anime-recommendation-system-content-based-filtering.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v3EJaPMzm-1a"
      },
      "source": [
        "# Data Collection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M8GmZ0NrnGGZ"
      },
      "source": [
        "Nama : Irbah Labibah Nur Saidah"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vu6dUMZlnGeN"
      },
      "source": [
        "Dataset ini diambil dari : https://www.kaggle.com/datasets/CooperUnion/anime-recommendations-database"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CNjEJC7Hm9S-"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ouR8RC1oXWX6",
        "outputId": "ab970065-49e3-4e08-f3a7-62260ff0d598"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gdown/cli.py:131: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  category=FutureWarning,\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1-9Y7fxzaY53xjetyh1OEAz5fVAjYJeUB\n",
            "To: /content/anime.csv\n",
            "100% 936k/936k [00:00<00:00, 140MB/s]\n",
            "/usr/local/lib/python3.7/dist-packages/gdown/cli.py:131: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  category=FutureWarning,\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1Gpi4HO8oVKLqabMFfn7e3boOm1agl149\n",
            "To: /content/rating.csv\n",
            "100% 111M/111M [00:01<00:00, 77.0MB/s] \n"
          ]
        }
      ],
      "source": [
        "!gdown --id \"1-9Y7fxzaY53xjetyh1OEAz5fVAjYJeUB\"\n",
        "!gdown --id \"1Gpi4HO8oVKLqabMFfn7e3boOm1agl149\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EnWIo-9AnktF"
      },
      "source": [
        "Terdapat 2 file, yaitu anime dan rating anime."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f9yBM4PcnZ_F"
      },
      "outputs": [],
      "source": [
        "anime = pd.read_csv(\"/content/anime.csv\")\n",
        "anime_rating = pd.read_csv(\"/content/rating.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "epddijDiol0p"
      },
      "source": [
        "# Data Understanding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 797
        },
        "id": "x75cAkTKoLxR",
        "outputId": "30b796d6-d80e-4256-f2c7-3105e1ee8ee2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       anime_id                                               name  \\\n",
              "0         32281                                     Kimi no Na wa.   \n",
              "1          5114                   Fullmetal Alchemist: Brotherhood   \n",
              "2         28977                                           Gintama°   \n",
              "3          9253                                        Steins;Gate   \n",
              "4          9969                                      Gintama&#039;   \n",
              "...         ...                                                ...   \n",
              "12289      9316       Toushindai My Lover: Minami tai Mecha-Minami   \n",
              "12290      5543                                        Under World   \n",
              "12291      5621                     Violence Gekiga David no Hoshi   \n",
              "12292      6133  Violence Gekiga Shin David no Hoshi: Inma Dens...   \n",
              "12293     26081                   Yasuji no Pornorama: Yacchimae!!   \n",
              "\n",
              "                                                   genre   type episodes  \\\n",
              "0                   Drama, Romance, School, Supernatural  Movie        1   \n",
              "1      Action, Adventure, Drama, Fantasy, Magic, Mili...     TV       64   \n",
              "2      Action, Comedy, Historical, Parody, Samurai, S...     TV       51   \n",
              "3                                       Sci-Fi, Thriller     TV       24   \n",
              "4      Action, Comedy, Historical, Parody, Samurai, S...     TV       51   \n",
              "...                                                  ...    ...      ...   \n",
              "12289                                             Hentai    OVA        1   \n",
              "12290                                             Hentai    OVA        1   \n",
              "12291                                             Hentai    OVA        4   \n",
              "12292                                             Hentai    OVA        1   \n",
              "12293                                             Hentai  Movie        1   \n",
              "\n",
              "       rating  members  \n",
              "0        9.37   200630  \n",
              "1        9.26   793665  \n",
              "2        9.25   114262  \n",
              "3        9.17   673572  \n",
              "4        9.16   151266  \n",
              "...       ...      ...  \n",
              "12289    4.15      211  \n",
              "12290    4.28      183  \n",
              "12291    4.88      219  \n",
              "12292    4.98      175  \n",
              "12293    5.46      142  \n",
              "\n",
              "[12294 rows x 7 columns]"
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              "      <th></th>\n",
              "      <th>anime_id</th>\n",
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              "      <td>Movie</td>\n",
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              "      <td>9.37</td>\n",
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              "      <td>5114</td>\n",
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              "      <td>TV</td>\n",
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              "      <td>28977</td>\n",
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              "      <td>Sci-Fi, Thriller</td>\n",
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              "      <td>9.17</td>\n",
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              "      <td>9969</td>\n",
              "      <td>Gintama&amp;#039;</td>\n",
              "      <td>Action, Comedy, Historical, Parody, Samurai, S...</td>\n",
              "      <td>TV</td>\n",
              "      <td>51</td>\n",
              "      <td>9.16</td>\n",
              "      <td>151266</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>9316</td>\n",
              "      <td>Toushindai My Lover: Minami tai Mecha-Minami</td>\n",
              "      <td>Hentai</td>\n",
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              "      <th>12290</th>\n",
              "      <td>5543</td>\n",
              "      <td>Under World</td>\n",
              "      <td>Hentai</td>\n",
              "      <td>OVA</td>\n",
              "      <td>1</td>\n",
              "      <td>4.28</td>\n",
              "      <td>183</td>\n",
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              "    <tr>\n",
              "      <th>12291</th>\n",
              "      <td>5621</td>\n",
              "      <td>Violence Gekiga David no Hoshi</td>\n",
              "      <td>Hentai</td>\n",
              "      <td>OVA</td>\n",
              "      <td>4</td>\n",
              "      <td>4.88</td>\n",
              "      <td>219</td>\n",
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              "    <tr>\n",
              "      <th>12292</th>\n",
              "      <td>6133</td>\n",
              "      <td>Violence Gekiga Shin David no Hoshi: Inma Dens...</td>\n",
              "      <td>Hentai</td>\n",
              "      <td>OVA</td>\n",
              "      <td>1</td>\n",
              "      <td>4.98</td>\n",
              "      <td>175</td>\n",
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              "    <tr>\n",
              "      <th>12293</th>\n",
              "      <td>26081</td>\n",
              "      <td>Yasuji no Pornorama: Yacchimae!!</td>\n",
              "      <td>Hentai</td>\n",
              "      <td>Movie</td>\n",
              "      <td>1</td>\n",
              "      <td>5.46</td>\n",
              "      <td>142</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12294 rows × 7 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-55bdfdf2-8d38-4434-8f2c-8c4c5c55ff65')\"\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "anime"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QYJs05dtPGiA"
      },
      "source": [
        "Ini adalah dataset dari setiap judul anime beserta dengan genre, tipe, epidoses, rata-rata rating, dan jumlah anggota komunitas anime tersebut."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eCGCJ2qVqbRj",
        "outputId": "4674d293-9a66-4ccd-eaa7-9ef298e40e41"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12294, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "anime.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z5YFKcuEqiNO"
      },
      "source": [
        "Data ini memiliki 12294 sample dan 7 fitur. Beberapa judul anime memiliki symbol."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "id": "AHl3dOBOoh3g",
        "outputId": "4bcfc430-d6c6-409c-de5a-2e51dde74f8e"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         user_id  anime_id  rating\n",
              "0              1        20      -1\n",
              "1              1        24      -1\n",
              "2              1        79      -1\n",
              "3              1       226      -1\n",
              "4              1       241      -1\n",
              "...          ...       ...     ...\n",
              "7813732    73515     16512       7\n",
              "7813733    73515     17187       9\n",
              "7813734    73515     22145      10\n",
              "7813735    73516       790       9\n",
              "7813736    73516      8074       9\n",
              "\n",
              "[7813737 rows x 3 columns]"
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5503c91c-6af1-4ad1-96d2-fecc064b654d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5503c91c-6af1-4ad1-96d2-fecc064b654d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "anime_rating"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g5zpXlKsPhuh"
      },
      "source": [
        "Ini adalah dataset dari pemberian rating anime dari setiap user."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4XyrAdCNqdeC",
        "outputId": "bb8398fa-a9ed-43cf-907c-2254ec22a528"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7813737, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "anime_rating.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KZqEO_2iqmdZ"
      },
      "source": [
        "Data ini memiliki 7813737 sample dan 3 fitur."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gT16nKQZn5HJ",
        "outputId": "dfa7290d-28cf-4b91-b711-e19d000dd488"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jumlah judul anime:  12294\n",
            "Jumlah genre anime:  3265\n",
            "Jumlah sample rating anime:  73515\n"
          ]
        }
      ],
      "source": [
        "print('Jumlah judul anime: ', len(anime.anime_id.unique()))\n",
        "print('Jumlah genre anime: ', len(anime.genre.unique()))\n",
        "print('Jumlah sample rating anime: ', len(anime_rating.user_id.unique()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QZkKJWhas-b2",
        "outputId": "664c8aaf-baba-4b59-e0f2-fea16dd9903e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 12294 entries, 0 to 12293\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   anime_id  12294 non-null  int64  \n",
            " 1   name      12294 non-null  object \n",
            " 2   genre     12232 non-null  object \n",
            " 3   type      12269 non-null  object \n",
            " 4   episodes  12294 non-null  object \n",
            " 5   rating    12064 non-null  float64\n",
            " 6   members   12294 non-null  int64  \n",
            "dtypes: float64(1), int64(2), object(4)\n",
            "memory usage: 672.5+ KB\n"
          ]
        }
      ],
      "source": [
        "anime.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A2dU9NvStBYA",
        "outputId": "3712e578-07df-4e19-e55d-f2e447f7892c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7813737 entries, 0 to 7813736\n",
            "Data columns (total 3 columns):\n",
            " #   Column    Dtype\n",
            "---  ------    -----\n",
            " 0   user_id   int64\n",
            " 1   anime_id  int64\n",
            " 2   rating    int64\n",
            "dtypes: int64(3)\n",
            "memory usage: 178.8 MB\n"
          ]
        }
      ],
      "source": [
        "anime_rating.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mxFnt-vetNNq",
        "outputId": "4f091825-57eb-4c0e-ffff-b2694cebe285"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "anime_id      0\n",
              "name          0\n",
              "genre        62\n",
              "type         25\n",
              "episodes      0\n",
              "rating      230\n",
              "members       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "anime.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kRzcE-FntlMd"
      },
      "outputs": [],
      "source": [
        "anime = anime.dropna()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TPLaTIHjt11A"
      },
      "source": [
        "Menghapus missing value pada dataset anime."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1YfT2aEFJzgF",
        "outputId": "5affa894-4216-46fd-a756-6c3f04693184"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12017, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "anime.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ysazl5VztSxp",
        "outputId": "14187d7d-5235-4199-efbe-ee96e4e4b358"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "user_id     0\n",
              "anime_id    0\n",
              "rating      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "anime_rating.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wmIMhhqjt7oU"
      },
      "source": [
        "Tidak ada missing value pada data rating anime"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G-9KDGbevUUU",
        "outputId": "b4f67363-0252-482c-a61b-656128c4eb50"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Terdapat 0 duplikat dari 12017 sample pada anime dataset.\n"
          ]
        }
      ],
      "source": [
        "dup_anime = anime[anime.duplicated()].shape[0]\n",
        "print(f\"Terdapat {dup_anime} duplikat dari {anime.shape[0]} sample pada anime dataset.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "06OpZ4qhuvwN",
        "outputId": "23c7b653-fdae-43a9-a8d6-e95303a19c7c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Terdapat 1 duplikat dari 7813737 sample pada rating anime dataset.\n"
          ]
        }
      ],
      "source": [
        "dup_rating = anime_rating[anime_rating.duplicated()].shape[0]\n",
        "print(f\"Terdapat {dup_rating} duplikat dari {anime_rating.shape[0]} sample pada rating anime dataset.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49R9UyHTvEvs",
        "outputId": "5cf91c76-1ce2-4ffd-9fa4-b74fc3f98515"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sample rating anime dataset sekarang adalah 7813736.\n"
          ]
        }
      ],
      "source": [
        "# Menghapus duplikat sample dari sample rating anime dataset\n",
        "anime_rating.drop_duplicates(keep='first',inplace=True)\n",
        "print(f\"Sample rating anime dataset sekarang adalah {anime_rating.shape[0]}.\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Menghapus symbol pada judul anime\n",
        "import re\n",
        "def text_cleaning(text):\n",
        "    text = re.sub(r'&quot;', '', text)\n",
        "    text = re.sub(r'.hack//', '', text)\n",
        "    text = re.sub(r'&#039;', '', text)\n",
        "    text = re.sub(r'A&#039;s', '', text)\n",
        "    text = re.sub(r'I&#039;', 'I\\'', text)\n",
        "    text = re.sub(r'&amp;', 'and', text)\n",
        "    \n",
        "    return text\n",
        "\n",
        "anime['name'] = anime['name'].apply(text_cleaning)"
      ],
      "metadata": {
        "id": "UuqpB5ftxbK1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db6004df-b30c-4073-8fcf-1779409c34c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  del sys.path[0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2iQmTtTgrCyz"
      },
      "source": [
        "## Univariate Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "aYaDv8vBuWJn",
        "outputId": "908cb42c-7a56-465c-933e-3b58dd2e0cf5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       anime_id    rating     members\n",
              "count  12017.00  12017.00    12017.00\n",
              "mean   13638.00      6.48    18348.88\n",
              "std    11231.08      1.02    55372.50\n",
              "min        1.00      1.67       12.00\n",
              "25%     3391.00      5.89      225.00\n",
              "50%     9959.00      6.57     1552.00\n",
              "75%    23729.00      7.18     9588.00\n",
              "max    34519.00     10.00  1013917.00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f9f5617a-e121-4867-a979-cdbd6e45ee1a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>anime_id</th>\n",
              "      <th>rating</th>\n",
              "      <th>members</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>12017.00</td>\n",
              "      <td>12017.00</td>\n",
              "      <td>12017.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>13638.00</td>\n",
              "      <td>6.48</td>\n",
              "      <td>18348.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>11231.08</td>\n",
              "      <td>1.02</td>\n",
              "      <td>55372.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.67</td>\n",
              "      <td>12.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3391.00</td>\n",
              "      <td>5.89</td>\n",
              "      <td>225.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9959.00</td>\n",
              "      <td>6.57</td>\n",
              "      <td>1552.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>23729.00</td>\n",
              "      <td>7.18</td>\n",
              "      <td>9588.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
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              "      <td>10.00</td>\n",
              "      <td>1013917.00</td>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
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              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-f9f5617a-e121-4867-a979-cdbd6e45ee1a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "          const element = document.querySelector('#df-f9f5617a-e121-4867-a979-cdbd6e45ee1a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "anime.describe().apply(lambda s: s.apply('{0:.2f}'.format))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3YZKcaR2MlrX"
      },
      "source": [
        "Dataset anime memiliki rating anime terendah 1.67 dan rating tertinggi 10 dengan rata-rata 6.48. Dataset ini juga memiliki jumlah anggota komunitas anime terendah 12 dan terbanyak 1013917 dengan rata-rata 18348. Perbedaan nilai min dan max dari jumlah anggota komunitas anime cukup jauh dan hal ini wajar dikarenakan beberapa anime memang sangat populer dan beberapa tidak."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
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          "height": 300
        },
        "id": "iGNC-17QuZQu",
        "outputId": "68982c80-5619-4b4e-83b8-b69fc6ca55b1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          user_id    anime_id      rating\n",
              "count  7813736.00  7813736.00  7813736.00\n",
              "mean     36727.96     8909.07        6.14\n",
              "std      20997.95     8883.95        3.73\n",
              "min          1.00        1.00       -1.00\n",
              "25%      18974.00     1240.00        6.00\n",
              "50%      36791.00     6213.00        7.00\n",
              "75%      54757.00    14093.00        9.00\n",
              "max      73516.00    34519.00       10.00"
            ],
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              "      <td>7813736.00</td>\n",
              "      <td>7813736.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>36727.96</td>\n",
              "      <td>8909.07</td>\n",
              "      <td>6.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>20997.95</td>\n",
              "      <td>8883.95</td>\n",
              "      <td>3.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>-1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
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              "      <td>1240.00</td>\n",
              "      <td>6.00</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>36791.00</td>\n",
              "      <td>6213.00</td>\n",
              "      <td>7.00</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>54757.00</td>\n",
              "      <td>14093.00</td>\n",
              "      <td>9.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>73516.00</td>\n",
              "      <td>34519.00</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "                                                     [key], {});\n",
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          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "anime_rating.describe().apply(lambda s: s.apply('{0:.2f}'.format))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nn83g6N4wh-M"
      },
      "source": [
        "Dataset rating anime memiliki rating terendah yang diberikan user pada suatu anime adalah -1 dan rating tertinggi adalah 10. Rating -1 menandakan bahwa user menonton anime, namun tidak memberikan rating."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CDtmnfWubqql"
      },
      "outputs": [],
      "source": [
        "anime_rating = anime_rating[~(anime_rating.rating == -1)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "id": "ZpKsT7z2b577",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          user_id    anime_id      rating\n",
              "count  6337240.00  6337240.00  6337240.00\n",
              "mean     36747.91     8902.87        7.81\n",
              "std      21013.40     8882.00        1.57\n",
              "min          1.00        1.00        1.00\n",
              "25%      18984.00     1239.00        7.00\n",
              "50%      36815.00     6213.00        8.00\n",
              "75%      54873.00    14075.00        9.00\n",
              "max      73516.00    34475.00       10.00"
            ],
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              "      <th></th>\n",
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              "      <td>6337240.00</td>\n",
              "      <td>6337240.00</td>\n",
              "      <td>6337240.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>36747.91</td>\n",
              "      <td>8902.87</td>\n",
              "      <td>7.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>21013.40</td>\n",
              "      <td>8882.00</td>\n",
              "      <td>1.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>18984.00</td>\n",
              "      <td>1239.00</td>\n",
              "      <td>7.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>36815.00</td>\n",
              "      <td>6213.00</td>\n",
              "      <td>8.00</td>\n",
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              "      <th>75%</th>\n",
              "      <td>54873.00</td>\n",
              "      <td>14075.00</td>\n",
              "      <td>9.00</td>\n",
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              "      <th>max</th>\n",
              "      <td>73516.00</td>\n",
              "      <td>34475.00</td>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "anime_rating.describe().apply(lambda s: s.apply('{0:.2f}'.format))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sgryA4Pob98Z"
      },
      "source": [
        "Sample user yang tidak memberikan rating tidak akan digunakan sehingga dihapus."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3l8QEzeHsNdp",
        "outputId": "3bb0718b-81c3-4a48-e2be-db44f20d16a8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "genre\n",
              "Action                                                     53\n",
              "Action, Adventure                                          15\n",
              "Action, Adventure, Cars, Comedy, Sci-Fi, Shounen            2\n",
              "Action, Adventure, Cars, Mecha, Sci-Fi, Shounen, Sports     1\n",
              "Action, Adventure, Cars, Sci-Fi                             1\n",
              "                                                           ..\n",
              "Super Power, Supernatural, Vampire                          1\n",
              "Supernatural                                               14\n",
              "Thriller                                                    1\n",
              "Vampire                                                     1\n",
              "Yaoi                                                        2\n",
              "Name: genre, Length: 3229, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "anime.groupby('genre')['genre'].agg('count')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "ulvW-8vLR0xw",
        "outputId": "fc2edab6-7731-456b-ff70-978698e1b156"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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LwCWFeY4600GEtCg6DGdxwQhgNTCjm63qmMsT319mOpMQBs0HlucXeXqZDiKkUHQIzuKCE4GV1FtYaLJ964xZ9uJV5lIJYdxEYHVw/JAwSAqFYc7igp8DbwJZ9Z9XCnVR4vLR/dXezWaSCdEh9ASWydQfZkmhMMhZXPAr4C+AvanXlSL9lqRXk9Opq4hsMiE6lGSsm9w/Mx0kXkmhMMRZXHAn8PCRtktQgT73JxfusBHwhT+VEB2WDfhrfpHnZtNB4pEUCgOcxQUPA3e0dPsM5Rp/o+O1j8MYSYhocV9+kecR0yHijXSPjSBncYENeBS4rC37v+kdv+xfvqNnhDaVEFHpr8AVhXmOgOkg8UBaFBHiLC6wA/+kjUUC4OSEtceMs337RehSCRG1fg78M7/I0+T9PRFaUigiwFlc4ABeBC5oz3GUIvFax9u9uqgDO0OTTIiodgFQmF/kSTAdJNZJoQgzZ3FBCvA61gpf7WZT5N6T9GJtEt7aUBxPiCh3FlbLQj7Lwkh+uGEUbEm8DpwYyuMmKd+wu5JeXAdyg0kI4HysVfNEmEihCJPgjeuwrSPRzVZ1zBUyzYcQB/1MekOFjxSK8PkjkB/OE0yyb50xW6b5EOKg6/KLPC3udi5aTgpFGDiLC24Brg73eZRCXZi4fMwAVbop3OcSIkrcmV/kuc50iFgj4yhCzFlc8FMgomsA+7Xa+UvXRSnVpORE8rxCdGA/L8xz/M10iFghLYoQchYXnIY1d1NE2ZXufV/y4u/s+L2RPrcQHdTj+UWekPQ0FFIoQsZZXDAdWMxhJvgLtwzlHn+j4/WVJs4tRAdkA57OL/JMMB0kFkihCAFnccForG6wySZzDLaXzjgnYZX0hBLCkga8nl/k6WE6SLSTQtFOzuKCnsA7QCfTWQBOkmk+hKivN/BafpEnxXSQaCaFoh2cxQWJWFNzdJjlGoPTfPTuKtN8CHHQJOAf+UUeWYO+jaRQtM/vgKmmQzRmU+TcnfSiU6b5EOJ784E7TYeIVlIo2shZXLCACIyVaKsk5Rt6d9IL62WaDyG+d3t+kec80yGikYyjaANnccEYYBWQajrLkaz2DfrwMe8Js0znEKKDcAHHFOY51poOEk2kRdFKzuKCLOBloqBIAEyyb50p03wI8b1krKnJ000HiSZSKFrBWVygsCb6G2w6S0vJNB9CHGIoMttsq0ihaJ2bgR+bDtFaSpF2S9KrqRnUlZvOEq38bhfvX3ws756fxzvzx7PhL3d//1rNrm28f8k03jpjBCtvWoDf6zlk/9rd23lpWhbvnT+J986fRNEDVwHgczlZ/j+n8fbZY3hn/njWLbqlyfPvXPIK78wfz5KfHYe70vo11uzcysqbFoTh3caFBflFnktNh4gWco+ihZzFBSdgjZeI2uJarZPW/tJ10Sg/9kTTWaKN1hpfXS2JqekEfF6WLJzNhF89Qs6YKXx80/n0nn06fefOp+iBq+g0ZCyDz2644m3t7u0s/+UZzCtc0+B5n8tJxYbVdM2bhd/rYemV8xhxyQ30OHZeg+0+uOwEpv/xNXYteRVP9X6G5F/FylsuZPRlt5PRd0jY33+McgJ5hXmOjaaDdHRR+6EXSc7igu7Ac0T5zytDucffmPS63K9oA6UUianWZe2Az0vA5wWl0Fqz99MP6X3cmQD0/9GF7Fr6eouPm5CcSte8WQDYEx10HjYe595dh57fZiPgceNzOVEJiZSt+YjknG5SJNonFXhBBuMdWVR/8EXQ40Cu6RChMNhWOl2m+WibgN/Pe+dP4vW5vek25XhyRk/Gc6AcR0YWtgRr2ebUrr2o27u7yf1rd2/nvQWT+eDncyhb89Ehr3uqK9m9/E26TZp9yGvDL7mepVedxO7lb9L3xHy+/L/7GfnTm0P7BuPTaKDAdIiOThYlP4LgeInTTOcIpZMS1h6zOdB97dpA//Gms0QTm93O3Oc+xVNdyYrr53NgSzHJud1atG9ybg9O+fcWkjrlULHxc1b8+hzmFa4hMT0TgIDPx6pbLmRI/lWk9x54yP7dp8yh+xRrscTtbz5Dj6nzqNmxmc+e+T2JGZ2Z8OtHSEiOio54HdHC/CLP+4V5jkLTQToqaVE0w1lc0IMY/GtDKRKvcbzTR6b5aBtHRie6TpzJnpXv4sjKwVN9gIDPB4Bz7y5SuvY8ZB+7I4mkTtZyIdkjjiK990Cqd2z+/vWi+68kve9ghp5/bbPn9rmcbH/jaQbPv4INf72byXf+H13GT+Xbt58P4TuMS3/KL/J0MR2io5JC0by/ANmmQ4SDTPPROq79ZXiqKwHwueooXf1fMvsPQylF17yZ7FzyMgDb33yaXjNObXL/gN8PQM3Ob6j5bgtpvQYAsP6xO/DWHGDCdUde8vnrp3/HkPwrsSUk4ne7QCmw2fC7nKF6q/EqF1hkOkRHJb2eDsNZXHAh1piJmLY3kLHqN+7zp1ifOOJwKjevZ/WdP0UH/OhAgD5zzmbUz6yurDU7v2HVLRfiqaqg07DxTLn7H9gdSexa+m/2b/yc0Zffwc4lr7Dh8buwJSSCzcbon99Gzxmn4CzdyRunDCKj/zDsiUkADJ5/BQNP/8khGerKdlN03xVM/8NrAHz3/ksU//UeEjM6cezDL5LcWf4gDoHTC/Mcr5kO0dFIoWhC8JJTMdDZdJZI+NQ/cOmjnrkzTecQogPYA4wszHNUmg7Skcilp6b9lTgpEgB5tm9myDQfQgDQAzjyNcA4I4WiEWdxwcXAKaZzRJJM8yFEAz/JL/KcYDpERyKXnuoJDqzbSAdZrS7S/Frt/KXropRqUnJMZxHCsG+B0YV5jhrTQToCaVE0dD9xWiQA7Er3vi958Xd2/F7TWYQwrB9wh+kQHYW0KIKcxQUTgCKkeLIl0HXZfe4zZ5jOIYRhHmBUYZ5ji+kgpsX9h2I9v0d+HgAMtu2dMT9h5XLTOYQwzAE8bDpERyAfjICzuOAMQLqH1jMv4Yujx9u2yypgIt6dll/kOc50CNPi/tKTs7jAAXwJDDKdpaMJaMpvcp9Xt1dn9TadRQiD1gMTCvMcftNBTJEWBVyLFIkmyTQfQgAwBlhoOoRJcd2icBYXdAE2A1mms3RkMs2HEJQBgwvzHFWmg5gQ7y2Ku5AicURdbdVHX+n4z1LTOYQwqAtwm+kQpsRti8JZXDAK+AKwm84SDbRGP+Od9skS/+ijTWcRwhAPMKgwzxF30/PHc4viQaRItJhSqAsSPxozUJV+bTqLEIY4gN+YDmFCXLYonMUF4wDp+tkGfq12Xue6MLmK1JhYGlaIVnIBAwvzHHtMB4mkeG1R3GQ6QLSypvko3CXTfIg4lUwctirirkXhLC4YDHyFXHZql62BrsvulWk+RHyqw2pVlJgOEinx2KK4ASkS7TbImuZjmekcQhiQAlxvOkQkxVWLwllc0BPYhnVTSrST1ngXeU4sXhMYMN50FiEizAn0L8xzlJkOEgnx1qL4FVIkQkYpEq92vNunqzoQd90FRdxLBX5tOkSkxE2LwllckA3sANJMZ4k1Hm3ffK3r4h5uHOmmswgRQTVA78I8xwHTQcItnloU1yJFIiwcyj/knqQXN0Cc/NUhhCUduMR0iEiIixaFs7ggHWtpw2zTWWLZp/6BSx/1zJXp2kU82QQML8xzxPQHaby0KH6KFImwy7N9M+N4+4aVpnMIEUFDgbmmQ4RbvBSKn5sOEA+UQi1I/GisTPMh4szVpgOEW8wXCmdxwdHASNM54oVSpN2c9GpaJs59prMIESEn5xd5BpgOEU4xXyiAn5gOEG9kmg8RZ2zAlaZDhFNM38x2FhekAXuADNNZ4tE3gS7L73GfNd10DiEioAKrq2yd6SDhEOstinOQImHMQFvZ9PyEj2WaDxEPsoFzTYcIl1gvFD81HSDenZiwbuoE27Y1pnMIEQGXmg4QLjF76clZXDAUkN43HYDWVNzkPre2VHfqYzqLEGGksWaV3W46SKi1qEWhlNJKqWfqfZ+glCpTSr3RlpMqpS5XSl3Uln1bQW5idxBKkX130ouuJDw1prMIEUYKuMB0iHBo6aWnWmC0Uiol+P0JwK62nlRr/bjW+qm27n8kzuKCBODicB1ftF5wmo9imeZDxLi4LhQAbwE/Cn59HvD8wReUUtlKqVeVUuuUUquUUmOVUjal1HalVKd6221WSnVTSt2plPp18LlBSql3lFKfKaWWK6WGh+B9nQx0D8FxRAh1sVVPucrxntzcFjFHKb01K7Nu6eBBZal31+6YaDpPqLWmUCwGzlVKJQNjgU/qvXYXsEZrPRa4GXhKax0AXgPOAFBKTQG+1VqXNjruX4FrtNYTsabtfbRN76Sh80NwDBEGE23bZJoPESP07vQ099IB/cu/Hjm8dFDvXgdmJjn8fYD5ppOFWkJLN9Rar1NK9cdqTbzV6OVpwFnB7ZYopXKUUplAIXA78CRW17HC+jsppdKBqcCLSqmDTye1+l3U4ywuSMJqUYgOKDjNx7jtgdyvt+ruw0znEaJ1dEVKsre4S25tVnq6e4xS9Gxio3OIsXW1W9s99nXgYepddjqClcBgpVQX4HTg5SbOX6m1Hl/vMaKVmRo7Hhk70aEpRepNSa+lyzQfIjpop8Ph+7hnjwOfjhxemjFwQMX0jAz3WKVQh9lhwN21OyZFNGKYtbZQPAHcpbVe3+j55cACAKXULGCf1rpKW31vXwF+B2zUWpfX30lrXQVsU0qdE9xXKaXGtf5tNHBGO/cXEWBXupdM8yE6Lu1LSPB/2q1r9ccjhu3VQwbtm9q5U90kpUhs4QFi6vJTqwqF1nqn1rqgiZfuBCYqpdYBD9Kwx1EhVk+Awib2A6vA/FQp9QVQDJzWmkz1OYsLbMCP27q/iKx05R53c9Krq0znEMKitc0WWJ+TXbts2JCyA8OGlE3KzamdarPptix4dk7I4xkUUwPunMUFxwIfmc4hWucd79hlhb6pM0znEPFJKb05M9O1u0tuzaAkh793CA897va0vutCeDxjWnwzO0qcYjqAaL0TE9ZN3RTosWZNYMAE01lEvNA709PdW7vm1vRMSfENAYaE4STHAzFRKGJtrifp7RSFlCLhGse7/bqpyu9MZxGxTJenpHiW9e1TsX7k8NJe/fpUzgwWiXA5LozHjqiYufTkLC7oBew0nUO0nUfbN1/ruriHG0e66SwiVujaJIf/i9zcmsSsTNcEpSJ6FaUayL49ra8vgucMi1hqUUhrIsrJNB8iNLTX6rFU9fGIYaVq8KB9UztluSZFuEiA1U0/L8LnDItYKhQnmQ4g2i84zcdS0zlEtNHabgusy8mpWT5syN5qq8eSc6rNRqrhYMcbPn9IxMSlJ2dxgQLKgBzTWUT7aY1+znvsqvf9Y44xnUV0bErpr7My60q65NYOcTj8TY2SNm3J7Wl9o75YxEqhGAZ8ZTqHCB2tcd7nPv07meZDHEp/l5Hu/qZLbk2vlBTfYNNpjsAFdL49ra/LdJD2iJVLT/KXZ4ypN81HmeksoiPQ+1JTPMv69anYMGpEaZ++Vo+ljl4kAJKBY02HaK9YKRRTTQcQoWdXutf9yYV7ZJqPeKVrkpK8K3r1rPxs5PDSTgP6V8xIT/eMNp2qDaK+m2ysFAppUcSoNOUee4tM8xFHtCcxwb+6e7eqlSOGldoHDyw/tlOWa6KBHkuhJPcoTHMWF2QC+4mdoiea8K537LLFMs1HjNLabtfrOndyVuXk1I5JsOtOR94nqvixxlNUmQ7SVrHw4Xo0sfE+RDPmJqybOsG2bY3pHCJ0lAp81bmTc+mQwWUlw4fuHdeta830GCwSAHZgeigOpJTqrZR6Lbha6Fal1B+VUg6l1CyllFZKnVpv2zeCs3kf/D5XKeVVSl3e2vPGwgesXHaKAzLNR6zQ32ZkuJYOGrDvm5HD9w7v2aNqpiMx0MN0qgg4qr0HUNbqbi8Dr2qthwBDgXTgvuAmO4FbmjnEOcAqrMXnWkUKRQRdfutz9JtxC3mnP9Dg+S++2sms83/H0Wc9xLT5D1O0/tsm97/lkdfIO+0Bjjr1fn59/0scvGy4pvg7Jp3xIGNOuqfB8/U99uwy8k5/gDOueByP15pR4OPPt3LDbxuvJT97DlkAACAASURBVNVxKUX23UkvupLw1JjOIlpDl6Wmepb261tRPGpEab++vStnJif7BppOFWHtXWcHrJviLq31kwBaaz/wS+AnQCrwBXBAKXXCYfY/D/gV0Esp1apZcqO6UAQH2h1tOkdLXXD6ZF59/NBW362PvM5NV8xj1Us3cOvVJ3HrI68fss2qNdtYtWYbn7z8Gz599UY+K97B8k+3APCLe17gz3eey7q3bmXLjjLe+2jjIfsXvlnE6pd/w9HjB/D+iq/QWvPg4+9x4+Unhv6NhpFM8xEtdFVSkndF716Vn48cXpo9oF/FzPQ0zyjTqQwaH4JjjAI+q/9EcPG3HcDBrsL3Abc23lEp1QfoobVeDbwA5LfmxFFdKIARQJbpEC01LW8w2VmHziiglKK6xhqPU1XjonvXzCa2AZfHi8frw+3x4fX66ZqTwZ6yA1TXupg8rj9KKc7/8STeWNJ4AULrY9XrC+Cs85CQYOf5fxcxd/oIsrPasiaLWV1s1VOudry3zHQO0Zh2Jyb6Pgn2WHIMHlh+bFam6yilsJtO1gEMvLt2R9iXaNZaLwNQSk1r9FI+VoEAWEwrLz9Fc5czgDGmA4TCQ785g9Mue4ybH36NgNYseeZ/DtlmyvgBzJg0hEGzb0drzWXnTWf4oO58vmEHPbv9cP+vV7dO7C6tPGT/y86bzqzzf8eIQd05ZsIA5l/zd17/yxVhfV/hNNG+beYc+/qVMs2HaTpgtwe+yO5cV5uTXTvGbtdTTCfqoBQwFljRjmN8CZzd4KBKZQJ9gS3A3ODTB1sV9WetPQ/orpRaEPy+p1JqiNZ6c0tOHO2FIpxzyUfM3wtX8NvfnMHpJ4znpXfWcMXtz/Pm369qsM3WHWV8/U0pm/57FwCn/uxRVny2lZSkli3he/6PJ3H+j6313h947B2uXDCD9z76kude/5Re3Tvx4PWnY7NFVwPz/MQV47YFunwt03xEns0W2JiV6drbJbdmWGJiQBacaplxtK9Q/Bd4UCl1kdb6KaWUHXgE+AfgPLiR1vo9pdQ9QA8ApdRQIF1r3evgNkqpu7CKx90tOXF0fTIcaqjpAKHw7OurOW2Oda/rzBPH81kTN7Nff38dk8f1Jz01ifTUJOZOG8EnX2ynR7esBi2IXaWVDVoYje3Ze4Ci9d9y6vFjKfjnBzz18CV0ykjhg1WbQv/Gwkym+YgspfT2zAzX0kED920bMWzviJ49qmYmJga6m84VRdp1j0ZbvVTOAM5RSm0GNmHNJXVzE5vfB/QJfn0e8Eqj11+iFZefpFB0AD26ZH1/Y/rDTzYxqF+XQ7bp06Mzy4u24PP58Xr9LC/awvCB3ejRJYuMtGRWf7EdrTXPvf4pP5p9+FkO7l70JrdebS3dUefyohTYbDbqXNE5S4ZM8xFuujQt1b20f9/yL0cOL+3fp3flzOQk3wDTqaLUiPYeQGv9ndb6VK31EK31IK31NVprt9b6Q631KfW2e11rrYLP36W1vrHRcdZprVucJ9ovPUVVobj4+n+y/NMtlFfWMOT427n1ypO4+Kxj+NNd+Vz/4Mv4fAGSkxL50x3nAvD5hh38/YUVPHr3eZwxdzxLV29m8hm/RSmYM20EJ8+yCsIfbj2Hn9/6LC6Xl7nTR3Li9JFNnn/tRmsBwAkjrT805v9oIpPP+C29unfilz+J3lkGgtN8LL/bfVZIBjUJfSA5ybe+S25NakaGe7xSdDOdKEYMNx2graJ2Cg9ncUEOsM90DtFxvOsbs2yx91iZ5qNNtCsx0b82N6dWdcqqG2+zkWQ6UYzKisapPKK5RRFVrQkRfnPt66du8vdY83lgoNxcbRHtt3osOZ052c4xdruOmjFJUWw4sNp0iNaK5nsUUihEA0qRcLXjvX7dVOUO01k6Mpst8GV259qlQweXlQ8fWnZU1y610+x2HTXjkaJcVF5+khaFiCnBaT7Kr3VdXOPGkW46T0ehlN6WkeHa0TW3pn9Skr/pm1it9PuRx+JIT8Nmt2NLsHPZ8jcO2earN95jyT2PoGw2bAl25v32DvpNtbppv3fbA2x+ZwkAM39zLaPPPvWQ/d+77QG2vPch3ceO5My//R6ALxa/jLN8P8dc9dNQvI1IG2Q6QFtEc6GIiTEUIvQcyj/k3qQXPrnevWCyNaY9Xuk9aameTV261HRNS/WOAELeW+mStxaTlpt92NcHzDqWK350AkopSjZs5MULr+KaNUvY9M5/2bN2A5evfBu/28OTJ+UzeO4skjN/GLzsOlDFnrUbuPKTd3ntqhso3fAV2YP6s+bpF7nw1adC/VYiJSo7BkTzpScpFOKwcm01U652vLvUdI7I05XJyd7lfXrvXztyeGm3/v32zwwWCSOS0tNQwVrtrXVa45OBvV9tpt+xk7EnJOBIS6Xb6OFs+U/DX5ey2Qh4fWit8TrrsCUmsOKPf2HK5ZdgT2zZQNMO6NC+71EgmgtFryNvIuLZRPv2WXPs61aazhF+us6R6FvZo/uB1SOGl6YOGlA+PdPq1href98Knj7tAh6f9iOKnnjusJttfP0dFk04jmfPvpTTH/tfALqPGcmW95ficdZRu6+C7ctWcmDX7gb7JWWkM+TE2Tw+9WTSu3clOSuDXUVrGXFqdE1k2UhX0wHaIiq7xwZnjfWCTDYmmqc1zvs9p+/YEugelTcRD0/7E+yBtdnZTld2Z+dYu12HfcK5xqp2l5DZszs1e/fx1I8v4OSH76L/tMNP9bT9o09Y+uAfufgNq6gsfWgRX77yFqm52aR1yaHXxHHN3nd47aobmPSzC9mzdgNb/7ucbqOHM/M314b8fYXZptvT+kbdlDPR2qLIRIqEaAGlSL3R8VpGrEzzYbMFNmR3rl02dEhZxbChZRO75NYea6JIAGT2tGbvSO+ay4hTT2TXZ2ub3b7/tCns376D2n0VAMy84RquWPk2F//7WdCanMGHv4Wy54sNaK3JHTKI4lfeYv7Tj1KxbQflW7aF7g1Fhlx6iqAc0wFE9Kg3zYfHdJa2UEpvzcqsWzp4YNmOEcP2ju7RvXpGYkLA6AeOp9aJu7rm+6+3LllG15GH/qFcvnX79wtp7V67Hp/bQ2pOZwJ+P87y/QCUbNhI6YavGHT84cdKLrnnEY677df4vV603w+Asim8dXWhfmvh1unu2h1Rd4MlWns9SaEQrRJ903zoPelpnq+75Nb0TE31DqWDdaus2buPxef9HICAz8eY+acx5IRZAHz692cAmLTwAr587W2+eO4l7ImJJKQkcc4//4xSCp/XyxNzrRmzkzIzOPP//oA9oemPo43/fpeeE8aS2cPqMNR97Ej+PHku3UYPp/uYkPT0jSSF1arYfaQNO5JovUdxIvCO6Rwi+rzrG7N0sffYmaZzNE3vT0n2buiSW5uZnu4eqxRx3LU3pk24Pa1v89fpOhhpUYi4Mte+/tiONc2Hdjoc/i9yc2rtnbLqJihFlLR4RDtE3X0KKRQirgSn+eh/k/vcHaW6U18zKbQvISGwNruz0xPssSSr9MWXqOsiG62F4vBDQYU4AqXofHfSi+W/cF1c7cIRoR5DWttsekPnTnX7c3NqRyYkBPIic17RAUmhiBBpUYh2cSj/4HuSXlh9vXvBpHBO86GU3pKZ6drZJbdmcJLDHxNrvIt2k0tPESItCtFuubaaydc43v1wkWferNAeWe9KT3Nv6dKltmdqincIMDi0xxdRLupm6o3WQpFqOoCIDUcFp/l43z+2nfcJdEVKire4S25NVnqaZ4xSMsWMOKyoGywcrYVCug2KkDk/8eNx23XXr1o/zYeuTXL4v8jNqUnMynKNlx5LooWi7nM36gILEWrBaT4yr3NdWFZF6hGuH2tvQkJgbU52rTe7c904m01PjUxKEUOkRREh0TdKUHRodqV73p+8eN0vXBdn+bE7Gr6qtc2m13fu5KzMzakdnZCgJ5lJKWJE1H3uRl1gIcIlTXnG3pr0yvK73GdPB1BKb8rKrNvTJbd2sMPhH2s6n4gZUfe5G3WBhQinXP+B7j/95pOXUypqUu0ppASf3hJ8CNFu7uSE3eQbGuvZRlIoRFzTGt/+usQNpTUpBw4kD/LX9Zg6IO+LF3slV9UcfmEFIdqnhHzTEVonWguF3KMQbeYLqKqy2qTispokXeezj9QJKb1c/eY4dVLWTIDE2jq/6YwipgVMB2itaC0UQrSKy2fbVVqdvGWfMynDF1BjQB2jIeDtOuEjX86IcShl9V7SOmDz+3sbjitimxQKIToCrdHV7oSvSmqS9x5wOXoEtBpKvXXW/Sm5X7n7HufHnthgtZzkyqo9StZjF+EVdS1WKRQiZgQ07gqnY31pTbKzxpMwBNQIYET9bbQtscrdZ+aaQGrXaSh1SH/29NLyEqRQiPCqMR2gtaK1UETlkpYi9Lx+VbG3JnljWW2S3e23jQZ12FlZvdnDVnq7HTUAZTvswkUZe/bWhiepEN+rNh2gtaK1UFSYDiDMcXrt20qqk3dU1Dk6+wNqFKhjm9s+4Mj81tVvThmJKUeczym9dF/oggrRtCrTAVorWgtFuekAInK0JnDAlVhcUpNcUeVK7KtRA4ABR9xP2dyenses9Gf2Oxql+rXkXMkVVTLhpAg3KRQRIoUixvkD1O5zJm3YW5Psc3rtw0G1ai0HX0afzz29ju2MzT6rNfsl1rmibq0AEXWkUESIFIoY5PbZSkprkjftcyalev1qDKhWD3rTCcllrn5zNuukrFZP1qf8Aa8KBKRrrAg3KRQRIheSY0SNO2FTSU3ynv11ji4BzQhQ3dtyHA0Bb7ejPvJlD/9hTEQrpZRX7lQtuKQlRDtJoYgQaVFEqYDGu7/Osb60Ormm2pMwENRQYGh7julP6bLR3Xe2bjwmorXSS8vKkEIhwk8KRYRIoYgivoA6sLcmqbisNlm5fLaRoI4KxXGtMRGz1gZSu0xDKVt7j5exp6wuFLmEOIJS0wFaSwqFCIs6r+270pqUbeVOR6YvoEZD2y4HHY43e/hKb7ejBqJUu1oR9aWXlre72AhxBAFgj+kQrRWVhSJ11LUHnMUFPqI0fyzSGl3lTviypDql7IArsbdGDQb6hPo8AUfmt67+c/aRcOQxEa2VfKAqI9THFKKR0qxFC32mQ7RWNH/QVgBdTYeIZ/4AdRXOpA2lNcmuWq99GKhR4TqXNSZi6kp/Zt8Wj4lorQSXp1s4jitEPTtNB2iLaC4Ue5FCEXEevyrbW5P8VVltUpLHmjIj7MuC+jL7fubpOTWntWMiWsPm8daidZt6XAnRCrtMB2iLaC4UW4DRpkPEg1qPfWtJdfLO/XWOHL9WI0FNj8R5Awkpe939jt/SljERrZW2r2KXamfvKyFaQFoUEbbJdIBYpTX+SlfihpLq5Mpqd2J/jRoEDIrY+UMwJqK10vfskw4SIhKkUESYFIoQ8gdUdVltUvHemiR/nc8+EtQ4IzlSu25095nV7jERrZVRsldmJBaRIJeeIkwKRTu5fbbdJdXJW8qdSWlea9W3o01l0bbEA+6+s74IpIRmTERrpZVVRPO/BRE9tpgO0BbR/I9DCkUbVLsTviqpTi6ptFZ9Gwb0NJ3Jmz38Y2+3owaHckxEayVV1WSZOreIK1+aDtAWUVsoUkddW+osLjgAyD/wZgQ0nuCqb7XBVd+GA8NN5wIIOLK2u/ofX0FCSkTuQzTH7vYaL5gi5u3OWrQw6qbvgCguFEGbgLB3z4w2Xr+q2FsbXPXNZxsFaqLpTPVpZXN7eh27yp/R52iU6m86T0Kda7+CbNM5RMyLytYESKGIGXVe+7cl1cnbK+ocnYJTZjS76pspvsx+n3l6HZOLsh92OdJIS9tbvhvobDqHiHkbTQdoq1goFHGp0apvfTRqIBCWEcuhEEhIKXX3m7NVJ2Uav8zUWMaeskrTGURckBaFIXFVKPwBnOXWlBmetqz6ZoJG+a0xEcMmRGpMRGull5RF3dw7IipJoTAkaptyLeXx2UpLa5I27XMmJXv8tjGgJpvO1FL+1K5fuvvOUtgSO8xlpqak7dvvMJ1BxIWo/byK9kJRDNQCaaaDhFKNx765pDpl1/66xK4BrUaAiqrJ6qwxEbO/CKTkGhkT0VqOGqfcyBbhtitr0cIy0yHaKqoLReqoa33O4oJPgVmms7SH1vj21znWl9QkV9e4EwZo1BBgiOlcbeHNGbHC23XCEJNjIlrL5vX1Mp1BxLxVpgO0R1QXiqCPicJC4QuoA2U1ScV7a5NxWV1YJ5jO1B6BpKxtrn5z9pOQ3CF7Wx2Oo6qmVEFUtdhEVJJCYdjHpgO0lMtr21lSk/xNuTMp3WdNmdEhb+62hlY2V3BMxDEoFXXrTafvLS9BCoUIv09MB2iPWCgUqwANKNNBGtMaXe1O2FhSk1J2wJXYM6DVEKC36Vyh4svsV+TpNbULyjbLdJa2ythTFpUjZUVU8QFFpkO0R9QXitRR15Y7iws2AcNMZwEIaFzlzqT1pTVJrlpPwlBQI1u6r98f4IprbyM3tzP33/XrQ173eLw8+MjjbNq8jczMDG6/6Wq6d+sCwNZtO/h9wRPUOuuw2RSP/fFuHI6GnXnu++2jbNv+HUdPGc/CS/IBePr5VxnQrzfTpua1/D0mpJa4+x2/TSdlhnw50khLLykLmM4gYt66rEUL60yHaI+oLxRBH2OwUHj9al9pTfJX+2qTEt3tWPXt5dfeoW/fnjidTf8/9fZ7H5KRnsYzT/yOJR+u5K9PLOb2m67B7/fzwEOPcdP1lzNoYD8OVFVjtzf81W7dtoOkpET+/tgDXH/zg9TUOnG73Wz8aisXnnd6i/JplN/bfeIKX+ehE1Aq6osEQErFgRTTGUTMi+r7EwAdvutiC0X8PoXTY/9ma0Xa0qKdndd/vrtz9q6q1Gluv30KqDZ11S0rK2fV6rWcfOKsw26zYuXnzJ1jLS43c/pkPl9bjNaaTz9bz8ABfRg00BqYnZWZgd3e8FebYLfjdnsJBAL4fD7sNhtPPv0Sl1x4Zovy+VO7FdcNP2ezL3vYDJTKaMt77Igctc5c0xlEzIvq+xMQWy2KsAqu+lZcWp28v8qd2Dc4ZcbAUB3/z395hst+eh7OusO3UPeV76drrtXl3263k5aaSlVVDTt3lYBS3HDLb6k8UMVxM4/h3HNOabBvv769yMrK4LJrbuWE46axa3cpOqAZOrj5+8/a5giuExEdYyJaJRDwK38gZu4ZiQ7rA9MB2itWCsVGYD8hntjNH6CmrDZpw97aZH+d1z4C1NhQHv+glZ+soVOnTIYOGcDada0f5e/3+9lQvInH/ng3SUkOfn3TAwwd3J+jJjRcUvzqyy/8/uub73iE6679Cc88/xpbt33LxAljOOWk2Q229+aM/NjbdXxUjYlojZTKqt0K+pjOIWLaV1mLFn5nOkR7xcRfiKmjrtWEqFXh9tn27KhMXfb5rs6fFe3KTvy2Mv3oOm/CsaDCNnp3w5eb+HjV55x38f9wz4N/Zs0XX3L/Q48esl1uTmf27qsArOJQ63SSmZlOl9xsxo4eRlZWBsnJSUyZNI5NW7cf9nwrVn7G0CH9qatzsXtPKXfcfC3LPlqNy+UGrDERzqFnrfF2mzAVpbqE5U13AOkl+/aaziBi3rumA4RCrLQowPqF/KgtO1a7E74OrvrWLaDVcKBHaKM172eX5vOzS61eSGvXfckLL73FzTdcech2U48+ivfeX86oEUNYunw1E8aNRCnFpIljWfyvN3C53CQmJvDF+q84+4x5TZ7L5/Px0qvvcP9dv2bX7hKUsnoVBwIBPP6AS/Wescqf0XtqNI6JaK30PWU1pjOImPee6QChEEuF4k2goCUbBjSe/XWODSXVyTU1noRBoIbRQbrXNvbkU/9i6NABHHv0RE4+cSb3/+/jXPCT68jISOe2G68GICMjjXPOPIkrfnE7SimmTBrH0ZObHuj96r/fZ+6c6SQnJzFwQF9cbjc/veJGRo6fWJKQ9xO3P4rHRLRW+t59HW7sjYgpHuBD0yFCQWmtTWcIGWdxwUYOs8ynz68q99Ymfbm3NlkFV33LjHC8DimQkLrH3X/Odu3IiInurq0x+dFnP3PU1nWo1f9ETFmStWjh8aZDhEIstSgA3qJeoajz2r4tqU45uOrbqFiYMiNUgmMiPvJ1HnpUrIyJaK2EOpdM3SHCKSYuO0GMFQqt+XeVO3FuSXVy+QFXYm+NGkQHXvXNFH9qt2J331kJ2BI69DoR4aR8frcK6J6mc4iYFhM3siHGCsXqnTkrsOZSGn2kbeORtjkq3X1nrwuk5MTemIhWSi3fv1PBINM5RMzalrVo4VrTIUIlpj4sZs9b4AXeMJ2jI/LmjFpRN+xsbyA1d0a8FwmA9JJ95aYziJj2L9MBQimmWhRBrwAXmA7RUQSSOn3j6nf8gWhbJyLcMvaUOU1nEDHtBdMBQikWC8U7QB0Q15O9aWWv8/Settqf3usYlArZVCOxIm1vud10BhGztmUtWhjV04o3FnOXIGbPW+AE3jadwyRf1oBP64bP3+fP6D0TpRxH3iP+JB+olu7RIlxi6rITxGaLAuBpoGXTosaQ4JiIb7Uj42jTWTq6BLcnoqPvRVyJqctOELuF4k2gDIjZeYrqs8ZE5H3k6zxkIkpJkTgCu9tTrbTuajqHiEkxd9kJYvDSE3zf++k50zkiwZ/WfUPd8PlbfNlDZ6JUuuk80SC1rGKX6QwiZsVcawJit0UB8A/gF6ZDhIu2OSrd/WavDyTnTPt+Zj/RIhklZftNZxAx6wnTAcIhJlsUALPnLVgLxMyAl/q8uaM+qht2ti+QkjtdikTrZewpc5vOIGLS0qxFCzeZDhEOsdyiAKtV8QfTIUIlkNRpq6vf8dUkJE8znSWape7bn2g6g4hJfzcdIFxitkUR9CzgNR2ivbSy17n7zFzqGnhyXxKSx5vOE+2Sqmo6mc4gYs5+YrBb7EExXShmz1uwjyif0qPRmAj5SzgE7B5vL9MZRMx5JmvRQpfpEOES65eeAJ4EzjAdorUCiWl73P3m7NCO9Cmms8SSRGdduYIc0zlEzPmb6QDhFNMtiqA3gW2mQ7SURvk8PSYvdQ0+LUOKROillZbvMZ1BxJxPshYtXG86RDjFfKGYPW9BgBYukWqaP637+rrh87/xdR4iYyLCJGNP2QHTGUTMecx0gHCLh0tPYPVtvhvIMB2kKdru2O/ue9yGQHK2jIkIs/SSsqjv3CA6lD3A86ZDhFvMtygAZs9bUEUHHQjjzR29om7o2f5ASo6MiYiA1PLKZNMZREz5U9aihR7TIcItXloUYI2nuBroENNLB5I7b3X1Pb6GhCRZJyKCHDW12aYziJjhBB43HSIS4qJFATB73oLtdIB5WLSyO119Zn3oGnBSXxKSxpnOE1e01jafv7fpGCJm/F/WooUVpkNEQkgKhVJKK6Weqfd9glKqTCnVpjEMSqm/K6VGhiJbIw+F4Zgt5ssauLpu+PyKQEavWTImIvKSqmpKFKSaziFigg94xHSISAnVpadaYLRSKkVrXQecALR5hk6t9cIQ5Wpg9rwFaz9459n3gLnhOP7hBBLTdrv7zflOurualV5aXgpE/ToUO6sruPz9JyhzVqEUXDxqBleMmwPApe/8hc2VJQAccNeRlZTCR+fe0WB/l8/LyS8/hNvvw6/9/HjQRG6echoAV//3H6zZ+y0azeBO3Xj0+EtJdzS8rfPals94YPVrdE5K49mTryI7JZ1tB/Zy98pXeHLeZRH4CXQIi7MWLfzWdIhICeU9ireAH2ENYz8PqyfAdACl1J1Ajdb64eD3G4BTsNaMeAHojXXv4B6tdaFS6kPg11rrIqXUPOD+4Ov7tNbHtzPnA0SoUFhjIiat8HcanIdSPSNxTnF4GXv2VpvOEAoJNhv3HnsO47v2o9rjYlbhPczuM5Lh2T0bfFDf8tELZDoOXRE4yZ7A66f/inRHMl6/j3kvP8QJ/UYzqfsg7p+e//0+Ny8v5G/rP+CXE09qsP9f1y1hyTm38O9v1vDipk+4bNzx3LvqVW49+vTwvvGOQwMPmg4RSaG8R7EYOFcplQyMBT5pwT7zgN1a63Fa69FY611/TynVBWvE41la63HAOe0NOXvegg+B99t7nCPxp/VYXzd8/ja/NSYiLdznE0eWXrovYDpDKHRP68T4rv0AyHAkMzS7B3tqKhtso7Xm1S1FnD108iH7K6W+byV4A368AT8Kq8PdwSKhtcbl99JUNzybUnj8Puq8HhLtdj7evYmuqVkM6tQthO+yQ1uctWhhsekQkRSyFoXWep1Sqj9Wa+KtFu62HnhEKfVb4A2t9fJGrx8NLNNabwueI1Q3jm4C5oToWA0Ex0QUB5Kzj5Xurh1LSsWBmLs/8W3VPtaXfcfE7gMaPP/x7s10Sck87Ie3PxBg5gv3sO1AGQvHzCKv+8DvX7vy/Sf5z7frGZbdk3uPPfRvs+smnsxpr/2O7mmd+OsJP+WSd/7CEyf+PLRvrOPyAbebDhFpoe719DrwMIcOQPE1OlcygNZ6E3AUVsG4VykVkV/A7HkLioCXQnlMDdqbO+ajuqFnBwIpsphQR5TorMs1nSGUajwuLnr7sQaXiw56afNqzmqiNXGQ3Wbjo3PvoPiSh/isdDtflv9wS/HROZfy1aUPM6xzD17efOiqnrP7jmRp/m0UnnINb21bywn9xrClsoSL3n6Ma5c8hdMb08t9PJG1aOEW0yEiLdSF4gngLq1143lPtmMVBJRSRwEDgl/3BJxa62eA/z24TT2rgBlKqYPbh7IP/C2APxQHCiR33lI37Ox13q5jp6GUTDjXEQUCPuUPxEzXWK/fx0VvP8Y5Q6fw40EN/9n4An7+vfVzzhySd8TjdEpKZXqvYfz32w0NnrfbbJw5ZBL/3vrZYfd1et08t/FjfjZmFg988jqPzfkJR/cYwlF/zQAAE91JREFUzIubWnLVOSq5sGZ4iDshLRRa651a66bmVXoJyFZKFWMNeju4CtQYYLVSai1wB3Bvo+OVAT8HXlZKfQEUhirr7HkLvsZa2KjNgmMilroGnNQPu4yJ6MhS9h/YpSAmuiRrrbl6yT8Zmt2Dqycc2i/jw+82MqRzD3qlN/131b66airdTgDqfB4+/O5LhnTujtaabyr3fn+Ot7d9wZDOh+8kVrDmXS4bdzyJ9gTqgvczbErh9MXsQOU/Zy1aGJfrrYfkHoXW+pAJ7LTWHwIfBr+uo+meRtuBd5vYd1a9r98G3g5FzibcCSwgeCmsNXydBq729JjSE2WbGfJUEfSHX/+ET5e8QVZOVx79zw9/VT798G188p/XUDYbnXK68j+P/IOcbod23Hrygd/w6ZI3ATj32tuYcWo+ACU7tvHQNedSvb+cwWMmct3vnybR4Wiw74q3XuLZ391Oeqdsbv3bq2R2zmHPt1t56qGb+c2fQ/Y3AQDpJfvKgH4hPaghq/ZsofDrVYzM6cW0xXcBcPvRZzK3/xjAuux09tBJDfbZU1PJtR/8kxdP/QUltQe44v0n8OsAWmtOH5zHvAHjCOgAV7z/BNUeFxrN6NzePDLrgiYz7Kmp5PPS7dw4+ccAXDb2OI578T6yHKk8+6OrwvjujanG6jEZl5TW2nQGoz5459mHgV+1dPtAYtoud785O2NlTMSGT5aRnJrO7667qEGhcFZXkZqRCcDrTxawY/OXXH1/w9kKPv3vm7z2xB+4659v4/W4uSl/Fvc9919SMzJ58Mr5HDPvTGb++Fz+dPPlDBwxjpMvvKLB/jfmz+LOf7zFyrdfpubAfk699BoeuuY8Flx3N70GDAnp+xz4/oplPddsnBHSg4p4cmfWooV3mQ5hStxM4dGM+4EjTj2tUT53jykfugaf1ilWigTA6CkzyOh06CWKg0UCwOWsbfLe/I7NXzJq8gzsCQkkp6bRf/hYPlv6Dlpr1n28hGknnw3A8WddzMr3Xj1kf5uy4fO4cbuc2BMT2bB6OZ27dA95kQBILy0P+TFF3PgO6x5q3Ir7QjF73oIK4L7mtvGn91xnjYkYPCuexkQ89dAtXHJ0Hz589VkuuO7Qe3gDRo7j86Xv4KpzcqBiH+tWfkDZ7u+o2l9OWmYn7AnWlc3cHr0pLzn00u45V93ELefP4ZP3/83MH59HYcE9nHvtbWF5L8mV1bK+h2irX2UtWug0HcKkeJo9tjl/AC4FRtR/UtuTKlx9jyvWyZ3jsrvrRTfcx0U33McLf36AN/75JxZc17DlfdSMuWz+4lOuP3MqWdldGH7UMdjtLZ+cd8L0E5gw/QQA/vvSU+TNPpld32zi5b8+THpWZ35+5x9JTgnN0IfEOlfcjAYTIfXfrEULXzQdwrS4b1EAzJ63wIvVGwuwxkR4uoxZXjf0LHRKdtyvEzHr9AWseLvpYSf519zCorfXcu+z/wGt6TlgKJmdc6itqsTv8wGwb89Ocrr3OuzxXXVO/vviP/jRRVfx7O/v4Lrf/ZNRk6bx4avPhiS/8vn+v707j46rvM84/v0JG7xIXEhDCNtNimNwmlIISw00pIUUknJxE1YbaFhvCD3sNKdhSw40kBOgCZALJDT0EIIPSwlbHTkBkmJSNyYsLdgQBwwBBmNJlmzJqyRrefvHvSbCSNeSPTPvLM/nnDmaMxrPPPKx/Mx7733ftwfntISKjFUfcIHvEJVARZE5/Aun/hfwwMCEDy3p3vuERf07/cVhFHfeRlV5980l793/7ROPsfuUaR94zsDAAKs702P/by5eyJu/X8j+nz0KM2OfQw5n/tyfAvCrh+7m4CO/OOJ7PXzHjcw480LGjR/Php5uMMMaGujtLs5of3JH5zvGsKtRiOT5fpDEi32HqAQ69DRE/w5TLtmwy/RF9TRp7oYLTmbRgnms7uzg9Om7c+ol13DUrLO5+zuXsfQPr9LQ0MBOu32M87IrnpYsfJ6fz/4hF95wJwN9fXz9hMOA9OT3126e/d55iTMvv57rz5/F7H+9ij0/9WmOmnn2sO+/om0Zr734LKdcnK5wOuOMC7h0xkFM3n4HrvrRB0+Ab4nG1o662DNAiqoFqNurnDZV95fHbqq5wPlA4juHFM/UuU8/vfMrS6p6vouU3T8ESVycY581QIeePuh24FnfIaR4Jrev1L9zGYufqyTeT79Am4hCBkmXDen3nUWKY7vVawLfGaRqdAFf8R2i0qgohhGFvMQm605J9RrXu0FXPMloXVKv6znlUVGM7Dp0CKrqbdPTu8ocNbW8uJTMz4Ik/rHvEJVIRTGCKKQf+DJQ1zMyq93k9pX6dCij0QnUzYbfY6WiyBGFvAb8s+8csuWaWtq7Nv8sES4KkniZ7xCVSkWxGVHIbQyzFLpUh6bW9j7fGaTiPRYk8T2+Q1QyFcXonAVo0lYVmtTRqUmlkucd0t9vyaGiGIUoZBlwru8cMnbbrl63o+8MUrH6gZlBEutD4GaoKEYpCnkQzdiuOtv09Y28GqHUu8uDJF7gO0Q1UFGMzT8Bv/EdQkZn/Nr1HQaabCfDmQN813eIaqGiGIMopA84CVjuO4tsXuPyFbqKRYZTAM4IklgL3Y2SimKMopB3gVnAgO8skq+pZflq3xmk4vSh8xJjpqLYAlHIU8AVvnNIvsbWDpW5bOrSIImf8R2i2qgotlAUcgPwiO8cMrJJK7q2851BKsrtQRLf6jtENVJRbJ0zgEW+Q8jwxq9bXzcbUMlmPQlc5DtEtVJRbIUoZDUQATppWmmccw39A3v4jiEV4VXgpCCJtXXAFlJRbKUo5B3gGGCt7yzyRxNWrVlmMMF3DvFuJXBMkMRa82srqCiKIAr5P2AmuhKqYkxu62jznUG86wOOD5L4dd9Bqp2KokiikLnA+b5zSKqppV0jPPlqkMTzfIeoBSqKIopCfgjc6DuHQGNrh+8I4tfXgiS+y3eIWqGiKL6vA/f5DlHvJnaumug7g3hzXZDEWp6jiFQURRaFOOA0NMfCq/Hrez7iO4N4cWuQxFf5DlFrVBQlkG2jOgto9p2lHtnAYJ8NDmrV2PozG7jQd4hapKIokShkA3A86UQfKaOJK7uWGmjDovryn8CZWuivNFQUJRSF9AJfBJ72naWeNLZ2tPvOIGX1JOlCf5pQVyIqihKLQrpJJ+RpH4syaWpZ3u07g5TNHGBGkMQ9voPUMhVFGUQha4G/Q2VRFpOXr9C/6/rwIOmEul7fQWqdfqHKJFsX6kjgcd9Zat2ErjWNvjNIyd0DnBwkcZ/vIPVARVFGUch6YAbwgO8stWx8T+9HfWeQkvo34PQgibVkTpnoypAyi0L6mgucAnQC5/rOU2sa+vrW45yKonbdEiTxxb5D1BuNKDyIQgajkH8ErvOdpdZMau9camC+c0hJfEMl4YeKwqMo5CrgUkDXfhdJU2v7Ct8ZpOj6gNOCJL7Wd5B6paLwLAq5iXQWty7pLIKmlvYNvjNIUa0Gjg6S+B7fQeqZiqICRCH/ARwGvOs7S7Wb1L5S591qx5vAIUES/9J3kHqnoqgQUcgLwEHAs76zVLMJq9du7zuDFMVvgOlBEv/OdxBRUVSUKKQF+GvgXt9ZqtU2vRt29Z1BttpPgCOCJNZSLBVCw/QKE4X0AKc2F3gFuBZdwTNq47p7ugz+xHcO2WK9wEVBEt/hO4i8n0YUFSoK+TbpgoKdvrNUi8nLV+ocT/V6GzhMJVGZVBQVLAqZA+wHLPCdpRo0tbZ3+c4gW+Rx4IAgiZ/zHUSGp6KocFFIAfgscD2ab5GrsaVdy0xXl0HgGtLLXzX/pYKpKKpAFNIfhVxGugKtTvCNYFJH57a+M8iotZIWxNVBEg8W60XNbHcze8zMlpjZG2Z2i5ntZWZLzaxhk+e+aGbTs/sXm1mPmQXFylJLVBRVJAp5HNgXmOc5SkXabu26HXxnkFH5KfDnQRIXdSVlMzPgYeBR59xUYC+gEfgKUCCdq7TxudOAJufcb7OHTgaeA44rZqZaoaKoMtkltJ8DrgQ0C3mIhr7+3X1nkFxdwJeDJD6xRIeajgB6nHN3ATjnBoBLgLNIV2yeNeS5s4D7AcxsCmmhXEVaGLIJFUUVyhYV/DZwIPC/vvNUgm3XrFtu0OQ7h4zoV8A+QRLPLuF7fAp4YegDzrnVpKOJF4EvmdnGKQEzgfuy+xtL47+Bvc1s5xJmrEoqiioWhSwCpgPfpM5HF41tHS2+M8iwuoGLgCODJF7qMUcn8DLwOTPbD+h3zr2cfe9k4H7n3CDwEHCip4wVSxPuqlwU0g98q7nAw8C/kxZH3WlsaV/tO4N8wJPAeUESLynT+/0OOGHoA2a2PRACr5OOIGYBbdl9zGwfYCrwZHqKg21J15i6tUyZq4JGFDUiCnkFOBS4GFjnOU7ZNbV1FO3KGdlqLcCsIImPKmNJQHp4a5KZnQZgZtsA3wV+7JxbT3qi+2jSw073Z3/mZOBq59zHs9uuwK5m9rEy5q54Kooakp27uAXYmzpbL2riiq6JvjMIA0ACTAuSuOzb/TrnHHAscKKZLQFeA3qAK7Lvd5FOXm1zzv0h+2OzgEc2ealHeP+J77pn6d+t1KLmAp8Bvg982neWUjv0e3e90TAwMMV3jjr2HHBukMS6uKIGaURRw6KQ+aRXRp1DLU/Uc27QBgZ0aawfrcBXgYNVErVLI4o60VxgB+Bq4Dxq7CKGCZ2rlh5454MqivJaA9wIfC9I4ro7J1ZvVBR1prnAXqSFMZMaGVF+ePEbL0z72VMH+M5RJ/qAO4BvBUm83HcYKY+a+mQpmxeFvAac0lzgOtIF2Y6jyve8aGppX+s7Q514ELgiSOLXfQeR8qqJT5SlYmZXmtkrZrZw6AJiRXrtuWaWuzaRmb1lZh8u1nsOFYW8EoWcAOwPzCnFe5RLY1tHVRddhXPAo8BBQRKfpJKoTxpRjMDMDgGOAfZ3zvVm/2EXbXVS59zRxXqtrRGFvAj8fXOBvwT+Bfi850hjNrFz1WTfGWpQP+mktO9o32rRiGJkuwAdzrleAOdch3NuWfYp/wYzW2Rmz5rZJwDMbCcze8jMnstuf5U93mhmd2XPX2hmx2ePvzdaMLNHzeyFbPRyjo8fNgp5Ngr5AulGST+hipYEGdfdu5PvDDWkB7gdmBok8WkqCQGNKPI8AXzTzF4Dfgk84Jx7OvveKufcPtkM0JtJRx63ADc55+abWUi6a9cngW9sfD6Ame04zHud5ZxbaWYTgefM7CHnnJeNXKKQl4DTmwtcTnqF1LnAh3xkGQ0bGNhgg4O7+c5RAzqBH5FexdTmO4xUFhXFCJxza83sANI17A8HHjCzy7Jv3zfk603Z/b8F/ixbLwZgezNrzB5/b5anc264PbAvNLNjs/t7kK4943XHryhkGXBldtL7DNKlQab6zDScSSu6lhrs6TtHFXuedARxf5DE3b7DSGVSUeTI1rOfB8wzs0XA6Ru/NfRp2dcG4GDnXM/Q1xhSHMMys78hLZNDnHPrzWweMGFrsxdLFLIeuL25wA9Id9g7G5gBjPcaLNPY2t6BimKsuknXOro9SOLnfYeRyqdzFCMws73NbOgn6P2At7P7M4d8XZDdfwK4YMif3y+7+yTpIZyNj2966CkAOrOSmAYcXJyfoLiiEBeFzI1Cjgd2Jd0QZpHnWDS1tOtT8Oj9HrgU2C1I4rNUEjJaGlGMrBFIsktY+0mXKT6H9HzEjma2EOjljztiXQjclj0+Dvg16fH9a7PHXyZdNO0a0lUsN/oFcK6ZLQZeBZ4p9Q+2taKQDtJzMzc3FzgQOBM4BSj7VqSTl6/Qh518S0l3d7tXS2zIltLM7DEys7eAA51zHb6zVJLmAhNIl3A+jrRMy7JJ/fRb73lpfHfvvuV4ryqyknRf6nuBXwdJrF9y2SoaUUhRRCE9pCOlh5sLjCfd1/s44EtAyS5fHdezQdtWpjqAuaSzpx8PkrjPcx6pIRpRSEk1F2ggvXLsWNIRR9GunGrY0Lfu0FvurufJdotIy2EOsCBIYm3eJCWhopCyai6wB+loY+Ntly19raZ3217d9945excrWxVoJ70K7xeko4Z3/caReqGiEK+aC3wSOIK0ND7DGA5T7fr8ywv2fOqZQ0qVrQIsAeYD/wPMD5L4Vc95pE7pHIV4FYUsBhYDtwE0FwiBg0g3XDoQOAAYbjY7ja3tvWWKWQ6dpIeSXiAth/laxlsqhYpCKkoUUgAKwEMbH2suMIW0NPYDpmW3KZPbV1bEpL8x6iOdz7CQtBgWAQuDJF7qNZVIDhWFVLwo5A3gDdL5AAA0Fxhnzv0p6cnxKcAngI+TnvP4KLAzRVztdwwGgGXAW6QTNN8ecv8t4C1dkSTVRucopGatuuDOHUlLY2NxfIR0IuUkYGL2dehtIumSLP2k/+H3D3NbQzpPoXPI16H324Ik7i/LDyhSJioKERHJpeUPREQkl4pCRERyqShERCSXikJERHKpKEREJJeKQkREcqkoREQkl4pCRERyqShERCSXikJERHKpKEREJJeKQkREcqkoREQkl4pCRERyqShERCSXikJERHKpKEREJJeKQkREcqkoREQkl4pCRERyqShERCSXikJERHKpKEREJJeKQkREcqkoREQkl4pCRERyqShERCSXikJERHKpKEREJJeKQkREcqkoREQkl4pCRERyqShERCSXikJERHKpKEREJJeKQkREcqkoREQkl4pCRERyqShERCSXikJERHKpKEREJJeKQkREcqkoREQkl4pCRERy/T+X0DELg9G34AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Anime Categories Distribution\n",
        "from pandas.core.tools.datetimes import overload\n",
        "ona = anime.loc[anime['type'] == 'ONA'].count()[0]\n",
        "tv = anime.loc[anime['type'] == 'TV'].count()[0]\n",
        "movie = anime.loc[anime['type'] == 'Movie'].count()[0]\n",
        "music = anime.loc[anime['type'] == 'Music'].count()[0]\n",
        "special = anime.loc[anime['type'] == 'Special'].count()[0]\n",
        "ova = anime.loc[anime['type'] == 'OVA'].count()[0]\n",
        "\n",
        "labels = ['ONA', 'TV', 'Movie', 'Music', 'Special', 'OVA']\n",
        "colors = ['#81F4E1', '#56CBF9', '#F5D491', '#BEB7A4', '#B4E1FF', '#F06C9B']\n",
        "\n",
        "plt.figure(figsize = (10,7))\n",
        "plt.title('Anime Categories Distribution')\n",
        "plt.pie([ona, tv, movie, music, special, ova],\n",
        "        labels = labels,\n",
        "        colors = colors,\n",
        "        autopct = '%.2f %%'\n",
        "        )\n",
        "        \n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "aGT-pIR4YhwH",
        "outputId": "bffcdfe7-9b0a-4cce-903e-0d2f0aa70143"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Anime's Average Ratings Distribution\n",
        "plt.hist(anime.rating, color='#B4E1FF', edgecolor='black')\n",
        "plt.ylabel('Total')\n",
        "plt.xlabel('Avg Rating')\n",
        "plt.title(\"Anime's Average Ratings Distribution\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "vtOYmrTga-c0",
        "outputId": "b817ba73-390a-4e6d-efc2-8d1a38f72ccc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# User Anime Ratings Distribution\n",
        "plt.hist(anime_rating.rating, color='#B4E1FF', edgecolor='black')\n",
        "plt.ylabel('Total')\n",
        "plt.xlabel('Rating')\n",
        "plt.title(\"User Anime Ratings Distribution\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AGYN0Xnkzeq9"
      },
      "source": [
        "## Multvarite Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 702
        },
        "id": "XvzxVDVcHJoG",
        "outputId": "30651321-a41a-4fd2-b197-94bd45a46f46"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     anime_id                              name  \\\n",
              "40       1535                        Death Note   \n",
              "86      16498                Shingeki no Kyojin   \n",
              "804     11757                  Sword Art Online   \n",
              "1        5114  Fullmetal Alchemist: Brotherhood   \n",
              "159      6547                      Angel Beats!   \n",
              "19       1575   Code Geass: Hangyaku no Lelouch   \n",
              "841        20                            Naruto   \n",
              "3        9253                       Steins;Gate   \n",
              "445     10620                  Mirai Nikki (TV)   \n",
              "131      4224                         Toradora!   \n",
              "\n",
              "                                                 genre type episodes  rating  \\\n",
              "40   Mystery, Police, Psychological, Supernatural, ...   TV       37    8.71   \n",
              "86        Action, Drama, Fantasy, Shounen, Super Power   TV       25    8.54   \n",
              "804          Action, Adventure, Fantasy, Game, Romance   TV       25    7.83   \n",
              "1    Action, Adventure, Drama, Fantasy, Magic, Mili...   TV       64    9.26   \n",
              "159        Action, Comedy, Drama, School, Supernatural   TV       13    8.39   \n",
              "19   Action, Mecha, Military, School, Sci-Fi, Super...   TV       25    8.83   \n",
              "841  Action, Comedy, Martial Arts, Shounen, Super P...   TV      220    7.81   \n",
              "3                                     Sci-Fi, Thriller   TV       24    9.17   \n",
              "445  Action, Mystery, Psychological, Shounen, Super...   TV       26    8.07   \n",
              "131             Comedy, Romance, School, Slice of Life   TV       25    8.45   \n",
              "\n",
              "     members  \n",
              "40   1013917  \n",
              "86    896229  \n",
              "804   893100  \n",
              "1     793665  \n",
              "159   717796  \n",
              "19    715151  \n",
              "841   683297  \n",
              "3     673572  \n",
              "445   657190  \n",
              "131   633817  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0a80b3a2-49c8-4c1a-a099-b24f31f46bbb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>anime_id</th>\n",
              "      <th>name</th>\n",
              "      <th>genre</th>\n",
              "      <th>type</th>\n",
              "      <th>episodes</th>\n",
              "      <th>rating</th>\n",
              "      <th>members</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>1535</td>\n",
              "      <td>Death Note</td>\n",
              "      <td>Mystery, Police, Psychological, Supernatural, ...</td>\n",
              "      <td>TV</td>\n",
              "      <td>37</td>\n",
              "      <td>8.71</td>\n",
              "      <td>1013917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>16498</td>\n",
              "      <td>Shingeki no Kyojin</td>\n",
              "      <td>Action, Drama, Fantasy, Shounen, Super Power</td>\n",
              "      <td>TV</td>\n",
              "      <td>25</td>\n",
              "      <td>8.54</td>\n",
              "      <td>896229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>804</th>\n",
              "      <td>11757</td>\n",
              "      <td>Sword Art Online</td>\n",
              "      <td>Action, Adventure, Fantasy, Game, Romance</td>\n",
              "      <td>TV</td>\n",
              "      <td>25</td>\n",
              "      <td>7.83</td>\n",
              "      <td>893100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5114</td>\n",
              "      <td>Fullmetal Alchemist: Brotherhood</td>\n",
              "      <td>Action, Adventure, Drama, Fantasy, Magic, Mili...</td>\n",
              "      <td>TV</td>\n",
              "      <td>64</td>\n",
              "      <td>9.26</td>\n",
              "      <td>793665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>6547</td>\n",
              "      <td>Angel Beats!</td>\n",
              "      <td>Action, Comedy, Drama, School, Supernatural</td>\n",
              "      <td>TV</td>\n",
              "      <td>13</td>\n",
              "      <td>8.39</td>\n",
              "      <td>717796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1575</td>\n",
              "      <td>Code Geass: Hangyaku no Lelouch</td>\n",
              "      <td>Action, Mecha, Military, School, Sci-Fi, Super...</td>\n",
              "      <td>TV</td>\n",
              "      <td>25</td>\n",
              "      <td>8.83</td>\n",
              "      <td>715151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>841</th>\n",
              "      <td>20</td>\n",
              "      <td>Naruto</td>\n",
              "      <td>Action, Comedy, Martial Arts, Shounen, Super P...</td>\n",
              "      <td>TV</td>\n",
              "      <td>220</td>\n",
              "      <td>7.81</td>\n",
              "      <td>683297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9253</td>\n",
              "      <td>Steins;Gate</td>\n",
              "      <td>Sci-Fi, Thriller</td>\n",
              "      <td>TV</td>\n",
              "      <td>24</td>\n",
              "      <td>9.17</td>\n",
              "      <td>673572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>445</th>\n",
              "      <td>10620</td>\n",
              "      <td>Mirai Nikki (TV)</td>\n",
              "      <td>Action, Mystery, Psychological, Shounen, Super...</td>\n",
              "      <td>TV</td>\n",
              "      <td>26</td>\n",
              "      <td>8.07</td>\n",
              "      <td>657190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>4224</td>\n",
              "      <td>Toradora!</td>\n",
              "      <td>Comedy, Romance, School, Slice of Life</td>\n",
              "      <td>TV</td>\n",
              "      <td>25</td>\n",
              "      <td>8.45</td>\n",
              "      <td>633817</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0a80b3a2-49c8-4c1a-a099-b24f31f46bbb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0a80b3a2-49c8-4c1a-a099-b24f31f46bbb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0a80b3a2-49c8-4c1a-a099-b24f31f46bbb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "# Top 10 Anime Community\n",
        "anime.sort_values(by='members', ascending=False).head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oCb1B6TxM5eh"
      },
      "source": [
        "Menampilkan daftar anime dengan jumlah anggota community terbanyak. Misalnya, anime Death Note memiliki jumlah anggota community terbanyak, yaitu sebesar 1013917."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "LzjfNdNCtmdo",
        "outputId": "bd5408d3-6c77-419c-ea39-7935012fba10"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1080 with 1 Axes>"
            ],
            "image/png": 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H3XB5kmT9fkcMXAmMlxxBPzIE/ckR9HfxJZcmSY594FEDV7Lj09hlR3NHkgcl+Zl0N227OsmfJfmtIYsa2AeTHJjkLa21f17A+Y9N8vfpbjr3x4tZ2GwrV61cqqFhp7D/rd09Kv0jABZOjqAfGYL+5Aj6u+Lqq5No7M6Hxi47lNbajelu+sVEa+3+Pc8/PYkFagAAAACWkV2GLgAAAAAAgG2jsQsAAAAAMDIauwAAAAAAI6OxCwAAAAAwMm6eBiyKTZs2DV0CjNrG3fYaugQYPTmCfmQI+pMj6G+f1XI0X9VaG7oGYBmoqnbSBbcPXQYAAADcw2kP3T36X4xVVa1rra2Za5+lGAAAAAAARkZjFwAAAABgZDR2gUVx0MEHD10CjNoxV52XY646b+gyYNTkCPqRIehPjqC/M846O2ecdfbQZYyCxi4AAAAAwMho7AIAAAAAjIzGLgAAAADAyGjsAgAAAACMjMYuAAAAAMDIaOwCAAAAAIzMiqELAJaHDTfdNHQJMGpX7fuAoUuA0ZMj6EeGoD85gv6OPerIoUsYDY1dYFHctnHj0CXAqN2w5yFDlwCjJ0fQjwxBf3IE/d33sMOGLmE0LMUAAAAAADAyGrvAothj5cqhS4BR2++Wr2W/W742dBkwanIE/cgQ9CdH0N8V69fnivXrhy5jFCzFACyKvffdN6c9dPehy4DReuFLT0mSnPbHfzRwJTBecgT9yBD0J0c7psPue7+hS2AbXHzpZUksyTAfGrvAoth79ep83W/UYMHOOOvsJMlf/dEfDlwJjJccQT8yBP3JEbA9WYoBAAAAAGBkNHYBAAAAAEZGYxcAAAAAYGQ0dgEAAAAARkZjFwAAAABgZKq1NnQNwDKwZs2atnbt2qHLAAAAAFg2qmpda23NXPvM2AUAAAAAGBmNXQAAAACAkdHYBRbFzbfeOnQJMGrnrluXc9etG7oMGDU5gn5kCPqTI+hPjuZvxdAFAMvDXXfdNXQJMGo3brh56BJg9OQI+pEh6E+OoD85mj8zdgEAAAAARkZjFwAAAABgZDR2AQAAAABGxhq7wKK46aYNqaqhy4DReuFLT0mS/PDjThi2EBgxOYJ+ZAj6kyPor0+O7nPIYbny6qsWuaIdl8YusChauztvOPKnhi4DRmvlnvdJEjmCHuQI+pEh6E+OoL8+OXrlZacudjk7NI1dYFFsvHVjsvfQVcB43fmla4cuAUZPjqAfGYL+5Aj6k6P509gFFsXNG25KDhm6ChivTZ/56tAlwOjJEfQjQ9CfHEF/cjR/bp4GAAAAADAyGrvAolixwgcAoI/af1Vq/1VDlwGjJkfQjwxBf3IE/cnR/GnsAotivwMOGLoEGLU9Tjw6e5x49NBlwKjJEfQjQ9CfHEF/cjR/GrsAAAAAACOjsQsAAAAAMDIauwAAAAAAI6OxCwAAAAAwMhq7AAAAAAAjo7ELAAAAADAyK4YuAFgebrjuumTvoauA8brtzC8MXQKMnhxBPzIE/ckR9CdH86exCyyKO++8c+gSYNTa9bcOXQKMnhxBPzIE/ckR9CdH82cpBgAAAACAkdHYBRbFXqutwwB97Hb8/bLb8fcbugwYNTmCfmQI+pMj6E+O5k9jF1gUK1etHLoEGLUVDzgoKx5w0NBlwKjJEfQjQ9CfHEF/cjR/GrsAAAAAACOjsQsAAAAAMDIauwAAAAAAI6OxCwAAAAAwMhq7AAAAAAAjs2LoAoDlYdOmTUOXAKN29w23Dl0CjJ4cQT8yBP3JEfQnR/NXrbWhawCWgapqbzjyp4YuAwAAANhJvfKyU7Pcep1Vta61tmaufZZiYFmrqtdUVZs87q6qG6rqgqr63ao6ZAmve1JVnTzH9rOq6p8XMN4Jk9dwXVXtNbXvJVW1zf/XqqqXV9UJ23oeAAAAAMPT2GVncGOShyd5RJKfSPIvSX46ycVVddwSXfOkJCcvwbj7J3nRIo318iQnLNJYAAAAAGxHGrvsDO5srZ0/eZzZWntDku9JcnWS91TVrgPXty3OSvJrVbXH0IVMO+jgg4cuAUZt5bPXZOWz5/x0DTBPcgT9yBD0J0fQnxzNn8YuO6XW2jfTzVg9IskTZrZX1R5V9caq+u+qur2qLqqqJ88+t6p+pqrOrarrJ0s7fLKq1sza/84kP5rksbOWgXjN1Bg/WVWXV9VNVfXhqjp8nqW/Md2s3edv6aCqOrCq3jVZuuHWyRIQs2v8SpIDkrx6Vo0nTPbtUlWvmNR3e1VdWlU/O8/6AAAAANgONHbZmZ2V5M4k3z9r2z+nW0Lh95I8NckFSf5PVT141jH3T/J3SX48yU8m+e8kn6qq75zsf12STyb5bLolIB6e5G9nnf+wJC9J8tIkL0jykCRvm2fN/z259surarctHHd6khOTnJLkWemy/smqOmKy/xnplqh4+6waL5zs+4skr5rU9JQk70/yjqr64XnWCAAAAMASWzF0ATCU1tptVfWNJAcnSVU9Pl0j84TW2tmTwz5aVUcl+c10jdy01l47M0ZV7ZLkY0mOT/KcJK9trX2pqq5Psktr7fw5Lr13kqe01m6YjHFIkj+tqpWttY3zKP33kzw3yc+ka8zeQ1U9MckjZ7+Oqvq3JF9J8rIk/6u19tmqujPJlbNrnDR+X5Tkua21d002f7yqDk3y6iQfmrrWC9I1p7PX6tXdKwMAAABgyZmxy86uZn3/g0m+luS8qlox80jyiSSzlzH4rqp6f1V9PcldSTYleWCSo+Z5zQtmmroTX5h8vc98Tm6tfSnJe5K8YjPrAx+f5JpZzem01m5J15R91FaGf3ySu5O8f4734MHT12utva21tqa1tmblqlXzKR8AAACARWDGLjutyQ3IDkjy9cmmA5Mckq5RO+2uyTmrk3x0cs6vJflqktvSLbUw3xuafXPq+R2Tr9tyQ7TfS/K5dMssTDs0yTVzbP96uvV5t+TAJLumW6ZhLocmuXKeNQIAAACwRDR22Zk9Ll0G/u/k+fVJrkryI1s45+FJDk/yhNbaF2c2VtU+S1XkXFprX6iq9yf5jSRvndp9dZJ7z3Hawele45Zcn27d4Uemm7k7ba6GMQAAAADbmcYuO6Wq2jfJHyS5PMnHJ5s/ke6GZjfPbtpOWTn5evussR6R7oZq62Ydd0e2bQbuQvzu5JrPmNr+6SS/U1WPaa2dM6lxVb59I7Qt1fhv6Wbs7tNa+9i2FLPhppussQs93HHBV4cuAUZPjqAfGYL+5Aj6k6P509hlZ7Ciqr5/8v3qJMelu0HYqiRPbK3dNdn3sSRnJvlYVf1Bks+na1U+OMkerbVXJjk/yc1J/qaq3phu9u5r0s30ne2LSZ5eVT+SbumC9a219Yv5olprF1bVh5M8aWr7mVX170n+qapekeS6JKeka0r/4VSNT6mqj0xe0yWttUuq6q+TvGfy+tama/5+d5KjWmvP31w9t22cz33fgM256/Jrhy4BRk+OoB8Zgv7kCPqTo/lz8zR2BvukW27h35O8N8mPJfmHJMe21r41y7a11pI8M8k7kvxKuibvW9Mtv3Du5JivJ/nxdGvxfmBy3AvTzfyd7S3p1uJ9R5ILkrxgaV5aXr+Z7T+SrlH9pnSvuZL8QGttdp0vS3JLkjMmNR432f7iJK9L8jNJ/jXJO9PN9j1nkWsHAAAAYIGq62UB9LNy1ar26sOfOXQZMFq7HnFQEr+dhj7kCPqRIehPjqC/Pjl65WWnZrn1OqtqXWttzVz7LMUALIrVe1tgF/q410PvlyTZ6B8BsGByBP3IEPQnR9CfHM2fpRgAAAAAAEZGYxcAAAAAYGQ0dgEAAAAARkZjFwAAAABgZDR2AQAAAABGRmMXAAAAAGBkqrU2dA3AMlBV7Q1H/tTQZQAAAAA7qVdedmqWW6+zqta11tbMtc+MXQAAAACAkdHYBQAAAAAYGY1dYFHsu//+Q5cAo7b7E4/O7k88eugyYNTkCPqRIehPjqA/OZq/FUMXACwPu+2229AlwKjtst+qoUuA0ZMj6EeGoD85gv7kaP7M2AUAAAAAGBmNXQAAAACAkdHYBQAAAAAYGY1dAAAAAICR0dgFAAAAABiZFUMXACwPG2/dmOw9dBUwXnd+6dqhS4DRkyPoR4agPzmC/uRo/jR2gUVx84abkkOGrgLGa9Nnvjp0CTB6cgT9yBD0J0fQnxzNn6UYAAAAAABGRmMXWBQrVvgAAPRR+69K7b9q6DJg1OQI+pEh6E+OoD85mj+NXWBR7HfAAUOXAKO2x4lHZ48Tjx66DBg1OYJ+ZAj6kyPoT47mT2MXAAAAAGBkfHYaWBRVu+SVl506dBkwWi+85dAkyV/LESyYHEE/MgT9yRH01ydH9znksMUuZ4dWrbWhawCWgSMf+MB22SWXDF0GjNYZZ52dJHnKCY8duBIYLzmCfmQI+pMj6E+O7qmq1rXW1sy1z1IMAAAAAAAjo7ELAAAAADAyGrsAAAAAACNjjV1gUXzfQx7SPnvhhUOXAaN144YNSZJ9Vq8euBIYLzmCfmQI+pMj6E+O7mlLa+yu2N7FAMvTrrv4AAD04S8t0J8cQT8yBP3JEfQnR/OnEwMAAAAAMDIau8Ci2HjbbUOXAKN28SWX5uJLLh26DBg1OYJ+ZAj6kyPoT47mT2MXWBR3bNo0dAkwaldcfXWuuPrqocuAUZMj6EeGoD85gv7kaP40dgEAAAAARkZjFwAAAABgZDR2AQAAAABGRmMXAAAAAGBkNHYBAAAAAEZmxdAFAMvDrrvuOnQJMGr7rN5r6BJg9OQI+pEh6E+OoD85mr9qrQ1dA7AMrFmzpq1du3boMgAAAACWjapa11pbM9c+SzEAAAAAAIyMxi4AAAAAwMho7AKL4sYNG4YuAUbtjLPOzhlnnT10GTBqcgT9yBD0J0fQnxzNn8YuAAAAAMDIaOwCAAAAAIyMxi4AAAAAwMho7AIAAAAAjIzGLgAAAADAyGjsAgAAAACMzIqhCwCWh5V77DF0CTBqxx515NAlwOjJEfQjQ9CfHEF/cjR/1VobugZgGVizZk1bu3bt0GUAAAAALBtVta61tmaufZZiAAAAAAAYGY1dYFHcsWnT0CXAqF2xfn2uWL9+6DJg1OQI+pEh6E+OoD85mj9r7AKLYuNttw1dAozaxZdeliS572GHDVwJjJccQT8yBP3JEfQnR/Nnxi4AAAAAwMho7AIAAAAAjIzGLgAAAADAyGjsAgAAAACMjMYuAAAAAMDIaOwCAAAAAIxMtdaGrgFYBqpqQ5JLhq4DRu7AJN8YuggYOTmCfmQI+pMj6E+Ovu1+rbWD5tqxYntXAixbl7TW1gxdBIxZVa2VI+hHjqAfGYL+5Aj6k6P5sRQDAAAAAMDIaOwCAAAAAIyMxi6wWN42dAGwDMgR9CdH0I8MQX9yBP3J0Ty4eRoAAAAAwMiYsQsAAAAAMDIauwAAAAAAI6OxC2xVVR1dVZ+oqluran1Vvbaqdp3HeftU1f+uqhuq6saqOrWqDtgeNcOOZCEZqqqHTvJz+eS8S6rq1VW1x/aqG3YkC/1ZNOv8XapqbVW1qvrhpawVdlR9clRVz6yqC6pqY1VdV1Ufqao9l7pm2JH0+HfRmqr6aFVdP3l8vKoetj1qhh1NVR1RVW+tqv+oqruq6qx5nqe/MIcVQxcA7Niqar8kH0/yhSRPT/KAJH+c7hdDr9rK6aclOSrJ85PcneQPkpye5NFLVS/saHpk6FmTY/8gyWVJvifJ6yZff3QJS4YdTs+fRTOen+TwJSkQRqBPjqrq+Un+Mskbk7wsyX5JfiD+PclOZKEZqqrvmJx3YZKfnmx+WZKPVdWxrbWvLmXdsAP67iRPTnJ+kt224Tz9hTm4eRqwRVX1yiQvT3K/1tpNk20vT/KaJIfMbJvjvIcn+fckj22tnTPZdnySTyd5Qmvt49uhfBhcjwwd2Fr7xtS2FyR5a5L7+0cAO5OF5mjW+fsluTTJK5L8bZKnttY+tKRFww6mz8+jJF9O8muttb/ZTuXCDqdHhl6Y5M1J9m+t3TjZtl+SbyR5SWvtr7ZD+bDDqKpdWmt3T77/5yQHttZO2Mo5+gubYSkGYGuelOTMqb+ovCfJyiSP3cp5X5/5n26StNY+k+4fBk9aikJhB7WgDE03dSc+O/l62OKVB6Ow0J9FM16X5Lwkn1iC2mAsFpqjkyZf37VUhcFILDRDuyW5M8kts7bdPNlWi10k7OhmmrrbSH9hMzR2ga15UJIvzt7QWrsiya2TffM+b+I/t3IeLDcLzdBcHp7uY0dfWpzSYDQWnKOq+p4kP5fklCWrDsZhoTl6WJJLkjyvqq6sqk1V9emqesTSlQo7pIVm6H2TY/64qu5dVfdO8qdJbkjy3iWqFZYb/YXN0NgFtma/JN+cY/sNk32LfR4sN4uShao6JN36bX/fWrtmkWqDseiTo79I8pettcsXvSoYl4Xm6JAkD0z3M+jXkzw13czDj1TVwYtdJOzAFpSh1tr6JI9Ld4+Er08ez0xyYmvt2iWoE5Yj/YXN0NgFgB1cVd0r3c0Cbk7yqwOXA6NRVT+RriH1+qFrgRGrJHsleV5r7dTW2keS/EiSu5K8ZNDKYASq6tB0M3PXpfvI+JMm359RVfcdsjZg/DR2ga25Ick+c2zfb7Jvsc+D5aZXFqqqkvxdJnePba3JDzujbc5RVe2W5A/T3TF5l6raN8nek917VtXqpSgUdmB9/k7Xkpw1s2Gyxui6JEcvYn2wo1tohl6Wbp3dH2utfWTyy5EfTffLEcsEwfzoL2yGxi6wNV/M1Jo1VfUdSVZl7jVuNnvexObWxoHlaqEZmvGmJE9P8vTWmuyws1pIjvZMcniSP0n3F/4bklw02feefPtmhLCzWOjPo/9MN2t3+iZPlW7dd9hZLDRDD0ry+dbappkNrbU7knw+yQOWoE5YjvQXNkNjF9iaDyc5cWpm07OSbExy9lbOO6SqHjWzoarWJPnOyT7YWSw0Q6mqV6b7mOtzWmvnLl2JsMNbSI5uTrem4ezHsyf7fiPJTy1NqbDDWujPow9Nvj5uZkNV7ZPkuHz7lyWwM1hohr6a5JjJ0lpJkqraPckxSb6yBHXCcqS/sBnVWhu6BmAHVlX7JflCks+l+zjrd6ab/fSm1tqrZh13eZKzW2vPm7XtzCRHpvuI0d2T869prT16+70CGNZCM1RVP5nk1CTvTPLWqWG/5GYb7Ez6/CyaGuf+Sb6c5KmttQ/NdQwsVz3/Tnd6kocleUWSbyR5ebplGI6yRBA7ix5/pzsuyflJPprkLelmu784yQ8mWdNa8wsSdipVtSrJkydPX5puqaxXT57/a2vtVv2F+VsxdAHAjq21dkNVPT7JXyb5YLo7Uf5pktdMHboiya5T2541OfYd6T4h8KEkv7SU9cKOpkeGfmjy9eTJY7bnpmv4wk6h588iIL1z9Jx0a1b/SbqPnZ+X5Ac0ddmZLDRDrbV1VfXEdI2rv59svjjJEzR12UndO90NBWebef7/pZvJrr8wT2bsAgAAAACMjDV2AQAAAABGRmMXAAAAAGBkNHYBAAAAAEZGYxcAAAAAYGQ0dgEAAAAARkZjFwAAAABgkVXVO6rqmqr63DyPP6mqvlBVn6+qf9zq8a21/lUCAAAAAPAtVfWYJDcn+bvW2jFbOfbIJKcl+YHW2g1Vde/W2jVbOseMXQAAAACARdZaOyfJ9bO3VdUDquojVbWuqj5VVQ+a7Pr5JG9urd0wOXeLTd1EYxcAAAAAYHt5W5JfbK0dl+SUJG+ZbD8qyVFVdV5VnV9VT9zaQCuWsEgAAAAAAJJU1V5JHpHkvVU1s3n3ydcVSY5MckKSw5OcU1XHtta+ubnxNHYBAAAAAJbeLkm+2Vp78Bz7rkzy6dbapiRfrqpL0zV6L9jSYAAAAAAALKHW2k3pmrY/niTV+d7J7tPTzdZNVR2YbmmG/9rSeBq7AAAAAACLrKreneT/JnlgVV1ZVc9L8lNJnldVFyX5fJKnTw4/M8l1VfWFJJ9M8rLW2nVbHL+1tnTVAwAAAACw6MzYBQAAAAAYGY1dAAAAAICR0dgFAAAAABgZjV0AAAAAgJHR2AUAAAAAGBmNXQAAAACAkdHYBQAAAAAYmf8fYT5Cn1UZ3eoAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Top 10 Anime Community Plot\n",
        "plt.figure(figsize = (20,15))\n",
        "top10_anime = anime[['name', 'members']].sort_values(by = 'members',ascending = False).head(10)\n",
        "\n",
        "colors = ['#87255B', '#56CBF9', '#F5D491', '#BEB7A4', '#B4E1FF', '#F06C9B', '#D3C4D1', '#81F4E1', '#C2AFF0', '#C57B57']\n",
        "\n",
        "\n",
        "labels = top10_anime[['name']].values.flatten()\n",
        "values = top10_anime[['members']].values.flatten()\n",
        "\n",
        "plt.barh(labels, values, color = colors, edgecolor='black')\n",
        "plt.grid(color='#95a5a6', linestyle='--', linewidth=2, axis='x', alpha=0.7)\n",
        "plt.xticks(fontsize = 15)\n",
        "plt.yticks(fontsize = 15)\n",
        "plt.title(\"Top 10 Anime Community\", fontdict = {'fontsize' : 20})\n",
        "plt.show()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "id": "KYeeiUInIOFp",
        "outputId": "dad551d4-d05b-4946-cb01-e640d6d8eac0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       anime_id                                     name  \\\n",
              "10464     33662  Taka no Tsume 8: Yoshida-kun no X-Files   \n",
              "10400     30120              Spoon-hime no Swing Kitchen   \n",
              "9595      23005                         Mogura no Motoro   \n",
              "0         32281                           Kimi no Na wa.   \n",
              "9078      33607                             Kahei no Umi   \n",
              "1          5114         Fullmetal Alchemist: Brotherhood   \n",
              "10786     26313          Yakusoku: Africa Mizu to Midori   \n",
              "2         28977                                 Gintama°   \n",
              "3          9253                              Steins;Gate   \n",
              "4          9969                                  Gintama   \n",
              "\n",
              "                                                   genre   type episodes  \\\n",
              "10464                                     Comedy, Parody  Movie        1   \n",
              "10400                                    Adventure, Kids     TV  Unknown   \n",
              "9595                                       Slice of Life  Movie        1   \n",
              "0                   Drama, Romance, School, Supernatural  Movie        1   \n",
              "9078                                          Historical  Movie        1   \n",
              "1      Action, Adventure, Drama, Fantasy, Magic, Mili...     TV       64   \n",
              "10786                                        Drama, Kids    OVA        1   \n",
              "2      Action, Comedy, Historical, Parody, Samurai, S...     TV       51   \n",
              "3                                       Sci-Fi, Thriller     TV       24   \n",
              "4      Action, Comedy, Historical, Parody, Samurai, S...     TV       51   \n",
              "\n",
              "       rating  members  \n",
              "10464   10.00       13  \n",
              "10400    9.60       47  \n",
              "9595     9.50       62  \n",
              "0        9.37   200630  \n",
              "9078     9.33       44  \n",
              "1        9.26   793665  \n",
              "10786    9.25       53  \n",
              "2        9.25   114262  \n",
              "3        9.17   673572  \n",
              "4        9.16   151266  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c86457f5-681e-4e0a-9481-e7a3cb54bfcc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>anime_id</th>\n",
              "      <th>name</th>\n",
              "      <th>genre</th>\n",
              "      <th>type</th>\n",
              "      <th>episodes</th>\n",
              "      <th>rating</th>\n",
              "      <th>members</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10464</th>\n",
              "      <td>33662</td>\n",
              "      <td>Taka no Tsume 8: Yoshida-kun no X-Files</td>\n",
              "      <td>Comedy, Parody</td>\n",
              "      <td>Movie</td>\n",
              "      <td>1</td>\n",
              "      <td>10.00</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10400</th>\n",
              "      <td>30120</td>\n",
              "      <td>Spoon-hime no Swing Kitchen</td>\n",
              "      <td>Adventure, Kids</td>\n",
              "      <td>TV</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>9.60</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9595</th>\n",
              "      <td>23005</td>\n",
              "      <td>Mogura no Motoro</td>\n",
              "      <td>Slice of Life</td>\n",
              "      <td>Movie</td>\n",
              "      <td>1</td>\n",
              "      <td>9.50</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>32281</td>\n",
              "      <td>Kimi no Na wa.</td>\n",
              "      <td>Drama, Romance, School, Supernatural</td>\n",
              "      <td>Movie</td>\n",
              "      <td>1</td>\n",
              "      <td>9.37</td>\n",
              "      <td>200630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9078</th>\n",
              "      <td>33607</td>\n",
              "      <td>Kahei no Umi</td>\n",
              "      <td>Historical</td>\n",
              "      <td>Movie</td>\n",
              "      <td>1</td>\n",
              "      <td>9.33</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5114</td>\n",
              "      <td>Fullmetal Alchemist: Brotherhood</td>\n",
              "      <td>Action, Adventure, Drama, Fantasy, Magic, Mili...</td>\n",
              "      <td>TV</td>\n",
              "      <td>64</td>\n",
              "      <td>9.26</td>\n",
              "      <td>793665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10786</th>\n",
              "      <td>26313</td>\n",
              "      <td>Yakusoku: Africa Mizu to Midori</td>\n",
              "      <td>Drama, Kids</td>\n",
              "      <td>OVA</td>\n",
              "      <td>1</td>\n",
              "      <td>9.25</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28977</td>\n",
              "      <td>Gintama°</td>\n",
              "      <td>Action, Comedy, Historical, Parody, Samurai, S...</td>\n",
              "      <td>TV</td>\n",
              "      <td>51</td>\n",
              "      <td>9.25</td>\n",
              "      <td>114262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9253</td>\n",
              "      <td>Steins;Gate</td>\n",
              "      <td>Sci-Fi, Thriller</td>\n",
              "      <td>TV</td>\n",
              "      <td>24</td>\n",
              "      <td>9.17</td>\n",
              "      <td>673572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9969</td>\n",
              "      <td>Gintama</td>\n",
              "      <td>Action, Comedy, Historical, Parody, Samurai, S...</td>\n",
              "      <td>TV</td>\n",
              "      <td>51</td>\n",
              "      <td>9.16</td>\n",
              "      <td>151266</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c86457f5-681e-4e0a-9481-e7a3cb54bfcc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c86457f5-681e-4e0a-9481-e7a3cb54bfcc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c86457f5-681e-4e0a-9481-e7a3cb54bfcc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# Top 10 Anime Based on Avg Ratings\n",
        "anime.sort_values(by='rating', ascending=False).head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cPX5nqoiNN5E"
      },
      "source": [
        "Menampilkan anime dengan rata-rata rating tertinggi. Misalnya, anime Taka no Tsume 8: Yoshida-kun no X-Files memiliki rata-rata rating tertinggi, yaitu 10."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "kpL9dr-bdizF",
        "outputId": "5677fca9-1370-4ad4-ecf8-7f84afdd2708"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1080 with 1 Axes>"
            ],
            "image/png": 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68yoB6IamMwAAe4s3jV//cFxRmSQZG3Uvz/Dfxm9c5rl/XFW3nHjObTOsWEyS/7sbak1r7fIkb0lyYIYVspOekWF17AfGm+3tzG9maJ6/tbX220s9Mqy6bUl+a2wyrWWvHL/+5bii9PtU1YHjGIObZfxsPGv8duPErvPHr8dPHX9okj9b4jy3r6q7L3GJAzOMyfhehps5JsPn6ZIkJ1bVjW7AV1X7VNX11x3n534ow0zsZ0wd+6isfJ5zWmtXZ5jTuz5TN8Crqv+UYWTENRk+l7vD4kiRp+3gc/r6DDcUvP6mn+PK/I8muU+GvG6f5G0Tc58XnZbhbzk8sKqmG6JPyU2b57yk1trWDGNZbpehAb3o/CS3raofmTy+qn4rN74Z4qJvZZhRvsNfZtxMu/zeVNX9q2qpvz29uCp6el45AHsR4zUAANgrjLNf/1eG0QefH2fpfifDary7ZbiB1p8u8dRtGWasfr6q3pNhtvAvZxg18OettY+v5PpVdUiG5vaiQ8avb5wYd/EnrbX/mDjmDzI0Np9TVT+a5NMZbiD3qCTfSPL0FVx3skH3V8sd11r7YlWdOl7vobnxKtE1o7X2kap6foY5y+dU1T8nOS9DA/eYDI3WTUl+bhdO+6NTN+47NMNN7X4ow6rZyZEZn8nQpPvF8QZrmzI02h6aYbXwhVPnPjLJZ6tqS5IzM4xguFWG1eeHJXlNa237+Nq+VVW/nORdSU6vqo8k+bcMvxA4KsONBm+X77/53NOTfDLJq6rqwUn+NcOK2kcnWbyB4Uo9P8Mq62dU1T0zrKQ/JMmvZmhGP6O1dt4unG9Fxkb6cUm2tNY+vYND35jkhUmeVFUnTqw0/+sMN3582cT332e8Id5vJ3l/kveMN3T8UpIfSfKzSd6XIcPrpp97E70sw807f7+qXjvOl35Vhubypqr6uwyjSjZkWGH8jgz/bJn2kST3TPL+qvp4hhXU/9pae++M6ryp781rkhxZVadlaKZfneQnMvzcXJDkb2dVHwD90XQGAGCv0Vp7XlV9NsOK0MdnaCB/KcOq5VeMKz2nXZ0bmlm/lqEBd26SP0ny2l24/EG5YdbspMdP/PnkJNc3nccG5H0z3DDvFzI0A7+VYTXsi8ZVrjvzoAyrYD/bWtvZWIS/zNB0fkrWcNM5SVpr/3Nsdj0zQ8PuURkaeF9N8oYkb9vFU95jfCy6MkMj7dVJ/ldr7fpGcmvt2qr6+QxzhB821vDVDE39lySZnmN7foYMj88wouKQJBdnaFA/P1PNubGp/iMZbkj3kAy5X52hmf3RJP8wdfw548ruP8mQ9/EZmtu/kGHV74qbzuMN++6b5AVJfjHJczKMefl0kj9trX1wpefaRYurnJf9xchY3/lV9eEMjdBHZmjOJ8k7M6wyv1WSzy/3WW+tbayqB2TI6eHj5k9lyOVx4/eXLfXcXdVa21ZV/yfDe/iCJCe01t5fVY/M8M+cx2QYnfHp8fp3ytJN55dkuHnjI5P8VIZRG3+d4RcKM3MT3puXZfjFxoYMn7vrkmwdt7+qtfbtWdYHQF+qteXuIQIAAHu3qjo/SVprx863EmB3G3+Jce8kB7fWvjPvetYS7w0Au2qtz2oDAACAmaiqH1jqJolV9cQMN8v74N7aVPXeADBLxmsAAACwtzg6w3ztDyX5Yob/J/6xDCNaLsn33/Rvb+O9AWBmjNcAAIBlGK8Be5aquk2GG4Y+IMNNHG+Z5GtJPpzkpa21L82xvLny3gAwS5rOAAAAAADMjPEawF7hkEMOaccee+y8ywAAAADYYywsLFzUWrv99HZNZ2CvcOyxx2bz5s3zLgMAAABgj1FVFyy1fZ/VLgQAAAAAgD2XpjMAa8IpG0/NKRtPnXcZ7ISc+iGrPsipD3Lqg5z6Ias+yKkPcurH3paVpjMAAAAAADOj6QwAAAAAwMxoOgMAAAAAMDOazgAAAAAAzIymMwAAAAAAM6PpDAAAAADAzKybdwEAkCR3P+4u8y6BFZBTP2TVBzn1QU59kFM/ZNUHOfVBTv3Y27Kq1tq8awDY7TZs2NA2b9487zIAAAAA9hhVtdBa2zC93XgNAAAAAABmRtMZgDVh64UXZuuFF867DHZCTv2QVR/k1Ac59UFO/ZBVH+TUBzn1Y2/LykxnANaELWefkyQ5+ogj5lwJOyKnfsiqD3Lqg5z6IKd+yKoPcuqDnPqxt2VlpTMAAAAAADOj6QwAAAAAwMxoOgMAAAAAMDOazgAAAAAAzIymMwAAAAAAM6PpDAAAAADAzFRrbd41AOx2GzZsaJs3b553GQAAAAB7jKpaaK1tmN5upTMAAAAAADOj6QwAAAAAwMxoOgOwJmxaWMimhYV5l8FOyKkfsuqDnPogpz7IqR+y6oOc+iCnfuxtWa2bdwEAkCSXbr983iWwAnLqh6z6IKc+yKkPcuqHrPogpz7IqR97W1ZWOgMAAAAAMDOazgAAAAAAzIymMwAAAAAAM6PpDAAAAADAzGg6AwAAAAAwM+vmXQAAJMnRhx8+7xJYATn1Q1Z9kFMf5NQHOfVDVn2QUx/k1I+9Latqrc27BoDdbsOGDW3z5s3zLgMAAABgj1FVC621DdPbjdcAAAAAAGBmNJ0BWBMu3b49l27fPu8y2Ak59UNWfZBTH+TUBzn1Q1Z9kFMf5NSPvS0rTWcA1oRNC2dk08IZ8y6DnZBTP2TVBzn1QU59kFM/ZNUHOfVBTv3Y27LSdAYAAAAAYGbWzbsAgNVw5plnpqrmXQY78DsnPDdJ8ogHHj/fQtghOfVDVn2QUx/k1Ac59UNWfZBTH2aV01FHHpGtX/nqDCqCQbXW5l0DwG5XVe20Fz5m3mWwA+cede8kyZ2+/Kk5V8KOyKkfsuqDnPogpz7IqR+y6oOc+jCrnH7qpW+PHuHudcrGU5MkDz/+AXOuZLaqaqG1tmF6u/EaAAAAAADMjKYzAAAAAAAzo+kMAAAAAMDMuJEgAGvCkV/7/LxLYAXk1A9Z9UFOfZBTH+TUD1n1QU59kFM/7vcTPz7vElaVpjMAa8Itr/nOvEtgBeTUD1n1QU59kFMf5NQPWfVBTn2QUz8OXr9+3iWsKuM1AAAAAACYGU1nANaEb97mjvnmbe447zLYCTn1Q1Z9kFMf5NQHOfVDVn2QUx/k1I8tZ52dLWedPe8yVo2mMwBrwvaDDs32gw6ddxnshJz6Ias+yKkPcuqDnPohqz7IqQ9y6sfWbduyddu2eZexajSdAQAAAACYGU1nAAAAAABmRtMZAAAAAICZ0XQGAAAAAGBmNJ0BAAAAAJiZdfMuAACSZL+rvzPvElgBOfVDVn2QUx/k1Ac59UNWfZBTH+TUj4PXHzTvElZVtdbmXQPAbldV7bQXPmbeZQAAAMCa81MvfXv0CLkpqmqhtbZhervxGgAAAAAAzIymMwAAAAAAM6PpDMCacO5R9865R9173mWwE3Lqh6z6IKc+yKkPcuqHrPogpz7IqR+nbDw1p2w8dd5lrBpNZwAAAAAAZkbTGQAAAACAmdF0BgAAAABgZjSdAQAAAACYGU1nAAAAAABmRtMZAAAAAICZWTfvAoDdo6p+IcnTkvxEkvVJvpnkk0n+qrX2/vGYluT3Wmuv24XzHjqe9+TW2vmzrpu91yEXnzfvElgBOfVDVn2QUx/k1Ac59UNWfZBTH+TUj7sfd5d5l7CqNJ1hD1RVr0zyzCRvTvJ/knwryTFJfi3J+6rqzq21LyW5b5Jd/TfUoUlOTLIxyfkzKhlyq+98Y94lsAJy6oes+iCnPsipD3Lqh6z6IKc+yKkfRx9xxLxLWFWazrCHqapHJXl2kie11k6e2v2WqnpkkiuSpLV2+iqXBwAAAMAezkxn2PM8O8lnlmg4J0laa+9trV2YDOM1quoZi/uqamNVvaOqHltVX6yqy6rqfVV1h3H/sUm2jId/bHx+G/cdWFWvq6qzquq7VXVeVf1ZVd1q8vrjc36/ql5RVd+qqouq6rnjvidU1blVdUlVvamq9p943uHjtnOr6oqqOruqXlJV+83qjWO+Ljvw0Fx24KHzLoOdkFM/ZNUHOfVBTn2QUz9k1Qc59UFO/dh64YXZeuGF8y5j1VjpDHuQqlqXYWTGy2/Gae6d5IgkJyQ5IMmrk7whycOSbEvyuCRvTfL0JGdMPO8HktwiyQszzI8+avzz3yd5yNQ1TkhySpL/luQRSf50nBV9zwxjQY5O8sokZyf5k/E5hyS5OMlzknw7yXFJTkpy+yRPvRmvlzXiotveMYm/HrbWyakfsuqDnPogpz7IqR+y6oOc+iCnfmw5+5wke8+YDU1n2LPcLsktk3x5cmNVVYaG8KJrW2ttmXPcKsnDW2vfHp97WJJXVtUBrbUrqurM8bgvTI7naK19M8nvTlxzXYZ50Zuq6ujW2taJa5zTWnvqeNyHk/xKkicnOaa1dtm4/fgkj87YdG6tbUny3Inzn5bkO0neVFW/11q7evqFVNVTkjxlmdcJAAAAwG5gvAbsmaYbyickuWbi8fQdPPcziw3n0RfGr0fu7KJV9RtV9dmquny8zqZx13FTh37k+kJbuy5Dc3phseE8+uLkNWvw7Kr6QlVdMZ7/rRma7EcvVU9r7Q2ttQ2ttQ07qx0AAACA2dB0hj3Lt5JcleQOU9vfkmF0xT1XcI5Lpr5fXEG8//SBk6rq0UnenOSTGVYu3yfDSuWlnrvUNZbaNvm8Z2cYG/KuJI9Kcq/c0DzfYW0AAAAArB7jNWAP0lr7XlV9MsmDk7xoYvvXk3w9SYZJG7vFryT5VGvtaYsbquoBMz7/O1prL5w4/11neH4AAAAAZsBKZ9jzvCrJvavqN3bT+Zdb+XxAhlXWkx43w+vu7vMDAAAAMANWOsMeprX27qp6VZKTq+qBSd6b5KIMNxl88HjY5TfjEluTXJHkCVV1aZJrWmubk3woyZ9V1QuTfCrJw5L8zM24zrQPJXlmVX0qyZcyNJzvPMPzAwAAADADms6wB2qt/X5VfTzJ05K8Mcn6JN/MMG/5Ya21992Mc19ZVU9OcmKSU5Psm6SSvD7JnZI8K8Mq6A8leWyS02/GS5n0R0lun+Ql4/fvTPLMDE119gB3+vKn5l0CKyCnfsiqD3Lqg5z6IKd+yKoPcuqDnPrx8ONnOYF07avW2rxrANjtqqqd9sLHzLsMAAAAWHN+6qVvjx4hN0VVLbTWNkxvN9MZAAAAAICZ0XQGYE34yg/eLV/5wbvNuwx2Qk79kFUf5NQHOfVBTv2QVR/k1Ac59WPTwkI2LSzMu4xVY6YzAGvC1fsdOO8SWAE59UNWfZBTH+TUBzn1Q1Z9kFMf5NSPS7dfPu8SVpWVzgAAAAAAzIymMwAAAAAAM6PpDAAAAADAzGg6AwAAAAAwM5rOAAAAAADMzLp5FwAASbL+8m/MuwRWQE79kFUf5NQHOfVBTv2QVR/k1Ac59ePoww+fdwmrqlpr864BYLerqnbaCx8z7zIAAABgzfmpl749eoTcFFW10FrbML3deA0AAAAAAGZG0xmANeGqfQ/MVfseOO8y2Ak59UNWfZBTH+TUBzn1Q1Z9kFMf5NSPS7dvz6Xbt8+7jFWj6QzAmvDVw+6Wrx52t3mXwU7IqR+y6oOc+iCnPsipH7Lqg5z6IKd+bFo4I5sWzph3GatG0xkAAAAAgJnRdAYAAAAAYGY0nQEAAAAAmBlNZwAAAAAAZkbTGQAAAACAmdF0BgAAAABgZtbNuwAASJIjv/b5eZfACsipH7Lqg5z6IKc+yKkfsuqDnPogp37c7yd+fN4lrCpNZwDWhFte8515l8AKyKkfsuqDnPogpz7IqR+y6oOc+iCnfhy8fv28S1hVxmsAAAAAADAz1Vqbdw0Au91+++3XrrnmmnmXwQ484GcfnCQ59UMfnHMl7Iic+iGrPsipD3Lqg5z6Ias+yKkPs8rpqCOPyNavfHUWJbGMLWednSS5+w8dN+dKZquqFlprG6a3G68B7BV+5Ed+JJs3b553GezAKRtPTZJs/OAH5lwJOyKnfsiqD3Lqg5z6IKd+yKoPcuqDnPqxddu2JHte03k5xmsAAAAAADAzms4AAAAAAMyMpjMAAAAAADOj6QwAAAAAwMxoOgMAAAAAMDPr5uh3mNEAACAASURBVF0AACTJwesPmncJrICc+iGrPsipD3Lqg5z6Ias+yKkPcurH3pZVtdbmXQPAbrdhw4a2efPmeZcBAAAAsMeoqoXW2obp7cZrAAAAAAAwM5rOAAAAAADMjKYzAGvCKRtPzSkbT513GeyEnPohqz7IqQ9y6oOc+iGrPsipD3Lqx96WlaYzAAAAAAAzo+kMAAAAAMDMaDoDAAAAADAz6+ZdAMBqOPPMLamqeZfBDvzOCc9NkjzigcfPtxB2SE79kFUf5NQHOfVBTv2QVR/k1IdZ5nTUUcdk69bzb/Z5IEmqtTbvGgB2u6pqH3/XJfMugx04/8rNSZJj998w50rYETn1Q1Z9kFMf5NQHOfVDVn2QUx9mmdP9H33r6BPuPos3EXz48Q+YcyWzVVULrbUbfQCN1wAAAAAAYGaM1wBgTbjdvsfMuwRWQE79kFUf5NQHOfVBTv2QVR/k1Ac59ePux91l3iWsKk1nANaE9be4/bxLYAXk1A9Z9UFOfZBTH+TUD1n1QU59kFM/jj7iiHmXsKqM1wAAAAAAYGY0nQFYE7Zf+81sv/ab8y6DnZBTP2TVBzn1QU59kFM/ZNUHOfVBTv3YeuGF2XrhhfMuY9UYrwHAmvCtay5I4q+HrXVy6oes+iCnPsipD3Lqh6z6IKc+yKkfW84+J8neM2bDSmcAAAAAAGZG0xkAAAAAgJnRdAYAAAAAYGY0nQEAAAAAmBlNZwAAAAAAZkbTGQAAAACAmanW2rxrANjtqqp9/F2XzLsMAAAAWJPu/+hbR5+QXVVVC621DdPbrXQGAAAAAGBmNJ0BAAAAAJgZTWcA1oQLr/5CLrz6C/Mug52QUz9k1Qc59UFOfZBTP2TVBzn1QU792LSwkE0LC/MuY9Wsm3cBAJAkV1/33XmXwArIqR+y6oOc+iCnPsipH7Lqg5z6IKd+XLr98nmXsKqsdAYAAAAAYGY0nQEAAAAAmBlNZwAAAAAAZkbTGQAAAACAmdF0BgAAAABgZtbNuwAASJKDbnHIvEtgBeTUD1n1QU59kFMf5NQPWfVBTn2QUz+OPvzweZewqqq1Nu8aYK9TVU9M8ntJjkvyvSTnJ/lYa+054/5DkzwtycmttfNvwvlbkt9rrb1uRiXv7Hp3SvL8JA9OcniSK5P8W5J3JvmL1trlu3CueyV5WGvtpBnX2D7+rktmeUoAAADYY9z/0beOPiG7qqoWWmsbprcbrwGrrKpekOSvknwgyS8meXySdyf5+YnDDk1yYpJjb+Jl7pvk7296lStXVfdL8rkkG5K8NEPj+bFJPpnkRUmet4unvFeG1w4AAABAh4zXgNX3jCSvb639wcS291bVi2d1gdba6bM6145U1QFJ3p7k9CQPb61dM7H7lKp6eZJ7rkYt9O+q676TJLnlPgfOuRJ2RE79kFUf5NQHOfVBTv2QVR/k1Ac59ePS7duTJAevXz/nSlaHlc6w+m6d5GvTG9v4d1iq6tgkW8bNH6uqNo7LyLj/tlX1hqr6elVdWVWfqKp7T55rfM4zJr7fWFXvqKrHVtUXq+qyqnpfVd1h6nkvGPdfOZ7//VV12A5ey68mOSLJ7081nBdf07bW2nsmzn/fqnpPVW2rqu9U1eeq6nET+5+Y5LUTr6FV1caJ/XerqlOqavv4+Pud1EdHtl3979l29b/Puwx2Qk79kFUf5NQHOfVBTv2QVR/k1Ac59WPTwhnZtHDGvMtYNVY6w+o7I8nvVdXWJP/UWvvW1P5tSR6X5K1Jnj4enySpqlsm+XCGxvV/T/KNJL+b5MNVdZfW2o2a2RPunaFBfEKSA5K8OskbkjxsPPfjk/xBhnEY/5bkdkn+a5Lrf11aVecn2dhae+K46f5Jvtpa+7cVvvZjkpyW5C8yzH3+qST/t6qua639TZJTkrxirPG+43MuG6995/G5m5P8eoZ/fv2PDKvE79UMngIAAABYEzSdYfU9Pck/Jjk5Sauqf0/yD0le3lq7rLV2VVWdOR77halRGb+e5G5J/ktr7ZwkqaoPJzkrQ6P2v+/gurfKMALj2+PzDkvyyqo6oLV2RYZZyh9srf35xHPeOXWO7yW5duL7I5J8efpCVTX5z5bWWrt2/MPfThxTST6e5A5Jnpzkb1pr3xwb20uNCDkxwwrxh7bWrh7PcWaS/8jQOD9liTqekuQpN34rAAAAANhdjNeAVdZaOzPJD2e4ceCfJ6kk/2+SzVV10E6e/qAkC0nOq6p1E83dUzPcyG9HPrPYcB59Yfx65Pj1c0keVlUvrqp7VdUtlqj9zq2135rePPlNVR2S5JqJx6cm9t2mql5TVRdM7H9KkuN2UnsyvPZ3Jblu4rWfl+T8LPPaW2tvaK1tWOouqgAAAADsHprOMAettataa+9trT2jtXbXJL+d5C5Jphu60w5Jcp98f1P3miRPSnLUTp57ydT3V49f9x+/vinDeI1fzdAo/npVvWSp5vOECzOsVJ6+zj3Hxz9N7Ts5yWOS/GmSB4/HvGmihh05JMPoj+nXfqfs/LUDAAAAsEqM14A1oLX2xqr6X0n+804OvTjDTOPfXWLfVTezhuuSvDLDyI2jMsyVfmmSr2SYwbyUjyf5zar64dbav4/n+d5YY6rqW0kOH/+8f5JHJHl6a+3681XVSn/5dXGGlc5/tcS+i1Z4DgAAAAB2M01nWGVVdWhr7RtT226f5OAkXx83Ta9CXvSRDCuEt06fY5Zaa19O8idV9aQkd93BoX+X5GVJ/ndV/Xxr7ZodHHvLDH+74vrmeFWtzzBmZHJEx+K85v1ba1dObP9Ikv+SZMFNAwEAAADWLk1nWH1bqurdST6Y5BtJjkny3CTfTfLX4zFbk1yR5AlVdWmSa1prm5O8OcnvJNlYVS9Pcm6S22W4CeDXWmuvvKlFVdXrM6wmPj3JpUkemGHkx/Mmjjk/ycbW2hOTpLV2RVX9Woab+J1eVX+R4aaG+ye5e5KfSfKl8dhLq+ozSV5UVZcluS7J88dr3WqilP8Yvz6rqj6a5LLW2llJTkry6SSnVNWbMqxuPjLJzyY5ubW28aa+dtaGw/f74XmXwArIqR+y6oOc+iCnPsipH7Lqg5z6IKd+3O8nfnzeJawqTWdYfX+U5FFJXpPktkm+luQTSR7TWjsvSVprV1bVk5OcmOEmgfsmqXH7A8dzvDjJD2ZoXH86yXtuZl2fTPLkJE/N0DT+YpInt9b+ceKYHxivd73W2r9U1Y9laCD/YYZxGlck+bckr873j+Z4bJLXZ2iefyvJ68ZzPmPimH/JMPP5WUn+OMMIj+Nba2dX1X2SvCTJG5IckOSrGVZAf/FmvnbWgFvuc+C8S2AF5NQPWfVBTn2QUx/k1A9Z9UFOfZBTPw5ev37eJayq8rfUgZWoqjtmaO7epbV27rzr2VVV1T7+rul7KQIAAABJcv9H3zr6hOyqqlporW2Y3r7SG3gB/GSSf+yx4UwfLrrm/Fx0zfnzLoOdkFM/ZNUHOfVBTn2QUz9k1Qc59UFO/dhy1tnZctbZ8y5j1Wg6AyvSWntra+2X5l0He67Lr70ol1970bzLYCfk1A9Z9UFOfZBTH+TUD1n1QU59kFM/tm7blq3bts27jFWj6QwAAAAAwMxoOgMAAAAAMDOazgAAAAAAzIymMwAAAAAAM6PpDAAAAADAzKybdwEAkCT77fMD8y6BFZBTP2TVBzn1QU59kFM/ZNUHOfVBTv04eP1B8y5hVVVrbd41AOx2VdU+/q5L5l0GAAAArEn3f/Sto0/IrqqqhdbahuntxmsAAAAAADAzms4AAAAAAMyMpjMAa8L5V27O+VdunncZ7ISc+iGrPsipD3Lqg5z6Ias+yKkPcurHKRtPzSkbT513GatG0xkAAAAAgJnRdAYAAAAAYGY0nQEAAAAAmBlNZwAAAAAAZkbTGQAAAACAmdF0BgAAAABgZtbNuwAASJLb7XvMvEtgBeTUD1n1QU59kFMf5NQPWfVBTn2QUz/uftxd5l3CqtJ0BmBNWH+L28+7BFZATv2QVR/k1Ac59UFO/ZBVH+TUBzn14+gjjph3CavKeA0AAAAAAGZG0xmANWH7td/M9mu/Oe8y2Ak59UNWfZBTH+TUBzn1Q1Z9kFMf5NSPrRdemK0XXjjvMlaN8RoArAnfuuaCJP562Fonp37Iqg9y6oOc+iCnfsiqD3Lqg5z6seXsc5LsPWM2NJ2BvcK+++6X+z/61vMugx34nROemyR5/CseNOdK2BE59UNWfZBTH+TUBzn1Q1Z9kFMfZpnTUUe5KSGzU621edcAsNtt2LChbd68ed5lsAOnbDw1SfLw4x8w50rYETn1Q1Z9kFMf5NQHOfVDVn2QUx/k1I89NauqWmitbZjebqYzAAAAAAAzo+kMAAAAAMDMaDoDAAAAADAzms4AAAAAAMyMGwkCewU3EgQAAACYLTcSBAAAAABgt9N0BgAAAABgZjSdAVgTNi0sZNPCwrzLYCfk1A9Z9UFOfZBTH+TUD1n1QU59kFM/9ras1s27AABIkku3Xz7vElgBOfVDVn2QUx/k1Ac59UNWfZBTH+TUj70tKyudAQAAAACYGU1nAAAAAABmRtMZAAAAAICZ0XQGAAAAAGBm3EgQ2CucuWVLqmreZbADv3PCc5Mkj3jg8fMthB2SUz9k1Qc59UFOfZBTP2TVBzn1YbVzOvKYo/OV8y9YlWvRt2qtzbsGgN2uqtqLL/cvxrVs/3O3JUmuvNPhc66EHZFTP2TVBzn1QU59kFM/ZNUHOfVhtXM68aBjopd402w56+wkyd1/6Lg5VzJbVbXQWttwo+0+KMDeQNMZAAAAbh5NZ6Yt13Q20xkAAAAAgJnRdAZgTdjn8iuyz+VXzLsMdkJO/ZBVH+TUBzn1QU79kFUf5NQHOfXj0u3bc+n27fMuY9VoOgOwJhz0+fNy0OfPm3cZ7ISc+iGrPsipD3Lqg5z6Ias+yKkPcurHpoUzsmnhjHmXsWo0nQEAAAAAmBlNZwAAAAAAZkbTGQAAAACAmdF0BgAAAABgZjSdAQAAAACYGU1nAAAAAABmplpr864BYLerqvbiyy+YdxnswD6XX5Ekue6gA+ZcCTsip37Iqg9y6oOc+iCnfsiqD3Lqw2rndOJBx0Qv8aa5dPv2JMnB69fPuZLZqqqF1tqG6e3r5lEMAEzzH7N9kFM/ZNUHOfVBTn2QUz9k1Qc59UFO/djTms07Y7wGAAAAAAAzo+kMwJqw/7nbsv+52+ZdBjshp37Iqg9y6oOc+iCnfsiqD3Lqg5z6seWss7PlrLPnXcaq0XQGYE3Y7xvfzn7f+Pa8y2An5NQPWfVBTn2QUx/k1A9Z9UFOfZBTP7Zu25at2/aeXxBoOgMAAAAAMDOazgAAAAAAzIymMwAAAAAAM6PpDAAAAADAzGg6AwAAAAAwM+vmXQAAJMm1B+4/7xJYATn1Q1Z9kFMf5NQHOfVDVn2QUx/k1I+D1x807xJWVbXW5l0DwG5XVe3Fl18w7zIAAACgWycedEz0EplUVQuttQ3T243XAAAAAABgZjSdoWNV9QtV9cGq+lZVXV1VX62qd1TVz00c06rqGbt43kOr6qSqOnbWNc9CVd2zqv69qr5dVS+Ydz0AAAAA3EDTGTpVVa9M8g9Jvprkt5M8KMnzkxyQ5H1V9Z/GQ++b5O938fSHJjkxybEzKXb23prkz5P8tyRPqqqfnHM9zMCtTv9CbnX6F+ZdBjshp37Iqg9y6oOc+iCnfsiqD3Lqg5z6ccrGU3PKxlPnXcaqcSNB6FBVPSrJs5M8qbV28tTut1TVI5NckSSttdNXubzdqqpun+Sy1tprx+9fm+Qnk3xiroUBAAAAkMRKZ+jVs5N8ZomGc5Kktfbe1tqFyY3Ha1TVxnEEx2Or6otVdVlVva+q7jDuPzbJlvHwj43Pb+O+A6vqdVV1VlV9t6rOq6o/q6pbTV5/fM7vV9UrxtEfF1XVc8d9T6iqc6vqkqp6U1XtP/G8w8dt51bVFVV1dlW9pKr2mzj9xUmOrKqfrqrDkvxyki/dnDcTAAAAgNmx0hk6U1XrMozMePnNOM29kxyR5IQM4zheneQNSR6WZFuSx2UYYfH0JGdMPO8HktwiyQuTfDPJUeOf/z7JQ6aucUKSUzKMwHhEkj+tqkOT3DPJM5McneSVSc5O8ifjcw7J0FR+TpJvJzkuyUlJbp/kqUnSWru2qp6X5CNJ9k3yriTvvhnvBQAAAAAzpOkM/bldklsm+fLkxqqqDA3hRde21toy57hVkoe31r49PvewJK+sqgNaa1dU1ZnjcV+YHM/RWvtmkt+duOa6JOcl2VRVR7fWtk5c45zW2lPH4z6c5FeSPDnJMa21y8btxyd5dMamc2ttS5LnTpz/tCTfSfKmqvq91trV43Fvrqr3Jrl1a+285d6oqnpKkqcstx8AAACA2TNeA/o13VA+Ick1E4+n7+C5n1lsOI8W7zpw5M4uWlW/UVWfrarLx+tsGncdN3XoR64vtLXrMjSnFxYbzqMvTl6zBs+uqi9U1RXj+d+aocl+9OTJW2vf3lHDeTzmDa21Da21DTt7XQAAAADMhqYz9OdbSa5Kcoep7W/JMLrinis4xyVT3189ft1/+sBJVfXoJG9O8skMK5fvk2Gl8lLPXeoaS22bfN6zM4wNeVeSRyW5V25onu+wNgAAAADWBuM1oDOtte9V1SeTPDjJiya2fz3J15NkmLSxW/xKkk+11p62uKGqHjDj87+jtfbCifPfdYbnZw274o6Hz7sEVkBO/ZBVH+TUBzn1QU79kFUf5NQHOfXj7sfdZd4lrCpNZ+jTq5L8Y1X9RmvtLbvh/MutfD4gwyrrSY+b4XV39/lZw675wdvMuwRWQE79kFUf5NQHOfVBTv2QVR/k1Ac59ePoI46YdwmrStMZOtRae3dVvSrJyVX1wCTvTXJRhpsMPng87PKbcYmtSa5I8oSqujTJNa21zUk+lOTPquqFST6V5GFJfuZmXGfah5I8s6o+leRLGRrOd57h+QEAAADYzTSdoVOttd+vqo8neVqSNyZZn+SbGeYtP6y19r6bce4rq+rJSU5McmqSfZNUktcnuVOSZ2VYBf2hJI9NcvrNeCmT/ijJ7ZO8ZPz+nUmemaGpzh5u368P97b0m/q1TU79kFUf5NQHOfVBTv2QVR/k1Ac59WPrhRcm2XtWPGs6Q8daa+/KcNO9HR1TU98fv8QxGzM0lSe3vTXJW6e2XZvkueNj0vRzbzRUepnrnpTkpInvL0/ypBu/iuy2IdWsHQecty2J/1ha6+TUD1n1QU59kFMf5NQPWfVBTn2QUz+2nH1Okr2n6bzPvAsAAAAAAGDPoekMAAAAAMDMaDoDAAAAADAzms4AAAAAAMyMpjMAAAAAADOj6QwAAAAAwMxUa23eNQDsdlXVXnz5BfMuAwAAALp14kHHRC+RSVW10FrbML3dSmcAAAAAAGZG0xkAAAAAgJnRdAZgTThwy7k5cMu58y6DnZBTP2TVBzn1QU59kFM/ZNUHOfVBTv3YtLCQTQsL8y5j1aybdwEAkCS3+M6V8y6BFZBTP2TVBzn1QU59kFM/ZNUHOfVBTv24dPvl8y5hVVnpDAAAAADAzGg6AwAAAAAwM5rOAAAAAADMjKYzAAAAAAAzo+kMAAAAAMDMrJt3AQCQJFcfept5l8AKyKkfsuqDnPogpz7IqR+y6oOc+iCnfhx9+OHzLmFVVWtt3jUA7HZV1V58+QXzLgMAAAC6deJBx0QvkUlVtdBa2zC93XgNAAAAAABmRtMZgDVhn8uvyD6XXzHvMtgJOfVDVn2QUx/k1Ac59UNWfZBTH+TUj0u3b8+l27fPu4xVo+kMwJpw0OfPy0GfP2/eZbATcuqHrPogpz7IqQ9y6oes+iCnPsipH5sWzsimhTPmXcaq0XQGAAAAAGBm1s27AIDVsO9+++XEg46ZdxnswO+c8NwkyV886KFzroQdkVM/ZNUHOfVBTn2QUz9k1Qc59WG1czrymKNX5Tr0r9xxEtgbbNiwoW3evHneZbADp2w8NUny8OMfMOdK2BE59UNWfZBTH+TUBzn1Q1Z9kFMf5NSPPTWrqlporW2Y3m68BgAAAAAAM6PpDAAAAADAzGg6AwAAAAAwM2Y6A3sFM53Xvku3b0+SHLx+/ZwrYUfk1A9Z9UFOfZBTH+TUD1n1QU59kFM/9tSslpvpvG4exQDAtD3tX7x7Kjn1Q1Z9kFMf5NQHOfVDVn2QUx/k1I+9LSvjNQAAAAAAmBlNZwDWhC1nnZ0tZ5097zLYCTn1Q1Z9kFMf5NQHOfVDVn2QUx/k1I+9LStNZwDWhK3btmXrtm3zLoOdkFM/ZNUHOfVBTn2QUz9k1Qc59UFO/djbstJ0BgAAAABgZjSdAQAAAACYGU1nAAAAAABmRtMZAAAAAICZ0XQGAAAAAGBm1s27AIDVsOXMLamqeZfBDvzSr/9GkuQRDzx+voWwQ3Lqh6z6IKc+yKkPcuqHrPogpz6sdk5HH3V0Lth6wapca09z8PqD5l3CqqrW2rxrANjtqqpt2fSv8y4DAAAAunX3+90jeolMqqqF1tqG6e3GawAAAAAAMDOazgAAAAAAzIymMwBrwue/cUE+/w2zwdY6OfVDVn2QUx/k1Ac59UNWfZBTH+TUj1M2nppTNp467zJWjaYzAAAAAAAzo+kMAAAAAMDMaDoDAAAAADAzms4AAAAAAMyMpjMAAAAAADOj6QwAAAAAwMysm3cBAJAkR6y/7bxLYAXk1A9Z9UFOfZBTH+TUD1n1QU59kFM/7n7cXeZdwqqq1tq8awDY7aqqbdn0r/MuAwAAALp19/vdI3qJTKqqhdbahuntxmsAAAAAAPD/s3fncZbU1f3/X0eGYZERRAVBNmNE/SlqpN1JBnEhRBIX3FCiaKJfjSQuaDQmKu5GcQ1+E0EjcfmBGncniArO6EQwTmNkBGQQgQFnBGQZmn2Q8/2jquFyp6eXoaarD/16Ph796L5161adW+87A4/TnznVGZvOkqQ54cobxrjyhrG+y9AUzKkOs6rBnGowpxrMqQ6zqsGcajCnOlavWcPqNWv6LmPWONNZkjQnrBm7EoAdt1nUcyWajDnVYVY1mFMN5lSDOdVhVjWYUw3mVMfKVecBsMeuu/ZcyexwpbMkSZIkSZIkqTM2nSVJkiRJkiRJnbHpLEmSJEmSJEnqjE1nSZIkSZIkSVJnbDpLkiRJkiRJkjpj01mSJEmSJEmS1JnIzL5rkKTNLiJy5fKf912GJEmSJEll7bPfI7CXqEERMZqZI8PbXeksSZIkSZIkSeqMTWdJkiRJkiRJUmdsOs9ARHwlIs6PiK0neO7kiDgnIhZOcYzDIyIjYrvNV+n0RMRREfG7Hs67f3sNlm/k+SdGxBkRcWNETPpvNiLi+IhYsXkq3eBc2X4dNsFzh40/P7Btr3bbwbNQ2ysi4pkdHm9pW/unJnhu94i4tX1+/4HtF0bE0VMc92HDr7uTdfbyGdbmcf6Vazn/yrV9l6EpmFMdZlWDOdVgTjWYUx1mVYM51WBOdSwfHWX56GjfZcyaBX0XUMxrgHOAfwDePr4xIp4DPA14Umbe3FNtlRzafn9CROyRmauHnv8kcBlwIHDTFMd6F7BNx/VN5lrgBcDnh7Yf2j43+MuEtcDjgV/OQl2vAH4BfL3DY14LPDsiXpWZ6we2vwC4jju+V4BnAVd0eP7p+BTwrVk+pzaTG27xr88KzKkOs6rBnGowpxrMqQ6zqsGcajCnOtaNXdt3CbPKlc4zkJmXAEcBb4qIPwSIiLsDHwE+m5lL+6uuhojYEngOcCoQNA3MYQ8Gvp6ZyzLz9I0cZxuAzDw/M3+xueqdwLeAp0XEPQdq2RF4KvDNwR0z86bMPD0zr57F+rq0DNiCpvk/6AUMvVeAzPzZBL9A2CwiYsuI2CIzL8nM+fNrQkmSJEmSpAJsOs/cx4BzgX9pH78d2BZ4Q0Q8PiK+GRFrI+K6iPjfiHjRVAeMiDe2oyT+YmD0xMOG9lkaEf858PihEfGdiLiyPdc5EfHqodccERHnRcRNEfGriHjdFHVERPxLRFwVEY/d2CiQ6YxRmMTTgB2BfwZO4/ZVz7eN3aBpdH6sPffxA+f8UES8NSIuAa5pt28wXiMi9oyIEyLidxFxfUScGREvHHj+/RGxMiKujYhLIuILEXHfadZ/GrAGOGRg2yHtttOG6rjDeI2B67nB10T7Dxxn0hEiEbEU2Bd4ycAxD2+f26IdQbG6/RycNXgtpnAj8A0GfjEQEQ8EHgWcOEEdG3wuIuJvIuLi9jP6LWCXCV63bUR8PCJ+2/45+GlEPG34PUbEf7ZjRM5va9vV8RqSJEmSJElzj+M1Zigzb4mIVwHLI+KtwGuBV2fm5RHxZOC/gX+jaYo9EfhMRNyamSdMdLyIeBvwZuAvMvO7M5h1+y2aUR+H0YygeBBwj4HjvpymMf5h4GTgScCHImKrzHz/BHXcjWasxTOBAzLzZxHxkOkU0jY4PwPcPzMvnGL3Q4HLgVPamj8eEQ/OzF8CZ9CMozgN+BDwn+2+414InAX8DRv57EbETu3rrwfeAFwMPAzYfWC3nYD30jSK7wMcCZwaEQ/LzFunqD+BL7bvY3ze8aFM0ISdwJL2/Y3bgua63Vl/A3wF+DXNuBGA89vv7wT+HngH8FOaBvkXIiI39pkccgLwpYjYJjNvoHmvPwEumOqFEfEM4BM0fx6+DiwG/n2CXY8D/gJ4C/Ar4OXAkoh4UmYOzv1+IvAA4E00+a6bRv2SJEmSJEmaZTadN0Fm/jgiPk3T0PsxbfMxM29rPEZEAD8EdqNpom3Q4IuI9wJ/CxyUmcume/6IuDdwf+AZmbmy3XzKwPN3oxkDcnxmHtlu/m5EbA/8Q0R8NDNvHNh/C+B44CnA/pl51nRrad0K/J6mITtZ3dsAzwA+l5m/j4gvRjprggAAIABJREFU0YwmORR4e2ZeA5zeXDou3MhojYMHa5/A64DtgX0zc3yS/imDO2TmywZq2oKmSX0JsB9NZlM5ETgyInamGRGyGHh9+/qNyszLGWiiR8QHaFb+Pm4a55zsuGdHxHXA5YPXLJqxH68F3p2Z7243nxwRu9F8PqbTdP4ezS81Dga+DDyfpkk8Hf8IfCczXzVw7vsAfz1Q40No8n9pZv5Hu+1k4EzgrdxxtMcOwCMz89KB10+zFEmSJEmSJM0Wx2tsug+23z+UmePjEe7Zjgm4CFjffr0C2HuC13+YZoXq02bScG5dSbOC998i4vnt6t5BuwG70jQJB32RZjX0PgPbtqBpou4PLN6EhjOZ+dnMXJCZF02x65/T3HzuxPZ1lwJLGRixMYVTpmg4AxxA0+jc6K1bI+KgiPhxRKwDbqFpOMPEOW0gM39GsyL3ee3Xqsz83+m8dqCG59OsxH7ZplzzaXoYzeiXiT4He7cN4Ell5i00q6hfEBEPp5m3/aWpXhcRC2jGcHxj6KmvDj1+NE3j/rYa29XmX2bDJv7oYMN5OtpxHCsmG08iSZIkSZKkbrnSedPdPPQdmtXCj6MZcXA2zdzhV9Gs7h12CDBKM/JgRjLz1nbm7XtoxhVsExH/Dfxd2xAdn5s73KAbf7zjwLZtgYOAr2TmqpnWMkOHtjWsjIgd2m3fAj4aEftO44Zw02k43otJrmlEPJrmJnhfA94PXEazQvt0YOtpHH/cF2lmHUf787S1zdt/p/mFxXBDuEvT+RxcztROBP4LWAv8KDPXtKuoJ3Nvml9oXDa0ffjxLsC1mXn9BDVu246DuWmo7mnLzGOBYwHGZ2dr7rrn1ttNvZN6Z051mFUN5lSDOdVgTnWYVQ3mVIM51bHHLhvc5uouzaZzRyJia5oRBK/OzH8b2L6x1eQHA98GPhsRhw3MEh5fybtwaP97ArfdMK2dgXxIRGwJ/DHNjfmWtKMTxlf5Dq+A3rn9fuXAtjGakQlLImJtZr554LnJapmRdrTHQcBWQ+cfdyhNE34y02kaXsEEN6sb8CyaRuvzB1ao7zmN4w47kWb8A8DLJttxUNus/RpNk/vNQ093dr1bg5+DKwa2T/Q5mMwy4CqaX6C8eop9x/2OZuTK8Gdw+PFaYLuI2Hao8bwzcP1Awxmml78Ku9897tV3CZoGc6rDrGowpxrMqQZzqsOsajCnGsypjn0eNK1/YH+X4XiN7mxFcz1va5JFxCKaG6RNZCVNE/ZgmhutjRsf9XDbTfwiYneasQYbyMz1mXkqzbiOXWjm3l5Cc5O85w7t/jya1dcrh45xSrvvkRHxj1PU8lgGblg4A8+muUYvobmp4eDXd4HnRzcDek8BDmznLU9kG2D9eMO59aKZniQzz6FZQXts+wuAKbXzo0+k+WXPCzLz90O7XEYzkmXwem8HPGEah7+ZDVdq/4LmhnsTfQ5WtTOmp9T+QuS9NKvS/3Oar7kF+BkbrvJ/9tDjn9I0k58zvqH9HDwHWI4kSZIkSZLKcaVzRzJzXUT8FHhbRFxDc3O9NwPr2EiTNjP/JyL+HDgpIq7JzDdk5iXt/Nl3RcT1NI3stzCwKrUdz3A0zViHX9OshH0T8PPMvLLd5yjgkxFxBc3N4BbTrFR9y0RzkTPzWxHxl8AX2lr+Bfgf4DfAxyPirTTjGP6epnF9m4h4Mc24iAdMMtf5UOCXmfnZ4Sfa1b9foVmxPZ0b+U3mI8CLgR9FxHtoZl8/BLh7Zn6A5lq8NiI+StNEfQJw2KacKDNfOcOXvAl4Ks3NIx8QEQ8YONbp7diUbwCva+eCXw0cCdwwjWP/kqbZfiDNquYLMvOK9n3+U0TcAqygafr+GdOfoz1e3zHAMTN5DU2j+qsR8a80q7sXA386dNxzIuIE4Jj2lzTn09x488E0n1fNIzesb35nt82WW/VciSZjTnWYVQ3mVIM51WBOdZhVDeZUgznVsW5sDIDtFy3quZLZ4Urnbr2Qpgn8WeBjNI3UDZqsg9qbCD4b+NuIeHu7+VBgNfB5msbdO4FzB172W5r5tv8InAT8X+AcBlZVZ+ZxwGtoxkl8uz3mkZn5/klqOZHmxocfjYjDM/Pm9vW30qxwPZKmEXjV0EvvRjO/d8KVyu2q4wOAz23k1EtoGqwzaoRu5D1cDjyRZpXtR2ne+ytorieZ+V80zd9DaGY7L6ZZbT4bxv8dxb8Apw19jTsC+G+aTD8BnACcOo1jv5vmM/AlmtXDf95ufxvwPprcvg38CXBYm/VmlZlfo2mw/znwdeCPgL+aYNeXA//R1voNYE/g4Mx0pfM8c/5Vv+X8q37bdxmagjnVYVY1mFMN5lSDOdVhVjWYUw3mVMfy0TNYPnpG32XMmrjjlAFJumuKiFy5/Od9l6FJ/OKy5h9KPGynTRmzrtliTnWYVQ3mVIM51WBOdZhVDeZUw2zntM9+j8Be4qZZsnQZAE/ff3HPlXQrIkYzc2R4uyudJUmSJEmSJEmdseksSZIkSZIkSeqMTWdJkiRJkiRJUmdsOkuSJEmSJEmSOmPTWZIkSZIkSZLUmQV9FyBJEsAD7nnfvkvQNJhTHWZVgznVYE41mFMdZlWDOdVgTnXst++j+i5hVtl0liTNCdtsuVXfJWgazKkOs6rBnGowpxrMqQ6zqsGcajCnOrZftKjvEmaV4zUkSZIkSZIkSZ2x6SxJmhN+c80V/OaaK/ouQ1MwpzrMqgZzqsGcajCnOsyqBnOqwZzqWHnuKlaeu6rvMmaNTWdJ0pxw1Y3XctWN1/ZdhqZgTnWYVQ3mVIM51WBOdZhVDeZUgznVsXrtWlavXdt3GbPGprMkSZIkSZIkqTM2nSVJkiRJkiRJnbHpLEmSJEmSJEnqjE1nSZIkSZIkSVJnbDpLkiRJkiRJkjqzoO8CJEkC2GbBwr5L0DSYUx1mVYM51WBONZhTHWZVgznVYE51bL9ou75LmFWRmX3XIEmbXUTkyuU/77sMSZIkSZLK2me/R2AvUYMiYjQzR4a3O15DkiRJkiRJktQZm86SJEmSJEmSpM7YdJYkzQm/uOwifnHZRX2XoSmYUx1mVYM51WBONZhTHWZVgznVYE51LFm6jCVLl/Vdxqyx6SxJkiRJkiRJ6oxNZ0mSJEmSJElSZ2w6S5IkSZIkSZI6s6DvAiRpNizcciH77PeIvsvQJF555BsAOPRDR/dciSZjTnWYVQ3mVIM51WBOdZhVDeZUw2zntMfue8zKeVRfZGbfNUjSZjcyMpIrVqzouwxNYvyGCk/ff3HPlWgy5lSHWdVgTjWYUw3mVIdZ1WBONZhTHXfVrCJiNDNHhrc7XkOSJEmSJEmS1BnHa0iS5oR99n5g3yVoGsypDrOqwZxqMKcazKkOs6rBnGowpzrmW1aO15A0LzheQ5IkSZIkqVuO15AkSZIkSZIkbXY2nSVJc8LqNWtYvWZN32VoCuZUh1nVYE41mFMN5lSHWdVgTjWYUx3zLStnOkuS5oSVq84DYI9dd+25Ek3GnOowqxrMqQZzqsGc6jCrGsypBnOqY75l5UpnSZIkSZIkSVJnbDpLkiRJkiRJkjpj01mSJEmSJEmS1BmbzpIkSZIkSZKkzth0liRJkiRJkiR1xqazJEmSJEmSJKkzkZl91yBJm93IyEiuWLGi7zIkSZIkSZLuMiJiNDNHhrcv6KMYSZptK888k4jouwxJkiRJkkrb8367ceElF/ddhuY4m86S5oWb16/n6iOO67sMSZIkSZJK2+GYl/ddggpwprMkaU5YsddCVuy1sO8yNAVzqsOsajCnGsypBnOqw6xqMKcazKmO5aOjLB8d7buMWeNKZ0nSnHDtVo4/qcCc6jCrGsypBnOqwZzqMKsazKkGc6pj3di1fZcwq1zpLEmSJEmSJEnqjE1nSZIkSZIkSVJnbDpLkiRJkiRJkjpj01mSJEmSJEmS1BmbzpIkSZIkSZKkzizouwBJkgB2Wff7vkvQNJhTHWZVgznVYE41mFMdZlWDOdVgTnXsscsufZcwqyIz+65Bkja7iMirjziu7zIkSZIkSSpth2Nejv1EjYuI0cwcGd7ueA1JkiRJkiRJUmdsOkuS5oSxrYOxraPvMjQFc6rDrGowpxrMqQZzqsOsajCnGsypjnVjY6wbG+u7jFlj01mSNCeM7rmQ0T0X9l2GpmBOdZhVDeZUgznVYE51mFUN5lSDOdWxfPQMlo+e0XcZs8amsyRJkiRJkiSpMzadJUmSJEmSJEmdseksSZIkSZIkSeqMTWdJkiRJkiRJUmdsOkuSJEmSJEmSOmPTWZIkSZIkSZLUmQV9FyBJEsC+F93cdwmaBnOqw6xqMKcazKkGc6rDrGowpxrMqY799n1U3yXMKpvOkqQ5YdGN2XcJmgZzqsOsajCnGsypBnOqw6xqMKcazKmO7Rct6ruEWeV4DUmSJEmSJElSZ2w6S5LmhHPvu4Bz7+s/wJnrzKkOs6rBnGowpxrMqQ6zqsGcajCnOlaeu4qV567qu4xZY9NZkjQnrN1+C9Zuv0XfZWgK5lSHWdVgTjWYUw3mVIdZ1WBONZhTHavXrmX12rV9lzFr5kXTOSKOioic4Ov7MzjG4e1rtmsf79U+PnjzVX6H8y9s38cjN/H1F0bE0dPc9/7te1sdETHB88dHxIoZnHtWr9Ukdcy07qdFxGvvxPmWDn3eroqIZRGx/6Yec4JzPCYijppg+1ER8buuzrORc8/oem6mGrZrr+3hfdYhSZIkSZKk282LpnNrHfD4oa+/7bWimVkIvB3YpKbzDL2g/b47sN8snG+2vAs4fAb7Pw3Y5KZz6wfc/nk7DLgR+K+I+MM7edxxj6H5XEiSJEmSJElzwnwa+nJLZp7edxFFHAqcDuzT/vyjfsvpRmae38Nprxz83EXED4GraRrav5roBe3q8q0y88bZKXFmImILwH+7I0mSJEmSpAnNp5XOG9X+8/wjhrbNeDzB+AiLiHhzRKyNiHUR8aFo/FlEnBURYxHx9Yi459Brd4yIYyPi0oi4MSJ+HBGPHdhlrP3+mYFxDXu1r31/RKyMiGsj4pKI+EJE3HfmVwIi4qE0zebPAN8EnhsRU/5yIiL2jIgTIuJ3EXF9RJwZES8c2m3biPhke10uiYh3RMTdho7zsIhY0l6nsYj48uB7iYj92/f+5Ij4RkRcFxHntaMwtoiID7Y1/CYiXj907DuMg4iIHSLiUxGxpr3mqyPiuPa5o4AjgT0HrvfxM7qYE7sBuAXYcqCOo9qa94uIn9Kshn5u+9zz2mxvioiLI+I943m0IyX+pf15vMalQ+/5jyLi9DaTn0XEHw8XFBF/3X42b4qIiyLi74eePz4iVkTEMyPirLa+xw48/9Q27+siYnn7GRp8/bYR8fGI+G17nX8aEU+boI4j2ixviohfRcTrJtjnkIhYFRE3RNPAf/Dkl1uSJEmSJEmzbV41nSNiwdDXBvOKO/ACmpEHLwU+ALwe+DDNaIe3Aq8EFgPvG6hrK+D7wFOANwLPBC4Hvj/QcD2g/f5ubh/XMD59fCfgvcDTacZB/AFw6nBDd5oOBdYDXwFOAO7d1rVREbETcBrwaOANwJ8Dn6YZzzHoA8C1wHOAzwNva38eP84fAv8NbE0ziuJw4KHAtybI6pPAcuBZwEXAfwLHAIuAF7aPPzTUuB/2YZrxIa8DDgTeAmT73KeA/x/4Lbdf73e1dY43vvef7Lrc/rZu+7ztDHywPcdJQ/ttC/xHe94/Bf6nbcx+ETgDeAZNg/kN7fsEWAJ8qP15vMa/meCYnwQOAW4CvhoR2w4U90bgX4GvAwe3P78rhn4JA+xFk9/7gIOAC9rte7Tv6T00n52dgC8O5XUczZ+H99DkdTGwJCJuG90SES9v3983aT4/X6bJ780D+zyqvR4/B54NfAv4EpIkSZIkSZpT5tN4jXvRNFMHPZWm2dulG4HnZubvge9ExDNoZkc/MDMvAIiIRwAvoWlAQ9NgfRjw0Mw8r93n+8C5NKtt3wj8tN33/OExIZn5svGfoxl9cBpwCU1D9YczrP8FwPcz84qIOBm4iqaZ+J1JXvM6YHtg38wcb4SfMsF+P8zMI9ufvxcRf0rTPBxvHL6dpsl7UGbe3L6fM4FfAn9G02Qd97nM/GC7zyXAWcCDMvOAdtv3gee3x//JRup+DPCJzPziwLbPA2TmJRGxFrhpgrEsCfye2xvUk3k2d/zc3QS8NDNXDe23DfD6zPzG+IaI+A9gaWa+pN30nbaX+76IeHdb44VtvRONjtkGeG1mntoeby3wM+BP2mPdg+aavzsz39G+5nttU/qfIuJf288xNH9+npKZ/ztQH8COwBMHPrd3A74GPAj4ZUQ8hObz89LM/I92n5OBM2l+CXNg+5qjgOMHPh/fjYjtgX+IiI+2o0beDKwCnpeZCZwUEQtpfhEzoYh4BfCKjT2vuWW7m6bzR0p9M6c6zKoGc6rBnGowpzrMqgZzqsGc6th+0XZ9lzCr5lPTeR0brtg9dzOcZ+lAow6aub07jjecB7bdJyIWts3VpwCjwAVxx1EWy4CRqU4YEQfRNPAeCtxj4Km9mUHTOSIeAzwAeCdAZt4cEV+lGbGx9SQzhg8AvjPQcN6Y7w49Pptmpey4p9CszL114DpcAFxIcx0Gm86DTe3x2cinjm/IzFsj4tfA/Sap53+BN0bE72ka7cON4All5jKm/2fnVOBN7c+LaJrnn4mI32Xm9wYPy8Dq5/aXB49iwxsZfhH4Z5pVzV+e4tw3A0sHHp/dft+t/f544O7Al4c+d6fSfJ52o1lFDvCbwYbzgAvHG84TnOOXNKvfY7DWNpsvA38/sO+uE7yfLwKvohn38lOaXxKc2Dacx32VSZrOmXkscCw0I0g2tp/mhpELb+67BE2DOdVhVjWYUw3mVIM51WFWNZhTDeZUx3777tt3CbNqPo3XuCUzVwx9jU39shm7eujxzRvZFsDC9vG9gcfRrIgd/HopG46ouIOIeDTNSIJLgL+kaSQ+rn166xnWPj5aY2k08453oGn03oNmdMfG3IvbR31MZqLrMFjjvWkatMPX4Q/Y8DrcdqzxVdHTOP6wI2jGSrwNOLedJ/yCqd/GjFw18Hn7QWa+kab5/r4J9hv8L8W9aeY+Xzq03/jjHadx7rHMvHX8wcDxx6/JvdvvZ3HH6/2DdvvgNR+uY9xE13zwHLsA12bm9UP7XUoz43urdp+JzjH8Xu8LXDa0z/BjSZIkSZIk9Ww+rXSezE3c3gAed8+JdtxMrgRW0KzqHHbTFK99Fs385+ePrwCNiD1nWkA74uB5NI3OiybY5VCaOc8TuYLbG4d3xpU0oxk+NcFzM7qp43Rk5tXA3wF/FxEPp1l5+4WIODMzz5781XfKOcCTh8sZevw7mgbwTkPbd26/X9lBHePHOJiJm8qD/xJgU1cJrwW2i4hthxrPOwPXZ+ZN7dgPmPq9/naCfYYfS5IkSZIkqWc2nRuXAA8Zf9A2YIebgpvTKcDTgNWZubGVm8MrSMdtA6wfGjnwok2oYTHNiIM3Af8z9NxLgedFxKKNrA4/haZxu3NmbmxF7HScQjMiZHTo/Wx2mXlme1O9FwEPphkTMdVK6U31MJqb6U1Wz+8jYhR4Ls3N/cY9D7iVZm43bY1MMf5kY04DbgB2zcwlU+28iX5K07B+DvBZaO6s2D5e3u5zCbCG5r0O3mDxecA1wMqBY/1FRPzDwOfj2ZupbvVg6YO2AmD/c6f6XZv6ZE51mFUN5lSDOdVgTnWYVQ3mVIM51bFk6TIAnr7/4p4rmR02nRtfA14dET8Dfg38NXecjby5fZbmpoJLI+LotoZ70cyw/W1mfqSdr3wBTfP3FzQ3LDwT+B7w2oj4KPAt4Ak0NyacqUNpRiV8LDPv8DdVRFwDvJhmVfVnJ3jtR9rnfxQR76FpqD4EuHtmfmAGNRxF0/BeEhH/TrPa9340N3w8PjOXzuQNTSUiltNk/wuaxujLgeu4ven+S2DniDi83ed3mXlhRCymaZA/uZ3vPJkdI2J83Mn4TOc/o7n54lTeDpwcEZ8BTqSZbfwu4LjMvGSgRoDXRMSpwDWZOa1Z5Zl5dUQcBXysXR3/Q5qRO3sDT8rMZ03nOFOc45yIOAE4JiIWAefTXOcH067sb2c8HwV8MiKuoPlML26ff8tAM/2faW4K+aWI+DRN8/6v7myNkiRJkiRJ6pZN58Y7aP6Z/rtpVo4eQzPn9tWzcfLMvDEinkRzA7930IwVuIym+fnNgV1fCRwNfB/YCrh/Zv5XRLwJ+FuaZt5pNOMSpnVTPICI2BI4BPjycMO5re+MiDiHpjG9QdM5My+PiCcCHwA+2tZ2HhvOLZ5UZq5qG7Tvprn52zbAb2gavL+a7LWb6DTgcGAv4PfAz4CDBhq6XwKeRPO+7kNzk8PDaeZxb9F+n8qTuH1V8nU07+P/AMdN9cLM/G47Y/qfaFZgXwZ8iKYZPe5HwAeB19Bc7x8C+0+jrvFzfCAi1tA0wY+k+WXGKpqb+HXl5TQN47cBO9CsXD44M8dXOpOZx0XE1jTv4zU0q5+PzMyPDOyzor0e76OZxb0CeD4brsyXJEmSJElSj2KWpxhIUi8iIq8+Yspev3rkPwurwZzqMKsazKkGc6rBnOowqxrMqYY+ctrhmJdjP3Hm7qrjNSJiNDNHhrffrY9iJEmSJEmSJEl3TTadJUmSJEmSJEmdseksSZIkSZIkSeqMNxKUJM0Je196S98laBrMqQ6zqsGcajCnGsypDrOqwZxqMKc69tn7gX2XMKtsOkuS5oRdr/593yVoGsypDrOqwZxqMKcazKkOs6rBnGowpzr22HXXvkuYVY7XkCRJkiRJkiR1xqazJGlOWLPDFqzZYYu+y9AUzKkOs6rBnGowpxrMqQ6zqsGcajCnOlavWcPqNWv6LmPWOF5DkjQnrNq5+U+S/zxsbjOnOsyqBnOqwZxqMKc6zKoGc6rBnOpYueo8YP6M2XClsyRJkiRJkiSpMzadJUmSJEmSJEmdseksSZIkSZIkSeqMTWdJkiRJkiRJUmdsOkuSJEmSJEmSOmPTWZIkSZIkSZLUmcjMvmuQpM0uIvLqI47ruwxJkiRJkkrb4ZiXYz9R4yJiNDNHhre70lmSJEmSJEmS1BmbzpIkSZIkSZKkzth0liTNCSv2WsiKvRb2XYamYE51mFUN5lSDOdVgTnWYVQ3mVIM51bF8dJTlo6N9lzFrFvRdgCRJANduFX2XoGkwpzrMqgZzqsGcajCnOsyqBnOqwZzqWDd2bd8lzCpXOkuSJEmSJEmSOmPTWZIkSZIkSZLUGZvOkiRJkiRJkqTO2HSWJEmSJEmSJHXGGwlKmhcWbrklOxzz8r7L0CReeeQbAHjmMUf3XIkmY051mFUN5lSDOdVgTnWYVQ3mVEMfOe15v91m7VyqKzKz7xokabMbGRnJFStW9F2GJrHy3FUA7POgvXuuRJMxpzrMqgZzqsGcajCnOsyqBnOqwZzquKtmFRGjmTmywXabzpLmA5vOkiRJkiRJ3dpY09mZzpIkSZIkSZKkzth0liTNCevGxlg3NtZ3GZqCOdVhVjWYUw3mVIM51WFWNZhTDeZUx3zLyqazJGlOWD56BstHz+i7DE3BnOowqxrMqQZzqsGc6jCrGsypBnOqY75lZdNZkiRJkiRJktQZm86SJEmSJEmSpM7YdJYkSZIkSZIkdcamsyRJkiRJkiSpMzadJUmSJEmSJEmdseksSZIkSZIkSepMZGbfNUjSZjcyMpIrVqzouwxNYt3YGADbL1rUcyWajDnVYVY1mFMN5lSDOdVhVjWYUw3mVMddNauIGM3MkeHtC/ooRpKkYXe1//DeVZlTHWZVgznVYE41mFMdZlWDOdVgTnXMt6wcryFJkiRJkiRJ6owrnSXNC2euXElE9F2GJrH4qU8DYNn3vttzJZqMOdVhVjWYUw3mVIM51WFWNZhTDXMtp9332JPVF13Ydxlz0spzVwGwz4P27rmS2eFMZ0nzQkTkty/y77u57LKzlgGw00MX91yJJmNOdZhVDeZUgznVYE51mFUN5lTDXMvp4D0De40TW7K0yerp+8+NrLqysZnOjteQJEmSJEmSJHXGprMkSZIkSZIkqTM2nSVJkiRJkiRJnbHpLEmSJEmSJEnqjE1nSZIkSZIkSVJnFvRdgCRJAAu22a7vEjQN5lSHWdVgTjWYUw3mVIdZ1WBONZhTHdsvml9ZRWb2XYMkbXYRkd++yL/vJEmSJEnaXA7eM7DXOL9ExGhmjgxvd7yGJEmSJEmSJKkzNp0lSZIkSZIkSZ2x6SxJmhMuO2sZl521rO8yNAVzqsOsajCnGsypBnOqw6xqMKcazKmOJUuXsWTp/MnKprMkSZIkSZIkqTM2nSVJkiRJkiRJnbHpLEmSJEmSJEnqjE1nSZIkSZIkSVJnbDpLkiRJkiRJkjpj01mSJEmSJEmS1JkFfRcgSRLAol0f2HcJmgZzqsOsajCnGsypBnOqw6xqMKcazKmOffaeX1lFZvZdgyRtdhGR377Iv+8kSZIkSdpcDt4zsNc4v0TEaGaODG93vIYkSZIkSZIkqTM2nSVJc8INV63hhqvW9F2GpmBOdZhVDeZUgznVYE51mFUN5lSDOdWxes0aVq+ZP1k501mSNCeMrTkPgG3uuWvPlWgy5lSHWdVgTjWYUw3mVIdZ1WBONZhTHStXNVntsev8yMqVzpIkSZIkSZKkzth0liRJkiRJkiR1xqazdCdFxFER8buhbXeLiC9ExI0RceAMjnVhRBzdUV0ZEUd0caxNPP8G12XguaMj4sKOztPZNZMkSZIkSdKd50xnqWMREcBxwHOBZ2fmyT2V8njggp7OPZueBVzRdxGSJEmSJElq2HSWuncM8GLgBZn57b6KyMzT+zr3bMrMn/VdgyRJkiRJkm7neA2pQxEYhhBgAAAgAElEQVTxYeCVwIsz8ytDz70/IlZGxLURcUk7fuO+GznO69p9roqIEyNih6Hnd4yIYyPi0naEx48j4rFD+0w5XqPd5zUR8d6IuDwiLouIT0TEVkP7PTIiTomI69uavhARO8/k2kxRx1ER8buIeGxErIiIGyJieUTcPyJ2ioivt9ftnIg4YOi1jteQJEmSJEmaQ1zpLHUkIt4DvBb4q8w8YYJddgLeC6wB7gMcCZwaEQ/LzFsH9nsecCbwCmA34MPt6/6mPc9WwPeBHYA3ApcBrwK+HxEPzMzfzrD0I4FTgcOAhwPvAy4CPtCe7z7AUuAc4IXAdsD7ge9FxEhm3jzD823MtsCx7XmvAz4OfA64CTgJ+L/A3wNfjojdM/P6js6rOWKnhy7uuwRNgznVYVY1mFMN5lSDOdVhVjWYUw3mVMfT959fWdl0lrpxL+AtwEcy8zMT7ZCZLxv/OSK2AE4DLgH2A344sOt64JmZeUu77/8HvIC26UzTHH4Y8NDMPK/d5/vAuTQN5DfOsPYLM/Pw9ueTI+KJwLNpm87tMQEOzMxr2vOdB5wOHAJM1GDfFNsAf5eZy9pz7Ap8Anh7Zh7dbrsEOAtYTNOInlREvIKmeS9JkiRJkqRZ4ngNqRvXAD8B/ioiHjnRDhFxUDsGYx1wC03DGWDvoV1/MN5wbp0N7BQRW7aPnwKMAhdExIKIGP/l0TJgZBNq/+7Q47NpVliPewzw3fGGM0Bm/gS4kKZh3pWbgR8NPP5V+/3UCbbdbzoHzMxjM3MkMzflukiSJEmSJGkT2HSWurEeeDrN6IyTIuIPBp+MiEcD36RpNP8l8Hjgce3TWw8d6+qhxzcDAYzPWb53+9r1Q18vBXbfhNonOt9gTbsAl07wukuBHSc57i3AFht5bov2+UFjQ2NGxsd23FbfwCiP4Wumu4Arfz3Klb8e7bsMTcGc6jCrGsypBnOqwZzqMKsazKkGc6pj+egoy0fnT1aO15A6kplXRMSBwI9px1Rk5mXt088CLgeen5kJEBF7buKprgRW0MxxHnbTJh5zMmtp5lEP25lmxfXGXA7cIyK2nWD+8i40s6il29xyw7V9l6BpMKc6zKoGc6rBnGowpzrMqgZzqsGc6lg3Nr+ycqWz1KHMXA0cSDPj+aSIWNQ+tQ2wfrzh3HrRJp7mFOAPgdWZuWLoa+UmF79xPwEOHHgv4yu39wKWT/K6H9H8HXPw4MaIuDvwZO44SkOSJEmSJEl3ETadpY5l5lk0jdaHAF+LiIXA94A9IuKjEfHkiHgr8JJNPMVngQuApRHxsojYPyIOiYh/jojXdfIm7ujD7feTI+IZEfEi4KvASuArG3tRZp4NfBH4dES8KSKeEhEvpLlpYgAf3wy1SpIkSZIkqWc2naXNIDN/DDwPWAx8DvgO8CbgEJrZzosZWgE8g2PfCDyJppH9DpobAX4MeCDwP3e29gnOd3l7vhuBE4BP0KxSfurAjOWNeTHwEeAVwEnAMcBFwH6Z+Zuua5UkSZIkSVL/nOks3UmZeRRw1ATbvw1sObDpA+3XoBh6zV4THOd44PihbeuA17RfG6srNvbcZPtM9H4y82fAAVMdb4Jj3Qy8rf2abL+JzrmUoevTbp/ymkmSJEmSJKk/rnSWJEmSJEmSJHXGlc6SpDlh63vu0ncJmgZzqsOsajCnGsypBnOqw6xqMKcazKmOPXaZX1lFZvZdgyRtdhGR377Iv+8kSZIkSdpcDt4zsNc4v0TEaGaODG93vIYkSZIkSZIkqTM2nSVJc8L6G8ZYf8NY32VoCuZUh1nVYE41mFMN5lSHWdVgTjWYUx3rxsZYNzZ/srLpLEmaE6769Rlc9esz+i5DUzCnOsyqBnOqwZxqMKc6zKoGc6rBnOpYPnoGy0fnT1Y2nSVJkiRJkiRJnbHpLEmSJEmSJEnqjE1nSZIkSZIkSVJnbDpLkiRJkiRJkjpj01mSJEmSJEmS1BmbzpIkSZIkSZKkzkRm9l2DJG12EZHfvsi/7+ay9TeMAbDlNot6rkSTMac6zKoGc6rBnGowpzrMqgZzqmGu5XTwnoG9xomtG2uy2n7R3MiqKxExmpkjw9sX9FGMJEnD5sr/JGly5lSHWdVgTjWYUw3mVIdZ1WBONZhTHXe1ZvNUHK8hSZIkSZIkSeqMTWdJ0pxwzZpVXLNmVd9laArmVIdZ1WBONZhTDeZUh1nVYE41mFMdK89dxcpz509WNp0lSXPCjVet5car1vZdhqZgTnWYVQ3mVIM51WBOdZhVDeZUgznVsXrtWlavnT9Z2XSWJEmSJEmSJHXGprMkSZIkSZIkqTM2nSVJkiRJkiRJnbHpLEmSJEmSJEnqjE1nSZIkSZIkSVJnFvRdgCTNhi0XLuTgPaPvMjSJQw77SwC+8vn9+y1EkzKnOsyqBnOqwZxqMKc6zKoGc6phruW0+x579l3CnLX9ou36LmFWRWb2XYMkbXYjIyO5YsWKvsuQJEmSJEm6y4iI0cwcGd7ueA1JkiRJkiRJUmdsOkuSJEmSJEmSOmPTWZI0JyxZuowlS5f1XYamYE51mFUN5lSDOdVgTnWYVQ3mVIM51THfsrLpLEmSJEmSJEnqjE1nSZIkSZIkSVJnbDpLkiRJkiRJkjpj01mSJEmSJEmS1BmbzpIkSZIkSZKkzth0liRJkiRJkiR1ZkHfBUiSBLDP3g/suwRNgznVYVY1mFMN5lSDOdVhVjWYUw3mVMd8yyoys+8aJGmzGxkZyRUrVvRdhiRJkiRJ0l1GRIxm5sjwdsdrSJIkSZIkSZI6Y9NZkjQnrF6zhtVr1vRdhqZgTnWYVQ3mVIM51WBOdZhVDeZUgznVMd+ycqazpHnhzDPPJCL6LkOTeOWRbwDg3z50dM+VaDLmVIdZ1WBONZhTDeZUh1nVYE41VMhp9913Y/Xqi/suo3crV50HwB677tpzJbPDmc6S5oWIyFNP+nzfZWgSF481vxTYfZH/XZrLzKkOs6rBnGowpxrMqQ6zqsGcaqiQ0wEHHYb9R1iydBkAT99/cc+VdMuZzpIkSZIkSZKkzc6msyRJkiRJkiSpMzadJUmSJEmSJEmdseksSZIkSZIkSeqMTWdJkiRJkiRJUmfCu0dKmg8iIk896fN9lyFJkiRJ0rxywEGHYf/xrisiRjNzZHi7K50lSZIkSZIkSZ2x6SxJkiRJkiRJ6oxNZ0nSnPDb64LfXhd9l6EpmFMdZlWDOdVgTjWYUx1mVYM51WBOdSwfHWX56GjfZcyaBX0XIEkSwPpb+65A02FOdZhVDeZUgznVYE51mFUN5lSDOdWxbuzavkuYVa50liRJkiRJkiR1xqazJEmSJEmSJKkzNp0lSZIkSZIkSZ2x6SxJkiRJkiRJ6oxNZ0mSJEmSJElSZxb0XYAkSQB33zL7LkHTYE51mFUN5lSDOdVgTnWYVQ3mVIM51bHHLrv0XcKssuksSZoTdty67wo0HeZUh1nVYE41mFMN5lSHWdVgTjWYUx37PGjvvkuYVY7XkCRJkiRJkiR1xqazJGlOuPn3zZfmNnOqw6xqMKcazKkGc6rDrGowpxrMqY51Y2OsGxvru4xZY9NZkjQnXHp9cOn10XcZmoI51WFWNZhTDeZUgznVYVY1mFMN5lTH8tEzWD56Rt9lzBqbzpIkSZIkSZKkzth0liRJkiRJkiR1xqazJEmSJEmSJKkzNp2lARFxVET8bmjb3SLiCxFxY0Qc2G67MCKO7uicGRFHdHGsTTz//m0NV0TEdkPPHRER2VdtkiRJkiRJqmdB3wVIc1lEBHAc8Fzg2Zl5cvvUs4ArOjrN44ELOjrWnbEj8Crgg30XIkmSJEmSpLpc6SxN7hjgxcChmfnt8Y2Z+bPMXN3FCTLz9My8tItj3UlLgddHxNZ9FyJJkiRJkqS6bDpLGxERHwZeCbw4M78y9NwdxmtExPERsSIinh4RZ0fE9RGxJCJ2jIg/jIgfRMR17T4PHzrWlOM12n1eExHvjYjLI+KyiPhERGw1tN8jI+KU9vxXtWNBdp7mW/4AzWrnv56ilvdHxMqIuDYiLmnPcd8pXrMsIo4deHxg+54+PLDtkIi4OSK2bR+/OCKWR8SV7Xv5QUSMTPO9qKCdt0123tZpLnOdOdVhVjWYUw3mVIM51WFWNZhTDeZUx377Por99n1U32XMGpvO0gQi4j3Aa4G/zswTpvmyPYB3Av8EvAJ4AnAscGL79RyakTYntmM7ZupIYFfgMJoRGP8HeM1AzfehWa28LfBC4G+BxcD3ImLhNI5/MfBZ4O8jYstJ9tsJeC/wdJpr9AfAqREx2d8nPwL+eODxnwA3TrDtjMy8vn28V1vPc9v3czHwo4j4g2m8FxW0cIvmS3ObOdVhVjWYUw3mVIM51WFWNZhTDeZUx/aLFrH9okV9lzFrnOksbehewFuAj2TmZ2bwuh2Bx2fm+QDtiuY3Ai/JzM+22wJYAjwYOGeGdV2YmYe3P58cEU8Enk2zQhmapjTAgZl5TXu+84DTgUOA6TTP3w+8lGakyKcn2iEzXzb+c0RsAZwGXALsB/xwI8f9EfCPEXGfzLycptn8aeCVEbFdZl7bbjtl4DzvHDjP3YDvAY+habq/E0mSJEmSJM1JrnSWNnQN8BPgryLikTN43YXjDefWr9rvp06w7X6bUNd3hx6fDew28PgxwHfHG84AmfkT4EKahvCU2vpPBN7cNpQ3EBEHRcSPI2IdcAtNwxlg70kO/WPg98B+7UiQxwCforkZ4+Mj4h7AI2ia0+PneUhEfC0iLm1fux540BTnGa71Fe1IkxXTfY36c+WNzZfmNnOqw6xqMKcazKkGc6rDrGowpxrMqY6V565i5bmr+i5j1th0lja0nmZ0xBrgpBmMc7h66PHNE2wf37YpN+ub6PiDx9kFmOiGhJfSrMKervcCDwCeP/xERDwa+CZNo/kvgccDj2uf3uh7yswx4H9pVjM/BrgBOJPbx248EQhgeXueRTRN9t2B17f7PBr4+WTnmeC8x2bmSGY6C7qA69YH163flMkzmk3mVIdZ1WBONZhTDeZUh1nVYE41mFMdq9euZfXatX2XMWscryFNIDOviIgDaVbonhwRT8zMy/quawpraeYtD9sZGJ3uQTLz7Ij4Gs2IkU8OPf0s4HLg+ZmZABGx5zQPPd5gvgL478y8NSJ+BDwT2BI4OzOvbPd9PM0q7qdm5i/HDxAR20/3fUiSJEmSJKkfrnSWNiIzVwMH0sx4PqldfTuX/QQ4cLDOdmXyXrQriGfgPcBDaZrMg7YB1o83nFsvmuYxfwj8EfBn3D77+YfAY4EnMzBaoz0PwE3jGyLiCTTvRZIkSZIkSXOYTWdpEpl5FnAw8BDgaxGxsOeSJvPh9vvJEfGMiHgR8FVgJfCVmRwoM88ATgKeNPTU94A9IuKjEfHkiHgr8JJpHnY5sAXwBG5vOv+cZpzJo7lj0/l04FrguIh4WkS8jGbW9G8GDxgRL46IW2aw2lqSJEmSJEmbmU1naQqZ+WPgecBi4HMRMSf/3GTm5TRN4huBE4BP0DRyn5qZN0/22o149wTn+C/gTcAhNLOdF9M05adb3y+B62nHfWTmrTQjTGBgNXZmXgo8F7gv8A3gtcAruf1GjOPuRtPIdoCVJEmSJEnSHBF3/FfyknTXFBF56kmf77sMTeLiseZ3B7sv8r9Lc5k51WFWNZhTDeZUgznVYVY1mFMNFXI64KDDsP8IS5YuA+Dp+y/uuZJuRcRoZo4Mb/dGgpKkOWHLOflvCDTMnOowqxrMqQZzqsGc6jCrGsypBnOqY/tF2/Vdwqyy6SxJmhPue3d/812BOdVhVjWYUw3mVIM51WFWNZhTDeZUx3777tt3CbPK34dIkiRJkiRJkjpj01mSJEmSJEmS1BmbzpKkOeHisbjtJhiau8ypDrOqwZxqMKcazKkOs6rBnGowpzqWLF12280E5wObzpIkSZIkSZKkzth0liRJkiRJkiR1xqazJEmSJEmSJKkzNp0lSZIkSZIkSZ2x6SxJkiRJkiRJ6oxNZ0mSJEmSJElSZxb0XYAkSQD33Cr7LkHTYE51mFUN5lSDOdVgTnWYVQ3mVIM51bHP3g/su4RZZdNZkjQnbLew7wo0HeZUh1nVYE41mFMN5lSHWdVgTjWYUx177Lpr3yXMKsdrSJIkSZIkSZI6Y9NZkjQnXHtz86W5zZzqMKsazKkGc6rBnOowqxrMqQZzqmP1mjWsXrOm7zJmjeM1JElzwlU3BQDbLXQm2VxmTnWYVQ3mVIM51WBOdZhVDeZUgznVsXLVecD8GbPhSmdJkiRJkiRJUmdsOkuSJEmSJEmSOmPTWZIkSZIkSZLUGZvOkiRJkiRJkqTO2HSWJEmSJEmSJHXGprMkSZIkSZIkqTORmX3XIEmbXUTkqSd9vu8yJEmSJEmaVw446DDsP951RcRoZo5ssN3QJc0HCxcuzPXr1/ddhiRJkiRJ88ruu+/G6tUX912GNpONNZ0X9FGMJM22hz/84axYsaLvMiRJkiRJku7ynOksSZoTlo+Osnx0tO8yNAVzqsOsajCnGsypBnOqw6xqMKcazKmO+ZaVK50lSXPCurFr+y5B02BOdZhVDeZUgznVYE51mFUN5lSDOdUx37JypbMkSZIkSZIkqTM2nSVJkiRJkiRJnbHpLEmSJEmSJEnqjE1nSZIkSZIkSVJnbDpLkiRJkiRJkjqzoO8CJEkC2GOXXfouQdNgTnWYVQ3mVIM51WBOdZhVDeZUgznVMd+yiszsuwZJ2uxGRkZyxYoVfZchSZIkSZJ0lxERo5k5Mrzd8RqSJEmSJEmSpM7YdJYkzQnrxsZYNzbWdxmagjnVYVY1mFMN5lSDOdVhVjWYUw3mVMd8y8qmsyRpTlg+egbLR8/ouwxNwZzqMKsazKkGc6rBnOowqxrMqQZzqmO+ZWXTWZIkSZIkSZLUmQV9FyBJs2HlyjOJiL7L0CReeeQbADj4Sfv3W4gmZU51mFUN5lSDOdVgTnWYVQ3mVMNdNac999iNCy+6uO8ydCdEZvZdgyRtdhGR1/3iY32XoUl8/4J7APCU+1/TcyWajDnVYVY1mFMN5lSDOdVhVjWYUw131Zzu/v/Yu/Mwucoy///vD4QdRJBFQBFHwRFhXAj4xRVXVFQUlVHREXdmXBDR36jfcdxQZtwddRRnVHS+boiACyqCGAQXJEEFFzYBGSBAWISwBZDn98dzCoqi091JKn36Sd6v6+qr06dOnXOf+lSU3PX0fXY6kFWtZ3nsvJMA2GuPx/dcyXglWVBKmTu63fEakiRJkiRJkqSxseksSZIkSZIkSRobm86SJEmSJEmSpLFxprOk1YIznWe/65bUz0Hvsc7tPVeiyZhTO8yqDebUBnNqgzm1w6zaYE5tWFVzWhVnOl+7eDEAG2+0Uc+VjNfSZjrP6aMYSZJGrWr/kbSqMqd2mFUbzKkN5tQGc2qHWbXBnNpgTu1Y1ZrNU3G8hiRJkiRJkiRpbGw6S5JmhT9euS5/vHLdvsvQFMypHWbVBnNqgzm1wZzaYVZtMKc2mFM7zjz7HM48+5y+y5gxNp0lSbPCJYvX5pLFa/ddhqZgTu0wqzaYUxvMqQ3m1A6zaoM5tcGc2nHRwoVctHBh32XMGJvOkiRJkiRJkqSxseksSZIkSZIkSRobm86SJEmSJEmSpLGx6SxJkiRJkiRJGhubzpIkSZIkSZKksZnTdwGSJAFstPZf+y5B02BO7TCrNphTG8ypDebUDrNqgzm1wZzasfFGG/ZdwoxKKaXvGiRppUtSbvjdJ/ouQ5IkSZIkTWGDnQ7EnmUbkiwopcwd3e54DUmSJEmSJEnS2Nh0liRJkiRJkiSNjU1nSdKscMIF9+CEC+7Rdxmagjm1w6zaYE5tMKc2mFM7zKoN5tQGc2rHsfNO4th5J/Vdxoyx6SxJkiRJkiRJGhubzpIkSZIkSZKksbHpLEmSJEmSJEkaG5vOkiRJkiRJkqSxseksSZIkSZIkSRobm85qRpJ3JylJzl3K4+d2j797hktrVpI9utfs9iT3neDx/+4en7eMx92iy2u7MZUqSZIkSZKkRszpuwBpGd0M3D/J3FLK/MHGJLsC23WPa9ndAPw98OHBhiRrA/sA1y/H8bYA3gXMAy5c8fK0Ovjbe93UdwmaBnNqh1m1wZzaYE5tMKd2mFUbzKkN5tSOnXfYvu8SZpRNZ7XmBuB04IXA/KHtLwROBHbpo6jJJFmvlDLb/1/gu9TX8MND2/YE1qQ2jjfqoaY7NPIaagXd5x639l2CpsGc2mFWbTCnNphTG8ypHWbVBnNqgzm1Y9utt+67hBnleA216OvAvkkC0H3ft9t+N0n2TXJmkiVJ/jfJ+5PMGdlnjyRnJLk5yWlJdkty5fCojiQXJvnwyPP278ZPbDh0nJJkzyTfSXI98KnusYO7Y1+b5PIk303ywKkutjvegUk+kGRRkiuSfDrJOiP7PSzJj5PcmOSaJF9JsuU0Xk+6126XkXpeCBwDLJmgpqWeqxupcWa360+6+svQc++f5Jgk1yVZPNHr0D3nzUk+nmTR4HhJNkvypSRXdeeel2TuNK9RkiRJkiRJM8Cms1p0FLAl8Jju58cCm3fb7yLJU4FvUFdH7w18EngLXSO422cb4PvAFcDzgcOArwDrrUCNnwd+Czy7+zPAfbrz7g28mrqK+OdJNp7G8Q4GtgZeAnwIeC1w4NA1bE5dkbw+8GLgDcDjgeO7MRlTOR/4FfCi7njrd7V/bXTHaZxrIbBft/vrgN27L7pG+Y+BB3evwf7A/YGTkmw6cqq3AlsBLwXe2G07hroC+y3UcSBrUBvbUzbvNftdfN1aXHzdWn2XoSmYUzvMqg3m1AZzaoM5tcOs2mBObTCndlx06aVcdOmlfZcxYxyvoeaUUv6S5IfUlbgnd99/WEq5tlv8POy9wLxSysu6n3/Y7XNokkNKKRcDbwJuBJ41GOGQ5Dpqs3p5fbOU8s6Rug8a/DnJmsDx1Eb33sCXpzjehaWU/bs/H5fk0dR5yx/sth3cfd+zlHJdd45zgV8Cz2OC5vEEvg68Engf8EzqfOwTgFeN7DfpuUopX0tyRrfPH0opvxx67suBbYEdSinnd889ldr0fi1w6NC+C0spfz/4IcnTgEcDe5RSTuq2nUidGf3W7vl3keQ1wGumce2aBc66qn7O46+HzW7m1A6zaoM5tcGc2mBO7TCrNphTG8ypHWeecy6w+ozZcKWzWvV14PndytnnM8Foja6x+wjgmyMPfYP63t+9+3lX4PiRmcHfWcH6jp2gnv+T5PgkVwG3URvdGwI7TON4Pxr5+Q/UldMDuwE/GjSBAUopp1Ibso9heo4AHpxkZ2oj/1ullNsm2G9FzrUbcPqg4dw992LgZxM89/sTPPeKQcO5e+4NwPeWdt5SyudKKXNLKY7gkCRJkiRJmiE2ndWq71Abtu8HNqDeCG/UZsBawOUj2wc/D8Y53BtYNLxDKeVm4PoVqO8u50yyLbVxHOqK3EdTm91XAOtO43h/Gfn5lpHnbTV6zqE6RsdWTKiUcglwSlff01nKjOwVPNeyPHd0v62or9fynFeSJEmSJEkzxPEaalIp5YYk3wMOoo6yuGGC3a4EbgW2GNk+uLne1d33y6gzoe+QZF1qU3vYzcDofORNllbiyM9Po85A3ntQa3czw3E1Sxdy9+uEeq0LluE4X6fOnb4M+OlKONdC4CFLee7VI9tGX8PJzjv6XEmSJEmSJPXElc5q2WeoK5w/O9GDpZS/UpugLxh5aF/gduAX3c+nAU9JMnzjwGdPcMiLqTfAG/bUada6XnfO4XEV+zK+D35OBfZMstFgQ5Jdge2oq5en65vU1/TQUsrtK3CuW7rvo6u4TwV2SXL/oeduAzxqGnWeCmyR5HFDz10f2Gsaz5UkSZIkSdIMsemsZpVS5pVSnlNKmTfJbu8CnpDki0n2TPIW6o3y/qubJQzwceoq5O8m2SvJK6ljO26kNooHjgaelOQdSZ6S5LNMvGp3IicCawJfTPKkJG8E/o27j81YXh/tvh+XZO8k+wFHAWcC35ruQUopV3av6adW8FwXATcBL0uye5LBTOXDu8d+kGTfJM8DfkBdlX7YFLUdB/wc+EaSlyV5JnXu83rAh6Z7jZIkSZIkSVq5bDprlVZK+RH1pnhzqSt43wR8BHj90D6XUFfLbkFtnr4BeAW1SXzd0OE+R21Qv5F6070lwCHTrONMYH/gkdQb372YugL72uW9tpHjLwKeQB0B8jXg08DJwFNKKbdM9tyVca5uJvargV2Ak6irySmlLAGeDJwFfB74ErUJvUcpZTojMp4DHE/N4ZvUGdlPLKWcN67rkyRJkiRJ0opJKaNjUyUleQy1kfrEUspP+q5HKy5JueF3n+i7DEmSJEmSNIUNdjoQe5ZtSLKglDJ3dLs3EpSAJP8O/Jp6A70HAe8EzqCu0pUkSZIkSZI0TTadpWod6lzgLYHFwI+AN09yMz1JkiRJkiRJE7DpLAGllDdR5z1L6smpl2wAwCO3uaHnSjQZc2qHWbXBnNpgTm0wp3aYVRvMqQ3m1I5TFiwA4DG77NJzJTPDprMkaVZYfMuafZegaTCndphVG8ypDebUBnNqh1m1wZzaYE7tuHbx9X2XMKPW6LsASZIkSZIkSdKqw6azJEmSJEmSJGlsbDpLkiRJkiRJksbGprMkSZIkSZIkaWxsOkuSJEmSJEmSxmZO3wVIkgSwzUa39F2CpsGc2mFWbTCnNphTG8ypHWbVBnNqgzm1Y9uttuq7hBmVUkrfNUjSSpek3PC7T/RdhiRJkiRJmsIGOx2IPcs2JFlQSpk7ut3xGpIkSZIkSZKksbHpLEmaFa5bsgbXLfH/lmY7c2qHWbXBnNpgTm0wp3aYVRvMqQ3m1I5rFy/m2sWL+y5jxviulCTNCr+6dG6W4nQAACAASURBVEN+demGfZehKZhTO8yqDebUBnNqgzm1w6zaYE5tMKd2nLLgdE5ZcHrfZcwYm86SJEmSJEmSpLGx6SxJkiRJkiRJGhubzpIkSZIkSZKksbHpLEmSJEmSJEkaG5vOkiRJkiRJkqSxseksSZIkSZIkSRqblFL6rkGSVrok5YbffaLvMjSJ65bUz0Hvsc7tPVeiyZhTO8yqDebUBnNqgzm1w6zaYE5tWFVz2mCnA1nVepbXLl4MwMYbbdRzJeOVZEEpZe7o9jl9FCNJ0qhV7T+SVlXm1A6zaoM5tcGc2mBO7TCrNphTG8ypHatas3kqNp0lrRbWXnstNtjpwL7LkCRJkiRJU7jftvfpuwStIJvOklYLO+/8d8yfP7/vMjSJM88+B4CdH7RDz5VoMubUDrNqgzm1wZzaYE7tMKs2mFMbzKkdq1tW3khQkjQrXLRwIRctXNh3GZqCObXDrNpgTm0wpzaYUzvMqg3m1AZzasfqlpVNZ0mSJEmSJEnS2Nh0liRJkiRJkiSNjU1nSZIkSZIkSdLY2HSWJEmSJEmSJI2NTWdJkiRJkiRJ0tjM6bsASZIANt5ow75L0DSYUzvMqg3m1AZzaoM5tcOs2mBObTCndqxuWaWU0ncNkrTSzZ07t8yfP7/vMiRJkiRJklYZSRaUUuaObne8hiRJkiRJkiRpbGw6S5IkSZIkSZLGxqazJGlWOHbeSRw776S+y9AUzKkdZtUGc2qDObXBnNphVm0wpzaYUztWt6xsOkuSJEmSJEmSxsamsyRJkiRJkiRpbGw6S5IkSZIkSZLGZk7fBUjSTDjjzDNJ0ncZmsQBB78FgGc+YY9+C9GkzKkdZtUGc2qDObXBnNphVm0wpzaYE2y97f245M8X9l2GRqSU0ncNkrTSJSn7nrak7zI0iZ0u+RkAv9vm0T1XosmYUzvMqg3m1AZzaoM5tcOs2mBObTAnOGLXdWihvzm4ieBeezy+50rGK8mCUsrc0e2O15AkSZIkSZIkjY3jNSRJs8Il93xA3yVoGsypHWbVBnNqgzm1wZzaYVZtMKc2mFM7dt5h+75LmFGO15C0WnC8hiRJkiRJq55WxmusqhyvIUmSJEmSJEla6Ww6S5JmhU1uuIxNbris7zI0BXNqh1m1wZzaYE5tMKd2mFUbzKkN5tSOiy69lIsuvbTvMmaMM50lSbPCNn/5EwDXbHDvnivRZMypHWbVBnNqgzm1wZzaYVZtMKc2mFM7zjznXAC23XrrniuZGa50liRJkiRJkiSNjU1nSZIkSZIkSdLY2HSWJEmSJEmSJI2NTWdJkiRJkiRJ0tjYdJYkSZIkSZIkjY1NZ0mSJEmSJEnS2KSU0ncNkrTSJSn7nrak7zIkSZIkSdIYHbHrOtjf7E+SBaWUuaPbXeksSZIkSZIkSRobm86SJEmSJEmSpLGx6SxJmhUecMVvecAVv+27DE3BnNphVm0wpzaYUxvMqR1m1QZzaoM5teOUBQs4ZcGCvsuYMXP6LkCSJID1br2+7xI0DebUDrNqgzm1wZzaYE7tMKs2mFMbzKkd1y5evbJypbMkSZIkSZIkaWxsOkuSJEmSJEmSxsamsyRJkiRJkiRpbGw6S5IkSZIkSZLGxqazJEmSJEmSJGls5vRdgCRJAFevv2XfJWgazKkdZtUGc2qDObXBnNphVm0wpzaYUzu23WqrvkuYUSml9F3DKiXJ/sAbgB2A24ALgZ+UUt7cY1nLLcmFwJGllLdMss8ewE+AnUspv5uh0mZUkrWoub4C+BvgRuBPwNGllH9bgeMW4A2llE+NpdCpz3c4sFMpZe7QtvWAY4GHAU8qpfx6tK4krwGuKKUcs6Ln60uSsu9pS/ouQ5IkSZIkjdERu66D/c3+JFkwUd/H8RpjlOTtwH8DxwH7AP8AfBt4dp91zYDTgd2pTdhV1aeA9wJfAZ4JvAY4CXjWCh53d+CbK3iM5ZZkHeBoYBdgz1LKr5dS12uA58xweZIkSZIkSWqQ4zXG6/XAYaWUdwxt+26S9/RV0EwopVwH/LLvOlaWJOsDLwf+bynlQ0MPHZUkK3LsUkpvr1u3evsI4DHUhvNps6Eurb7WveV6AG5ee8OeK9FkzKkdZtUGc2qDObXBnNphVm0wpzaYUzuuXbwYgI032qjnSmaGK53H657AZaMby9Aa/yTbJSlJXpzkf5IsTnJFkneNPi/JE5OcmuTmJJcn+c8kG47sc/8kxyS5rjvWd5M8cGSfkuTAJB9Isqg736e7Va7TkuSgJBcnuSbJ15Pcc+ixPbpz7DRyzoOSfCTJVUmuTPKW7rGXJTk/yV+SfCHJuiPn2rY7x9VJbkxyXJIHTVHf/t05d05yfJIbkpyVZJ8J9n19knOTLElyXpKDprj8DYC1mDrbPyd5x9DPr+1qeuPQtoOTXDLyOr1+6Od5SY7s3h/ndbn+IMl9JniNfpDkpiQXdNd/ZJJ5U1zL4PlrUldtPwV4ZinlZyOP31FXd8xdgJd120s3Rmaw76uTnDn0Pj0yycYjx3tKkjO6XE5J8pCRx9dI8rbumpckOSfJy0b2mdZro3Y9cNFveeCi3/ZdhqZgTu0wqzaYUxvMqQ3m1A6zaoM5tcGc2nHKgtM5ZcHpfZcxY2w6j9fpwBu6puq9ptj3Q9S5wM8H/gt4V5LXDR7smnI/BK4Enge8C3gxcOTQPusAPwYeDLwa2B+4P3BSkk1HzncwsDXwku7crwUOnOZ17Qs8iTpi4Z+p4yU+MI3nHQxsCLwI+CrwoSQf7Op8I/AOYD/gTUPXtClwCvAg4IDu3BsAJ6TOHp7KV4HvAM8FzgW+PtyUTPJq4JPdPs+ijpD4SJK3Le2ApZRFwP8C706yT5KlfSR1MvDYoZ8fB9w8wbaTp7iGR1JXzR9Mfc0fAXxu6BrS1f9g6ozpN1Nfz0dOcdyBNYDDqWNfnlNKmTfF/v8EnAV8nzp2Y3fqDGiS/AtwGHXUyHOAfwSupeY+sC31Pfd+6nthC+Ab3XUMfBL4l+4696KO/PhCkmeO1DLpayNJkiRJkqT+OV5jvF4HHENt6JUkfwS+BXy4G0Ex7PellNd2fz4uyRbAO5J8ppRyO/BO4M/As0spfwVIcjW1Wbd7KeUX1JEP2wI7lFLO7/Y5FTif2lQ+dOh8F5ZS9h8636Opc6c/OI3rupXanLytO8eOwAupzcjJnDu4xiQnAC+gNsfvN3g9Um9C+FxgcDO+g6hN5oeVUq7u9vkZ9YaMrwA+PcU5P1ZK+UL3vAXA5dQm+WeTrAG8Gzi8lHJwt/+PulW5b0/y8VLKzUs57v7A16l53p7k193P/1FKuaXb52Tgg0nW6DJ8LPB56gcLg2bxY4B/neIa7gHsVUq5pnvevYGPJVmvlHIT8AzgocBug5EYSX7VvUbTmav98O7rwFLKj6bauZTyhyQ3AIuGx26krnZ/B/DxkRtlHjVyiE2BR5dSzu2etwa1qfwg4KzUlfn/CLy8lPKl7jknJNmK+mHL94aONdVrcxepN0B8zVTXKEmSJEmSpPFxpfMYlVLOoK4+fTbwn0CozeP5GRmLQW26DTuKuhJ5sCp3N+DoQcO58y3gNmrjcrDP6YOGc1fDxcDPhvYZGG0u/mHoXCSZM/w1su9PBg3noedukToTeDI/HqrrduACYMFIA/48YJuhn58MHA9cN1TLYmABcLc7YU7gjusspVwFXMGd13kf6ms8euO+b1CbmTsv7aCllBOBB1Cb7V8A7kVdvXti10QF+Gl3nIcm2a473weBzZJsDzyE2oCdaqXzaYOmaucP3ffB67QrcNnIDOZLqK/RdJxPbU6/Ncl9p/mciewOrAd8cYr9Lhw0nDuD6xnk8iTgduDokffgj4GHdaNABqZ6be6ilPK5Usrcie6iKkmSJEmSpJXDpvOYlVKWlFK+W0p5fSllR+BVwPbAK0d2vWIpP2819P3ykWP/FbiK2riccJ/O5UP7DPxl5OdbgOFZyreOfE313ABTzYSe6HlT1bEZ8PcT1PMEYDoN0smOP3htR1+zwc+jr9ldlFIWl1K+UUp5NfA3wPuAR1PHdEAdQXEldYXzY4HflVIuAn4ztO0vwO+W4xoYuo57A4smeN5E2yZyDfBU6m86HDfBKJbpGoyQWTjFflNdz2bAmtSxHMOZH97VuNXQc6c6liRJkiRJknrmeI2VrJTy+W6O8d+OPLTFUn5eOPT9Lvt0Kz7vBVw9tM9dbsjW2XJon+nadRn3X1mups4rft8Ejy1ewWMPXtvR137LoXNPSymlJPkQdSX73wLf7radwp3N5Z92uw9mPa8L/Kxb9b0iLgM2n2D75tQZ0tOp//wkT6fOYj42yZNKKTcuYx1Xdd+3ojbbl9fV1BX8j6aueB41+gGNJEmSJEmSZjFXOo9RN5d5dNvmwMbcfXXtc0d+3ofaFL24+/lU4LkjowX2oX5QcMrQPrskuf/Q+bYBHjW0z7SUUuYPfy3Lc8fsx9RG+u9HayqlnL2Cx74YuJQ6W3rYvsB1wJkTPSnJWt384lHbd9+Hs/0ptcH8OO5sOg+2PZapR2tMx2nAvZPsNlTjNsAuy3KQUspvqDf/ezjwzQnGqgwbXZEO8AvgJuBly3LeCZxIXem88QSZzx+amS1JkiRJkqQGuNJ5vM5M8m3qXOErgPsBbwFuBL40su9DkhxGndP8OOr4jQOHVsEeAvwaOCbJZ6jzb/8dOK67iSDU8QP/DPwgyb8Cf6XeeO1K4LCVcoUr30eBl1BnJX8SuIS6EvnxwCmllK8t74FLKbcneTdwWJKrqLOjH0+9id07JrmJ4MbAOUm+BPyEOgbiQcDbu/qG53Of3F3DltzZdD6FOg968PiK+j7wW+CIJG+nNn7fRW1+L9Mq6lLKT5LsBxwBfD7J/qWUMsGuZwF7JtmTusL5glLKVUneB7w/ydpdXesAewHv6eZMT6eGs5N8Fvh691sB86kN7odQb5L5qmW5JrXrvM0f2ncJmgZzaodZtcGc2mBObTCndphVG8ypDebUjsfs8oi+S5hRNp3H673A3sB/UOcDXwb8HPj7UsoFI/v+f8AzqU3nm6njJD41eLCU8vtu/MEHqDcZvA74Wve8wT5LkjyZ2uT8PHXO8jzgeaWUZR2vMSuUUq5M8n+A9wMfA+5JXQF+CnDGGI7/X0nWBQ7svi4GDi6lfGySp11HvSHgM4AXU28WeAlwHHBIKeXaoX1/DVwPLCylXNadc1GSs4DtqA3VFb2GkmRv6gcLX6Q2m98PPJ/6AceyHu9bSV4HfIb6YclbJ9jtEGBbanP6HsDLgcNLKYcmuZr6Wr6WOi/6pyz7KJTXAecAr6b+PbqOepPAzy/r9ahdN689er9VzUbm1A6zaoM5tcGc2mBO7TCrNphTG8ypHRtvtFHfJcyoTLyoUStLku2AC4BnlVK+1281WlUk2Rg4H/hUKeVdfdczGyUp+562pO8yJEmSJEnSGB2x6zrY3+xPkgWllLmj213pLDUoyQHUURrnUm8g+GbqaIsv9FmXtCK2vuY8AC7d5IE9V6LJmFM7zKoN5tQGc2qDObXDrNpgTm0wp3acefY5AOz8oB16rmRmeCNBqU03AwcBx1JHbNwEPLmU8udeq5JWwKY3Xs6mN47ec1WzjTm1w6zaYE5tMKc2mFM7zKoN5tQGc2rHRQsXctHChX2XMWNc6TzDSikXUmcvS8utlHI49UaSkiRJkiRJ0qziSmdJkiRJkiRJ0tjYdJYkSZIkSZIkjY1NZ0mSJEmSJEnS2Nh0liRJkiRJkiSNjTcSlCTNCjettWHfJWgazKkdZtUGc2qDObXBnNphVm0wpzaYUzs23mj1yiqllL5rkKSVLknZ97QlfZchSZIkSZLG6Ihd18H+Zn+SLCilzB3d7ngNSZIkSZIkSdLY2HSWJEmSJEmSJI2NTWdJ0qyw0yU/Y6dLftZ3GZqCObXDrNpgTm0wpzaYUzvMqg3m1AZzasex807i2Hkn9V3GjLHpLEmSJEmSJEkaG5vOkiRJkiRJkqSxseksSZIkSZIkSRobm86SJEmSJEmSpLGx6SxJkiRJkiRJGhubzpIkSZIkSZKksZnTdwGSJAFccs8H9F2CpsGc2mFWbTCnNphTG8ypHWbVBnNqgzm1Y+cdtu+7hBmVUkrfNUjSSpek7Hvakr7LkCRJkiRJY3TErutgf7M/SRaUUuaObne8hiRJkiRJkiRpbGw6S5JmhU1uuIxNbris7zI0BXNqh1m1wZzaYE5tMKd2mFUbzKkN5tSOiy69lIsuvbTvMmaMM50lrRbWWnttjth1nb7L0CQOOPgtABzxkQ/3XIkmY07tMKs2mFMbzKkN5tQOs2qDObXBnGDrbe/XdwnTcuY55wKw7dZb91zJzHCms6TVwty5c8v8+fP7LkOTOHbeSQDstcfje65EkzGndphVG8ypDebUBnNqh1m1wZzaYE7tWFWzcqazJEmSJEmSJGmls+ksSZIkSZIkSRobm86SJEmSJEmSpLGx6SxJkiRJkiRJGhubzpIkSZIkSZKksUkppe8aJGmlmzt3bpk/f37fZUiSJEmSJK0ykiwopcwd3e5KZ0mSJEmSJEnS2Nh0liRJkiRJkiSNjU1nSdKscMqCBZyyYEHfZWgK5tQOs2qDObXBnNpgTu0wqzaYUxvMqR2rW1Zz+i5AkiSAaxdf33cJmgZzaodZtcGc2mBObTCndphVG8ypDebUjtUtK1c6S5IkSZIkSZLGxqazJEmSJEmSJGlsbDpLkiRJkiRJksbGmc6SVgtn/PYMkvRdhiZxwMFvAeCZT9ij30I0KXNqh1m1wZzaYE5tMKd2mFUbzKkN5tSOAw5+C+uusy577fH4vkuZETadJa0Wbr3tVg7dfr++y9Ak1ttgGwBzmuXMqR1m1QZzaoM5tcGc2mFWbTCnNphTO9bbYBvOuuGSvsuYMTadJUmzwm1/WtR3CZoGc2qHWbXBnNpgTm0wp3aYVRvMqQ3m1I7b/rSIP15xRt9lzJiUUvquQZJWuiTFT34lSZIkSVJf3n7uV1jVerFJFpRS5o5u90aCkiRJkiRJkqSxseksSZoVsun6ZNP1+y5DUzCndphVG8ypDebUBnNqh1m1wZzaYE7tyKbrs/mWW/Zdxoyx6SxJmhXW3XNH1t1zx77L0BTMqR1m1QZzaoM5tcGc2mFWbTCnNphTO9bdc0ee95KX9l3GjLHpLEmSJEmSJEkaG5vOkiRJkiRJkqSxseksSZIkSZIkSRobm86SJEmSJEmSpLGx6SxJkiRJkiRJGhubzpIkSZIkSZKksUkppe8aJGmlS1IO3X6/vsvQJLLp+gCUq2/suRJNxpzaYVZtMKc2mFMbzKkdZtUGc2qDObUjm67PRy78DldcdlnfpYxVkgWllLmj2+f0UYwkSaP8j6Q2mFM7zKoN5tQGc2qDObXDrNpgTm0wp3aUq29k0eWX913GjHG8hiRJkiRJkiRpbFzpLEmaFdba7X4A3PqrP/dciSZjTu0wqzaYUxvMqQ3m1A6zaoM5tcGc2rHWbvfj8ds9te8yZowrnSVJs8KcB2zOnAds3ncZmoI5tcOs2mBObTCnNphTO8yqDebUBnNqx5wHbM6D/+7v+i5jxth0liRJkiRJkiSNjU1nSZIkSZIkSdLY2HSWJEmSJEmSJI2NTWdJkiRJkiRJ0tjYdJYkSZIkSZIkjc2cvguQJAng9mtu7LsETYM5tcOs2mBObTCnNphTO8yqDebUBnNqx+3X3Miiay7vu4wZk1JK3zVI0kqXpBy6/X59lyFJkiRJklZTbz/3K6xqvdgkC0opc0e3TzpeI0mZxtceUxzj8CTzV7D+GZXkwmlc9/5917msUr0+ye+T3Jjkz0k+meSey3icjyS5JskWEzx2WJIrkmy6grXu0b3OO02x37TeX0muTPLuFalp6FglyevHcazZJsk/JLk9ySNHtm+U5JIkX5nkudtN8vflPknmJTlyaP93J7lyZV6PJEmSJEmSZt5U4zV2H/rzesCJwCHAsUPb/zDuomaB5wLrDP38Q+BI4L+Htv1pRisajzcAHwfeB8wDdgA+AGwL7L0Mx3kXsC/wIeBlg41JdgNeBbyilHL1eEqe0vuo702NQSnly0leBXwmya6llL92D70P2AA4eBqHeQvws5FtVwD/BNw6tmIlSZIkSZI0K03adC6l/HLw5yQbdn/80/D2VVEp5dfDPye5Dbh4FbjuFwNHl1Le1f38kyTrAB9LskEp5YbpHKSUcn2SNwFHJvnvUsrJSdYA/hM4uZTypZVT/oS1tNj8n+3+EfgN8DrgP5I8DHg9cFAp5bJpPP/spfxdWRU/oNIYrfei+ts4N32tqV+OWe2YUzvMqg3m1AZzaoM5tcOs2mBObTCndqz3orkccMNWfZcxYyYdrzGV7lfxT0lydTdu4SdJ7jbDY+Q5ayc5KslFSR7Ybfu3JGcmuT7JxUm+kuTeUxxn8Kv8+3YjHa7tnvuergE6vO8Tk5ya5OYklyf5z6Em+vJc92OSnJzkuu7rN0leMPT43cYvjI4SSLJ/t98jurEDN3bHeUSSDZJ8sbum85O8aIIa9k4yv7umy5J8MMlaU5S+FnDtyLa/AOm+pq2U8i3gB8B/JpkDHADsTG1YkuRhSX7cXdc1XaZbjlzD25OcN5TLDyfIfbMk3+zeG+cn+aeRY9xtvEaSxyX5bXfcBUkeNVp/kr2SHN+NArkuyS+TPHVZXoOhY+3UZfA/SdZMHc/y4ZF9Bnlv2P08GB+yx2TXt5TzXZjkw0kO6t7z1yT5ekbGpCS5f5JjuutbnOS7g79zkyml/J5uRXySbYDPAr+lfqiw3DIyXmMp+2ya5HPd++HmJD/P3Ud9vDLJH5LclDo25aQkD1mR2iRJkiRJkjQ+K9R0BrYDvgy8gLqK9n+Bk5P8zUQ7J1kXOBp4KPDYUsp53UNbUMc87AW8Cfgb4MTR5vFSfBC4Hng+8P+Af+3+PDjnQ6jjMa4EnkcdDfFi6riMZZbkHsD3gPO74z0f+B9gmeYiD/kS8LXuWOnq+jxwaXfsU4EvJ7nPUA37AkcBvwKeDbwHeA1w6BTn+m9g3yTPSJ3R+3DgbcDhpZTrh45/YZLDp1H764AHUEeuHAJ8uJTyxySbU8d3rE99rd8APB44Psna3Tn+AXgH8FFgT2qz+jzqCIdh/0VteD63O+anU8d4TCjJ1tRm+NXU1+8w4CtdLcPuD3wXeCn1tf858IMkj57GdQ+f7+FdXd8BXjY0jmK6lun6huwLPIma+z8Dz6T+HRrUtQ7wY+DBwKuB/anXfFKmN2/7PcB1wCnArsA/LcO1rZFkztDXtP53pqv5BODJwFuB5wCLgBMGH0YkeRy1Cf4/wNOBV1Cz23iatUmSJEmSJGklm2qm86RKKe8d/LlrLB0P7Aa8BHjv8L5J1qc25u4DPK6UcsnQcV4xtN+awC+Ai4HHAD+dooyfllIGc2aPT/I0YB/giG7bO4E/A88eNM2SXA18I8nupZRfLNNF1znIGwOvL6Us7rb9aBmPMezDg3EUSUKdlz2vlPJ/u22/ojZPn0WdsxvqLOUvl1LuWBWbZAm1YXloKeWqiU5USvlMko2ozdZBI/AY4LUju94GTNlgLKVckOT91Ibz+d13uHPu756llOu6+s4Ffklt8H6N+j75USllePXsUROc5mullEO6Y8yjvg77UBvuE3kTcDOwVynlxu55N1A/kBiu/VODP3fv3Z8ADwFeyd3nEU+oW4H7Q2oD9MCyfLcfXdbrG7gVeE4p5bbuuTsCL6TOTQZ4OXVW9w6llPO7fU6l5vRapviAohuh8u/AJ6nvtVOX4Zq+PfLzV6j/mzCVlwA7AQ8ppZzb1XwCcDb1PfVW6vvmjFLKcP3fWdoBk7yG2piXJEmSJEnSDFnR8RoPTnJ0ksupTcpbgQdRG7PDNqA257YAHj/ccO6O8/Tu1+ivpTY8L+4eGj3OREYbvn+gNrYHdqPOMR5uon6rO89jpnH8UX+irqz+auqIi+Vd4Tzw46E/D1Z+nzjYUEq5lrrac5tu0w7UZuIRw6tJu+esS23aTSh1TMc7gX+hrjx+BXUV6+eH9yulPLCU8spp1v+h7vunSyk3dX8eNJSvGzrmqcCF3Pma/wZ4Ruo4lN26Dxsmcke+pZRbgXO5a76jdgOOHzScO0eP7pTkPkm+lOQS6nvhVuCpDL3nRlbrjtb3aOqHLJ8rpbxxORvOsOzXN/CTQcO58wdgi9w5YmU34PRBw7k7/sXUhvqU7/vuel8BFGD3wQr17rFMsZL5IOr7avD1zmlcD9QVzguAC4be1wAnAYOxPb8BHp7kY6ljVNae6EADpZTPlVLmllImHfsjSZIkSZKk8VnupnO3YvZHwH2BNwOPpTaYfkttfg7bGngUtfl7+chxdqWuVLyYOupgd+D/dA+PHmcifxn5+ZaR520F3OWcXQP6KmA6YwbuopRyDfAU6nzkI4BFSY5d2kiRaRiu/5YJtg22D65ps+7796mN0sHXBd32+050kq4x+EngP0oph5ZSflpK+SJ1Ze9LkzxieYovpQxqvmVo891e887l3Pmaf4E6XmNf6giRy5McMkFzd6p8R90buGKkxhupHxQAd7wW36G+J/8VeAL1vfuDwbGTbMddX9/RGxY+lfqbAl+epJbpWNbrm+x5Adbpfp5OBpN5HfUDjH2oH3K8deixl3HX1+YLI889r5Qyf+jrAqZnM+rf/VtHvl5O974upZzQ/fw46jiSK5N8OsnoWBZJkiRJkiT1ZEXGa+xOXZH5lFLKWYONSSaarXou8Ang8CSXlVI+M/TYc6kref9+sFo0yf1WoK5RC6krrO/QNTbvRZ37u8xKKb8EnpZkPerqzI8CX+XOZvkSYHQF5ibLc64JDGp+DfDrCR5fWoNvM+o1/2Zk++AYDwBOX+Hqqru95p0tqStZKaXcDnwM+FiS+wL7p7G5+QAAEtJJREFUAe+nfvjw2RU492Wj5+5GuwzfOPKBwMOBp5dSfji033pD+1xKbUQPLBk5zyHU7H+U5LHDK4qp4z1WVv7TtZA6LmTUlkzxvu/mJ78P+Hgp5ZjUmyK+I8n/K6X8mTqeZfi1uXKi4yyHq4H5dDejHHHH69+No/lSNzt8H+r7aDF1PrkkSZIkSZJ6tiJN50GD7o5mUJJHUW8uuGB051LK/yTZEPhUksWllMGM3fWAW0fGE+y3AnWNOhV4bpJ3DI3Y2Id67aesyIG7cRLfTbIT8Pahhy6m3sANuGNl7ZNW5FxDzgYuAbYrpfzXMjxvEXAj8Ajgm0Pbd+m+XziW6qpTgX9MstFg7nW3on07JnjNSyn/C/xbkpcDO67guU8DXpFk/aERG88d2Wei9+79qCMzzuhquoXaAF2aW6mztr8P/DjJY4bGxtwl/85Tl/VCVtCpwD8kuf9gpXGSbairu989xXM/Sr2J4GAu+/up85Y/Djy3mxk+4dzwFfRj6ut0USnliql2LqUsAg5Lsg8r/r7RLHDLaX/uuwRNgzm1w6zaYE5tMKc2mFM7zKoN5tQGc2rHLaf9mZ9eciJ3Tqpdta1I0/mX1JEF/5Xkg9RVz++mNkQn1N3IbkPgi0muL6UcQ52L+6YkH6euoHwU07vp2HQdQl3Ne0ySz3R1/jtw3HLcRJAke1Fn3R4DXESdtfxahuYwU2cIvy7Jr6k3bnsVcI8VuYiBUsrtSQ4G/ifJPagjIW4B/gZ4DvD8kXnGg+eVJJ8DDkpyI3W27wOA91CzvOODgiTnASctw1znUR+lrlY9rrsZ3YbAvwFnUudpk+Qw6srWXwLXUkdcbA/883Kec+Dj1NEQ30vyUepol7cDNw3tcxa1MfyRJO8ENqK+Dkt9706klHJTkmcBJwAnJHlc1wg9GvhkkndQm+DPY+JVxyvT4dTX8gdJ/pU6c/1d1FXJhy3tSUmeCLwIeEEp5Xq44zrfBByd5GnDq8PH7MvAAcC8bnX1+dTV+bsBl5VSPpbkPdTxIPO6a3k4dT65q5xXAX89b1HfJWgazKkdZtUGc2qDObXBnNphVm0wpzaYUzv+et4i/nDuGX2XMWOWe6ZzN5v5BdQZut8G3kRtGJ03xfM+BBwKfD3JU0op36c2x55HnbP7eOCZy1vXBOf7PfB06siFo6hN6K9RV6kuj/OoN1f7AHWm9QepN0l8xdA+76GuJj6E2vz7DfDF5Tzf3ZRSvgHsDTysO89RwD9Rx2PcMslT30Ydm7Af8D3qDQW/B+zdjbsYmAMs7cZ+06lvEbWJfDP1tf40cDJ1FMugvl9Q5/J+kbpa+LnAq7sPIpZbt9r4GdRxIt+ivi4voa7yHuyzhLra/TbgSOprcij1hnXLer7rqe+vJdQm+8bA56jN7zdS534vob4XZkx3jU+mNtg/D3yJ+iHJHqWUCcdrdDfl+zT1JpBHjhzvGGpOn0yyzkTPH0PNN1PfN8dT/w79iDqWZ3vgV91up1FXNX8WOI764ca7u/0kSZIkSZI0C+SuUy0kadWUpBy6/Tgn92jc1nzg5oCf1M925tQOs2qDObXBnNpgTu0wqzaYUxvMqR1rPnBzDr/kRH7/29/2XcpYJVlQSpk7un1FxmtIkjQ2a+9a7yF7k/+xNKuZUzvMqg3m1AZzaoM5tcOs2mBObTCndqy96/143I4zfcuv/iz3eA1JkiRJkiRJkkbZdJYkSZIkSZIkjY1NZ0mSJEmSJEnS2Nh0liRJkiRJkiSNjU1nSZIkSZIkSdLY2HSWJEmSJEmSJI1NSil91yBJK12Scuj2+/VdhiRJkiRJWk29/dyvsKr1YpMsKKXMHd3uSmdJkiRJkiRJ0tjYdJYkSZIkSZIkjc2cvguQJAlgnaftCMCSH/6h50o0GXNqh1m1wZzaYE5tMKd2mFUbzKkN5tSOdZ62I8975Ev7LmPG2HSWJM0Ka2yyft8laBrMqR1m1QZzaoM5tcGc2mFWbTCnNphTO9bYZH02X3vLvsuYMY7XkCRJkiRJkiSNjU1nSZIkSZIkSdLY2HSWJEmSJEmSJI2NTWdJkiRJkiRJ0tjYdJYkSZIkSZIkjc2cvguQJAngtj8t6rsETYM5tcOs2mBObTCnNphTO8yqDebUBnNqx21/WsQfrzij7zJmTEopfdcgSStdknLo9vv1XYYkSZIkSVpNvf3cr7Cq9WKTLCilzB3d7ngNSZIkSZIkSdLY2HSWJM0K2XR9sun6fZehKZhTO8yqDebUBnNqgzm1w6zaYE5tMKd2ZNP12XzLLfsuY8bYdJYkzQrr7rkj6+65Y99laArm1A6zaoM5tcGc2mBO7TCrNphTG8ypHevuuSPPe8lL+y5jxth0liRJkiRJkiSNzZy+C5CkmbDWnLV4+7lf6bsMTeKAG7YC4LPmNKuZUzvMqg3m1AZzaoM5tcOs2mBObTCndhxww1asu866fZcxY7Kq3TFRkiYyd+7cMn/+/L7L0CSOnXcSAHvt8fieK9FkzKkdZtUGc2qDObXBnNphVm0wpzaYUztW1aySLCilzB3d7ngNSZIkSZIkSdLY2HSWJEmSJEmSJI2NTWdJkiRJkiRJ0tg401nSasGZzrPftYsXA7DxRhv1XIkmY07tMKs2mFMbzKkN5tQOs2qDObXBnNqxqma1tJnOc/ooRpKkUava//GuqsypHWbVBnNqgzm1wZzaYVZtMKc2mFM7VresHK8hSZIkSZIkSRobm86SpFnhzLPP4cyzz+m7DE3BnNphVm0wpzaYUxvMqR1m1QZzaoM5tWN1y8qmsyRpVrho4UIuWriw7zI0BXNqh1m1wZzaYE5tMKd2mFUbzKkN5tSO1S0rm86SJEmSJEmSpLGx6SxJkiRJkiRJGhubzpIkSZIkSZKksbHpLEmSJEmSJEkaG5vOkiRJkiRJkqSxmdN3AZIkAWy80YZ9l6BpMKd2mFUbzKkN5tQGc2qHWbXBnNpgTu1Y3bJKKaXvGiRppZs7d26ZP39+32VIkiRJkiStMpIsKKXMHd3ueA1JkiRJkiRJ0tjYdJYkSZIkSZIkjY1NZ0nSrHDsvJM4dt5JfZehKZhTO8yqDebUBnNqgzm1w6zaYE5tMKd2rG5Z2XSWJEmSJEmSJI2NTWdJkiRJkiRJ0tjYdJYkSZIkSZIkjY1NZ0mSJEmSJEnS2Nh0liRJkiRJkiSNjU1nSZIkSZIkSdLYzOm7AEmSAHbeYfu+S9A0mFM7zKoN5tQGc2qDObXDrNpgTm0wp3asblmllNJ3DZK00s2dO7fMnz+/7zIkSZIkSZJWGUkWlFLmjm53vIYkSZIkSZIkaWxsOkuSZoWLLr2Uiy69tO8yNAVzaodZtcGc2mBObTCndphVG8ypDebUjtUtK2c6S5JmhTPPOReAbbfeuudKNBlzaodZtcGc2mBObTCndphVG8ypDebUjtUtK1c6S5IkSZIkSZLGxqazJEmSJEmSJGlsbDpLkiRJkiRJksbGprMkSZIkSZIkaWxsOkuSJEmSJEmSxsamsyRJkiRJkiRpbFJK6bsGSVrpkiwC/tx3HZrSZsCVfRehKZlTO8yqDebUBnNqgzm1w6zaYE5tMKd2rIpZ3a+UsvnoRpvOkqRZI8n8UsrcvuvQ5MypHWbVBnNqgzm1wZzaYVZtMKc2mFM7VqesHK8hSZIkSZIkSRobm86SJEmSJEmSpLGx6SxJmk0+13cBmhZzaodZtcGc2mBObTCndphVG8ypDebUjtUmK2c6S5IkSZIkSZLGxpXOkiRJkiRJkqSxseksSZIkSZIkSRobm86SpF4leWCSw5KckeSvSeb1XZPuLskLknwnySVJrk+yIMmL+q5Ld5Xk+Ul+nuSqJDcnOTvJvyRZu+/atHRJtun+XpUkG/Zdj+6UZP8ul9GvA/quTXeVZE6StyU5N8mSJBcn+VjfdelOSeYt5e9TSbJ73/XpTklemOT07v+bLkny5SRb912X7i7Jc7p/Ry1JckGSN/dd0+puOv++TfWOJP+b5KYkP03ysB7KXanm9F2AJGm19xDgGcAvgbV6rkVL92bgAuAg4EpqZl9Nslkp5ZO9VqZh9wJOBD4E/AXYDXg3cG/g9f2VpSl8CLge2KDvQrRUTwRuGvr5/L4K0VIdTs3pPcBZwH2BHfssSHfzT8A9Rra9F3g4cNrMl6OJJHk28DXg08Bbga2AQ4Bjk+xSSrm9z/p0pySPBo4CvgC8BXgk8O9Jbi+lfLzX4lZv0/n37duAd1L/jp1F/bfWCUl2KqVcNiNVzgBvJChJ6lWSNQb/8ZrkSGCzUsoe/ValUV1z+cqRbV8Fdi+l3L+nsjQNSd4PvA7YpPgffrNOkscBxwAfoDafNyqlXN9vVRpIsj/wRcxlVkvyNOC7wENLKX/oux5NT/dbOJcB3yil/GPf9ahK8nVg+1LKLkPbng18G9ixlPLH3orTXSQ5Dli/lPLYoW0fAV4O3LuUcktvxa3Gpvr3bZJ1gcuBj5RS3ttt2wC4EDislPIvM170SuJ4DUlSr1wt0YbRhnPn14C/ajn7XQU4XmMWSrIm8EnqSr+J/o5Jmp5XACfacG7O04BNqKtqNXusBVw7su0v3ffMcC2a3MOA40e2/Yj698qRNT2Zxr9vH0X9rY8jhp5zA/XD06evxNJmnE1nSZK0vHYHzum7CN1dkjWTrJ/kMcAbgc+4ynlWOgBYh/orzJrd/pTktm5O+mv7LkZ380jgnCSfSnJdkhuTHOUM2lnvhcDFwMl9F6K7+ALw2CT/kOQeSXagjtfwg53ZZ11gdDXz4OcHz3Atmr6/Bf4KnDuy/Y/dY6sMm86SJGmZJXkS8BzgI33Xognd0H2dDJxEnRenWSTJvYD3AW8updzadz1aqoXUmYsvBZ5Fnc/42SQH9VqVRt0b2J+66u+F1F8t3wU4OokrM2ehJOsDzwaO8EPR2aWUciz179PnqCuezwbWBJ7XY1ma2HnAriPbduu+bzrDtWj6NgGuL6X8dWT7NcD6q9INwL2RoCRJWiZJtgO+Cny7lHJ4r8VoaR4FrE/9h8e/Ap+i3sBJs8f7gV+WUr7fdyFaulLKccBxQ5t+0M1i/Jckn3BE1KyR7mvvUspVAEkWUj90eyLw4x5r08SeRb15qqM1ZpkkTwA+C3wC+AGwJfWmxEcnefIEjTL157PUD0JfDRxJ/e++N3eP+f9P6p1NZ0mSNG1JNqX+A+TPwH49l6OlKKWc3v3xlCRXAl9K8pFSyp/6rEtVkodQZ9A+Lsk9u83rd983TvLXUspN/VSnaTgS2BfYDji/31LUuQY4f9Bw7pxC/TXzHbHpPBu9EDivlDK/70J0Nx8BvlNK+efBhiS/Ac4C9gaO6qsw3c0XgIcCn6GuTL8R+Gfq/SIu67EuTe4aYMMka458iLMJcOOqdANIx2tIkqRp6X4V9nvUm9I9s5RyY88laXoGDej791qFhm1PvVHTL6j/8LiGO+c6X0z9x6JmrzLyXf37IxPf4Cy42m/WSbIx/3979xJqUxgFcPy/ysSAicdEihgwMTCSK4U8UgaKMDFGIozugJiIkRhIpiYkA5EMDBRJKK+8RpgoUiSS0DL49s3BvVy1u9/u9P/Vabe/3alVu9Npr72+tcqwLKucu2kOcK93ITOfAZ+BWVUi0rAy83tmbgemAPMoVek3m8s3R/yiantKaVkz+7f1Oc21vmGlsyRJ+qeIGAecpSTLFmbmm8ohafQGmuPzqlGo13VgyW9rqyjVSauxerbr1gFvKTs+1A0XgQMRMTkz3zZriykvd+7XC0sjWEsZomrSuZteAvN7FyJiLjAeeFEjIP1dZg69wCYitgE3MrOvkpd95gbwAVhPGdI5VNyzhlKx3jdMOkuSqmr+YFc3p9OAiRGxrjm/ZDVtZxyn3KedwKRmCNqQu5n5pU5Y6hURl4ErwCPKVOwBYA9wxtYa3dEkxa72rjW90gGuZebHMQ5JI4iIc8At4AGlKmlD89lhP+dOOQnsAC5ExEFgAnAYuJKZ16tGpuFsBO5n5pPagWhYJ4AjEfGKnz2d91ESzs4h6JCIWAAsolSmTwQ2ASubNVUymufbiDgE7I2Id5Tq5t2UbhR9tdvNpLMkqbaplAraXkPnM7GioitWNMejw1zzPnXHbcrE+RnAN0rF7CDlAVLS/3tG6b89ndKq4TGwOTNPVY1Kv8jMDxGxFDgGnKb0cj4P7KoamP4QEZOBZcDe2rFoRMcov6GtwBbgPWWHzmBmfqoZmP7wlfIidD+lldA1YCAzH9YMSqN6vj1ESTIPApOAO8DyzHw9RjGOici0FZkkSZIkSZIkqR0OEpQkSZIkSZIktcaksyRJkiRJkiSpNSadJUmSJEmSJEmtMeksSZIkSZIkSWqNSWdJkiRJkiRJUmtMOkuSJEmSJEmSWmPSWZIkSZIkSZLUGpPOkiRJkiRJkqTW/ADi6FzxoRJ1NAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Top 10 Anime Based on Avg Ratings Plot\n",
        "plt.figure(figsize = (20,15))\n",
        "top10_anime = anime[['name', 'rating']].sort_values(by = 'rating',ascending = False).head(10)\n",
        "\n",
        "colors = ['#87255B', '#56CBF9', '#F5D491', '#BEB7A4', '#B4E1FF', '#F06C9B', '#D3C4D1', '#81F4E1', '#C2AFF0', '#C57B57']\n",
        "\n",
        "bins = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
        "\n",
        "\n",
        "labels = top10_anime[['name']].values.flatten()\n",
        "values = top10_anime[['rating']].values.flatten()\n",
        "\n",
        "plt.barh(labels, values, color = colors, edgecolor='black')\n",
        "plt.grid(color='#95a5a6', linestyle='--', linewidth=2, axis='x', alpha=0.7)\n",
        "plt.xticks(bins, fontsize = 15)\n",
        "plt.yticks(fontsize = 15)\n",
        "plt.title(\"Top 10 Anime Based on Avg Ratings\", fontdict = {'fontsize' : 20})\n",
        "plt.show()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "SBti8xziIcxd",
        "outputId": "38b0c286-384d-4334-f6b5-40a37db3e6e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     user_id  anime_id  rating\n",
              "47         1      8074      10\n",
              "81         1     11617      10\n",
              "83         1     11757      10\n",
              "101        1     15451      10\n",
              "153        2     11771      10"
            ],
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              "\n",
              "  <div id=\"df-38ed9f02-8656-4859-aee9-26d9bebd3349\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>anime_id</th>\n",
              "      <th>rating</th>\n",
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              "  <tbody>\n",
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              "      <th>47</th>\n",
              "      <td>1</td>\n",
              "      <td>8074</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>1</td>\n",
              "      <td>11617</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>1</td>\n",
              "      <td>11757</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>1</td>\n",
              "      <td>15451</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>2</td>\n",
              "      <td>11771</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-38ed9f02-8656-4859-aee9-26d9bebd3349')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-38ed9f02-8656-4859-aee9-26d9bebd3349 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-38ed9f02-8656-4859-aee9-26d9bebd3349');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "anime_rating.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "hTlTNymNI-l0",
        "outputId": "31a21826-44c2-4bc9-cd7f-43db491c5fc9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          user_id  rating\n",
              "anime_id                 \n",
              "1           13449   13449\n",
              "5            5790    5790\n",
              "6            9385    9385"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>anime_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13449</td>\n",
              "      <td>13449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5790</td>\n",
              "      <td>5790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>9385</td>\n",
              "      <td>9385</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5dcf9d6e-73a7-4822-8b78-817d0f0e0d7c')\"\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5dcf9d6e-73a7-4822-8b78-817d0f0e0d7c');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "# Count anime rating contribution\n",
        "anime_rating_contribution = anime_rating.groupby('anime_id').count()\n",
        "anime_rating_contribution.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BUDzvQWEKN6a"
      },
      "source": [
        "Anime dengan ID 1 mendapat rating sebanyak 13449 kali, anime dengan ID 5 mendapat rating sebanyak 5790 kali, dan anime dengan ID 6 mendapat rating sebanyak 9385 kali."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7EQ_Gz4PKkoc",
        "outputId": "64a59499-9a1a-40f6-fa43-00f3ed019037"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      anime_id  user_id  rating                                name  \\\n",
              "1388      1535    34226   34226                          Death Note   \n",
              "6323     11757    26310   26310                    Sword Art Online   \n",
              "7059     16498    25289   25289                  Shingeki no Kyojin   \n",
              "1426      1575    24126   24126     Code Geass: Hangyaku no Lelouch   \n",
              "4567      6547    23565   23565                        Angel Beats!   \n",
              "201        226    23528   23528                          Elfen Lied   \n",
              "10          20    22071   22071                              Naruto   \n",
              "3936      5114    21494   21494    Fullmetal Alchemist: Brotherhood   \n",
              "99         121    21332   21332                 Fullmetal Alchemist   \n",
              "2642      2904    21124   21124  Code Geass: Hangyaku no Lelouch R2   \n",
              "\n",
              "                                                  genre type episodes  \\\n",
              "1388  Mystery, Police, Psychological, Supernatural, ...   TV       37   \n",
              "6323          Action, Adventure, Fantasy, Game, Romance   TV       25   \n",
              "7059       Action, Drama, Fantasy, Shounen, Super Power   TV       25   \n",
              "1426  Action, Mecha, Military, School, Sci-Fi, Super...   TV       25   \n",
              "4567        Action, Comedy, Drama, School, Supernatural   TV       13   \n",
              "201   Action, Drama, Horror, Psychological, Romance,...   TV       13   \n",
              "10    Action, Comedy, Martial Arts, Shounen, Super P...   TV      220   \n",
              "3936  Action, Adventure, Drama, Fantasy, Magic, Mili...   TV       64   \n",
              "99    Action, Adventure, Comedy, Drama, Fantasy, Mag...   TV       51   \n",
              "2642  Action, Drama, Mecha, Military, Sci-Fi, Super ...   TV       25   \n",
              "\n",
              "        members  \n",
              "1388  1013917.0  \n",
              "6323   893100.0  \n",
              "7059   896229.0  \n",
              "1426   715151.0  \n",
              "4567   717796.0  \n",
              "201    623511.0  \n",
              "10     683297.0  \n",
              "3936   793665.0  \n",
              "99     600384.0  \n",
              "2642   572888.0  "
            ],
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              "\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8abe8b64-81e4-4570-9333-1dd7b2fb1afa')\"\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8abe8b64-81e4-4570-9333-1dd7b2fb1afa button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8abe8b64-81e4-4570-9333-1dd7b2fb1afa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "# Menggabungkan table anime_rating_contribution dengan table anime untuk mendapat nama anime\n",
        "# Top 10 anime rating contribution\n",
        "anime2 = anime.drop(['rating'], axis = 'columns')\n",
        "name_anime_rating_contribution = pd.merge(anime_rating_contribution, anime2, on = 'anime_id', how = 'left')\n",
        "name_anime_rating_contribution.sort_values(by='rating', ascending=False).head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "WMmizcNVLuHg",
        "outputId": "aaaed362-18b8-478a-b401-cec0738e0f89"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1080 with 1 Axes>"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Top 10 Anime Rating Contribution Plot\n",
        "plt.figure(figsize = (20,15))\n",
        "top10_anime = name_anime_rating_contribution[['name', 'rating']].sort_values(by = 'rating',ascending = False).head(10)\n",
        "\n",
        "colors = ['#87255B', '#56CBF9', '#F5D491', '#BEB7A4', '#B4E1FF', '#F06C9B', '#D3C4D1', '#81F4E1', '#C2AFF0', '#C57B57']\n",
        "\n",
        "\n",
        "labels = top10_anime[['name']].values.flatten()\n",
        "values = top10_anime[['rating']].values.flatten()\n",
        "\n",
        "plt.barh(labels, values, color = colors, edgecolor='black')\n",
        "plt.grid(color='#95a5a6', linestyle='--', linewidth=2, axis='x', alpha=0.7)\n",
        "plt.xticks(fontsize = 15)\n",
        "plt.yticks(fontsize = 15)\n",
        "plt.title(\"Top 10 Anime Rating Contribution\", fontdict = {'fontsize' : 20})\n",
        "plt.show()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-BunU6wLAC7F"
      },
      "source": [
        "# Content Based Filtering Model & Result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Et7DuyD65jk",
        "outputId": "22951160-266d-4297-c518-0e2f7f0abf9e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['action',\n",
              " 'adventure',\n",
              " 'ai',\n",
              " 'arts',\n",
              " 'cars',\n",
              " 'comedy',\n",
              " 'dementia',\n",
              " 'demons',\n",
              " 'drama',\n",
              " 'ecchi',\n",
              " 'fantasy',\n",
              " 'fi',\n",
              " 'game',\n",
              " 'harem',\n",
              " 'hentai',\n",
              " 'historical',\n",
              " 'horror',\n",
              " 'josei',\n",
              " 'kids',\n",
              " 'life',\n",
              " 'magic',\n",
              " 'martial',\n",
              " 'mecha',\n",
              " 'military',\n",
              " 'music',\n",
              " 'mystery',\n",
              " 'of',\n",
              " 'parody',\n",
              " 'police',\n",
              " 'power',\n",
              " 'psychological',\n",
              " 'romance',\n",
              " 'samurai',\n",
              " 'school',\n",
              " 'sci',\n",
              " 'seinen',\n",
              " 'shoujo',\n",
              " 'shounen',\n",
              " 'slice',\n",
              " 'space',\n",
              " 'sports',\n",
              " 'super',\n",
              " 'supernatural',\n",
              " 'thriller',\n",
              " 'vampire',\n",
              " 'yaoi',\n",
              " 'yuri']"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "# TF-IDF\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "tf = TfidfVectorizer()\n",
        "tf.fit(anime['genre']) \n",
        "tf.get_feature_names()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "T0AxW2Iw8cCb",
        "outputId": "dc022cde-4833-4899-fbe5-f2ebed0ac8ca"
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      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12017, 47)"
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          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "tfidf_matrix = tf.fit_transform(anime['genre'])\n",
        "tfidf_matrix.shape "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQW14Ndg8t4_",
        "outputId": "532c0465-c7b0-4a2e-bdae-57de4e6a4d65"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "tfidf_matrix.todense()"
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    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
        "id": "tW63MUdD8w0J",
        "outputId": "cfe4e30e-bbce-43d0-9f45-5be546604b85"
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              "                                                    martial      life  \\\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "pd.DataFrame(\n",
        "    tfidf_matrix.todense(), \n",
        "    columns=tf.get_feature_names(),\n",
        "    index=anime.name\n",
        ").sample(22, axis=1).sample(10, axis=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dataframe ini menunjukkan matriks tf-idf untuk beberapa anime dan genre. Semakin tinggi nilai matriks menunjukkan hubungan antara anime dengan genre tersebut. Misalkan anime Asa da yo! Kaishain\tmerupakan genre comedy terlihat dari nilai matriks 1.0 yang didapat anime tersebut pada genre comedy"
      ],
      "metadata": {
        "id": "neS0KH0byTk9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KWFLubIf8-HK",
        "outputId": "da571280-0a3f-4d43-91cc-c9fa9872c28b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "        1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        " \n",
        "# Menghitung cosine similarity pada matrix tf-idf\n",
        "cosine_sim = cosine_similarity(tfidf_matrix) \n",
        "cosine_sim"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 646
        },
        "id": "2XYzCRo99D-3",
        "outputId": "5c74c298-7386-4e09-ea72-41b7c7f32965"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape: (12017, 12017)\n"
          ]
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              "name                                                                \n",
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              "name                                                Queens Blade: Gyokuza wo Tsugu Mono Specials  \\\n",
              "name                                                                                               \n",
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              "                                                     [key], {});\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "# Membuat dataframe dari variabel cosine_sim dengan baris dan kolom berupa nama resto\n",
        "cosine_sim_df = pd.DataFrame(cosine_sim, index=anime['name'], columns=anime['name'])\n",
        "print('Shape:', cosine_sim_df.shape)\n",
        " \n",
        "# Melihat similarity matrix pada setiap resto\n",
        "cosine_sim_df.sample(5, axis=1).sample(10, axis=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dataframe ini menunjukkan nilai kesamaan antar anime. Semakin tinggi nilai cosline similarity, kedua anime akan semakin memiliki kesamaan.Misalnya, anime Anata dake Konbanwa memiliki kesamaan dengan anime Kisaku Spirit terlihat dari nilai cosine similarity 1.0 antar kedua anime tersebut."
      ],
      "metadata": {
        "id": "5z-IO76P5rHL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yvSjhJZb9SAd"
      },
      "outputs": [],
      "source": [
        "def anime_recommendations(name, similarity_data=cosine_sim_df, items=anime[['name', 'genre']], k=5):\n",
        " \n",
        "    # Mengambil data dengan menggunakan argpartition untuk melakukan partisi secara tidak langsung sepanjang sumbu yang diberikan    \n",
        "    # Dataframe diubah menjadi numpy\n",
        "    # Range(start, stop, step)\n",
        "    index = similarity_data.loc[:,name].to_numpy().argpartition(\n",
        "        range(-1, -k, -1))\n",
        "    \n",
        "    # Mengambil data dengan similarity terbesar dari index yang ada\n",
        "    closest = similarity_data.columns[index[-1:-(k+2):-1]]\n",
        "    \n",
        "    # Drop name agar nama resto yang dicari tidak muncul dalam daftar rekomendasi\n",
        "    closest = closest.drop(name, errors='ignore')\n",
        " \n",
        "    return pd.DataFrame(closest).merge(items).head(k)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fungsi anime_recommendations dibuat untuk menemukan rekomendasi anime menggunakan similarity yang telah didefinisikan sebelumnya. Fungsi ini bekerja dengan cara mengambil anime dengan similarity terbesar dari index yang ada."
      ],
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              "     anime_id    name                                              genre type  \\\n",
              "841        20  Naruto  Action, Comedy, Martial Arts, Shounen, Super P...   TV   \n",
              "\n",
              "    episodes  rating  members  \n",
              "841      220    7.81   683297  "
            ],
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              "      <td>Naruto</td>\n",
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      "source": [
        "anime[anime.name.eq('Naruto')]"
      ]
    },
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            "text/plain": [
              "                                                name  \\\n",
              "0        Naruto: Shippuuden Movie 4 - The Lost Tower   \n",
              "1               Naruto Shippuuden: Sunny Side Battle   \n",
              "2  Boruto: Naruto the Movie - Naruto ga Hokage ni...   \n",
              "3                                        Naruto x UT   \n",
              "4                                 Naruto: Shippuuden   \n",
              "\n",
              "                                               genre  \n",
              "0  Action, Comedy, Martial Arts, Shounen, Super P...  \n",
              "1  Action, Comedy, Martial Arts, Shounen, Super P...  \n",
              "2  Action, Comedy, Martial Arts, Shounen, Super P...  \n",
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              "4  Action, Comedy, Martial Arts, Shounen, Super P...  "
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      "source": [
        "anime_recommendations('Naruto')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sistem telah berhasil merekomendasikan top 5 persen anime yang mirip dengan naruto, yaitu beberapa film dan seri dari naruto itu sendiri. Jadi, jika pengguna menyunkai naruto, maka sistem dapat merekomendasikan seri atau movie naruto lainnya."
      ],
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        "id": "OmNtg3hwOe6r"
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