuOtkwl4Z6y4bgENcTvi/+AOVrvxuirgXdqMwTZ0tbfd7Pa2l5Nwl8pe8erwJVzrZPUo1UsiroX6iZD6WIw7YDfCtdBM8dZTf28dNo5R3ZyOO0h2CHntRNyl5i9xyfvHQKMoxnBICxCuxfNd73GS9/8ZYID3uEmM6qcPrv/wOO+7tMJbZ1O87edfuG4L2VGMITHkcVfgXtzBaAHw51LT3k70r1CFxvMA7l6BwI1ts7xtp07IND2jGEtvrx5O9Z5nA2+GlP8Wd/WsFjAA9yMsbaNcP9W8feE9QOdSZSNwVxxmevudhbih/mKxLY/BnagEugENwJ1sneI9vxW4MUax9PD2K1NxJ+J1cQ0HebirMeNxDQW3ld7GoxRPoHW/J64VdA1ei7r3+hDvf+0oxzEF130sCa9bpPf6n/n5uNoIWI67EntbjNZX6NWY5739cx3cFb0ZuPHk/xGLWMqNtbIDiNkHda00ZwB3es9beTvfv3nPr8bdhHd5uBVZ0RsHLrmZB5zlPT8Hd3dxa+/5fd7OuGo04vCWcRrgx11avQiXoCfg+hRPwvX9aYnrIH+H956odJ/wljUPd4Y/3vvsjXEHyhrel+dkb9oXvbhPiGLdhF42vN07GNTEtYr8G3fwPA6XYNwPPBDuvRUUS6CrTSru5KAfLlmehDsw1sK11DyIG1QfopCgh8RT+sSuN64VezTu8vPL3o6vNu7gNTBa6ynMuhqPSz4D/ejeBR4MKX8DGBStWMqI72LcXdaB53fzc8J+Em7c66hccsZ1HQl0QQhNRnNwlzHnhryW7P2P2roqFVtN3LBVL3vbzTu4VvTjvMdXlN6Oo/DdugCXIAwPea2Ltx9qy8/763dwJ6NVCbnXIEr1EmjIaex9r24BWpUqS8Xtry8OibFCu06Eq29cK/cHAAAgAElEQVRc//c3cN21Al3HrsAlWFFtpCj1Pe+Cu9KRgTt2rcN1KauG6172AK4/8fHeNlXhiTHupKC29/j/cMeJdl4MzwNTQqZ9FdeoEfXvFu5EezAu38n19oeZuC4un4RsL08E9pPe8wrtxsXBifBDuIR9hPf8DuAG73ED7zufFe26OezYKzuAmHxIl0hswLVE/IT7EQSALNyZ5oPeDi/0ixfNA/kJuBaKm0OXhTuzXevFUo8ot9p4y7wXN3bgH4APcWeaN3v1Mh6XKPcN/QJFIYYmuGHNJnvPG3rrqm3IAeE5XGvAhbiWlFOiGE9on9kU3KgBvw8p/wv8//bOO9yuqlr7vxGSkEASQAKhEwgtIEXphBJ6kRKlSAsdQg1FSkBUQEPvEOlC6Ehvkd5BkKLC5wXLVbAL4sWGIsL4/njHYs+z2EGQPdc+JPN9nvWcvco+a+xZRx88TQgxte922q/v4OiXoXG+N9qE+seifxYt4W5t4ADy+s9VG3M/pLkagczcR8SYHosYvVObWOhqi+/JSIDZATF91wed30M5eh8BLs1NU0LPvsg3dSxwQe3eXcDIzO9fFGnIj6yPz5jXmyIheOem2iR59wCUNzr1c5wXbdzLI6vHgyiSPxcdSyALYeXTOHOswYshDdaeyfozmcwCFW32HFSZ8XKktR4W10aivMBzJc91et1J59XKsQ6tiQTyLVBmg+2SZ75BcxaP9RHTt3jQ9DzyBb841ubKV7Y/EiqeqPqxg/SsEmNiL1pM+MkoiHYRxJieH/30CBmD1IOe9Whpg79NWBlirGyLGOSdkMW3utc3+X7H9gx6xp3MGn+H0SpQdiiyDr1IQ5aOj/wbuk1A9h8oM+Z9yG8WtGFentyvUn7NnJEGq50vA1ySnKdBAbehdGNNtc9gxHSeHZ8PQQzFyyj92b4N0NAnFrVL43wSrfRnryBGeQxiAl8ERjVA02LITDcOuXZMpBV0MwPwGtIUD5haP3eIjmORJeE0JODNiqTvQ+P+4sisOrHBMbMAMhWeiRi8g2rtsDpiepbLSEO16R2eXLuEhNlEG+KBSKO+NJmyuaTjuHY+GVmh+qPsD7tF/y2OGMD5G+irTZHLxA7JtYo5HoSY9m8DyzbZNnFtV5Q6b/Hk2nEVrTSQ6SHG0ES0aT9OK7vMZ5Cl7Dq0md+SY37Hu1ImdE5qGmnEBF6BLHxfRD6ZB2Zsk5SeXWLsHoD2zglxfc+Y/9mzF9Ro2yvo6I8YsJOIQGe0bzxc0YSswhOAGTv4/n7J550QM3xrcu1EtIfPgfaJFcjoBpS8d7sYF8vGulN3v1kDac7/huJO3uvnjON6U6SUuJYIkkdKsBuQy+a/gK2bHD8fmvZuE5ChM1LJsi/yifoJrYj4PrEAnlCdN0VPcm008Gjt2syEzyOZM2G0oWdeZJaqJszAaLf7yRyVntBQVfn6Ez3TtpwBTEmfa4CWEUhjfXJyPgVJuqvEJnE5sHCm989FMP9Is3Z9vG8vpIndH5nt5onFdxSwSQPtYrEhnQBsGtdeBnaLz7PGZvETYJ3MtAxAJtQLgG3i2n0kG3XMs7OaGL/JOxeMsbMUMqlegyrpLYvSIF2B/LF3aYCWKk/oHUijtX7al8lYy6rZr63J2yFmfM44PxGl2Kq0ofcSTB+J5ikDTZXloy+KFXgBlQdPn5kHadw2a2jsLI402J9tc29nxPT9DhXt6dGPHaRhG1rM7zxIqJo1rr8Q/bMfrXRk4zL20ZBknBhyb3mAxJKKsiWdgRQZDxJxOpnomRm5kiwZ8+bzaC1+gp4p8i5HglQjWZuS945HColfIneSDZHFahAtYXhNtNfnzra1JuIf5kBWzt/SchOdHQmetxHW+952dJ2ADndGtdj1R1L2p+N8b2TWqCTLBZH7wIqZ6UlNFacgZqbyKZ6CNq2q5POD1HLf5mibD7j/WbRhZ8kD/CFpnA9p0sYn11aKxTmna0vdZ3YJpMF+gjD1ID/aY2LBu4OWiajTPo8zIbeWB2gxfGshE+8yyKT6ItJmb5qDhg9qm7h2KNKEPg3sGteGIAFrJTKax2JjGo201n1iA7gk+mwDpMmvTIVfI0lp1dAYHhpry81IgPgiLX/nuZEbVfa8rjGO7o6xtEKsPxfQStPWiB9xjaZzY+4cjywOy8b1G1BZ41OppYTMTE+1X8wW+8N4EveoJtsIMVnPVHO+HQ3A1kQgNhmEBiQkjKWnsDl7zLmnUSD0Tiig9vMkVqIM7bF2rHEP0LIgbISy8aTC73xIo/8wIaCnfdthmgahWKBHEfP5aSR8HoWsDpUyow+Kg8nmBtTudyIlyQko/uZy5IL3GNrXq+xbC5Dkcs5BR5yvFGNlP7SnVxb7bJb5jv6ebhOQYaAsgNT3xyJt1r7I1+4w5C5QbZrZIvZjAr0XEIGk7JNQkMJjyKTah5YG6SmSfLwZ6EknzwJMRUpDpo8/UDPDNNx/y6NNc91YdJ7M3Dap8LJrTOiZo53ORMxw/7hvJBHFORbf+L9DkR/fn1CavPWQ7/fwuH8wYr4epQGtWnzeAvn2zRjj92lCS4I21LvInKweCQfPI6Hl4min+ZGJ8IKYd3ujzfSW+DtPRnoGJZ/H0sqUsj/ysz4Pbe6PkBSnyd1XcT5XrDVV4N1iyBXpHBrII420jSfFmJkJMRST4t6JaG2+lpaV7H7g9uT7nQ4EqrePpdcR0zAZCZ7Zs5XwfmF8QZSK7bg4T032ddo7HmSX/O+ZaGW9qPKQ705LuNseud5ly9yE1uHvI0vYBoipqxQRe6Ky7fMnz89MzwwmnRYYUovH55E7wtW09oXFUUDtMWTMGZ/QMDdTCfZG7pCX0TPgeBjhzhFrU0eLGdHT+jJHjKGV0Vp9Cz3rNJyfc/x27Dd1m4AOdo7FcTYtafclIpk20iidiYIE+pIEoXSYjrXRhnwDCgL6YkLP7chn7WqS1C00xIiijfpGpAndrt0CQtS0z9lP8bcftc0peWYzlJPyFUIjmYGOuQmTJNIY3Yk0j6cB18T1lZGW67A238/qghPvGBNj5WjEeKZBVCuTMfNEjY4bENNwK2JI10H+fJci//zH04U4Ew3XAffE56pSUlW5bhjS2JyMGPdBKFo95zheHAXYzIaEhu1iXo1FDGHF4OyHUvxNyEhLunHPkcyrS4ksPHF+CBI0D8jcV2siYf+oWHertXamaJvz4vwptHHOEn32VzKk+qIn47AKNcY3aa91kEZ708ztU8+isiaycq6CmOOqUlruYi+G3AIeql0/E+2dFyKl0uYo4PdY5PayckaaPotcNqoKjP1jzB5NFDEJOh6imeI86dyqMlAsEWP7RFoM++qxJm7cAE0Do9/SSqzpmJoXWaCPT/s6/uYqgjUcKUtORe5As8T4OQ9ZWzeJft0tx/s7/nu6TcDH7Ix2Zt6DYxI9l0yu+REjthh5K/FUZVTXQEE/ZyFNyQCkqakc0K+OTSG7X2i8z5Abx9lx/jBidKrcrk2kkElNghsgV5KTiACtel8if9qO+4slC8RWSJs3HDF3+yHNwz2xKVQV9r6AGOYFGmobq52vGrT9AGXu+FzmflqAxJ0GMXoT4/MNRNAo0nB9Dmlrd8xM01wor/fpyfj5C2LSr0HpfhZCQswp7daFDDSNjPf9kFZZ3s/EvD8buXSMjesjctMT77kYafquQMLCBkHLCYjBuIGMwbQxdocjRqZtBoegrXIBOh35GVbuSkvTYcaiNpeORQzeY0hhUQXTpkzF2g2Nn2FoDzgVCTDVerM/MtN/qk5/RlqeAS6Mz9+KcfLZmNunI6vnumhvfZ//cwZ6JqJUlDsmbfR1tGftGM88BOyXm5Z416zxvusR87cosuKdHvN9GRSkmC3YuD4WYj4/RqL9rd1fGSniBpF5DMVYvhftB0NRUN26SEkxEWmJbwPWbaK/OvKbuk3Ax+iMdDHbDkn7A5Ap9TckUm102h7tBlAH6RkYkyYtu3gBrRRkN9PyQbokJlQu6W1epNGaj1Zu0t2QBvIqIitHE4tuG9qWRz5OY5F7wAuEWwuZNSQ1OkYgwWVMtNNsSMo+MD7/glbu5mx5S0nMULXrqaaiT2wSb5BJm0XL4rI+Eio3jOsbIA3So/TMxbtIw+NmGbRp348EmlHAwtGHN8Qz69OmNG6H2yhdd85FQtT2ybXZkTDzO6Rhz1bWNBk7VVGMSnB4DWlrlo3jLmQRyarVj3ePAK6Kz+/lYE/+7o+0sjcjYadKQ5hbO3osLU319kT1P8LqQsPm3ViPK9/ZH5BoypGA83QDNFSKkT7IXeF14BvJ/ZWQoHk6DQSSJeN5ViTQvUjPNJkTaAnm2a2a1bhALj9Hx/mJyFVhBGKOJyP3w9wZb9opAVegp0vb+0rPZ6LlABS7sF0yfg5BWuFniMBierqafSJ8i9+jt9sEdKCTLkdaiDOjU6qgk7OQz88jwBkN0bIoYiAqX8OTkwl1ODJDPQJcn5GGLyJTxhTERFRMw3jg17WF5gQiyXZGetJFpgri+Gpy7SQSU16uyZz0we20zF9HolLgIE3pxfF5YIyp20lS6WWkayXE9E1Akn7bTZpmfENnRBHwD8R4/nTMqzQt2jeD3r45N6gaXX2RJeY5ejIRn0n6rSlaRiMLzBDEZJ3G+4OmViOTv2FtTi0cf8cEPdchxupGVBxi5ti4sjHo8f4qdmMU8P+mQusc0XYbI01SW1eqHG0Ua+F9ybV9EPO1eVPjpkbfnmif+h6t/WIQrWqnTVWyq5jj+VBMwyZxXrm/bEhmi1CNnoo5nh8xoHvQCjjcLsb3zAl9nc7fnAq+Y5A29BwSSwbaG86PzwNyr8s1mk5Byr/RCY0v0AqqrStYOu1vfS7K/nMs0gIfjHiu7yH+ZnhFB+LNslsYsrR5twn4iJ1SNzNvSMuX72Ii/y/S2myJoudTs3ATJrK1kNP5g8gklfoBrUredDJHIZPPSjEwF0bM3R1x/2HEHG6IGNLHck7qdu2NtOjX1NrlJpISrJlo6Yck3Z8hE+FWSBvwAnLbGIqElv2Qz2xTZU1HxXhZFWkYb6EWMNbpxa0NDScircjStIK1joiFb0C016XRd9chjV/jGgCkgRwT7VT5G55Dkte0ARr2iTFTJdMfiPI4n47M8EcAazXUHiug3KXrxvn2tKxBywP/QJrJrOseMqHumJxfQVRgjPN+8Xc9au5jnaaNnvvDPLSC+5ZHWvN9kvvHxzqYU6u/fPJ5GyJANebaI/QUOG+tnWcVGOp9gPaFv9RobqRiZfouWszx6oi5+gJyrfg+GS1CNVoqBdOomNt7A7PEvUVjLWzMyoB4mnuRMmtnFIMzMu4dgFyEsgaNIu34M8n5GMKPOeb2n1DmkI1ibJ+Wk56sv7XbBPyXHTQKSU47Id+je6pOQBqv9/n6dHqRQZq00fG57h+7PT3LOzcRJDAapWmpUtS95z8ck+ag2CiOQz4/59HBxOdTa+9455nIFWBuFGx4O2LQq2CGmYEtM9ExG8FoInP8gzGhJwYNqyBGbybkNnAI8LWM46Y+VnZFJt1lkL9qxeQ0wnhG29+PIq0nR/usgRiJrxDps+J8d2BcZnraCVOpxmRI0HEn0mq/Fx2eiZ66xvP6aLMBKMBvs1hzDkYawPvIVzZ9RlpMzGcRozA2ub8xEn7nQ3EWF5ExpqINbSchF651kLZvQnJ/NBIAGynHHWPkh0j4PzP6agMkeG6ejKVsRURQBoOLkb/lJMQsfC/aZihak29DWrgmqqNVjOdMxNpPLQgdKQb+QX4LQ92vez5auazTe1uioNZHSVLVZaDnlHjX/Eix9DKtVJTroj3iKOSudRcZSky366vk/AvIyjAE7Z+PIZepag89FVg1M01VhpBKO13lKl4NKQhGBx3XEHUjPqlH1wn4kB2yF8E4oWT598fGsFgMjnQBPhVJcwPIa5bfISbPYnFeZ3iORJvkLA2200lIe1YtdtXitwbSyjbhJ5amM1seadfGoepak5G0vTDh901e5nwGxCD8mgieQ5L/1UiDfBPalKYQGsD69ztNT/J5o/i7KXIPeJrw2yVDENJU6Kk2w0URk/lF5M5xYtB0UPTf+7JyZKbHkOZ1B1qCXrpZzoncFyZlpqcdk/44EsSvREzP34j4BfKmgLwMua9UbiMLxBxKU5yNQILvc8halXP9WxcxEgfQ0qRdFf2yEC23l5uinbIWfaGnALMcYjTnRIGZk2I8zYeUKd+lmVzSw5Cm8wwiuC6uP4SsmTOiQjDbkCgGMqw7adtsg2I8bqbl3123xJ5Ic5mSzo5xPDHm1JxtnlkrWRs63TbzIGHyXJTpYXCMm/NJCmHQSpl5HXBE5jZJU4im2ZsGR799Kc6fBV5sqJ8qGvZBAu5oJOQ9goSGX6HUuEvTYKxQtt/bbQI+RIecjhjMeVB0+mTERFTmuQNQXsOvxqCZQnPatsORJFuZn1PGZwYkOTXi3xzvHIA2p0tr1zeINszqL4uk6bHIjaM/0jiugqTwH8fCdyaSeteP9smecgxpA+5AmvyhSPIfFXQchrT7U2r9lys38ODYmCZHOy2P/EEr5moxpOnaJ8f729BTLXibI01IVQRnS5Ta8A3kOzZ/xjZJS6J/CjGel6HN8he0/C7TDSPrHKclXFYWjx1i7MyCNDeVGfMLSCDNshkgE+pNiCleCjG8VZGgkWijPrz2nSUyt82RaHM8MsbqFUio6hv0fRn5NQ9F2uMtyVuYoq6UGIkEvSp/alVRbjWk7d+gk++vvbuu6VsECQtP0RLyRiC//ffNcTJaqJAP823IBedMJOQNavdsEwdywTk3Pl8bx4Cp0dNJ+mIcDoi5dVSb+wsiN61zatezWn9jrlRuYscgV5LL0V7VFzHw1f0T0N4xsNNzql17JZ9PQPvCXsm1McjvOJtyoMmj6wR8QEfMEIv+48m1+ZCJ7Gok+Veb+qYxoPZLv5+BpnXpuYn3I3IDt3svMlk1FRBUtcWchHN8cq8KNOmXkx6Uqqk/2sAHx+e5kVS5amxQ/5vQktv3MZ3Mu8d7r0Ga6iqidubYKLIVgqj1z5nUfK8Qw34ucmN4johWz90mbe7tjwSEpZNrywGLZqRnkZjPVXqqDYHrkvsTSCL0G9gA5k4+t7N4pP6X+6LI+bUy0dIHuUs8kly7DgXSbh3na6HNsZH8oMg9435axQ0GRPtcjZjgeZBgM64+v8njM1sJMHMgE/PiyCJ1HvJ1rJQWp5LJXavd2ETKiLUQgzUnYrIOpOXzvDFap7MJePTci05Dgu43k2vXEjnbGxo71RpYZUraGmnTLwKuTJ5bsiF6ZoxxW5Wd7hFMjDK6XETiNpFz/Yn5fiEStA9DCpTFYs5fgjTa5yALyP8QaTRz0NXu/9XG0zW0UvxZDhq6eXSdgA/omGEoq8PJcb4zknZHIHP4GUwldVV9Qe4QPfsiH95J9Azoq3xmz0yuZS3+gBjQKsXZ+5gWxJg+hYSFg5CGPYvpEEnecyXnS8aE3h4xnUsQDvtoE72LWgR/h+mpb8aplnEEcrN5A1kZRnzQdztM15D4ewFhUqaVHaNvLNIjCM1aE22TXE/dFCYiq0M2Zjh9L3IJeE8zg5irK5BQVTE9U8icvznecyDKuDErEmrbWTxOQ8z84shalFs7uzoKtPkM0sS+iHKmvkxoupA2+0by+4WuijSxVS7ZakOcAwkwZ8T5BigFWTYGh54CzJLxvqOSa/vGuDoN7R2/oIGqZPHucdE/ZyM3rmWiH8+Je5V5vgnXtn4oePZm5GbzMJHeK+4/TeYy3PRkNtdFQst8SIB5iZ5M8ZeQ5jFbuenkXf1j3GzY5t6MSGFS5StuqpjSnNE+d5EUt0KM6NFoD10N2Dm5l9Nlah96avHTFH/PkvA909LRdQL+Q6csE5Pke4jRq1ITzYrcGCaR2eE8oWXt2Ag3RpkNxtMqs7gg8hs7sgE6+gctk5FwMKndhhib07sxwXJqJT6DfMA3Ru4KC8ZGdAktP9r7kB/ZS8D4THSk0mxfYKcPeHYiir5uJEAgFrITkKBwFPBo7f4h7fowIz3HIDP4nMm1VIC4mYxa6zb9tSPSMu4bG9JjSMNf5Zi9gwj4yEjPyciykZaanRG5b6UWj58ihmcgDZmeoy3epac/8cgYw8MREz+4IVr2Qq5JY2rXV0fMZ+W3unhGGhZGzEqleRyLrB1DUOzJwfF3E+RidyGZiy8ktH0JuQFVMQ3jkNZ/CKqQOJnYN+J+jkAyQxbX/kSpbcRQ9UdZSi4E1o5nh9X7MmPbbIYsMFXF0UEx56cgC3Dl3pG9IA4tofsgtIcuGOfVmKqKdmRl0GOcnov2horpXBIxwicl42g4EmoGt/sdnRw7tXH0AxLNdFyv6FyAKKI2rR1dJ+BDdNRKSIN1bO36wsjc8D5n/Yy0PIPMP/MiZuY5WtqTFdGGPgf5fDHTTA9PIm3EHHGtXaDQqIxtkU6gq5Cf7qTk2mFIO/qZWJC3Ip/JeR6ksaqq6G1PG9/uZDE0Mmr66v2P3APORv7wMyLT2O3Iz/o24Nu5aKmNmz5IGzEZuQZ8k4ThTBa87Lmbk3feFLRcjRjQzZDm/D5kxnwUuCBn28R770iurY60sH2ROfXpuJ7d4vEBdB5Lz/zAfWPszN/Q+9MAoMMRc7xGcn+W6MtZ2n2vw7TMGGvKujFWNkNWqu8iAfQB5JNd+RjntATVU45dhJi/1WnFwVyBtLV9yCjgAcu26a/NkfZ69ThfEAkOV5LZZQEx4WlQ4XGEYiRZkwYFjV9H2v0B6f3cB7IInRZHFUQ6b4ylQzK/22INfhfVXdiPVpn7tWPcHIYsVl9CCoIm8urPRZJPG1kV9q4906g/etNHH3oJzMymcv40YrxmM7O9q/vu/nPkq/lq/bsZaKva6Xiktf4NCt76N7CPmV2GNoz13P01j5HTYRrM3d81s9mQZP19tDEeAODu79S/4+6Pd5qOoKVP9RvNrEph9TTw/8xsgXj3KSiv4b5o877B3R/OQQ9aYJYDNjSzWdEm/VrQN6B6yN3fqWh395eq39JRQuL/m1lfMzsk3ns38h8egQSr7VHw0jrAk+6+TSdpqCPGzUi0Wb/h7jujFHFvAVuY2eLx3L/jK2/npKear2a2JNoAt0GatePR5jAQBbWdi/LhjstFi7u/iwTceYKmbZG14zfu/m93/yHwTzO7B2lPprj7t3PR8wF0fg34XzO7wcxmQpvkr939Vw2932MN+isSYv4BbGRmC8UjX0Hr4T/r3+sUDWY2Q/zPt9B8H4UEhilIUbGnux+JNMULoU297drYKcTcWsTMPmdm8yON+rPIBD93PPYj4P/c/V13fyZ+S0f3LDP7Ksr+gZntDJwWY/k+xHSeY2ZD3f0VtBY9jirEdhxm1j8+3gfcZGZrxHkfpMkm+Ts7cK+7f8Xdv+Tu/zSzGWJedooea8NfVGPpSZTCbwDwqJmdGnRf4e6nd4qGdoi5cSZSqP0WKftuMrOl3P1BpMAYgxRMK6Lx/a9O01HtgdFMs6GMKduZ2XzB6xwAHGlmWyS0Z5tTvQLd5sz9A6QPWhq+gSjNzCVE0FSX6FwTuQQ8QyuR/lxIY9GEv9h8yMxdVUpaCm1SB8X5cmQsX9yGnglIMzMoaLkC+SRVaeIWIdIS5R43yLR8JfKrPpk2rgBkLiNKS0uzMGK0niMyBiBfv31Q/t1sUfFToW0U8EskYL5NqzjFwkjD9TUaCj6sXVs16Kp8redCvrKPkwQAZqQp9a2+FXgVbYqL1J7rj3LSjm6y36ZC80tIw7Rv5vdMbU2uxvgySDt7FBLQbyWvy1aag/f6GLMLI+HpjOS59ZGQ/r5MA5no2gIpKa6Kdjg81sO7EMN+HPILz+bfHGvwd5EVYT9kTTwIaSO/jZi+k4B7GmiPfmiPOjjOV0f75crIpew5tE8ZciO4i8jwkvZzp8dNfF6SnoHF/WrPboj2+EaqtUUbzIrS41WuLZNjjl+IFDyHkuSOn9q8/Bg0pGtgFVC7OtJg70srKPpsFOuwcBNt0+2j+wS0TCqzIMZqbyJlVO3+UCS5bNI0jTV6LwMea7Jt4vNMMUl+B6wY12ZGWse7EcN1O5lcS2ISL5icT4yFf1hybVXkG3UgSuOUrX58rW32Q75yG6FI6xdQsNSkoOe8piY0kux/gsy8y6NNs8og8KnYtE6MBbGJSm0bx5itUnztFDQtGedroc07my9dra82R9Hx1ftPJMqrxvlZyEy/QEZ60s0y9a1+nJrbRqw5bYN8u3HEOrl25nekAuduyJ1tRJt7oxFDPKnddzP01VDEEF+TXFsGMRNHxPk3qvmWqW1mTD73IbJxxPlCMYY2Q0qBx5G2tnJ363Qqttljzf8pihtYEDEwVUGjOWNuHxbnTxGV9zKPn3URA1y1y55IaJg9xtOtSAB+loz+zbWxswtyPbwduDsdr53ul/+CzpOR5ngRtG99KdbBp4gSy5nGTzqXL0d759FIEbBxjKX9kHvo6cBXu9lOjfZJtwmITpk3OuGCWNguSyZVOrgbiQz9D7R+NuicI+eEqg3aOZA2YChi8s6lJcn1R1X4JhL+SRloMWSqrIIN+yKtzZqxMe0fC91wxAx+meY0NjcgDUWVBmmroGV/FFX8OWD7jO//T9XINkM+fhtEH32LjEVfeH9Wjj3QBn0QLU3+BMSgD2mijxJadgpaLkIb5Rdi7F6DhLsbkQYp2zynfaBq5Vv9KeD3wC5xfgkSOBsJbOv2UVtzZkO+ujfHevcg7fNJLzi1sdcBetK1f0PEPFyI/M6rQOx+yCLyUMz1nGvysoTGEWmKhwYtKyfP7EBE6qPczvcihUFH81zHuvMdVLhkoVhbjkSB4Wl2jrHA2fV+a2As7YBcjyqB6mtI8TUAadRXJPzjyawgQML45bQEhqdJBODc7/8AuioryCDgFeR6uFlyP3tpbqRw2yPm+K3oxOAAAB1USURBVNqosMmkuFcFi/4vDSQW6E1Hd18uiXse4M/ASXFtXqQ1voyWVqmrEl2N5sHI3DqsofddGZP6IcSEroY0akc10S60NPb9YwKPRVqr7ZBp5SIk4Z6CpPGOJ/D/ANo2JlxaatcPj4m+cu16pyXuy2hfjeyO2nNjUcDdVTQQCBSbz2a0Ajn2QQLVqsmzl5M580SNtm2RJr/KWjAeaUVGx5jZhCQFUSYaFoxxOgwxFBsl9yrmeHnkP/tjovjA9HCgfKlp8M9WtIovDERWoHtoCVf1ANOcDOlGKCJ/oVgDz0aCb0XrzMjykbXKKBJur0DC3Y1xbQI9C07tQE+m6+tkKtZDzzSZGyF/6+Oinyor1fiY6wNyrT0oO9KV1CwZSEHySIyfIdFv1+YeN/S0UM2FhLtHafETg5Cy4vBOv/s/0RPn9cwPfVHw37i4NnBqz3eaJqTgep6W0LsQ0hynwlUjeaV709F48F3l9A4KXHD33yLmate49hukqfgJ8DUzm8076Ij/ceEKPtnE3f+Q6x3hBN/HzM4GXnH3nVDg1hik6bsnzsfnoiHo6JO0/TIoaGxr5CJxDfLF2tPdT0MBJi+jCec5aGlzeS5aQVNpgMWpwB9QOqv30KlxZGazm9lNyNfzNGBWM9vM3X+J+uTvZnZ48t4rUFqbHTx/INDiKEXT54ETzWwHdz8PeB0YY2bLxeO7uftVuWhJ+yvm/O+RULlLXJ6EBKttgJXcfYq7T85FD4Ar8Ai0tizh7ncl9/4d4/1ZpNm+zt33z0lPL8PSSCO6RfTXsDgA3kGMz2vI3Et9jndyjU4DpcxsTcQE/9Ldf+HuzyNmawTweTPr5+5/d/eH3f3PnaIhpSUZyw+gNhmMmFDc/UTEWDwcwbYTkHBM3P9KzL+Ow91/X7VVjOX/QcqmH6Fgu4uQG8PX3P2fOdaeGCt90LjY18zOjwBo3H0immuXuvtfkCB8d+03dHRvr/YsM5vNzGZ399+jzA6/BlYws2Hu/je0lx5vZsNzBu+ne6iZrW1mcyM3uiqg3l1Bzz9Bwifu/o/0f3RqP63tof3i76Hxd734+wqKHdjMzHaL9/9PJ97/iUKTXDg9TXV7IOm6MifcgqJTq/srktEE3tsO2mhaEZO1HpLqzqs9uxViKJqgbR3k7zQImb+vQBrJQUgTcS1ixjpeDjJ+a2q6XYlWWrb5EIO1cXL/WBJNYAZ6Pko1sl1z0ZG8e3labkfDoi/WRRvVz5AWf2tkMruajMGQVX8ln0ciE/jycT4m6NkkzudEgsWnm6Anzo9EGr7L6s+0m4PT+lGbW8ciJcXqSAv7fSJNE9Jq3Uv+FF+VJis1Mx+MtH5p7MneQWvOIiKp5nFNJNQthLKnfJ0kSAvlbt+bJEVmU2OpNufGIWviBCQ8NJHeaweUWWFwrIUnAocm/XcnSRxB7rYB1kAKpHOR7/UsyE3gyliDqrRsjVh9411nI6vvqTF+hsX1ylI1Q8y/LGtQbYzsF3RsHueroRzko+O8H9pnG8ut39uO5l8oRuoWFDF7NUo7VPmHPgl864M6dVo86FlZZr5k4p4dA3bv5P65tKnUk4GmamP6EpG/Obk3PvpvbRRQcWBGOuZNPn8VMehXIQZwNhQcdT3SRlwdYytbdHzQ8ZGqkeUav7GYbh2bT5XZYYk4nkdWmHFIaBmFGNFPNTSmxyAN1r4o88R6cX1PFHxTMctNMenLkuSuRoGjJyfnjfpb97YDuQTdgvxCr0P+8ksDv0Lm+YfIXyGtYooXRdkUxiGGdCbEiH6DEKLiWlNM8UEomLgqTDEvSo12GGKUx9XWqcb3q2S9HoyYwUaKdiTvn0IIBbEO/xPtXxsjF6bVG6JjldgjRiIG8EVUcXBGZPG8OaGzKcHlwGruoFzfjyP3k6qAUT0uJKfQcCqK79gU+Dmt7CE7IK119oqnn4QjuytFGzPFQcBT7l5VuVoU+Eqo+TcCNjWzkana36PnpkWY2a5ELmIzOwy5SXw7TPFnAW8Cr5vZ8uFaMRJF8+aipy/0aPObiU2oesbdz0ZuCtshifesTLSMAX5pZkPMbGuUjWNlxHBtiQICL0aS9s+A77r7GHf/e6fzE6dw98eQj+qzKCBxpLtfhjaBCWY2HLXbLu7+Ro7xGya6d9z9euTmMtHMlnPlZ14GuMrdL0VM6b+Dzlfd/U+dpiXoGW9mm8fnEciveTNUensG4DYzW9rdL0Kb1TaxNnQ8L2eFqt3NbDwSnq4zs/Pj9iHA+ma2r5l9HWn8phvUcpdujKw9Y9CG+SM0t3+FLFZPAZfEmp0j9/fc8J4r0FqIebgduUmdgNaeSWi/2Dbyq77pmUy8ZrYT2pcI16TN3H1Z4F4zWxRpII9CTPFlyIr22+r73div3HvkmT7F3W9p4r2JC9t9wFJmdg5ql+WAvyKL0d9jzczx/hlql/6FXKGqnNInI+XEIe5+NWLan4Z8/dSG53kQ7QuXIIHhSDSmqzz3PVxcOklXzS1paRQ/MQYFpr8BrGRmO7vc6m5Byq6CnFw3PaXu4fG3H3LEvx6ZyAagRaWK5J2p29JCkweKWH4Emb6uRFq9RVEuw88jk8aZSEOaTWODgm9Wis+LowWlKum8CtJcp5WuBtOAKwfaEB+jlWPxFKQxXgD5Eb8v+wXNleptvBoZbTI2oMwOJyHt9dwxbt5Ai9x3kT9x7rbYHTHBI+N8GNoUn4s5PhGlGhxE/jKrayWft0KMVh8UBPQUrbzfKyD3gIubGC+97aCVUWZ94Hu0LFWfQZrjM0iyTsS9TgewrhDrXrX27B7jeTkUrHk6YrrmiWePInO+ZFrWjXmQS8nTiKGaFGPpTaTJnpFEa800btn8D+22HioY9J3k2iAy5venp0VoM5LgS5Rd4TPx+Uq0x66VuQ1SfmdllJWkStX3KaSsqO7fBVxKYmnodNvQsiLUq1HujgqYgPawn5Ixteon8WhqkGyAUpHsnwyS79CqA34TMs0PTL4zTS8ysVFXA3eL2JiuphXdvDxiJIbHeTaBATF0+8cG9DkUAf7N+LtfPDMWmeebygecLnr3AdfH52tppUy6EjFjjZUFb0PnrShl3Eyx2OUUXmZAzELVJ6nP/qqIkTkkzg9E6cbGZ/797/mEIjeJJ4BBce0A4CvxeQtUaSsrk45cRraNz7OjdEPP0Erv9RnErG8X59OlCwVi7K5AmvJZEAO6Y3L/emR2zTq3kNvPl5HwP3eMo3kRI1O5UTyLtG5Z/WVp+XsORgz4qShV5tLIL7RitMbXxzHTeIncD9l+JwEHpG0Zn7P5zCLBZUrM6fOBPeL64yg70UjkSrZmp2n4ANo2QVaxu4kYmBjHf0J5nE+MfSxLetU2tNyNFBN7xB5yJK1MSgeidJkLdnv89KajiUFyVmxOk4C/0dJCnooYinuRD1CvScmWuT1SSW4oLU3olrEZjKTFHH+Llk9dLj/VsUgjvDTyJ34C+GLc+xzKG7pTnJ8JTG6yrZLPP40xdCgyr56KJO4RvaBPG6lGFu8aHQts5SfXL7m3ScylfWMBbCRvKQr0uxNp8b8J3BLXd40+Og5p/VfISMOnq80PaYbPCboWjjGcVnH6InIXGJprXvW2ozaXBgaz8P3k2m7ILelaFNR2B5nSn1HzrUQM8WSkvZoZMcY3xL3h0X9jc9DShrZVYs/aKNbfg0jyWCPz/I9pqDraJ+mIMdRDwZXhHek4/lTsUZXCbdsYK2shq+uDyEKUNYifsDDE53HIHXJorMEHx3iaH1l/L0UWz4H139MhWo6kFb+xBrJ2VGvgAyiT0xLIkvhArMtzd3vs9LYjxyCpNEh9kDn1CVr5JndEfmuLxMDZjajKE/enG6kbmZmfQDkmv4nMzUciP599UeDAyySVbzLQcAnS3K8Tm9HCyLfvjuSZsfHclnHeKCORjKd5kcvNlShQ4E5aOXG7XbkoezWy2vt2R+WL54rzmZN7h8WROwCxEu5GIiZqhzgfEmN4YqwB28b4WSczPTvEfFkszk+KDWgmlKXjXBQgWQmdWUyYvfGgp2WhYkaXRSmsdo/zGdHmPb62Jne82hayeoxPru2NAoEup1Wl7b6Y67+ngewu8c4xyFVrizhfM2jYkVZlzafoBcJ4bzxQrvv1GnrXF1HWlGdoFaSYA/EUlxBCOPlzWy8ATEjOd0HpDVeN80VpWUQWqn230wVxZkDa4Wpf+DwS5DaKdqraZA7kJrR9p+f3tHLkHCxXIt+1q5HfbKUZvRV4tl2ndrsxGmt0SWwvIMl2mViMb417ZyBt1lfI5KuKXCcuJ0lZldxbJvruiOTalwnGuEvtVTHHqyFt6aeTe9PNuKm1yckoCLFiUAeiIh7b5WwT5LKxADBfnE8I5iUtHrIA0kRMiPOcUdY7IfeJWVEwS1VQYDCKiq+qfm2LXAfWy01TbzqSuTMw5vXJRFlgJJy/wFTMzLnGEbJ6vI4yXxyNmOCFY2xdiNxu+qKUnTmtDP1q51NQla+RybWtkTZ7m2jDyt2iMBRdOlAluyko20WVXaFK4Tl/7J2HZV53FiAEqDi/higohar8PUFYGpC/8Wn0rJDYsfFDS/s8BLlwVBUFV4rxnLq3rQjs2e0+7O1HjgGzEPJPqzRId8bmWTHG+6IMApcm35mmN6nYjI6lpXVdgnDER1q1GZBbySYoBdnRudsEmQmrNHl9a/fWQumS9qxo7AVtWGm69kZp0GaY1sfNh2iTW1BqrcHIZHZF5vd9G/md34yCs9ZFpu5TkOvCnMmzo2gF1OZyA7okNsj1kEWhD/IzrPzRh6OsAVUKvU2nxzET7fB4rL1fQJHxG8e93WLzXLD2ndzrz+7I/ej25NoQJNjdSORUzfTu1P3IaMUs9EXm92Nrz08gfNLjvOvr4fR00DPAcTXkmvBDWkqBQ5EfeqUpnSczPX1jzXmOlvvWMUgrWwXbTQJuSr6TJU0m7487eZEWozw81uWJyOKxFlK67dztPu3tRyc6Jg2+GYKYwJeIDAZIE/AI0t7cgRjAlWIDm6a1fSj45xbkV3Qi0thsiUzvL5NoYZFE2UjuSWRKeYlWMEm9CMKnkYbkRsSo9xpmAvmUXUlD/rO9/aDl35ytvGlsBNcQwizKLLNtbEbrIs3faSjPdLYo9Bo9U7N4zIIyg5wW5ysjX7rpxie0zrihvNvboBzp9yPFxc9o+SIeScYiKx9A57HAC7Vrc8YczzK/kWvNhShbSR+Un/k55HozFJmYf0ISiFiO7h3RJ0cgZdGlMY5Xiz3g0OS5S5HfbG6BbhwtS9h4pPirSk1PQgHYhqwLjwDHNdBGo4nKpkhomDG5txStaoyP0EANhGnhqCSu/wpmNoNHDj4zG+Luf4mclMchxu8yd/+NmS2CNDqLu/uFZnY8MoOM9V5U7rmTiFyfzwJ/dvfRZjYQZX74l7ufFblDz0fO+X9DC/Nh7n5fQ/R9HW0Sx7j7X81soLv/w8wWQJH7DwK4Snn2CkSbHgy86O5Tuk1Pb4CZDUEMzoMZ3zED0s4e5u6vRRnet81sf+TKsCYaM3ui9HWn56IloelbwJfd/Xdm1tdV0tnc3c1sQaQpvtPdTzWz+d39V7lp6g2olaAdArwZbTMYBdXd5+4Xm9kUFOsxyt1fi+fNP86G8N/Reytigj/XwLvmRinFNkYKineR8P8QKiDySxTUuyxicDZx98eT7zfePgXv5fo/C43dL8S1McgV6HuufO2Y2Zru/khGOk5GQuZ2HuXlg5dZCBW5etXMbgd+7+57mtmcwN/c/c1cNCW07YKswCClwSzI1e6FuPZM0PW33LRMC/ivE7VXBQbi87XApWb2GIoUvRhFP25qZjO7+8/c/WHgitjQFnH3HaZVphjeqwE/HljazEa56p//EdjAzNZBpu8d0CI9HjiyKaY4cCsKltgr6K3qs5+OikH8pTcxxfBem55RmOIWop+yMcWB2ZGWZt44fzfefS4KAtwTBSXdilwbssLM5gh65opL7wQ9HgVq3kHptrYys9mnU6b4PLRBXgngKvzwZ+DtEDCrcuF/rL7fDabP3bcAFjezC3O+x8z2RYzDyohJuAuNoRfd/XfABWh87+Hu30UZVX5Xo7UwxQ2gTQGZl5GfN2a2cly7D8UxrB9KJnIxxWbW18weR9VFV3P3V6Lg1rLufhQqoPTlUCBsg/b4TVwFld5sU4Sk43AVmDoUeBu5tn0bWfZGIT/5XxSm+MPjI2uMzWwe4O3QHPVHPiyOsigciBaUyry6NfCwu18e3+2DzPfZKrf1NpjZ7sjH53BkDvoB8gldFEWqTgF+5e5vd4G2jVF/zYTcXHZBgZH7NU1LQe9GGwvD4Ph7IvBjd780Zcy6QE9q8VjJ3W8IofzvTdDTWxBr7D4o7dihSNPfx903MbO9kcZrFEq7eEx8p6uaUDObDbm63J/p/5+N3MMOAH4e42Q2VGJ6dpT14h+xHu4BXOjud8d3GxvTBe8T7sYCrwE/cfefm9lByG1gd3d/3cw+h5i/+3IzfWb2a+Aidz/WzHZDwX27uvuTZvYpZKF6zt2PMbNZ3f2NnPR8AJ23oUqD23Xj/dMKPjRjHKUFl0SazhtRYNY/zew0NDC/E8+dgTTCm5nZOOARd38xD/mfDMQm/mVktvxuCBTrokpOx3utJGTDtA1GLh7/B/zD3Sd3i5aC3gszWwEVg3jZ3U9Lrn8HBZJe2UvouQH4qbsf2SQ93UYwxI7WmY2Ac9392rj3IxSQeIyZzY9c2u6rvjctM35mNhK5qW3t7m8l7jaGTOCHIEXPwfH8qqExLugiTKWlF0NWqMNRXNLrSMm0DLJ23+nu5zREz1yowNXzSCt7hLs/n9xfCikJd0GKLu/W3DKznwIPufueTb97WsFH0hiHSeBhtAA/7e6HBCP8N3f/SjwzDJjo7ntM64vuR4GZ3Qy84+5bdZuWgoL/BomFYSDKTLEn8CN336PL9EyXFo+adm2mMNvOi5iHl4Fvufsvw/XkD8De7n5hu+9Pq4gxMs7dxyS+8e9pyM1seaT9e6nSoMf1ab5tehNqY3kMChLbx8xOQnmk30L5o93MDkWB+yc1TOOKKLvL5u5+V3J9LCrq8dcm/In/E3JbYKYHfCjGOFl0ByLz3F+QK8BLyIftAVQx6VGUTgu0SRW/rAQhyT3o7nt1m5aCgv8GYWE4EPgr2gi+9R++0gQ907XFI4KTtqLlN/xTVB3yLmCKu//RzJYB/uLuL3eN0C4gXGu+japSPhfXZnD3d8xsBMqkNAR4y92f7CKp0yVCc1/FB8wHvIE0sqBMTrO4+25m9jTwT3dfo0ukAu+5Rh6PSsz/3cwuQZkzdgw//oJpAH0/6GYEstwJ/N7MDnT3N8zsecQcT0BmqB+jCN9xKIjsd+5+SF6yP7FYCfleFxR8IhGL/ze6TUeFoOeEbtPRLcRGPQ6Zm+dDea3XQ6mj9gXeMrNbK7OvJZmEphP8HlX33MrMXnX3Xye/fy/gSXe/uXvkTd9INPfrowJFB6PKq25m/YjgUSTkbWtmC6NAsq4o3Nz9EjNbDHjBzF4AfusKIO26r35B5/AfNcah5RyBcpS+iSKdd0GpQGZECeKPD9/ZgR7ZDabDBbigoKAgK+omflO2hbfc/ZI43x74EhLCD0H+jtd2hdheAjNbAjHBs6M8+79FfscLANt2K1BqeoaZzeGtNIGLIYHuEI8MO2Y2E+qjN4HFkSb5QHf/c5dI7gEzuwnFMhwR54XfmYbwgRrjwMooH+8wlNv2TpSi6TV3nxymqglmtiMaxD1SuRUUFBQUfHxUTLEpP/HbwL9Q5autUfYJUPGODVGl0VO6Q2nvgru/FMFc+6Hc8a8Db7j7RlD8iZuGmV0OvGJm57j7qyjF6wvu/mDEMc0QrpsXAiNR5onDehNP4ZFPGQq/My3iw/oYr4Kcy1dEldD2B15x9y+b2VCUDujVrJQWFBQUTOeItfibyD1gpLt/wcyeQ0qLbwCbo3RtmwN/DEa6mHgDIVQMdPc/xHnR9DWEiAe4FfgFMN4jlaIpBez9wAFJtpRlgAHu/r1u0fthUISqaRMfJV3bXqge+LKuHMZzpsxwWXwLCgoK8sHMFkeFDvZHla1uQfl5/4GKKv0JBUXv6e7/0y06ezuSlG2FqWkQZrYtsIG77xbng1BGmTeB9ZEL0MR4/FTgBG84DWRBAXw4VwoAXKWcR6BqM4vXNcSFKS4oKCjoLGoazXdRIYFhqITx511VuGZy983NbACAK798YfqmgmqvKu3TOF4HFgkN8ZYoT/HGKAXaLcAZKP3iYOBgd7+nW4QWTN/4byrf3Q9c4u5X5yGpoKCgYPpFZAM6yt2Pi/PKt3gxpJj4A6ru948o2HEYcLK7/zp9vlv0FxS0g6lC3KHADigA8lKU+nVWYDV338lUqKafu7/VPUoLpnd8ZMa4oKCgoCAvzOyPSAHRI+rdzHYBvgZsCiyIcqpOdvczukZsQcFHgJkt7+7PmtmMrmqEGwJbeanUVtBL8KFdKeooWomCgoKCbFgK5Ur9cRRRMTPr6+6XheZtPDA/cGyVh7fEeRR8EuDuz8bft0yllo8AHuoqUQUFCYrGuKCgoKAXwsxGoewTG7v748n1DYAXUcrMf8a1whQXfGIQ2azWQplULnD3M7tMUkHBeyiMcUFBQUEvRVS2OxGY193/ZWa3AIOALap0VwUFnzREvuJVEQ/yaLfpKShIURjjgoKCgl4MMzsZFfH4Napkt31cL1rigoKCgg6jMMYFBQUFvRxmdgPwS3c/JM5LYYqCgoKCDCiMcUFBQcEnCIUpLigoKMiHPt0moKCgoKDgwyGyARWmuKCgoCATisa4oKCgoKCgoKCggKIxLigoKCgoKCgoKAAKY1xQUFBQUFBQUFAAFMa4oKCgoKCgoKCgACiMcUFBQUFBQUFBQQFQGOOCgoKCXgczG21mq3Xofy1hZt81s7fM7NBO/M+CgoKCaRV9u01AQUFBQcH7MBr4G/DEh/2CmfV193+3ufUnYDwwpjOkFRQUFEy7KBrjgoKCgoZgZreY2bNm9iMz2yuubWRmz5nZD83sfjMbDuwNHGxmPzCzNcxsuJk9YGbPxzMLxHcvM7Pzzewp4OR273T3V939aeDtD0HfcDN70cwuChrvMbOBcW9PM3s66LzRzGZKaDjPzJ40s5+Htvtb8X8uS/73BqG5fs7MrjezQR+rMQsKCgoyoDDGBQUFBc1hN3dfHlgBGG9mw4CLgC3dfVlga3d/GTgfOMPdl3P3R4FzgMnuvgxwFXB28j/nA1arykV3AIsCk9x9KeANYMu4fpO7rxh0vgjsnnxnNmBV4GDgNuAMYClgaTNbzsyGAkcD67n7Z4FngE7RW1BQUNAxFFeKgoKCguYw3sw+H5/nB/YCHnH3XwC4+5+m8r1VgS/E5yvoqR2+vsPV8H7h7j+Iz88Cw+Pzp83sG8CswCDg7uQ7t7u7m9kLwB/c/QUAM/tRfH8+YEngcTMD6A98t4M0FxQUFHQEhTEuKCgoaABmNhpYD1jV3d80s4eAHwBLfMx//feP+f063ko+vwMMjM+XAWPc/Ydmtgvyg65/593a999F+8w7wL3uvl2HaS0oKCjoKIorRUFBQUEzmAX4v2CKlwBWAQYAa5rZQgBm9ql49q/A4OS7TwDbxucdgEebIbkHBgO/M7N+QcNHwZPAKDNbBMDMZjazxTpNYEFBQcHHRdEYFxQUFDSDu4C9zexF4MeIWXwNuVPcZGZ9gFeB9YHbgRvMbAvggDguNbPD4ju7ftiXmtlcyKd3CPCumR0ELOnuf/mI9H8FeCre/xQ9GfcPhLu/Flrma8xsxrh8NPCTj0hDQUFBQVaYu3ebhoKCgoKCgoKCgoKuo7hSFBQUFBQUFBQUFFBcKQoKCgqmCZjZrsCBtcuPu/t+bZ6dHbi/zb9Z191fz0FfQUFBwScBxZWioKCgoKCgoKCggOJKUVBQUFBQUFBQUAAUxrigoKCgoKCgoKAAKIxxQUFBQUFBQUFBAVAY44KCgoKCgoKCggIA/j8zIA2dwprAMAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXswRtzAc5xD",
        "colab_type": "text"
      },
      "source": [
        "Após análise desses gráficos, podemos melhorar os dados:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G6XgJQS_dCnm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Retirando algumas colunas com dados categóricos\n",
        "df.drop(columns=['director_name', 'actor_1_name', 'actor_2_name', \n",
        "                 'actor_3_name', 'plot_keywords', 'movie_title'], axis=1, inplace=True)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mccqle0YdJqL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "44e6d763-07a0-4fa6-b564-7e7db5e87bb8"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4695, 21)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g-DCioDRdLps",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "da6e5691-1fb9-4762-ef91-70f095eeb964"
      },
      "source": [
        "# Verificando os valores da coluna de genero ('genre')\n",
        "df['genres'].value_counts()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Drama                                      209\n",
              "Comedy                                     186\n",
              "Comedy|Drama|Romance                       182\n",
              "Comedy|Drama                               180\n",
              "Comedy|Romance                             149\n",
              "                                          ... \n",
              "Comedy|Documentary|Drama                     1\n",
              "Biography|Documentary|History                1\n",
              "Adventure|Comedy|Crime|Family|Musical        1\n",
              "Adventure|Comedy|Music|Sci-Fi                1\n",
              "Adventure|Comedy|Family|Fantasy|Musical      1\n",
              "Name: genres, Length: 875, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vABwjhQ8dWh2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Retirando esses dados nao interessates para o modelo\n",
        "df.drop('genres', axis=1, inplace=True)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xT7KzMCSdjpX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Retirando as colunas criadas\n",
        "df.drop(columns=['Profit', 'Profit_Percentage'], axis=1, inplace=True)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lV9gdyW_drky",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4c6981c4-fa3b-4bc9-a75f-b7f3d38eb812"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4695, 18)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZsHbA6ZVdv1T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "outputId": "4a1eda42-716d-4fa7-fe41-a44d1e215526"
      },
      "source": [
        "# Verificando se existem colunas fortemente correlacionadas\n",
        "# Método corr --> Mapa de calor\n",
        "\n",
        "corr = df.corr()\n",
        "sns.set_context(\"notebook\", font_scale=1.0, rc={\"lines.linewidth\": 2.5})\n",
        "plt.figure(figsize=(13,7))\n",
        "mask = np.zeros_like(corr)\n",
        "mask[np.triu_indices_from(mask, 1)] = True\n",
        "a = sns.heatmap(corr,mask=mask, annot=True, fmt='.2f')\n",
        "rotx = a.set_xticklabels(a.get_xticklabels(), rotation=90)\n",
        "roty = a.set_yticklabels(a.get_yticklabels(), rotation=30)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WzBv1d1_jWST",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Criando uma nova coluna combinando as duas colunas muito correlacionadas\n",
        "df['Other_actors_facebook_likes'] = df['actor_2_facebook_likes'] + df['actor_3_facebook_likes']"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHcrSNPejc2i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Removendo as colunas correlacionadas\n",
        "df.drop(columns=['actor_2_facebook_likes', 'actor_3_facebook_likes',\n",
        "                 'cast_total_facebook_likes'], axis=1, inplace=True)"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fUXTJHuMjpRQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Criando uma nova coluna combinando as duas colunas muito correlacionadas\n",
        "# Razão entre o número de críticas por reviews e o número de usuários que fizeram reviews\n",
        "\n",
        "df['critic_review_ratio'] = df['num_critic_for_reviews']/df['num_user_for_reviews']"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FWAbm9Jhj-Bh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Removendo as colunas anteriores correlacionadas\n",
        "df.drop(columns=['num_critic_for_reviews', 'num_user_for_reviews'], axis=1, inplace=True)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5S1UVXdmkETS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "outputId": "f3bdcc46-3169-4a91-ba35-e859031e246e"
      },
      "source": [
        "# Verificando se ainda existem colunas fortemente correlacionadas\n",
        "\n",
        "corr = df.corr()\n",
        "sns.set_context(\"notebook\", font_scale=1.0, rc={\"lines.linewidth\": 2.5})\n",
        "plt.figure(figsize=(13,7))\n",
        "mask = np.zeros_like(corr)\n",
        "mask[np.triu_indices_from(mask, 1)] = True\n",
        "a = sns.heatmap(corr,mask=mask, annot=True, fmt='.2f')\n",
        "rotx = a.set_xticklabels(a.get_xticklabels(), rotation=90)\n",
        "roty = a.set_yticklabels(a.get_yticklabels(), rotation=30)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K_op7L_0kLj-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Categorizando os valores de nota do imdb\n",
        "df['imdb_binned_score']=pd.cut(df['imdb_score'], bins=[0,4,6,8,10], right=True, labels=False)+1"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNSjIfM1kdmw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "bb587d18-6186-46b6-a6bf-7e35c488b686"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>duration</th>\n",
              "      <th>director_facebook_likes</th>\n",
              "      <th>actor_1_facebook_likes</th>\n",
              "      <th>gross</th>\n",
              "      <th>num_voted_users</th>\n",
              "      <th>facenumber_in_poster</th>\n",
              "      <th>movie_imdb_link</th>\n",
              "      <th>content_rating</th>\n",
              "      <th>budget</th>\n",
              "      <th>title_year</th>\n",
              "      <th>imdb_score</th>\n",
              "      <th>aspect_ratio</th>\n",
              "      <th>movie_facebook_likes</th>\n",
              "      <th>Other_actors_facebook_likes</th>\n",
              "      <th>critic_review_ratio</th>\n",
              "      <th>imdb_binned_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1937</th>\n",
              "      <td>142.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11000.0</td>\n",
              "      <td>28341469.0</td>\n",
              "      <td>1689764</td>\n",
              "      <td>0.0</td>\n",
              "      <td>http://www.imdb.com/title/tt0111161/?ref_=fn_t...</td>\n",
              "      <td>R</td>\n",
              "      <td>25000000.0</td>\n",
              "      <td>1994.0</td>\n",
              "      <td>9.3</td>\n",
              "      <td>1.85</td>\n",
              "      <td>108000</td>\n",
              "      <td>1206.0</td>\n",
              "      <td>0.048021</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3466</th>\n",
              "      <td>175.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14000.0</td>\n",
              "      <td>134821952.0</td>\n",
              "      <td>1155770</td>\n",
              "      <td>1.0</td>\n",
              "      <td>http://www.imdb.com/title/tt0068646/?ref_=fn_t...</td>\n",
              "      <td>R</td>\n",
              "      <td>6000000.0</td>\n",
              "      <td>1972.0</td>\n",
              "      <td>9.2</td>\n",
              "      <td>1.85</td>\n",
              "      <td>43000</td>\n",
              "      <td>13000.0</td>\n",
              "      <td>0.092940</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2837</th>\n",
              "      <td>220.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>57300000.0</td>\n",
              "      <td>790926</td>\n",
              "      <td>1.0</td>\n",
              "      <td>http://www.imdb.com/title/tt0071562/?ref_=fn_t...</td>\n",
              "      <td>R</td>\n",
              "      <td>13000000.0</td>\n",
              "      <td>1974.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.85</td>\n",
              "      <td>14000</td>\n",
              "      <td>17000.0</td>\n",
              "      <td>0.229231</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>152.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>23000.0</td>\n",
              "      <td>533316061.0</td>\n",
              "      <td>1676169</td>\n",
              "      <td>0.0</td>\n",
              "      <td>http://www.imdb.com/title/tt0468569/?ref_=fn_t...</td>\n",
              "      <td>PG-13</td>\n",
              "      <td>185000000.0</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.35</td>\n",
              "      <td>37000</td>\n",
              "      <td>24000.0</td>\n",
              "      <td>0.138204</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4822</th>\n",
              "      <td>96.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>359.0</td>\n",
              "      <td>26005908.0</td>\n",
              "      <td>447785</td>\n",
              "      <td>1.0</td>\n",
              "      <td>http://www.imdb.com/title/tt0050083/?ref_=fn_t...</td>\n",
              "      <td>Not Rated</td>\n",
              "      <td>350000.0</td>\n",
              "      <td>1957.0</td>\n",
              "      <td>8.9</td>\n",
              "      <td>1.66</td>\n",
              "      <td>40000</td>\n",
              "      <td>512.0</td>\n",
              "      <td>0.199324</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      duration  director_facebook_likes  ...  critic_review_ratio  imdb_binned_score\n",
              "1937     142.0                      0.0  ...             0.048021                  4\n",
              "3466     175.0                      0.0  ...             0.092940                  4\n",
              "2837     220.0                      0.0  ...             0.229231                  4\n",
              "66       152.0                  22000.0  ...             0.138204                  4\n",
              "4822      96.0                      0.0  ...             0.199324                  4\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TY_lw8oWkpOy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Criando novas colunas para transformar os valores categóricos de 'content rating' (classificação indicativa) em valores numéricos\n",
        "df = pd.get_dummies(data = df, columns=['content_rating'], prefix=['content_rating'], drop_first=True)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EAuQOzzkk3OC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Salvando todos os dados feitos até o momento em um novo dataset\n",
        "df.to_csv('dados_imdb_com_nota.csv', index=False)"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bmRlsaL8lH5x",
        "colab_type": "text"
      },
      "source": [
        "A partir desse ponto vamos treinar o modelo escolhido, assim devemos escolher os dados e fornecer para o modelo a ser treinado e ver os resultados que ele vai prever. \n",
        "Faremos um Aprendizado Supervisionado"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QEr2sIJjlaeu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Escolhendo as colunas do dataframe que serão nossos valores de entrada para o modelo\n",
        "X=pd.DataFrame(columns=['duration','director_facebook_likes','actor_1_facebook_likes','gross',\n",
        "                        'num_voted_users','facenumber_in_poster','budget','title_year','aspect_ratio',\n",
        "                        'movie_facebook_likes','Other_actors_facebook_likes','critic_review_ratio',\n",
        "                        'content_rating_G','content_rating_GP',\n",
        "                        'content_rating_M','content_rating_NC-17','content_rating_Not Rated',\n",
        "                        'content_rating_PG','content_rating_PG-13','content_rating_Passed',\n",
        "                        'content_rating_R','content_rating_TV-14','content_rating_TV-G',\n",
        "                        'content_rating_TV-PG','content_rating_Unrated','content_rating_X'],data=df)"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_52xPYtuln6I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Escolhendo a(s) coluna(s) do dataframe que serão a saída (resposta) do modelo\n",
        "y = pd.DataFrame(columns=['imdb_binned_score'], data=df)"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-n5fcYaRlsZZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importando o pacote de divisão dos dados em treinamento e teste\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z8wYzj_0l069",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Dividindo os dados em treinamento e teste\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sLQZ1DAel5aT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Normalizando os dados\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_X = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_test = sc_X.transform(X_test)"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nycGcLjkl-MF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "89a78035-84b1-447b-d25e-190f474f94cc"
      },
      "source": [
        "#Verificando se existe colunas vazias\n",
        "X.isna().sum()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "duration                       0\n",
              "director_facebook_likes        0\n",
              "actor_1_facebook_likes         0\n",
              "gross                          0\n",
              "num_voted_users                0\n",
              "facenumber_in_poster           0\n",
              "budget                         0\n",
              "title_year                     0\n",
              "aspect_ratio                   0\n",
              "movie_facebook_likes           0\n",
              "Other_actors_facebook_likes    0\n",
              "critic_review_ratio            0\n",
              "content_rating_G               0\n",
              "content_rating_GP              0\n",
              "content_rating_M               0\n",
              "content_rating_NC-17           0\n",
              "content_rating_Not Rated       0\n",
              "content_rating_PG              0\n",
              "content_rating_PG-13           0\n",
              "content_rating_Passed          0\n",
              "content_rating_R               0\n",
              "content_rating_TV-14           0\n",
              "content_rating_TV-G            0\n",
              "content_rating_TV-PG           0\n",
              "content_rating_Unrated         0\n",
              "content_rating_X               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cfcCxVZumUx1",
        "colab_type": "text"
      },
      "source": [
        "Utilização de modelo de regressão logística. Ele tenta descobrir uma função matemática que simule a distribuição dos dados. Modelo mais simples, disponível no sklearning."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ksE83z4omYEU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "3364fb77-0f21-44cb-c678-ae88f573400a"
      },
      "source": [
        "# Importando, configurando e treinando o modelo de regressão \n",
        "from sklearn.linear_model import LogisticRegression\n",
        "logit =LogisticRegression(verbose=1, max_iter=1000)\n",
        "logit.fit(X_train,np.ravel(y_train,order='C'))\n",
        "y_pred=logit.predict(X_test)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.3s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LNqcnJ-vmgUd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f0a82ff7-e17e-443d-f4cc-ac237677f810"
      },
      "source": [
        "# Verificando os valores preditos\n",
        "y_pred"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 3, 3, ..., 2, 3, 3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9mOllHaJmsSh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importando o pacote de métricas e calculando a matriz de confusão\n",
        "from sklearn import metrics\n",
        "cnf_matrix =  metrics.confusion_matrix(y_test, y_pred)"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QEzgeynXm568",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "4e4c815c-045a-4842-e8ad-7e81fe09c694"
      },
      "source": [
        "# Visualização simples da matriz de confusão:\n",
        "print(cnf_matrix)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[  0  28  17   0]\n",
            " [  1 159 232   0]\n",
            " [  3 103 789   9]\n",
            " [  0   0  31  37]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ircUxqVSmydl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Código para melhor visualização da matriz de confusão\n",
        "import itertools\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "    plt.tight_layout()"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ffhodCXsnG5S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "f52e74fd-3f50-45c7-8522-2a6701b05e54"
      },
      "source": [
        "# Imprimindo a matriz de confusão\n",
        "plot_confusion_matrix(cnf_matrix, classes=['1','2', '3', '4'],\n",
        "                      title='Matriz de confusão não normalizada', normalize=False)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BEUW3XLWnM-u",
        "colab_type": "text"
      },
      "source": [
        "Um modelo de Machine Learn não se dá muito bem com dados desbalanceados, ou seja, quando se tem mais dados de uma categoria, ela é que vai ser melhor prevista, como por exemplo a categoria 3 de filmes bons, foi da qual mais o modelo se aproximou, será que é esta categoria que tem mais dados? Vamos ver abaixo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I-vkfk0NqNQR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "c4350d2f-913f-47f9-ec87-0633c3ac5fd1"
      },
      "source": [
        "# Verificando quantos valores existem de cada categoria em 'imdb_binned_score'\n",
        "df['imdb_binned_score'].value_counts()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    3015\n",
              "2    1323\n",
              "4     203\n",
              "1     154\n",
              "Name: imdb_binned_score, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWNZ7dKkqP0O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "c9fdbdfe-e98d-4ef6-a7f3-ef5b550d4f8d"
      },
      "source": [
        "# Métricas finais, outro modo de olhar o número de amostras de cada categoria/classe\n",
        "print(metrics.classification_report(y_test, y_pred, target_names=['1','2', '3', '4']))"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.00      0.00      0.00        45\n",
            "           2       0.55      0.41      0.47       392\n",
            "           3       0.74      0.87      0.80       904\n",
            "           4       0.80      0.54      0.65        68\n",
            "\n",
            "    accuracy                           0.70      1409\n",
            "   macro avg       0.52      0.46      0.48      1409\n",
            "weighted avg       0.66      0.70      0.67      1409\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQ9LSVNHqPq0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Definindo em qual caminho vamos salvar o modelo em uma variável para ficar mais organizado. ex: modelo_treinado.\n",
        "modelo_treinado = 'modelo_imdb.sav'"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kzXlNWhZqbAQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Salvando o modelo\n",
        "pickle.dump(logit, open(modelo_treinado, 'wb'))"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F6gEGHL3qqu3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Carregando o modelo treinado\n",
        "modelo_carregado = pickle.load(open(modelo_treinado, 'rb'))"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXLMyDQHqsJC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "fbcb7131-c310-4641-8c02-4a161da8c1da"
      },
      "source": [
        "# Olhando o conteúdo de um vetor de teste\n",
        "X_test[0]"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.11125319,  0.12234245, -0.47077098, -0.56684582,  0.102644  ,\n",
              "       -0.18064455, -0.29363035,  0.56016395, -0.41895874,  0.10788607,\n",
              "       -0.40176212,  0.17842293, -0.14967295, -0.04276995, -0.03491087,\n",
              "       -0.04276995, -0.14093771, -0.41056461, -0.65997074, -0.03903749,\n",
              "        1.05121875, -0.0302291 , -0.0302291 , -0.01744746, -0.11240473,\n",
              "       -0.05240624])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QSXb_leGqtj1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e10f24b2-fcee-4bf1-8cbc-79befc86ff9d"
      },
      "source": [
        "# Fazendo predição do novo dado com o modelo carregado\n",
        "modelo_carregado.predict([X_test[0]])"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y-fz6a4oqu2F",
        "colab_type": "text"
      },
      "source": [
        "O resultado significa o filme com os valores do vetor testado (X_test[0]) estão dentro da categoria de filme 3, que significa a classe de filmes bons."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VVZKzqoK3LKZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a72809a2-1419-4085-947e-48bc2db93001"
      },
      "source": [
        "# Fazendo predição de outro vetor\n",
        "modelo_carregado.predict([X_test[8]])"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    }
  ]
}