superheroes_figures_recommendation.ipynb

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      "name": "superheroes-figures-recommendation.ipynb",
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    "kernelspec": {
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      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U1cCtFP9CVaO"
      },
      "source": [
        "# <center> <i>Recommendation Systems</i>: Rekomendasi Tokoh Superhero bagi Penggemar Action Figure </center>\n",
        "--- \n",
        "\n",
        "<small>by: Muhammad Difagama Ivanka</small><br>\n",
        "[<sup><sub>sklearn</sub></sup>](https://scikit-learn.org/dev/about.html#citing-scikit-learn)\n",
        "\n",
        "<p align=\"center\">\n",
        "  <img width=\"460\" height=\"300\" src=\"https://user-images.githubusercontent.com/59215827/138017796-b083cf9c-1610-4d14-8287-b3efe152cc73.png\">\n",
        "  <img width=\"460\" height=\"300\" src=\"https://user-images.githubusercontent.com/59215827/138018614-de84c049-e638-49b7-8078-633f68ff3a26.png\"><br>\n",
        "  <sup><sub><a href=\"https://www.pxfuel.com/en/free-photo-xtdqo\"><i>image source</i></a></sub></sup>\n",
        "  <sup><sub><a href=\"https://www.flickr.com/photos/levork/4966756896\"><i>image source</i></a></sub></sup>\n",
        "</p>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YfIFaVToDnLH"
      },
      "source": [
        "# *Preview*\n",
        "\n",
        "Penggemar superhero terutama dari DC atau Marvel sering merasa bingung dalam memilih siapa karakter favoritnya terlebih bagi pengolektor *action figure* superhero tersebut. Maka dibuat sistem rekomendasi terhadap karakter superhero bagi mereka."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "by6EmiTIEpUh"
      },
      "source": [
        "# 1.&nbsp;Sumber Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2YQ9eidBwVu",
        "outputId": "99c7f95f-7787-4666-a04d-b107e94ea741"
      },
      "source": [
        "!pip install --upgrade --force-reinstall --no-deps kaggle\n",
        "!pip install -U plotly\n",
        "!pip install -U scikit-learn"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting kaggle\n",
            "  Using cached kaggle-1.5.12-py3-none-any.whl\n",
            "Installing collected packages: kaggle\n",
            "  Attempting uninstall: kaggle\n",
            "    Found existing installation: kaggle 1.5.12\n",
            "    Uninstalling kaggle-1.5.12:\n",
            "      Successfully uninstalled kaggle-1.5.12\n",
            "Successfully installed kaggle-1.5.12\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (5.3.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from plotly) (1.15.0)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from plotly) (8.0.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (0.22.2.post1)\n",
            "Collecting scikit-learn\n",
            "  Downloading scikit_learn-1.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (23.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 23.1 MB 1.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.19.5)\n",
            "Collecting threadpoolctl>=2.0.0\n",
            "  Downloading threadpoolctl-3.0.0-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.4.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.0.1)\n",
            "Installing collected packages: threadpoolctl, scikit-learn\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 0.22.2.post1\n",
            "    Uninstalling scikit-learn-0.22.2.post1:\n",
            "      Successfully uninstalled scikit-learn-0.22.2.post1\n",
            "Successfully installed scikit-learn-1.0 threadpoolctl-3.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7pxl9QbGG5TC"
      },
      "source": [
        "## Upload file kaggle.json (api kaggle token) dari pengaturan akun,\n",
        "1. klik 'expire api token'\n",
        "2. 'create new token' (auto download)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 56
        },
        "id": "3vRHBh77G6Im",
        "outputId": "67f4ce4f-ee9a-4787-faf8-2a069789d16b"
      },
      "source": [
        "from google.colab import files\n",
        "\n",
        "!rm -r -f -- kaggle.json\n",
        "files.upload()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-7910ea5f-23a9-437b-95d4-2d7001e98502\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-7910ea5f-23a9-437b-95d4-2d7001e98502\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kldoHjH6FgPz"
      },
      "source": [
        "## Mendapatkan file dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pv3UW2eAHWF3",
        "outputId": "64f37c36-c790-4a8c-bcfd-9934f3ad5fdf"
      },
      "source": [
        "# Copy api\n",
        "# !rm -r -f -- ~/.kaggle/kaggle.json\n",
        "!rm -rf ~/.kaggle\n",
        "!mkdir ~/.kaggle/\n",
        "\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "\n",
        "# Mengganti permission\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "\n",
        "# Unduh dataset\n",
        "# !rm -r -f -- test_timeseries.csv.zip\n",
        "# !rm -r -f -- soil_data.csv.zip\n",
        "!kaggle datasets download -d fivethirtyeight/fivethirtyeight-comic-characters-dataset\n",
        "\n",
        "# direktori dataset\n",
        "!mkdir dataset\n",
        "!unzip fivethirtyeight-comic-characters-dataset -d dataset"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading fivethirtyeight-comic-characters-dataset.zip to /content\n",
            "\r  0% 0.00/597k [00:00<?, ?B/s]\n",
            "\r100% 597k/597k [00:00<00:00, 114MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QPzd44mtKuyb"
      },
      "source": [
        "# 2.&nbsp;*Data Preprocessing* 1\n",
        "\n",
        "Detail | Keterangan\n",
        "-- | --\n",
        "Source | [Kaggle: FiveThirtyEight Comic Characters Dataset](https://www.kaggle.com/fivethirtyeight/fivethirtyeight-comic-characters-dataset)\n",
        "License | [Public (CC0 1.0)](https://creativecommons.org/publicdomain/zero/1.0/)\n",
        "Domain | Superheroes, Figures, Fantasy\n",
        "Data | dc-wikia-data.csv & marvel-wikia-data.csv\n",
        "Size | < 4 MB"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HBQS1YEcLz46"
      },
      "source": [
        "## Cek data original"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUtQG_XCLcID",
        "outputId": "012b6789-3f6a-4020-87fb-10b11f00275b"
      },
      "source": [
        "import numpy as np\n",
        "np.random.seed(233)\n",
        "import pandas as pd\n",
        "\n",
        "dc = pd.read_csv('dataset/dc-wikia-data.csv')\n",
        "marvel = pd.read_csv('dataset/marvel-wikia-data.csv')\n",
        "marvel = marvel.rename(columns= {'Year': 'YEAR'})\n",
        "\n",
        "dc.shape, marvel.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((6896, 13), (16376, 13))"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2-3_e31GNfEK"
      },
      "source": [
        "## Menambahkan asal superhero (DC atau MARVEL)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4WPcMj2bNiuy"
      },
      "source": [
        "dc.loc[:, 'origins'] = 'DC'\n",
        "marvel.loc[:, 'origins'] = 'Marvel'"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "paTD6bj4NQKZ"
      },
      "source": [
        "## Gabung data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "diddGZuLNTDq",
        "outputId": "6616ae1a-094f-472c-8ae6-6c6d0b33c764"
      },
      "source": [
        "superhero = pd.concat([dc, marvel], axis=0).reset_index(drop=True)\n",
        "superhero.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23272, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b9IYxWC2RcRw"
      },
      "source": [
        "# 3.&nbsp;*Data Undestanding*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 840
        },
        "id": "HTmwhJUbRpfF",
        "outputId": "2f01b027-a4cd-45bb-bcdb-5a2fb97e506e"
      },
      "source": [
        "superhero"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>page_id</th>\n",
              "      <th>name</th>\n",
              "      <th>urlslug</th>\n",
              "      <th>ID</th>\n",
              "      <th>ALIGN</th>\n",
              "      <th>EYE</th>\n",
              "      <th>HAIR</th>\n",
              "      <th>SEX</th>\n",
              "      <th>GSM</th>\n",
              "      <th>ALIVE</th>\n",
              "      <th>APPEARANCES</th>\n",
              "      <th>FIRST APPEARANCE</th>\n",
              "      <th>YEAR</th>\n",
              "      <th>origins</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1422</td>\n",
              "      <td>Batman (Bruce Wayne)</td>\n",
              "      <td>\\/wiki\\/Batman_(Bruce_Wayne)</td>\n",
              "      <td>Secret Identity</td>\n",
              "      <td>Good Characters</td>\n",
              "      <td>Blue Eyes</td>\n",
              "      <td>Black Hair</td>\n",
              "      <td>Male Characters</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Living Characters</td>\n",
              "      <td>3093.0</td>\n",
              "      <td>1939, May</td>\n",
              "      <td>1939.0</td>\n",
              "      <td>DC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23387</td>\n",
              "      <td>Superman (Clark Kent)</td>\n",
              "      <td>\\/wiki\\/Superman_(Clark_Kent)</td>\n",
              "      <td>Secret Identity</td>\n",
              "      <td>Good Characters</td>\n",
              "      <td>Blue Eyes</td>\n",
              "      <td>Black Hair</td>\n",
              "      <td>Male Characters</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Living Characters</td>\n",
              "      <td>2496.0</td>\n",
              "      <td>1986, October</td>\n",
              "      <td>1986.0</td>\n",
              "      <td>DC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1458</td>\n",
              "      <td>Green Lantern (Hal Jordan)</td>\n",
              "      <td>\\/wiki\\/Green_Lantern_(Hal_Jordan)</td>\n",
              "      <td>Secret Identity</td>\n",
              "      <td>Good Characters</td>\n",
              "      <td>Brown Eyes</td>\n",
              "      <td>Brown Hair</td>\n",
              "      <td>Male Characters</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Living Characters</td>\n",
              "      <td>1565.0</td>\n",
              "      <td>1959, October</td>\n",
              "      <td>1959.0</td>\n",
              "      <td>DC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1659</td>\n",
              "      <td>James Gordon (New Earth)</td>\n",
              "      <td>\\/wiki\\/James_Gordon_(New_Earth)</td>\n",
              "      <td>Public Identity</td>\n",
              "      <td>Good Characters</td>\n",
              "      <td>Brown Eyes</td>\n",
              "      <td>White Hair</td>\n",
              "      <td>Male Characters</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Living Characters</td>\n",
              "      <td>1316.0</td>\n",
              "      <td>1987, February</td>\n",
              "      <td>1987.0</td>\n",
              "      <td>DC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1576</td>\n",
              "      <td>Richard Grayson (New Earth)</td>\n",
              "      <td>\\/wiki\\/Richard_Grayson_(New_Earth)</td>\n",
              "      <td>Secret Identity</td>\n",
              "      <td>Good Characters</td>\n",
              "      <td>Blue Eyes</td>\n",
              "      <td>Black Hair</td>\n",
              "      <td>Male Characters</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Living Characters</td>\n",
              "      <td>1237.0</td>\n",
              "      <td>1940, April</td>\n",
              "      <td>1940.0</td>\n",
              "      <td>DC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23267</th>\n",
              "      <td>657508</td>\n",
              "      <td>Ru'ach (Earth-616)</td>\n",
              "      <td>\\/Ru%27ach_(Earth-616)</td>\n",
              "      <td>No Dual Identity</td>\n",
              "      <td>Bad Characters</td>\n",
              "      <td>Green Eyes</td>\n",
              "      <td>No Hair</td>\n",
              "      <td>Male Characters</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Living Characters</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Marvel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23268</th>\n",
              "      <td>665474</td>\n",
              "      <td>Thane (Thanos' son) (Earth-616)</td>\n",
              "      <td>\\/Thane_(Thanos%27_son)_(Earth-616)</td>\n",
              "      <td>No Dual Identity</td>\n",
              "      <td>Good Characters</td>\n",
              "      <td>Blue Eyes</td>\n",
              "      <td>Bald</td>\n",
              "      <td>Male Characters</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Living Characters</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Marvel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23269</th>\n",
              "      <td>695217</td>\n",
              "      <td>Tinkerer (Skrull) (Earth-616)</td>\n",
              "      <td>\\/Tinkerer_(Skrull)_(Earth-616)</td>\n",
              "      <td>Secret Identity</td>\n",
              "      <td>Bad Characters</td>\n",
              "      <td>Black Eyes</td>\n",
              "      <td>Bald</td>\n",
              "      <td>Male Characters</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Living Characters</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Marvel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23270</th>\n",
              "      <td>708811</td>\n",
              "      <td>TK421 (Spiderling) (Earth-616)</td>\n",
              "      <td>\\/TK421_(Spiderling)_(Earth-616)</td>\n",
              "      <td>Secret Identity</td>\n",
              "      <td>Neutral Characters</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Male Characters</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Living Characters</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Marvel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23271</th>\n",
              "      <td>673702</td>\n",
              "      <td>Yologarch (Earth-616)</td>\n",
              "      <td>\\/Yologarch_(Earth-616)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Bad Characters</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Living Characters</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Marvel</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23272 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       page_id                             name  ...    YEAR origins\n",
              "0         1422             Batman (Bruce Wayne)  ...  1939.0      DC\n",
              "1        23387            Superman (Clark Kent)  ...  1986.0      DC\n",
              "2         1458       Green Lantern (Hal Jordan)  ...  1959.0      DC\n",
              "3         1659         James Gordon (New Earth)  ...  1987.0      DC\n",
              "4         1576      Richard Grayson (New Earth)  ...  1940.0      DC\n",
              "...        ...                              ...  ...     ...     ...\n",
              "23267   657508               Ru'ach (Earth-616)  ...     NaN  Marvel\n",
              "23268   665474  Thane (Thanos' son) (Earth-616)  ...     NaN  Marvel\n",
              "23269   695217    Tinkerer (Skrull) (Earth-616)  ...     NaN  Marvel\n",
              "23270   708811   TK421 (Spiderling) (Earth-616)  ...     NaN  Marvel\n",
              "23271   673702            Yologarch (Earth-616)  ...     NaN  Marvel\n",
              "\n",
              "[23272 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RJQTeCB3QRi2"
      },
      "source": [
        "## Informasi dasar dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LbrFfeemO2AE",
        "outputId": "2ed3bb9e-347b-42f3-b098-845a5ceb4511"
      },
      "source": [
        "superhero.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 23272 entries, 0 to 23271\n",
            "Data columns (total 14 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   page_id           23272 non-null  int64  \n",
            " 1   name              23272 non-null  object \n",
            " 2   urlslug           23272 non-null  object \n",
            " 3   ID                17489 non-null  object \n",
            " 4   ALIGN             19859 non-null  object \n",
            " 5   EYE               9877 non-null   object \n",
            " 6   HAIR              16734 non-null  object \n",
            " 7   SEX               22293 non-null  object \n",
            " 8   GSM               154 non-null    object \n",
            " 9   ALIVE             23266 non-null  object \n",
            " 10  APPEARANCES       21821 non-null  float64\n",
            " 11  FIRST APPEARANCE  22388 non-null  object \n",
            " 12  YEAR              22388 non-null  float64\n",
            " 13  origins           23272 non-null  object \n",
            "dtypes: float64(2), int64(1), object(11)\n",
            "memory usage: 2.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pO_hmdnkuWLw",
        "outputId": "fac6ba3f-7646-45b8-bc03-507cc39f7aac"
      },
      "source": [
        "23272*.01"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "232.72"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cYsl_NbWAzAC"
      },
      "source": [
        "## Statistik dasar kolom continuous"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "z7lnpAYiA2QC",
        "outputId": "9a8635a6-35c6-4a85-f99e-1ff422fa9231"
      },
      "source": [
        "superhero.describe().T"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>APPEARANCES</th>\n",
              "      <td>21006.0</td>\n",
              "      <td>19.390650</td>\n",
              "      <td>94.738369</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>4043.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YEAR</th>\n",
              "      <td>21006.0</td>\n",
              "      <td>1986.193897</td>\n",
              "      <td>19.177425</td>\n",
              "      <td>1935.0</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>1990.0</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>2013.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               count         mean        std  ...     50%     75%     max\n",
              "APPEARANCES  21006.0    19.390650  94.738369  ...     4.0    11.0  4043.0\n",
              "YEAR         21006.0  1986.193897  19.177425  ...  1990.0  2001.0  2013.0\n",
              "\n",
              "[2 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 246
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mb-L4CUFQ0IE"
      },
      "source": [
        "## Cek NAN Values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z37TUYlhQ1yQ",
        "outputId": "174c0c2d-50bb-4d14-ac09-08e5b26fd72e"
      },
      "source": [
        "superhero.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "page_id                 0\n",
              "name                    0\n",
              "urlslug                 0\n",
              "ID                   5783\n",
              "ALIGN                3413\n",
              "EYE                 13395\n",
              "HAIR                 6538\n",
              "SEX                   979\n",
              "GSM                 23118\n",
              "ALIVE                   6\n",
              "APPEARANCES          1451\n",
              "FIRST APPEARANCE      884\n",
              "YEAR                  884\n",
              "origins                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "id": "aJXloibrSiMg",
        "outputId": "ba6f2e3f-7a25-411b-e3c8-fc472cba687b"
      },
      "source": [
        "# Visualisasi NAN values\n",
        "import missingno as msno\n",
        "\n",
        "fig = msno.matrix(superhero, color=(1, 0.43, 0.43))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xlZUnkjIep2w"
      },
      "source": [
        "# 4.&nbsp;*Data Preprocessing* 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F2hYqRLje9bG"
      },
      "source": [
        "## Handling Nan Values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fc6tcPSafJDS"
      },
      "source": [
        "### Drop Columns\n",
        "\n",
        "sekaligus dilakukan pada kolom2 lain yang tidak diperlukan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3otUBRXaeviJ",
        "outputId": "3e799f0b-affa-4082-bace-05efed12db4d"
      },
      "source": [
        "superhero = superhero.drop(['page_id', 'urlslug', 'GSM'], axis=1)\n",
        "superhero.isna().sum()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name                    0\n",
              "ID                   5783\n",
              "ALIGN                3413\n",
              "EYE                 13395\n",
              "HAIR                 6538\n",
              "SEX                   979\n",
              "ALIVE                   6\n",
              "APPEARANCES          1451\n",
              "FIRST APPEARANCE      884\n",
              "YEAR                  884\n",
              "origins                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cj08DHi5f_xk"
      },
      "source": [
        "### *Drop rows* berdasarkan *columns* dengan Nan Values\n",
        "\n",
        "jika tidak memungkinkan di*impute* nilai baru"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uCfyuSHcgaKX",
        "outputId": "a26491d2-39c6-47b1-ee32-85009a3a13e6"
      },
      "source": [
        "superhero = superhero[superhero.APPEARANCES.notna()]\n",
        "superhero = superhero[superhero['FIRST APPEARANCE'].notna()]\n",
        "superhero = superhero[superhero.YEAR.notna()]\n",
        "superhero = superhero[superhero.ALIVE.notna()]\n",
        "superhero = superhero.reset_index(drop=True)\n",
        "superhero.isna().sum()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name                    0\n",
              "ID                   5152\n",
              "ALIGN                3017\n",
              "EYE                 12124\n",
              "HAIR                 5657\n",
              "SEX                   824\n",
              "ALIVE                   0\n",
              "APPEARANCES             0\n",
              "FIRST APPEARANCE        0\n",
              "YEAR                    0\n",
              "origins                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "69BMIzhhh-Ba"
      },
      "source": [
        "### *Impute* dengan kategori 'others'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "maqCEkaIhOwh",
        "outputId": "833b618d-2001-488f-a31d-646ad6e4de1c"
      },
      "source": [
        "superhero = superhero.fillna(value='others')\n",
        "superhero.isna().sum()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name                0\n",
              "ID                  0\n",
              "ALIGN               0\n",
              "EYE                 0\n",
              "HAIR                0\n",
              "SEX                 0\n",
              "ALIVE               0\n",
              "APPEARANCES         0\n",
              "FIRST APPEARANCE    0\n",
              "YEAR                0\n",
              "origins             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kO3YE3aHxoZg"
      },
      "source": [
        "## Buat fitur *MONTH*\n",
        "bulan pertama kali kemunculan tokoh"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "94tLdXPfxqkY"
      },
      "source": [
        "superhero.loc[:, 'MONTH'] = superhero.loc[:, 'FIRST APPEARANCE'].apply(\n",
        "    lambda x: (x.split('-')[0]).split(' ')[0]\\\n",
        "    if len((x.split('-')[0]).split(' ')[0]) <= 4 else\\\n",
        "    (x.split('-')[0]).split(' ')[1]\n",
        "    )"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MwDNNUhzzgwO"
      },
      "source": [
        "## Perbaiki format fitur *MONTH*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mnnC06gqyCcm",
        "outputId": "df043794-6926-4f32-fd59-276655cdeba3"
      },
      "source": [
        "superhero.loc[:, 'MONTH'] = superhero.MONTH.replace({\n",
        "    'Aug': 'August',\n",
        "    'Dec': 'December',\n",
        "    'Oct': 'October',\n",
        "    'Nov': 'November',\n",
        "    'Sep': 'September',\n",
        "    'Mar': 'March',\n",
        "    'Jan': 'January',\n",
        "    'Jun': 'June',\n",
        "    'Jul': 'July',\n",
        "    'Feb': 'February',\n",
        "    'Apr': 'April'\n",
        "})\n",
        "\n",
        "superhero.MONTH.value_counts()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "August       1937\n",
              "December     1930\n",
              "October      1908\n",
              "September    1845\n",
              "June         1806\n",
              "March        1744\n",
              "November     1743\n",
              "January      1741\n",
              "July         1719\n",
              "May          1591\n",
              "February     1549\n",
              "April        1493\n",
              "1993           28\n",
              "1987           14\n",
              "1988           13\n",
              "1986           11\n",
              "1989           11\n",
              "1992           10\n",
              "1995            9\n",
              "1991            8\n",
              "1999            8\n",
              "1997            5\n",
              "1990            5\n",
              "1996            3\n",
              "1984            3\n",
              "Holiday         2\n",
              "1998            2\n",
              "1981            1\n",
              "2000            1\n",
              "1985            1\n",
              "1982            1\n",
              "1940            1\n",
              "1983            1\n",
              "Name: MONTH, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tMrXpbltzwRT"
      },
      "source": [
        "## Hapus value pada fitur *MONTH* jika freq < 1000"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TjVoPKuQzYHY",
        "outputId": "d514fef2-e514-4bb0-9657-740b50e96ece"
      },
      "source": [
        "v = superhero.loc[:, ['MONTH']]\n",
        "superhero = superhero[v.replace(v.apply(pd.Series.value_counts)).gt(1000).all(1)]\n",
        "superhero = superhero.reset_index(drop=True)\n",
        "superhero.MONTH.value_counts()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "August       1937\n",
              "December     1930\n",
              "October      1908\n",
              "September    1845\n",
              "June         1806\n",
              "March        1744\n",
              "November     1743\n",
              "January      1741\n",
              "July         1719\n",
              "May          1591\n",
              "February     1549\n",
              "April        1493\n",
              "Name: MONTH, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0oWSLzOa4e_4"
      },
      "source": [
        "## Ganti penamaan kategori pada fitur lain"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6bYg_00h4jmQ"
      },
      "source": [
        "# ID\n",
        "superhero.loc[:, 'ID'] = superhero.ID.replace({\n",
        "    'others': 'Identity Unknown',\n",
        "    'Known to Authorities Identity': 'Secret Identity'\n",
        "    })\n",
        "\n",
        "# ALIGN\n",
        "superhero.loc[:, 'ALIGN'] = superhero.ALIGN.replace({\n",
        "    'Reformed Criminals': 'others'})\n",
        "\n",
        "# SEX\n",
        "sx = superhero.SEX.value_counts().to_dict()\n",
        "superhero.loc[:, 'SEX'] = superhero.SEX.apply(lambda x : x  if sx[x] > 50 else 'others' )\n",
        "\n",
        "# EYE\n",
        "yy = superhero.EYE.value_counts().to_dict()\n",
        "superhero.loc[:, 'EYE'] = superhero.EYE.apply(lambda x : x  if yy[x] > 100 else 'rare' )\n",
        "\n",
        "# HAIR\n",
        "superhero.loc[:, 'HAIR'] = superhero.HAIR.replace({\n",
        "    'Bald': 'No Hair'\n",
        "    })\n",
        "hr = superhero.HAIR.value_counts().to_dict()\n",
        "superhero.loc[:, 'HAIR'] = superhero.HAIR.apply(lambda x : x  if hr[x] > 100 else 'rare' )"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BbxfWFTCTheq"
      },
      "source": [
        "# 5.&nbsp;Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVOytdXUTmGI"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set_style('darkgrid')\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "import plotly.figure_factory as ff\n",
        "\n",
        "def pie_chart(df, title:str, values:str, names:list, hole:float, colors:str, labels:dict):\n",
        "  \"\"\"helper function to create pie chart\n",
        "  * df: the dataframe contain data\n",
        "  * title: the title for the chart\n",
        "  * values: values to be visualize\n",
        "  * names: the category names\n",
        "  * hole: if prefer to have a doughnut model\n",
        "  * colors: custom color for every category\n",
        "  * labels: adjust the hover of the pie info\n",
        "  \"\"\"\n",
        "  fig = px.pie(\n",
        "      data_frame=df,\n",
        "      title=title,\n",
        "      values=values,\n",
        "      names=names,\n",
        "      hole=hole,\n",
        "      color_discrete_sequence=colors,\n",
        "      labels=labels)\n",
        "\n",
        "  fig.show()\n",
        "\n",
        "def plot_dist(df, column:str, title:str, cat_type:str='default', color:str='origins'):\n",
        "  '''use to plot distribution interactively but very high computation'''\n",
        "  if cat_type=='default':\n",
        "    colors=['#ffff00', '#fcd37f', '#ffaa00', '#e60000', '#730000']\n",
        "  else:\n",
        "    colors=['#9FD8DF', '#FF7171']\n",
        "  fig = px.histogram(\n",
        "      data_frame=df,\n",
        "      title=title,\n",
        "      x=column,\n",
        "      color=color,\n",
        "      template='plotly_white',\n",
        "      marginal='box',\n",
        "      nbins=200,\n",
        "      barmode='relative',\n",
        "      histfunc='count')\n",
        "\n",
        "  fig.show()"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ov3mXCTQVYxU"
      },
      "source": [
        "## Distribusi data Categorical"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n1i2DDp9WTKj",
        "outputId": "100a25e5-306d-443e-88e5-2952ef5d554b"
      },
      "source": [
        "superhero.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['name', 'ID', 'ALIGN', 'EYE', 'HAIR', 'SEX', 'ALIVE', 'APPEARANCES',\n",
              "       'FIRST APPEARANCE', 'YEAR', 'origins', 'MONTH'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jMcCVK0PVeBd",
        "outputId": "b63b3985-91e7-4e77-bdca-11c4bf92b6a4"
      },
      "source": [
        "col_cat = superhero.drop(['name', 'APPEARANCES', 'FIRST APPEARANCE', 'YEAR'], axis=1).columns\n",
        "\n",
        "for col in col_cat:\n",
        "  col_df = pd.DataFrame(superhero.loc[:, col].value_counts())\n",
        "\n",
        "  pie_chart(col_df, \n",
        "            f'Distribusi kelas pada kolom {col}', \n",
        "            col, \n",
        "            col_df.index, \n",
        "            .5, \n",
        "            None,\n",
        "            {'index':col, col:'Jumlah Data'})"
      ],
      "execution_count": null,
      "outputs": [
        {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "esq01MrEbxKl",
        "outputId": "3c9adfb7-d712-49fc-abf1-5578c056db94"
      },
      "source": [
        "superhero.loc[:, 'YEAR'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1993.0    698\n",
              "1994.0    661\n",
              "2006.0    643\n",
              "1992.0    593\n",
              "2010.0    582\n",
              "         ... \n",
              "1958.0     17\n",
              "1938.0     10\n",
              "1936.0      8\n",
              "1937.0      4\n",
              "1935.0      1\n",
              "Name: YEAR, Length: 79, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-e4XnN31_0h"
      },
      "source": [
        "## Distribusi data Continuous"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "wgNkN769ZCgY",
        "outputId": "25c72e60-fe3c-4d86-a1aa-1f8cfd693770"
      },
      "source": [
        "plot_dist(superhero, 'APPEARANCES', 'Distribusi data fitur APPEARANCES')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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data fitur APPEARANCES\"},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"APPEARANCES\"}},\"xaxis2\":{\"anchor\":\"y2\",\"domain\":[0.0,1.0],\"matches\":\"x\",\"showgrid\":true,\"showticklabels\":false},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,0.7326],\"title\":{\"text\":\"count\"}},\"yaxis2\":{\"anchor\":\"x2\",\"domain\":[0.7426,1.0],\"matches\":\"y2\",\"showgrid\":false,\"showline\":false,\"showticklabels\":false,\"ticks\":\"\"}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
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              "            observer.disconnect();\n",
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              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
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              "\n",
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              "</body>\n",
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fUw0j9n73G0J"
      },
      "source": [
        "## *Relationships*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "Qj7-48siGsdk",
        "outputId": "94f473c8-7402-4dfa-d954-58c5bc8ccef5"
      },
      "source": [
        "sns.catplot(x='ID', y='APPEARANCES', hue='origins', aspect=1.7,\n",
        "            col='ALIVE', kind='strip', data=superhero);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IeDEiRpefYWV9RMSvI2JCRIyJiA9U3Ou4iHiu/BxXrZglSZIkSZKketSjiveeC+ydmbMioidwX0TcVJ47PTP/tkT7g4BNy88uwO+AXSJiTeAHwHAggcciYkRmzqhi7JIkSZIkSVLdqNpIwizMKos9y0+2cMmhwCXldQ8Bq0fEIOAA4NbMnF4mBm8FDqxW3JIkSZIkSVK9qeqahBHRPSJGA1MpEn0Pl6d+Uk4pPjciepd1g4FXKi6fVNY1Vy9JkiRJkiSpDVQ1SZiZCzNzGDAE2DkitgHOALYAdgLWBL7dFs+KiJMiYmREjJw2bVpb3FKSJKnTsm8kSZKk1miX3Y0z8y3gTuDAzJxSTimeC/wZ2LlsNhkYWnHZkLKuufoln3F+Zg7PzOEDBw6sxmtIkiR1GvaNJEmS1BrV3N14YESsXh6vAuwHPF2uM0hEBHAYMLa8ZATw2XKX412BmZk5BbgF2D8i1oiINYD9yzpJkiRJkiRJbaCauxsPAi6OiO4UycirM/P6iLgjIgYCAYwGvli2vxE4GJgAvAN8DiAzp0fEj4BHy3Y/zMzpVYxbkiRJkiRJqitVSxJm5hhghybq926mfQKnNHPuQuDCNg1QkiRJkiRJEtBOaxJKkiRJkiRJ6riqOd1YUkc0+w144Dfw1kuw1aGw9eG1jkiSJEmSJNWYSUKpnmTCpYfDa2OK8rjrYP67MOzY2sYlSZIkSZJqyunGUj15fdziBGGDJ66oTSySJEmSJKnDMEko1ZO+a0F0b1y36rq1iUWSJEmSJHUYJgmlerLaIPjQNxaX+60De5xeu3gkSZIkSVKH4JqEUr3Z5/uw/THw1kRYfzfo1bfWEUmSJEmSpBozSSjVo7U3KT6SJEmSJEk43ViSJEmSJEmqeyYJJUmSJEmSpDpnklCSJEmSJEmqcyYJJUmSJEmSpDpnklCSJEmSJEmqcyYJJUmSJEmSpDpnklCSJEmSJEmqcyYJJUmSJEmSpDpnklCSJEmSJEmqcyYJJUmSJEmSpDpnkrALmj13AQsWLqp1GJIkSZIkSeoketQ6ALWdWXMXcOqVo7n96ddZs28v/utjW3HosMG1DkuSJEmSJEkdnCMJu5Df3jmB2556nUx4c/Y8Tv/bGKbPnlfrsCRJkiRJktTBmSTsQsa++naj8rwFi3ju9X/XKBp1WW8+Dxd/DM4aClccC7Om1joiSZIkSZK0kkwSdiG7b7xWo/JqfXqw7ZABNYpGXdY1J8CL98Dct+GZG+CGb9Y6IkmSJEmStJJck7ALOeFDGzJ99jz+MfpVBq3eh+8evCV9e/mfWG1o3jvw6qjGdRPvr00skiRJkiSpzVQtgxQRfYB7gN7lc/6WmT+IiA2BK4G1gMeAz2TmvIjoDVwC7Ai8CXwyMyeW9zoDOAFYCHwtM2+pVtydWY/u3Tjj4C054+Atax2KuqpefWGdrWDq+MV1g3esXTySJEmSJKlNVHO68Vxg78zcHhgGHBgRuwI/Bc7NzE2AGRTJP8qfM8r6c8t2RMRWwNHA1sCBwG8jonsV45bUko+fD+tuUxyv/0E45Be1jUeSJEmSJK20qo0kzMwEZpXFnuUngb2BY8v6i4Ezgd8Bh5bHAH8DzouIKOuvzMy5wIsRMQHYGXiwWrFLasF628KX7oeF86F7z1pHI0mSJEmS2kBVNy6JiO4RMRqYCtwKPA+8lZkLyiaTgMHl8WDgFYDy/EyKKcnv1TdxjaTWmPcOPPVPmHgfZK7cvUwQSpIkSZLUZVR1V4vMXAgMi4jVgeuALar1rIg4CTgJYP3116/WY6TO6+1X4U/7wduTivJmB8KxV9U2JklS1dg3kiRJUmtUdSRhg8x8C7gT+CCwekQ0JCeHAJPL48nAUIDy/ACKDUzeq2/imspnnJ+ZwzNz+MCBA6vyHlKn9sj5ixOEAM/eDC89ULt4JElVZd9IkiRJrVG1JGFEDCxHEBIRqwD7AU9RJAuPLJsdB/yjPB5RlinP31GuazgCODoiepc7I28KPFKtuKUu6923lq9OkiRJkiTVnWpONx4EXFzuRNwNuDozr4+I8cCVEfFjYBRwQdn+AuDScmOS6RQ7GpOZ4yLiamA8sAA4pZzGLKk1dvg0jLoUFpVLgg5YHzbeu7YxSZIkSZKkDiFyZTcv6ICGDx+eI0eOrHUYUscz6TEYfRmssgbs/AXov16tI5IktU6syEX2jSRJUhe0Qv0iNa+qG5dI6mCG7Fh8JEmSJEmSKrTLxiWSJEmSJEmSOi6ThJIkSZIkSVKdM0koSZIkSZIk1TmThJIkSZIkSVKdM0koSZIkSZIk1TmThJIkSZIkSVKdM0koSZIkSZIk1TmThJIkSZIkSVKdM0koSZIkSZIk1TmThJIkSZIkSVKdM0koSZIkSZIk1TmThJIkSZIkSVKdM0koSZIkSZIk1TmThJIkSZIkSVKdM0koSZIkSZIk1TmThJIkSZIkSVKdM0koSZIkSZIk1TmThJIkSZIkSVKdM0koSZIkSZIk1TmThJIkSZIkSVKdM0koSZIkSZIk1TmThJIkSZIkSVKdM0koSZIkSZIk1bmqJQkjYmhE3BkR4yNiXER8vaw/MyImR8To8nNwxTVnRMSEiHgmIg6oqD+wrJsQEd+pVsySJEmSJElSPepRxXsvAL6VmY9HRH/gsYi4tTx3bmaeU9k4IrYCjga2Bt4H3BYRm5Wn/w/YD5gEPBoRIzJzfBVj79JeenM2f75/IrPmLuDonYYyfIM1ax2SJEmSJEmSaqjZJGFE7AS8kpmvleXPAkcALwFnZub0lm6cmVOAKeXxvyPiKWBwC5ccClyZmXOBFyNiArBzeW5CZr5QxnFl2dYk4Qp4e858Pv7bB3hz9jwA/j5qMtd+eTe2G7J6jSOTJEmSJElSrbQ03fgPwDyAiNgDOBu4BJgJnN+ah0TEBsAOwMNl1VciYkxEXBgRa5R1g4FXKi6bVNY1V68VcOfTU99LEAIsWJRcN2pyDSOSJEmSJElSrbWUJOxeMVrwk8D5mXlNZn4f2GR5HxARqwLXAKdm5tvA74CNgWEUIw1/vkKRL/2ckyJiZESMnDZtWlvcsktaq1/vperWXnXpOkmS1LnZN5IkSVJrtJgkjIiG6cj7AHdUnFuutQwjoidFgvCyzLwWIDNfz8yFmbkI+COLpxRPBoZWXD6krGuuvpHMPD8zh2fm8IEDBy5PeHVp903WYt8t132vvMk6q3LszuvXMCJJklQN9o0kSZLUGi0l+64A7o6IN4B3gXsBImITiinHLYqIAC4AnsrMX1TUDyrXKwQ4HBhbHo8ALo+IX1BsXLIp8AgQwKYRsSFFcvBo4NjlfkM1EhH86bjhjHp5BrPnLmTXjdakR/eqbXItSZIkSZKkTqDZJGFm/iQibgcGAf/KzCxPdQO+uhz33h34DPBkRIwu674LHBMRw4AEJgInl88bFxFXU2xIsgA4JTMXAkTEV4BbgO7AhZk5rlVvqaXssP4ay24kSZIkSZKkutDS7sZ7Z+Yd5fGGwIsAmflsRHwceLylG2fmfRSjAJd0YwvX/AT4SRP1N7Z0nSRJkiRJkqQV19I803Mqjq9Z4tz/q0IskiRJkiRJkmqgpSRhNHPcVFmSJEmSJElSJ9VSkjCbOW6qLEmSJEmSJKmTaml3440iYgTFqMGGY8ryhlWPTJIkSZIkSVK7aClJeGjF8TlLnFuyLEmSJEmSJKmTailJOB4YmJnjKysjYitgWlWjkiRJkiRJktRuWlqT8DfA2k3UrwX8qjrhSJIkSZIkSWpvLSUJN8nMe5aszMx7ge2qF5IkSZIkSZKk9tRSkrB/C+d6tnUgkjqpsdfCJYfBXz8Hr42tdTSSOoMX74Xf7Q5nDYURX4X5c2odkSRJklT3WlqTcEJEHJyZN1ZWRsRBwAvVDUtSp/DcrfC3zy0uP38HnDoG+gyoXUySOrZ5s+GqT8GcmUX58Uug/yD4yHdrG5ckSZJU51pKEp4K3BARnwAeK+uGAx8EDql2YJI6gfF/b1ye8xa8cDds9R+1iUdSx/f6+MUJwgYvPVCbWCRJkiS9p9npxpn5HLAtcDewQfm5G9guM59tj+AkdXCrv3/pujWaqJOkButsAb2WWNFk8I61iUWSJEnSe1oaSUhmzgX+3E6xSOpsdj4Jnr0FJo+E6AY7nwyDtq91VJI6st794cgL4cbTYOYk2OpQ2OP0WkclSZIk1b1mk4QR8SKQzZzOzNy4OiFJ6jRWWR2+cHsxfbDPajBgSK0jktQZbLY/bDYGFi2Cbi3toSZJkiSpvbQ0knD4EuVuwCeA04BRVYtIUuez7la1jkBSZ2SCUJIkSeowWlqT8M3MfBOYQbFRyZ0Um5Z8NDOPaKf4JEmSJEmSpE4jIm6MiNWX0eaHEbFve8W0PFqabtwT+DzwDeA+4LDMnNBegUmSJEmSJEmdRUQEEJl58LLaZuZ/tUNIrdLSPJ8XgTOA3wM3AttFxMcbPu0SnSRJkiRJktRBRMQ3I2Js+Tk1IjaIiGci4hJgLDA0IiZGxNpl+++X5++LiCsi4rSy/qKIOLI8nhgR/x0Rj0fEkxGxRVm/Z0SMLj+jIqJ/Nd+tpTUJb6PYuGT78lMpgWurFZQkSZIkSZLUkUTEjsDngF2AAB4G7gY2BY7LzIfKdg3tdwKOoMir9QQeBx5r5vZvZOYHIuLLFPuBnFj+PCUz74+IVYE5VXo1oIUkYWYe39y5iFi3KtFIkiRJkiRJHdOHgOsyczZARFwLfBh4qSFBuITdgX9k5hxgTkT8s4V7NwzGewxomMF7P/CLiLgMuDYzJ7XFSzRnubcVjIjVI+KEiLgddzeWurZXHoVrT4Z/fAVeH1/raCRJkiRJ6shmt8E95pY/F1IO6svMsylGFK4C3N8wDblaWkwSRsQqEXF0RIwAngR+DvwIGFLNoCTV0NSn4KKDYcyVMOpSuGB/+PdrtY5KkiRJkqRauxc4LCL6RkQ/4PCyrjn3Ax+LiD7ldOFDWvOwiNg4M5/MzJ8CjwK1SRJGxOXAs8B+wG+ADYAZmXlXZi6qZlCSamjstbBw3uLyvH/D09fXLh5JkiRJkjqAzHwcuAh4hGI9wj8BM1po/ygwAhgD3EQxAG9mKx55arlByhhgfnmPqmlp45KtKF70KeCpzFwYEVnNYCRVUSbMmwW9l7EZ0qrrNFHnMqSSJEmSJGXmL4BfLFG9zRJtNqgonpOZZ0ZEX+Aeyo1LKvcCqWyfmSOBvcrjr7Zd5MvW7EjCzBwGfALoD9wWEfcB/Zd305KIGBoRd0bE+IgYFxFfL+vXjIhbI+K58ucaZX1ExK8jYkJEjImID1Tc67iy/XMRcdzKvLBUlybeB7/aDs4aAhccAG+/2nzbYcfC4B0XlzfeBzY7qPoxSpIkSZLU9ZwfEaMpdja+phyN2CFF5vINDiy3eT4WOAqYlJm7LaP9IGBQZj4eEf0pMqWHAccD0zPz7Ij4DrBGZn47Ig4GvgocTLGV9K8yc5eIWBMYCQwHsrzPjpnZ7HDO4cOH58iRI5frvaQub+EC+OW28O+KxODWh8NRFzV/TSa8/BD06NU4YShJqrVYkYvsG0mSpC5ohfpFat5y726cmY9l5reAjYGbl6P9lIbsaGb+m2La8mDgUODistnFFIlDyvpLsvAQsHqZaDwAuDUzp5eJwVuBA5c3bqnuzZ7aOEEIMOWJlq+JgPd/0AShJEmSJEl1oqWNS1aLiDMi4ryI2L+cDvwVis1MWpU5iIgNgB0oFnVcNzOnlKdeAxqmLw8GXqm4bFJZ11y9pOXRfxCsvVnjug33rE0skiRJkiSpQ2ppJOGlwOYUO6+cCNxJMdX48Mw8dHkfUG7xfA1wama+XXkui7nObbIZSkScFBEjI2LktGnT2uKWUtcQAZ/8S5EY7LcODPsU7PfDlbvnzElw6eHw4/XgokNg+ottE6skqc3YN5IkSVJrtLS78UaZuS1ARPwJmAKsn5lzlvfmEdGTIkF4WWZeW1a/HhGDMnNKOZ14alk/GRhacfmQsm4y5a4uFfV3LfmszDwfOB+KdXeWN0apLgzcHI4b0Xb3G/FVeP6O4njivXDhgbDn6bDDZ6BH77Z7jiRphdk3kiRJUmu0NJJwfsNBZi6k2DRWXmMAACAASURBVKykNQnCAC4Aniq3h24wAmjYofg44B8V9Z8tpzXvCswspyXfAuwfEWuUOyHvX9ZJqpWXHmxcnvUa3PAt+NvnaxOPJEmSJEkdVEQsjIjRETEuIp6IiG9FRLeK8ztHxD0R8UxEjIqIP0VE3/aOs6WRhNtHRMP04ABWKctBMVN4tWXce3fgM8CT5VbPAN8FzgaujogTgJeAT5TnbqTY2XgC8A7wOYoHTY+IHwGPlu1+mJnTl/cFJVXBkOHFCMIlPX09vD0FVhvU/jFJkiRJkrQSNvjODd2AY4BTKWa7vgL8Erhi4tkfXbQSt343M4cBRMQ6wOXAasAPImJd4K/A0Zn5YNnmSKA/RX6s3USxLGDXMnz48Bw5cmStw5C6rukvwt+/BC8vMaKwWw847Tnou2Zt4pKkri9W5CL7RpIkqQtaoX5Rc8oE4TXAfkC/ilOzgX8BR65oojAiZmXmqhXljSgGw60N/DdAZv7XCobeZlqabryUiOgXEZ+OiBuqFZCkTmDNDeHzN8Nn/wHdK9Yg3PXLJgglSZIkSZ3RMSydIKQs7w8c3VYPyswXgO7AOsA2wGNtde+V0dJ0YwAiohfwUeBY4ACKrOrvqxyXpM5go73ga6OKTUwGbg5Dd651RJIkSZIkrYhTWTpB2KAf8A2KacJdVrNJwojYnyKLuj9wJ3AJsFNmfq6dYpPUGQwYDB/4TK2jkCRJkiRpZQxdyfPLrZxuvBCYCowDdmTxxr4109J045uBjYAPZeanM/OfwMos0iipq3n7VRh1Gbzy6LLbSpIkSZLUcb2ykueXS0QMpJihe14WG4WcBxwXEbtUtPl4uaFJu2ppuvEHKOZb3xYRLwBXUsyXliR48V647EhYMKco7/Y12P9HtY1JkiRJkqQV80vgDzQ95Xg2cO5K3HuViBgN9AQWAJcCvwDIzNcj4mjgnHLn40XAPRSD99pVs0nCzBwNjAa+ExG7UUw97hkRNwHXZeb57RSjpI7onp8tThACPPRb+NA33LhEkiRJktQZXQEcSfO7G1+5ojfOzBYH3WXmg8CHV/T+bWW5djfOzAcy86vAEIrM6S7LuERSVzdvduPyogWNk4aSJEmSJHUSE8/+6CLgCOAkYCTwevnzJODI8nyX1tLGJZ/OzL+Ux7tn5v2ZuQj4V0Rs1m4RSuqYdjoBJo9cXN78o7Da+2oXjyRJkiRJK6FMBF5OF9/FuDktrUn4TeAv5fFvKNYobPB5ioUVJdWrYcfCquvCszfD2pvBDu5wLEmSJElSZ9VSkjCaOW6qLKkebbJP8ZEkSZIkSZ1aS2sSZjPHTZUlSZIkSZIkdVItjSTcIiLGUIwa3Lg8pixvVPXIJFXf6+PgH6fAlCdgwz3hsN/BaoNqHZUkSZIkSWpnLSUJt2y3KCTVxt9OgGlPFccv3Ak3fAuOqcv1WSVJkiRJqoqISOCyzPx0We4BTAEezsxDqvjcDYDrM3Ob5WnfbJIwM1+KiMOATYAnM/OWNolQUscw5+3FCcIGkx6pTSySJEmSJNXamQO6AccApwJDgVeAXwJXcObMRStx59nANhGxSma+C+wHTG7NDSKiR2YuWIkYlqnZNQkj4rfAN4C1gB9FxPerGYikdtZnNVhn68Z16+9am1gkSZIkSaqlIkF4DfAHYDiwbvnzD8DfyvMr40bgo+XxMcAVDSciYueIeDAiRkXEAxGxeVl/fESMiIg7gNsj4sqI+GjFdRdFxJER0T0ifhYRj0bEmIg4eUUCbOkF9wD2zswzgL2Aw1bkAZI6sCMvhCE7Q/desOn+cPDPax2RJEmSJEm1cAzFCL9+S9T3A/YHjl7J+18JHB0RfYDtgIcrzj0NfDgzdwD+C/ifinMfAI7MzD2Bq4BPAEREL2Af4AbgBGBmZu4E7AR8ISI2bG2ALa1JOC8zFwJk5jsREa29uaQObp0t4MRbax2FJEmSJEm1dipLJwgb9KOYbbvCi/hn5phyjcBjKEYVVhoAXBwRmwIJ9Kw4d2tmTi+PbwJ+FRG9gQOBezLz3YjYH9guIo6suN+mwLOtiXF5djeGxjscR/FuuV1rHiRJkiRJkiR1UENX8vzyGAGcQzFjd62K+h8Bd2bm4WUi8a6Kc7MbDjJzTkTcBRwAfJJidCIUubqvLrmfSHmv5ebuxpIkSZIkSap3r1CsQ9jS+ZV1IfBWZj4ZEXtV1A9g8UYmxy/jHlcBJ1Ksl9jQ9hbgSxFxR2bOj4jNaOXGKNDCmoSZ+RKwA3AUsEVmvlT5ae2DJEmSJEmSpA7ql1SM2lvCbODclX1AZk7KzF83cep/gbMiYhQtD+gD+BewJ3BbZs4r6/4EjAcej4ixFJutLOs+S4nMbPpEsbvx1sADFAsh/jMzf9TaB9TC8OHDc+TIkbUOo6bmzF/IuFffZqO1+7FGv161DkeSJLWNFVoj2r6RJEnqgtp274zFuxsvuXnJbIrE3JGcOXNRmz6zg2kpq7gHsH1mLoyIvsC9FHOk1cGNnTyT4//8CG/MmkevHt046/BtOWLHIbUOS5IkSZIkqWM6c+YizhxwBMUuxt+gWIPwFYoRhFd29QQhuLtxl3TWTU/xxqxixOm8BYv44fXjOWT7QfTu0b3GkUmSJEmSJHVQRSLwclZiF+PObEV3N16UmdtXPTqtkFffmtOoPPPd+cyas4Deq5oklCRJkiRJ0tKa3biEYnfjj5WfQ8ryfwBfZjl2SImICyNiarlgYkPdmRExOSJGl5+DK86dERETIuKZiDigov7Asm5CRHyn9a9Yfw7ZblCj8m4br8Vaq/auUTSqG+9Mh5u/C5ceDg/9HhZ1+ZHYkiRJkiR1Gc2OJKzcwTgidgCOpdjp+EWKhRyX5SLgPOCSJerPzcxzKisiYiuKOd9bA+8Dbiu3awb4P4pFIycBj0bEiMwcvxzPr1un7rsZ/fv04O5np7HFeqvx1b03qXVIqgdXfgpefqA4fv4OmPdv2OP02sYkSZIkSZKWS7NJwjJJd0z5eQO4imI35I8sz40z856I2GA54zgUuDIz5wIvRsQEYOfy3ITMfKGM6cqyrUnCFnTvFpy0x8actMfGtQ5F9eLtVxcnCBs8eY1JQkmSJEmSOomWphs/DewNHJKZH8rM3wAL2+CZX4mIMeV05DXKusEUO8Y0mFTWNVcvqSPpszr06t+4boA7akuSJEmS1Fm0lCT8ODAFuDMi/hgR+1BsWrIyfgdsDAwr7/3zlbzfeyLipIgYGREjp02b1la3lbQ8evWFA34M3XoW5X7rwD7/VduYJKnO2TeSJElSa7S0JuHfgb9HRD+KKb6nAutExO+A6zLzX619WGa+3nAcEX8Eri+Lk4GhFU2HsHhzlObql7z3+cD5AMOHD8/WxiZpJe14PGz+UZj+PAwaBj371DoiSapr9o0kSZLUGi2NJAQgM2dn5uWZ+TGKJN0o4Nsr8rCIqNx293CgYefjEcDREdE7IjYENgUeAR4FNo2IDSOiF8XmJiNW5NmS2sGqA2H9XU0QSpIkSZLUyTQ7krApmTmD4hvp85fVNiKuAPYC1o6IScAPgL0iYhiQwETg5PK+4yLiaooNSRYAp2TmwvI+XwFuAboDF2bmuNbELEmSJEmSJKllrUoStkZmHtNE9QUttP8J8JMm6m8EbmzD0CS1pXdnwPXfhAm3w7pbw0d/DutuVeuoJEmSJElSKyxzurEkteiW78G4a2HuTHj5Abj6s5AufSVJkiRJUmdiklDSypl4X+Pym8/BrKm1iUWSJEmSJK0Qk4SSVtyUMbDWJo3rBqwP/QbWJh5JkiRJkrRCqrYmoaQubMFcuPyT8MKdRXmVNYq1CdfeHA49D7r5/YMkSZIkSZ2JSUJJLZv6FPReDQYMZs78hfz69ufoMfZqvjnrzsVt3p0Bh58P23+ydnFKqj+ZEFHrKCRJkqQuwSShpKbNmQl/OQImPQrRDXb5EmfNPZaLH3yJL3d/BXou0X7W6zUJU1IdencG/P0UePYmWHNj+NgvYYMP1ToqSZIkqVNzTqCkpj18fpEgBMhF8ND/MXHsgwDcvGhn5mbFdww9+sCWh9QgSEl16fYfwTM3FH83vfkc/PV4WDCv1lFJkiRJnZpJwnr2+nh4+A/w8kO1jkQd0YyJS1UNW/UtAF7I93HsvO9xa+zGoq2PgOOuhzU3aucAJXUJMybCvb+Axy6Gee8s3zWTHmlcnj2tyb+zJEmSJC0/pxvXqzF/hWu/AGRR/sj/gz1Pr2lI6mC2OhRG/2Vxuc/qHHToMfz1imd4deYcnu29NXHUp+i21bq1i1FS5/baWLhgf5g/uyg/dhGcePuyNz9afzd47cnF5VXXgzU3rFqYkiRJUj0wSdjFjJw4nevHTGHw6qtwzC7rs2rvZv4T3/Mz3ksQAtx3Luz+dejRq13iVAfyxFXwykOwzlaww2egZ5+ifrP94YgLYNSl0Gd12OM0tlhvCDd9fR0efOFNdt94bfqvsuTChJLUCiMvWJwgBHj1cZh4L2y0Z8vX7fN9eHc6PH0jrL0JHPxz6O7fR5I6iFdHw79fgw33gF59ax2NJEnLzSRhF3LnM1P5/EWPkmXu76axU7j2y7s33Xjh3MblRQuKtZ1UP2ZOhgsPgJmvLK574S44+rLF5W2PLD6lm56cwml/fYLZ8xYyaEAfLjhuJ7Z632rtF7OkLqaJnYljOVZC6d0fjvhT24cjSStrxNfg8YuL41XXg8/f5JIskqROwzUJu5DLHnr5vQQhwOMvv8XYyTObbrzrlxuXh39u8Qgy1Yfbf9g4QQjw9PXFN99NmL9wEf/v72OZPW8hAFNmzuF/bnyq2lFK6sp2/gL06r+4PHQXeH8zX25JUkc37ZnFCUKAWa/B/b+uXTySJLWSIwm7kL69ui9V16+56ca7nAxrb1qMHBu0PWz98eoGp45n2tNL10UP6N70lPNZcxbw5uzGu4dOfHN2k20labmssyWc8hCM/wf0XbtYC3VZ6xFKUkf1zpvLVydJUgdlkrALOXnPjbjz6an8e+4CAD7+gcFsuHa/5i/YeO/io/q06f4wZXTjug9+Gfqu2WTzNfr1YpcN1+ThF6e/V3fQNutVM0JJ9WDAEPjgKbWOQpJW3tBdYO3N4I1nF9ft8OnaxSNJUiuZJOxCtn7fAO46fS/uemYa71t9FT648VorfrN578Cjf4SpT8Om+8I2R7RdoOoY9vxPWDS/GMHTa1X48Ddh68Mbt5k3Gx6/tJiWvNVh/PZTH+Dntz7LU1PeZo9NB3LKRzapTeySJEkdTbfucPwN8NDviuVbtj0SNtmn1lFJkrTcIisXsesihg8fniNHjqx1GJ3bFcfCMzcsLh94Nuz6pdrFo9q48CB4+YHiOLrBsVfDpvvVNiZJqm9N7PaybPaNJElSF7RC/SI1z4V/tLTZbzROEAI8fkltYlHtvDZ2cYIQit2vH3U3UUmt9MLdcN5O8KOBcM2JMHdWrSOSJEmS1ASThFpajz7QY5XGdausUZtYVB2ZcPfPin+4X/wxmNTE6JKeqzRR17fZW07791xOvnQkw374L47/8yNMmvFOGwYsqVOa/y789bhifa6F8+DJv8LdP611VJJUXV1wppYkqT6YJKxDc+Yv5IlX3uLtOfObbtB7Vdjr24vLPfvCR77bPsGpfYy8AO78cfEP9xfvgb8csfTonrU2hu2OXlzutSrs/rVmb3nGtU9yy7jXeeud+dz1zDS+cdXoZttKqhNvPAvvzmhc98rDtYlFktrD7T+Es4bATzco1iaUJKkTceOSOjPq5RmcePFI3pw9j769uvPzo7bnoG0HLd3wQ9+AzQ+GqU/BBh+GfiuxCYo6nudua1ye8xZMHgkb7dW4/vDfw7Bj4K1Xit2Q+6/b7C0feuHNRuVHJ85gwcJF9OjudxFS3Vp7s2IkemWicOgutYtHkqrpqX/CvT9fXL75OzB0Zxi8Y+1ikiSpFfzXe535yQ1P8ebseQC8M28h3//HOBYuamZKxMDNYevDTBB2Retu1bjcrQesvfnS7SKYstYunDxuS3b9zVi+fuUoZpR/fpa07eABjcpbDlrNBKFU73quAkddXPz90r03bHsU7PntZV8nSZ3RK48sXdfUki6SJHVQ/gu+zkya8W6j8qxZbzNn3oIaRaOa2f3rsNFHiuNe/eGg/4XVmhhRCpx65WhuGfc6r709hwdGj+fCKy6H+XOWavc/H9+W7YYUicIt1uvPLz6xfdXCl9SJbLQnfOUR+P5UOOJPxZIWktQVvX+3pevW37X945AkaQVFdsGFdYcPH54jR/qtXVN++M/xXHj/iwzkLX7V8zx26z4eVl8f/uO84h9yqi+zphX/YG9qkxJg/sJFbPq9mwD4dPdb+UGPS+gZC6HfOvCZ62C9bZa6Zs78hfTp2b2qYUtSHYsVuci+kdRO7jkHHv59MXp6z9Nhx+NrHZEkdWUr1C9S81yTsAt68Pk3+f3dzzNvwSKO330DDth6vffOfeegLVi9b0+Gjfw2u707vqh862XymhP52VbXcf+Lb7HN4AGctv/mrNGvV43eQO1m1YEtnu7ZvRubrrMqk6e+wXd7XF4kCAFmT4U7fgTHXrXUNSYIJUlS3drjtOIjSVInVLXpxhFxYURMjYixFXVrRsStEfFc+XONsj4i4tcRMSEixkTEByquOa5s/1xEHFeteLuKV6a/w3F/foS7n53Ggy+8yRf/8hiPv7x4wfhePbrxtX02ZY8+zze6LmZPZcR9j/LEpJlc9vDLfO3KUe0dujqoc47anm3WWEjfmNv4xMzJtQlIkiRJkiS1uWquSXgRcOASdd8Bbs/MTYHbyzLAQcCm5eck4HdQJBWBHwC7ADsDP2hILKppdz0zlXkLFr1XzoRbx7/euNHD58OMiY2qJrEuk3Pt98r3PvcG77hWoYDth67OVf95FAsG79T4xLZH1CYgSV3PooXwr+/D/24M/7cLPHNTrSOSJEmS6k7VkoSZeQ8wfYnqQ4GLy+OLgcMq6i/JwkPA6hExCDgAuDUzp2fmDOBWlk48qsL71+q3VN0Ga/VtXHHXWY3L3Xtz7lo/ICv+OAwa0Ic+PZw2qkJE0OPYq2DXU2CTfeHgc2D3U2sdlqSu4rE/wwO/hnfegGlPw9XHwayptY5KkiRJqivtvbvxupk5pTx+DVi3PB4MvFLRblJZ11y9mvHhTdfm6J2GEuXynftuuS6H7VDxK8uEBUvsTNtnNY4//BDWXa03AKv16cFPDt+Gbt1cA1QV+q0FB/4PfPoa2PkLvPeHTJJW1sT7G5cXzoVJbrKh6vm/Oyewx//eyX+cdx93Pzut1uFIkiR1CDXbuCQzMyLabGvliDiJYqoy66+/flvdttOJCM4+YjtO3Xcz5i9cxNA1+y7ZAHY6sRixUbqt/6Fs0a8n9317b16YNpv11+zLKr0cRVivHp04nYsfmEjP7t044UMbss3gAbUOSVJXN3hHGHft4nJ0h0Hb1S6eLsK+UdOufXwSP7vlGQBeng5fuGQk9397bwb2713jyCRJkmqrvUcSvl5OI6b82TCXaDIwtKLdkLKuufqlZOb5mTk8M4cPHNjyjq31YL0BfZZOEDbY74eM3uXnXLDgIE6e9w1OnLg3n/zDQ2TC5uv1N0FYx5557d8c+8eHuH7MFK4bNZlP/OFBXn3r3ffOvzFrLr++/Tl+dP14xr06s9G1c+Yv5I6nX+exl2YseVtJatnOJ8GwT0O3ntBvHTjstzBgSK2j6vTsGzXtvglvNCrPW/D/2bvr8Kiu9IHj3zMSN0ISNBDcHSq0SFso1N0FutWt29b2V92t7G5t221L3YWW0kJL8RYp7hI0uEQJcRs5vz/ukGQyEyXJZJL38zx5Mvfcc+89k5zcnHnvESdr9lecIUcIIYQQouVp7J6EM4FJwCuu7zPKpd+rlPoOY5GSbK11slJqLvBSucVKzgWebOQyNz9K8f6xIcy2tytNOpJVyNoDmYzsFlPFgaK5m7UlGZujrINvQYmDeYkpTD6jC8V2B1e8t5wDxwoA+HLFAX7860gGdIwkNaeIK95bzuHjRkDxvP5tee/GYT55D0IIP2QJgEvfgYv+CyazTGcgGlTfdhFML/fMWSno0y7ChyUSQgghhGgaGqwnoVLqW2AF0EspdVgpdStGcHC8Umo3MM61DfAbsBdIAj4E7gbQWmcC/wDWuL5ecKWJk9QmIsgjra2XNNGyeKsDbSODAVi6K6M0QAhQ4nAyde1BAD5bvr80QAgwe2sK6w7In6oQopbMFgkQigZ30+mduXhQe0wKwgMtPHthX7rEeC78JoQQQgjR0jRYT0Kt9XWV7DrHS14N3FPJeT4BPqnHojU/xbmw/VcwW6H3BWANrvaQO8d0ZdHONPYfK0ApuPWMLnSNDWuEwgqfcjoABSbX8wF7iVF/QlsDcPnQDsxel8T6g5nkE8w5veMY1ycOgNBAz9vFibSsghKPfcfzbQ3zHoQQQoiTEGgx89Z1Q3j58gFYzSYCLI09+44QQgghRNPks4VLRD0pyIQPxkLWAWM7tg/cvhACqn4i3i4ymAUPj2HDoSxiwwJJkCfozd/CF2DlFGMo3xkPQHQXmPUoFGZCVALgJMhWxJeFmThDFFl9biD6yjdLe/Wc1jWaUT1iWLrbmMspLjyQm09PAOCKoR35fu1hHE5jqHL7yCDO7CFD14UQQjRd3h5+CSGEEEK0ZMroxNe8DB8+XK9du9bXxWgcK96BuU+5p106BQZX1pFTtEg7Z8O317qnmYPAUVT1cdd+Y/ROdXE6NUuTMsgutHF27zjCyn3AWrM/kx/WHqJVSACTz0igXWT1PVqFEC1Exm5IWgAxPaHb2TKk+OTU6YfXotpGQgghhGgppFFZz+QRqr+zewnyeEsTLduR9Z5p1QUIAVK2ugUJTSbFmJ7eV8gckRDNiIToupZQCNFc7ZoH310HTruxfcodcP5/fFsmIYQQQgghhAeZhMXfDbwWgluVbYe3g76X+K48omlKOMMzLaR19cd195hCVAghaufPN8oChABrPjamyhBCCCGEEEI0KdKT0N9FdoA7l8LGb4xVIQffCCHSm0tU0HUsTHgZVvzPmJPwzIehwzCY/zRk7gNLEBzbDeYAI+gc0hpG3gcdh/u65EIIf+essIiRdroWURJCCCGEEEI0JRIkbA6i4mHs4wB8ufIAUxb9jtaaO8d0Y9LIBN+WTTQdp99tfJV384yy18W5RpDQEti45RJCNG+n3gWH1wKuOZAHXAVh3qctEEIIIYQQQviOBAmbkXUHMnn6562l28/OTKR323BO7VqDYaVCBIb7ugRCiOZm5RRY8xG07gZt+kHvC6Hf5b4ulRBCCCGEEMILmZOwGVm513OOp1X7ZN4nIYQQPrDjN5jzuDGVwbEk2DEL4k8xpsYQQgghhBBCNDkSJGxGBsdHeaQN7Bjpg5IIIYRo8fb+4b7ttMO+pcbrgyth9hOw4h0ozmv8sgkhhBBCCCE8yOP8ZuSM7jE8PL4nHyzZi9aaW0d1ZWyvOF8XS/ijxJ+NIYIBocYiJ51O9XWJhBD+pk1/z7S2/WHXPPjmakrnKNw2E26d26hFE0IIIYQQQniSIGEzc/85Pbj3rO5owGxSXvOU2J3sSs2lc+sQwoOsjVtA0fTt/xN+mFS2vXcx3L8BItp55j2wAnbPg9je0P8KGUYohCgz+AY4tAo2fWcsiDTqEYjrB9NOozRACHBoJaRsgbYDfFZUIYQQQgghhAQJmyVTJcFBgK1HsrnlszWk5xYTEmDm9Ut7MDF3OqRuga5nwbDJoCo/XrQA239137YXwsZvIHMP5KXCwGth4FWw+QeYfltptm1//sSyAS9x1fCORIUENHKhhRBNjtkCl74L5/0LTFawBsH8Z4x7SUXWkMYvnxBCCCGEEMKNBAlbmI9mzGdCwZ/sUh1ZXdKHwJl3AauNndtmQF4ajH3cp2UUPua0e6YtfxuKjhuvkxaAJQBWvuuWpVfaHG787RJ2L5/BK9EzMRVlw7BJcMYDjVBoIUSTVX7l9B2zPPf3vcxY/VgIIYQQQgjhUxIkbEl2zeXVtDuwWB0AfGKfwBi9Bsp3HNz0jQQJW7JtM2DNh6WbGlBdzoJ9FRYg2DodzO69BZ2YiCSXfxS+iOmozUic/wxEdIABVzZwwYUQfiG6m7HS8QmBEXD5B74rjxBCCCGEEKKUrG7srw6vg6SF4DCCMcfyiskv9tIDrLwlr2LBUbp5o3kBhSrYPU+oLHTSkhWs/MRtWwHF5iDPjIFhMOphMJU9Z/jSMZ6+pkMEKpt73j2/N0BJhRB+6dx/QFRn43VAOFz8ltEzWQghhBBCCOFz0pPQH029CbbPBMAZ3Y1Hwl7hp102gqwmHhzXk7vGlBu2VZwLRzdCXF+wF7mdxqI0trGPw+IXQDsgIAzGPduY70Q0MYcKAuhVbtupFenZeXSsmLHbOdBzAty9iqwts3lyaQmz7b3ppo54ntTbCqdCiJYptpexEFLGbojsaDxwEEIIIYQQQjQJEiT0NweWlwYIAUyZe0hI+xa4kiKbk1dm7+DC1kfpuPIFyNgFJfngtIE5EIbcACmby44deBWhYx9kW5vxzFrwO3+WdGX83jbc01mjZPGSFmlH91tpm/4nkaoAgC8d4+jX9UI6pi8tyxTWFnqdb7yO6U7UWffx8qkljE1MISxwKPacYixLXgFbAfS9FEbc6oN3IoRoskxmiOvt61IIIYQQQgghKpAgob/Jz/BIak126WsrdmJ/mQzFFfI5io1Va2+eaSw8EdcHBlxNQYmd6384QlZBVwA2zdtFZLCVm05PMI5L3gTL3jICPsNvhR7jGuiNiabg7LHnMGnbx7TNWEGeDmZQnImBQ06FDh/Bpm8hrA2MesRYpbScqJAArhnRybV1H5x6u1HngiIb/00IIYQQQgghhBCi1iRI6G+6nW305MpLAcCpzEx3MP8hZgAAIABJREFUjCrd3d+aTGDFAOEJBRkQfyp0HQOA3eHkiR83klXgPofcop3pRpAwLw0+vQBKco0du+bAX+ZC/Cn1/rZE0xAeZGXqAxPZ+qeNgUvuwJxdBFNehvEvUHTtD2xPzqFbWBgRVZzjWF4xUSGBmK1e5jIUQojyso9A4k/GA4X+V0BAiK9LJIQQQgghRIslQUJ/ExgGt86DP9+APb9jKsnn1dhlPFXYFR0Wx6NjB8L0F6E4x/PY3he69QD7YOleZm5K9sjWs2248WL3vLIAIYB2QuLPEiRsATptehOzo2wOS8cfL3P2wm4cLVAEW828fvUgzhvQzu2Y/Rn5/PXr9WxPzqFDVDCvXT2I07q2buyiCyH8RUYSfHg2FLt6w6/+AG7/A8zSNBFCCCGEEMIXZHVjf9SqM+SlQtYBKMiga9p8vov9lKl3ns6IXvFw+YcQ0QFQENMTuoyF0X+Dy6a4nWbpLs8eh4M6RpYtfBLpsVyF9zTRrNzz9XoyM1Ld0pS9iPyCfAAKbQ6emZmIw6nd8jz/SyLbk43g9JGsQh75fhPOCnmEEKLU2k/KAoRgzJm794/anaPwOGyZBvuX1W/ZhBBCCCGEaIHkcb2f0nv+oPzSInrvIpTWoBT0mmisPGsvAmuw58G2QjiynmExDlbsLUsOtJj44i+nEhlsNRK6jIGB18DmqcZ2/Gkw9OYGe0/CN2wOJ0lpeSS0DiU1p4h521KJN5/F06avSvMs0CMIppgCgrBhISOvmCKbg9DAslvI9mSj1+l15oVcal5Gan4r8o+0JTy+b6O/JyFEC5C2Az6daAQKwfh/dfkHvi2TEEK4LN6Vzid/7sOk4PZRXRnZPcbXRRJCCCGqJUFCP5Ue2oO47LKVinfqTrQrspcF+JTyHiA8ugG+ugIKjvGIyUpU27v5Z8qpRIVYee6ifkSGWMvyKmV84Br9mLFwSbuBDfyuRGPbdCiL279YS1puMeFBFp46rw8AHzvOJ1W3YoxpE1mW1ozSa1kZdB8ZOoLHbHdAz4luAUKAUT1icG74hpetH5clfn85PLAJLIGN+baEEP5g+C2w/ouyaS3aDoSuZ9X8+OVvlwUIwXigNeoRiO1Vv+UUQoha2p6cw18+W1M66uLPpAzmPDiabrFhPi6ZEEIIUTUZbuyn3gu/jyRnewD2OtvySMkdbDqUBUBBiZ0im8P7gQtfgIJjACinjdsKPmHDE2ey+qlxXDqkg3veoxtg9uOwdRqEt/NyMuHvXvh1G2m5xQDkFtl56/fdTOzXBoBfnafzpPMuOjoO09t0CIAYlcOrAR/wxpUVegcWZPJiwCf8M+gr9/TcZDi0usHfhxCiiSnOhTlPwvtj4Le/QVG2+36nA1a8A/ZCMAdAn4vgL3NqNx+ht7l3i3M904SozrE9MOtR+Plu+Z/V0tkKoaTgpE8zf1uq27QsNodm4fbUKo4QQgghmgaf9CRUSu0HcgEHYNdaD1dKRQNTgQRgP3C11vq4UkoB/wXOBwqAyVrr9b4od1MSlTCEcUmv0oocjhOOxWSiR1wYT/20he/XHMJqNnHXmG48MK6H+4HZR9y3S3JpZS4CS6R7+uF18MkEcLpWPt70HdyzSnqENRcOG5TkceBYPu04Rj6B5BBGcnYR8x4czUWDMjh0vIAze8QQ+v4jbodGkwOOLCC0LHH6HQQkzfdyIWXMoSmEaFl+fRi2fG+8Tt4IuSlwzZdl+zd/D+s+Ldve/guc+RB0GFbzawybDDt+NRbVAqMnYm2OFwKM3qgfjy99gMrm7+H2hdBukG/LJRrf/Gdg5RRAw4jbYcKLxqiaOujc2nOl9k7RoV5yCiGEEE2LL4cbn6W1Lr9yxhPAQq31K0qpJ1zbjwPnAT1cX6cC77m+t2h3jEqg19bXGHH8V3JVGEeHP87Kfcf4ZtVBAOxOB28s2MWZPWIY1rlV2YEDroI//lm2nTAKwtt4XmDDF2UBQoDj+2DvImOuQ+Hftk43evYUZLDEHEFIUA7F2sJ7jovZ0PWvhAdbuWBgWc/Rfe0GQkrZKtgaE8pU7tZhL4akBd6v1e1siOrUUO9ECNFU7fi1wvYs9+2UzXhI3ly7IF/3c+CW2cbCJRHtYfhf6vyBXrRgu+aWBQjBaPts/l6ChC1N0gJY9t+y7ZXvQMKZ0Pv8Op3unN5xnN07lt93pANw0aD2jO/rpb0thBBCNDFNaU7CS4CxrtefA4swgoSXAF9orTWwUikVpZRqp7VO9nqWFiI48TsmZn0LClqTS8L6v/GWbRqtyeYS83IcmPjZMZIdh9Pdg4SjHoGgSNg9D+J6w5kPe79AYHjN0oR/KcqGGfeCzVipOMRhDNcLVHYetEwn55wHPQ7p0rkLpJRtK5xG/Rl6k5FgDsAW1gFr3mHP6+Wn1/tbEEL4gehukLql3HZX9/1dxsDKd8slmIyHVrXV6TSI7Q35GRAcVaeiihYu1MtiEqGxjV8O4VspW7yn1SFI+OWK/fxrzk7yiu2M7NaaZy/qS6+2ESdfRiGEEKIR+GpOQg3MU0qtU0rd4UprUy7wlwKceNzWAThU7tjDrrSW7cAy922njcB9C5kd+CTPWL/keevnrAv8K9fPPwW+vqpscneTCU69A26cBuf+E0KivZ//lDshrG3Zdlw/Y3Vj4d8y95YGCL2JyNvnmdiqi2daVHzZa6X4ovWDZGnPoTXSi1CIFuqCVyE0zngd0houfN19f6+JxnA+5WqGmC2Qvr3211n1PrzWC/43DKaMglyZ80vUUtezoefEsu3YPjBsku/KI3yjyxigQk/krmNrfZrDxwt4dmYiecV2AJbvOcbcRLkvCSGE8B++ChKeqbUeijGU+B6l1OjyO129BrXXIyuhlLpDKbVWKbU2Pb0F9F6qMCTLiQl75n7iVFZpmkU5UWij19fv/6x4hqpFxcPwyWXbaYkw5/GTKLBoEuL6QVglw10CwqBLuT/FvHTY+A207g6dRpalD7zW1ZgusyV4OKcWv8t/bZdRoo0OyiUhbeHsp+v7HQgh/EGn0+ChRLh7JTy83f3ecsKhVWXzCTpK4LfHQNfiX39eGsx9CuxFxnbKZljyn5MvezPS4tpGdWEywfVT4baFcPNMuOtPCG5V/XGieekwFC6bYgSJY3rBxf+DTrWf3Wh3ah7OCrexHSleFlkSQgghmiifDDfWWh9xfU9TSv0EnAKknhhGrJRqB6S5sh8BynVboqMrreI5PwA+ABg+fHitAox+adgtkJoIG7+GoEje1NeTn51Ref59Sz3TCrOMoceuOZwyD+9i46wPSCk0Yx52I1ev/9L9meq6z2HCS2C21utbEY3IEgDXfw/z/g+yDhgBwMIs4wPR2CdKe5ZmJq2i1feXo0ryjOOG3ATn/xusIdC6m9splydlsPlwNsUE8IbjKr5wnMvZbQr49303G9cTQrRMlgCI61P5/uwKUxTkpRiLKtX0vnH8ADjt7mnHkmpXxmauxbWNqqG1ZsOhLIIsZvq2rzD8s+Nw3xRKNB2DrjW+TsLQzq0IC7SU9iQEGN1Dhq8LIYTwH40eJFRKhQImrXWu6/W5wAvATGAS8Irr+wzXITOBe5VS32EsWJLd0ucjBIyhWRe9CRe8BsrEsZ+3MnvVVm6zzKadyvRyQLnPBhlJ8MNkY76o6G5wxUcQFEngx2M4WxcAsG/+T2SHBuI2w5MlqGxomPBf7QfD5LJFBVbuPcZr83aS+X0Blw9NImf3CsYf+i/DzXllx2z4EkY/ijOyM9uOZNMmIojY8EByimzc/sVa8kscpVmH9+3Js9cMRlma0pSnQogmp/8VsObDsu0+F3kPEBbnwR8vGtNsdBxh9FAOjjLuZREdIadcsLH3BQ1fbuGX8ort3PDRKjYdMkZcTOjXhvduGIbJVPViNwUldl6bt4vV+zIZFB/Jo+f2IipEHoAJ7yKDrXwyeQT/mbuDjLwSrhjagWtGxFd/oBBCCNFE+OJTfBvgJ2X0XrMA32it5yil1gDfK6VuBQ4AV7vy/wacDyQBBcAtjV/kJsxkBuCxib3JyCvmwu2v8EjYPK4vmeaWzdmmP6v3HiMs0EL/+Q+WTSifuQe+u57snlcQ6QoQAnQxpTJDX8wl6nDZcLCxj5deTzQPx/NLuOXTNRTajCBf7MKHuceyGLz8mtOOZXLdJ/vZk56PxaR4dEIvBnSIpI8tkdusszHh5FPHRBw6jrBACRAKIaox4SUIbwP7/4T2Q42FtbyZ9TBsnmq8Tt4EOclw3TdGr/abfjICiNmHjKDjiNsar/zCr3y/5lBpgBBgbmIqi3enc1avuCqPe/rnRH5cf5hzTWuIT93F10eGc889lSz6JgRwSpdofrhrZPUZhRBCiCao0T/Ja633AoO8pB8DzvGSroF7GqFo/kFrSJwO22cZ8yv3vxJ6nUdksJX3bxoODCcp7XwOfZ9KfIYxxFgrM1u27+SjDe+wwDmMbUFrcVtiIjeZ4KI0j0vtCx3C32MuJyxlFUGdhnPrkEuQtdmal9X7Mym0OYjlOF3VUa62LPaesdNI3thsZU+6seiJ3al5de5O5t7cgW8CXiJAGcNqxpo28l2rgY1VfCGEP7MEwOi/GV9V2fGb+/au2eB0GnPJxfaEqz9vuDKKZiM1t8gjLS3HM62i+dtSeNAyjQct042E9FnM+u9epobfzA2ndmJCv7ZVn0A0G0lpuUxff4SIYCvXDI+nVaj0KBVCNJCM3cbUUJ1GQoCXxSGFaEDS3cffzH8Glr9Vtr31R2Ml4gkvwbpPObr5d3YcyCRFR5GvL+E+66+YtINBOpGPAhJ52XYtB5wx9DEdcjttwL7fKbRGEmzLBmCH6sp8+yASk4qAUbANDs1M5PWrBzfimxUNrVdcGP+yfsBVpsWYlOd0VQd0G6LH3k34yNs4/FWi2z67U3N4xTS6qbJ5dwKUg+vCNgETEUKIOinOMxYycc2RSuuuRg/CE1p1MQKEQtRAXrGdP3akEd8qBLMCh+tfXXighbN7V7KQVzldYkKZnD7XLe2szKmYM3aQsq8V6656hmEDBzRE0YWvpO2ATd9CUAQMnQyhrdmenMNl7y6jyGaMrpm65hBzHhxFoMXL0At7Cfz5Buz4BVDQ+0I47S5jHnAhhKjOwn/A0leN16GxMOlXiOvt2zKJFkWChP5Ea1j9oWf62k/AVgAbvqQ90N7VXnFqz+Wrn7R+xwz76R5BQgoyCI7pQc7gp8kosRA79HISX/nTLcvS3VUsjCL8UkLWChLMi7zuc6LIOetFIk+5hFVHcjk3wUxx0nY2664UEYjZpCjcu9xjaLK1dUKDl1sI0Uwt/jcsfQ3sxdDvUrjsfTj/NZh6A+SlQkhrYz5eIWrg8PECLn93OWm5xQAMiY+iQ6tggqxmbhvVhdjwwNK8C7en8tny/QSYTdw5phundDGC1M9f0h/bxwFAfmneEFXCRPMaADJ/3Qr9thhzRQv/l7IVPjqnbNX09V/C3SuZuuZQaYAQYF9GPkt2ZTC+r5dA84LnYOU75c652QgY3rFEHnAIIaqWkwx/vl62nZ8OS/4DV37suzKJFkdaNP5EeZ9cWzttsKHCSsRAZXNxjzev4zP7OCaZF7ifMmM3EUOvICIkGq013VqZuTz3a0aatrHVmcCKuL/Wy9sQvvPD2kP8tiWZ+OgQ7h7bnbYZuz0zRXSEqI5MC76G/1sQRsmc+VxlXsSLlo+5KdBBrimCFyKe59eUSM6ybnI/NiAM+l3WOG9GCNG8JG8y5hc8IfEn6HwGnHI7PJQIx/ZAdBewBFZ+DiHK+Xz5/tIAIcCGQ1k8dUEfRiREu+XbeCiL279Yi9PVy3BpUgYLHx5DfHQIg+Oj2Hvqw8SuetrrNaJLjsLh1dBZ5qBrFjZ8VRYgBDi+D5LmE2Tt4ZE1yFpJwG/bDM+0lC1waKXUEyFE1Qozy9YDOCHfc1owIRqSPM7yNx2GeiQp11dNhagSLjGvIJNwz517/zDOqRTfxs/kHstMhpiSuMmygNfMb9etzKJJ+G71Qf42bTN/7EznixUHuOnjVege48Fkdc+YcxgOrqTz9vcpsTsJwMbTlq8IUMbiJuHOHF7KfoIvrS9jxe5+bEk+bPwGPhoH746ENfLUSwhRhX1LYf0XkHMU0rZ77k/bZgQHd/xqrGgsAUJRC3nFds+0Is+0eYkppQFCgBK7kz92ln0oezl9JOcXv8TTtsn86jjV80Jh1Q9bFn7CGuwlLYQbT+tETFjZ/eeUhGjO6Bbj/RytEio5t8wrJoSoRlxfaFthfvdB1/umLKLFkp6E/uacZ9GfTEDhOX9cRU4NH9gv4FLzMtqastz2tVL5OE0BUOFBBa26wNy/w5qPiLO7T+gdfHCREQQKCD3JNyF8Yeamo27bu9PymJcaxoTht8D6z8Fhc3tydappBwPVHg7pWCJUgduxVl3McLOXXoho+PWBss1ZD0NkPPQ8tz7fihDCnx0/YAT85j1t3HvA+PB8xUdgDgRHWc8vNPD2MOOFyQq9zoPj+6HdIBj7pNHjp1UCmLzMCyZavKuHxzNt3WFsrokIE1qHcEZ3z8BO59aewZtO0WVpDqdmm05gmyOBhY6hDFR76WRKN3aefi+07tYwb0A0vhG3Gg8781KM7c5nQNez6GgysfDhMczblkJEsJWze8dhqmzIzoR/wldXQMGxsrReF0B7mddbCFENpeCmn2HF20Z7p+8lMkpLNDoJEvqbmB7kmyMJcxhBP4dWmL0sOAHGcGOTgtNK3uV800reDXjLfb+zBDsmLDhxaJgRcBEpX0/l7oIp3q8d0QEsXp6wCr/QNjLII+33OT8zIfeDSo+xY+Y4ESxxDGC0eUvdLpw0X4KEQggoyITvroeDKzyDgbYCWPcFjH0cEmeA0w5Db4ZFL8OJh2JOG2yfabxO2QybvjPSwtpB34uh4wijMW2RFUeFwWo2GcOF0/MZ0CGS164eRIDFcxDNpUM6MDcxld93pKEUXD6kI2N6xpbunzwygcW70nE4NUeJ4fmEL/j4bCeEt5MAYXMT2RHuXQ075xgLl3QfXzqPYGSIlauGx1d/jvZD4OEdxr0uYzdEJ0DXs3E4NfuP5dMhypgXUwghvAptDeOe83UpRAsmQUI/k7HiK2IcZb0CzUqT4QwjxpTnNX9PZSxQ8pvzNPY7p5JgSnXbb8HJfmccCaY0RpUsoag4wPsg9MBIuOB1mXDZj91/dg+mrz/ilnZF9qeVTjqQqqPooNIpwcKDtrt5XH/HNZbFtb9wXN86lFYI0axobUzEfXCFsV0+QHjC3oWwew6gYMzjMGwSzH2q8nM6bcb3vGRY/b7xteV7uOGHei++8D8FJXZu+ngVxwuMerJoVzqLdqZzxbCOHnkDLWY+mTyC/Rn5WC0mOkS5PxAd3TOWmfeewdytKXSMDuGSwe3B26q2onkIioRB15zcOSwB0HWM8QVsO5rD7V+s5UhWIVEhVt64ZjBn9Yqrh8IKIYQQ9UsiPn7E6dR8s+qAR3qYKnKbS6e8ZF02Ofds5yme59SQYDLm3YlV2XRQ6Z4nUWaIaA+BXuYwFH6jxOEkPrrsg89I01ZOMe30yGfTxgefNiqLD6xvsCDwMWYF/p2vHONI05Ee+VOdFdJMVqOXkDJB/yth8A31+0aEEP5l72L47yBYXs28tg5X0A8NS1+F4lwYcFXtrrV7nve5DUWLs3b/8dIA4Qnzt6VWktuQEBPqESA8oZ86wMMJB7h6UAyBEiAUNZCUlktWQQnkJPPPmRs5klUIQFaBjaemb8FZWeNdCNHyOOxg9/IAVQgfkJ6EfmR3Wh6f5gznqsAfaaeOl6YHKc9JuE8437qevzuMYOAMx+ncZf7FbUVjbysia11hIWXtgPTt8P1N8NA2sHoOWxVN24o9x7j5k1XYHJozTVvoYzrIwKhi8NIB1epaoATK6kE7lcm/rB8Sp7Ld8mboCFqpPDSgAsIgpidMeAna9Icja43J3GXonxAtl70EfrwV8r08gAqKgtF/MxYt2b/EWP3zBKfdGJ588dvGPF7JmyC6qzGHYfZhMFmMPEJ4obXmy5WeD1W7xdViTuXsIzDrEWwHV3PcZiXO4ZqjLrwd3DLbWGlbtGw5yaTZA3n3z2R67P+aS4pmEBYSQvYpD3Ddis5kJu/jw4A3iDLt5V3Cedx0K3NdD+yTs4sosDkIC5SPYkK0eCvehUWvGFOvDLkRLnhN5loWPiX/mfxI+6ggsojg0uIXWBF4P6ZK5iIsL5wCfrY+RRT5gNM9+Ace25WlAcYEzBm7oN3ASjKIpuqz5fuwOTRPWb7mDsssAHRebdbEhgTrcRwOE+Zyq93EqJyyDCV5cHQ9rP0Uju+Dw6uN9D4Xw1Wfy1B1IVqi7EPeA4QAgRHG98OrwVzhYUL7IRDX23h92l/L0kc9AhlJMO//IGme5zl7TIC4PidfbuHXVuw55tFrML5VMHeMKps/MH37MgqXvEVYgCL6rPug7QAICCUpo5Api/dw65776VO0ESvgNig0N9noFXvh643yXkTDS8sp4qtVB8kvtnPV8I70bhtRad7tyTmsStzNpTsfJyp9DWEEE28fww2WOUaGAgj/7V4cxS/zmPVXBpj2AhBFLv+2fsDi4kEUEcjIbq0lQCiEgNREmPtk2fa6T6HDUGNeZiF8RP47+ZHwICttI4NIzm7NPOdwJprXVHuMSdsZaNpfL9cvNIejI7rguQagaOpMShFKIZPMc0vTarJCdnmzigcTQSHnmtdWHkgG2DLVfXv7TNizEHqMr9X1hBDNQKsEiOwE2Qc992UfhHl/L9sOCDNWOQ4Igdg+sPl76H+F8TTdVgSr3oOjG4yFSioGCENi4Lx/GQuXiBYvObvII21c3zZEhlgBWLthPf1+vpRYVQKA/mwOCk1JcCzvFN3Cz4UDeTVoY+UXqCzwLfxOQYmdS99ZxlFXnflq5QFm3HuG10DhrM3J3Pftep40f0WUxWiDh1DI5HJtKwATmtNN2+jlmhf8hEhVwKg2JbSK78ZjE3s30DsSQviVo17+1xzdWHWQMHMvzP07pG2DHufCuOeNtpMQ9US69viZMCtcb15Ing5igWMIiY54irS1dH+Rrv+uyVpDkbbyXfHp3PvDjno/v2h4t43qQpBFYapFYHC3s51HWj/zfrcAoa7p6XKOVJ9HCNH8mMxw7VcQf1r1eUvyID8Nju+HTd/A9Nvhl/uNfT/fDQueg20zYPUUz2MLMoxey2ar5z7R4pzVO47woLLn4CYFFw5sDw47aTlFzP/pU4JdAUIoe2gWUJjO8863CaKEJC//A09IjLuw4QovGtXvO9JKA4QAxXYn09Ye9pp3yuI9ODX0UO5tGrOXkT1bnQkscg5yT4zuxocPXsO/rxxETFjgyRdeCOH/Oo805v8vr8to73nTdsDS1+GzC2Hnb0Z7afUHsPD5Bi+maFkkSOhHimwOJme9w0vWj7nSspRx5g30Mh0hSJVNzJ3hZWGJk6UUBCkbt1jmEbFruky07IeGdY7mp4fOY3HohBof08OU7LZ9jnk9wbhPqGvzmNUS7NrkFjy0m4Oh53m1K7AQwn/ZimDe0/DeGfDj7RAaC7fOhe516E288Vs4ugkSf6w6X1hbCRCKUtGhAXx/5+lcNqQDE/q1YfoFMGz6aPhHa5zvjiTbXvlAmghVyF/Nv/CK/TqPfUd0ayaVPM7kZa2xO5xejhb+JtTLkF9vaQAOV/v3D+dgt/QMHcHn9vEUayv5OpCZ0bcw8fzLWNruFtaHjcYW2taYCuH6qVXM6SOEaJGiu8BVnxojKCI6wjnPQr9LPfPt+QOmnGEEBCt2vkha0DhlFS2GDDf2I0EmJ1dZFrulWZR7I7WjKdNt22MRkpM00boek0kaOP6oU9YqVhDJEkd/Rpu31vr4YzqSWc7TeNAyvTQtQGmPOlaxTh4M7k3X8DZ1LrcQws8seM4YGgyQuhXSd8Gl/4OR9xsrFh9aWfNzmcyw7pNqMim49B358C3c9GkXwUuXDWDelkP0nnMF2LIAaFu0h0es6cx3DGG8eYPXY++3/kSWDuUosbTHGFpcqAO4t+R+bNpMYN4hjuWX0CZCFnLzd6N7xHJa12hW7jXazx2igrnhtE5e894+ugsPTd3EF47xhFLIxeYVpOhoDgx+hOfWWHjJfgNOTNiOWnhqQDHfBb4E6caw5KP7LKQftzIoptHemhCiKSs8DnnpENvTmCqlqulSbIUw877KF2yL69swZRQtlgQJ/Ykyo81B4PCyJG0t1TV42K6D94aTaOKW/RfmP8M1AHUckT7LcSpvOq5kr7Mt/7Z+WNqDVXlbEbucsKJU7zuEEM3Trtnu2ykbYcqZxuteF8DlH8H022p2rtPuhmN7qs4z9knoPq725RTNRlpuES//toPNh7M4vVtrnjivDxaT4rJ3l5GfmsQlgVlu+WNVDu/ZL+E/9mtpFWDnPtOPnKnXu+WJUvlEkc86R3dmOM9gu7MTnwb8myiVb2T48QeY9IusQOnnzCbF1zf14+jPzxKWvp7DEYOYNKWEElMQ953ZnktHdC9deO2y2FTiui3ku33BTHFczLsOo7fPc237otlGMWULMAXsng1Hy+YOb28/zFdfvUbCw/8gMiJSHmqIShWWONh/LJ/ucWFYzTLor1la+R7MfxYcxdBmgDGnsiXIWLDE271h0cvGQnDeWEOMh69z/w7jnpNRFaJeSJDQn5hMFDjMeJvFxK4hlxBaqQK39MraIHVtm+zOD2FQ9dlEU7P87ZM6PEuH8rnDGKq8XvdyG+IOVdenSFsq5KZAeNuTKoMQwk+07mHMk+PNzlkw6FoYcTus+8y4eQRHQ16Ke76gVlB0HPb+AW0Hej9XeDsY/wJ0GAZZhyAqvj7fhfAjD3y7kSP7EhlvWscf6UN4qtDOuD5x7EjJxUQs+TqQUOU+XUYsWazXPbioV3sSho2Eb8/yeu6hpiSuJq4xAAAbRElEQVTec1zCk9ZvywKEAAeWQeJ0GHBVQ7410QhMv9xH/M6fAWiVuZG7HTuJJJ/Rs7dg+70V1ujOEBiO3r+UM4AzAuAc5yaet9zPe322cMryB7kiMJfPHBN4zX4VoIixFntc5zr9G5FvfGEs5nTxW9DNe50TLdcfO9K4/7sN5BbZiQsP5MObhzMoPsrXxRL1pTgXFv7DmEfwxDzxqVvgs/ON1+0Gw80zILjC73zPH57najMAMnaBrcD4WvE/CGkNox42FjaZ9Qgkb4auY+D8VyEkukHfmmhe5PGEPynOI0Lnet1lBhwNsGhJRYvTwyiyORr8OqKeqer/1AucZq8LkWgNZhx8ZH2VgSqJwzqGJGf7Gl86UDkoWnpyQUrhX+YmpnDmv36n5//N5rFpm0jJKeSjpXv574LdHMosqP4Ewr9NeAmsoZXvn/032DbTCOr1vxL6X+aZp+i48T15ExxY7v08uclGL+m3h8KbA2DGPbVYTUk0F0U2B0MPfMTigId52vo1CwMfZXDiyzz9szGthhMTT9huw6HLnmbZtJn3A99kYcAjhO36mbicRHTFieNdlIIHLT/SWXnpFZ+8uUHek2g8drsD57aZbmkTTasZbd4CgLX4OCRvhP1L3WZhvsT0JwsuN3Fa4guY8tMIV4XcZ/mZfUE3sDfoei448Arlp/DWGuJNGcZG9kFjYSZ7CUKcoLXm7z9tIbfIGFKallvMC79u83GpRL2afgesfh8qW0gyeSPMftwYXlxeuwoPS00BRnDRUeFhxI5fje8/TIY9vxuLum39EWY/Vh+lFy2I9CT0J7YCzMr7RNlKQYzyHkCsL1pDSAAEWiS27HfOfBjmPF5llhCT9+CvUhBOEcPMSfxseobX7FeSQwiZOowgigmp0KvQm+xdywg6v04lF34mM7+E+7/dQLHduFd9v/Yw8xJTySo06slHS/fyy31nkhBTRRBJ+LfYnnDh6/DTnd7357p6DeZjPO2uTmYVw41TT8yvqmHDV9D/Cuh2dm1KK/xcoEnzgPWn0gCOUjDZPJfxznWYAjUf2c/nU8d5bCjpweWmxfzFMpco16iLbqYUXua/MKvqa4RSyGLHIC63/FmapgE18JqGeVOi0Xy56iBjnHF0NZX1ZrZjwUrVATytNYXf3+nR3UK5vsB9lIXHiIv8dGPxgegudS67aD5sDifT1h12W2kb4MCx/EqOEH6nOA92zq4+3+bvYN8SuGU2pG+H/Aw4/T44fgD2LzVGUTjtxj2koiPrYfWHxgPW8vYtqZ/3IFoMCRL6k7A4FjsHM8a00SeXVwr+oT5CqRd9cn1Rd3NyE5hQD4vYmBQ8aplW6/McztPI0iXNW3ahjWdnbGXp7ozSAOEJJwKEALnFdh7+fiPpecVYTSasZhNxEYHcf04PRiTIUIhmY9C1xnw7FYcRN7SMJAkStjBKO7DiPpm7SUG8MnptPWv9krvMM4lROZhVzXqaOrVxjhM69h5Oh4h49NplgMahFUucg9i3/AC3Xt6/vt6K8IHvVh9kgf0W3rG+RZTK55gOZ7mjLxdZVlV5nElBR1cdq4vDxNEmIh6ZPcz/pOcW88GSPRw+Xsj5A9px0aCaj66pzKM/bGLGxqMe6RP6yVQ9zYY5EALDoTinLM0aYvTCsVfoOZh7FD6/sGwewuBWcNtCCI2BgDD433DvQUK0MX9hRZVN2yJEJSRI6GcsAYFQycJGjSFAF0FeGoTF+a4QolZK7E7i/3y8TgFCbwuS1OU8KQGy4E1zd/X7y9mZUrNFlVIOJvGAZTqdTanMdpzC56nnsnb/cZY8dhax4d5mXRV+Z9svjR8gBFj4HMSfAu0HN/61hW9YAlFtB0JK5UN/25iya3y6igFCAOsu966GFqU527yR4k2TONxrBh37nVGrIosmwulkYsGv9Dev42vHOfzuGMIR3ZppQf8szVKThf60NnqWVqw3VXm75GIezLfRLlI+ivkTrTU3fbyKHSnG6K3ZW1OwOZxcPrRjnc95LK+YmZvcA4QhAWYmj0zg/nN6nFR5RRPy64OeAcIrP4H0HZA4E5LdF89yW6ik8DismgLn/8fYHvWoMcWKt2HLRV7+342876SLL1oW+c/kTzL3coa96iebDU0DKriVT8sgaqfI7qArnk8na6K6lYtrapxeDU6HrALpr/YuhrWfQEAonH4vOEogbRt0HMGubBOZKsprgLC/2seEmGOsNQ1gcaoR/LOYnHxheYXuJqNOnmbajkLzmW0icxOT2XroGPsOHKBr1x48cV5vIoOln4XfObYHfpjkm2uX5Btz/ty72jfXF42uoMTOs7a7+Je+u1ZBmsr+t9XmHIHKRvHKj0GChH4pb87zPGT7AMwwnnX0UQdY5+xFR9JK89SkHaSUMcS4RJuw4qxRmykqIpy2EUEn/yZEo9qWnFMaIDxh+vojJxUktJhMmJXCXm5O3X7tI3hsYu86n1M0MVmHYOPX7mm2Qph6Ezhdo20CI8oFERUeAUBbIexZBDPvh7xU6HUexJ8Ky940gogn6ApTk4W3h87yP0rUjgQJ/cpJRmrqgU1bCJCl1f1KRJCVk5nR5GQDhACBhSlQmAWhrU/+ZKJxHV4HX14G2jVn5dZpYDcmStZAT2Cpoz/hPEguIaWH3WeeziPWaZBrzO/0mOk2fnKeSTd9uDRAeML55lVMdYyl/cL7uaJkOcHKxo6N8byR/TLP3XJRI71RUW/WfVpWX3zh+AHfXVs0uk+X7efu9Ocw1XK65Pr43waQUeCgW/2cSjSiI1mFRK6a4ta0Ptu8iYEmz3lSK9YVb71NAQKUkyJtwartmKuoX1pDn1GXo+qrEopG0zo0EJPCbVGauJMcAREZYmXSyAQ+/nMfAFaz4u6zup/UOUUT4yjBs9efLgsQghEgbDsQorvC4Jtg2iQoKf8AXsGXl5Rt7vwNIjpAeFvPIGHvi4yFS2J6wAWvgVlCPqJ2ZAUKP5Jibke207dPHQuUDAX0Rwd1rE+vv091kgChnyrcMNU94GMvW0ntxMebUeatvG59iwBsPGD+gR+sz/CQZVppPgt2Xg+YwtbAWxmokiiusBJ7pg5lVeBdnG1bTLBrIZzepkOM3v9Gg70v0YAc1S9m1KBCY3x7fdGoDhw5SmeVVn3GBtL52BKOZmT67Pqibl6bs4MQijzSK1sE8EQnL11JgPCEIFV1gBCMoOPS375m65GaD4MXTUPbyCDuHtu9NHAcFx7I3TEbsP+nN7zYDj49H7IOGkGbFe/C4n9D5j4ozGLv2nnsnHIjR759AI4f4JdNR7j/2/Xc8NFKFu9Kp0dcGDee1on5D43hrF5xOJ2anCIf/z8V9aN1N+g+vvp8KZth28/ww00VAoTAhi8886/5EMI7eKaHtIZxz0Dr7pC+s+wGJkQNSVjZj0SHBmBRng2axhRxUn3ShK/Eq1SfXt/iyMPp1JhqM45L+NzUNQfpunohI2owSnycaTM7AydV2TsnVBXzb+tHVFyjfaJ5vdf8/dT+GpdVNCEdTzXmzvGVXB/MhSh8pmdsMGq3767flkyW/PgP2t8pDzX8yaGsQoqxEkzNgjAn/rfVV+e/S01L+W1LMv07RNbPCUWjeXRCL86Jy0GtmkJB6h66LdlQVi8OLEN/dSW2wlwC8o1RE87fX8SkoMuJYespkL3zB54qeoM4jtNJpTHUlEOxthCQkc0XayDKqskjhLVFHRnW1so9p4QTpXOg9wVGwEn4n06nQdL8muW1FdT8vHsWeKat/6zs9dZpsPIduHOpTPskasxvgoRKqYnAfwEz8JHW+hUfF6nRBRxP8vmIY6UBWxFYZR4VfxKqfLjaDdBRZWLLSSUgSlZp8xdOpyZ5xjNcbU6qUf6afnBSyriJ10RrjtUwp2hSatO4bQjabvTikPlzW4SCLb/VW+CmrrpkLvFtAUSt3R04u7Tnui+MMieS4tgHyLxz/ubLN5/ihuPvlPUorbjAX8ZOAsptm7wEmCPJZ3PgHdXfu4KALGCesemc/xymW+cYC3QJ/7F/Gfz+D99dPzURkhZAzwm+K4PwK34x3FgpZQbeAc4D+gLXKaX6+rZUjW/z1k2+LgJKwbGjNQsaiCYiL83XsWWUgoD0bT4uhaiNIpuD+80/+fTDt1nD5q1bfFcAUTcz7/F1CSAl0dclEI3kjrzXfV0EWhVLu8jfJBz61ddFIC/xD18XQdTS9t+/4sbyAcKTUJf2lQkHuTMeO/mLi8a1rwk8SFr2lq9LIPyIXwQJgVOAJK31Xq11CfAdcEk1xzQ7tjVfV5+pgWkNqZtq2FVaNAlZWxf6uggA7PhaGjX+xGkraRLB5ehpsnCJqIPF//F1CUQjCWwCUy2FNIEyiNppZ/dcoKSxTcp739dFELXUZcn9Pu+5HJa+wbcFELW3eVr1eRragT99XQLhR/wlSNgBOFRu+7ArrUXpXOD7nlhKQeutn/q6GKIWSnZ5mavCBzohPS38SfqO5T5vCAO05Xj1mYSo6Jjcb0TjaQK3SlFLAU3gl9YU/seK2rHiqD6TEBXlHfV1CYSoFX8JElZLKXWHUmqtUmptenq6r4vTIKIeXunzxYm0hthHVvm2EKJW4q6f0iTqje2qmb4thKiVLsPHNYl645z4tm8LIfzTZe/5ugRNQktoG9mV7ydid0qwx++kBnb1dRE8FvESTV9ml0t83jYqUn6zpIA44ZS7fF0CmlHYRzQCf6ktR4D4ctsdXWmltNYfaK2Ha62Hx8bGNmrhGoslIppiZXxw9tVXiQJTYLCvfxSiNixWclWoT+uNQ0Fkv9G+/kmIWkpWrXxebwJOu9nXPwZRW7cu8u31LWHQVe430DLaRtZnj+HEh20jwPx/Gb7+MYhaavfkeuz4tk1te/Swr38MopZiJ31BsTL7rM44NQQ86sPl3EXdjH8WAny8kvnfZISFqDmltfZ1GaqllLIAu4BzMIKDa4DrtdZeZyYfPny4Xrt2bSOWUAghhBCiUdSp35q0jYQQQgjRDEl//nrmF/2VtdZ2pdS9wFzADHxSWYBQCCGEEEIIIYQQQghRO34RJATQWv8G/ObrcgghhBBCCCGEEEII0dz4y5yEQgghhBBCCCGEEEKIBiJBQiGEEEIIIYQQQgghWjgJEgohhBBCCCGEEEII0cJJkFAIIYQQQgghhBBCiBZOgoRCCCGEEEIIIYQQQrRwEiQUQgghhBBCCCGEEKKFkyChEEIIIYQQQgghhBAtnAQJhRBCCCGEEEIIIYRo4ZTW2tdlqHdKqXTggK/L0UTFABm+LoTwS1J3RF1IvRF1IfWmchla64m1PUjaRlWS+ibqQuqNqAupN6IupN5Urk7tIlG5ZhkkFJVTSq3VWg/3dTmE/5G6I+pC6o2oC6k3ojFJfRN1IfVG1IXUG1EXUm9EY5LhxkIIIYQQQgghhBBCtHASJBRCCCGEEEIIIYQQooWTIGHL84GvCyD8ltQdURdSb0RdSL0RjUnqm6gLqTeiLqTeiLqQeiMajcxJKIQQQgghhBBCCCFECyc9CYUQQgghhBBCCCGEaOEkSCiEEEIIIYQQQgghRAsnQcJGoJT6u1IqUSm1WSm1USl1agNeK0EpdX0V+7ZWsm+RUqpOy6orpcYqpUaW275LKXWz6/VkpVT7upxXgFLK4aozW5VSPyilQqrJv18pFeMl/Tml1KOu1y8opcbV8PpSZ5ogpVReJemfKaWurOM5Byulzi+3fbFS6gnX60uVUn1reT63+lFVXapwnNf3JuqXUkorpV4rt/2oUuq5Whw/WSmVrpTaoJTarZSaW/5vug7l8Vo/5B7UPEm7SOpXXUm7SHgj7SJRH6RtJPchYZAgYQNTSp0OXAgM1VoPBMYBh07ynJYqdicAXhvDDWgsUHpD0VpP0Vp/4dqcDMgNpe4KtdaDtdb9gRLgrpM9odb6Ga31gpMv2kkZi9SZpmYwUNoY1lrP1Fq/4tq8FKhVY1g0ecXA5d4+PNfCVK31EK11D+AVYLpSqk/9FK/BjUXuQT4h7SKpXydJ2kWisUi7qOWRtpHchwQSJGwM7YAMrXUxgNY6Q2t9FEApNUwptVgptc71pKGdK727UmqBUmqTUmq9UqqbK7K/VCk1E9imlDIrpf6jlFrjehJ/p+t6rwCjXE9ZH6qsUEqpYKXUd0qp7Uqpn4DgcvvOVUqtcF37B6VUmCt9v1LqeVf6FqVUb6VUAkYD7SHXNUedeDrrenI3HPjate8CpdTP5a4z3nVtUTNLge6uuvDriUSl1P+UUpPL5XvM9ftZrZTqXvEk5Z+qKqVGKKWWu+raaqVUeGUXlzrT9CjD/5RSO5VSC4C4cvsqu78sUkr9y/X73uX6+QcALwDXuH7u17ieGP7P9UTxYuA/rn3dlFLry12nR/ntGpZ7slJqulJqjjKetP7bS54YV526wFXnFymlpimldiilvlZKKVe+c5TxxHaLUuoTpVSgq15Pd+2/RClVqJQKUEoFKaX2VvZzqPUvwL/ZMVbK8/g/oYwn1L8r43/LQqVUp+pOprX+w3W+O1znKH2S7fpd7i937qWue8J6VYsn7HIPajakXST1q75Iu0jqjBtlkHaRtIvqStpGch8SSJCwMcwD4l0323eVUmMAlFJW4G3gSq31MOAT4EXXMV8D72itB2FE85Nd6UOBB7TWPYFbgWyt9QhgBHC7UqoL8ASw1PWU9Y0qyvVXoEBr3Qd4FhjmKlcM8H/AOK31UGAt8HC54zJc6e8Bj2qt9wNTgDdc11x6IqPWeprr+Bu01oOB34DeSqlYV5ZbXO9bVEMZvSTOA7bUIHu21noA8D/gzSrOGQBMxahTgzB6cxRWcV6pM03PZUAvjKfZN+N6+lfN/QXAorU+BXgQeFZrXQI8g/H0c7DWeuqJjFrr5cBM4G+ufXuAbKXUYFeWW4BP61D2wcA1wACMRnj8iR1KqTbALOAZrfUsV/IQV3n7Al2BM5RSQcBnwDWuOm/BqKcbXOcHGAVsxbhPngqsquznUIf34O/eAW5QSkVWSH8b+NzVy+tr4K0anm890LuaPGnAeNc94ZpanBvkHtRcSLtI6tdJk3aR1JlKSLtI2kUnS9pGch9q8SRI2MC01nkYf6x3AOnAVGU83ewF9AfmK6U2YvwRd1TGE8sOWuufXMcXaa0LXKdbrbXe53p9LnCz69hVQGugRy2KNhr4ynWNzcBmV/ppGP9slrnOPQnoXO646a7v6zCG8NSY1loDXwI3KqWigNOB2bU5RwsU7Po9rAUOAh/X4Jhvy30/vYp8vYBkrfUaAK11jtbaXkV+qTNNz2jgW621w9UT53dXutf7S7nj6vw7cfkIuEUpZcZozHzjJY+uJm2h1jpba10EbKOszliBhcBjWuv55fKv1lof1lo7gY2ucvcC9mmtd7nyfA6MdtXjPcoY3nEK8DrGz2oURs+TE0725+DXtNY5wBfA/RV2nU7Z7/RL4MwanlLVII8V+FAptQX4gdoN15J7UDMg7aIyUr/qRNpFUmeqIu0iaRedFGkbVU/uQ81fVXO4iHqitXYAi4BFrj/+SRh/kIlaa7fGiqpiWAOQXz4rcJ/Wem6F48eeZHEVMF9rfV0l+4td3x3Urf58CvwCFAE/VNP4Eq65d8onKKXsuAf4gyocoyt53VCkzjQ9Ci/3l3JO9nfyI8bTyt+BdVrrY17yHANalduOBjK8lKFiOewY98cJwOIa5K/MEoxeJjZgAcaTdTPwNy/nrOvPoTl4E+Mpd116PVQ0BNjuel3+PlX+HvUQkAoMcu0vqofryj3Iz0i7yI3Ur9qRdpHUmbqQdpG0i2pD2kbVk/tQMyY9CRuYUqqXUqr8k+zBwAFgJxCrjAm8UUpZlVL9tNa5wGGl1KWu9EDlfeW2ucBfXd3nUUr1VEqFArlAVQ3qE5bgmshbKdUfGOhKX4nRXb27a1+oUqpnNeeq6ppu+1xP9Y5iPMGrjxtvS3QA6OuqG1HAORX2X1Pu+4oqzrMTaKeUGgHGBzFV9eTvUmeaniUYQ1LMyphb5yxXutf7SzXnqs3vpAjjHvQelf9OFmE8YTzxBHXS/7d3/y5yVVEcwL8HLSwCwUrLYArT5l8I+AeIgooEg4WFYAKiCIJgK6yNEfEXGIWgKJgipUUKIQEhWRPFRUURUio2Cv4Acy3uW7K7bpyszubN7Pt8mlnmvZ09y96978yZe89Lcm5GDEl/A/d4+jaG52ac+3WSA3W9x9TRXE+gP03fLnOhtfZj+qqie9O32DBorf2c5MP0rZrrzid5ePj60WxeZbCtYcvoE0neGp76IcOWlyQb7yy5P32lzrX0v9dtOwjXHLQHyIuMr10gL2KdvEhe9L/JjWYfMw/tbYqEu29fkner6ququpK+7PfFodfFg0leqqrL6cvE15uUHk1yfDj/fJK7t3ndt9OXol+qfgv0N9I/BbiS5K/qDZdv2KA7/SK2r6rW0hvzXkyS4aJxLMn7w8+/kNl9FM4mub+GJqdbjp1K8vpwbL2R6ukkV1tra2HHWmtX0y9cXw6Pq1tOuXP4253INo13N7zOn+kJ88lhDH6Sf376vpExs3jOJPk2fS54L8Obnxnzy42cS3+T9XlVPbTl2AdJnq3eCPvg8NzpJNfS+4tt5830hOLyEMO+JCs380sNq4weSXKkqp78l/N+T++D8tGwGulaej+VpG83vCs9eUr63PjFsEWCzV5OsvFOfk+lb5u6kn49OnGD71tv6P5NkueTPLDhf3QlvWCzuuW1X0vy2DAmDmXzSrBZzEF7g7zI+JoreREbyIvkRfMiN9rsVMxDk1HmBW61qno1yWpr7Wb6yIAxs4Cq6pkk+1trL4wdC+w2cxC7yfhip4yZxSMvYmrMQ3uXIiG3VFVdTP905L7W2h+zzgdjZvFU1ZkkB5Mcaa39NOt8WGbmIHaT8cVOGTOLR17E1JiH9jZFQgAAAACYOD0JAQAAAGDiFAkBAAAAYOIUCQEAAABg4hQJAXagqn4dHg9U1W9VtVpVa1X1WVUdGzk8AIBbSm4EsHfcPnYAAEvsu9ba4SSpqnuSfFxV1Vp7Z+S4AADGIDcCWGJWEgLMQWvt+yRPJzk+diwAAGOTGwEsH0VCgPm5lOTQ2EEAACwIuRHAElEkBJifGjsAAIAFIjcCWCKKhADzczjJ2thBAAAsCLkRwBJRJASYg6o6kGQlyclxIwEAGJ/cCGD5uLsxwH93sKpWk9yR5Jckr7TWTo0bEgDAaORGAEusWmtjxwAAAAAAjMh2YwAAAACYOEVCAAAAAJg4RUIAAAAAmDhFQgAAAACYOEVCAAAAAJg4RUIAAAAAmDhFQgAAAACYuL8BCtj3w9hZ3I4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1293.75x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "id": "cTltwcEuw1El",
        "outputId": "540ca10f-1566-4924-9612-cf213e265dca"
      },
      "source": [
        "sns.catplot(x='MONTH', y='APPEARANCES', hue='origins', aspect=2.2,\n",
        "            col='ALIVE', kind='strip', data=superhero);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1660.97x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "U9EVJnw_9bfi",
        "outputId": "6866d633-6b3d-4102-b377-21dfcfdbd668"
      },
      "source": [
        "sns.catplot(x='SEX', y='APPEARANCES', hue='origins', aspect=1.3,\n",
        "            col='ALIVE', kind='strip', data=superhero);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1012.98x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "id": "9KA8Etgm_NoU",
        "outputId": "e3967ac5-38b5-4696-c31e-03553b593570"
      },
      "source": [
        "sns.catplot(x='EYE', y='APPEARANCES', hue='origins', aspect=2.2,\n",
        "            col='ALIVE', kind='strip', data=superhero);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1660.97x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "id": "FGNatGem_RpK",
        "outputId": "95f7daa8-92fe-4299-b3c6-9455456fbb5a"
      },
      "source": [
        "sns.catplot(x='HAIR', y='APPEARANCES', hue='origins', aspect=2.2,\n",
        "            col='ALIVE', kind='strip', data=superhero);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1660.97x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ujl-e11wMhBK"
      },
      "source": [
        "# 6.&nbsp;Data Wrangling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l2fBSYcFN8GJ"
      },
      "source": [
        "## *Discretize* `APPEARANCES`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iv9q2NTRip5z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8dcbd4fd-ae76-478b-c458-40e9f9711e67"
      },
      "source": [
        "from sklearn.preprocessing import KBinsDiscretizer\n",
        "\n",
        "def discrete_appear(col):\n",
        "  if col > 1000:\n",
        "    return '>1000'\n",
        "  elif col > 500:\n",
        "    return '>500'\n",
        "  elif col > 400:\n",
        "    return '>400'\n",
        "  elif col > 300:\n",
        "    return '>300'\n",
        "  elif col > 200:\n",
        "    return '>200'\n",
        "  elif col > 100:\n",
        "    return '>100'\n",
        "  elif col > 60:\n",
        "    return '>60'\n",
        "  elif col > 30:\n",
        "    return '>30'\n",
        "  return str(int(col))\n",
        "\n",
        "# kbind = KBinsDiscretizer(strategy='uniform', encode='ordinal', n_bins=10)\n",
        "# superhero.loc[:, 'APPEAR_BIN'] = kbind.fit_transform(superhero.loc[:, ['APPEARANCES']])[:, 0]\n",
        "superhero.loc[:, 'APPEAR_CAT'] = superhero.APPEARANCES.apply(discrete_appear)\n",
        "\n",
        "superhero.loc[:, ['APPEAR_CAT']].value_counts()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "APPEAR_CAT\n",
              "1             5530\n",
              "2             2646\n",
              "3             1824\n",
              "4             1519\n",
              "5             1134\n",
              ">30           1007\n",
              "6              929\n",
              "7              703\n",
              "8              576\n",
              "9              479\n",
              ">60            422\n",
              "10             410\n",
              ">100           368\n",
              "11             339\n",
              "12             305\n",
              "13             281\n",
              "14             268\n",
              "16             210\n",
              "15             209\n",
              "17             181\n",
              "19             167\n",
              "18             162\n",
              "20             138\n",
              ">200           129\n",
              "21             120\n",
              "23             102\n",
              "22              99\n",
              "24              95\n",
              "25              90\n",
              "26              83\n",
              "28              80\n",
              "29              73\n",
              ">300            70\n",
              "27              68\n",
              "30              65\n",
              ">500            59\n",
              ">1000           41\n",
              ">400            25\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZUpktq3jOBtF"
      },
      "source": [
        "## *Discretize* `YEAR`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5_H_zqrhUda",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62de8b41-b4e5-4622-f539-117535b7b28b"
      },
      "source": [
        "def discrete_year(col):\n",
        "  if col < 1970:\n",
        "    return '<1970'\n",
        "  elif col < 1980:\n",
        "    return '<1980'\n",
        "  elif col < 1985:\n",
        "    return '<1985'\n",
        "  elif col < 1990:\n",
        "    return '<1990'\n",
        "  elif col < 1995:\n",
        "    return '<1995'  \n",
        "  elif col < 2000:\n",
        "    return '<2000'\n",
        "  elif col < 2005:\n",
        "    return '<2005'\n",
        "  return '>=2005'\n",
        "\n",
        "# kbind = KBinsDiscretizer(strategy='uniform', encode='ordinal')\n",
        "# superhero.loc[:, 'YEAR_BIN'] = kbind.fit_transform(superhero.loc[:, ['APPEARANCES']])[:, 0]\n",
        "superhero.loc[:, 'YEAR_CAT'] = superhero.YEAR.apply(discrete_year)\n",
        "\n",
        "superhero.loc[:, ['YEAR_CAT']].value_counts()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "YEAR_CAT\n",
              ">=2005      4008\n",
              "<1970       3865\n",
              "<1995       2917\n",
              "<1980       2515\n",
              "<1990       2185\n",
              "<2000       2095\n",
              "<2005       1861\n",
              "<1985       1560\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2s5DyooPn_Hu"
      },
      "source": [
        "## One-Hot Fitur"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kDsbiV43nWVr"
      },
      "source": [
        "superhero2 = superhero.copy()"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "KeO9mlNfoPlc",
        "outputId": "74c8131e-b272-4264-f6a9-1f80bd48d4e3"
      },
      "source": [
        "superhero = superhero.set_index('name').sample(frac=1.0, random_state=233)\n",
        "superhero = superhero.drop(['APPEARANCES', 'YEAR', 'FIRST APPEARANCE'], axis=1)\n",
        "\n",
        "superhero_names = superhero.index\n",
        "superhero_names = pd.DataFrame({'name':superhero_names})\n",
        "\n",
        "superhero"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>ALIGN</th>\n",
              "      <th>EYE</th>\n",
              "      <th>HAIR</th>\n",
              "      <th>SEX</th>\n",
              "      <th>ALIVE</th>\n",
              "      <th>origins</th>\n",
              "      <th>MONTH</th>\n",
              "      <th>APPEAR_CAT</th>\n",
              "      <th>YEAR_CAT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Sir Lancelot (Earth-616)</th>\n",
              "      <td>Public Identity</td>\n",
              "      <td>Good Characters</td>\n",
              "      <td>others</td>\n",
              "      <td>Black Hair</td>\n",
              "      <td>Male Characters</td>\n",
              "      <td>Living Characters</td>\n",
              "      <td>Marvel</td>\n",
              "      <td>May</td>\n",
              "      <td>5</td>\n",
              "      <td>&lt;1970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tani Uiruson (Earth-616)</th>\n",
              "      <td>Identity Unknown</td>\n",
              "      <td>Bad Characters</td>\n",
              "      <td>others</td>\n",
              "      <td>others</td>\n",
              "      <td>Male Characters</td>\n",
              "      <td>Living Characters</td>\n",
              "      <td>Marvel</td>\n",
              "      <td>August</td>\n",
              "      <td>2</td>\n",
              "      <td>&lt;1985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Remora (Atlantean) (Earth-616)</th>\n",
              "      <td>Identity Unknown</td>\n",
              "      <td>Good Characters</td>\n",
              "      <td>others</td>\n",
              "      <td>others</td>\n",
              "      <td>Male Characters</td>\n",
              "      <td>Living Characters</td>\n",
              "      <td>Marvel</td>\n",
              "      <td>January</td>\n",
              "      <td>2</td>\n",
              "      <td>&lt;1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Paul Hoben (New Earth)</th>\n",
              "      <td>Public Identity</td>\n",
              "      <td>Good Characters</td>\n",
              "      <td>others</td>\n",
              "      <td>Brown Hair</td>\n",
              "      <td>Male Characters</td>\n",
              "      <td>Living Characters</td>\n",
              "      <td>DC</td>\n",
              "      <td>September</td>\n",
              "      <td>15</td>\n",
              "      <td>&lt;1985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Radiant (New Earth)</th>\n",
              "      <td>Identity Unknown</td>\n",
              "      <td>Good Characters</td>\n",
              "      <td>others</td>\n",
              "      <td>others</td>\n",
              "      <td>others</td>\n",
              "      <td>Deceased Characters</td>\n",
              "      <td>DC</td>\n",
              "      <td>May</td>\n",
              "      <td>4</td>\n",
              "      <td>&lt;1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brain (Business Man) (Earth-616)</th>\n",
              "      <td>Secret Identity</td>\n",
              "      <td>Bad Characters</td>\n",
              "      <td>others</td>\n",
              "      <td>No Hair</td>\n",
              "      <td>Male Characters</td>\n",
              "      <td>Deceased Characters</td>\n",
              "      <td>Marvel</td>\n",
              "      <td>November</td>\n",
              "      <td>1</td>\n",
              "      <td>&lt;1970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Junzo Muto (Earth-616)</th>\n",
              "      <td>Public Identity</td>\n",
              "      <td>Bad Characters</td>\n",
              "      <td>others</td>\n",
              "      <td>Black Hair</td>\n",
              "      <td>Male Characters</td>\n",
              "      <td>Living Characters</td>\n",
              "      <td>Marvel</td>\n",
              "      <td>February</td>\n",
              "      <td>7</td>\n",
              "      <td>&lt;2005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Baron Zebek (Earth-616)</th>\n",
              "      <td>No Dual Identity</td>\n",
              "      <td>Bad Characters</td>\n",
              "      <td>others</td>\n",
              "      <td>others</td>\n",
              "      <td>Male Characters</td>\n",
              "      <td>Living Characters</td>\n",
              "      <td>Marvel</td>\n",
              "      <td>May</td>\n",
              "      <td>2</td>\n",
              "      <td>&lt;2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vincenzo Baldi (New Earth)</th>\n",
              "      <td>Identity Unknown</td>\n",
              "      <td>Neutral Characters</td>\n",
              "      <td>others</td>\n",
              "      <td>Black Hair</td>\n",
              "      <td>Male Characters</td>\n",
              "      <td>Deceased Characters</td>\n",
              "      <td>DC</td>\n",
              "      <td>July</td>\n",
              "      <td>4</td>\n",
              "      <td>&gt;=2005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rachel Carpenter (Earth-616)</th>\n",
              "      <td>Public Identity</td>\n",
              "      <td>Good Characters</td>\n",
              "      <td>Brown Eyes</td>\n",
              "      <td>Blond Hair</td>\n",
              "      <td>Female Characters</td>\n",
              "      <td>Living Characters</td>\n",
              "      <td>Marvel</td>\n",
              "      <td>January</td>\n",
              "      <td>&gt;30</td>\n",
              "      <td>&lt;1990</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21006 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                ID  ... YEAR_CAT\n",
              "name                                                ...         \n",
              "Sir Lancelot (Earth-616)           Public Identity  ...    <1970\n",
              "Tani Uiruson (Earth-616)          Identity Unknown  ...    <1985\n",
              "Remora (Atlantean) (Earth-616)    Identity Unknown  ...    <1995\n",
              "Paul Hoben (New Earth)             Public Identity  ...    <1985\n",
              "Radiant (New Earth)               Identity Unknown  ...    <1995\n",
              "...                                            ...  ...      ...\n",
              "Brain (Business Man) (Earth-616)   Secret Identity  ...    <1970\n",
              "Junzo Muto (Earth-616)             Public Identity  ...    <2005\n",
              "Baron Zebek (Earth-616)           No Dual Identity  ...    <2000\n",
              "Vincenzo Baldi (New Earth)        Identity Unknown  ...   >=2005\n",
              "Rachel Carpenter (Earth-616)       Public Identity  ...    <1990\n",
              "\n",
              "[21006 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 988
        },
        "id": "q9_Wkr2qpdK-",
        "outputId": "ef6e2637-903a-4530-d699-1e360168d7a3"
      },
      "source": [
        "super_hot = pd.get_dummies(superhero)\n",
        "super_hot"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID_Identity Unknown</th>\n",
              "      <th>ID_No Dual Identity</th>\n",
              "      <th>ID_Public Identity</th>\n",
              "      <th>ID_Secret Identity</th>\n",
              "      <th>ALIGN_Bad Characters</th>\n",
              "      <th>ALIGN_Good Characters</th>\n",
              "      <th>ALIGN_Neutral Characters</th>\n",
              "      <th>ALIGN_others</th>\n",
              "      <th>EYE_Black Eyes</th>\n",
              "      <th>EYE_Blue Eyes</th>\n",
              "      <th>EYE_Brown Eyes</th>\n",
              "      <th>EYE_Green Eyes</th>\n",
              "      <th>EYE_Grey Eyes</th>\n",
              "      <th>EYE_Red Eyes</th>\n",
              "      <th>EYE_White Eyes</th>\n",
              "      <th>EYE_Yellow Eyes</th>\n",
              "      <th>EYE_others</th>\n",
              "      <th>EYE_rare</th>\n",
              "      <th>HAIR_Black Hair</th>\n",
              "      <th>HAIR_Blond Hair</th>\n",
              "      <th>HAIR_Brown Hair</th>\n",
              "      <th>HAIR_Green Hair</th>\n",
              "      <th>HAIR_Grey Hair</th>\n",
              "      <th>HAIR_No Hair</th>\n",
              "      <th>HAIR_Red Hair</th>\n",
              "      <th>HAIR_White Hair</th>\n",
              "      <th>HAIR_others</th>\n",
              "      <th>HAIR_rare</th>\n",
              "      <th>SEX_Female Characters</th>\n",
              "      <th>SEX_Male Characters</th>\n",
              "      <th>SEX_others</th>\n",
              "      <th>ALIVE_Deceased Characters</th>\n",
              "      <th>ALIVE_Living Characters</th>\n",
              "      <th>origins_DC</th>\n",
              "      <th>origins_Marvel</th>\n",
              "      <th>MONTH_April</th>\n",
              "      <th>MONTH_August</th>\n",
              "      <th>MONTH_December</th>\n",
              "      <th>MONTH_February</th>\n",
              "      <th>MONTH_January</th>\n",
              "      <th>...</th>\n",
              "      <th>APPEAR_CAT_15</th>\n",
              "      <th>APPEAR_CAT_16</th>\n",
              "      <th>APPEAR_CAT_17</th>\n",
              "      <th>APPEAR_CAT_18</th>\n",
              "      <th>APPEAR_CAT_19</th>\n",
              "      <th>APPEAR_CAT_2</th>\n",
              "      <th>APPEAR_CAT_20</th>\n",
              "      <th>APPEAR_CAT_21</th>\n",
              "      <th>APPEAR_CAT_22</th>\n",
              "      <th>APPEAR_CAT_23</th>\n",
              "      <th>APPEAR_CAT_24</th>\n",
              "      <th>APPEAR_CAT_25</th>\n",
              "      <th>APPEAR_CAT_26</th>\n",
              "      <th>APPEAR_CAT_27</th>\n",
              "      <th>APPEAR_CAT_28</th>\n",
              "      <th>APPEAR_CAT_29</th>\n",
              "      <th>APPEAR_CAT_3</th>\n",
              "      <th>APPEAR_CAT_30</th>\n",
              "      <th>APPEAR_CAT_4</th>\n",
              "      <th>APPEAR_CAT_5</th>\n",
              "      <th>APPEAR_CAT_6</th>\n",
              "      <th>APPEAR_CAT_7</th>\n",
              "      <th>APPEAR_CAT_8</th>\n",
              "      <th>APPEAR_CAT_9</th>\n",
              "      <th>APPEAR_CAT_&gt;100</th>\n",
              "      <th>APPEAR_CAT_&gt;1000</th>\n",
              "      <th>APPEAR_CAT_&gt;200</th>\n",
              "      <th>APPEAR_CAT_&gt;30</th>\n",
              "      <th>APPEAR_CAT_&gt;300</th>\n",
              "      <th>APPEAR_CAT_&gt;400</th>\n",
              "      <th>APPEAR_CAT_&gt;500</th>\n",
              "      <th>APPEAR_CAT_&gt;60</th>\n",
              "      <th>YEAR_CAT_&lt;1970</th>\n",
              "      <th>YEAR_CAT_&lt;1980</th>\n",
              "      <th>YEAR_CAT_&lt;1985</th>\n",
              "      <th>YEAR_CAT_&lt;1990</th>\n",
              "      <th>YEAR_CAT_&lt;1995</th>\n",
              "      <th>YEAR_CAT_&lt;2000</th>\n",
              "      <th>YEAR_CAT_&lt;2005</th>\n",
              "      <th>YEAR_CAT_&gt;=2005</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Sir Lancelot (Earth-616)</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tani Uiruson (Earth-616)</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Remora (Atlantean) (Earth-616)</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Paul Hoben (New Earth)</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Radiant (New Earth)</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brain (Business Man) (Earth-616)</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Junzo Muto (Earth-616)</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Baron Zebek (Earth-616)</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vincenzo Baldi (New Earth)</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rachel Carpenter (Earth-616)</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21006 rows × 93 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                  ID_Identity Unknown  ...  YEAR_CAT_>=2005\n",
              "name                                                   ...                 \n",
              "Sir Lancelot (Earth-616)                            0  ...                0\n",
              "Tani Uiruson (Earth-616)                            1  ...                0\n",
              "Remora (Atlantean) (Earth-616)                      1  ...                0\n",
              "Paul Hoben (New Earth)                              0  ...                0\n",
              "Radiant (New Earth)                                 1  ...                0\n",
              "...                                               ...  ...              ...\n",
              "Brain (Business Man) (Earth-616)                    0  ...                0\n",
              "Junzo Muto (Earth-616)                              0  ...                0\n",
              "Baron Zebek (Earth-616)                             0  ...                0\n",
              "Vincenzo Baldi (New Earth)                          1  ...                1\n",
              "Rachel Carpenter (Earth-616)                        0  ...                0\n",
              "\n",
              "[21006 rows x 93 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HvGlAksAqaKw"
      },
      "source": [
        "# 7.&nbsp;Recommendation Systems\n",
        "\n",
        "<small>note: nama superhero harus `sama persis` dengan yang terdapat di dataset</small>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t4WKp-SuJvoH"
      },
      "source": [
        "super_hot[super_hot.index.str.contains('Captain America')]"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gDt1Yt_vwaEw"
      },
      "source": [
        "from sklearn.neighbors import NearestNeighbors\n",
        "\n",
        "def get_recommended_heroes(model, df, name_df, hero_name:str, kind:str='jaccard', num_recommend:int=5):\n",
        "  '''\n",
        "  Fungsi mendapatkan rekomendasi superheroes figures \n",
        "  berdasarkan dua hitungan jarak, 'dice' atau 'cosine'\n",
        "  '''\n",
        "  print(f'Apabila anda menyukai action figure \\'{hero_name}\\'')\n",
        "  print('5 action figure berikut ini juga mungkin anda sukai:')\n",
        "\n",
        "  # handle nama hero itu sendiri (yang sedang diberikan rekomendasi)\n",
        "  num_recommend = num_recommend+1\n",
        "\n",
        "  if kind == 'jaccard':\n",
        "    distances, neighbors = model.kneighbors(df.loc[[hero_name]], n_neighbors=num_recommend)\n",
        "    similar_heroes = []\n",
        "    for name_hero in name_df.loc[neighbors[0][:]].values:\n",
        "      similar_heroes.append(name_hero[0])\n",
        "\n",
        "    similar_distances = []\n",
        "    for distance in distances[0]:\n",
        "      similar_distances.append(f'{round(100-distance, 3)}%')\n",
        "\n",
        "  elif kind == 'cosine':\n",
        "    cos_df = df.loc[:, [hero_name]].sort_values(by=hero_name, ascending=False)\n",
        "    cos_df = cos_df.head(num_recommend)\n",
        "\n",
        "    similar_heroes = cos_df.index.tolist()\n",
        "    similar_distances = cos_df.loc[:, hero_name].apply(\n",
        "        lambda x: f'{round(x*100, 3)}%'\n",
        "    ).values.tolist()\n",
        "\n",
        "  else:\n",
        "    similar_heroes, similar_distances = [0], [0]\n",
        "\n",
        "  # hasil rekomendasi\n",
        "  results_df = pd.DataFrame(data={\n",
        "      'Superheroes' : similar_heroes, \n",
        "      'Tingkat Kesamaan' : similar_distances\n",
        "      }).set_index('Superheroes')\n",
        "  \n",
        "  results_df.loc[:, 'temp_score'] = results_df.loc[:, 'Tingkat Kesamaan'].apply(\n",
        "      lambda x: float(x.split('%')[0])\n",
        "  )\n",
        "\n",
        "  # menghilangkan nama tokoh yang sedang coba direkomendasikan\n",
        "  results_df = results_df.drop([hero_name])\n",
        "\n",
        "  # urutkan N kemiripan tertinggi\n",
        "  results_df = results_df.sort_values(by='temp_score', ascending=False)\n",
        "  results_df = results_df.drop(['temp_score'], axis=1)\n",
        "\n",
        "  return results_df"
      ],
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B8DC_QFyWnFo"
      },
      "source": [
        "## Nearest Neighbors (NN) - Jaccard"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aa0Xvrk6Wqlp",
        "outputId": "da7102c1-2f9b-4cb4-c667-6bac242a872b"
      },
      "source": [
        "nnjc = NearestNeighbors(metric='jaccard')\n",
        "\n",
        "nnjc.fit(super_hot)"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NearestNeighbors(metric='jaccard')"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "Hu4LV0FEWwZB",
        "outputId": "8b23ae9e-ea57-414d-cc1a-65288abfe560"
      },
      "source": [
        "nnjac_recom_aquaman = get_recommended_heroes(nnjc, super_hot, superhero_names, 'Aquaman (Arthur Curry)')\n",
        "nnjac_recom_aquaman"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Apabila anda menyukai action figure 'Aquaman (Arthur Curry)'\n",
            "5 action figure berikut ini juga mungkin anda sukai:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/pairwise.py:1870: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
            "  warnings.warn(msg, DataConversionWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tingkat Kesamaan</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Superheroes</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Dinah Laurel Lance (New Earth)</th>\n",
              "      <td>99.818%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jonathan Storm (Earth-616)</th>\n",
              "      <td>99.818%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Warren Worthington III (Earth-616)</th>\n",
              "      <td>99.667%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Steven Trevor (New Earth)</th>\n",
              "      <td>99.667%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bulldozer Nichols (New Earth)</th>\n",
              "      <td>99.667%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   Tingkat Kesamaan\n",
              "Superheroes                                        \n",
              "Dinah Laurel Lance (New Earth)              99.818%\n",
              "Jonathan Storm (Earth-616)                  99.818%\n",
              "Warren Worthington III (Earth-616)          99.667%\n",
              "Steven Trevor (New Earth)                   99.667%\n",
              "Bulldozer Nichols (New Earth)               99.667%"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "jiahRb8fXnAi",
        "outputId": "5a985595-f104-417f-c0eb-f6559245d618"
      },
      "source": [
        "nnjac_recom_captain = get_recommended_heroes(nnjc, super_hot, superhero_names, 'Captain America (Steven Rogers)')\n",
        "nnjac_recom_captain"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Apabila anda menyukai action figure 'Captain America (Steven Rogers)'\n",
            "5 action figure berikut ini juga mungkin anda sukai:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/pairwise.py:1870: DataConversionWarning: Data was converted to boolean for metric jaccard\n",
            "  warnings.warn(msg, DataConversionWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tingkat Kesamaan</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Superheroes</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Iron Man (Anthony \\\"Tony\\\" Stark)</th>\n",
              "      <td>99.818%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lieutenant General Fredricks (Earth-616)</th>\n",
              "      <td>99.667%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Clay Harder (Earth-616)</th>\n",
              "      <td>99.667%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alexander Summers (Earth-616)</th>\n",
              "      <td>99.667%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Norrin Radd (Earth-616)</th>\n",
              "      <td>99.667%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Tingkat Kesamaan\n",
              "Superheroes                                              \n",
              "Iron Man (Anthony \\\"Tony\\\" Stark)                 99.818%\n",
              "Lieutenant General Fredricks (Earth-616)          99.667%\n",
              "Clay Harder (Earth-616)                           99.667%\n",
              "Alexander Summers (Earth-616)                     99.667%\n",
              "Norrin Radd (Earth-616)                           99.667%"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bVvqziVHtUz0"
      },
      "source": [
        "## Cosine Similarity"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8J_MduBrtPHv",
        "outputId": "84e4271a-c1cc-41a7-a501-308cebd63e08"
      },
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "cos_sim = cosine_similarity(super_hot)\n",
        "cos_sim_df = pd.DataFrame(cos_sim, \n",
        "                          index=superhero_names['name'], \n",
        "                          columns=superhero_names['name'])\n",
        "cos_sim_df"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>name</th>\n",
              "      <th>Sir Lancelot (Earth-616)</th>\n",
              "      <th>Tani Uiruson (Earth-616)</th>\n",
              "      <th>Remora (Atlantean) (Earth-616)</th>\n",
              "      <th>Paul Hoben (New Earth)</th>\n",
              "      <th>Radiant (New Earth)</th>\n",
              "      <th>Dolores Downing (Earth-616)</th>\n",
              "      <th>Achilles (New Earth)</th>\n",
              "      <th>Peter Cannon (New Earth)</th>\n",
              "      <th>D.C.1. (A.I.M.) (Earth-616)</th>\n",
              "      <th>Claudio Volpe (New Earth)</th>\n",
              "      <th>Sari Tartar (Earth-616)</th>\n",
              "      <th>Samuel Benjamin (New Earth)</th>\n",
              "      <th>Michael Duffy (Earth-616)</th>\n",
              "      <th>Benjamin Addison (New Earth)</th>\n",
              "      <th>Scandium (Element) (Earth-616)</th>\n",
              "      <th>Roadkill (Earth-616)</th>\n",
              "      <th>William Hastings (Earth-616)</th>\n",
              "      <th>Eileen Flowers (New Earth)</th>\n",
              "      <th>Requiem (Earth-616)</th>\n",
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              "      <th>Mark Armstrong (New Earth)</th>\n",
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              "      <th>Romnar (Earth-616)</th>\n",
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              "      <th>Mark Sheppard (Earth-616)</th>\n",
              "      <th>X (Thanosi) (Earth-616)</th>\n",
              "      <th>Germaine Caruso (Earth-616)</th>\n",
              "      <th>Farley Stillwell (Earth-616)</th>\n",
              "      <th>Sally (Barmaid) (Earth-616)</th>\n",
              "      <th>Robert Bruce Banner Jr. (Counter-Earth) (Earth-616)</th>\n",
              "      <th>Pyotr Vasilievich Starkovsky (Earth-616)</th>\n",
              "      <th>Lucifer (Quist) (Earth-616)</th>\n",
              "      <th>Stearn (New Earth)</th>\n",
              "      <th>Carson Bells (Earth-616)</th>\n",
              "      <th>Lemar Hoskins (Earth-616)</th>\n",
              "      <th>Piotr Phobos (Earth-616)</th>\n",
              "      <th>Cynas (Earth-616)</th>\n",
              "      <th>Sarah Mumford (Earth-616)</th>\n",
              "      <th>Gareth Thomas (Earth-616)</th>\n",
              "      <th>...</th>\n",
              "      <th>Janos Quested (Earth-616)</th>\n",
              "      <th>Elaine Kendrick (Earth-616)</th>\n",
              "      <th>Little Barda (New Earth)</th>\n",
              "      <th>Patch (Wildkat Klub) (Earth-616)</th>\n",
              "      <th>Phoebe Marrs (Earth-616)</th>\n",
              "      <th>Alpha (Mad Thinker Robot) (Earth-616)</th>\n",
              "      <th>Abbas al-Abbad (Earth-616)</th>\n",
              "      <th>Arthur Blackwood (Earth-616)</th>\n",
              "      <th>Richard Drakunovski (New Earth)</th>\n",
              "      <th>Dominic Mndawe (New Earth)</th>\n",
              "      <th>Appa Ali Apsa (New Earth)</th>\n",
              "      <th>Strangler (Earth-616)</th>\n",
              "      <th>Walter Haley (New Earth)</th>\n",
              "      <th>Twist Staley (Earth-616)</th>\n",
              "      <th>Jacob Freeman (New Earth)</th>\n",
              "      <th>Andrew Franklin (New Earth)</th>\n",
              "      <th>Eric Hertz (Earth-616)</th>\n",
              "      <th>J.A.K.E. I (New Earth)</th>\n",
              "      <th>Falcona (Earth-616)</th>\n",
              "      <th>Mildred Manor (Earth-616)</th>\n",
              "      <th>Nick Iona (Earth-616)</th>\n",
              "      <th>Carmella (Earth-616)</th>\n",
              "      <th>Squagga (New Earth)</th>\n",
              "      <th>Gigantus (New Earth)</th>\n",
              "      <th>Singer (Earth-616)</th>\n",
              "      <th>Gullywhump (Earth-616)</th>\n",
              "      <th>Tisiphone (New Earth)</th>\n",
              "      <th>Gale (Institute) (Earth-616)</th>\n",
              "      <th>Winston Kranpuff (Earth-616)</th>\n",
              "      <th>Promise (New Earth)</th>\n",
              "      <th>Lisa Jennings (New Earth)</th>\n",
              "      <th>Deb Kafka (New Earth)</th>\n",
              "      <th>Frederick Hudson (Earth-616)</th>\n",
              "      <th>Sigmund Adler (Earth-616)</th>\n",
              "      <th>Sebastian Shaw (Earth-616)</th>\n",
              "      <th>Brain (Business Man) (Earth-616)</th>\n",
              "      <th>Junzo Muto (Earth-616)</th>\n",
              "      <th>Baron Zebek (Earth-616)</th>\n",
              "      <th>Vincenzo Baldi (New Earth)</th>\n",
              "      <th>Rachel Carpenter (Earth-616)</th>\n",
              "    </tr>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Sir Lancelot (Earth-616)</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>...</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tani Uiruson (Earth-616)</th>\n",
              "      <td>0.4</td>\n",
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              "      <td>0.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Remora (Atlantean) (Earth-616)</th>\n",
              "      <td>0.5</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
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              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.5</td>\n",
              "      <td>...</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Paul Hoben (New Earth)</th>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
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              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
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              "      <td>0.2</td>\n",
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              "      <td>0.3</td>\n",
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              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
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              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
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              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Radiant (New Earth)</th>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brain (Business Man) (Earth-616)</th>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>...</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Junzo Muto (Earth-616)</th>\n",
              "      <td>0.6</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Baron Zebek (Earth-616)</th>\n",
              "      <td>0.5</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vincenzo Baldi (New Earth)</th>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rachel Carpenter (Earth-616)</th>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>...</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21006 rows × 21006 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "name                              Sir Lancelot (Earth-616)  ...  Rachel Carpenter (Earth-616)\n",
              "name                                                        ...                              \n",
              "Sir Lancelot (Earth-616)                               1.0  ...                           0.4\n",
              "Tani Uiruson (Earth-616)                               0.4  ...                           0.2\n",
              "Remora (Atlantean) (Earth-616)                         0.5  ...                           0.4\n",
              "Paul Hoben (New Earth)                                 0.5  ...                           0.3\n",
              "Radiant (New Earth)                                    0.3  ...                           0.1\n",
              "...                                                    ...  ...                           ...\n",
              "Brain (Business Man) (Earth-616)                       0.4  ...                           0.1\n",
              "Junzo Muto (Earth-616)                                 0.6  ...                           0.3\n",
              "Baron Zebek (Earth-616)                                0.5  ...                           0.2\n",
              "Vincenzo Baldi (New Earth)                             0.3  ...                           0.0\n",
              "Rachel Carpenter (Earth-616)                           0.4  ...                           1.0\n",
              "\n",
              "[21006 rows x 21006 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "PTocWx0k5z05",
        "outputId": "ff1ee9cc-3559-4e6d-b9b2-2ee16e665030"
      },
      "source": [
        "cos_recom_aquaman = get_recommended_heroes(None, cos_sim_df, superhero_names, 'Aquaman (Arthur Curry)', kind='cosine')\n",
        "cos_recom_aquaman"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Apabila anda menyukai action figure 'Aquaman (Arthur Curry)'\n",
            "5 action figure berikut ini juga mungkin anda sukai:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tingkat Kesamaan</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Superheroes</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Dinah Laurel Lance (New Earth)</th>\n",
              "      <td>90.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jonathan Storm (Earth-616)</th>\n",
              "      <td>90.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bulldozer Nichols (New Earth)</th>\n",
              "      <td>80.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Clinton Barton (Earth-616)</th>\n",
              "      <td>80.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Phillip Mason (New Earth)</th>\n",
              "      <td>80.0%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Tingkat Kesamaan\n",
              "Superheroes                                    \n",
              "Dinah Laurel Lance (New Earth)            90.0%\n",
              "Jonathan Storm (Earth-616)                90.0%\n",
              "Bulldozer Nichols (New Earth)             80.0%\n",
              "Clinton Barton (Earth-616)                80.0%\n",
              "Phillip Mason (New Earth)                 80.0%"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "ZhrV1QI4o6i3",
        "outputId": "35aca7c1-c1a1-4f38-8221-d9fa8fa5a663"
      },
      "source": [
        "cos_recom_captain = get_recommended_heroes(None, cos_sim_df, superhero_names, 'Captain America (Steven Rogers)', kind='cosine')\n",
        "cos_recom_captain"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Apabila anda menyukai action figure 'Captain America (Steven Rogers)'\n",
            "5 action figure berikut ini juga mungkin anda sukai:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tingkat Kesamaan</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Superheroes</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Iron Man (Anthony \\\"Tony\\\" Stark)</th>\n",
              "      <td>90.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Benjamin Grimm (Earth-616)</th>\n",
              "      <td>80.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Scoops Winston (Earth-616)</th>\n",
              "      <td>80.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cactus (American Frontier) (Earth-616)</th>\n",
              "      <td>80.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Clinton Barton (Earth-616)</th>\n",
              "      <td>80.0%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                       Tingkat Kesamaan\n",
              "Superheroes                                            \n",
              "Iron Man (Anthony \\\"Tony\\\" Stark)                 90.0%\n",
              "Benjamin Grimm (Earth-616)                        80.0%\n",
              "Scoops Winston (Earth-616)                        80.0%\n",
              "Cactus (American Frontier) (Earth-616)            80.0%\n",
              "Clinton Barton (Earth-616)                        80.0%"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VBR11bLj9wwV"
      },
      "source": [
        "# 8.&nbsp;*Evaluation*\n",
        "\n",
        "menerapkan pendekatan kebalikan dari setiap metrik (untuk menandakan hasil rekomendasi tidak terpisah jauh atau terpisah dengan jelas dan tentu hal ini semakin baik)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y54AaVPXuN2q"
      },
      "source": [
        "import statistics as statcs\n",
        "from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score\n",
        "\n",
        "def eval_recom(df, recommended_heroes:list, hero_truth:str):\n",
        "  '''\n",
        "  Mendapatkan kelompok cluster hasil dari rekomendasi\n",
        "  '''\n",
        "  def eval_calc(X, labels):\n",
        "    # -1 (sangat buruk(salah cluster)) s.d. 1 (terbaik (cluster sangat terpisah)). dekat 0 ada tumpang tindih\n",
        "    silh = silhouette_score(X, labels)\n",
        "\n",
        "    # semakin besar cluster semakin terpisah\n",
        "    cal_har = calinski_harabasz_score(X, labels)\n",
        "\n",
        "     # mendekati 0, maka cluster makin terpisah\n",
        "    dav_bou = davies_bouldin_score(X, labels)\n",
        "\n",
        "    # metrics_df = pd.DataFrame({\n",
        "    #     'Silhouette': [silh],\n",
        "    #     'Calinski & Harabasz': [cal_har],\n",
        "    #     'Davies-Bouldin': [dav_bou]\n",
        "    # }).T\n",
        "\n",
        "    # metrics_df.columns = ['score']\n",
        "\n",
        "    # return metrics_df\n",
        "    return silh, cal_har, dav_bou\n",
        "\n",
        "  # pelabelan cluster (dengan perputaran label)\n",
        "  # hero_truth = ['Aquaman (Arthur Curry)', 'Captain America (Steven Rogers)']\n",
        "  hero_truth = [hero_truth]\n",
        "  hero_clus = hero_truth +  recommended_heroes\n",
        "  # hero_clus = [hero_clus[0]] + hero_clus[2:7] + [hero_clus[1]] + hero_clus[7:]\n",
        "\n",
        "  hero_clus_temp = hero_clus.copy()\n",
        "  hero_clus2 = hero_truth * 6\n",
        "  hero_clus2_temp = hero_clus2.copy()\n",
        "\n",
        "  # ambil informasi superhero asli (fitur2 dari df superhot)\n",
        "  filtered_recom = df.filter(items = hero_clus, axis=0)\n",
        "\n",
        "  # label_clus = [hero_truth[0]] * 6 + [hero_truth[1]] * 6\n",
        "\n",
        "  # label_clus =  hero_truth * 2 + hero_clus[2:] \n",
        "  # label_clus =  hero_truth + hero_clus[1] + hero_truth + hero_clus[3:]\n",
        "  # label_clus =  hero_truth + hero_clus[1] + hero_clus[2] + hero_truth + hero_clus[4:]\n",
        "  # label_clus =  hero_truth + hero_clus[1] + hero_clus[2] + hero_clus[3] + hero_truth + hero_clus[5]\n",
        "  # label_clus =  hero_truth + hero_clus[1] + hero_clus[2] + hero_clus[3] +  hero_clus[4] + hero_truth \n",
        "\n",
        "  label_list = []\n",
        "  silh_list, calhar_list, davbou_list = [], [], []\n",
        "\n",
        "  # proses perputarannya\n",
        "  for i in range(1, len(hero_clus)):\n",
        "    hero_clus[i] = hero_clus_temp[0]\n",
        "    hero_clus2[i] = hero_clus_temp[i]\n",
        "\n",
        "    label_list.append(hero_clus)\n",
        "    label_list.append(hero_clus2)\n",
        "\n",
        "    hero_clus = hero_clus_temp.copy()\n",
        "    hero_clus2 = hero_clus2_temp.copy()\n",
        "\n",
        "  filtered_arr = filtered_recom.values\n",
        "\n",
        "  # pengumpulan hasil perhitungan tiap metrik\n",
        "  for lbl in label_list:\n",
        "    label_arr = np.array(lbl)\n",
        "    silh_list.append(eval_calc(filtered_arr, label_arr)[0])\n",
        "    calhar_list.append(eval_calc(filtered_arr, label_arr)[1])\n",
        "    davbou_list.append(eval_calc(filtered_arr, label_arr)[2])\n",
        "  \n",
        "  silh_mean = statcs.mean(silh_list)\n",
        "  calhar_mean = statcs.mean(calhar_list)\n",
        "  davbou_mean = statcs.mean(davbou_list)\n",
        "\n",
        "  metrics_df = pd.DataFrame({\n",
        "      'Silhouette': [silh_mean],\n",
        "      'Calinski & Harabasz': [calhar_mean],\n",
        "      'Davies-Bouldin': [davbou_mean]\n",
        "      }).T\n",
        "\n",
        "  metrics_df.columns = ['score']\n",
        "\n",
        "  return metrics_df"
      ],
      "execution_count": 183,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PHU4IpcZuDl6"
      },
      "source": [
        "## NN - Jaccard"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "XO944B1wxPIF",
        "outputId": "93984f32-ee08-4c47-a560-1a7bde2996bc"
      },
      "source": [
        "aquaman_nnjac = nnjac_recom_aquaman.index.tolist()\n",
        "\n",
        "eval_recom(super_hot, aquaman_nnjac, 'Aquaman (Arthur Curry)')"
      ],
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Silhouette</th>\n",
              "      <td>0.000081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calinski &amp; Harabasz</th>\n",
              "      <td>1.448557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Davies-Bouldin</th>\n",
              "      <td>0.641715</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        score\n",
              "Silhouette           0.000081\n",
              "Calinski & Harabasz  1.448557\n",
              "Davies-Bouldin       0.641715"
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "Jb18PxLSxiFv",
        "outputId": "4673b0b0-3072-4a60-c1a9-d1da1aaa0890"
      },
      "source": [
        "captain_nnjac = nnjac_recom_captain.index.tolist()\n",
        "\n",
        "eval_recom(super_hot, captain_nnjac, 'Captain America (Steven Rogers)')"
      ],
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Silhouette</th>\n",
              "      <td>-0.017955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calinski &amp; Harabasz</th>\n",
              "      <td>1.203731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Davies-Bouldin</th>\n",
              "      <td>0.690078</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        score\n",
              "Silhouette          -0.017955\n",
              "Calinski & Harabasz  1.203731\n",
              "Davies-Bouldin       0.690078"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-cc7emP3uHH1"
      },
      "source": [
        "## Cosine"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "dby-dcXyx_2k",
        "outputId": "47ee2975-9055-40b0-bb72-48233d0fa3c2"
      },
      "source": [
        "aquaman_cos = cos_recom_aquaman.index.tolist()\n",
        "\n",
        "eval_recom(super_hot, aquaman_cos, 'Aquaman (Arthur Curry)')"
      ],
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Silhouette</th>\n",
              "      <td>0.000081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calinski &amp; Harabasz</th>\n",
              "      <td>1.448557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Davies-Bouldin</th>\n",
              "      <td>0.641715</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        score\n",
              "Silhouette           0.000081\n",
              "Calinski & Harabasz  1.448557\n",
              "Davies-Bouldin       0.641715"
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "aX_VUcDjx3bj",
        "outputId": "7be10cc1-9c9a-4086-e99e-431eafc637a5"
      },
      "source": [
        "captain_cos = cos_recom_captain.index.tolist()\n",
        "\n",
        "eval_recom(super_hot, captain_cos, 'Captain America (Steven Rogers)')"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Silhouette</th>\n",
              "      <td>0.010948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calinski &amp; Harabasz</th>\n",
              "      <td>1.411004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Davies-Bouldin</th>\n",
              "      <td>0.640964</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        score\n",
              "Silhouette           0.010948\n",
              "Calinski & Harabasz  1.411004\n",
              "Davies-Bouldin       0.640964"
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pDul6Fj76MBV"
      },
      "source": [
        "# Penutup\n",
        "\n",
        "Hasil sistem rekomendasi telah menunjukkan keberhasilannya dengan baik dalam merekomendasikan superhero terhadap pengoleksi *action figures*. "
      ]
    }
  ]
}