IA_DeepLearning_Projet.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f0iuiH0M0Ym5"
      },
      "source": [
        "# 💻  **Step 1: Preparation**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wjJiRJ3x4GSV"
      },
      "source": [
        "## 🎥 **Loading the Movies dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "GR8b4aWN2rPe"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 144,
      "metadata": {
        "id": "ghBfChlt0YG4"
      },
      "outputs": [],
      "source": [
        "# Load the dataset from Github\n",
        "url = 'https://raw.githubusercontent.com/siliataider/Neural-Network-Analysis-for-Predictive-Modeling/main/Top_rated_movies1.csv'\n",
        "df1 = pd.read_csv(url)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yfNyi-BE4OI-"
      },
      "source": [
        "## 📶 **Dataset characteristics**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "U6JkEJ4X2uj2",
        "outputId": "ec5e6231-cef4-4d93-cf18-720de3aa76e7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       id                                 title  \\\n",
              "0  168705                            BloodRayne   \n",
              "1   19766                    Inspector Gadget 2   \n",
              "2  248705            The Visitors: Bastille Day   \n",
              "3   17711  The Adventures of Rocky & Bullwinkle   \n",
              "4     580                     Jaws: The Revenge   \n",
              "\n",
              "                                            overview  popularity release_date  \\\n",
              "0  In 18th-century Romania, after spending much o...      17.499   2005-10-22   \n",
              "1  After capturing Claw, all the criminals have g...      20.772   2003-03-11   \n",
              "2  Stuck in the corridors of time, Godefroy de Mo...      18.828   2016-03-23   \n",
              "3  Rocky and Bullwinkle have been living off the ...      16.436   2000-06-30   \n",
              "4  After another deadly shark attack, Ellen Brody...      30.996   1987-07-17   \n",
              "\n",
              "   vote_average  vote_count  \n",
              "0         4.105         501  \n",
              "1         4.100         342  \n",
              "2         4.090         636  \n",
              "3         4.075         335  \n",
              "4         4.064         931  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-14f12d0f-3f9e-46b7-87f8-a7481abb2b93\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>title</th>\n",
              "      <th>overview</th>\n",
              "      <th>popularity</th>\n",
              "      <th>release_date</th>\n",
              "      <th>vote_average</th>\n",
              "      <th>vote_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>168705</td>\n",
              "      <td>BloodRayne</td>\n",
              "      <td>In 18th-century Romania, after spending much o...</td>\n",
              "      <td>17.499</td>\n",
              "      <td>2005-10-22</td>\n",
              "      <td>4.105</td>\n",
              "      <td>501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>19766</td>\n",
              "      <td>Inspector Gadget 2</td>\n",
              "      <td>After capturing Claw, all the criminals have g...</td>\n",
              "      <td>20.772</td>\n",
              "      <td>2003-03-11</td>\n",
              "      <td>4.100</td>\n",
              "      <td>342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>248705</td>\n",
              "      <td>The Visitors: Bastille Day</td>\n",
              "      <td>Stuck in the corridors of time, Godefroy de Mo...</td>\n",
              "      <td>18.828</td>\n",
              "      <td>2016-03-23</td>\n",
              "      <td>4.090</td>\n",
              "      <td>636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>17711</td>\n",
              "      <td>The Adventures of Rocky &amp; Bullwinkle</td>\n",
              "      <td>Rocky and Bullwinkle have been living off the ...</td>\n",
              "      <td>16.436</td>\n",
              "      <td>2000-06-30</td>\n",
              "      <td>4.075</td>\n",
              "      <td>335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>580</td>\n",
              "      <td>Jaws: The Revenge</td>\n",
              "      <td>After another deadly shark attack, Ellen Brody...</td>\n",
              "      <td>30.996</td>\n",
              "      <td>1987-07-17</td>\n",
              "      <td>4.064</td>\n",
              "      <td>931</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-14f12d0f-3f9e-46b7-87f8-a7481abb2b93')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-14f12d0f-3f9e-46b7-87f8-a7481abb2b93 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-14f12d0f-3f9e-46b7-87f8-a7481abb2b93');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0770f9e2-67cd-49bb-8085-8013b35bec6e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0770f9e2-67cd-49bb-8085-8013b35bec6e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0770f9e2-67cd-49bb-8085-8013b35bec6e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df1.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DzkQrvlH3vvV",
        "outputId": "09525ed5-f60e-4c37-c461-983613f789a1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8831, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df1.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tYSQO6Vh4DD0",
        "outputId": "0fc15e23-accf-4327-ea50-1b5dc5dc5f40"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.describe of           id                                 title  \\\n",
              "0     168705                            BloodRayne   \n",
              "1      19766                    Inspector Gadget 2   \n",
              "2     248705            The Visitors: Bastille Day   \n",
              "3      17711  The Adventures of Rocky & Bullwinkle   \n",
              "4        580                     Jaws: The Revenge   \n",
              "...      ...                                   ...   \n",
              "8826   12142                     Alone in the Dark   \n",
              "8827    5491                     Battlefield Earth   \n",
              "8828   11059                     House of the Dead   \n",
              "8829   14164                  Dragonball Evolution   \n",
              "8830   40016            Birdemic: Shock and Terror   \n",
              "\n",
              "                                               overview  popularity  \\\n",
              "0     In 18th-century Romania, after spending much o...      17.499   \n",
              "1     After capturing Claw, all the criminals have g...      20.772   \n",
              "2     Stuck in the corridors of time, Godefroy de Mo...      18.828   \n",
              "3     Rocky and Bullwinkle have been living off the ...      16.436   \n",
              "4     After another deadly shark attack, Ellen Brody...      30.996   \n",
              "...                                                 ...         ...   \n",
              "8826  Edward Carnby is a private investigator specia...      12.499   \n",
              "8827  In the year 3000, man is no match for the Psyc...      19.133   \n",
              "8828  Set on an island off the coast, a techno rave ...      10.795   \n",
              "8829  On his 18th birthday, Goku receives a mystical...      38.646   \n",
              "8830  A platoon of eagles and vultures attacks the r...      13.853   \n",
              "\n",
              "     release_date  vote_average  vote_count  \n",
              "0      2005-10-22         4.105         501  \n",
              "1      2003-03-11         4.100         342  \n",
              "2      2016-03-23         4.090         636  \n",
              "3      2000-06-30         4.075         335  \n",
              "4      1987-07-17         4.064         931  \n",
              "...           ...           ...         ...  \n",
              "8826   2005-01-28         3.228         550  \n",
              "8827   2000-05-12         3.221         761  \n",
              "8828   2003-04-11         3.100         359  \n",
              "8829   2009-03-12         2.891        1925  \n",
              "8830   2010-02-27         2.106         306  \n",
              "\n",
              "[8831 rows x 7 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "df1.describe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vIF2XenU4-X7",
        "outputId": "691dfb07-3d09-4da8-a98e-fb8e19b2af7a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id              0\n",
              "title           0\n",
              "overview        1\n",
              "popularity      0\n",
              "release_date    0\n",
              "vote_average    0\n",
              "vote_count      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "df1.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "inTobSnAMweE"
      },
      "outputs": [],
      "source": [
        "df1 = df1.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "US7lZ0RD5BfK",
        "outputId": "d17a0d4d-9377-4e8b-ab1b-55069bea4440"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                int64\n",
              "title            object\n",
              "overview         object\n",
              "popularity      float64\n",
              "release_date     object\n",
              "vote_average    float64\n",
              "vote_count        int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "df1.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "id": "1dS0RgME5ENq",
        "outputId": "983d6f8a-7e23-415a-bff2-02e01a8953aa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-18-49b3fcfeb4d1>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  df1.corr()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    id  popularity  vote_average  vote_count\n",
              "id            1.000000    0.161383      0.030725   -0.076822\n",
              "popularity    0.161383    1.000000      0.141408    0.331585\n",
              "vote_average  0.030725    0.141408      1.000000    0.278212\n",
              "vote_count   -0.076822    0.331585      0.278212    1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1f60431a-8065-4d4f-9d2e-deb4c8300f7e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>popularity</th>\n",
              "      <th>vote_average</th>\n",
              "      <th>vote_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.161383</td>\n",
              "      <td>0.030725</td>\n",
              "      <td>-0.076822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>popularity</th>\n",
              "      <td>0.161383</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.141408</td>\n",
              "      <td>0.331585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vote_average</th>\n",
              "      <td>0.030725</td>\n",
              "      <td>0.141408</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.278212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vote_count</th>\n",
              "      <td>-0.076822</td>\n",
              "      <td>0.331585</td>\n",
              "      <td>0.278212</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1f60431a-8065-4d4f-9d2e-deb4c8300f7e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1f60431a-8065-4d4f-9d2e-deb4c8300f7e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1f60431a-8065-4d4f-9d2e-deb4c8300f7e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-07698538-e347-4859-a0e1-510c0f4d54aa\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-07698538-e347-4859-a0e1-510c0f4d54aa')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-07698538-e347-4859-a0e1-510c0f4d54aa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "df1.corr()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTO9_9do7RPz"
      },
      "source": [
        "## ➿ **Data transformation**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "iz7KdtEI9x3G"
      },
      "outputs": [],
      "source": [
        "df2 = df1.copy()\n",
        "\n",
        "columns_to_drop = ['id']\n",
        "df2 = df2.drop(columns=columns_to_drop)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "Pv1XL__-9-xr"
      },
      "outputs": [],
      "source": [
        "df2['title_length'] = df1['title'].apply(len)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "0UlzI7bCupxF",
        "outputId": "e8941ea8-73ae-4698-8314-725b021fffa9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "popularity_values = df2['vote_average']\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(popularity_values, bins=200, color='skyblue', edgecolor='black')\n",
        "plt.title('Distribution of vote_average Values')\n",
        "plt.xlabel('Popularity')\n",
        "plt.ylabel('Frequency')\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "b24OAuDsuGJL"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "import numpy as np\n",
        "\n",
        "conditions = [\n",
        "    (df2['vote_average'] < 3),\n",
        "    (df2['vote_average'] >= 3) & (df2['vote_average'] < 4.5),\n",
        "    (df2['vote_average'] >= 4.5) & (df2['vote_average'] < 5.5),\n",
        "    (df2['vote_average'] >= 5.5) & (df2['vote_average'] < 6.5),\n",
        "    (df2['vote_average'] >= 6.5) & (df2['vote_average'] < 7.5),\n",
        "    (df2['vote_average'] >= 7.5) & (df2['vote_average'] < 8),\n",
        "    (df2['vote_average'] >= 8)\n",
        "]\n",
        "\n",
        "values = ['very bad', 'bad', 'mediocre', 'acceptable', 'good', 'very good', 'excellent']\n",
        "\n",
        "df2['popularity_class'] = np.select(conditions, values)\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "df2['popularity_class_labels'] = label_encoder.fit_transform(df2['popularity_class'])\n",
        "\n",
        "df2['overview_len'] = df2['overview'].apply(len)\n",
        "\n",
        "# Convert 'release_date' to the number of days since the minimum date\n",
        "min_date = min(pd.to_datetime(df2['release_date']))\n",
        "df2['release_date'] = (pd.to_datetime(df2['release_date']) - min_date).dt.days"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "gnd2sqRc-Bvx",
        "outputId": "fcba9ecc-619d-4f6d-ef20-788317f154fb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  title  \\\n",
              "0                            BloodRayne   \n",
              "1                    Inspector Gadget 2   \n",
              "2            The Visitors: Bastille Day   \n",
              "3  The Adventures of Rocky & Bullwinkle   \n",
              "4                     Jaws: The Revenge   \n",
              "\n",
              "                                            overview  popularity  \\\n",
              "0  In 18th-century Romania, after spending much o...      17.499   \n",
              "1  After capturing Claw, all the criminals have g...      20.772   \n",
              "2  Stuck in the corridors of time, Godefroy de Mo...      18.828   \n",
              "3  Rocky and Bullwinkle have been living off the ...      16.436   \n",
              "4  After another deadly shark attack, Ellen Brody...      30.996   \n",
              "\n",
              "   release_date  vote_average  vote_count  title_length popularity_class  \\\n",
              "0         37809         4.105         501            10              bad   \n",
              "1         36853         4.100         342            18              bad   \n",
              "2         41614         4.090         636            26              bad   \n",
              "3         35869         4.075         335            36              bad   \n",
              "4         31137         4.064         931            17              bad   \n",
              "\n",
              "   popularity_class_labels  overview_len  \n",
              "0                        1           359  \n",
              "1                        1           348  \n",
              "2                        1           406  \n",
              "3                        1           337  \n",
              "4                        1           243  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8b5b9c20-21c3-4360-95fe-6204b24e5d37\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>overview</th>\n",
              "      <th>popularity</th>\n",
              "      <th>release_date</th>\n",
              "      <th>vote_average</th>\n",
              "      <th>vote_count</th>\n",
              "      <th>title_length</th>\n",
              "      <th>popularity_class</th>\n",
              "      <th>popularity_class_labels</th>\n",
              "      <th>overview_len</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BloodRayne</td>\n",
              "      <td>In 18th-century Romania, after spending much o...</td>\n",
              "      <td>17.499</td>\n",
              "      <td>37809</td>\n",
              "      <td>4.105</td>\n",
              "      <td>501</td>\n",
              "      <td>10</td>\n",
              "      <td>bad</td>\n",
              "      <td>1</td>\n",
              "      <td>359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Inspector Gadget 2</td>\n",
              "      <td>After capturing Claw, all the criminals have g...</td>\n",
              "      <td>20.772</td>\n",
              "      <td>36853</td>\n",
              "      <td>4.100</td>\n",
              "      <td>342</td>\n",
              "      <td>18</td>\n",
              "      <td>bad</td>\n",
              "      <td>1</td>\n",
              "      <td>348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The Visitors: Bastille Day</td>\n",
              "      <td>Stuck in the corridors of time, Godefroy de Mo...</td>\n",
              "      <td>18.828</td>\n",
              "      <td>41614</td>\n",
              "      <td>4.090</td>\n",
              "      <td>636</td>\n",
              "      <td>26</td>\n",
              "      <td>bad</td>\n",
              "      <td>1</td>\n",
              "      <td>406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>The Adventures of Rocky &amp; Bullwinkle</td>\n",
              "      <td>Rocky and Bullwinkle have been living off the ...</td>\n",
              "      <td>16.436</td>\n",
              "      <td>35869</td>\n",
              "      <td>4.075</td>\n",
              "      <td>335</td>\n",
              "      <td>36</td>\n",
              "      <td>bad</td>\n",
              "      <td>1</td>\n",
              "      <td>337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Jaws: The Revenge</td>\n",
              "      <td>After another deadly shark attack, Ellen Brody...</td>\n",
              "      <td>30.996</td>\n",
              "      <td>31137</td>\n",
              "      <td>4.064</td>\n",
              "      <td>931</td>\n",
              "      <td>17</td>\n",
              "      <td>bad</td>\n",
              "      <td>1</td>\n",
              "      <td>243</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b5b9c20-21c3-4360-95fe-6204b24e5d37')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8b5b9c20-21c3-4360-95fe-6204b24e5d37 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8b5b9c20-21c3-4360-95fe-6204b24e5d37');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-270c7afc-b6df-462b-96c0-1ff039d6caa6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-270c7afc-b6df-462b-96c0-1ff039d6caa6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-270c7afc-b6df-462b-96c0-1ff039d6caa6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "df2.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qr8rXJYe-NNo",
        "outputId": "f6a1fda3-894f-4d72-db36-ce27d67664f4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "good          3857\n",
              "acceptable    3056\n",
              "very good      948\n",
              "mediocre       583\n",
              "excellent      304\n",
              "bad             80\n",
              "very bad         2\n",
              "Name: popularity_class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "popularity_counts = df2['popularity_class'].value_counts()\n",
        "popularity_counts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rG1GyTcDxzN7",
        "outputId": "d49f45bf-dc1f-4050-aff9-f06a35fabe81"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    3857\n",
              "0    3056\n",
              "6     948\n",
              "4     583\n",
              "2     304\n",
              "1      80\n",
              "5       2\n",
              "Name: popularity_class_labels, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "popularity_counts = df2['popularity_class_labels'].value_counts()\n",
        "popularity_counts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hdc_1bV1yiQy",
        "outputId": "d62a61fe-5ef0-4ff8-f849-a5b4bef7dfb1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(998, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "max_overview = df2['overview_len'].max()\n",
        "min_overview = df2['overview_len'].min()\n",
        "\n",
        "max_overview, min_overview"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jaioD6Cxx1I2",
        "outputId": "35aa8bc6-6727-403d-8605-df80e3c7c826"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(83, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "max_title = df2['title_length'].max()\n",
        "min_title = df2['title_length'].min()\n",
        "\n",
        "max_title, min_title"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n_VH6Byl5dxI"
      },
      "source": [
        "## 📈 **Dataset features**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Ktd0Wygy5Yii",
        "outputId": "8ad1d3c0-14f7-49cd-a4db-9ca9484d3ec0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   release_date  title_length  overview_len  vote_count  popularity\n",
              "0         37809            10           359         501      17.499\n",
              "1         36853            18           348         342      20.772\n",
              "2         41614            26           406         636      18.828"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-075ecb74-8c91-4068-96be-ecb31d4460a4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>release_date</th>\n",
              "      <th>title_length</th>\n",
              "      <th>overview_len</th>\n",
              "      <th>vote_count</th>\n",
              "      <th>popularity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>37809</td>\n",
              "      <td>10</td>\n",
              "      <td>359</td>\n",
              "      <td>501</td>\n",
              "      <td>17.499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>36853</td>\n",
              "      <td>18</td>\n",
              "      <td>348</td>\n",
              "      <td>342</td>\n",
              "      <td>20.772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41614</td>\n",
              "      <td>26</td>\n",
              "      <td>406</td>\n",
              "      <td>636</td>\n",
              "      <td>18.828</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-075ecb74-8c91-4068-96be-ecb31d4460a4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-075ecb74-8c91-4068-96be-ecb31d4460a4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-075ecb74-8c91-4068-96be-ecb31d4460a4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d2c076c7-632a-433a-a0a8-d47205f010fb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d2c076c7-632a-433a-a0a8-d47205f010fb')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d2c076c7-632a-433a-a0a8-d47205f010fb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# Targets\n",
        "y_regression = df2['vote_average']\n",
        "y_classification = df2['popularity_class_labels']\n",
        "\n",
        "# Features\n",
        "X_numeric_regression = df2[['release_date', 'title_length', 'overview_len', 'vote_count', 'popularity']]\n",
        "X_numeric_regression.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "dxzvY0gF6m_Q",
        "outputId": "b29fd0c1-d618-486b-c190-71955ca2234c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   release_date  title_length  overview_len  vote_count  vote_average\n",
              "0         37809            10           359         501         4.105\n",
              "1         36853            18           348         342         4.100\n",
              "2         41614            26           406         636         4.090"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6d39d8be-67ca-46df-9b9e-551b3e6c07f2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>release_date</th>\n",
              "      <th>title_length</th>\n",
              "      <th>overview_len</th>\n",
              "      <th>vote_count</th>\n",
              "      <th>vote_average</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>37809</td>\n",
              "      <td>10</td>\n",
              "      <td>359</td>\n",
              "      <td>501</td>\n",
              "      <td>4.105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>36853</td>\n",
              "      <td>18</td>\n",
              "      <td>348</td>\n",
              "      <td>342</td>\n",
              "      <td>4.100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41614</td>\n",
              "      <td>26</td>\n",
              "      <td>406</td>\n",
              "      <td>636</td>\n",
              "      <td>4.090</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d39d8be-67ca-46df-9b9e-551b3e6c07f2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6d39d8be-67ca-46df-9b9e-551b3e6c07f2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6d39d8be-67ca-46df-9b9e-551b3e6c07f2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-112383e1-6318-42ab-8457-c894ab149005\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-112383e1-6318-42ab-8457-c894ab149005')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-112383e1-6318-42ab-8457-c894ab149005 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "X_numeric_classification = df2[['release_date', 'title_length', 'overview_len', 'vote_count', 'vote_average']]\n",
        "X_numeric_classification.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uBOQA8sR-whi",
        "outputId": "2c2cac6c-416b-4055-fefe-e40dbc6a4d4b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.series.Series'> <class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ],
      "source": [
        "print(type(y_regression), type(X_numeric_regression))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nXwVZRnOEhRE"
      },
      "source": [
        "##  \t✏️ **Text vectorization**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aW-5itLdw5W_"
      },
      "source": [
        "### 📋 **Overview**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q7sJJGqO6k0o",
        "outputId": "542db9f2-7f7c-4d51-b0be-351cc7c76482"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.layers.experimental.preprocessing import TextVectorization\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50rMh8KKEoSA",
        "outputId": "a9e339dd-ac01-44b9-ed3c-d62f317941cd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[   0,    0,    0, ...,    1,  786,  720],\n",
              "       [   0,    0,    0, ...,  984, 4051,  818],\n",
              "       [   0,    0,    0, ...,    1, 2127,    1],\n",
              "       ...,\n",
              "       [   0,    0,    0, ...,  135,  579, 1079],\n",
              "       [   0,    0,    0, ...,   44,   84,  803],\n",
              "       [   0,    0,    0, ...,   23,  213,    1]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "vocabulary_size = 10000\n",
        "max_len_overview = 500\n",
        "\n",
        "def remove_stop_words(text):\n",
        "    text = word_tokenize(text.lower())\n",
        "    stop_words = set(stopwords.words('english'))\n",
        "    text = [word for word in text if word.isalpha() and not word in stop_words]\n",
        "    return ' '.join(text)\n",
        "\n",
        "df2['overview'] = df2['overview'].apply(remove_stop_words)\n",
        "\n",
        "tokenizer = Tokenizer(num_words=vocabulary_size, oov_token=\"<OOV>\")\n",
        "tokenizer.fit_on_texts(df2['overview'].astype(str))\n",
        "X_text_seq = tokenizer.texts_to_sequences(df2['overview'].astype(str))\n",
        "X_text_padded = pad_sequences(X_text_seq, maxlen=max_len_overview)\n",
        "\n",
        "X_text_vectorized = X_text_padded\n",
        "X_text_vectorized"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JM0byrEKw8xt"
      },
      "source": [
        "### 🔖 **Title**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "co67tKv6ZZ15",
        "outputId": "6352cb29-6c07-4ac5-e253-7363ed8414a9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[   0,    0,    0, ...,    0,    0, 2688],\n",
              "       [   0,    0,    0, ..., 1637, 1638,    7],\n",
              "       [   0,    0,    0, ..., 1147, 1639,   25],\n",
              "       ...,\n",
              "       [   0,    0,    0, ...,    3,    2,   20],\n",
              "       [   0,    0,    0, ...,    0, 6987, 1123],\n",
              "       [   0,    0,    0, ..., 6989,    5, 1437]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "max_len_title = 90\n",
        "\n",
        "tokenizer = Tokenizer(num_words=vocabulary_size, oov_token=\"<OOV>\")\n",
        "tokenizer.fit_on_texts(df2['title'].astype(str))\n",
        "X_text_seq_title = tokenizer.texts_to_sequences(df2['title'].astype(str))\n",
        "X_text_padded_title = pad_sequences(X_text_seq_title, maxlen=max_len_title)\n",
        "\n",
        "X_text_vectorized_title = X_text_padded_title\n",
        "X_text_vectorized_title"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sjF3AEcxeqox"
      },
      "source": [
        "## 🔢 **Numeric normalization**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C_JmJ7zOh5sh",
        "outputId": "72bc0e18-1049-4c3a-e48d-33bd30cdf3a6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "release_date      int64\n",
              "title_length      int64\n",
              "overview_len      int64\n",
              "vote_count        int64\n",
              "vote_average    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "X_numeric_classification.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OpG4bHcAU1Se",
        "outputId": "a3e98196-2eaa-4084-977a-28d57ffdbf38"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "release_date      int64\n",
              "title_length      int64\n",
              "overview_len      int64\n",
              "vote_count        int64\n",
              "popularity      float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "X_numeric_regression.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "itIbyNWLFzw9"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import Normalizer\n",
        "\n",
        "def normalize_numeric(X_numeric):\n",
        "  normalizer = Normalizer()\n",
        "  X_numeric_normalized = normalizer.fit_transform(X_numeric)\n",
        "  return X_numeric_normalized"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VXv1m0du7QmE",
        "outputId": "1590977d-e240-49d2-b2ee-bfc8105c6cfb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9.99867014e-01, 2.64452118e-04, 9.49383105e-03, 1.32490511e-02,\n",
              "        4.62764762e-04],\n",
              "       [9.99912089e-01, 4.88384056e-04, 9.44209174e-03, 9.27929706e-03,\n",
              "        5.63595200e-04],\n",
              "       [9.99835360e-01, 6.24686869e-04, 9.75472572e-03, 1.52808019e-02,\n",
              "        4.52369399e-04],\n",
              "       ...,\n",
              "       [9.99900814e-01, 4.60858742e-04, 1.01660017e-02, 9.73225226e-03,\n",
              "        2.92645301e-04],\n",
              "       [9.98731916e-01, 5.11566827e-04, 1.04359633e-02, 4.92383071e-02,\n",
              "        9.88500580e-04],\n",
              "       [9.99955490e-01, 6.59902603e-04, 5.30460169e-03, 7.76654602e-03,\n",
              "        3.51601183e-04]])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "X_numeric_regression_normalized = normalize_numeric(X_numeric_regression)\n",
        "X_numeric_regression_normalized"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YTt3-6TH7Ucr",
        "outputId": "1475b4f4-f9fb-49b8-f8ea-6677f72a9c29"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9.99867115e-01, 2.64452145e-04, 9.49383201e-03, 1.32490525e-02,\n",
              "        1.08557606e-04],\n",
              "       [9.99912241e-01, 4.88384130e-04, 9.44209318e-03, 9.27929847e-03,\n",
              "        1.11243052e-04],\n",
              "       [9.99835458e-01, 6.24686930e-04, 9.75472667e-03, 1.52808034e-02,\n",
              "        9.82680594e-05],\n",
              "       ...,\n",
              "       [9.99900853e-01, 4.60858760e-04, 1.01660021e-02, 9.73225264e-03,\n",
              "        8.40389504e-05],\n",
              "       [9.98732402e-01, 5.11567076e-04, 1.04359683e-02, 4.92383310e-02,\n",
              "        7.39470208e-05],\n",
              "       [9.99955551e-01, 6.59902643e-04, 5.30460201e-03, 7.76654649e-03,\n",
              "        5.34521141e-05]])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "X_numeric_classification_normalized = normalize_numeric(X_numeric_classification)\n",
        "X_numeric_classification_normalized"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MX8VF4Ls6G_8"
      },
      "source": [
        "## 🔪 **Dataset split: test and train**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "9yclYQaGFQ65"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "9wiRgE_07b6c"
      },
      "outputs": [],
      "source": [
        "# Split the data into train and test sets (70% train, 30% test)\n",
        "def split_dataset(features, target):\n",
        "  X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.3, random_state=42)\n",
        "  print(\"Training set shape:\", X_train.shape, y_train.shape)\n",
        "  print(\"Testing set shape:\", X_test.shape, y_test.shape)\n",
        "  return X_train, X_test, y_train, y_test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kUkRAGfi_oJX"
      },
      "source": [
        "# 🐌 **Step 2: Model Training**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wRZYp0gnCUwR"
      },
      "source": [
        "## 🍀 **Build the model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "maiGDBjx_rnG"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Embedding, Bidirectional, LSTM, Dense\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.losses import MeanSquaredError\n",
        "from keras.layers import Dense, Conv1D, MaxPooling1D, GlobalMaxPooling1D"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "N-DebUarhJ5Y"
      },
      "outputs": [],
      "source": [
        "def build_model1(dim):\n",
        "  model1 = Sequential()\n",
        "  model1.add(Dense(10, input_dim = dim, activation='relu'))\n",
        "  model1.add(Dense(50, activation='relu'))\n",
        "  model1.add(Dense(50, activation='relu'))\n",
        "  model1.add(Dense(1))\n",
        "  return model1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "pM9gP6V34pZJ"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.regularizers import l2\n",
        "\n",
        "def build_model1_classification(dim):\n",
        "    model1 = Sequential()\n",
        "    model1.add(Flatten())\n",
        "    model1.add(Dense(128, input_dim=dim, activation='relu', kernel_regularizer=l2(0.01)))\n",
        "    model1.add(Dropout(0.4))\n",
        "    model1.add(Dense(128, activation='relu', kernel_regularizer=l2(0.01)))\n",
        "    model1.add(Dropout(0.4))\n",
        "    model1.add(Dense(7, activation='softmax'))\n",
        "    return model1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "Jbrkb5KqCZwQ"
      },
      "outputs": [],
      "source": [
        "def build_model2(dim):\n",
        "  model2 = Sequential()\n",
        "  model2.add(Dense(10, input_dim = dim))\n",
        "  model2.add(Dense(50, activation='relu'))\n",
        "  model2.add(Dropout(0.2))\n",
        "  model2.add(Dense(50, activation='relu'))\n",
        "  model2.add(Dropout(0.2))\n",
        "  model2.add(Dense(50, activation='relu'))\n",
        "  model2.add(Dropout(0.2))\n",
        "  model2.add(Dense(50, activation='relu'))\n",
        "  model2.add(Dense(1))\n",
        "  return model2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "Syly_8UR1QyQ"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Embedding, LSTM, Dense, Flatten, SpatialDropout1D\n",
        "from tensorflow.keras.regularizers import l2\n",
        "\n",
        "def build_model_embedding():\n",
        "    model = Sequential([\n",
        "        Embedding(input_dim=vocabulary_size, output_dim=64, input_length=max_len),\n",
        "        Flatten(),\n",
        "        Dense(128, activation='relu'),\n",
        "        Dense(units=64, activation='relu', kernel_regularizer=l2(0.01)),\n",
        "        Dense(7, activation='softmax')\n",
        "    ])\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "quIN6ztBW8Hz"
      },
      "outputs": [],
      "source": [
        "def build_model_embedding_2():\n",
        "    model = Sequential()\n",
        "    model.add(Embedding(vocabulary_size, 128, input_length=max_len))\n",
        "    model.add(Conv1D(32, 7, activation='relu'))\n",
        "    model.add(MaxPooling1D(5))\n",
        "    model.add(Conv1D(32, 7, activation='relu'))\n",
        "    model.add(GlobalMaxPooling1D())\n",
        "    model.add(Dense(7, activation='softmax'))\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "_46whWhcbINZ"
      },
      "outputs": [],
      "source": [
        "def build_model_embedding_3():\n",
        "    model = Sequential([\n",
        "        Embedding(input_dim=vocabulary_size, output_dim=64, input_length=max_len),\n",
        "        LSTM(64),\n",
        "        Dense(7, activation='softmax')\n",
        "    ])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "O7FDEl4GbNAn"
      },
      "outputs": [],
      "source": [
        "def build_model_embedding_4():\n",
        "    model = Sequential([\n",
        "        Embedding(input_dim=vocabulary_size, output_dim=64, input_length=max_len),\n",
        "        Flatten(),\n",
        "        Dense(32, activation='relu'),\n",
        "        Dropout(0.4),\n",
        "        Dense(32, activation='relu'),\n",
        "        Dropout(0.4),\n",
        "        Dense(32, activation='relu'),\n",
        "        Dense(7, activation='softmax')\n",
        "    ])\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def build_lstm_model():\n",
        "    model = Sequential()\n",
        "    model.add(Embedding(input_dim=vocabulary_size, output_dim=128, input_length=max_len))\n",
        "    model.add(SpatialDropout1D(0.2))\n",
        "    model.add(LSTM(50, dropout=0.2, recurrent_dropout=0.2))\n",
        "    model.add(Dense(7, activation='softmax'))\n",
        "    return model"
      ],
      "metadata": {
        "id": "dtLwYfmmpcMr"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JwK3btE7CpVc"
      },
      "source": [
        "## 💭 **Make predictions**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 🔧 **Utility functions**:"
      ],
      "metadata": {
        "id": "D3pvh68kb-Mr"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 142,
      "metadata": {
        "id": "_BLxXTnabVHf"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import explained_variance_score\n",
        "\n",
        "def predict_from_model_regression(model):\n",
        "  y_pred = model.predict(X_test)\n",
        "  r_squared = r2_score(y_test, y_pred)\n",
        "  print(\"R-squared:\", r_squared)\n",
        "  explained_variance = explained_variance_score(y_test, y_pred)\n",
        "  print(\"Explained Variance:\", explained_variance)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 157,
      "metadata": {
        "id": "Nvd-6iTOPwwt"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "import warnings\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "def predict_from_model_classification(model):\n",
        "    y_pred_class = np.argmax(model.predict(X_test), axis=1)\n",
        "    confusion_mat = confusion_matrix(y_test.argmax(axis=1), y_pred_class)\n",
        "    accuracy = accuracy_score(y_test.argmax(axis=1), y_pred_class)\n",
        "    print(\"Confusion Matrix:\\n\", confusion_mat)\n",
        "    sns.heatmap(confusion_mat, annot=True, fmt=\"g\", xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)\n",
        "\n",
        "    with warnings.catch_warnings():\n",
        "        warnings.simplefilter(\"ignore\")\n",
        "        print(\"Classification Report:\\n\", classification_report(y_test.argmax(axis=1), y_pred_class, zero_division=1))\n",
        "    print(\"Accuracy:\", accuracy)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jy0NnjVSCi0R"
      },
      "source": [
        "## 💪 **Train & Evaluate the model**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 🔧 **Utility functions**:"
      ],
      "metadata": {
        "id": "eSkeUgrhb1NJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 143,
      "metadata": {
        "id": "-z-mBfEbNpUE"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "\n",
        "def fit_evaluate_model(model, learning_rate, loss, metric, epochs):\n",
        "  '''class_values = np.unique(np.argmax(y_train, axis=1))\n",
        "  class_weights = compute_class_weight('balanced', classes=class_values, y=np.argmax(y_train, axis=1))\n",
        "  class_weight_dict = dict(zip(class_values, class_weights))'''\n",
        "\n",
        "  model.compile(optimizer=Adam(learning_rate=learning_rate), loss=loss, metrics=metric)\n",
        "  if 'accuracy' not in metric:\n",
        "      metric.append('accuracy')\n",
        "  history = model.fit(X_train, y_train, epochs=epochs, batch_size=32, validation_split=0.2)\n",
        "\n",
        "  test_loss = model.evaluate(X_test, y_test)\n",
        "\n",
        "  plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "  plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.legend()\n",
        "  plt.show()\n",
        "\n",
        "  print(\"Test Loss (mean squared error):\", test_loss)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ViAsnzKBsPMO"
      },
      "source": [
        "### ⛔ **Regression**:\n",
        "***target = vote_average***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0AqV7wJbYIDB"
      },
      "source": [
        "#### 🔢 **Numeric features**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W9-OUyZ6Ykf7",
        "outputId": "ffeabc8b-bb8e-424d-96c1-5a1ab0054aa7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set shape: (6181, 5) (6181,)\n",
            "Testing set shape: (2649, 5) (2649,)\n"
          ]
        }
      ],
      "source": [
        "X_train, X_test, y_train, y_test = split_dataset(X_numeric_regression_normalized, y_regression)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "B31bu_Q_lHUF"
      },
      "outputs": [],
      "source": [
        "model1 = build_model1(X_numeric_regression_normalized.shape[1])\n",
        "model2 = build_model2(X_numeric_regression_normalized.shape[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 831
        },
        "id": "R825TsNmjCLw",
        "outputId": "d385e8cb-e70c-4936-9649-9673d686ab35"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "155/155 [==============================] - 2s 4ms/step - loss: 38.8529 - mean_squared_error: 38.8529 - accuracy: 0.0000e+00 - val_loss: 35.0556 - val_mean_squared_error: 35.0556 - val_accuracy: 0.0000e+00\n",
            "Epoch 2/10\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 31.0685 - mean_squared_error: 31.0685 - accuracy: 0.0000e+00 - val_loss: 25.8118 - val_mean_squared_error: 25.8118 - val_accuracy: 0.0000e+00\n",
            "Epoch 3/10\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 20.3582 - mean_squared_error: 20.3582 - accuracy: 0.0000e+00 - val_loss: 13.9520 - val_mean_squared_error: 13.9520 - val_accuracy: 0.0000e+00\n",
            "Epoch 4/10\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 8.7835 - mean_squared_error: 8.7835 - accuracy: 0.0000e+00 - val_loss: 4.2120 - val_mean_squared_error: 4.2120 - val_accuracy: 0.0000e+00\n",
            "Epoch 5/10\n",
            "155/155 [==============================] - 1s 5ms/step - loss: 2.2139 - mean_squared_error: 2.2139 - accuracy: 0.0000e+00 - val_loss: 0.9135 - val_mean_squared_error: 0.9135 - val_accuracy: 0.0000e+00\n",
            "Epoch 6/10\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 0.7324 - mean_squared_error: 0.7324 - accuracy: 0.0000e+00 - val_loss: 0.5788 - val_mean_squared_error: 0.5788 - val_accuracy: 0.0000e+00\n",
            "Epoch 7/10\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 0.6201 - mean_squared_error: 0.6201 - accuracy: 0.0000e+00 - val_loss: 0.5681 - val_mean_squared_error: 0.5681 - val_accuracy: 0.0000e+00\n",
            "Epoch 8/10\n",
            "155/155 [==============================] - 1s 5ms/step - loss: 0.6158 - mean_squared_error: 0.6158 - accuracy: 0.0000e+00 - val_loss: 0.5670 - val_mean_squared_error: 0.5670 - val_accuracy: 0.0000e+00\n",
            "Epoch 9/10\n",
            "155/155 [==============================] - 1s 3ms/step - loss: 0.6143 - mean_squared_error: 0.6143 - accuracy: 0.0000e+00 - val_loss: 0.5651 - val_mean_squared_error: 0.5651 - val_accuracy: 0.0000e+00\n",
            "Epoch 10/10\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 0.6125 - mean_squared_error: 0.6125 - accuracy: 0.0000e+00 - val_loss: 0.5633 - val_mean_squared_error: 0.5633 - val_accuracy: 0.0000e+00\n",
            "83/83 [==============================] - 0s 2ms/step - loss: 0.6258 - mean_squared_error: 0.6258 - accuracy: 0.0000e+00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss (mean squared error): [0.625832200050354, 0.625832200050354, 0.0]\n"
          ]
        }
      ],
      "source": [
        "fit_evaluate_model(model1, 0.0001, MeanSquaredError(), ['mean_squared_error'], 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uoR1mbzRJTJV",
        "outputId": "a2bfe03e-8bce-43e4-97f0-67e6162dd0cb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "83/83 [==============================] - 0s 2ms/step\n",
            "R-squared: 0.03407154007708535\n",
            "Explained Variance: 0.03417086248133461\n"
          ]
        }
      ],
      "source": [
        "predict_from_model_regression(model1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 831
        },
        "id": "eT_bbfaCT2jU",
        "outputId": "d472da1b-93a0-4da6-edf8-6382353087ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "155/155 [==============================] - 2s 4ms/step - loss: 8.2816 - mean_squared_error: 8.2816 - accuracy: 0.0000e+00 - val_loss: 0.7023 - val_mean_squared_error: 0.7023 - val_accuracy: 0.0000e+00\n",
            "Epoch 2/10\n",
            "155/155 [==============================] - 1s 3ms/step - loss: 1.3840 - mean_squared_error: 1.3840 - accuracy: 0.0000e+00 - val_loss: 0.8302 - val_mean_squared_error: 0.8302 - val_accuracy: 0.0000e+00\n",
            "Epoch 3/10\n",
            "155/155 [==============================] - 1s 3ms/step - loss: 1.1380 - mean_squared_error: 1.1380 - accuracy: 0.0000e+00 - val_loss: 1.3466 - val_mean_squared_error: 1.3466 - val_accuracy: 0.0000e+00\n",
            "Epoch 4/10\n",
            "155/155 [==============================] - 1s 3ms/step - loss: 1.0477 - mean_squared_error: 1.0477 - accuracy: 0.0000e+00 - val_loss: 1.0518 - val_mean_squared_error: 1.0518 - val_accuracy: 0.0000e+00\n",
            "Epoch 5/10\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 0.9931 - mean_squared_error: 0.9931 - accuracy: 0.0000e+00 - val_loss: 1.4143 - val_mean_squared_error: 1.4143 - val_accuracy: 0.0000e+00\n",
            "Epoch 6/10\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 0.9227 - mean_squared_error: 0.9227 - accuracy: 0.0000e+00 - val_loss: 0.9565 - val_mean_squared_error: 0.9565 - val_accuracy: 0.0000e+00\n",
            "Epoch 7/10\n",
            "155/155 [==============================] - 1s 3ms/step - loss: 0.8534 - mean_squared_error: 0.8534 - accuracy: 0.0000e+00 - val_loss: 1.2235 - val_mean_squared_error: 1.2235 - val_accuracy: 0.0000e+00\n",
            "Epoch 8/10\n",
            "155/155 [==============================] - 1s 3ms/step - loss: 0.8410 - mean_squared_error: 0.8410 - accuracy: 0.0000e+00 - val_loss: 0.7075 - val_mean_squared_error: 0.7075 - val_accuracy: 0.0000e+00\n",
            "Epoch 9/10\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 0.8023 - mean_squared_error: 0.8023 - accuracy: 0.0000e+00 - val_loss: 0.6311 - val_mean_squared_error: 0.6311 - val_accuracy: 0.0000e+00\n",
            "Epoch 10/10\n",
            "155/155 [==============================] - 1s 3ms/step - loss: 0.7497 - mean_squared_error: 0.7497 - accuracy: 0.0000e+00 - val_loss: 0.6520 - val_mean_squared_error: 0.6520 - val_accuracy: 0.0000e+00\n",
            "83/83 [==============================] - 0s 2ms/step - loss: 0.7224 - mean_squared_error: 0.7224 - accuracy: 0.0000e+00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss (mean squared error): [0.7223867177963257, 0.7223867177963257, 0.0]\n"
          ]
        }
      ],
      "source": [
        "fit_evaluate_model(model2, 0.001, MeanSquaredError(), ['mean_squared_error'], 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ZoBhrtCJWFx",
        "outputId": "073fbae4-cf49-47be-e4b1-6b5206e2a401"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "83/83 [==============================] - 0s 2ms/step\n",
            "R-squared: -0.11495344476510216\n",
            "Explained Variance: 0.07976940588025316\n"
          ]
        }
      ],
      "source": [
        "predict_from_model_regression(model2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UaCpBYqkYN-P"
      },
      "source": [
        "#### 🔠 **Text features**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lMDjSrjZYRN3",
        "outputId": "e5922e7d-e2eb-4daa-dfba-a37f5c909c94"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set shape: (6181, 500) (6181,)\n",
            "Testing set shape: (2649, 500) (2649,)\n"
          ]
        }
      ],
      "source": [
        "X_train, X_test, y_train, y_test = split_dataset(X_text_vectorized, y_regression)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "ISknPkAjmcD5"
      },
      "outputs": [],
      "source": [
        "model1 = build_model1(X_text_vectorized.shape[1])\n",
        "model2 = build_model2(X_text_vectorized.shape[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        },
        "id": "NTxtDJC5ZLih",
        "outputId": "555bc0be-1e47-4aa4-d792-852177c4a0c1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "155/155 [==============================] - 2s 7ms/step - loss: 23467.0547 - mean_squared_error: 23467.0547 - accuracy: 0.0000e+00 - val_loss: 4858.9707 - val_mean_squared_error: 4858.9707 - val_accuracy: 0.0000e+00\n",
            "Epoch 2/10\n",
            "155/155 [==============================] - 2s 10ms/step - loss: 3313.0925 - mean_squared_error: 3313.0925 - accuracy: 0.0000e+00 - val_loss: 1906.1348 - val_mean_squared_error: 1906.1348 - val_accuracy: 0.0000e+00\n",
            "Epoch 3/10\n",
            "155/155 [==============================] - 1s 8ms/step - loss: 1632.6887 - mean_squared_error: 1632.6887 - accuracy: 0.0000e+00 - val_loss: 1203.0353 - val_mean_squared_error: 1203.0353 - val_accuracy: 0.0000e+00\n",
            "Epoch 4/10\n",
            "155/155 [==============================] - 1s 7ms/step - loss: 1069.5007 - mean_squared_error: 1069.5007 - accuracy: 0.0000e+00 - val_loss: 870.3325 - val_mean_squared_error: 870.3325 - val_accuracy: 0.0000e+00\n",
            "Epoch 5/10\n",
            "155/155 [==============================] - 1s 8ms/step - loss: 759.9596 - mean_squared_error: 759.9596 - accuracy: 0.0000e+00 - val_loss: 670.1645 - val_mean_squared_error: 670.1645 - val_accuracy: 0.0000e+00\n",
            "Epoch 6/10\n",
            "155/155 [==============================] - 1s 6ms/step - loss: 565.2001 - mean_squared_error: 565.2001 - accuracy: 0.0000e+00 - val_loss: 535.4111 - val_mean_squared_error: 535.4111 - val_accuracy: 0.0000e+00\n",
            "Epoch 7/10\n",
            "155/155 [==============================] - 1s 7ms/step - loss: 439.6220 - mean_squared_error: 439.6220 - accuracy: 0.0000e+00 - val_loss: 435.2075 - val_mean_squared_error: 435.2075 - val_accuracy: 0.0000e+00\n",
            "Epoch 8/10\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 352.7681 - mean_squared_error: 352.7681 - accuracy: 0.0000e+00 - val_loss: 364.7919 - val_mean_squared_error: 364.7919 - val_accuracy: 0.0000e+00\n",
            "Epoch 9/10\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 290.4719 - mean_squared_error: 290.4719 - accuracy: 0.0000e+00 - val_loss: 311.1450 - val_mean_squared_error: 311.1450 - val_accuracy: 0.0000e+00\n",
            "Epoch 10/10\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 243.0459 - mean_squared_error: 243.0459 - accuracy: 0.0000e+00 - val_loss: 266.2161 - val_mean_squared_error: 266.2161 - val_accuracy: 0.0000e+00\n",
            "83/83 [==============================] - 0s 2ms/step - loss: 262.3179 - mean_squared_error: 262.3179 - accuracy: 0.0000e+00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss (mean squared error): [262.31787109375, 262.31787109375, 0.0]\n"
          ]
        }
      ],
      "source": [
        "fit_evaluate_model(model1, 0.0001, MeanSquaredError(), ['mean_squared_error'], 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wUm7HhYRJYOZ",
        "outputId": "7ec268cb-6bd6-4431-875f-75bd54a2454f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "83/83 [==============================] - 0s 2ms/step\n",
            "R-squared: -403.86942271288484\n",
            "Explained Variance: -399.3624725722653\n"
          ]
        }
      ],
      "source": [
        "predict_from_model_regression(model1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        },
        "id": "cTHl2-Q5iE87",
        "outputId": "8c33395b-5469-491b-e12d-a4e477899d70"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "155/155 [==============================] - 2s 5ms/step - loss: 11323.1484 - mean_squared_error: 11323.1484 - accuracy: 0.0000e+00 - val_loss: 343.9087 - val_mean_squared_error: 343.9087 - val_accuracy: 0.0000e+00\n",
            "Epoch 2/10\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 904.5927 - mean_squared_error: 904.5927 - accuracy: 0.0000e+00 - val_loss: 165.0285 - val_mean_squared_error: 165.0285 - val_accuracy: 0.0000e+00\n",
            "Epoch 3/10\n",
            "155/155 [==============================] - 1s 5ms/step - loss: 514.2545 - mean_squared_error: 514.2545 - accuracy: 0.0000e+00 - val_loss: 76.4917 - val_mean_squared_error: 76.4917 - val_accuracy: 0.0000e+00\n",
            "Epoch 4/10\n",
            "155/155 [==============================] - 1s 5ms/step - loss: 325.6237 - mean_squared_error: 325.6237 - accuracy: 0.0000e+00 - val_loss: 52.1277 - val_mean_squared_error: 52.1277 - val_accuracy: 0.0000e+00\n",
            "Epoch 5/10\n",
            "155/155 [==============================] - 1s 5ms/step - loss: 226.7923 - mean_squared_error: 226.7923 - accuracy: 0.0000e+00 - val_loss: 37.7312 - val_mean_squared_error: 37.7312 - val_accuracy: 0.0000e+00\n",
            "Epoch 6/10\n",
            "155/155 [==============================] - 1s 6ms/step - loss: 174.1987 - mean_squared_error: 174.1987 - accuracy: 0.0000e+00 - val_loss: 28.5555 - val_mean_squared_error: 28.5555 - val_accuracy: 0.0000e+00\n",
            "Epoch 7/10\n",
            "155/155 [==============================] - 1s 6ms/step - loss: 123.3549 - mean_squared_error: 123.3549 - accuracy: 0.0000e+00 - val_loss: 24.2187 - val_mean_squared_error: 24.2187 - val_accuracy: 0.0000e+00\n",
            "Epoch 8/10\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 107.1901 - mean_squared_error: 107.1901 - accuracy: 0.0000e+00 - val_loss: 21.3175 - val_mean_squared_error: 21.3175 - val_accuracy: 0.0000e+00\n",
            "Epoch 9/10\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 83.9434 - mean_squared_error: 83.9434 - accuracy: 0.0000e+00 - val_loss: 18.6524 - val_mean_squared_error: 18.6524 - val_accuracy: 0.0000e+00\n",
            "Epoch 10/10\n",
            "155/155 [==============================] - 1s 3ms/step - loss: 68.9275 - mean_squared_error: 68.9275 - accuracy: 0.0000e+00 - val_loss: 20.0113 - val_mean_squared_error: 20.0113 - val_accuracy: 0.0000e+00\n",
            "83/83 [==============================] - 0s 2ms/step - loss: 20.2841 - mean_squared_error: 20.2841 - accuracy: 0.0000e+00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss (mean squared error): [20.28413963317871, 20.28413963317871, 0.0]\n"
          ]
        }
      ],
      "source": [
        "fit_evaluate_model(model2, 0.001, MeanSquaredError(), ['mean_squared_error'], 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hXCMLxS6JaIy",
        "outputId": "c12d0d70-8353-4b48-9ba4-e32b598aea05"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "83/83 [==============================] - 0s 2ms/step\n",
            "R-squared: -30.307158624117204\n",
            "Explained Variance: -14.881390716479256\n"
          ]
        }
      ],
      "source": [
        "predict_from_model_regression(model2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BpCgaEcyspLE"
      },
      "source": [
        "### 📉 **Classification**\n",
        "***target = popularity_class_label***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3aVYaNwhsubJ"
      },
      "source": [
        "#### 🔢 **Numeric features**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 170,
      "metadata": {
        "id": "_1iJmFy_5cEy"
      },
      "outputs": [],
      "source": [
        "y_classification_encoded = to_categorical(y_classification, num_classes=7)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 171,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qh8l_45WsxaS",
        "outputId": "4e5e4352-4842-466c-86df-d4b114bae5e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set shape: (6181, 5) (6181, 7)\n",
            "Testing set shape: (2649, 5) (2649, 7)\n"
          ]
        }
      ],
      "source": [
        "X_train, X_test, y_train, y_test = split_dataset(X_numeric_classification_normalized, y_classification_encoded)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 172,
      "metadata": {
        "id": "9nWeCBpY1BcV"
      },
      "outputs": [],
      "source": [
        "max_len = max_len_overview\n",
        "model1 = build_model1_classification(X_numeric_classification_normalized.shape[1])\n",
        "model2 = build_model2(X_numeric_classification_normalized.shape[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "da-5xWS82EF0",
        "outputId": "0cf801eb-4467-43dc-c5c6-503dfaafe31e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "155/155 [==============================] - 2s 4ms/step - loss: 1.5874 - accuracy: 0.4258 - val_loss: 1.3165 - val_accuracy: 0.4543\n",
            "Epoch 2/50\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 1.3428 - accuracy: 0.4288 - val_loss: 1.3027 - val_accuracy: 0.4406\n",
            "Epoch 3/50\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 1.3281 - accuracy: 0.4373 - val_loss: 1.2897 - val_accuracy: 0.4406\n",
            "Epoch 4/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3205 - accuracy: 0.4361 - val_loss: 1.2868 - val_accuracy: 0.4406\n",
            "Epoch 5/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3218 - accuracy: 0.4389 - val_loss: 1.2825 - val_accuracy: 0.4406\n",
            "Epoch 6/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3201 - accuracy: 0.4381 - val_loss: 1.2838 - val_accuracy: 0.4406\n",
            "Epoch 7/50\n",
            "155/155 [==============================] - 1s 3ms/step - loss: 1.3196 - accuracy: 0.4375 - val_loss: 1.2912 - val_accuracy: 0.4406\n",
            "Epoch 8/50\n",
            "155/155 [==============================] - 1s 3ms/step - loss: 1.3205 - accuracy: 0.4397 - val_loss: 1.2844 - val_accuracy: 0.4406\n",
            "Epoch 9/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3185 - accuracy: 0.4399 - val_loss: 1.2815 - val_accuracy: 0.4406\n",
            "Epoch 10/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3149 - accuracy: 0.4369 - val_loss: 1.2905 - val_accuracy: 0.4406\n",
            "Epoch 11/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3181 - accuracy: 0.4393 - val_loss: 1.2818 - val_accuracy: 0.4406\n",
            "Epoch 12/50\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 1.3180 - accuracy: 0.4399 - val_loss: 1.2846 - val_accuracy: 0.4406\n",
            "Epoch 13/50\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 1.3152 - accuracy: 0.4387 - val_loss: 1.2814 - val_accuracy: 0.4406\n",
            "Epoch 14/50\n",
            "155/155 [==============================] - 1s 6ms/step - loss: 1.3178 - accuracy: 0.4391 - val_loss: 1.2852 - val_accuracy: 0.4406\n",
            "Epoch 15/50\n",
            "155/155 [==============================] - 1s 5ms/step - loss: 1.3178 - accuracy: 0.4393 - val_loss: 1.2857 - val_accuracy: 0.4406\n",
            "Epoch 16/50\n",
            "155/155 [==============================] - 1s 5ms/step - loss: 1.3173 - accuracy: 0.4401 - val_loss: 1.2845 - val_accuracy: 0.4406\n",
            "Epoch 17/50\n",
            "155/155 [==============================] - 1s 5ms/step - loss: 1.3167 - accuracy: 0.4397 - val_loss: 1.2837 - val_accuracy: 0.4406\n",
            "Epoch 18/50\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 1.3170 - accuracy: 0.4391 - val_loss: 1.2824 - val_accuracy: 0.4406\n",
            "Epoch 19/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3189 - accuracy: 0.4395 - val_loss: 1.2828 - val_accuracy: 0.4406\n",
            "Epoch 20/50\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 1.3147 - accuracy: 0.4397 - val_loss: 1.2825 - val_accuracy: 0.4406\n",
            "Epoch 21/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3161 - accuracy: 0.4395 - val_loss: 1.2843 - val_accuracy: 0.4406\n",
            "Epoch 22/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3159 - accuracy: 0.4397 - val_loss: 1.2847 - val_accuracy: 0.4406\n",
            "Epoch 23/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3173 - accuracy: 0.4395 - val_loss: 1.2855 - val_accuracy: 0.4406\n",
            "Epoch 24/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3164 - accuracy: 0.4389 - val_loss: 1.2837 - val_accuracy: 0.4406\n",
            "Epoch 25/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3172 - accuracy: 0.4395 - val_loss: 1.2830 - val_accuracy: 0.4406\n",
            "Epoch 26/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3158 - accuracy: 0.4395 - val_loss: 1.2830 - val_accuracy: 0.4406\n",
            "Epoch 27/50\n",
            "155/155 [==============================] - 1s 3ms/step - loss: 1.3158 - accuracy: 0.4395 - val_loss: 1.2827 - val_accuracy: 0.4406\n",
            "Epoch 28/50\n",
            "155/155 [==============================] - 1s 3ms/step - loss: 1.3175 - accuracy: 0.4391 - val_loss: 1.2857 - val_accuracy: 0.4406\n",
            "Epoch 29/50\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 1.3146 - accuracy: 0.4395 - val_loss: 1.2832 - val_accuracy: 0.4406\n",
            "Epoch 30/50\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 1.3176 - accuracy: 0.4395 - val_loss: 1.2832 - val_accuracy: 0.4406\n",
            "Epoch 31/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3160 - accuracy: 0.4395 - val_loss: 1.2823 - val_accuracy: 0.4406\n",
            "Epoch 32/50\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 1.3164 - accuracy: 0.4395 - val_loss: 1.2826 - val_accuracy: 0.4406\n",
            "Epoch 33/50\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 1.3167 - accuracy: 0.4395 - val_loss: 1.2833 - val_accuracy: 0.4406\n",
            "Epoch 34/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3176 - accuracy: 0.4391 - val_loss: 1.2833 - val_accuracy: 0.4406\n",
            "Epoch 35/50\n",
            "155/155 [==============================] - 1s 3ms/step - loss: 1.3151 - accuracy: 0.4395 - val_loss: 1.2813 - val_accuracy: 0.4406\n",
            "Epoch 36/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3157 - accuracy: 0.4401 - val_loss: 1.2856 - val_accuracy: 0.4406\n",
            "Epoch 37/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3165 - accuracy: 0.4395 - val_loss: 1.2830 - val_accuracy: 0.4406\n",
            "Epoch 38/50\n",
            "155/155 [==============================] - 1s 5ms/step - loss: 1.3169 - accuracy: 0.4395 - val_loss: 1.2846 - val_accuracy: 0.4406\n",
            "Epoch 39/50\n",
            "155/155 [==============================] - 1s 5ms/step - loss: 1.3141 - accuracy: 0.4395 - val_loss: 1.2822 - val_accuracy: 0.4406\n",
            "Epoch 40/50\n",
            "155/155 [==============================] - 1s 5ms/step - loss: 1.3159 - accuracy: 0.4393 - val_loss: 1.2816 - val_accuracy: 0.4406\n",
            "Epoch 41/50\n",
            "155/155 [==============================] - 1s 5ms/step - loss: 1.3162 - accuracy: 0.4395 - val_loss: 1.2824 - val_accuracy: 0.4406\n",
            "Epoch 42/50\n",
            "155/155 [==============================] - 1s 5ms/step - loss: 1.3168 - accuracy: 0.4395 - val_loss: 1.2829 - val_accuracy: 0.4406\n",
            "Epoch 43/50\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 1.3155 - accuracy: 0.4395 - val_loss: 1.2816 - val_accuracy: 0.4406\n",
            "Epoch 44/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3169 - accuracy: 0.4395 - val_loss: 1.2822 - val_accuracy: 0.4406\n",
            "Epoch 45/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3139 - accuracy: 0.4395 - val_loss: 1.2817 - val_accuracy: 0.4406\n",
            "Epoch 46/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3155 - accuracy: 0.4395 - val_loss: 1.2839 - val_accuracy: 0.4406\n",
            "Epoch 47/50\n",
            "155/155 [==============================] - 1s 3ms/step - loss: 1.3167 - accuracy: 0.4393 - val_loss: 1.2832 - val_accuracy: 0.4406\n",
            "Epoch 48/50\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3162 - accuracy: 0.4395 - val_loss: 1.2837 - val_accuracy: 0.4406\n",
            "Epoch 49/50\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 1.3135 - accuracy: 0.4395 - val_loss: 1.2814 - val_accuracy: 0.4406\n",
            "Epoch 50/50\n",
            "155/155 [==============================] - 1s 3ms/step - loss: 1.3147 - accuracy: 0.4397 - val_loss: 1.2838 - val_accuracy: 0.4406\n",
            "83/83 [==============================] - 0s 2ms/step - loss: 1.3155 - accuracy: 0.4300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss (mean squared error): [1.3155431747436523, 0.4299735724925995]\n"
          ]
        }
      ],
      "source": [
        "fit_evaluate_model(model1, 0.003, 'categorical_crossentropy', ['accuracy'], 50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 173,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 926
        },
        "id": "HThfbmy8JcAS",
        "outputId": "70009da0-1d91-430c-e5fa-084d2b1fb06f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "83/83 [==============================] - 0s 2ms/step\n",
            "Confusion Matrix:\n",
            " [[   0   12    0    0    0  919    0]\n",
            " [   0    0    0    0    0   26    0]\n",
            " [   0   31    0    0    0   62    0]\n",
            " [   0   77    0    0    0 1062    0]\n",
            " [   0    0    0    0    0  171    0]\n",
            " [   0    0    0    0    0    1    0]\n",
            " [   0   40    0    0    0  248    0]]\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.00      0.00       931\n",
            "           1       0.00      0.00      0.00        26\n",
            "           2       1.00      0.00      0.00        93\n",
            "           3       1.00      0.00      0.00      1139\n",
            "           4       1.00      0.00      0.00       171\n",
            "           5       0.00      1.00      0.00         1\n",
            "           6       1.00      0.00      0.00       288\n",
            "\n",
            "    accuracy                           0.00      2649\n",
            "   macro avg       0.71      0.14      0.00      2649\n",
            "weighted avg       0.99      0.00      0.00      2649\n",
            "\n",
            "Accuracy: 0.00037750094375235937\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "predict_from_model_classification(model1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "r7Zx7xTB8zkV",
        "outputId": "8701b1a0-ab61-4e17-eaf7-80b6042be25b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "155/155 [==============================] - 2s 5ms/step - loss: 1.3146 - accuracy: 0.4395 - val_loss: 1.2831 - val_accuracy: 0.4406\n",
            "Epoch 2/20\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 1.3141 - accuracy: 0.4395 - val_loss: 1.2823 - val_accuracy: 0.4406\n",
            "Epoch 3/20\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3136 - accuracy: 0.4395 - val_loss: 1.2824 - val_accuracy: 0.4406\n",
            "Epoch 4/20\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 1.3127 - accuracy: 0.4395 - val_loss: 1.2816 - val_accuracy: 0.4406\n",
            "Epoch 5/20\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3129 - accuracy: 0.4395 - val_loss: 1.2814 - val_accuracy: 0.4406\n",
            "Epoch 6/20\n",
            "155/155 [==============================] - 1s 3ms/step - loss: 1.3135 - accuracy: 0.4395 - val_loss: 1.2833 - val_accuracy: 0.4406\n",
            "Epoch 7/20\n",
            "155/155 [==============================] - 1s 3ms/step - loss: 1.3138 - accuracy: 0.4395 - val_loss: 1.2834 - val_accuracy: 0.4406\n",
            "Epoch 8/20\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3139 - accuracy: 0.4395 - val_loss: 1.2816 - val_accuracy: 0.4406\n",
            "Epoch 9/20\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 1.3136 - accuracy: 0.4395 - val_loss: 1.2830 - val_accuracy: 0.4406\n",
            "Epoch 10/20\n",
            "155/155 [==============================] - 1s 5ms/step - loss: 1.3139 - accuracy: 0.4395 - val_loss: 1.2821 - val_accuracy: 0.4406\n",
            "Epoch 11/20\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 1.3139 - accuracy: 0.4395 - val_loss: 1.2832 - val_accuracy: 0.4406\n",
            "Epoch 12/20\n",
            "155/155 [==============================] - 1s 5ms/step - loss: 1.3129 - accuracy: 0.4395 - val_loss: 1.2824 - val_accuracy: 0.4406\n",
            "Epoch 13/20\n",
            "155/155 [==============================] - 1s 5ms/step - loss: 1.3139 - accuracy: 0.4395 - val_loss: 1.2823 - val_accuracy: 0.4406\n",
            "Epoch 14/20\n",
            "155/155 [==============================] - 1s 5ms/step - loss: 1.3139 - accuracy: 0.4395 - val_loss: 1.2822 - val_accuracy: 0.4406\n",
            "Epoch 15/20\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 1.3135 - accuracy: 0.4395 - val_loss: 1.2819 - val_accuracy: 0.4406\n",
            "Epoch 16/20\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3135 - accuracy: 0.4395 - val_loss: 1.2837 - val_accuracy: 0.4406\n",
            "Epoch 17/20\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3144 - accuracy: 0.4395 - val_loss: 1.2821 - val_accuracy: 0.4406\n",
            "Epoch 18/20\n",
            "155/155 [==============================] - 1s 3ms/step - loss: 1.3135 - accuracy: 0.4395 - val_loss: 1.2822 - val_accuracy: 0.4406\n",
            "Epoch 19/20\n",
            "155/155 [==============================] - 1s 4ms/step - loss: 1.3134 - accuracy: 0.4395 - val_loss: 1.2814 - val_accuracy: 0.4406\n",
            "Epoch 20/20\n",
            "155/155 [==============================] - 0s 3ms/step - loss: 1.3129 - accuracy: 0.4395 - val_loss: 1.2814 - val_accuracy: 0.4406\n",
            "83/83 [==============================] - 0s 2ms/step - loss: 1.3156 - accuracy: 0.4300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss (mean squared error): [1.3156248331069946, 0.4299735724925995]\n"
          ]
        }
      ],
      "source": [
        "fit_evaluate_model(model1, 0.001, 'categorical_crossentropy', ['accuracy'], 20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 174,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 926
        },
        "id": "oWykqJ0NJdXS",
        "outputId": "000b5a2c-4be7-4de8-bc0e-bee521e67da4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "83/83 [==============================] - 0s 2ms/step\n",
            "Confusion Matrix:\n",
            " [[ 931    0    0    0    0    0    0]\n",
            " [  26    0    0    0    0    0    0]\n",
            " [  93    0    0    0    0    0    0]\n",
            " [1139    0    0    0    0    0    0]\n",
            " [ 171    0    0    0    0    0    0]\n",
            " [   1    0    0    0    0    0    0]\n",
            " [ 288    0    0    0    0    0    0]]\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.35      1.00      0.52       931\n",
            "           1       1.00      0.00      0.00        26\n",
            "           2       1.00      0.00      0.00        93\n",
            "           3       1.00      0.00      0.00      1139\n",
            "           4       1.00      0.00      0.00       171\n",
            "           5       1.00      0.00      0.00         1\n",
            "           6       1.00      0.00      0.00       288\n",
            "\n",
            "    accuracy                           0.35      2649\n",
            "   macro avg       0.91      0.14      0.07      2649\n",
            "weighted avg       0.77      0.35      0.18      2649\n",
            "\n",
            "Accuracy: 0.35145337863344656\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "predict_from_model_classification(model2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0rvGuBJTsyHD"
      },
      "source": [
        "#### 🔠 **Overview: Text features**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 151,
      "metadata": {
        "id": "j0lh29i0dkv1"
      },
      "outputs": [],
      "source": [
        "y_classification_encoded = to_categorical(y_classification, num_classes=7)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 152,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3jrrVjzZssma",
        "outputId": "8527f01e-8e07-4c26-b5ca-a205d351badb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set shape: (6181, 500) (6181, 7)\n",
            "Testing set shape: (2649, 500) (2649, 7)\n"
          ]
        }
      ],
      "source": [
        "X_train, X_test, y_train, y_test = split_dataset(X_text_vectorized, y_classification_encoded)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 153,
      "metadata": {
        "id": "DMZ5zPd18pEY"
      },
      "outputs": [],
      "source": [
        "max_len = max_len_overview\n",
        "model_embedding = build_model_embedding()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_embedding.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cd6fv17_XNWD",
        "outputId": "70cb365e-3890-4687-a552-d6f705f477ad"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_18\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_6 (Embedding)     (None, 500, 64)           640000    \n",
            "                                                                 \n",
            " flatten_5 (Flatten)         (None, 32000)             0         \n",
            "                                                                 \n",
            " dense_64 (Dense)            (None, 128)               4096128   \n",
            "                                                                 \n",
            " dense_65 (Dense)            (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_66 (Dense)            (None, 7)                 455       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4744839 (18.10 MB)\n",
            "Trainable params: 4744839 (18.10 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 155,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 831
        },
        "id": "m7K9IwRk8_Y1",
        "outputId": "7f75c6a5-e5d8-4012-eb06-dae84c2bb564"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "155/155 [==============================] - 14s 81ms/step - loss: 1.7097 - accuracy: 0.4047 - val_loss: 1.4141 - val_accuracy: 0.4406\n",
            "Epoch 2/10\n",
            "155/155 [==============================] - 13s 82ms/step - loss: 1.1895 - accuracy: 0.5649 - val_loss: 1.5357 - val_accuracy: 0.3751\n",
            "Epoch 3/10\n",
            "155/155 [==============================] - 12s 80ms/step - loss: 0.5694 - accuracy: 0.8087 - val_loss: 1.9784 - val_accuracy: 0.3848\n",
            "Epoch 4/10\n",
            "155/155 [==============================] - 12s 80ms/step - loss: 0.2579 - accuracy: 0.9181 - val_loss: 2.4872 - val_accuracy: 0.3581\n",
            "Epoch 5/10\n",
            "155/155 [==============================] - 12s 75ms/step - loss: 0.1486 - accuracy: 0.9640 - val_loss: 2.4710 - val_accuracy: 0.3848\n",
            "Epoch 6/10\n",
            "155/155 [==============================] - 12s 75ms/step - loss: 0.0764 - accuracy: 0.9846 - val_loss: 2.4606 - val_accuracy: 0.3533\n",
            "Epoch 7/10\n",
            "155/155 [==============================] - 13s 82ms/step - loss: 0.0406 - accuracy: 0.9933 - val_loss: 2.6129 - val_accuracy: 0.3791\n",
            "Epoch 8/10\n",
            "155/155 [==============================] - 14s 93ms/step - loss: 0.0226 - accuracy: 0.9974 - val_loss: 2.5852 - val_accuracy: 0.3840\n",
            "Epoch 9/10\n",
            "155/155 [==============================] - 12s 80ms/step - loss: 0.0220 - accuracy: 0.9976 - val_loss: 2.4813 - val_accuracy: 0.3541\n",
            "Epoch 10/10\n",
            "155/155 [==============================] - 13s 82ms/step - loss: 0.0146 - accuracy: 0.9994 - val_loss: 2.5537 - val_accuracy: 0.3614\n",
            "83/83 [==============================] - 1s 10ms/step - loss: 2.6973 - accuracy: 0.3575\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAGwCAYAAABVdURTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABfdUlEQVR4nO3deVhUZf8G8HtmgGFHlB1RZFFRERSQ0NwtUjO1zCVN1LS3Ust4/ZXmmpW2mFlpWuaSlUqWW1n6GuVugRLmvivIjij7OnN+fxwYGVlkFDjDzP25rrlkzpyZ+Q6g5/Z5vuc5MkEQBBAREREZCLnUBRARERHVJ4YbIiIiMigMN0RERGRQGG6IiIjIoDDcEBERkUFhuCEiIiKDwnBDREREBsVE6gIam1qtRnJyMmxsbCCTyaQuh4iIiOpAEATk5ubCzc0NcnntYzNGF26Sk5Ph4eEhdRlERET0ABITE9GyZcta9zG6cGNjYwNA/ObY2tpKXA0RERHVRU5ODjw8PDTH8doYXbipmIqytbVluCEiImpi6tJSwoZiIiIiMigMN0RERGRQGG6IiIjIoDDcEBERkUFhuCEiIiKDwnBDREREBoXhhoiIiAwKww0REREZFIYbIiIiMigMN0RERGRQJA03Bw8exJAhQ+Dm5gaZTIYdO3bc9zn79+9H165doVQq4ePjgw0bNjR4nURERNR0SBpu8vPzERAQgJUrV9Zp/2vXrmHw4MHo27cv4uPjMWPGDEyePBl79+5t4EqJiIioqZD0wpkDBw7EwIED67z/6tWr0aZNG3z88ccAAD8/Pxw+fBiffPIJwsPDG6pMIiIiuodaLaBUrYZKLaBUJaBMpUaZWkCpSg0TuRwuduaS1dakrgp+7NgxDBgwQGtbeHg4ZsyYUeNziouLUVxcrLmfk5PTUOUREVEjEgQBJSo1ikrVEAQBglC+vdLjd7+ueEx7p+oer+51Ku8DXZ5zz74VWwSh+vcuUwkoU4tBoVQloEytLr9fvq38sTJVzcGi8mtU3laqFv9UVWzT2l98v8qPadVQua7yP9X3fD8qC25tjx9f7l7zDg2sSYWb1NRUODs7a21zdnZGTk4OCgsLYWFhUeU5S5Yswdtvv91YJRIRUSWlKjUKSlQoLFGhsFSFgpIyFJaoxG2lKs3XBSVlKCqt+Lry/ioUlpZV3Vb+taq2Iyw1OrkMMFHIYaKQSVpHkwo3D2L27NmIjIzU3M/JyYGHh4eEFRER6Q+VWtAEjsJqw0X1YaSwVHv7vc8pKFGhqFSFUpX+hA9Z+fFWprkvu+d+xeMyzcZqH6vhtTSH85rep5bnVDymkMtgIpfDVCETQ4JcBhNFpW1yMTiYKuRQyGXa2+RyKBQymMplmoBhWv6Yifzu65lWekxR/vqm9zymkFfdZiK/W4+ppjaxLoVcfD25XNpQU6FJhRsXFxekpaVpbUtLS4OtrW21ozYAoFQqoVQqG6M8IiLJqdUCsgtLkZlXjIy8YmTmlSAjtxiZecXIrPgzrwSZecXIyi9BcZm6UepSyGWwNFXAwkwBSzMFzE3FPy3NTGBhpoBF+f2Kxy1MFbAwMynfp/L+CliYmtzdz0wBcxMFFPIagopMPw621LiaVLgJCwvDr7/+qrVt3759CAsLk6giIqKGp1YLuF1QogklmXnFyMgtDy+5d7dl5hXjVl4Jyh5gqkYmQ3n4MKkULhTVhIv7hxGt55YHEVOFjEGDGo2k4SYvLw+XL1/W3L927Rri4+PRvHlztGrVCrNnz0ZSUhI2btwIAHjppZewYsUKvPHGG5g0aRL++OMP/PDDD9i9e7dUH4GI6IGo1AKy8rWDSUVQ0QSXSiMsuvaW2FmYwsHaDA7WSjjaKCv9KW5zsFaiuZUZrJVi+FCayBk+yGBIGm6OHz+Ovn37au5X9MZERERgw4YNSElJQUJCgubxNm3aYPfu3Xj99dfx6aefomXLlvj66695GjgR6YUylRpZ+SV3g0lupeBSeXqoPLDoOsBib2mqCSYO5UFFE1w0283QwkoJMxMuQE/GSyYI957cZthycnJgZ2eH7Oxs2NraSl0OETUhWfkliE+8jYtpeVpBRfy6BLcLSqqcLlwbmQxobmmmCSWa4GJdPsJiI4YWRxtxlMVUwcBCxkuX43eT6rkhImosRaUqnE3JQXzCHcQnireErIL7Pk8uA5pbaU8HVZ4K0oy4lE8LmTCwENU7hhsiMnpqtYDrt/I1ISY+8Q7OpeRUexqzl6MVOrnZwdXOvNoRl+ZWZpozd4hIGgw3RGR0buUV4+TNO4hPuIN/Eu/gZOId5BSVVdmvhZUZAj2aibdWzdDZvRnsLE0lqJiIdMFwQ0QGrahUhTPJOZoRmZM1TC8pTeTo5G6HQI9mCPBohi4ezdDS3oJnEBE1QQw3RGQw1GoB127lIz7hjjgyU8v0krejlSbEBHrYo72rDRt2iQwEww0RNVkPPL3UshnsLDi9RGSoGG6IqEm4d3opPvE2ErMKq+xXeXqp4sbpJSLjwnBDRHqn8vRS5bOXqrusgLejFQI97BHYSpxiaufC6SUiY8dwQ0SSu5VXrHUadk3TSw7W4vRSQEtOLxFRzRhuiKhRidNL2fgn4Q5O3szm9BIR1TuGGyJqcNmFpfjurxvYczqV00tE1OAYboiowWTkFmPdkWv49tgN5BXfnWaqmF4KLD8Nu7OHHWzNOb1ERPWD4YaI6t3N2wVYc/AqtsQmorhMDQBo62yNyY96Icy7BaeXiKhBMdwQUb25nJ6H1QeuYMc/SZqppwCPZpjW1wf92ztBzmsuEVEjYLghood2OikbX+y/jN9Op0Iob6fp4dMCU/v4IMy7BUdpiKhRMdwQ0QOLuZaFlX9exoGLGZptj3Vwxit9vNGllb2ElRGRMWO4ISKdCIKA/Rcz8MWflxF7/TYAQC4Dngpww8t9fNDOxUbiConI2DHcEFGdqNQC9pxOxco/L+NsSg4AwEwhx4jglvhPLy+0bmElcYVERCKGGyKqVUmZGjvik7B6/xVczcwHAFiaKTA2tBUm9/SCs625xBUSEWljuCGiahWWqBAVm4CvDl5FcnYRAMDOwhQTuntiQndP2FuZSVwhEVH1GG6ISEtOUSm+PXYD6w5fw638EgCAo40SU3q2wXOhrWGt5D8bRKTf+K8UEQEAMvOKsf7INWw8egO55asJt7S3wEu9vTEiqCXMTRUSV0hEVDcMN0RGLvlOIb46eBVbYhNQVCquJuzrZI1X+npjSGc3mPD6TkTUxDDcEBmpqxniasLb/0lCqUpcea9zSztM7euDx/ycuZowETVZDDdERuZMcja+2H8Fv55K0awmHObVAlP7+qCHD1cTJqKmj+GGyEgcvy6uJvznhburCQ/wc8LLfXwQ1JqrCROR4WC4ITJggiDg4KVMrPzzMmKuZQEQVxN+srMbXu7jDT9XW4krJCKqfww3RAZIrRaw90wqVu6/jNNJ4mrCpgoZRgS1xH96ecPTgasJE5HhYrghMiClKjV2xidj1f7LuJIhriZsYarAc6GtMLlnG7jaWUhcIRFRw2O4ITIARaUq/HA8EV8euIqkO4UAAFtzE3E14R5t0JyrCROREWG4IWrCcotK8d1fCVh7+Coy88TVhB2slZjcsw3GhraCjbmpxBUSETU+hhuiJigrvwTrj1zDN0evI6dIXE3YvZkFXurthWeDPbiaMBEZNYYboiYkJbsQaw5ew+aYBBSWqgAA3o5WeKWPD54KdIMpVxMmImK4IWoKrmfmY/WBK/gp7qZmNWF/dztM7euNxzu4cDVhIqJKGG6I9JggCHj757PYeOw61OWrCYe2aY6pfX3Q09eBqwkTEVWD4YZIj3209wI2HL0OAOjX3gmv9PFGsGdzaYsiItJzDDdEeuq7v27gi/1XAAAfjuiMkcEeEldERNQ0sPuQSA/9fjYN83eeBgC8PqAtgw0RkQ4Yboj0zMnEO5i++R+oBWBUsAde7e8jdUlERE0Kww2RHrlxKx+TNsSisFSF3m0d8e7wTmwaJiLSEcMNkZ7Iyi/BhPWxuJVfgo5utlg5tivXrSEiegCS/8u5cuVKeHp6wtzcHKGhoYiJialx39LSUixatAje3t4wNzdHQEAA9uzZ04jVEjWMolIVJn8Ti2uZ+XBvZoH1E0JgrWS/PxHRg5A03ERFRSEyMhILFixAXFwcAgICEB4ejvT09Gr3nzt3Lr788kt8/vnnOHv2LF566SUMHz4c//zzTyNXTlR/VGoBr235B3EJd2BrboJvJoXAydZc6rKIiJosmSAIglRvHhoaipCQEKxYsQIAoFar4eHhgenTp2PWrFlV9ndzc8OcOXMwdepUzbZnnnkGFhYW+O677+r0njk5ObCzs0N2djZsbW3r54MQPaCKRfo2HL0OM4Uc300ORbc2XMeGiOheuhy/JRu5KSkpwYkTJzBgwIC7xcjlGDBgAI4dO1btc4qLi2Furv0/WgsLCxw+fLjG9ykuLkZOTo7WjUhfrD18TbNI37JRAQw2RET1QLJwk5mZCZVKBWdnZ63tzs7OSE1NrfY54eHhWLZsGS5dugS1Wo19+/Zh27ZtSElJqfF9lixZAjs7O83Nw4PrhZB++OXfZLy7+xwAYM4gPzzZ2U3iioiIDIPkDcW6+PTTT+Hr64v27dvDzMwM06ZNw8SJEyGX1/wxZs+ejezsbM0tMTGxESsmqt7fV28hMuokAGBCd09M7tlG4oqIiAyHZOHGwcEBCoUCaWlpWtvT0tLg4uJS7XMcHR2xY8cO5Ofn48aNGzh//jysra3h5eVV4/solUrY2tpq3YikdDk9F1M2HkeJSo3wjs6Y92QHrmVDRFSPJAs3ZmZmCAoKQnR0tGabWq1GdHQ0wsLCan2uubk53N3dUVZWhp9++glDhw5t6HKJ6kV6ThEi1sUip6gMXVs1w6eju0AhZ7AhIqpPki6kERkZiYiICAQHB6Nbt25Yvnw58vPzMXHiRADA+PHj4e7ujiVLlgAA/v77byQlJSEwMBBJSUlYuHAh1Go13njjDSk/BlGd5BWXYdI3sUi6U4g2Dlb4OiIE5qYKqcsiIjI4koabUaNGISMjA/Pnz0dqaioCAwOxZ88eTZNxQkKCVj9NUVER5s6di6tXr8La2hqDBg3Ct99+i2bNmkn0CYjqplSlxtTv43A6KQctrMywYWIImluZSV0WEZFBknSdGylwnRtqbIIgYNZPpxB1PBHmpnJseTEMgR7NpC6LiKhJaRLr3BAZi8+iLyPqeCLkMmDFmK4MNkREDYzhhqgBbT2eiE9+vwgAWDS0EwZ0cL7PM4iI6GEx3BA1kIMXMzB72ykAwCt9vDHukdYSV0REZBwYbogawJnkbLz83QmUqQUMC3TD/4W3k7okIiKjwXBDVM+S7hRi4vpY5JeoEObVAh+OCOAifUREjYjhhqgeZReUYsK6GKTnFqOdsw1WPx8EMxP+NSMiakz8V5eonhSXqfDit8dxKT0PLrbmWD8xBHYWplKXRURkdBhuiOqBWi1g5tZ/8fe1LFgrTbB+YgjcmllIXRYRkVFiuCGqBx/sPY+fTybDRC7D6nFB8HPlApFERFJhuCF6SBuPXceXB64CAD54pjMe9XWQuCIiIuPGcEP0EP53JhULd50BAMx8vC2eCWopcUVERMRwQ/SA4hJu49Ut/0AtAGO6eWBqXx+pSyIiIjDcED2Q65n5mPzNcRSVqtG3nSPeGdqJa9kQEekJhhsiHd3KK0bE+hhk5ZfA390OK57rChMF/yoREekL/otMpIPCEhVe+OY4btwqQEt7C6ydEAwrpYnUZRERUSUMN0R1pFILeHXLP4hPvINmlqb4ZlI3ONmYS10WERHdg+GGqA4EQcDCXWew72wazEzk+Hp8MLwdraUui4iIqsFwQ1QHXx68im//ugGZDFg+KhDBns2lLomIiGrAcEN0Hzvjk/D+b+cBAHMHd8Agf1eJKyIiotow3BDV4tiVW/i/rf8CACb1aIMXHm0jcUVERHQ/DDdENbiYlosXvz2OEpUaAzu5YO5gP6lLIiKiOmC4IapGWk4RJqyLQW5RGYJb2+OTUYGQy7lIHxFRU8BwQ3SP3KJSTFgfi+TsIng5WmHN+GCYmyqkLouIiOqI4YaoklKVGq98H4dzKTlwsDbDNxO7wd7KTOqyiIhIBww3ROUEQcDsbadw6FImLEwVWDchBB7NLaUui4iIdMRwQ1Tuk98v4ccTNyGXASvHdkHnls2kLomIiB4Aww0RgKjYBHwWfQkA8O4wf/Rr7yxxRURE9KAYbsjo/XkhHW9tPw0AmNbXB8+FtpK4IiIiehgMN2TUTidlY+r3cVCpBTzd1R3/fbyt1CUREdFDYrgho5WYVYCJG2JRUKLCoz4OeP/pzpDJuJYNEVFTx3BDRulOQQkmrI9BRm4x2rvY4ItxXWFmwr8ORESGgP+ak9EpKlXhxY0ncCUjH6525lg/MQS25qZSl0VERPWE4YaMilot4L9bTyLmehZslCZYPzEErnYWUpdFRET1iOGGjMqS385h978pMFXI8OXzQWjvYit1SUREVM8YbshorD9yDWsOXQMAfDQiAN19HCSuiIiIGgLDDRmFPadTsOiXswCA/wtvh2Fd3CWuiIiIGgrDDRm8Ezey8NqWeAgCMDa0FV7p4y11SURE1IAYbsigXc3Iw+RvjqO4TI3+7Z3w9lMduZYNEZGBY7ghg5WZV4wJ62Nxu6AUAS3t8PlzXWCi4K88EZGh47/0ZJAEQcCLG48jIasArZpb4uuIEFiamUhdFhERNQKGGzJIx67eQlzCHViaKbBhYggcbZRSl0RERI1E8nCzcuVKeHp6wtzcHKGhoYiJial1/+XLl6Ndu3awsLCAh4cHXn/9dRQVFTVStdRUbIlJBAAM6+IOL0driashIqLGJGm4iYqKQmRkJBYsWIC4uDgEBAQgPDwc6enp1e6/adMmzJo1CwsWLMC5c+ewdu1aREVF4a233mrkykmfZeWXYM/pVADAmJBWEldDRESNTdJws2zZMkyZMgUTJ05Ehw4dsHr1alhaWmLdunXV7n/06FH06NEDzz33HDw9PfH4449jzJgx9x3tIeOyLe4mSlRqdHSzhX9LO6nLISKiRiZZuCkpKcGJEycwYMCAu8XI5RgwYACOHTtW7XO6d++OEydOaMLM1atX8euvv2LQoEE1vk9xcTFycnK0bmS4BEHAllhxSmp0N47aEBEZI8lOH8nMzIRKpYKzs7PWdmdnZ5w/f77a5zz33HPIzMzEo48+CkEQUFZWhpdeeqnWaaklS5bg7bffrtfaSX+duHEbl9PzYGGqwNBAN6nLISIiCUjeUKyL/fv3Y/Hixfjiiy8QFxeHbdu2Yffu3XjnnXdqfM7s2bORnZ2tuSUmJjZixdTYNpc3Ej/Z2RW25qYSV0NERFKQbOTGwcEBCoUCaWlpWtvT0tLg4uJS7XPmzZuH559/HpMnTwYA+Pv7Iz8/Hy+++CLmzJkDubxqVlMqlVAqeRqwMcguLMXuU8kAOCVFRGTMJBu5MTMzQ1BQEKKjozXb1Go1oqOjERYWVu1zCgoKqgQYhUIBQOy1IOO2Mz4JRaVqtHW2RtdWzaQuh4iIJCLpkq2RkZGIiIhAcHAwunXrhuXLlyM/Px8TJ04EAIwfPx7u7u5YsmQJAGDIkCFYtmwZunTpgtDQUFy+fBnz5s3DkCFDNCGHjJMgCJopqdEhrXj9KCIiIyZpuBk1ahQyMjIwf/58pKamIjAwEHv27NE0GSckJGiN1MydOxcymQxz585FUlISHB0dMWTIELz33ntSfQTSE//ezMa5lByYmcjxdFd3qcshIiIJyQQjm8/JycmBnZ0dsrOzYWtrK3U5VE9mb/sXm2MSMSzQDctHd5G6HCIiqme6HL+b1NlSRNXJKy7Drng2EhMRkYjhhpq8n08mI79EBS8HK4S2aS51OUREJDGGG2rytsQkAABGhXiwkZiIiBhuqGk7m5yDkzezYaqQ4ZmgllKXQ0REeoDhhpq0LbHiqM3jHVzgYM3FGomIiOGGmrDCEhW2/5MEABjdzUPiaoiISF8w3FCTtftUCnKLyuDR3AI9vB2kLoeIiPQEww01WZpG4mAPyOVsJCYiIhHDDTVJl9JycfzGbSjkMjwbzCkpIiK6i+GGmqQtseJ1pPq2c4KzrbnE1RARkT5huKEmp7hMhW1xNwEAz4Vy1IaIiLQx3FCTs/dMGm4XlMLVzhy92zpJXQ4REekZhhtqcjb/LTYSPxvsAQUbiYmI6B4MN9SkXM/Mx7GrtyCTASODuSIxERFVxXBDTUpFI3EvX0e0tLeUuBoiItJHDDfUZJSq1PjxhNhIPKZbK4mrISIifcVwQ01G9Lk0ZOYVw8Faif5+bCQmIqLqMdxQk7EpRpySeja4JUwV/NUlIqLq8QhBTUJiVgEOXcoAAIwO4do2RERUM4YbahK2Hk+EIADdvVugdQsrqcshIiI9xnBDeq9MpcYPx9lITEREdcNwQ3rvwMUMpOYUwd7SFI93dJa6HCIi0nMMN6T3Npc3Ej/TtSWUJgqJqyEiIn3HcEN6LTW7CH+cTwMAjO7GRmIiIro/hhvSa1uPJ0ItACGe9vBxspG6HCIiagIYbkhvqdUCoo6LU1JsJCYiorpiuCG9dfhyJm7eLoStuQkG+btKXQ4RETURDDekt7bEJgAAhndxh7kpG4mJiKhuGG5IL2XkFuN/ZyoaiTklRUREdadzuPH09MSiRYuQkJDQEPUQAQB+iruJMrWAAI9m8HO1lbocIiJqQnQONzNmzMC2bdvg5eWFxx57DFu2bEFxcXFD1EZGShAERMWWNxLzOlJERKSjBwo38fHxiImJgZ+fH6ZPnw5XV1dMmzYNcXFxDVEjGZm/rmbhWmY+rMwUGBLgJnU5RETUxDxwz03Xrl3x2WefITk5GQsWLMDXX3+NkJAQBAYGYt26dRAEoT7rJCNS0Uj8VKA7rJQmEldDRERNzQMfOUpLS7F9+3asX78e+/btwyOPPIIXXngBN2/exFtvvYXff/8dmzZtqs9ayQjczi/Bb6dSAQBjuCIxURVqtRolJSVSl0HUIMzMzCCXP/y5TjqHm7i4OKxfvx6bN2+GXC7H+PHj8cknn6B9+/aafYYPH46QkJCHLo6Mz7Z/klCiUqODqy383e2kLodIr5SUlODatWtQq9VSl0LUIORyOdq0aQMzM7OHeh2dw01ISAgee+wxrFq1CsOGDYOpqWmVfdq0aYPRo0c/VGFkfARBwJYYcUpqTDcPyGQyiSsi0h+CICAlJQUKhQIeHh718r9bIn2iVquRnJyMlJQUtGrV6qGOATqHm6tXr6J169a17mNlZYX169c/cFFknOISbuNSeh7MTeUY2sVd6nKI9EpZWRkKCgrg5uYGS0tLqcshahCOjo5ITk5GWVlZtYMndaVz9E9PT8fff/9dZfvff/+N48ePP3AhRJtjxNO/n+zsBlvzB/+lJjJEKpUKAB56uJ5In1X8flf8vj8oncPN1KlTkZiYWGV7UlISpk6d+lDFkPHKKSrFL/8mA2AjMVFtOF1Lhqy+fr91Djdnz55F165dq2zv0qULzp49Wy9FkfHZ+U8SikrV8HWyRtdW9lKXQ0RETZjO4UapVCItLa3K9pSUFJiYcE0S0p0gCJopqdHdHq6JjIgMn6enJ5YvX17n/ffv3w+ZTIY7d+40WE2kX3QON48//jhmz56N7OxszbY7d+7grbfewmOPPfZARaxcuRKenp4wNzdHaGgoYmJiaty3T58+kMlkVW6DBw9+oPcm6Z1KysbZlByYmcjxNBuJiQxGdf9WV74tXLjwgV43NjYWL774Yp337969O1JSUmBn13jLS7Rv3x5KpRKpqamN9p50l85DLUuXLkWvXr3QunVrdOnSBQAQHx8PZ2dnfPvttzoXEBUVhcjISKxevRqhoaFYvnw5wsPDceHCBTg5OVXZf9u2bVoLWN26dQsBAQF49tlndX5v0g8VozYDO7nA3orNkkSGIiUlRfN1VFQU5s+fjwsXLmi2WVtba74WBAEqlapOMwCOjo461WFmZgYXFxednvMwDh8+jMLCQowYMQLffPMN3nzzzUZ77+qUlpY+1JlHTZHOIzfu7u74999/8eGHH6JDhw4ICgrCp59+ilOnTsHDQ/dG0GXLlmHKlCmYOHEiOnTogNWrV8PS0hLr1q2rdv/mzZvDxcVFc9u3bx8sLS0Zbpqo/OIy7IpPAgCMDmklcTVEVJ8q/1ttZ2cHmUymuX/+/HnY2Njgt99+Q1BQEJRKJQ4fPowrV65g6NChcHZ2hrW1NUJCQvD7779rve6901IymQxff/01hg8fDktLS/j6+mLXrl2ax++dltqwYQOaNWuGvXv3ws/PD9bW1njiiSe0wlhZWRleffVVNGvWDC1atMCbb76JiIgIDBs27L6fe+3atXjuuefw/PPPV3ssu3nzJsaMGYPmzZvDysoKwcHBWmch//zzzwgJCYG5uTkcHBwwfPhwrc+6Y8cOrddr1qwZNmzYAAC4fv06ZDIZoqKi0Lt3b5ibm+P777/HrVu3MGbMGLi7u8PS0hL+/v7YvHmz1uuo1Wp8+OGH8PHxgVKpRKtWrfDee+8BAPr164dp06Zp7Z+RkQEzMzNER0ff93vS2B6oScbKykqnIcGalJSU4MSJE5g9e7Zmm1wux4ABA3Ds2LE6vcbatWsxevRoWFlZVft4cXGx1lXLc3JyHq5oqlc/n0xGfokKbRys8IhXc6nLIWoyBEFAYenDnS77oCxMFfXWGzdr1iwsXboUXl5esLe3R2JiIgYNGoT33nsPSqUSGzduxJAhQ3DhwgW0alXzf4DefvttfPjhh/joo4/w+eefY+zYsbhx4waaN6/+35WCggIsXboU3377LeRyOcaNG4eZM2fi+++/BwB88MEH+P7777F+/Xr4+fnh008/xY4dO9C3b99aP09ubi62bt2Kv//+G+3bt0d2djYOHTqEnj17AgDy8vLQu3dvuLu7Y9euXXBxcUFcXJxm1endu3dj+PDhmDNnDjZu3IiSkhL8+uuvD/R9/fjjj9GlSxeYm5ujqKgIQUFBePPNN2Fra4vdu3fj+eefh7e3N7p16wYAmD17NtasWYNPPvkEjz76KFJSUnD+/HkAwOTJkzFt2jR8/PHHUCqVAIDvvvsO7u7u6Nevn871NbQH7gA+e/YsEhISqlzj5Kmnnqrza2RmZkKlUsHZ2Vlru7Ozs+YbWpuYmBicPn0aa9eurXGfJUuW4O23365zTdS4NseKU1KjQrgiMZEuCktV6DB/ryTvfXZROCzN6ucEkkWLFmn1azZv3hwBAQGa+++88w62b9+OXbt2VRk5qGzChAkYM2YMAGDx4sX47LPPEBMTgyeeeKLa/UtLS7F69Wp4e3sDAKZNm4ZFixZpHv/8888xe/ZszajJihUr6hQytmzZAl9fX3Ts2BEAMHr0aKxdu1YTbjZt2oSMjAzExsZqgpePj4/m+e+99x5Gjx6tddyq/P2oqxkzZuDpp5/W2jZz5kzN19OnT8fevXvxww8/oFu3bsjNzcWnn36KFStWICIiAgDg7e2NRx99FADw9NNPY9q0adi5cydGjhwJQBwBmzBhgl7+2/1AKxQPHz4cp06dgkwm01z9u+LDPezCO7pYu3Yt/P39NamzOrNnz0ZkZKTmfk5OzgNNn1H9O5eSg5OJd2Ail+GZri2lLoeIJBAcHKx1Py8vDwsXLsTu3buRkpKCsrIyFBYWIiEhodbX6dy5s+ZrKysr2NraIj09vcb9LS0tNcEGAFxdXTX7Z2dnIy0tTevYolAoEBQUdN/req1btw7jxo3T3B83bhx69+6Nzz//HDY2NoiPj0eXLl1qHFGKj4/HlClTan2Purj3+6pSqbB48WL88MMPSEpKQklJCYqLizWrXZ87dw7FxcXo379/ta9nbm6umWYbOXIk4uLicPr0aa3pP32ic7h57bXX0KZNG0RHR6NNmzaIiYnBrVu38N///hdLly7V6bUcHBygUCiqnFqelpZ23+av/Px8bNmyRStpV0epVGqG0Ei/VFxH6vGOznC04c+ISBcWpgqcXRQu2XvXl3tbCmbOnIl9+/Zh6dKl8PHxgYWFBUaMGHHfK6Hf2zArk8lqDSLV7V/xn/UHdfbsWfz111+IiYnRaiJWqVTYsmULpkyZAgsLi1pf436PV1dnaWlplf3u/b5+9NFH+PTTT7F8+XL4+/vDysoKM2bM0Hxf7/e+gDg1FRgYiJs3b2L9+vXo16/ffS/HJBWdG4qPHTuGRYsWwcHBAXK5HHK5HI8++iiWLFmCV199VafXMjMzQ1BQkFYzklqtRnR0NMLCwmp97tatW1FcXKyVkKnpKCxRYfs/bCQmelAymQyWZiaS3BpyGuLIkSOYMGEChg8fDn9/f7i4uOD69esN9n7VsbOzg7OzM2JjYzXbVCoV4uLian3e2rVr0atXL5w8eRLx8fGaW2RkpKZ9onPnzoiPj0dWVla1r9G5c+daG3QdHR21Gp8vXbqEgoKC+36mI0eOYOjQoRg3bhwCAgLg5eWFixcvah739fWFhYVFre/t7++P4OBgrFmzBps2bcKkSZPu+75S0TncqFQq2NjYABBHXpKTxSXzW7durXWKX11FRkZizZo1+Oabb3Du3Dm8/PLLyM/Px8SJEwEA48eP12o4rrB27VoMGzYMLVq00Pk9SXq/nkpBTlEZWtpb4FEfB6nLISI94evri23btiE+Ph4nT57Ec889d9+poIYwffp0LFmyBDt37sSFCxfw2muv4fbt2zUGu9LSUnz77bcYM2YMOnXqpHWbPHky/v77b5w5cwZjxoyBi4sLhg0bhiNHjuDq1av46aefNCfRLFiwAJs3b8aCBQtw7tw5nDp1Ch988IHmffr164cVK1bgn3/+wfHjx/HSSy/V6TRvX19f7Nu3D0ePHsW5c+fwn//8R2vWxNzcHG+++SbeeOMNbNy4EVeuXMFff/1Vpad18uTJeP/99yEIgtZZXPpG53DTqVMnnDx5EgAQGhqKDz/8EEeOHMGiRYvg5eWlcwGjRo3C0qVLMX/+fAQGBiI+Ph579uzRNBknJCRopVQAuHDhAg4fPowXXnhB5/cj/bAlVpySGhXsAblc/5rRiEgay5Ytg729Pbp3744hQ4YgPDy82kv+NLQ333wTY8aMwfjx4xEWFgZra2uEh4fD3Ny82v137dqFW7duVXvA9/Pzg5+fH9auXQszMzP873//g5OTEwYNGgR/f3+8//77UCjEqb4+ffpg69at2LVrFwIDA9GvXz+thW0//vhjeHh4oGfPnnjuuecwc+bMOl0lfu7cuejatSvCw8PRp08fTcCqbN68efjvf/+L+fPnw8/PD6NGjarStzRmzBiYmJhgzJgxNX4v9IFM0HGSce/evcjPz8fTTz+Ny5cv48knn8TFixfRokULREVF6eUpYZXl5OTAzs4O2dnZsLW1lboco3Q5PRcDlh2EXAYcndUfLnb6+xeESF8UFRXh2rVraNOmjV4fVAyVWq2Gn58fRo4ciXfeeUfqciRz/fp1eHt7IzY2tkFCZ22/57ocv3VuKA4Pv9vA5uPjg/PnzyMrKwv29vZ6eToY6Z8t5SsS92vvzGBDRHrpxo0b+N///ofevXujuLgYK1aswLVr1/Dcc89JXZokSktLcevWLcydOxePPPKIJKNputBpWqq0tBQmJiY4ffq01vbmzZsz2FCdFJep8FPcTQDAmG48JZ+I9JNcLseGDRsQEhKCHj164NSpU/j999/h5+cndWmSOHLkCFxdXREbG4vVq1dLXc596TRyY2pqilatWjXqWjZkWP53Jg23C0rhYmuO3m11uz4MEVFj8fDwwJEjR6QuQ2/06dPnoU+Vb0w6NxTPmTMHb731Vo2nsRHVZnP52jYjg1vCRKHzrx8REdF96dxzs2LFCly+fBlubm5o3bp1lYWC7rcOABmvG7fycfTKLchkwMgQTkkREVHD0Dnc1OWKqETV2VJ+Halevo5oaX//UxeJiIgehM7hZsGCBQ1RBxm4UpUaW4+zkZiIiBoemx6oUUSfS0dmXjEcrJXo7+d8/ycQERE9IJ1HbuRyea2nffNMKqpORSPxiKCWMGUjMRERNSCdjzLbt2/Htm3bNLeoqCjMmjULrq6u+OqrrxqiRmribt4uwMFLGQCA0WwkJiId9enTBzNmzNDc9/T0xPLly2t9jkwmw44dOx76vevrdahx6TxyM3To0CrbRowYgY4dOyIqKorXe6Iqfjh+E4IAdPduAU8Hq/s/gYgMwpAhQ1BaWoo9e/ZUeezQoUOaK2h37txZp9eNjY2tcqbuw1q4cCF27NiB+Ph4re0pKSmwt7ev1/eqSWFhIdzd3SGXy5GUlASlUtko72uI6m1+4JFHHqn1UulknFRqAVuPi2dJje7WSuJqiKgxvfDCC9i3bx9u3rxZ5bH169cjODhY52ADAI6OjnW6WGR9cHFxabSQ8dNPP6Fjx45o37695KNFgiCgrKxM0hoeRr2Em8LCQnz22Wdwd3evj5cjA3LgYjpSsotgb2mK8I5sJCYyJk8++SQcHR2xYcMGre15eXnYunUrXnjhBdy6dQtjxoyBu7s7LC0t4e/vj82bN9f6uvdOS126dAm9evWCubk5OnTogH379lV5zptvvom2bdvC0tISXl5emDdvHkpLSwEAGzZswNtvv42TJ09CJpNBJpNpar53WurUqVPo168fLCws0KJFC7z44ovIy8vTPD5hwgQMGzYMS5cuhaurK1q0aIGpU6dq3qs2a9euxbhx4zBu3DisXbu2yuNnzpzBk08+CVtbW9jY2KBnz564cuWK5vF169ahY8eOUCqVcHV1xbRp0wCIF7uUyWRao1J37tyBTCbD/v37AQD79++HTCbDb7/9hqCgICiVShw+fBhXrlzB0KFD4ezsDGtra4SEhOD333/Xqqu4uBhvvvkmPDw8oFQq4ePjg7Vr10IQBPj4+GDp0qVa+8fHx0Mmk+Hy5cv3/Z48KJ2npe69QKYgCMjNzYWlpSW+++67ei2Omr7N5RfJfLprSyhNFBJXQ2RABAEoLZDmvU0tgTpcT9DExATjx4/Hhg0bMGfOHM2xY+vWrVCpVBgzZgzy8vIQFBSEN998E7a2tti9ezeef/55eHt7o1u3bvd9D7VajaeffhrOzs74+++/kZ2drdWfU8HGxgYbNmyAm5sbTp06hSlTpsDGxgZvvPEGRo0ahdOnT2PPnj2aA7ednV2V18jPz0d4eDjCwsIQGxuL9PR0TJ48GdOmTdMKcH/++SdcXV3x559/4vLlyxg1ahQCAwMxZcqUGj/HlStXcOzYMWzbtg2CIOD111/HjRs30Lp1awBAUlISevXqhT59+uCPP/6Ara0tjhw5ohldWbVqFSIjI/H+++9j4MCByM7OfqDLR8yaNQtLly6Fl5cX7O3tkZiYiEGDBuG9996DUqnExo0bMWTIEFy4cAGtWomj8ePHj8exY8fw2WefISAgANeuXUNmZiZkMhkmTZqE9evXY+bMmZr3WL9+PXr16gUfHx+d66srncPNJ598ohVu5HI5HB0dERoa2mjzktQ0pOUU4Y/z6QC4tg1RvSstABa7SfPebyUDZnXreZk0aRI++ugjHDhwAH369AEgHtyeeeYZ2NnZwc7OTuvAN336dOzduxc//PBDncLN77//jvPnz2Pv3r1wcxO/H4sXL8bAgQO19ps7d67ma09PT8ycORNbtmzBG2+8AQsLC1hbW8PExAQuLi41vtemTZtQVFSEjRs3anp+VqxYgSFDhuCDDz6As7M4Om1vb48VK1ZAoVCgffv2GDx4MKKjo2sNN+vWrcPAgQM1x9Hw8HCsX78eCxcuBACsXLkSdnZ22LJlC0xNTQEAbdu21Tz/3XffxX//+1+89tprmm0hISH3/f7da9GiRXjsscc095s3b46AgADN/XfeeQfbt2/Hrl27MG3aNFy8eBE//PAD9u3bhwEDBgAAvLy8NPtPmDAB8+fPR0xMDLp164bS0lJs2rSpymhOfdM53EyYMKEByiBDtPV4IlRqAcGt7eHjZCN1OUQkgfbt26N79+5Yt24d+vTpg8uXL+PQoUNYtGgRAHH5kMWLF+OHH35AUlISSkpKUFxcXOeemnPnzsHDw0MTbAAgLCysyn5RUVH47LPPcOXKFeTl5aGsrAy2trY6fZZz584hICBAq5m5R48eUKvVuHDhgibcdOzYEQrF3ZFqV1dXnDp1qsbXValU+Oabb/Dpp59qto0bNw4zZ87E/PnzIZfLER8fj549e2qCTWXp6elITk5G//79dfo81QkODta6n5eXh4ULF2L37t1ISUlBWVkZCgsLkZAgLu8RHx8PhUKB3r17V/t6bm5uGDx4MNatW4du3brh559/RnFxMZ599tmHrrU2Ooeb9evXw9raukphW7duRUFBASIiIuqtOGq61GoBUeWNxGPYSExU/0wtxREUqd5bBy+88AKmT5+OlStXYv369fD29tYcDD/66CN8+umnWL58Ofz9/WFlZYUZM2agpKSk3so9duwYxo4di7fffhvh4eGaEZCPP/643t6jsnsDiEwmg1qtrnH/vXv3IikpCaNGjdLarlKpEB0djcceewwWFhY1Pr+2xwBxhgWA1lW9a+oBuvcstJkzZ2Lfvn1YunQpfHx8YGFhgREjRmh+Pvd7bwCYPHkynn/+eXzyySdYv349Ro0a1eAN4To3FC9ZsgQODg5Vtjs5OWHx4sX1UhQ1fUeuZCIxqxA25iYY5O8qdTlEhkcmE6eGpLjVod+mspEjR0Iul2PTpk3YuHEjJk2apGlvOHLkCIYOHYpx48YhICAAXl5euHjxYp1f28/PD4mJiUhJSdFs++uvv7T2OXr0KFq3bo05c+YgODgYvr6+uHHjhtY+ZmZm912E1s/PDydPnkR+fr5m25EjRyCXy9GuXbs613yvtWvXYvTo0YiPj9e6jR49WtNY3LlzZxw6dKjaUGJjYwNPT88az1h2dHQEAK3v0b2nvNfkyJEjmDBhAoYPHw5/f3+4uLjg+vXrmsf9/f2hVqtx4MCBGl9j0KBBsLKywqpVq7Bnzx5MmjSpTu/9MHQONwkJCWjTpk2V7a1bt9YMUxFtKW8kHt7FHRZmbCQmMmbW1tYYNWoUZs+ejZSUFK32Bl9fX+zbtw9Hjx7FuXPn8J///AdpaWl1fu0BAwagbdu2iIiIwMmTJ3Ho0CHMmTNHax9fX18kJCRgy5YtuHLlCj777DNs375dax9PT09cu3YN8fHxyMzMRHFxcZX3Gjt2LMzNzREREYHTp0/jzz//xPTp0/H8889rpqR0lZGRgZ9//hkRERHo1KmT1m38+PHYsWMHsrKyMG3aNOTk5GD06NE4fvw4Ll26hG+//RYXLlwAIK7T8/HHH+Ozzz7DpUuXEBcXh88//xyAOLryyCOP4P3338e5c+dw4MABrR6k2vj6+mLbtm2Ij4/HyZMn8dxzz2mNQnl6eiIiIgKTJk3Cjh07cO3aNezfvx8//PCDZh+FQoEJEyZg9uzZ8PX1rXbasL7pHG6cnJzw77//Vtl+8uRJtGjRol6KoqYtM68Y/zubCgAYHcIpKSISp6Zu376N8PBwrf6YuXPnomvXrggPD0efPn3g4uKCYcOG1fl15XI5tm/fjsLCQnTr1g2TJ0/Ge++9p7XPU089hddffx3Tpk1DYGAgjh49innz5mnt88wzz+CJJ55A37594ejoWO3p6JaWlti7dy+ysrIQEhKCESNGoH///lixYoVu34xKKpqTq+uX6d+/PywsLPDdd9+hRYsW+OOPP5CXl4fevXsjKCgIa9as0UyBRUREYPny5fjiiy/QsWNHPPnkk7h06ZLmtdatW4eysjIEBQVhxowZePfdd+tU37Jly2Bvb4/u3btjyJAhCA8PR9euXbX2WbVqFUaMGIFXXnkF7du3x5QpU7RGtwDx519SUoKJEyfq+i16IDKh8iRcHbz55puIiorSnMoFAAcOHMCkSZMwYsSIBu+Aflg5OTmws7NDdna2zs1kVDdfHriCJb+dR0BLO+yc9qjU5RAZhKKiIly7dg1t2rSBubm51OUQ6eTQoUPo378/EhMTax3lqu33XJfjt84Nxe+88w6uX7+O/v37w8REfLparcb48ePZc0MQBAFRsWwkJiIicYG/jIwMLFy4EM8+++wDT9/pSudwY2ZmhqioKLz77ruIj4+HhYUF/P39NQsNkXH7+1oWrmbmw8pMgSEBEq3BQUREemHz5s144YUXEBgYiI0bNzba++ocbir4+vrC19e3PmshA7AlRmwqfyrQDVbKB/71IiIiAzBhwgRJ1sfTuaH4mWeewQcffFBl+4cfftjgi/KQfrtTUIJfT7ORmIiIpKVzuDl48CAGDRpUZfvAgQNx8ODBeimKmqZtcUkoKVPDz9UWnVtWvS4LET08Hc8BIWpS6uv3W+dwk5eXBzMzsyrbTU1NkZOTUy9FUdMjCAK2xIpTUs9189C6/hgRPbyK5fzrc+VeIn1T8ftd+fIVD0Lnpgh/f39ERUVh/vz5Wtu3bNmCDh06PFQx1HTFJdzBxbQ8mJvKMbSLu9TlEBkcExMTWFpaIiMjA6amppol9YkMhVqtRkZGBiwtLTVnYz8onZ89b948PP3007hy5Qr69esHAIiOjsamTZvw448/PlQx1HRVNBIP9neDrXnVC7sR0cORyWRwdXXFtWvXqlw6gMhQyOVytGrV6qFH/3UON0OGDMGOHTuwePFi/Pjjj7CwsEBAQAD++OMPNG/e/KGKoaYpp6gUv/wrXrNkTDcPiashMlxmZmbw9fXl1BQZLDMzs3oZlXygcZ/Bgwdj8ODBAMQVAzdv3oyZM2fixIkT973wGBmenfHJKCxVwcfJGkGt7aUuh8igyeVyrlBMdB8PHI8OHjyIiIgIuLm54eOPP0a/fv2qXImVjEPFlNSYbg8/lEhERPSwdBq5SU1NxYYNG7B27Vrk5ORg5MiRKC4uxo4dO9hMbKRO3czGmeQcmCnkeJqNxEREpAfqPHIzZMgQtGvXDv/++y+WL1+O5ORkzeXUyXhtLj/9+4lOLrC3qrpEABERUWOr88jNb7/9hldffRUvv/wyL7tAAID84jLsik8GAIxmIzEREemJOo/cHD58GLm5uQgKCkJoaChWrFiBzMzMhqyN9Nwv/yYjr7gMni0sEebVQupyiIiIAOgQbh555BGsWbMGKSkp+M9//oMtW7bAzc0NarUa+/btQ25ubkPWSXpoc0wiAGBUCBuJiYhIf+h8tpSVlRUmTZqEw4cP49SpU/jvf/+L999/H05OTnjqqacaokbSQ+dTcxCfeAcmchlGBLWUuhwiIiKNh1opp127dvjwww9x8+ZNbN68ub5qoiZgS/mozWMdnOFoo5S4GiIiorvq5eIkCoUCw4YNw65du+rj5UjPFZWqsC3uJgBgdLdWEldDRESkjVdeI539djoFOUVlcG9mgZ4+DlKXQ0REpEXycLNy5Up4enrC3NwcoaGhiImJqXX/O3fuYOrUqXB1dYVSqUTbtm3x66+/NlK1BACb/65oJPaAXM5GYiIi0i8Pd03xhxQVFYXIyEisXr0aoaGhWL58OcLDw3HhwgU4OTlV2b+kpASPPfYYnJyc8OOPP8Ld3R03btxAs2bNGr94I3U5PQ8x17MglwEjg7m2DRER6R9Jw82yZcswZcoUTJw4EQCwevVq7N69G+vWrcOsWbOq7L9u3TpkZWXh6NGjMDU1BQB4eno2ZslGL6p8ReJ+7Z3gYseL9xERkf6RbFqqpKQEJ06cwIABA+4WI5djwIABOHbsWLXP2bVrF8LCwjB16lQ4OzujU6dOWLx4ca1XIi8uLkZOTo7WjR5McZkKP8UlAQBGh7CRmIiI9JNk4SYzMxMqlQrOzs5a252dnZGamlrtc65evYoff/wRKpUKv/76K+bNm4ePP/4Y7777bo3vs2TJEtjZ2WluHh6cSnlQ+86mISu/BM62SvRp5yh1OURERNWSvKFYF2q1Gk5OTvjqq68QFBSEUaNGYc6cOVi9enWNz5k9ezays7M1t8TExEas2LBsjhGnpEYGe8BE0aR+dYiIyIhI1nPj4OAAhUKBtLQ0re1paWlwcXGp9jmurq4wNTWFQqHQbPPz80NqaipKSkpgZlb1qtRKpRJKJReZe1g3buXjyOVbkLGRmIiI9Jxk//02MzNDUFAQoqOjNdvUajWio6MRFhZW7XN69OiBy5cvQ61Wa7ZdvHgRrq6u1QYbqj9RseKIV09fR3g0t5S4GiIioppJOrcQGRmJNWvW4JtvvsG5c+fw8ssvIz8/X3P21Pjx4zF79mzN/i+//DKysrLw2muv4eLFi9i9ezcWL16MqVOnSvURjEKpSo2tJ8QViceEcNSGiIj0m6Sngo8aNQoZGRmYP38+UlNTERgYiD179miajBMSEiCX381fHh4e2Lt3L15//XV07twZ7u7ueO211/Dmm29K9RGMwh/n05GRWwwHazP093O+/xOIiIgkJBMEQZC6iMaUk5MDOzs7ZGdnw9bWVupymoQJ62Ow/0IG/tPbC7MH+kldDhERGSFdjt885YVqlXSnEAcuZgDg2jZERNQ0MNxQrX6ITYQgAGFeLdDGwUrqcoiIiO6L4YZqpFIL2HpcPEtqdDc2EhMRUdPAcEM1OngxA8nZRWhmaYrwjtWvPURERKRvGG6oRhUrEj/dpSXMTRX32ZuIiEg/MNxQtdJzihB9Ph0AMIZTUkRE1IQw3FC1tp64CZVaQHBre/g620hdDhERUZ0x3FAVarWALbHilNTobjz9m4iImhaGG6ri6JVbSMwqhI25CQb7u0pdDhERkU4YbqiKzeWjNsMC3WFhxkZiIiJqWhhuSMvN2wX435lUAFzbhoiImiaGG9Lyzi9nUaoSEObVAh3d7KQuh4iISGcMN6Tx54V07D2TBoVchreHdpS6HCIiogfCcEMAgKJSFRbuOgMAmNTDE215+jcRETVRDDcEAFhz8Cpu3CqAs60Srw1oK3U5RERED4zhhpCYVYAVf14GAMwZ3AHWShOJKyIiInpwDDeERb+cRXGZGmFeLTCkM9e1ISKipo3hxsj9eT4d+86mwUQuw6KhHSGTyaQuiYiI6KEw3BixolIVFv5c3kT8aBteQ4qIiAwCw40R+6pSE/Gr/X2lLoeIiKheMNwYqcSsAqwsbyKeyyZiIiIyIAw3Rurtn8Um4u7eLfAkm4iJiMiAMNwYoehzafj9HJuIiYjIMDHcGJnKTcQv9GwDHyc2ERMRkWFhuDEyqw9cQWJWIVxszfFqPzYRExGR4WG4MSIJtwrwxf4rAIB5T3aAFZuIiYjIADHcGJG3fz6DkjI1HvVxwCB/F6nLISIiahAMN0bi97NpiD6fDlOFDAufYhMxEREZLoYbI1C5iXhyTy/4OFlLXBEREVHDYbgxAl/sv4KbtwvhZmeO6f18pC6HiIioQTHcGLjrmflYfeBuE7GlGZuIiYjIsDHcGDBBELCwvIm4p68DnujEJmIiIjJ8DDcGbN/ZNOy/kAFThQxvs4mYiIiMBMONgSosUeHtn88CAF7s5QUvRzYRExGRcWC4MVBf7L+MpDuFcG9mgal92URMRETGg+HGAF3LzMeXB64CYBMxEREZH4YbAyMIAhbsOoMSlRq92zoivKOz1CURERE1KoYbA7P3TBoOXsyAmULOlYiJiMgoMdwYkIKSMrzzi9hE/J/eXmjjYCVxRURERI2P4caArPzzbhPxK33YRExERMaJ4cZAXM3Iw1cHxSbiBUM6wMJMIXFFRERE0tCLcLNy5Up4enrC3NwcoaGhiImJqXHfDRs2QCaTad3Mzc0bsVr9U9FEXKoS0LedIx7rwCZiIiIyXpKHm6ioKERGRmLBggWIi4tDQEAAwsPDkZ6eXuNzbG1tkZKSornduHGjESvWP3tOp+LQpUyYmbCJmIiISPJws2zZMkyZMgUTJ05Ehw4dsHr1alhaWmLdunU1Pkcmk8HFxUVzc3Y23pGKyk3EL/X2RusWbCImIiLjJmm4KSkpwYkTJzBgwADNNrlcjgEDBuDYsWM1Pi8vLw+tW7eGh4cHhg4dijNnztS4b3FxMXJycrRuhmTFH5eRnF2ElvYWeKWPt9TlEBERSU7ScJOZmQmVSlVl5MXZ2RmpqanVPqddu3ZYt24ddu7cie+++w5qtRrdu3fHzZs3q91/yZIlsLOz09w8PDzq/XNI5UpGHtYcqmgi7ghzUzYRExERST4tpauwsDCMHz8egYGB6N27N7Zt2wZHR0d8+eWX1e4/e/ZsZGdna26JiYmNXHHDEAQBC8ubiPu1d8IAPyepSyIiItILkl50yMHBAQqFAmlpaVrb09LS4OLiUqfXMDU1RZcuXXD58uVqH1cqlVAqlQ9dq775rVIT8YIhHdhETEREVE7SkRszMzMEBQUhOjpas02tViM6OhphYWF1eg2VSoVTp07B1dW1ocrUO/nFd5uIX2YTMRERkRbJLxcdGRmJiIgIBAcHo1u3bli+fDny8/MxceJEAMD48ePh7u6OJUuWAAAWLVqERx55BD4+Prhz5w4++ugj3LhxA5MnT5byYzSqz/+4jJTsIng0t8DLbCImIiLSInm4GTVqFDIyMjB//nykpqYiMDAQe/bs0TQZJyQkQC6/O8B0+/ZtTJkyBampqbC3t0dQUBCOHj2KDh06SPURGtXl9Fx8Xd5EvJBNxERERFXIBEEQpC6iMeXk5MDOzg7Z2dmwtbWVuhydCIKAcWv/xpHLtzDAzwlfR4RIXRIREVGj0OX43eTOljJmu0+l4MjlW1CayLFgSEepyyEiItJLDDdNRF6lJuJX+vjAo7mlxBURERHpJ4abJuLz6EtIyylGq+aW+E9vL6nLISIi0lsMN03ApbRcrD18DQCw8KkObCImIiKqBcONnhMEAfN3nkGZWsBjHZzRr73xXiSUiIioLhhu9NzP/6bg2FWxiXj+k8ZxujsREdHDYLjRY3nFZXi3vIl4Wl82ERMREdUFw40e+/T3i0jPLYZnC0tM6cUmYiIiorpguNFTF1Jzse7IdQDAwqe4EjEREVFdMdzoIbGJ+DRUagHhHZ3Rp52T1CURERE1GQw3emjXyWT8fS0L5qZyzGMTMRERkU4YbvRMblEp3t19DgAwvZ8vWtqziZiIiEgXDDd6Zvnvl5CRW4w2DlaY3LON1OUQERE1OQw3euR8ag42HL0OQGwiVpqwiZiIiEhXDDd6QhAEzN9xBiq1gIGdXNC7raPUJRERETVJDDd6Ykd8EmKuZ8HCVIG5bCImIiJ6YAw3eiCnqBTv7T4PAJje3wfuzSwkroiIiKjpYrjRA8v3XUJmXjG8HK0w+VGuRExERPQwGG4kdi4lB98cuw4AePupjjAz4Y+EiIjoYfBIKqHKKxEP9ndFT182ERMRET0shhsJbf8nCbHXb8PCVIE5g/2kLoeIiMggMNxIJLuwFIt/FVcifrW/L9zYRExERFQvGG7qi1oN/DQFuPR7nXb/ZN9FZOaVwMvRCi88ypWIiYiI6gvDTX2J/x449QPw/TPAz68Bxbk17no2OQcby5uIFz3ViU3ERERE9YhH1frS6Rkg9GXx6xMbgFXdgWuHquymVotNxGoBGNzZFY/6OjRunURERAaO4aa+mFkCA98HIn4B7FoBdxKAb54EfpsFlBZqdtv2TxKO37gNSzMF5rKJmIiIqN4x3NS3Nj2BV44CXSPE+3+vAlb3BG4eR3ZhKZaUNxG/1t8XrnZsIiYiIqpvDDcNQWkDPPUZMPZHwMYVuHUJWPsYTm54Hbn5+fBxssbEHmwiJiIiaggMNw3J9zHglWOA/0hAUKNX2rfYaTYPS3vK2URMRETUQHiEbWgW9lAP/wof2r6FW4IN/OQJCPxtOHDwI0BVJnV1REREBofhphH8GHcTX6R3wjDhYxT5DALUpcAf7wJrHwMyLkhdHhERkUFhuGlg2QWleP+38wCA8QNCYD52EzD8K0BpByTHic3GR1eIiwASERHRQ2O4aWBL/3cBWfkl8HWyxoQenoBMBgSMEntxvPsDqmLgf3PE08azrkldLhERUZPHcNOATt3Mxnd/3wAALBraCaaKSt9uO3dg3E/AkE8BM2vgxhFgVQ8gdi0gCBJVTERE1PQx3DQQtVrAvJ2nIQjA0EA3hHm3qLqTTAYETQBePgK0fhQozQd2RwLfPQ1kJzV6zURERIaA4aaBbD2RiPjEO7BWmuCtQfdZidjeE4j4GXjifcDEHLjyB/BFGBC/maM4REREOmK4aQB3Cko0TcQzBvjC2db8/k+Sy4FHXgZeOgy4BwPF2cCOl4AtY4G89AaumIiIyHAw3DSAj/ZewO2CUrRztkFEd0/dnuzgC0zaC/SfD8hNgQu7gZWhwJkdDVEqERGRwWG4qWf/3ryDTTEJAIBFQztqNxHXlcIE6Plf4MX9gLM/UJgFbI0AfnwBKMiq34KJiIgMDMNNPVKrBczbITYRD+/ijlCvapqIdeHSCZjyB9Dr/wCZAjj9o9iLc3Fv/RRMRERkgBhu6lHU8UScvJkNG6UJZg9qXz8vamIG9JsLvLAPcGgL5KUCm0YCO6cBRTn18x5EREQGRC/CzcqVK+Hp6Qlzc3OEhoYiJiamTs/bsmULZDIZhg0b1rAF1sHt/BJ8sEdsIn79sbZwsqlDE7EuWgYB/zkIhE0DIAP++RZY1R24eqB+34eIiKiJkzzcREVFITIyEgsWLEBcXBwCAgIQHh6O9PTazxC6fv06Zs6ciZ49ezZSpbU7eCkD2YWlaO9ig/FhrRvmTUwtgPD3gAm7gWatgexEYONTwK9vACUFDfOeRERETYxMEKRdSCU0NBQhISFYsWIFAECtVsPDwwPTp0/HrFmzqn2OSqVCr169MGnSJBw6dAh37tzBjh076vR+OTk5sLOzQ3Z2NmxtbevrYwAATidlQy0I6NyyWb2+brWK84B984Dj68T7zb2B4asBj24N/95ERESNTJfjt6QjNyUlJThx4gQGDBig2SaXyzFgwAAcO3asxuctWrQITk5OeOGFF+77HsXFxcjJydG6NZRO7naNE2wAQGkNPPmJeAkHGzcg6wqwLhzYtwAoK26cGoiIiPSQpOEmMzMTKpUKzs7OWtudnZ2Rmppa7XMOHz6MtWvXYs2aNXV6jyVLlsDOzk5z8/DweOi69YrPAPEinAFjAEENHFkOfNUHSI6XuDAiIiJpSN5zo4vc3Fw8//zzWLNmDRwcHOr0nNmzZyM7O1tzS0xMbOAqJWDRTJySGvU9YOUIpJ8Fvu4P7P8AUJVKXR0REVGjMpHyzR0cHKBQKJCWlqa1PS0tDS4uLlX2v3LlCq5fv44hQ4ZotqnVagCAiYkJLly4AG9vb63nKJVKKJXKBqheD/k9CbR6BPjldeDcLmD/YuDCr8DwLwGnejo1nYiISM9JOnJjZmaGoKAgREdHa7ap1WpER0cjLCysyv7t27fHqVOnEB8fr7k99dRT6Nu3L+Lj4w1vyulBWDkAIzcCz6wFzJsBKfHAl72AI58BapXU1RERETU4SUduACAyMhIREREIDg5Gt27dsHz5cuTn52PixIkAgPHjx8Pd3R1LliyBubk5OnXqpPX8Zs2aAUCV7UZNJgP8RwCtewA/vwpc+p94ZtX53cCwL4AW3vd/DSIioiZK8nAzatQoZGRkYP78+UhNTUVgYCD27NmjaTJOSEiAXN6kWoP0h60r8NwP4oJ/e2YDiX8Bqx8FHlsEBL8gXomciIjIwEi+zk1ja8h1bvTa7RvAzqnA9UPifa8+wFMrgGYGMpUnCEDRHSA3DchNAXJTxT+VNoBXX3G0SiaTukrjlZcBXP0TyM8Uz/BzbCt1RWQIBAEoyQdUJYBlc6mroQamy/Gb4caYqNVA7JrytXAKAaUt8MT7QOBz+nvgFwSgOOduWLk3vORVul9WVPPrNGsFePcHfPoDbXoB5naN9xmMUVkJkPg3cCUauPIHkHJS+3FHP6DDUPHm5Ke/v39UvwQBKC0QA0lxLlCSJy5IWpJX9b7WYzXtkw+g/BDWwhdoPwho/yTgHsyRaQPEcFMLow43FTIvAzteAm7GivfbDgSGfArYONf+vPpWnFsptNQSXkp1uLSEhT1g4wpYOwM2LkBOEpDwl/g/uwoyhbiSs3c/MfC4BQJyRb1/PKNz64oYZC5HiyOEJXnaj7v4i0sVXDsEqCstUdDC527QcenMoKNPBEH8T0O1gaO6cJJfaVtu1XBSkieux9XQrJyAdgOB9oOBNr0B03q+1h9JguGmFgw35dQq4OhnwJ+LxQO/hT0weBnQ6emHf+3iPDGc5KXeE17uuV+aX/fXNLcTQ4uNC2DtIv5Zcd/GVQxm1i7V/yNWkg9cPywedK9EA7cuaz9uYS9OXfn0FwOPrdvDfX5jUZQDXDsoBpor0cDt69qPWzneDZDefQFrJ3F74R3g4h7g7E7xZ6KqtKJ2s9blQWcY4N6VQaeh5KYCV/cDSXHiyGiVoFIpnAgNdJalmbV4U1b8aXPPfes67GMDmFkB6jLxd+n8buDSPqA4++77mFqJf7fbPwm0fVz8+05NEsNNLRhu7pF2Btj+EpD6r3i/49PA4I+rn78uydcOKJWnhCrfSnLr/v5K2/KAcp/QYmZZP58XEPuPKg7IVw+I/7hX5tSh/KDcTzzjjP/rE6nV4tICV6KBy38AN2PEg0oFuam4zpJ3P/Fg4ux//6mBohzxbL6zO8WDUlnh3cfsPAC/p4AOTwEtu3Ga4WGU5APXj4iB5uqf4kKfujK1Kg8VVrUEDetK+9jcE1Qq3Te1bLifZ1kJcOMwcP5XMezkJt99TKYAPHsA7QaLU1jNWjVMDdQgGG5qwXBTjbIS4NBS4OBS8X9pVk5A55FAfoZ2eLk3BNTGzFo7qFg73xNayoOMmVXDfa66UJUBScfvjuokxUEzhw8AJuZiwPHpL45AOLYzrtGE3NS7U01X/wQKbmk/3tz77oiXZ0/x4PWgSvLFgHN2J3Bxr/bIno0r4DdEHNVpFcZpxPtRq4Dkf8Sf2ZX9Yv9T5alAyADXAMDzUXFErfIoSHVhxMyqaX7PBUEM5Od3i2En/Yz24y7+4ohOu0Hi18b0d7sJYripBcNNLZLixFGczAs172NqWXV0pUpwcRb/oWyKCrLEA8Ll8pGd3BTtx23d745MePUxvCHu0iIg4Vj5yNYfQNpp7cfNbACv3ne/B/aeDVRHofj+Z3cCF37TDtZWjuIBqcNQMVApJF/RQnqCAGRdFX93r+4XpwuLsrX3adZKnH717gt49gKsWkhSqqSyromrtp/fLf6eV+7/sWtV3pA8GGjVnb9XeojhphYMN/dRWgTEfAXkJFczReQihhZj+d+NIAAZ5++O6tw4qn1GlkwOuHW9O6rjHtT0/kEUBCDzUvlUU7TYm1R5aggyseG64kyzliGAwrRxaywrFg/YZ3eKB6WiO3cfs2guHow6DBUbR03MGrc2KRVklU8z7RdDzZ0E7cfN7cQzA736ikG8uZfx/N2ti/xbYu/XhV/F3/3Kv/fmzYC2T4hhx7v/w41IUr1huKkFww09sNJC4MaRu6M6Gee1Hze3Ew+wFWFHX9cQKrwDXDtQHtr+ALLvuZistcvdqSavvvr1P3xVqTgqcXYncP4X7WkypZ14MOowVKzb0HqlSovEhTiv7geu/Fl+en2lf77lpoBHKODdR/z8bl2a5lSSFEoKxO/r+d3Axd+0f68USjEcth8snoFV0RhPjY7hphYMN1Rvsm+KB5kr0eKflUcUAMCh7d0Rj9Y96rcpWhdqlTjlWDE6k3RcezheoQRah92t1alD0/gfvqoMSDgqBp1zP4sN7hXMbIC24WLQ8Rkg3ff+YajV4rRgxVTTjWP3jKpB/FlVjMy07s4RhvqgVok9Sud3i7fb1yo9KBNHL9sPFm8OvpKVaYwYbmrBcEMNoqKBs2IK6+Zx7VNoFWZiI2zFqI5zx4YNENlJd8PM1f3VBK92d2tp3b1pHvwrqzggnd0JnN2lfYaMqSXg+7gYdHwf1+8AUBGYK6abCjK1H7d2EXtmvPqINxuXxq/RmFRMTVcEneQ47ccd2orNyO2fFKeleUZfg2K4qQXDDTWKiqmfK3+I01jZ9/RDWLtUakyuh6mf0kLxVN+KFYGrmzLz6lO+5kw//Z0yqw9qNZB0Aji7Qww6lb/3JubiSE6HoeLIjtQrVRfliAseVkw13bqk/biplXhGk1cfMdQ4tm8ao2qGKie5vCH5V3F6tPIZaNbO4rRVu8Fir5OhTYvqAYabWjDcUKMTBHHhwIpRneuH71l1uaJpt3zBO49u92/aFQQg/dzd0ZkbR7UXw5PJxSXoKwKUW9em1+xcHwRBHFE7tws4s0N7ikFhJgbLDkPFg1JjXJtIVSqO6lU0Ad87wieTiyMAXn3E2lqGGFeTdFNSlA1c/r3SwoGVzugzs767cKDvY4Z3VuW9VGVA4W2gMEtsdC+4JS4f4N23Xt+G4aYWDDckubJi8TTUiobe6k63btML8CkPO83biNu1TlP/Q3vqBQBsW959jldvw/8HVVeCIH6vz+4Ub5kX7z4mNxGbwTsMFXsprBzq7z0zL96darp+uOoil829747MePYELJrVz3tT4ykrEUfgKkZ1Kv/dlJuIPXftB4tTWPo+alpWfDegVA4rhVlAwe3qt9+77AAgnk4/6bd6LY3hphYMN6R3clMrNSb/Uc1CeV7i9ElyPLQXGLQQV1utaAR2aMspC12kn78bdCov7iaTi1NBHYYC7Yfofs21vPS7PTNX/qwaQi2ai+GzYs0ZrpJrWCpGC8/vFsPOvatBu3QWR3TaDwKcOzXc39mKK6ZrBZHb9w8uulwW517mzcQRUIvm4iKRTy6rt48DMNzUiuGG9JpaDaSevDuqk/i39iUOnDreHZ1pFcZ5/fqSeelu0Km4FAkAQCZ+nzsMFVdItnOv+tySAnFasOKspntH4irORquYanLpzMZTY5J19e6lIBL/0j5TsVmr8ktBDBZ/z2qaOhYEcXTkvgGl/FbxdeWpal3IFOLIr2WLu2HFsnmlr1tU/dq8WYNPfTPc1ILhhpqU4lzxKtoleeKUha2r1BUZvqxrYo/O2Z1iY3JlLbuJ17py6yoeqK78KQbQyledB8QAUzHV1CoMMLVotPJJj+VnigsHnv9V/M9L5VP7LezFZncTc+2AUhFoHvQCpgpl3QNKRaBR2uplAGe4qQXDDRHV2Z1EcQ2dszvFMFMT25Z3F8/z6lN/PTtkuEoKxNG+87+K01eFWfd/jpl1eRgpDyGawNKimvBSvt3U0mCmqxluasFwQ0QPJCcZOPeLOKqTcUE8k8m7rxhoWngbzAGEJFCxTtPVA+LUTk3BxUQpdaWSYripBcMNERFR06PL8Vv/JtWIiIiIHgLDDRERERkUhhsiIiIyKAw3REREZFAYboiIiMigMNwQERGRQWG4ISIiIoPCcENEREQGheGGiIiIDArDDRERERkUhhsiIiIyKAw3REREZFAYboiIiMigMNwQERGRQTGRuoDGJggCAPHS6URERNQ0VBy3K47jtTG6cJObmwsA8PDwkLgSIiIi0lVubi7s7Oxq3Ucm1CUCGRC1Wo3k5GTY2NhAJpPV62vn5OTAw8MDiYmJsLW1rdfXJt3x56Ff+PPQL/x56B/+TGonCAJyc3Ph5uYGubz2rhqjG7mRy+Vo2bJlg76Hra0tfzH1CH8e+oU/D/3Cn4f+4c+kZvcbsanAhmIiIiIyKAw3REREZFAYbuqRUqnEggULoFQqpS6FwJ+HvuHPQ7/w56F/+DOpP0bXUExERESGjSM3REREZFAYboiIiMigMNwQERGRQWG4ISIiIoPCcFNPVq5cCU9PT5ibmyM0NBQxMTFSl2S0lixZgpCQENjY2MDJyQnDhg3DhQsXpC6Lyr3//vuQyWSYMWOG1KUYraSkJIwbNw4tWrSAhYUF/P39cfz4canLMkoqlQrz5s1DmzZtYGFhAW9vb7zzzjt1un4S1Yzhph5ERUUhMjISCxYsQFxcHAICAhAeHo709HSpSzNKBw4cwNSpU/HXX39h3759KC0txeOPP478/HypSzN6sbGx+PLLL9G5c2epSzFat2/fRo8ePWBqaorffvsNZ8+exccffwx7e3upSzNKH3zwAVatWoUVK1bg3Llz+OCDD/Dhhx/i888/l7q0Jo2ngteD0NBQhISEYMWKFQDE61d5eHhg+vTpmDVrlsTVUUZGBpycnHDgwAH06tVL6nKMVl5eHrp27YovvvgC7777LgIDA7F8+XKpyzI6s2bNwpEjR3Do0CGpSyEATz75JJydnbF27VrNtmeeeQYWFhb47rvvJKysaePIzUMqKSnBiRMnMGDAAM02uVyOAQMG4NixYxJWRhWys7MBAM2bN5e4EuM2depUDB48WOvvCjW+Xbt2ITg4GM8++yycnJzQpUsXrFmzRuqyjFb37t0RHR2NixcvAgBOnjyJw4cPY+DAgRJX1rQZ3YUz61tmZiZUKhWcnZ21tjs7O+P8+fMSVUUV1Go1ZsyYgR49eqBTp05Sl2O0tmzZgri4OMTGxkpditG7evUqVq1ahcjISLz11luIjY3Fq6++CjMzM0REREhdntGZNWsWcnJy0L59eygUCqhUKrz33nsYO3as1KU1aQw3ZNCmTp2K06dP4/Dhw1KXYrQSExPx2muvYd++fTA3N5e6HKOnVqsRHByMxYsXAwC6dOmC06dPY/Xq1Qw3Evjhhx/w/fffY9OmTejYsSPi4+MxY8YMuLm58efxEBhuHpKDgwMUCgXS0tK0tqelpcHFxUWiqggApk2bhl9++QUHDx5Ey5YtpS7HaJ04cQLp6eno2rWrZptKpcLBgwexYsUKFBcXQ6FQSFihcXF1dUWHDh20tvn5+eGnn36SqCLj9n//93+YNWsWRo8eDQDw9/fHjRs3sGTJEoabh8Cem4dkZmaGoKAgREdHa7ap1WpER0cjLCxMwsqMlyAImDZtGrZv344//vgDbdq0kboko9a/f3+cOnUK8fHxmltwcDDGjh2L+Ph4BptG1qNHjypLI1y8eBGtW7eWqCLjVlBQALlc+1CsUCigVqslqsgwcOSmHkRGRiIiIgLBwcHo1q0bli9fjvz8fEycOFHq0ozS1KlTsWnTJuzcuRM2NjZITU0FANjZ2cHCwkLi6oyPjY1NlX4nKysrtGjRgn1QEnj99dfRvXt3LF68GCNHjkRMTAy++uorfPXVV1KXZpSGDBmC9957D61atULHjh3xzz//YNmyZZg0aZLUpTVpPBW8nqxYsQIfffQRUlNTERgYiM8++wyhoaFSl2WUZDJZtdvXr1+PCRMmNG4xVK0+ffrwVHAJ/fLLL5g9ezYuXbqENm3aIDIyElOmTJG6LKOUm5uLefPmYfv27UhPT4ebmxvGjBmD+fPnw8zMTOrymiyGGyIiIjIo7LkhIiIig8JwQ0RERAaF4YaIiIgMCsMNERERGRSGGyIiIjIoDDdERERkUBhuiIiIyKAw3BAREZFBYbghIqMnk8mwY8cOqcsgonrCcENEkpowYQJkMlmV2xNPPCF1aUTURPHCmUQkuSeeeALr16/X2qZUKiWqhoiaOo7cEJHklEolXFxctG729vYAxCmjVatWYeDAgbCwsICXlxd+/PFHreefOnUK/fr1g4WFBVq0aIEXX3wReXl5WvusW7cOHTt2hFKphKurK6ZNm6b1eGZmJoYPHw5LS0v4+vpi165dDfuhiajBMNwQkd6bN28ennnmGZw8eRJjx47F6NGjce7cOQBAfn4+wsPDYW9vj9jYWGzduhW///67VnhZtWoVpk6dihdffBGnTp3Crl274OPjo/Ueb7/9NkaOHIl///0XgwYNwtixY5GVldWon5OI6olARCShiIgIQaFQCFZWVlq39957TxAEQQAgvPTSS1rPCQ0NFV5++WVBEAThq6++Euzt7YW8vDzN47t37xbkcrmQmpoqCIIguLm5CXPmzKmxBgDC3LlzNffz8vIEAMJvv/1Wb5+TiBoPe26ISHJ9+/bFqlWrtLY1b95c83VYWJjWY2FhYYiPjwcAnDt3DgEBAbCystI83qNHD6jValy4cAEymQzJycno379/rTV07txZ87WVlRVsbW2Rnp7+oB+JiCTEcENEkrOysqoyTVRfLCws6rSfqamp1n2ZTAa1Wt0QJRFRA2PPDRHpvb/++qvKfT8/PwCAn58fTp48ifz8fM3jR44cgVwuR7t27WBjYwNPT09ER0c3as1EJB2O3BCR5IqLi5Gamqq1zcTEBA4ODgCArVu3Ijg4GI8++ii+//57xMTEYO3atQCAsWPHYsGCBYiIiMDChQuRkZGB6dOn4/nnn4ezszMAYOHChXjppZfg5OSEgQMHIjc3F0eOHMH06dMb94MSUaNguCEiye3Zsweurq5a29q1a4fz588DEM9k2rJlC1555RW4urpi8+bN6NChAwDA0tISe/fuxWuvvYaQkBBYWlrimWeewbJlyzSvFRERgaKiInzyySeYOXMmHBwcMGLEiMb7gETUqGSCIAhSF0FEVBOZTIbt27dj2LBhUpdCRE0Ee26IiIjIoDDcEBERkUFhzw0R6TXOnBORrjhyQ0RERAaF4YaIiIgMCsMNERERGRSGGyIiIjIoDDdERERkUBhuiIiIyKAw3BAREZFBYbghIiIig/L/7ZPFFoCDID4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss (mean squared error): [2.697305917739868, 0.35749340057373047]\n"
          ]
        }
      ],
      "source": [
        "fit_evaluate_model(model_embedding, 0.01, 'categorical_crossentropy', ['accuracy'], 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 158,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 926
        },
        "id": "ufRl7PmDUFSS",
        "outputId": "2a1fde3a-6f09-4900-b4c8-b9557c8e0b81"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "83/83 [==============================] - 1s 17ms/step\n",
            "Confusion Matrix:\n",
            " [[420   5   4 338 109   0  55]\n",
            " [ 13   0   0   7   4   0   2]\n",
            " [ 32   1   2  45   8   0   5]\n",
            " [463   7   9 478 107   0  75]\n",
            " [ 66   1   0  68  25   0  11]\n",
            " [  0   0   0   0   0   0   1]\n",
            " [126   0   1 109  30   0  22]]\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.38      0.45      0.41       931\n",
            "           1       0.00      0.00      0.00        26\n",
            "           2       0.12      0.02      0.04        93\n",
            "           3       0.46      0.42      0.44      1139\n",
            "           4       0.09      0.15      0.11       171\n",
            "           5       1.00      0.00      0.00         1\n",
            "           6       0.13      0.08      0.10       288\n",
            "\n",
            "    accuracy                           0.36      2649\n",
            "   macro avg       0.31      0.16      0.16      2649\n",
            "weighted avg       0.35      0.36      0.35      2649\n",
            "\n",
            "Accuracy: 0.3574933937334843\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkYAAAHbCAYAAAAuzVVsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACSm0lEQVR4nOzdd1QUVx/G8e/SOxYEe0Hsioq919hL1Bh7C5rYC2rUxN7QWKPGEjWIxq6xGxtGY0dBQQWxi42iCApI3/cPyOq+mFhYGIHfJ2fOYe/cnX3GLMvdW2ZUarVajRBCCCGEQE/pAEIIIYQQnwtpGAkhhBBCpJCGkRBCCCFECmkYCSGEEEKkkIaREEIIIUQKaRgJIYQQQqSQhpEQQgghRAppGAkhhBBCpDBQOkB299rjV6UjpDvrVtOVjiDSKGx4NaUjpLsB27L+tW63P72odIR0VTpnIaUjpLtrwefT/TXin93VyXEMbex1cpyMJg0jIYQQQryRlKh0AkXJUJoQQgghRArpMRJCCCHEG+okpRMoShpGQgghhHgjKXs3jGQoTQghhBAihfQYCSGEEEJDLUNpQgghhBApZChNCCGEEEKA9BgJIYQQ4m0ylCaEEEIIkUIu8CiEEEIIIUB6jIQQQgjxNhlKE0IIIYRIkc1XpUnDSAghhBAa2f06RjLHSAghhBAihfQYCSGEEOINGUoTQgghhEghQ2lCCCGEEAKkx0gIIYQQb5MLPAqAdevWkSNHjv+sM3XqVCpVqpQheYQQQghFqJN0s2VSWa7HaOrUqezevZsrV64oHUURvx2+wJI9p+neyInvOzciIuo1K/af5Zz/A4JevCKnhSmNKjowuG0dLE2NNc97GvaSWZuPcenmQ0yNDWlbsxzD29fDQD9ztJ0nTXRh0iQXrbKAgNtUcGyoTKB0NnbMEGbNmsCSpWsYM2aq0nE+iEGt5hjWaoFeTlsAkoIfEnd0G4kB3gAYdxqIfomKqKxyQmwMiQ8CiD2wHnXoY80x9Ao6YNSqF/oFi4NaTeLDW8QdWE/S0/tKnFIqpauXpc13HbCvUJycdrlYMMCVS0cuaNX5yqUbjbt9gbmVOQGXbvDbjysJuv9Us79oeXu6j++NvWMJkpIS8fzzPBtm/EZsdExGn06aDBrYh9Eug8ibNw++vn6MGDmJi5euKB3row0e05/BY/trld29dZ92dbsC4PbHcqrVcdLav839D6Z//1OGZRS6leUaRtnZtftB7DjtS8kCeTRloRFRhEZE4dKxAfb5cvM07CUzNx8jNCKS+QPaAZCYlMSw5bvIbWXGujHdePYyiknuf2Kgr8fw9vWUOp2Pdv36DVq07KZ5nJCQoGCa9FOlSkX6D+iBr6+f0lE+ijr8OXEHN5D07CmgwrBqI0z6juf14tEkBT8k8dEd4r3/Rh0eisrMEqMvumA6YArRrgOTv30amWDafzIJfp5E71qFSk8fo2ZdMRkwmeiZAz6L7n9jMxMC/e9xYtsxRv86IdX+tgM70KJvG1aM/pnQh8F0Ht2d8RumMLbpMOJj48lpm5MfN07j3L7TuE3+FVMLM3pPcWbQguEsHpR5/tB27tyO+fOmMHjIeDwvXmb4sP4cPLCRsuXrExr6XOl4H+3WjTv0/2qY5nFiovZ7bfuG3Syb+6vmcczrzNWITSWbr0r76O6AQ4cOUbduXXLkyEHu3Llp06YNd+7c0ex/9OgR3bp1I1euXJibm1O1alUuXHjzjWnfvn1Uq1YNExMTbGxs6NChg2ZfbGwsY8aMoUCBApibm1OjRg1OnDih2f/PcNfu3bspUaIEJiYmNG/enIcPH2r2T5s2DR8fH1QqFSqVinXr1gGwcOFCKlSogLm5OYUKFWLw4MFERkamOr9/O/a/WbNmDWXKlMHExITSpUuzfPnyj/0n1YnomDh+WHeQyT2aYWn2pifIIb8NC75tRwPH4hTKk4PqpQoztF0dTl69S0Ji8pv/nP8D7j59zuy+rShdyJa65YoxuE0dtp28QnyC8n9sPlRCQiLBwaGa7fnzF0pH0jlzczPWuy9l0KDvefEiQuk4HyXR/xKJN7xRP3uK+tkT4g5thLgY9AqXBCDhwlGS7vmhfhFK0uO7xB3ehF7OPKhyJfcw6dkWQGVuSdzhzahDn6T0OG1FzzInqpx5/uulM4zPCW+2zd/EpcMX3rm/pXNbdi3bhtdRTwJvPGC5y8/ktM1F1WY1AKjcpBqJ8Ym4TfqVp3efcNf3Nmt/WEmNVrWxK5I3I08lTUaNGMCatZtwX78Nf/9bDB4ynujo1/Tr21XpaJ8kMSGR56Fhmi08TPt3L+Z1jNb+qMhohZLqSDYfSvvohlFUVBQuLi5cunQJDw8P9PT06NChA0lJSURGRtKgQQMeP37M3r178fHx4fvvvycppfV54MABOnToQKtWrbh8+TIeHh5Ur15dc+yhQ4dy7tw5tmzZgq+vL507d6ZFixbcunVLUyc6OppZs2axfv16zpw5Q3h4OF27Jv+ydenShdGjR1OuXDmePn3K06dP6dKlS/KJ6umxZMkSrl+/jru7O8ePH+f777/XOrf/Ova7bNy4kcmTJzNr1iz8/f2ZPXs2kyZNwt3d/WP/WdNs9lYP6pUvRs3SRd5bN/J1LBYmRpphMt+7T3AoYENuK3NNndplixIZE8edp8/SLbOuOTgU4/69S9y4cQb3dUspVCi/0pF0bsnPszj4pwfHj59WOkraqPQwqFgXjExIfBCQer+hMQZVG5P0PAh1ePJ7MCn0MeqolxhWbwr6BmBghEH1piQFP0T9IiSDT+Dj2RayI6dtLq6d9tWUvX4VzZ0rNynhVAoAQ2NDEuITUKvVmjpxMbEAlKpWNmMDfyJDQ0OcnBzxOH5KU6ZWq/E4fpqaNasomOzTFbYvxHGfffzpuZM5y6eRt4Cd1v7WHZtzyu8Qu05uZOSPgzB5a5qCyHw+eiitU6dOWo9/++038uTJg5+fH2fPniU0NJSLFy+SK1cuABwcHDR1Z82aRdeuXZk2bZqmrGLFigAEBgbi5uZGYGAg+fMn/0EbM2YMhw4dws3NjdmzZwMQHx/PsmXLqFEj+RuWu7s7ZcqUwdPTk+rVq2NhYYGBgQF582p/uxo5cqTm56JFizJz5kwGDhyo1cPzvmP/vylTprBgwQI6duwIQLFixfDz82PVqlX06dMnVf3Y2FhiY2O1ypLi4jE2MkxV92McunSDGw9D2Diux3vrvoiMZvWf5+lYx1FT9uxlFLktzbTq5bIyS9mXOb75eF68TP/+o7h58y5589ky8cdRHPf4g8pOTYiMjFI6nk583bkdlStXoFbt1kpH+WR6eQtjOnQOGBhBXAwx7nNQhzzS7Deo1QLj1r1RGZuSFPKI16unQWLKkGhsDK9XTMKk73gMm3YGQP3sKa9XT88UXf/WtjkAiHgWrlUe8SyCHHlyAnD9jC89J/ajzXdf8udv+zExNabb+N4A5LTNmZFxP5mNTS4MDAwICdb+UhUSEkrpUsUVSvXpfL2vM3H4DO7fCcTGNjeDxzizfs9KvmzQg+ioaA7sOsyTh0GEBj+jZFkHRk0cQtHiRRj5zXilo3+6TPD7lJ4+umF069YtJk+ezIULF3j27JmmNygwMJArV65QuXJlTaPo/125coUBAwa8c9/Vq1dJTEykZMmSWuWxsbHkzp37TWADA6pVq6Z5XLp0aXLkyIG/v/87Gy//OHbsGK6urty4cYOXL1+SkJBATEwM0dHRmJmZffSxo6KiuHPnDs7OzlrnlJCQgLW19TszuLq6ajUKAX7o1YaJfdr+a+73CQp7yU/b/2LlsK8wNvzv/52Rr2MZtnwX9nlzM7BNrU9+zc/R4cN/aX6+es0fT8/L3L51nq++asu6dVsUTKYbBQvmY8GCabRq1T1V4zozSQp9QvQiF1QmZhg41saky3CiV0zUNI4SLv9N4i0fVJY5MWrQHpOeY3j9ywRIiAcDI4y/HkLi/RvEb1wIenrJdZwn8vrnsZAQp/DZpd2jWw9ZMXoJvSb2o+v3vUhKTOLQuv2Eh7zQfNaKjHX6+DnNzzf9bnPV+zpHvHbTon0T/ti0jx0b9mj23/K/Q2jwM37b+QuFihTg4YPH7zrkZ0+tzjxTKNLDRzeM2rZtS5EiRVi9ejX58+cnKSmJ8uXLExcXh6mp6X8+97/2R0ZGoq+vj5eXF/r6+lr7LCwsPjamlvv379OmTRsGDRrErFmzyJUrF6dPn8bZ2Zm4uDhNw+hj/DM/afXq1Zoepn/8f/5/TJgwARcX7ZVTSWc2fPRrv80vMJiwV9F0m/PmOIlJarxvP2Lryct4LhmJvp4eUTFxDF62E3NjIxZ+1x7DtzLaWJlz7UGQ1nHDUnqKbKw+/t/mcxAR8ZJbt+7iULyo0lF0wsnJETu7PFy48KemzMDAgHr1ajB4UF8sLO0zxx/OxATUz4NQA3GP76JXyAGjem2I3bkyeX9MNOqYaNTPnhITeBPz6RswKF+DhCunMahcD1VOW2KXjYeUoaaYTYuS65SrToLP5z28GBESDoC1TQ7CQ97Mf7O2sea+3z3N47N7/ubsnr+xtrEmJjoW1Gpa929HSGBwRkf+JM+ehZGQkICtnY1Wua1tHoKCQxVKpTuvXkby4E4ghYsVfOf+q97XAShUrGCmbRhl5vlBuvBRDaPnz58TEBDA6tWrqVcvebXS6dNvPowcHR1Zs2YNYWFh7+w1cnR0xMPDg379+qXaV7lyZRITEwkJCdEc+10SEhK4dOmSpgcnICCA8PBwypQpA4CRkVGqFQNeXl4kJSWxYMEC9PSS59Vs27bto4/9Njs7O/Lnz8/du3fp0eP9Q1gAxsbGGBtrjz2/TuMwWo3SRdgxUXvYbvL6QxTLm4t+zaqjr6dH5OtYBi/biaGBPosHfZmqZ8nRPj9rDl0g7FU0uVKG1M7deICFiRH2eXOTGZmbm2FvX5SNm/5QOopOHD9+msqVm2iVrV69gICAO8yfvzxzNIreRaUHBv/1O6DS7FcZGadM6nwz/0bzWKVK35w6EPIwmBchYZSv48iDlIaQqYUpxSuV5Ojvh1LVj3iWPMG34ddNiIuN5+ppnwzN+6ni4+Px9valcaO67N17GACVSkXjRnVZvsJN4XRpZ2pmSqGiBdi3I/X/M4DS5ZJHPZ6FZL7VdyLZRzWMcubMSe7cufn111/Jly8fgYGBjB//Zhy1W7duzJ49my+//BJXV1fy5cvH5cuXyZ8/P7Vq1WLKlCk0adKE4sWL07VrVxISEjh48CDjxo2jZMmS9OjRg969e7NgwQIqV65MaGgoHh4eODo60rp18rwKQ0NDhg0bxpIlSzAwMGDo0KHUrFlT05gpWrQo9+7d48qVKxQsWBBLS0scHByIj49n6dKltG3bljNnzrBy5cpU5/e+Y/+/adOmMXz4cKytrWnRogWxsbFcunSJFy9epOoZSi/mJkY45Nf+ZmZqbIi1uSkO+W2IfB3LoKU7iYmLZ1bfVkS9jiPqdfKQQ05LU/T19KhVpgj2+XLz47qDjOxQn+cvo/ll72m+blAJo/cMz30u5syZyIEDxwgMfES+fHZMnjyaxMREtm7drXQ0nYiMjOK6n/Yk5aio1zwPe5Gq/HNl1LInCTe8k5fjG5tiULk++vbliFkzHVUuOwwq1iHx5hXUUS9RWefGqFFHiI8j0T/5OkcJN30wat0H4w7fEnfmICqVCsNGHSEpicQ71xQ+u2TGZibkLZpP8zhPIVuKlC1GZPgrnj95xp9r9/HlsM4E3XtCyMMQOo/uzouQMK1rHTXr04qbXjeIiYqhQr2K9PihL5vnrCf6ZeaZK7fo59W4rV2El7cvFy9eZviwAZibm7LOfavS0T7amCnDOHHkNE8eBWFrZ8OQ7weQmJjEwV1HKFSkAK06NuOUx1nCX7ykZFkHxk0fwcWz3tz0u6109E+XWb9o6chH/dXT09Njy5YtDB8+nPLly1OqVCmWLFlCw4YNgeTemiNHjjB69GhatWpFQkICZcuW5ZdffgGgYcOGbN++nRkzZjBnzhysrKyoX7++5vhubm7MnDmT0aNH8/jxY2xsbKhZsyZt2rTR1DEzM2PcuHF0796dx48fU69ePdauXavZ36lTJ/744w8aNWpEeHg4bm5u9O3bl4ULFzJ37lwmTJhA/fr1cXV1pXfv3lrn975j/7/+/ftjZmbGvHnzGDt2LObm5lSoUEFrorfS/B+GcDXl4nFtp2ify4EZ/SmQ2xp9PT2WDOrArC3H6DNvc/IFHmuUZXCbOkpE/iQFC+Rjw/pl5M6dk9DQMM6e9aRe/XY8examdDSRQmVhjUnXEaiscqKOiSbp6X1i1kxPnlNklRP9YmUxrNcWlak56sgIEu9eJ/qX8aijkntO1KGPiXGbjdEXXTAbOgfUSSQ+vsfrNdNRv/o8Ls1g7+jA5K0zNY97T3YG4OT246wcs4R9K3dhbGZCf9fBmFmZE3DJnzm9pxMfG695TvGKJfhqVFdMzEx5cucRayas4PSuExl9Kmmyffte8tjkYurkMeTNmwcfn+u0btOTkJDMs8r1H3b5bflp5XRy5LQm7Hk4lz196NGqPy+eh2NsbETN+tXo9W1XTM1MCHoSwtH9J1i16DelY6dNNh9KU6nfXhf6mVu3bh0jR44kPDxc6Sg689rj1/dXyuSsW01XOoJIo7Dh1d5fKZMbsC3TfBR+su1PLyodIV2VzllI6Qjp7lrw+XR/jRiv3To5jkmVL3VynIyWOcZJhBBCCJExPoOryCtJGkZCCCGEeCObD6VljjuEpujbt2+WGkYTQgghxOdFeoyEEEII8YasShNCCCGESCFDaUIIIYQQAqTHSAghhBBvk6E0IYQQQogU0jASQgghhEimVmfv6xjJHCMhhBBCiBTSYySEEEKIN2QoTQghhBAihSzXF0IIIYQQID1GQgghhHibDKUJIYQQQqSQoTQhhBBCCAHSYySEEEKIt8lQmhBCCCFEChlKE0IIIYQQID1GQgghhHibDKUJIYQQQqSQhpFQkk3b2UpHSHdJarXSEUQa5V56SekI6U4t79NML+DFQ6UjZA0yx0gIIYQQQoD0GAkhhBDibTKUJoQQQgiRQobShBBCCCE+H3PmzEGlUjFy5EhNWUxMDEOGDCF37txYWFjQqVMngoODtZ4XGBhI69atMTMzw9bWlrFjx5KQkPBRry0NIyGEEEK8kZSkm+0TXbx4kVWrVuHo6KhVPmrUKPbt28f27ds5efIkT548oWPHjpr9iYmJtG7dmri4OM6ePYu7uzvr1q1j8uTJH/X60jASQgghxBvqJJ1ssbGxvHz5UmuLjY39z5eOjIykR48erF69mpw5c2rKIyIiWLt2LQsXLqRx48ZUqVIFNzc3zp49y/nz5wE4cuQIfn5+/P7771SqVImWLVsyY8YMfvnlF+Li4j749KVhJIQQQgidc3V1xdraWmtzdXX9z+cMGTKE1q1b07RpU61yLy8v4uPjtcpLly5N4cKFOXfuHADnzp2jQoUK2NnZaeo0b96cly9fcv369Q/OLZOvhRBCCPGGjlalTZgwARcXF60yY2Pjf62/ZcsWvL29uXjxYqp9QUFBGBkZkSNHDq1yOzs7goKCNHXebhT9s/+ffR9KGkZCCCGEeENHDSNjY+P/bAi97eHDh4wYMYKjR49iYmKik9f/VDKUJoQQQghFeXl5ERISgpOTEwYGBhgYGHDy5EmWLFmCgYEBdnZ2xMXFER4ervW84OBg8ubNC0DevHlTrVL75/E/dT6ENIyEEEII8YZarZvtIzRp0oSrV69y5coVzVa1alV69Oih+dnQ0BAPDw/NcwICAggMDKRWrVoA1KpVi6tXrxISEqKpc/ToUaysrChbtuwHZ5GhNCGEEEK8ocCVry0tLSlfvrxWmbm5Oblz59aUOzs74+LiQq5cubCysmLYsGHUqlWLmjVrAtCsWTPKli1Lr169+OmnnwgKCmLixIkMGTLkg4f0QBpGQgghhHjbZ3pLkEWLFqGnp0enTp2IjY2lefPmLF++XLNfX1+f/fv3M2jQIGrVqoW5uTl9+vRh+vTpH/U6KrXcUlpR5mZFlY6Q7mIT4pWOINJIXy/rj7pnh4/CpCx+jiqlA2SA+LjH6f4arzdO0slxTHvM0MlxMpr0GAkhhBDijWx+rzRpGAkhhBDijc90KC2jZP3+cSGEEEKIDyQ9RkIIIYR4I4vPRXsfaRgJIYQQ4g0ZSsueGjZsyMiRI3V6zBMnTqBSqVJdmVMIIYQQmUO2bRhlZXXqVGf7jjXcvnOBqOj7tGnbTGv/Dz+OxPuyByGhfjx67MP+/b9TtVolZcLq2KCBfbh98zyRL+9w9vQ+qlWtpHQkncuq5xgQcJbYmIeptp8Xz1Q6WroZO2YIcbGPmD9/qtJRdC6rvk+//34o584eIOx5AI8f+bBjx1pKliyudCzdSkrSzZZJScMoCzI3N+PqVX9GjZr8zv23b91ltMtkqldrzhdNv+JB4CP27l2PjU2uDE6qW507t2P+vCnMmLmQajVa4OPrx8EDG8mTJ7fS0XQmK59jnTptKFzESbO1bNUNgJ1/7Fc4WfqoUqUi/Qf0wNfXT+koOpeV36f169VkxQp36tZrS8tW3TA0MOTggU2YmZkqHU131Em62TKpbN0wSkhIYOjQoVhbW2NjY8OkSZM0F3nbsGEDVatWxdLSkrx589K9e3et+68AHDx4kJIlS2JqakqjRo24f/++AmeR2pEjJ5g+bQH79h5+5/5t2/by119nuH//If7+txg/bibW1laUL186g5Pq1qgRA1izdhPu67fh73+LwUPGEx39mn59uyodTWey8jk+exZGcHCoZmvVsgl37tzn77/PKx1N58zNzVjvvpRBg77nxYsIpePoXFZ+n7Zp25P1G7bh53cTX18/nPuPpEiRgjg5OSodTehItm4Yubu7Y2BggKenJz///DMLFy5kzZo1AMTHxzNjxgx8fHzYvXs39+/fp2/fvprnPnz4kI4dO9K2bVuuXLlC//79GT9+vEJn8ukMDQ355ptuhIe/5OpVf6XjfDJDQ0OcnBzxOH5KU6ZWq/E4fpqaNasomEx3ssM5/sPQ0JBu3Tqyzn2r0lHSxZKfZ3HwTw+OHz+tdBSdy07vUwBraysAXrwIVzaIDqmT1DrZMqtsvSqtUKFCLFq0CJVKRalSpbh69SqLFi1iwIABfPPNN5p69vb2LFmyhGrVqhEZGYmFhQUrVqygePHiLFiwAEDz/Llz5/7r68XGxhIbG6tVplarUaky/kL2LVo2xt19KWZmpgQFhdC2bU+eP3+R4Tl0xcYmFwYGBoQEP9MqDwkJpXSprDH+nx3O8R/t2jUnRw4rNmzYrnQUnfu6czsqV65ArdqtlY6SLrLT+1SlUrFg/jTOnPHk+vUApePoTiaeH6QL2brHqGbNmlqNklq1anHr1i0SExPx8vKibdu2FC5cGEtLSxo0aABAYGAgAP7+/tSoUUPreLVq1frP13N1dcXa2lpri09Qphv975PnqFWzFY0bdeLo0ZNs2PBLlhj/F1lDv75dOXz4L54+DVY6ik4VLJiPBQum0afPsFRfkkTms3TJbMqVK0WPnoOVjqJbMsdI/L+YmBiaN2+OlZUVGzdu5OLFi+zatQuAuLi4Tz7uhAkTiIiI0NoMDax1FfujREe/5u7dB1y8eJnBg8aRkJBAnz5dFMmiC8+ehZGQkICtnY1Wua1tHoKCQxVKpVvZ4RwBChcuQOPGdXFz26J0FJ1zcnLEzi4PFy78SXTUfaKj7tOgQS2GDvmG6Kj76GWBm/Vml/fpz4tn0qpVU75o1pnHj58qHUfoUOb/LUyDCxcuaD0+f/48JUqU4MaNGzx//pw5c+ZQr149SpcunWridZkyZfD09Ez1/P9ibGyMlZWV1qbEMNq76OnpYWRspHSMTxYfH4+3ty+NG9XVlKlUKho3qsv5814KJtOd7HCOAL17f01IyDMO/umhdBSdO378NJUrN6Fateaa7dKlK2zevItq1ZqTlAWGMLLD+/TnxTNp374FzZp/zf37D5WOo3tJat1smVS2nmMUGBiIi4sL3333Hd7e3ixdupQFCxZQuHBhjIyMWLp0KQMHDuTatWvMmDFD67kDBw5kwYIFjB07lv79++Pl5cW6deuUOZH/Y25uRvHiRTWPixYphKNjWcLCwgkLe8H344ZyYP8xgoJCyG2Tk+++603+/HnZ9ccB5ULrwKKfV+O2dhFe3r5cvHiZ4cMGYG5umqUm8Gb1c1SpVPTu/TW//76DxMREpePoXGRkFNf9tOeiREW95nnYi1TlmVlWfp8uXTKbrl2/pGOnb3j1KhI7uzwARES8IiYmRuF0OpIFGuhpka0bRr179+b169dUr14dfX19RowYwbfffotKpWLdunX88MMPLFmyBCcnJ+bPn0+7du00zy1cuDA7d+5k1KhRLF26lOrVqzN79mytSdtKcXJy5NDhN8MQc3+aBMDvG3YwfPiPlCxZnB6bO5E7d07CwsLx8vLliy864+9/S6nIOrF9+17y2ORi6uQx5M2bBx+f67Ru05OQkGfvf3ImkdXPsUmTehQpXBD3LPAHNDvLyu/TgQP7AHDcY6dWubPzKNZv2KZEJKFjKrU6m98tTmHmZkWVjpDuYhPilY4g0kg/C8x9eZ/s8FGYlMXP8fOYmJC+4uMep/trRP88UCfHMRuxUifHyWjZusdICCGEEP8nizeg3yfrfw0UQgghhPhA0mMkhBBCiDdk8rUQQgghRIpMvNReF2QoTQghhBAihfQYCSGEEOKNTHw7D12QhpEQQggh3sjmQ2nSMBJCCCGEhjqbT76WOUZCCCGEECmkx0gIIYQQb8hQmhBCCCFEimw++VqG0oQQQgghUkiPkRBCCCHekKE0IYQQQogUsipNCCGEEEKA9BgJIYQQ4m0ylCaEEEIIkUJWpQkhhBBCCJAeIyGEEEK8TYbShBBCCCGSZfd7pUnDSAghhBBvSI+RUFIOY3OlI6S74IRwpSOkO5XSAdJZYUtbpSOku4evQpWOkO6S1IlKR0hXKlVW/00UGUEaRkIIIYR4Q3qMhBBCCCFSyHJ9IYQQQggB0mMkhBBCiLfJUJoQQgghRDJ1Nm8YyVCaEEIIIUQK6TESQgghxBvZvMdIGkZCCCGEeCObX/lahtKEEEIIIVJIj5EQQggh3pChNCGEEEKIFNIwEkIIIYRIplZn74aRzDESQgghhEghPUZCCCGEeEOG0oQQQgghUmTzhpEMpQkhhBBCpJAeIyGEEEJoZPd7pUnDSAghhBBvZPOGkQylCSGEEEKkyPINo3Xr1pEjRw7N46lTp1KpUiXF8gghhBCftSQdbZlUlm8YZYT/b3wprfc3XTh6+g9uPLjAjQcX2Ht4I42a1gUgRw5rZsz9gb8993P7iReeV48xfc4ELK0sFE6ddvXq1mD3rnUE3vciIe4x7do1VzqSTn3//VDOnT1A2PMAHj/yYceOtZQsWVzpWDrz7fC+3Ar14seZozVlv+9exa1QL61t+rwJCqZMGz09PaZMGc2NG6d58eImfn6nmDBhuNKx0sWggX24ffM8kS/vcPb0PqpVraR0JJ2YNNGFuNhHWttV3xNKx9IpdZJaJ1tmJXOMsqCnT4JxnbaIe3ceoFKp6NytPb9tXEbzBp1QqVTY5bVlxuT53Lxxh4KF8jNn4WTy5rXl276jlI6eJubmZvj6+uG2bgs7t69VOo7O1a9XkxUr3LnkdQUDAwNmTB/PwQObcKzYkOjo10rHS5MKlcrStXdH/K/dTLVvy/o/+HnuSs3jmOiYjIymU2PGDGLAgF707++Cv/9NnJwc+fXX+UREvGL5cjel4+lM587tmD9vCoOHjMfz4mWGD+vPwQMbKVu+PqGhz5WOl2bXr9+gRctumscJCQkKphG6liE9RklJSbi6ulKsWDFMTU2pWLEiO3bsQK1W07RpU5o3b665BHlYWBgFCxZk8uTJmufv27ePatWqYWJigo2NDR06dNDsi42NZcyYMRQoUABzc3Nq1KjBiRMnPirfmjVrKFOmDCYmJpQuXZrly5dr9t2/fx+VSsUff/xBo0aNMDMzo2LFipw7dw6AEydO0K9fPyIiIlCpVKhUKqZOnfrp/1g6cPTQCY4fPcW9u4HcvfOAuTOXEBUVjVPVigT43+bbPiM5eugED+4/5MypC8yd+TNNWzREX19f0dxpdejwX0ye8hN79hxSOkq6aNO2J+s3bMPP7ya+vn449x9JkSIFcXJyVDpampiZm7Jg5UwmuszkZcTLVPtjXsfwLOS5ZouMjFIgpW7UrFmV/fuPcOjQcR48eMSuXQc5duxvqlWrqHQ0nRo1YgBr1m7Cff02/P1vMXjIeKKjX9Ovb1elo+lEQkIiwcGhmu358xdKR9KtJLVutkwqQxpGrq6urF+/npUrV3L9+nVGjRpFz549+fvvv3F3d+fixYssWbIEgIEDB1KgQAFNw+jAgQN06NCBVq1acfnyZTw8PKhevbrm2EOHDuXcuXNs2bIFX19fOnfuTIsWLbh169YHZdu4cSOTJ09m1qxZ+Pv7M3v2bCZNmoS7u7tWvR9//JExY8Zw5coVSpYsSbdu3UhISKB27dosXrwYKysrnj59ytOnTxkzZoyO/uXSTk9Pj3YdW2JmZorXRZ931rG0siTyVSSJiYkZnE6khbW1FQAvXoQrGySNpswdz4mjpzn7t+c797fr1JILNzw48PdWRk8ciompSQYn1J3z5y/RqFEdHByKAVChQhlq167G4cMnlA2mQ4aGhjg5OeJx/JSmTK1W43H8NDVrVlEwme44OBTj/r1L3LhxBvd1SylUKL/SkXQrm88xSvehtNjYWGbPns2xY8eoVasWAPb29pw+fZpVq1axadMmVq1aRe/evQkKCuLgwYNcvnwZA4PkaLNmzaJr165MmzZNc8yKFZO/XQUGBuLm5kZgYCD58ye/MceMGcOhQ4dwc3Nj9uzZ7803ZcoUFixYQMeOHQEoVqwYfn5+rFq1ij59+mjqjRkzhtatWwMwbdo0ypUrx+3btyldujTW1taoVCry5s373n+L2NhYrTK1OgmVSvft09JlS7D38CaMTYyIioqmf6/h3Aq4k6pezlw5GDl2IBvdt+s8g0g/KpWKBfOnceaMJ9evBygd55O1/rIZ5SqUpmOzXu/cv2/nIR4/CiIkKJTSZUswdvIw7IsXYUi/sRmcVDfmzVuOpaUlvr5/kZiYiL6+PlOmzGPLlt1KR9MZG5tcGBgYEBL8TKs8JCSU0qUy/5w4z4uX6d9/FDdv3iVvPlsm/jiK4x5/UNmpSabuzXxbZp4fpAvp3jC6ffs20dHRfPHFF1rlcXFxVK5cGYDOnTuza9cu5syZw4oVKyhRooSm3pUrVxgwYMA7j3316lUSExMpWbKkVnlsbCy5c+d+b7aoqCju3LmDs7Oz1mskJCRgbW2tVdfR8c1wRb58+QAICQmhdOnS732df7i6umo18AAsjG2wMrX94GN8qDu37tOsficsrSxo3b4Zi5fPplObvlqNIwtLc9ZvXcHNgDssmLP8P44mPjdLl8ymXLlSNGzU4f2VP1N589sxcdYY+nYeTFxs3DvrbN2wS/PzTf/bhAQ/Y8OulRQuWpDA+48yKqrOfPVVG7p1+5I+fYbh53eTihXLMW/eFJ4+Deb333coHU98gMOH/9L8fPWaP56el7l96zxffdWWdeu2KJhM6Eq6N4wiIyOB5CGxAgUKaO0zNjYGIDo6Gi8vL/T19VMNgZmamv7nsfX19TXPfZuFxftXWf2TbfXq1dSoUUNr3/8fz9DQUPOzSqUCkudOfYwJEybg4uKiVVa6cI1/qZ028fHx3L8XCMBVHz8qVS5P/4E9GTcquWFmbmHGxh2riIqMon/P4TJ5MBP5efFMWrVqSuMmHXn8+KnScT5Z+YplsLHNzW6PjZoyAwMDqtVyoqfz15QrUCvV75iP91UAChcrlCkbRq6uPzJv3nK2b98HwPXrARQuXICxYwdnmYbRs2dhJCQkYGtno1Vua5uHoOBQhVKln4iIl9y6dReH4kWVjqI7mXgYTBfSfY5R2bJlMTY2JjAwEAcHB62tUKFCAIwePRo9PT3+/PNPlixZwvHjxzXPd3R0xMPD453Hrly5MomJiYSEhKQ69vuGtQDs7OzInz8/d+/eTfX8YsWKffA5GhkZfdD8HGNjY6ysrLS29BhGexc9PT2MjIyA5J6izTtXExcXT9/uQ4n9l2/r4vPz8+KZtG/fgmbNv+b+/YdKx0mTc3970qre17Rr1F2z+V6+zt4df9KuUfd3fvEoU74UAKGZ9A+sqalpqvNKTExCTy/rXDklPj4eb29fGjeqqylTqVQ0blSX8+e9FEyWPszNzbC3L8rToBClo+iMEsv1V6xYgaOjo+ZvY61atfjzzz81+2NiYhgyZAi5c+fGwsKCTp06ERwcrHWMwMBAWrdujZmZGba2towdO/aTvvSne4+RpaUlY8aMYdSoUSQlJVG3bl0iIiI4c+YMVlZW2NjY8Ntvv3Hu3DmcnJwYO3Ysffr0wdfXl5w5czJlyhSaNGlC8eLF6dq1KwkJCRw8eJBx48ZRsmRJevToQe/evVmwYAGVK1cmNDQUDw8PHB0dNXOC/su0adMYPnw41tbWtGjRgtjYWC5dusSLFy9S9e78m6JFixIZGYmHhwcVK1bEzMwMMzOztP7TfbLxk0fy17FTPH74FAtLc778qjW16laje6dvNY0iEzMThn03HktLCywtk3vXnj8L++hesM+JubmZZlIrQLGihalYsRxhYS94+PCJgsl0Y+mS2XTt+iUdO33Dq1eR2NnlASAi4hUxMZlvCXtUVDS3bmjPe3sd/ZrwFxHcunGHwkUL0rZjC04cO034iwhKlS3BjzNG43nWiwC/2wqlTpuDB48xbtwwHj58gr9/8lDa8OH9cXffpnQ0nVr082rc1i7Cy9uXixcvM3zYAMzNTVnnvlXpaGk2Z85EDhw4RmDgI/Lls2Py5NEkJiaydetupaNlagULFmTOnDmUKFECtVqNu7s77du35/Lly5QrV45Ro0Zx4MABtm/fjrW1NUOHDqVjx46cOXMGgMTERFq3bk3evHk5e/YsT58+pXfv3hgaGn7QfOO3Zch1jGbMmEGePHlwdXXl7t275MiRAycnJyZMmECXLl2YOnUqTk5OQHJD5ciRIwwcOJCtW7fSsGFDtm/fzowZM5gzZw5WVlbUr19fc2w3NzdmzpzJ6NGjefz4MTY2NtSsWZM2bdp8ULb+/ftjZmbGvHnzGDt2LObm5lSoUIGRI0d+8PnVrl2bgQMH0qVLF54/f86UKVMUXbJvY5OLn1e4YmuXh1cvX+F//SbdO33LqRPnqFWnGk4pS4PPXtZe1l7D8QseZeIGRNUqFfE49mY4YsH8qQC4r9+Gc//MfY0mgIEDkxcDHPfYqVXu7DyK9Ruy1h9WgLi4eGo3qE6f77phZmbK0yfBHN7vwfKFmfcaVaNGTWbKlDEsWTKTPHlsePo0mLVrNzJr1s9KR9Op7dv3kscmF1MnjyFv3jz4+FyndZuehIQ8e/+TP3MFC+Rjw/pl5M6dk9DQMM6e9aRe/XY8examdDTdUeD7cdu2bbUez5o1ixUrVnD+/HkKFizI2rVr2bRpE40bNwaS//aXKVOG8+fPU7NmTY4cOYKfnx/Hjh3Dzs6OSpUqMWPGDMaNG8fUqVM1IyYfQqX+5wJCQhEFcpZTOkK6C44KVzpCulMpHSCdFbV+/9B0ZvfwVeYcnvsYCUlZ+5Iceqqs/psIcbHpP7fuedsGOjmOxY4jqVZiGxsba+YX/5vExES2b99Onz59uHz5MkFBQTRp0oQXL15o3WWiSJEijBw5klGjRjF58mT27t3LlStXNPvv3buHvb093t7emsVeHyLrDGwLIYQQ4rPh6uqKtbW11ubq6vqv9a9evYqFhQXGxsYMHDiQXbt2UbZsWYKCgjAyMkp16y07OzuCgoIACAoKws7OLtX+f/Z9DLkliBBCCCHe0NFQ2rtWYv9Xb1GpUqW4cuUKERER7Nixgz59+nDy5EndhPkI0jASQgghhIZaRw2jDxk2e5uRkREODg4AVKlShYsXL/Lzzz/TpUsX4uLiCA8P1+o1Cg4O1qxAz5s3L56e2lfP/2fV2oesUn+bDKUJIYQQ4rOTlJREbGwsVapUwdDQUOvSPQEBAQQGBmruqFGrVi2uXr1KSMibyyYcPXoUKysrypYt+1GvKz1GQgghhHhDgVVpEyZMoGXLlhQuXJhXr16xadMmTpw4weHDh7G2tsbZ2RkXFxdy5cqFlZUVw4YNo1atWtSsWROAZs2aUbZsWXr16sVPP/1EUFAQEydOZMiQIR/VawXSMBJCCCHEW3Q1lPYxQkJC6N27N0+fPsXa2hpHR0cOHz6suZ3YokWL0NPTo1OnTsTGxtK8eXOWL39zKyt9fX3279/PoEGDqFWrFubm5vTp04fp06d/dBZZrq8wWa6fNWT1RcKyXD9rkOX6mV9GLNcPaaKb5fq2Hhk/cVoXZI6REEIIIUQKGUoTQgghhIYSQ2mfE2kYCSGEEOINddYfkvwvMpQmhBBCCJFCeoyEEEIIoSFDaUIIIYQQKdRJMpQmhBBCCCGQHiMhhBBCvEWG0oQQQgghUqhlVZoQQgghhADpMRJCCCHEW2QoTQghhBAiRXZflSYNIyGEEEJoZPdby0vDSGH3LrsrHSHdWZbuoHSEdKcia3/D8vPfrnSEdNeziovSEdLdzqcXlY6QrmrYlFI6gsgCpGEkhBBCCA0ZShNCCCGESJHdG0ayXF8IIYQQIoX0GAkhhBBCQyZfCyGEEEKkkKE0IYQQQggBSI+REEIIId6S3e+VJg0jIYQQQmhk91uCyFCaEEIIIUQK6TESQgghhEaSDKUJIYQQQiSTOUZCCCGEEClkub4QQgghhACkx0gIIYQQb5ErXwshhBBCpJChNCGEEEIIAUiPkRBCCCHeIsv1hRBCCCFSZPfl+jKUJoQQQgiRQnqMhBBCCKGR3VelSY+RjhUtWpTFixcrHUMIIYT4JElqlU62zEp6jLKYNVv38vNvW+n5ZQvGDeqlKb/id4ul67Zx9cYd9PRVlLIvwqrZ4zExNgJg2JQF3LjzgLDwl1hZmlOzcjlGOXfDNndOpU7lowQEnKVokUKpyleudGfEyIkKJNI9CwtzpkwZQ/v2zcmTx4YrV64xZsxUvLx8lY720dZs2MbilW707Nye8SMH8vhpMM2/6vvOugtm/EDzxvUAuOofwOIVbvgF3EalUlG+TElcBjtTuoR9Bqb/d2Wql6Xtdx0oVqE4uexyMW+AK5eOXNCq09mlG026fYG5lTkBl26w5seVBN1/CkDZmuWZsnXmO4/9Q9sx3PG9ne7noCuDBvZhtMsg8ubNg6+vHyNGTuLipStKx/po285vJF+hvKnK/1i3h0U/LmHJ9gVUrl1Ja9/uDftYMH5xxgQUOicNoyzkWsAddhw4TslihbXKr/jdYtCPc3Hu2o4Jg/ugr69HwN1A9FRvWvTVKpalf9d25MmVg5BnL5i/ehMuM37m98VTM/gsPk2dOm3Q19fXPC5XrhR/HtzMzj/2K5hKt1as+Ily5UrxzTcjefIkmO7dO3Lw4CYqV27CkyfBSsf7YFf9A9i+5yAlHYppyvLa2nBi70atetv3/Inbpp3Uq1kVgOjo1wx0mUSjujWZOHooiYmJ/LJ2A9+5TOTYrvUYGij/cWZsZsID/3v8te0YY36dkGp/u4EdaNm3DctH/0zIw2C+Ht2dHzZMYXTTYcTHxhPgdYNvq/bVek6X0d0pX8cxUzWKOndux/x5Uxg8ZDyeFy8zfFh/Dh7YSNny9QkNfa50vI/ybavB6Om/GVwpVroYi7fM46/9JzVle3/fz9r56zSPY17HZmREnZPJ11nUq1ev6NGjB+bm5uTLl49FixbRsGFDRo4cCcCLFy/o3bs3OXPmxMzMjJYtW3Lr1i2tY+zcuZNy5cphbGxM0aJFWbBggdb+kJAQ2rZti6mpKcWKFWPjRu0P9owU/TqG8XOXM2Vkf6wszbX2zVu1ge5fNqd/l3Y4FC1IsUL5adGgJkZGhpo6vTu2pGKZEuS3y0OlciVx7tIW3xu3iU9IyOhT+STPnoURHByq2Vq1bMKdO/f5++/zSkfTCRMTYzp0aMkPP8zm9GlP7t59wMyZi7hz5wHfftvr/Qf4TERHv2b8tHlMHTcCK0sLTbm+vj42uXNpbR5/n6V5k3qYmZkCcPfBQyJevmJI/14UK1IQB/siDPqmB8/DXvA0KESpU9Jy5YQ3W+dv4uLhC+/c38q5LX8s28alo54E3njALy4/k9M2F9Wa1QAgMT6BiNBwzRb54hVVv6jOie3HM/I00mzUiAGsWbsJ9/Xb8Pe/xeAh44mOfk2/vl2VjvbRwsMiCAt9odlqN63Jo3uPuXLOR1MnJiZWq050ZLSCidNOrdbNllll2YaRi4sLZ86cYe/evRw9epRTp07h7e2t2d+3b18uXbrE3r17OXfuHGq1mlatWhEfHw+Al5cXX3/9NV27duXq1atMnTqVSZMmsW7dOq1jPHz4kL/++osdO3awfPlyQkKU+YCetWwd9apXopZTea3y5+ER+N64Q64cVvQcOZUGXQbRd8wMvK8F/OuxIl5GcuD4GSqVLfFZfAv/WIaGhnTr1pF17luVjqIzBgYGGBgYEBur/U00JiaG2rWrKZTq481c8Av1a1WjVrXK/1nv+o1b3Lh1l45tmmvKihUuSA5rK/7Yf5j4+HhiYmP5Y99h7IsWIn9eu/SOnma2hezIaZuLq6ffDH2+fhXN7Ss3KeFU6p3PqfJFdSxzWnJim0dGxUwzQ0NDnJwc8Th+SlOmVqvxOH6amjWrKJgs7QwMDWjWsSkHtx7SKm/WoQn7rv6Bu8cavhvvjLGJsUIJdUPmGGVBr169wt3dnU2bNtGkSRMA3NzcyJ8/PwC3bt1i7969nDlzhtq1awOwceNGChUqxO7du+ncuTMLFy6kSZMmTJo0CYCSJUvi5+fHvHnz6Nu3Lzdv3uTPP//E09OTatWS/zCtXbuWMmXK/Guu2NjYVH/YVLFxGKfM8/lUf544h9/te2xZOiPVvkdPkxtqKzb8wegB3SldvAh7j52i//jZ7Fo1lyIF3oydL1yzmS17j/I6NhbHMg78Mn1MmnIppV275uTIYcWGDduVjqIzkZFRnDt3iQkThnPjxm2Cg0Pp0qU9NWo4cefOfaXjfZCDx07gf/MOW9b8/N66f+xPbvBUrlBWU2ZubobbsrkMHz+dVes2A1CkYH5WLZqJgYH+vx3qs5HDNgcAEc/CtcojnkWQI8+75/I17tIUn7+vEBaUeYafbGxyYWBgQEjwM63ykJBQSpcqrlAq3ajXog4WVhYc3HZYU3Z093GCHwXzLPg5xcvYM/DHARQqXoiJA6YqF1SkSZbsMbp79y7x8fFUr15dU2ZtbU2pUsnfyvz9/TEwMKBGjRqa/blz56ZUqVL4+/tr6tSpU0fruHXq1OHWrVskJiZqjlGlyptvQKVLlyZHjhz/msvV1RVra2ut7acV69J0rkEhz5mzYj1zxg3B2Ch1A0udlNyf2blVYzo0b0AZh6KMG9iLogXzsevwCa26/Tq3YdvyWayaPR59PT1+mLcSdSbsD+3XtyuHD//F06eZZ97Nh3B2HoVKpeLevYu8fHmbwYP7sW3bHpKSkpSO9l5Pg0OZs3gVc6Z8/94vAjGxsRw8ekKrt+if8smui6lcoSwbf13IhhXzcbAvwuAxU4iJzdxzOt4lV97cVKxfieNbjykdRaRo07UlF/7y5Hnwm4bqvo0H8Dx5ibs37nF0lwezRsyhQat65C+ST8GkaaNWq3SyZVZZssfoczVhwgRcXFy0ylRPr6XpmNdv3yMs/CVdhvyoKUtMSsLr6g027z3CvrXzAbAvUkDrefaF8vM0RPtbaE5rS3JaW1K0YD7sC+fni57D8fG/TaWyJdKUMSMVLlyAxo3r0qXLt0pH0bm7dx/wxRdfY2ZmipWVJUFBIWzY8Av37gUqHe29/AJuEfYinK+/GaopS0xMwuvKNTb/sQ/vv/ZqJs8f+es0r2NiadeiidYxDhw5weOnwWxctRA9veTvdD9NHUftFp05fuocrZo2zLDz+RThIeEAWNvkIDzkhabc2saa+373UtVv+HUTXr14hddRz4yKqBPPnoWRkJCArZ2NVrmtbR6CgkMVSpV2dgVsqVLPiYn9p/5nPT/vGwAULFqAJw+eZkAy3cvMw2C6kCUbRvb29hgaGnLx4kUKF05eoRUREcHNmzepX78+ZcqUISEhgQsXLmiG0p4/f05AQABlyyZ33ZcpU4YzZ85oHffMmTOULFkSfX19SpcuTUJCAl5eXpqhtICAAMLDw/81l7GxMcbG2mPPcWFpG0arWakcf6yao1U2acGvFCuUj2++bkvBfLbY5s7J/Ufav6APHgdRt2rFfz3uPz1F/8y5yix69/6akJBnHPwz88zJ+FjR0a+Jjn5NjhzWfPFFfX780VXpSO9Vs0oldm1YoVU2cdZCihUphHPPzlorCv/Yf5hGdWuQK2cOrfoxMTHo6alQvbWaUqXSA5VK0zP6OQt5GMyLkDAq1HHkQUpDyNTCFIdKJTn6+6FU9Rt2bszff5wgMSExo6OmSXx8PN7evjRuVJe9e5OHnFQqFY0b1WX5CjeF0326Vl1aEP4snHMe/72go0S55OHC5yFhGRFLpIMs2TCytLSkT58+jB07lly5cmFra8uUKVPQ09NDpVJRokQJ2rdvz4ABA1i1ahWWlpaMHz+eAgUK0L59ewBGjx5NtWrVmDFjBl26dOHcuXMsW7aM5cuXA1CqVClatGjBd999x4oVKzAwMGDkyJGYmppm6Lmam5lSoqj29XtMTYzJYWmpKe/7VWuWb9hJKfvClLYvwp5jp7j38AkLJ44AwPfGba4F3MWpfEmsLMx5+DSEZe7bKZTPjoplMk9vkUqlonfvr/n99x0kJmauPyYfomnT+qhUKm7dukvx4kWZPfsHAgLu4O6+Telo72VubkYJ+6JaZaamJuSwstQqD3z0BK8r11gxf3qqY9Sq7sSC5WuZueAXun/VDnWSmjW/b8NAX5/qTv/eyM9IxmYm5C36ZgjFtpAtRcoWIzL8Fc+fPOPg2n10GNaZp/eeEPIwhC6ju/MiJIyL/3eto/J1HLErnJfjW45m9CnoxKKfV+O2dhFe3r5cvHiZ4cMGYG5ummkXRKhUKlp1acGf24+QmPhm6Dp/kXx80aEJ5zwu8PLFS4qXsWfY1MFcOefDHf+7CiZOm8//a0b6ypINI4CFCxcycOBA2rRpg5WVFd9//z0PHz7ExMQESJ6MPWLECNq0aUNcXBz169fn4MGDGBomL2F3cnJi27ZtTJ48mRkzZpAvXz6mT59O3759Na/h5uZG//79adCgAXZ2dsycOVMzWftz0qtjS2Lj4/lp5e+8fBVFSfvC/Oo6gUL5k1fymBgb4XHmIss37OR1TCx5cuWgTlVHvv3xS60l/Z+7Jk3qUaRwQdwz6Yfv+1hbWzFjxjgKFMhLWFgEu3cfZMqUeSRkkksqfIg/9h/BztaG2tWdUu2zL1KIZXOnssJtIz2/c0GlUlGmZHFWLphBHptcCqRNrbijg9YFGvtMdgbgxPbjrBizhL0rd2FsZsK3roMxszIn4JI/rr2nEx+r3TPbqEtTAi758+TO4wzNryvbt+8lj00upk4eQ968efDxuU7rNj0JCXn2/id/hqrWcyJvQbtUq9ES4hOoWteJzv07YWJqQsjTEE4ePIX7z78rlFQ3svtQmkqdGWfXfoKoqCgKFCjAggULcHZ2VjqORtz9S0pHSHeWpTsoHSHdqcjaHySvHp1QOkK661nF5f2VMrmdTy8qHSFd1cpTWukI6e7U4/SfJnA2XyedHKf20506OU5Gy7I9RpcvX+bGjRtUr16diIgIpk9P7pr/Z6hMCCGEEKll5hVlupBlG0YA8+fPJyAgACMjI6pUqcKpU6ewsbF5/xOFEEKIbOrzvwBI+sqyDaPKlSvj5eWldAwhhBBCZCJZtmEkhBBCiI+nzuJzJt9HGkZCCCGE0MgElwVLV9IwEkIIIYRGUjbvMcqS90oTQgghhPgU0mMkhBBCCA2ZYySEEEIIkSK7L9eXoTQhhBBCiBTSYySEEEIIDRlKE0IIIYRIIUNpQgghhBACkB4jIYQQQrwlu/cYScNICCGEEBrZfY6RDKUJIYQQQqSQHiMhhBBCaCRl7w4jaRgJIYQQ4o3sfq80aRgJIYQQQkOtdACFyRwjIYQQQogU0mOksDq1XZSOkO4Sk7L74s/Mr45jP6UjpLt7UUFKRxBpdCnsttIRsgQlPrFdXV35448/uHHjBqamptSuXZu5c+dSqlQpTZ2YmBhGjx7Nli1biI2NpXnz5ixfvhw7OztNncDAQAYNGsRff/2FhYUFffr0wdXVFQODD2/uSI+REEIIITSSVCqdbB/j5MmTDBkyhPPnz3P06FHi4+Np1qwZUVFRmjqjRo1i3759bN++nZMnT/LkyRM6duyo2Z+YmEjr1q2Ji4vj7NmzuLu7s27dOiZPnvxRWVRqtTq7Dycqqlr++kpHSHeXn91ROoJIIycbB6UjpLvs0GP04nWk0hHSlaF+1h8Eef36Qbq/xo58PXRynK+ebvzk54aGhmJra8vJkyepX78+ERER5MmTh02bNvHVV18BcOPGDcqUKcO5c+eoWbMmf/75J23atOHJkyeaXqSVK1cybtw4QkNDMTIy+qDXlh4jIYQQQmiodbTFxsby8uVLrS02NvaDMkRERACQK1cuALy8vIiPj6dp06aaOqVLl6Zw4cKcO3cOgHPnzlGhQgWtobXmzZvz8uVLrl+//sHnLw0jIYQQQmgk6WhzdXXF2tpaa3N1dX3/6yclMXLkSOrUqUP58uUBCAoKwsjIiBw5cmjVtbOzIygoSFPn7UbRP/v/2fehsn6/oxBCCCEy3IQJE3Bx0V5gZGxs/N7nDRkyhGvXrnH69On0ivafpGEkhBBCCA1dXfna2Nj4gxpCbxs6dCj79+/n77//pmDBgpryvHnzEhcXR3h4uFavUXBwMHnz5tXU8fT01DpecHCwZt+HkqE0IYQQQmgkodLJ9jHUajVDhw5l165dHD9+nGLFimntr1KlCoaGhnh4eGjKAgICCAwMpFatWgDUqlWLq1evEhISoqlz9OhRrKysKFu27AdnkR4jIYQQQihqyJAhbNq0iT179mBpaamZE2RtbY2pqSnW1tY4Ozvj4uJCrly5sLKyYtiwYdSqVYuaNWsC0KxZM8qWLUuvXr346aefCAoKYuLEiQwZMuSjeq6kYSSEEEIIDSWu4bNixQoAGjZsqFXu5uZG3759AVi0aBF6enp06tRJ6wKP/9DX12f//v0MGjSIWrVqYW5uTp8+fZg+ffpHZZHrGClMrmMkMgO5jlHWINcxyvwy4jpG6wv01Mlxej/+XSfHyWhZ/10khBBCiA+W3W/iJJOvhRBCCCFSSI+REEIIITSy+/waaRgJIYQQQkNX1zHKrGQoTQghhBAihfQYCSGEEEIju0++loaREEIIITSye8NIhtKEEEIIIVJIj5EQQgghNNTZfPK1NIyEEEIIoSFDaVlAw4YNGTlypOZx0aJFWbx4sWJ5hBBCiMwqSUdbZpUlGkb/7+LFi3z77bdKx1BUnrw2TF86kaPX9nHqzlE2e6yjjGMprTpFHYqwYJ0rf904yN+3D+N+cBV2BWwVSpx29erWYPeudQTe9yIh7jHt2jVXOlK6GDSwD7dvnify5R3Ont5HtaqVlI70yfLktWHa0h85em0vf985wiYPN633qamZKWNmjWDfpe38fecIW06407FXOwUTf5wRLt9y5K8d3Hvkjd/ts7hv/IXiDsW06uzev57QiACtbd6iaQol1p2s8j6tU6c6O3as5e5dT16/fkDbts209rdv34J9+zbw6NEVXr9+gKNjWYWSCl3JkkNpefLkSdfjx8XFYWRklK6vkRaW1has2fMLXmcvM6Ln94Q/D6eQfUFeRrzS1ClQJD+rdy9j75YDrJr/G1GvoiheqhhxMXEKJk8bc3MzfH39cFu3hZ3b1yodJ1107tyO+fOmMHjIeDwvXmb4sP4cPLCRsuXrExr6XOl4H8XS2oLVe5bhdfbKv75PR04dQtU6lZkybBZPHwZRo0E1vncdSWjwM04dOatg+g9Tu051flu9kcveVzEw0OfHyS5s37WWujVaEx39WlNv/bqtzJ21RPM4+vXrdx0u08hK71NzczOuXvVn/fptbN36a6r9ZmamnD17kZ07D7BixVwFEupedr/ydbr2GDVs2JBhw4YxcuRIcubMiZ2dHatXryYqKop+/fphaWmJg4MDf/75p+Y5165do2XLllhYWGBnZ0evXr149uyZZn9UVBS9e/fGwsKCfPnysWDBglSv+/9DaYGBgbRv3x4LCwusrKz4+uuvCQ4O1nrOvn37qFatGiYmJtjY2NChQwet482YMYPevXtjZWWl6Y06ffo09erVw9TUlEKFCjF8+HCioqJ09c/3yfoM6UHwkxCmj5qD3xV/njx8yoWTF3n84ImmzuDxAzh7/DxLZ67k5rVbPH7whL+PnOHF83DlgqfRocN/MXnKT+zZc0jpKOlm1IgBrFm7Cff12/D3v8XgIeOJjn5Nv75dlY720XoP6U7Ik1BmjJqD35UbPHkYxIWTl7Tep45Vy3Fg+2G8z13h6aMgdm/cxy2/O5SrVEbB5B+uS6f+bNm0i4Abt7l+LYBhg8ZTqHABKlYqp1XvdXQMISHPNFvkK+U/R9IiK71Pjxw5wbRp89m79/A792/evAtX1yUcP346g5OlnySVbrbMKt2H0tzd3bGxscHT05Nhw4YxaNAgOnfuTO3atfH29qZZs2b06tWL6OhowsPDady4MZUrV+bSpUscOnSI4OBgvv76a83xxo4dy8mTJ9mzZw9HjhzhxIkTeHt7/+vrJyUl0b59e8LCwjh58iRHjx7l7t27dOnSRVPnwIEDdOjQgVatWnH58mU8PDyoXr261nHmz59PxYoVuXz5MpMmTeLOnTu0aNGCTp064evry9atWzl9+jRDhw7V/T/iR6rXrA7+PgG4rprGYd89/H5kDV92b6PZr1KpqNOkFoF3H7Jk03wO++7Bbf9KGrSoq2Bq8T6GhoY4OTnicfyUpkytVuNx/DQ1a1ZRMNmnSX6f3sB11TQO+e5mw5E1tH/rfQrge+k69ZvVIU9eGwCq1K5MYftCXDh5UYnIaWZlbQnAixcRWuWdvm7Ljbvn+fvcPiZOccHU1ESJeDqR1d6nIvtJ96G0ihUrMnHiRAAmTJjAnDlzsLGxYcCAAQBMnjyZFStW4Ovry7Fjx6hcuTKzZ8/WPP+3336jUKFC3Lx5k/z587N27Vp+//13mjRpAiQ3vAoWLPivr+/h4cHVq1e5d+8ehQoVAmD9+vWUK1eOixcvUq1aNWbNmkXXrl2ZNu3NuH7FihW1jtO4cWNGjx6tedy/f3969OihmfRdokQJlixZQoMGDVixYgUmJqk/2GJjY4mNjdUqS1InoafSbfu0QOF8dOrdnk2/bsNt6e+Uq1ia0TNGEB+fwIHth8hlkxNzCzP6DO3BirlrWDZrJbUa1eCnNTMZ9NUIvM/76DSP0A0bm1wYGBgQEvxMqzwkJJTSpYorlOrTFSicj46927Pp1+24Lf2dshVLM3rGcBLi4zmwPfnb+fyJP/PDT2M44L2ThPgEkpKSmD12Ppcv+Cqc/uOpVCpmuv7AhXNe3PC/pSnfuWM/jx4+IehpCGXLlWLytDEUL1GMfj2HKZj202W192l2lJknTutCujeMHB0dNT/r6+uTO3duKlSooCmzs7MDICQkBB8fH/766y8sLCxSHefOnTu8fv2auLg4atSooSnPlSsXpUqVSlX/H/7+/hQqVEjTKAIoW7YsOXLkwN/fn2rVqnHlyhVNQ+3fVK1aVeuxj48Pvr6+bNy4UVOmVqtJSkri3r17lCmTuqvf1dVVq/EFkM+iMAUsi/zna38sPT09/H0DWD5nNQA3r93CvnQxOvZqx4Hth1DpJfdxnjx8ms2rtyfXuX4bx6rl6di7vTSMRIb453264q33afHSxejYq72mYfT1Nx0pX6UsLn0mEPQoiMo1KzJ2dvIco4unvJSM/9HmLphC6TIlaNOiu1b5hnXbND/7+90kODiUXfvcKVqsEPfvPczomEJIwyi9X8DQ0FDrsUql0ipTqZL/SCclJREZGUnbtm2ZOzf1BLZ8+fJx+/btdMloamr63jrm5uZajyMjI/nuu+8YPnx4qrqFCxd+5zEmTJiAi4uLVlmjUq0+IumHeRbynLs372uV3b/1gMatGgAQHhZBQnwC924+0Kpz79YDKlWvgPg8PXsWRkJCArZ2NlrltrZ5CAoOVSjVp3sW8px773ifNmpVHwBjEyMGjx/A984TOeNxHoDb/ncpWc6BngO7ZKqG0Zx5k2jWvCHtWvXk6ZPg/6zrfSn5i0kx+yKZsmGU1d6nIvv5rJbrOzk5cf36dYoWLYqDg4PWZm5uTvHixTE0NOTChQua57x48YKbN2/+6zHLlCnDw4cPefjwzQeMn58f4eHhlC2bvKzS0dERDw+Pj87q5+eXKqeDg8O/rlgzNjbGyspKa9P1MBqAz8WrFCleSKussH0hgh4nfyAnxCfg53PjHXUK8vRRkM7zCN2Ij4/H29uXxo3ezAVTqVQ0blSX8+czTyPhH74Xr1GkuPaXiML2BTXvUwMDAwyNDElK0l4jk5iYhErvs/ro+k9z5k2iVZsv6Ni2D4EPHr23fvkKyb3NwUGZsxGR1d6n2ZFaR1tm9Vl9ugwZMoSwsDC6devGxYsXuXPnDocPH6Zfv34kJiZiYWGBs7MzY8eO5fjx41y7do2+ffui9x8fkk2bNqVChQr06NEDb29vPD096d27Nw0aNNAMj02ZMoXNmzczZcoU/P39uXr16jt7rd42btw4zp49y9ChQ7ly5Qq3bt1iz549n8Xk682/bqeCUzn6DutJwaIFaN6hKR16tmW72y5NnQ3LN/NFu8Z82b0NBYsWoHO/jtT7ojY73HcrFzyNzM3NqFixHBUrJq/4KVa0MBUrlqNQofwKJ9OdRT+vpr9zd3r16kzp0g78smwO5uamrHPfqnS0j7bp1+2Udyqr9T798q33aVRkNF5nLzN80kCcalUif6G8tP66Ba2+as7JP0+95+ifh7kLpvDV1+0Y2H80kZFR2NraYGtrg4mJMQBFixXCZexgHCuVo1DhAjRv2Zhlq+Zy9rQnftcDFE7/6bLS+9Tc3AxHx7Ka6xMVLVoIR8eyms+VnDmtcXQsS5kyJQAoWdIeR8ey2Nml72Vj0lN2X5X2WV3HKH/+/Jw5c4Zx48bRrFkzYmNjKVKkCC1atNA0fubNm6cZcrO0tGT06NFERET86zFVKhV79uxh2LBh1K9fHz09PVq0aMHSpUs1dRo2bMj27duZMWMGc+bMwcrKivr16/9nVkdHR06ePMmPP/5IvXr1UKvVFC9eXGu1m1L8fG4w1vlHhkz4jv6j+vDkYRALJy/l0K6jmjonDp3CdfwC+g7tyegZIwi8G8i4AZPx8byqYPK0qVqlIh7HdmgeL5g/FQD39dtw7j9KoVS6tX37XvLY5GLq5DHkzZsHH5/rtG7Tk5CQZ+9/8mfG3+cG3ztPZPCEb3Ee1TvlfbqMw7uOaepMHDSdwT98y/RlE7HKYUXQ4yBWzl3DzvV7FEz+4b7pnzyfaM/B37XKhw0az5ZNu4iLi6dBw1p8N7g3ZmZmPHn8lP17j7Bw3nIl4upMVnqfOjk5cuTImwbdTz9NBmDDhu18++0YWrf+gtWr31w2ZsOGXwCYOXMRs2YtztCsQjdUarU6M/d4ZXrV8v93AywruPzsjtIRRBo52TgoHSHd3YvK+sPIL15HKh0hXRnqf1bf9dPF69cP3l8pjeYU6amT44x/8Pv7K32Gsv67SAghhBAfLLv3lkjDSAghhBAaSdm8afRZTb4WQgghhFCS9BgJIYQQQkMu8CiEEEIIkSJ7D6TJUJoQQgghhIb0GAkhhBBCQ4bShBBCCCFSZOarVuuCDKUJIYQQQqSQHiMhhBBCaGT36xhJw0gIIYQQGtm7WSRDaUIIIYQQGtJjJIQQQggNWZUmhBBCCJFC5hgJIYQQQqTI3s0imWMkhBBCCKEhPUZCCCGE0JA5RkIIIYQQKbL7HCMZShNCCCGESCE9RkIIIYTQyN79RdIwUtzlZ3eUjiDEe3k/u610BCHeKz4xQekIWUJ2n2MkQ2lCCCGEECmkx0gIIYQQGupsPpgmDSMhhBBCaMhQmhBCCCGEAKTHSAghhBBvye7XMZKGkRBCCCE0snezSBpGQgghhHhLdu8xkjlGQgghhBAppMdICCGEEBrZfVWaNIyEEEIIoZHdr2MkQ2lCCCGEECmkx0gIIYQQGjKUJoQQQgiRQobShBBCCCEEID1GQgghhHiLDKUJIYQQQqRIUstQmhBCCCGEQHqMhBBCCPGW7N1fJA0jIYQQQrxF7pWWDfXt25cvv/xS58e9f/8+KpWKK1eu6PzYQgghREZQ6+i/zCpbNoyyq0ED+3D75nkiX97h7Ol9VKtaSelIOifnmPll9fMDOcfMrl7dGuzetY7A+14kxD2mXbvmSkcSOpSpG0bx8fFKR8g0Ondux/x5U5gxcyHVarTAx9ePgwc2kidPbqWj6YycY+aX1c8P5ByzAnNzM3x9/Rg24kelo6SLJB1tmVWGNIx+/fVX8ufPT1KS9j9V+/bt+eabbzSP9+zZg5OTEyYmJtjb2zNt2jQSEhI0+1UqFStWrKBdu3aYm5szc+ZMHBwcmD9/vtZxr1y5gkql4vbt2/+Za9q0aeTJkwcrKysGDhxIXFycZt+hQ4eoW7cuOXLkIHfu3LRp04Y7d+5oPd/T05PKlStjYmJC1apVuXz58kf/22SUUSMGsGbtJtzXb8Pf/xaDh4wnOvo1/fp2VTqazsg5Zn5Z/fxAzjErOHT4LyZP+Yk9ew4pHSVdJKHWyZZZZUjDqHPnzjx//py//vpLUxYWFsahQ4fo0aMHAKdOnaJ3796MGDECPz8/Vq1axbp165g1a5bWsaZOnUqHDh24evUqzs7OfPPNN7i5uWnVcXNzo379+jg4OPxrJg8PD/z9/Tlx4gSbN2/mjz/+YNq0aZr9UVFRuLi4cOnSJTw8PNDT06NDhw6axl1kZCRt2rShbNmyeHl5MXXqVMaMGZPmf6v0YGhoiJOTIx7HT2nK1Go1HsdPU7NmFQWT6Y6cY+Y/x6x+fiDnmFXOUWRtGdIwypkzJy1btmTTpk2ash07dmBjY0OjRo2A5N6b8ePH06dPH+zt7fniiy+YMWMGq1at0jpW9+7d6devH/b29hQuXJi+ffsSEBCAp6cnkDy8tmnTJq2eqHcxMjLit99+o1y5crRu3Zrp06ezZMkSTcOnU6dOdOzYEQcHBypVqsRvv/3G1atX8fPzA2DTpk0kJSWxdu1aypUrR5s2bRg7dux/vmZsbCwvX77U2tQZcCEtG5tcGBgYEBL8TKs8JCSUvHZ50v31M4KcY+Y/x6x+fiDnmFXOMatTavL133//Tdu2bcmfPz8qlYrdu3dr51KrmTx5Mvny5cPU1JSmTZty69YtrTphYWH06NEDKysrcuTIgbOzM5GRkR+VI8PmGPXo0YOdO3cSGxsLwMaNG+natSt6eskRfHx8mD59OhYWFpptwIABPH36lOjoaM1xqlatqnXc/Pnz07p1a3777TcA9u3bR2xsLJ07d/7PPBUrVsTMzEzzuFatWkRGRvLw4UMAbt26Rbdu3bC3t8fKyoqiRYsCEBgYCIC/vz+Ojo6YmJhoHeO/uLq6Ym1trbWpk17953OEEEKIjKTUHKOoqCgqVqzIL7/88s79P/30E0uWLGHlypVcuHABc3NzmjdvTkxMjKZOjx49uH79OkePHmX//v38/ffffPvttx+VI8OuY9S2bVvUajUHDhygWrVqnDp1ikWLFmn2R0ZGMm3aNDp27JjquW83PszNzVPt79+/P7169WLRokW4ubnRpUsXrUbPp+YtUqQIq1ev1syPKl++vNY8pI81YcIEXFxctMpy5i6dppwf4tmzMBISErC1s9Eqt7XNQ1BwaLq/fkaQc8z855jVzw/kHLPKOYoPExsbq+kM+YexsTHGxsbvrN+yZUtatmz5zn1qtZrFixczceJE2rdvD8D69euxs7Nj9+7ddO3aFX9/fw4dOsTFixc1nShLly6lVatWzJ8/n/z5839Q7gzrMTIxMaFjx45s3LiRzZs3U6pUKZycnDT7nZycCAgIwMHBIdX2T6/Sv2nVqhXm5uasWLGCQ4cOvXcYDZJ7qF6/fq15fP78eSwsLChUqBDPnz8nICCAiRMn0qRJE8qUKcOLFy+0nl+mTBl8fX21Wqrnz5//z9c0NjbGyspKa1OpVO/Nmlbx8fF4e/vSuFFdTZlKpaJxo7qcP++V7q+fEeQcM/85ZvXzAznHrHKOWZ1ardbJ9q5REldX10/KdO/ePYKCgmjatKmmzNramho1anDu3DkAzp07R44cObRGlpo2bYqenh4XLlz44NfK0Ctf9+jRgzZt2nD9+nV69uyptW/y5Mm0adOGwoUL89VXX6Gnp4ePjw/Xrl1j5syZ/3lcfX19+vbty4QJEyhRosR7h7QA4uLicHZ2ZuLEidy/f58pU6YwdOhQ9PT0yJkzJ7lz5+bXX38lX758BAYGMn78eK3nd+/enR9//JEBAwYwYcIE7t+/n2p13Odk0c+rcVu7CC9vXy5evMzwYQMwNzdlnftWpaPpjJxj5pfVzw/kHLMCc3MzHByKaR4XK1qYihXLERb2gocPnyiYTDd0taLsXaMk/9Zb9D5BQUEA2NnZaZXb2dlp9gUFBWFra6u138DAgFy5cmnqfIgMbRg1btyYXLlyERAQQPfu3bX2NW/enP379zN9+nTmzp2LoaEhpUuXpn///h90bGdnZ2bPnk2/fv0+qH6TJk0oUaIE9evXJzY2lm7dujF16lQA9PT02LJlC8OHD6d8+fKUKlWKJUuW0LBhQ83zLSws2LdvHwMHDqRy5cqULVuWuXPn0qlTpw96/Yy2ffte8tjkYurkMeTNmwcfn+u0btOTkJBn739yJiHnmPll9fMDOcesoGqVingc26F5vGD+VADc12/Duf8ohVJ9fv5r2OxzplJnxLKoDHDq1CmaNGnCw4cPU7UoP2cGRgWUjiCEECKTSIh7nO6v0bZwG50cZ1/g/k9+rkqlYteuXZrbd929e5fixYtz+fJlKlWqpKnXoEEDKlWqxM8//8xvv/3G6NGjtaa+JCQkYGJiwvbt2+nQocMHvXamvvI1JE/uevToEVOnTqVz586ZqlEkhBBCfG4+x3ulFStWjLx58+Lh4aEpe/nyJRcuXNBMn6lVqxbh4eF4eb2Zy3b8+HGSkpKoUaPGB79Whg6lpYfNmzfj7OxMpUqVWL9+vdJxhBBCiExNqatWR0ZGat2x4t69e1y5coVcuXJRuHBhRo4cycyZMylRogTFihVj0qRJ5M+fX9OrVKZMGVq0aMGAAQNYuXIl8fHxDB06lK5du37wijTIQkNpmZUMpQkhhPhQGTGU1qpwK50c52DgwY+qf+LECc1Fn9/Wp08f1q1bh1qtZsqUKfz666+Eh4dTt25dli9fTsmSJTV1w8LCGDp0KPv27UNPT49OnTqxZMkSLCwsPjiHNIwUJg0jIYQQHyojGkYtC737WkIf68+Hf+rkOBkt0w+lCSGEEEJ3PuWq1VlJpp98LYQQQgihK9JjJIQQQggNXa8oy2ykYSSEEEIIDaVWpX0uZChNCCGEECKF9BgJIYQQQiO7L1aXhpEQQgghNGQoTQghhBBCANJjJIQQQoi3yKo0IYQQQogUSTLHSAghhBAiWfZuFskcIyGEEEIIDekxEkIIIYRGdl+VJg0jIYQQQmhk94aRDKUJIYQQQqSQHiMhhBBCaMiVr4UQQgghUmT3oTRpGCnMOX9tpSOku7VPziodQaRR53zVlI6Q7k5E3FQ6QroLjY5QOkK6sjI2UzqCyAKkYSSEEEIIDbnytRBCCCFEiuw+x0hWpQkhhBBCpJAeIyGEEEJoyORrIYQQQogU2X0oTRpGQgghhNDI7j1GMsdICCGEECKF9BgJIYQQQkOW6wshhBBCpEjK5nOMZChNCCGEECKF9BgJIYQQQkOG0oQQQgghUshQmhBCCCGEAKTHSAghhBBvkaE0IYQQQogUMpQmhBBCCCEA6TESQgghxFtkKE0IIYQQIkV2H0qThpEQQgghNLJ7j5HMMdKxhg0bMnLkSKVjCCGEEOITSMMoCyhRvQxD1oxj7oVVrLq/nYrNqmn26Rno03F8DyYfWsASvw3MvbCKvguGYm2bM9VxyjdyYvzu2Sy9sZGFPm4M+nVsRp6GTgwa2IfbN88T+fIOZ0/vo1rVSkpH0pl6dWuwe9c6Au97kRD3mHbtmisd6aOUrl6WMWt/ZLnnb2x+sJuqzWqkqvOVSzeWX/wN94Ct/LBxGnmL5tPaX7S8PT/8PpU1vhv59cp6+rsOxtjMJKNO4aP1/qYLHmd2cTPQk5uBnuw7sonGTetp9hsbGzF73kSu3z3L7UeXWLN+MTZ5ciuYWHeyyu/iyNHfcezETh48uUzA3fNs2LwchxLFNPtz5LRmzrxJXPA+zOOQq/j6ncT1p0lYWlkomDpt1OoknWyZ1WfdMIqPj1c6QqZgZGbMI/8HbJ68NvU+U2MKlbPnwNIdzGozjpUD55O3eH6GrBmnVa9yixp8s2gYZ7f/xYyWY5jXaRKee05n1CnoROfO7Zg/bwozZi6kWo0W+Pj6cfDARvJkkT805uZm+Pr6MWzEj0pH+STGZiYE+t/jt0mr3rm/7cAOtOjbhrU/rGRS+++JjY5h/IYpGBobApDTNic/bpxG0P2nTPpyLHN6T6dgyUIMWjA8I0/jozx9EsysqYto3rAzLRp15szfF3DbtIySpR0AmDZ7PM1aNOLbvqPo2Lo3dnltWbvhZ4VTp11W+l2sU6c6a1dvpHnjznRs1xdDQ0N27nbDzMwUgHx5bcmXz47JP86lTo3WDBk4jiZf1GPpL64KJ/90Sah1smVWOmkY/frrr+TPn5+kJO0WYvv27fnmm280j/fs2YOTkxMmJibY29szbdo0EhISNPtVKhUrVqygXbt2mJubM3PmTBwcHJg/f77Wca9cuYJKpeL27dvvzJOQkMDw4cPJkSMHuXPnZty4cfTp04cvv/xSUyc2Npbhw4dja2uLiYkJdevW5eLFi1rHOXnyJNWrV8fY2Jh8+fIxfvx4rbxRUVH07t0bCwsL8uXLx4IFCz76304Xrp+4wp4FW7hy2DPVvphX0fzcawZeB84RfPcJ9y7fYvPktRRxLE7O/DYA6Onr0WVKP3bO3sDfG48Scu8pT28/wuvAuYw+lTQZNWIAa9Zuwn39Nvz9bzF4yHiio1/Tr29XpaPpxKHDfzF5yk/s2XNI6SifxOeEN9vmb+LS4Qvv3N/SuS27lm3D66gngTcesNzlZ3La5tL0LFVuUo3E+ETcJv3K07tPuOt7m7U/rKRGq9rYFcmbkafywY4eOsHxo39z7+4D7t55wJyZPxMVFU2Vao5YWlnQrVcnpvw4lzN/X8DXx49RQ36kek0nnKo6Kh09TbLS72Lnjs5s3vgHN27c5vq1GwwZOI5ChQtQsXJ5APz9b9Gn51AO/3mc+/cCOfX3eWZNW0jzlo3R19dXOL34FDppGHXu3Jnnz5/z119/acrCwsI4dOgQPXr0AODUqVP07t2bESNG4Ofnx6pVq1i3bh2zZs3SOtbUqVPp0KEDV69exdnZmW+++QY3NzetOm5ubtSvXx8HB4d35pk7dy4bN27Ezc2NM2fO8PLlS3bv3q1V5/vvv2fnzp24u7vj7e2Ng4MDzZs3JywsDIDHjx/TqlUrqlWrho+PDytWrGDt2rXMnDlTc4yxY8dy8uRJ9uzZw5EjRzhx4gTe3t6f/O+YUUwtzUhKSuL1yygACpe3J2e+3KjVSfx44Cd+8vyVYet+IH/JQgon/XCGhoY4OTnicfyUpkytVuNx/DQ1a1ZRMJn4ELaF7Mhpm4trp301Za9fRXPnyk1KOJUCwNDYkIT4BNRvrZiJi4kFoFS1shkb+BPo6enRvmNLzMxM8fL0wbFSOYyMDDl18s0XkNu37vHo4ROqVq+kXNA0yuq/i1YpQ2ThYeH/XsfaklevIklMTMygVLqlVqt1smVWOmkY5cyZk5YtW7Jp0yZN2Y4dO7CxsaFRo0YATJs2jfHjx9OnTx/s7e354osvmDFjBqtWaXerd+/enX79+mFvb0/hwoXp27cvAQEBeHom94bEx8ezadMmrZ6o/7d06VImTJhAhw4dKF26NMuWLSNHjhya/VFRUaxYsYJ58+bRsmVLypYty+rVqzE1NWXt2uThqOXLl1OoUCGWLVtG6dKl+fLLL5k2bRoLFiwgKSmJyMhI1q5dy/z582nSpAkVKlTA3d1dq0fp/8XGxvLy5UutLVGdsb84BsaGdBzfk4t7zxAT+RoAm8K2ALQZ8TUHl+5k2TdziI6IYvSWqZhZZ45xchubXBgYGBAS/EyrPCQklLx2eRRKJT6UtW0OACKehWuVRzyLIEee5Plw18/4Yp0nB22++xJ9QwPMrczpNr43kDzM9rkqXbYEtx9d4kHIFeYumsI3PYdzM+AOtrY2xMbG8TLilVb90JBn5LG1USht2mXl30WVSsXsuRM5f+4S/v633lknV+6cjPl+CO5uWzI4ne7IUJqO9OjRg507dxIbm/wNbuPGjXTt2hU9veSX8PHxYfr06VhYWGi2AQMG8PTpU6KjozXHqVq1qtZx8+fPT+vWrfntt98A2LdvH7GxsXTu3PmdOSIiIggODqZ69eqaMn19fapUefNN5c6dO8THx1OnTh1NmaGhIdWrV8ff3x8Af39/atWqhUql0tSpU6cOkZGRPHr0iDt37hAXF0eNGm8mkObKlYtSpUr967+Rq6sr1tbWWtvliBv/Wl/X9Az0+XaZCyoVbJq4WlOuUiX/P/rzlz+4fOgCgdfu4j72F9RqqNK6ZoblE+K/PLr1kBWjl9C6f3vcb2xlxaV1hDwMJjzkRaph/M/JnVv3aVqvI62bdGX92q0sWTGbkqWKKx1LfIJ5C6dSpkwJ+vcd9c79lpYWbN2+moAbt5k7e2kGpxO6orPrGLVt2xa1Ws2BAweoVq0ap06dYtGiRZr9kZGRTJs2jY4dO6Z6ronJm1Ul5ubmqfb379+fXr16sWjRItzc3OjSpQtmZma6ip5hJkyYgIuLi1aZS4W+GfLaegb6fPuLC7kK2rCo2zRNbxFAROgLAJ7ceqQpS4hL4NnDYHLlzxzf8J49CyMhIQFbO+1v2ra2eQgKDlUolfhQESHhAFjb5CA85IWm3NrGmvt+9zSPz+75m7N7/sbaxpqY6FhQq2ndvx0hgcEZHfmDxcfHc/9eIAC+Pn5UdCpP/4G92LPrT4yNjbCyttTqNcpja0NoyLN/O9xnL6v+Ls6dP5nmLRrRukV3njwJSrXfwsKc7bvW8ioykl7dB//n6MHnLjMPg+mCznqMTExM6NixIxs3bmTz5s2UKlUKJycnzX4nJycCAgJwcHBItf3Tq/RvWrVqhbm5OStWrODQoUP/OYxmbW2NnZ2d1kTqxMRErbk/xYsXx8jIiDNnzmjK4uPjuXjxImXLJs9VKFOmDOfOndN6g5w5cwZLS0sKFixI8eLFMTQ05MKFNxNJX7x4wc2bN/81m7GxMVZWVlqbvir9J+f90yiyLZqXxT1mEBUeqbU/8Opd4mPjyGufX+s5uQvkIexx5vggi4+Px9vbl8aN6mrKVCoVjRvV5fx5LwWTiQ8R8jCYFyFhlK/zZtKxqYUpxSuV5JZ3QKr6Ec8iiI2OoVbbusTFxnP1tE9Gxk0TPT0VRsaG+F65TlxcPPUavOmVLe5QlIKF8nPJ84pyAdMoK/4uzp0/mdZtv6B9m14EPniUar+lpQU797gRFxdPjy4DiY2NUyCl7iSp1TrZMiudXvm6R48etGnThuvXr9OzZ0+tfZMnT6ZNmzYULlyYr776Cj09PXx8fLh27ZrWhOZ30dfXp2/fvkyYMIESJUpQq1at/6w/bNgwXF1dcXBwoHTp0ixdupQXL15ohsXMzc0ZNGgQY8eOJVeuXBQuXJiffvqJ6OhonJ2dARg8eDCLFy9m2LBhDB06lICAAKZMmYKLiwt6enpYWFjg7OzM2LFjyZ07N7a2tvz444/vbeSlB2MzE/IUfbMqx6aQLQXLFiUqPJKIkBd8t2I0hcsV4xfnOejp62GVJwcAUeGRJMYnEBP5mr83HqXtqK8Je/qMsMfPaPZtO4BMtTJt0c+rcVu7CC9vXy5evMzwYQMwNzdlnftWpaPphLm5GQ4Ob66fUqxoYSpWLEdY2AsePnyiYLIPY2xmonVdojyFbClSthiR4a94/uQZf67dx5fDOhN07wkhD0PoPLo7L0LCuHTkzZePZn1acdPrBjFRMVSoV5EeP/Rl85z1RKcsJPjc/DB5FMeP/c2jR0+xsDCn41dtqF23Ot06DuDVy0g2b9jJ1FnjePEigsiXkcz86UcuXriM9yXf9x/8M5aVfhfnLZzKV53b0qPrICJfRWGbMv/r5ctXxMTEahpFpqYmfNd/DJaWFlhaJs/NfPYs7LMe5hXvptOGUePGjcmVKxcBAQF0795da1/z5s3Zv38/06dPZ+7cuRgaGlK6dGn69+//Qcd2dnZm9uzZ9OvX7711x40bR1BQEL1790ZfX59vv/2W5s2bay2dnDNnDklJSfTq1YtXr15RtWpVDh8+TM6cyZM4CxQowMGDBxk7diwVK1YkV65cODs7M3HiRM0x5s2bR2RkJG3btsXS0pLRo0cTERHxQeejS0Uc7Rm9ZZrm8deT+gJwdscJ9i/eRqUvki/4OOlP7cseLOg6hZvn/QDYMXsDiQmJfLNwGIYmRty7cpuF3ad9tn9w3mX79r3kscnF1MljyJs3Dz4+12ndpichmXhY4m1Vq1TE49gOzeMF86cC4L5+G8793z3n4XNi7+jA5K1vvgT1npz8JeTk9uOsHLOEfSt3YWxmQn/XwZhZmRNwyZ85vacTH/vmembFK5bgq1FdMTEz5cmdR6yZsILTu05k9Kl8sNx5crFk5Rxs7fLw6uUr/K7fpFvHAfx9IvkLx5Qfkj+H1qz/GWMjQ04cP8P40TMUTp12Wel30XlA8srq/Yc2apUPGTiOzRv/wLFiWapWqwSAt6+HVp2K5RryMPBxhuTUpex+SxCVOpMMJp46dYomTZrw8OFD7OzsPuq5SUlJlClThq+//poZMz6vD53vir57EnlWsvbJWaUjiDTqnK/a+ytlcici/n0YPKsIjc74L24Zyco48809/Vhhr969Gk6X7KxL6+Q4wRm4uEiXPvubyMbGxhIaGsrUqVPp3LnzBzWKHjx4wJEjR2jQoAGxsbEsW7aMe/fuperFEkIIIYS2zLzUXhc+61uCAGzevJkiRYoQHh7OTz/99EHP0dPTY926dVSrVo06depw9epVjh07RpkyZdI5rRBCCCEys8++x6hv37707dv3o55TqFAhrRVnQgghhPgwmWSGTbr57BtGQgghhMg4mXmpvS589kNpQgghhBAZRXqMhBBCCKEhQ2lCCCGEEClkVZoQQgghhACkx0gIIYQQb5GhNCGEEEKIFLIqTQghhBBCANJjJIQQQoi3ZPebyErDSAghhBAa2X0oTRpGQgghhNDI7pOvZY6REEIIIUQK6TESQgghhIbMMRJCCCGESCFDaUIIIYQQn4FffvmFokWLYmJiQo0aNfD09MzwDNIwEkIIIYSGWq3Wyfaxtm7diouLC1OmTMHb25uKFSvSvHlzQkJC0uEs/500jIQQQgihodbR9rEWLlzIgAED6NevH2XLlmXlypWYmZnx22+/pfWUPoo0jIQQQgihc7Gxsbx8+VJri42NfWfduLg4vLy8aNq0qaZMT0+Ppk2bcu7cuYyKnEwtspWYmBj1lClT1DExMUpHSRdZ/fzUajnHrCCrn59aLeco1OopU6ak6kiaMmXKO+s+fvxYDajPnj2rVT527Fh19erVMyDtGyq1OptPP89mXr58ibW1NREREVhZWSkdR+ey+vmBnGNWkNXPD+QcRXKP0f/3EBkbG2NsbJyq7pMnTyhQoABnz56lVq1amvLvv/+ekydPcuHChXTP+w9Zri+EEEIInfu3RtC72NjYoK+vT3BwsFZ5cHAwefPmTY94/0rmGAkhhBBCUUZGRlSpUgUPDw9NWVJSEh4eHlo9SBlBeoyEEEIIoTgXFxf69OlD1apVqV69OosXLyYqKop+/fplaA5pGGUzxsbGTJky5YO7NzObrH5+IOeYFWT18wM5R/HxunTpQmhoKJMnTyYoKIhKlSpx6NAh7OzsMjSHTL4WQgghhEghc4yEEEIIIVJIw0gIIYQQIoU0jIQQQgghUkjDSAghhBAihTSMhBBCCCFSyHJ9ITKB6dOnM2bMGMzMzLTKX79+zbx585g8ebJCyUR25uvr+8F1HR0d0zFJ+nFxcfngugsXLkzHJCKjyHL9bGLDhg2sXLmSe/fuce7cOYoUKcLixYspVqwY7du3VzqeeA99fX2ePn2Kra2tVvnz58+xtbUlMTFRoWSfbsmSJR9cd/jw4emYJOPFxcVx7949ihcvjoFB5v1+qqenh0qlQq1Wo1Kp/rNuZnyPAjRq1Ejrsbe3NwkJCZQqVQqAmzdvoq+vT5UqVTh+/LgSEYWOZd7fSPHBVqxYweTJkxk5ciSzZs3SfEDlyJGDxYsXZ9qG0cuXLz+4bma/weO//eHx8fEhV65cCiRKu0WLFmk9Dg0NJTo6mhw5cgAQHh6OmZkZtra2WaZhFB0dzbBhw3B3dweS/6ja29szbNgwChQowPjx4xVO+HHu3bun+fny5cuMGTOGsWPHam7hcO7cORYsWMBPP/2kVMQ0++uvvzQ/L1y4EEtLS9zd3cmZMycAL168oF+/ftSrV0+piELX1CLLK1OmjHrXrl1qtVqttrCwUN+5c0etVqvVV69eVefOnVvBZGmjUqnUenp6H7RlVjly5FDnzJlTraenp/n5n83Kykqtp6enHjx4sNIx02zjxo3qOnXqqG/cuKEpu3HjhrpevXrq33//XcFkujV8+HB1lSpV1KdOnVKbm5trfhd3796trlSpksLp0qZatWrqAwcOpCo/cOCA2snJSYFEupc/f371tWvXUpVfvXpVnS9fPgUSifQgPUbZwL1796hcuXKqcmNjY6KiohRIpBtvf5O7f/8+48ePp2/fvlrfVt3d3XF1dVUqYpotXrwYtVrNN998w7Rp07C2ttbsMzIyomjRohl+g8X0MGnSJHbs2KEZngAoVaoUixYt4quvvqJHjx4KptOd3bt3s3XrVmrWrKnVA1iuXDnu3LmjYLK0u3r1KsWKFUtVXqxYMfz8/BRIpHsvX74kNDQ0VXloaCivXr1SIJFID9IwygaKFSvGlStXKFKkiFb5oUOHKFOmjEKp0q5Bgwaan6dPn87ChQvp1q2bpqxdu3ZUqFCBX3/9lT59+igRMc3+yV2sWDFq166NoaGhwonSx9OnT0lISEhVnpiYSHBwsAKJ0kdoaGiqeWIAUVFR752j87krU6YMrq6urFmzBiMjIyB5LpWrq2um/px5W4cOHejXrx8LFiygevXqAFy4cIGxY8fSsWNHhdMJXZGGUTbg4uLCkCFDiImJQa1W4+npyebNmzUfYlnBuXPnWLlyZaryqlWr0r9/fwUS6VaDBg1ISkri5s2bhISEkJSUpLW/fv36CiXTjSZNmvDdd9+xZs0anJycAPDy8mLQoEE0bdpU4XS6U7VqVQ4cOMCwYcMANI2hNWvWZPqev5UrV9K2bVsKFiyoWYHm6+uLSqVi3759CqfTjZUrVzJmzBi6d+9OfHw8AAYGBjg7OzNv3jyF0wldkVVp2cTGjRuZOnWqprs+f/78TJs2DWdnZ4WT6UapUqVo3759qkme33//PXv27CEgIEChZLpx/vx5unfvzoMHD/j/X1mVSpVpV/z8IzQ0lD59+nDo0CFNr1hCQgLNmzdn3bp17+xlyYxOnz5Ny5Yt6dmzJ+vWreO7777Dz8+Ps2fPcvLkSapUqaJ0xDSJiopi48aN3LhxA0juRerevTvm5uYKJ9OtqKgozWdp8eLFs9z5ZXfSMMpmoqOjiYyMzDJ/aP5x8OBBOnXqhIODAzVq1ADA09OTW7dusXPnTlq1aqVwwrSpVKkSJUuWZNq0aeTLly/VsMvbc48ys5s3b+Lv749KpaJ06dKULFlS6Ug6d/fuXVxdXfHx8SEyMhInJyfGjRtHhQoVlI4mPsKjR48AKFiwoMJJhK5Jw0hkGY8ePWLFihX4+/sDyd9WBw4cSKFChRROlnbm5ub4+Pjg4OCgdJR0989HUmafc/P/4uPj+e6775g0adI7JylnFX5+fgQGBhIXF6dV3q5dO4US6U5SUhIzZ85kwYIFREZGAmBpacno0aP58ccf0dOTm0lkBdIwyqIqV678wX9YvL290zmNSKvGjRvz/fff06JFC6WjpJv169czb948bt26BUDJkiUZO3YsvXr1UjiZ7lhbW3PlypUs2TC6e/cuHTp04OrVq5qLPsKbBm5mH+4FmDBhAmvXrmXatGnUqVMHSB4enTp1KgMGDGDWrFkKJxS6IJOvs6gvv/xS6QiKiI6Ofue31cx6O4J/DBs2jNGjRxMUFESFChVSrU7L7Oe3cOFCJk2axNChQ7X+4AwcOJBnz54xatQohRPqxpdffsnu3buzzPm8bcSIERQrVgwPDw+KFSuGp6cnz58/Z/To0cyfP1/peDrh7u7OmjVrtHq/HB0dKVCgAIMHD5aGURYhPUYiSwgNDaVfv378+eef79yf2b+tvquL/u1bMWT28ytWrBjTpk2jd+/eWuXu7u5MnTpV6wrLmdk/wzBNmjShSpUqqSbtZuYrfNvY2HD8+HEcHR2xtrbG09OTUqVKcfz4cUaPHs3ly5eVjphmJiYm+Pr6ppr7FhAQQKVKlXj9+rVCyYQuSY9RNnLp0iXN/JuyZctm+hUwbxs5ciTh4eFcuHCBhg0bsmvXLoKDgzV/iDK7rNIw+DdPnz6ldu3aqcpr167N06dPFUiUPtauXUuOHDnw8vLCy8tLa59KpcrUDaPExEQsLS2B5EbSkydPKFWqFEWKFMn0q0L/UbFiRZYtW5bqPn/Lli2jYsWKCqUSuiYNo2zg0aNHdOvWjTNnzmjdh6p27dps2bIlS6yqOH78OHv27KFq1aro6elRpEgRvvjiC6ysrHB1daV169ZKR0yT/784Z1bj4ODAtm3b+OGHH7TKt27dSokSJRRKpXtZuYFbvnx5fHx8KFasGDVq1OCnn37CyMiIX3/9FXt7e6Xj6cRPP/1E69atOXbsmNYV9h8+fMjBgwcVTid0RabQZwP9+/cnPj4ef39/wsLCCAsLw9/fn6SkpCxx8UNIvq7IP5cgyJkzp+ay/RUqVMgyk8s3bNhAnTp1yJ8/Pw8ePACSbxmyZ88ehZOl3bRp05g8eTItWrRgxowZzJgxgxYtWjBt2jSmT5+udDzxASZOnKi58Oj06dO5d+8e9erV4+DBg6l6WDKrBg0acPPmTTp06EB4eDjh4eF07NiRgIAAuYlsVpLxt2cTGc3ExETt7e2dqvzSpUtqU1NTBRLpXtWqVdWHDh1Sq9Vqddu2bdW9evVSP3r0SP3999+r7e3tFU6XdsuXL1fb2NioZ86cqTY1NdXcfNTNzU3dsGFDhdPpxqVLl9Q9evRQOzk5qZ2cnNQ9evR45/s2M+vYsaN6zpw5qcrnzp2r/uqrrxRIlL6eP3+uTkpKUjqGEB9FJl9nAyVLluT333/X3NvnH56ennTv3p3bt28rlEx3fv/9dxISEujbty9eXl60aNGC58+fY2RkhLu7O126dFE6YpqULVuW2bNn8+WXX2JpaYmPjw/29vZcu3aNhg0b8uzZM6Ujig+QJ08ejh8/nupijlevXqVp06ZZ5r5wDx8+BMgS1xD7f+Hh4axdu1YzX7NcuXJ88803WeYiq0KG0rKFefPmMWzYMC5duqQpu3TpEiNGjMgyy2h79uxJ3759AXBycuLBgwdcunSJR48eZfpGESTPTalcuXKqcmNjY6KiohRIpHuJiYns3LmTmTNnMnPmTHbt2pXpV9v9v8jISM0NVt9maGjIy5cvFUikOwkJCUyaNAlra2uKFi1K0aJFsba2ZuLEiZr7imV2ly5donjx4ixatEgzLWHhwoUUL148ywzZC2QoLavKkSOHOmfOnJrNyMhIraenpzYyMtL6OWfOnEpH1Zk1a9aoy5UrpznHcuXKqVevXq10LJ0oU6aMevfu3Wq1Wq22sLDQDKUtWbJEXblyZSWj6cStW7fUJUuWVJuZmakrV66srly5strMzExdqlQp9e3bt5WOpzPVqlVTT5s2LVX5lClT1E5OTgok0p2BAweqbW1t1StXrlT7+PiofXx81CtXrlTnzZtXPXDgQKXj6UTdunXVffv2VcfHx2vK4uPj1X369FHXq1dPwWRCl2RVWha1ePFipSNkqMmTJ7Nw4UKGDRumtVpk1KhRBAYGZvoJvC4uLgwZMoSYmBjUajWenp5s3rwZV1dX1qxZo3S8NBs+fDj29vacO3eOXLlyAfD8+XN69uzJ8OHDOXDggMIJdWPSpEl07NiRO3fu0LhxYwA8PDzYvHkz27dvVzhd2mzatIktW7bQsmVLTZmjoyOFChWiW7durFixQsF0unHp0iVWr16NgcGbP50GBgZ8//33VK1aVcFkQqeUbpkJoQs2NjbqTZs2pSrftGmTOnfu3Aok0r3ff/9d7eDgoFapVGqVSqUuUKCAes2aNUrH0gkzMzO1r69vqvIrV66ozc3NFUiUfvbv36+uXbu22szMTJ07d251o0aN1CdOnFA6VprlyZNH7efnl6rcz89PbWNjo0Ai3bO1tVUfPnw4VfmhQ4fUtra2CiQS6UF6jLKZmJiYVLfLsLKyUiiN7sTHx7/zG1uVKlVISEhQIJHu9ejRgx49ehAdHU1kZKTm8gRZgbGxMa9evUpV/m9zcjKz1q1bZ/rrar3L0KFDmTFjBm5ubhgbGwMQGxvLrFmzGDp0qMLpdKNLly44Ozszf/58zQVJz5w5w9ixY+nWrZvC6YSuSMMoG4iKimLcuHFs27aN58+fp9qfFSa49urVixUrVrBw4UKt8l9//ZUePXoolCp9mJmZYWZmpnQMnWrTpg3ffvsta9eu1ayevHDhAgMHDswSd2X/f15eXlqrmt41sT4z6Nixo9bjY8eOUbBgQc1VoH18fIiLi6NJkyZKxNO5+fPno1Kp6N27t+YLl6GhIYMGDWLOnDkKpxO6Isv1s4EhQ4bw119/MWPGDHr16sUvv/zC48ePWbVqFXPmzMm0DQcXFxfNzwkJCaxbt47ChQtTs2ZNIPkPa2BgIL1792bp0qVKxfxklStX1tyZ/H0y+4qY8PBw+vTpw759+zQ3yI2Pj6d9+/a4ublprtie2YWEhNC1a1dOnDihdRX6Ro0asWXLFvLkyaNswI/Ur1+/D67r5uaWjkkyVnR0NHfu3AGgePHiWe6LSnYnDaNsoHDhwqxfv56GDRtiZWWFt7c3Dg4ObNiwgc2bN2faS9k3atTog+qpVCqOHz+ezml0b9q0aR9cd8qUKemYJOPcvn1b05NSpkwZHBwcFE6kW126dOHu3busX7+eMmXKAODn50efPn1wcHBg8+bNCicUQkjDKBuwsLDAz8+PwoULU7BgQf744w+qV6/OvXv3qFChApGRkUpHFNnc271/b1OpVJiYmODg4ED79u01K9YyK2tra44dO0a1atW0yj09PWnWrBnh4eHKBBMfpEOHDu/sxX37fdq9e3dKlSqlQDqhKzLHKBuwt7fn3r17FC5cmNKlS7Nt2zaqV6/Ovn37sswQhcjcLl++jLe3N4mJiZo/Kjdv3kRfX5/SpUuzfPlyRo8ezenTpylbtqzCaT9dUlKSZqjwbYaGhpr7jInPl7W1Nbt37yZHjhxUqVIFSB7GDg8Pp1mzZmzdupW5c+fi4eFBnTp1FE4rPpX0GGUDixYtQl9fn+HDh3Ps2DHatm2LWq0mPj6ehQsXMmLECKUjinfImTPnB88xCgsLS+c06Wvx4sWcOnUKNzc3zSrJiIgI+vfvT926dRkwYADdu3fn9evXHD58WOG0n659+/aEh4ezefNm8ufPD8Djx4/p0aMHOXPmZNeuXQonFP9l/PjxvHz5kmXLlqGnl3zjiKSkJEaMGIGlpSWzZs1i4MCBXL9+ndOnTyucVnwqaRhlQw8ePMDLywsHBwccHR2VjiP+hbu7+wfX7dOnTzomSX8FChTg6NGjqXqDrl+/TrNmzXj8+DHe3t40a9YsU98X7uHDh7Rr147r169r7iP28OFDypcvz969eylYsKDCCcV/yZMnD2fOnKFkyZJa5Tdv3qR27do8e/aMq1evUq9ePRkWzcRkKC0bWL9+PV26dNFcW6RIkSIUKVKEuLg41q9fT+/evRVOKN4lszd2PkZERAQhISGpGkahoaGae4jlyJEj1TW4MptChQrh7e3NsWPHuHHjBpA8ybxp06YKJ0u7u3fvYm9vr3SMdJWQkMCNGzdSNYxu3LihueyJiYnJB/f0is+T3EQ2G+jXrx8RERGpyl+9evVRy22Fsu7cucPEiRPp1q0bISEhAPz5559cv35d4WRp1759e7755ht27drFo0ePePToEbt27cLZ2Zkvv/wSSJ6g/P9/kDIjlUrFF198wbBhwxg2bFiWaBQBODg40KhRI37//XdiYmKUjpMuevXqhbOzM4sWLeL06dOcPn2aRYsW4ezsrPmCefLkScqVK6dwUpEWMpSWDejp6REcHJzqGik+Pj40atQo089PyQ5OnjxJy5YtqVOnDn///Tf+/v7Y29szZ84cLl26xI4dO5SOmCaRkZGMGjWK9evXay6cZ2BgQJ8+fVi0aBHm5uZcuXIFgEqVKikX9BMsWbKEb7/9FhMTE5YsWfKfdYcPH55BqXTvypUruLm5sXnzZuLi4jRXif7ngp1ZQWJiInPmzGHZsmUEBwcDYGdnx7Bhwxg3bhz6+voEBgaip6cnw6KZmDSMsrB/LhDo4+NDuXLltG58mJiYyL1792jRogXbtm1TMKX4ELVq1aJz5864uLhgaWmJj48P9vb2eHp60rFjRx49eqR0RJ2IjIzk7t27QPJqSgsLC4UTpV2xYsW4dOkSuXPnplixYv9aT6VSac49M0tISGDv3r2sW7eOQ4cOUbJkSb755ht69eqV6S5g+V/+GeLNCrdUEtqkYZSF/XOBwGnTpjF69GitPzJGRkYULVqUTp06Zbl7UWVFFhYWXL16lWLFimk1jO7fv0/p0qWz7NCFyLxiY2NZvnw5EyZMIO5/7d1/TNT1Hwfw5wkcHEJIBFjERAwrEpRgOX+ASUqIaErgD05Qg9UWqyY/HLrBIBEDJ39ULpIkoAKMdJi0CRgdnDJPfnOuiR4lxsEQBURAQu4+3z+EW+fpd2Bnb+4+r8fGpm/+efIHtxefe9/zNTYGoVCILVu2ICMjA88//zzreIQ8Fl2+NmKTbcguLi7YunUrLCwsGCciT2rOnDno7u7WeeLQ1NQEJycnRqkI0VVfX4/c3FwUFxdj9uzZiI+PR1RUFDo7O5Gamop33nkHly5dYh2TkMeiJ0Y8Ul9fr1m34O7urikoIzNffHw8ZDIZSkpKsHDhQjQ2NqKnpweRkZGIjIw0mpUgxuhxrd6P8vASZEOSlZWFb7/9Fm1tbQgKCkJ0dDSCgoI0fT8A0NnZCRcXF809MkJmIhqMeECpVGLbtm24cOGC1uLK5cuXo7i4mC4JGoCxsTHExMQgLy8PKpUKpqamUKlUCA8PR15eHkxMTFhHJI/x8E6/xsZGjI+P6zR8e3t7G+ROv0lubm547733sGvXrse+VTY2NoaioiJeVVEQw0ODEQ8EBgZiYGAA+fn5mhfjtrY27N69G8888wzOnj3LOCGZqr/++gtyuRxDQ0Pw8vKCm5sb60hkGrKysiCRSJCfnw9bW1sAQH9/P3bv3g1fX1/ExcUxTvhkxsfHkZaWhujoaKP+Q4sPXU2EBiNeEIlEqK2thZeXl9Z5Q0MDfH19MTIywigZIfzi5OSEiooKnZ6by5cvIyAgAF1dXYyS/XvW1taQy+VwcXFhHeWpmTVrFlatWoWoqCiEhobSvU0jRQWPPODs7Iz79+/rnKtUKs2+JjKzvfvuu8jIyNA5z8zMRFhYGINE5EkMDg6it7dX57y3txd3795lkEh//P39UV1dzTrGU9XY2AhPT0/ExsZi7ty5+OCDD+giuRGiJ0Y8cPr0aaSnp+Po0aPw8fEB8OAi9mQp2WSzMJm57O3tUVVVBQ8PD61zuVyONWvWaMrmyMwWGRkJqVSKI0eOaIoPZTIZEhIS4OvrO639eDNNdnY2UlNTIRaL4e3tjdmzZ2t9f+PGjYyS6R9fupr4igYjHrC1tcXIyAjGx8c1JY+T/374xYtasGcmkUiE5uZmzR2xSVeuXIGXlxfu3bvHKBmZjpGREcTHxyM3N1fzFNfU1BRRUVE4fPiwzu+jIfnnp88eJhAINLvEjAl1NRknGox4gE9b2o3VG2+8geDgYCQnJ2udp6Sk4MyZM2hoaGCUjDyJ4eFhtLe3AwAWLFhg0AMRHz3c1bRz506trqbBwUF6i82A0WBEiAE4c+YMQkJCEB4eDn9/fwDAr7/+iqKiIpSUlNDboQZGoVCgvb0dfn5+EIlE4DjOqDayj46OGuXFZOpq4ge6fM0TxryZnQ82bNiA0tJSKBQKfPjhh4iLi0NnZyfOnTtHQ5EBuX37Nt566y0sXLgQQUFB6O7uBgBERUUZ7Ef1J6lUKhw4cABOTk6wsrLS7H1LSkrC8ePHGafTj6+++grh4eHo6OhAaWkpgoODdd5CdHBwMJqfl69oMOKB6upqeHh4QCaT4dSpUxgaGgIAtLS0UGOyAVm/fj0uXLiA4eFh3Lp1C1VVVVi1ahXrWGQa9uzZAzMzM9y4cQOWlpaa861btxp8n9jBgweRl5eHzMxMrf2LixYtwjfffMMwmX6Mj49DLBYjIiLi/94fEgqFdCXBwNFgxAOJiYlIS0tDZWWl1guWv78/Ll68yDAZmaqUlBSo1Wqd8zt37mD79u0MEpEnUVFRgYyMDJ0SRDc3N3R0dDBKpR8FBQU4duwYxGKxVhP74sWLceXKFYbJ9MPU1BRHjhyht8h4gAYjHpDL5di8ebPOuYODA27dusUgEZmu48ePY+XKlZq3JwBAIpHAw8NDc4mXzHzDw8NaT4om9fX1wdzcnEEi/VEqlXjppZd0ztVq9SN71AwRH7qaCA1GvDC5mf1htJndcLS2tuLFF1/EkiVLkJOTg4SEBAQEBCAiIgK1tbWs45Ep8vX1RUFBgeb/AoEAarUamZmZOjvVDI27uzukUqnO+U8//aTTum+o1q1bh8TERMTHx6OoqAg///yz1hcxEhwxenFxcdzKlSu57u5uztramrt27Rp3/vx5ztXVlUtJSWEdj0zDvn37OIFAwJmZmXHnzp1jHYdMk1wu5xwcHLjAwEBOKBRyoaGh3Kuvvso5OjpyCoWCdbx/pbS0lLOxseE+++wzztLSkjt8+DAXHR3NCYVCrqKignU8vRAIBI/9mjVrFut4RE/o4/o8QJvZjcMXX3yBxMREbNq0CQ0NDTAxMUFhYSEWL17MOhqZhoGBARw9ehQtLS0YGhrC66+/jpiYGKMoBJRKpfj000+1frbk5GQEBASwjkbIlNFgxCO0md1wBQYGoq6uDl9//TVCQ0Nx7949xMbGIi8vD6mpqdi7dy/riGSKRkdH0draips3b+pcqDemtRnGzli7mggNRoQYhLVr1yI/P19n6e8vv/yC6OjoR94hIzPP2bNnERERgb6+Pjz80mvoazOio6OxY8cOvPnmm6yjPDUqlQrp6enIzs5GT08Prl69CldXVyQlJcHFxQVRUVGsIxI9oMvXPECb2Q1fZWUl2tvbsWPHDixbtgxKpRLAg08z/fjjj4zTkan66KOPsGXLFnR1dUGtVmt9GfJQBAC9vb0IDAyEs7MzEhIS0NzczDqS3hl7VxN5gAYjHqipqUFQUJDO+bp161BTU8MgEZmukydP4u2334ZIJEJTUxP+/vtvAA96jA4dOsQ4HZmqnp4exMbGwtHRkXUUvTt9+jS6u7uRlJSEuro6eHt747XXXkN6ejquX7/OOp5eGHtXE3mABiMeGBoa0vrrZpKZmRkGBwcZJCLTlZaWhuzsbOTk5MDMzExzvmLFCjQ2NjJMRqYjNDQUEomEdYynxtbWFu+//z4kEgk6Ojqwa9cufPfdd4/sNzJEfOhqIoAp6wDk6fPw8MCJEyd0NrMXFxfD3d2dUSoyHW1tbfDz89M5t7GxwcDAwH8fiDyRL7/8EmFhYZBKpfDw8NAacgHg448/ZpRMv+7fv4/6+nrIZDJcv37daJ6QTXY1zZs3T+vcmLqaCA1GvJCUlISQkBC0t7c/cjM7mfnmzp0LhUIBFxcXrfPz58/D1dWVTSgybUVFRaioqICFhQUkEgkEAoHmewKBwOAHo99++w2FhYU4efIk1Go1QkJCUFZWpnndMXTJycnYuXMnlEol1Go1Tp06hba2NhQUFKCsrIx1PKIvDDuUyH+orKyMW758OWdpacnZ2dlxq1ev5iQSCetYZIrS09M5d3d37uLFi5y1tTUnlUq577//nrO3t+c+//xz1vHIFDk6OnIHDx7kVCoV6yh698ILL3AWFhbcpk2buJKSEm50dJR1pKeipqaGW7NmDWdvb8+JRCJuxYoVXHl5OetYRI/o4/qEGACO45Ceno5Dhw5hZGQEAGBubo74+HgcOHCAcToyVc8++yzq6uqwYMEC1lH0LicnB2FhYZgzZw7rKIT8KzQY8UBdXR3UajWWLl2qdS6TyWBiYgIfHx9Gych0jY2NQaFQYGhoCO7u7rCysmIdiUzDnj17YG9vj/3797OOQp4AH7qaCN0x4oWYmBjs3btXZzBSKpXIyMiATCZjlIxMl1AopAvzBkylUiEzMxPl5eXw9PTUuXydlZXFKBmZismuJnt7e2zbtg1isRhLlixhHYvoGT0x4gErKyu0trbqXNL9888/4enpibt37zJKRgi/rF69+rHfEwgEqKqq+g/TkCfR39+PkpISFBYWQiqV4pVXXoFYLEZ4eLjOhyOIYaLBiAfs7OxQVlaGZcuWaZ3X1tZi/fr16O/vZ5SMEEIMV2dnJ4qKipCbm4tr165hfHycdSSiB1TwyAMBAQHYt28f7ty5ozkbGBjA/v37sXbtWobJCCHEMBlrVxOhJ0a8oFQq4efnh9u3b2tKyJqbm+Ho6IjKyko4OzszTkgIIYbhUV1NYrEY/v7+Wr1UxHDRYMQTw8PD+OGHH9DS0gKRSARPT09s375d5/InIYSQR3NyckJfXx8CAwMhFouxYcMGmJubs45F9IwGIx75/fffcePGDYyNjWmdb9y4kVEiQggxHNTVxA80GPHAH3/8gc2bN0Mul0MgEIDjOK1HviqVimE6QgghZOagy9c88Mknn2D+/Pm4efMmLC0tcfnyZVRXV8PHx8eoN30TQggh00VPjHjgueeeQ1VVFTw9PWFjY4NLly7h5ZdfRlVVFeLi4tDU1MQ6IiGEEDIj0BMjHlCpVLC2tgbwYEjq6uoCAMybNw9tbW0soxFCCCEzCq0E4YFFixahpaUF8+fPx9KlS5GZmQmhUIhjx47ptGETQgghfEZvpfFAeXk5hoeHERISAoVCgeDgYFy9ehV2dnY4ceIE/P39WUckhBBCZgQajHiqr68Ptra2VEhGCCGE/AMNRoQQQgghE+jyNSGEEELIBBqMCCGEEEIm0GBECCGEEDKBBiNCCCGEkAk0GBFCCCGETKDBiBBCCCFkAg1GhBBCCCET/geBI6IwU5H+ywAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "predict_from_model_classification(model_embedding)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "id": "DZPpvKp2W6gk"
      },
      "outputs": [],
      "source": [
        "max_len = max_len_overview\n",
        "model_embedding_2 = build_model_embedding_2()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_embedding_2.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBqXQNodXdZ7",
        "outputId": "b045926a-600e-4515-f530-41ab6b9abec2"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_1 (Embedding)     (None, 500, 128)          1280000   \n",
            "                                                                 \n",
            " conv1d (Conv1D)             (None, 494, 32)           28704     \n",
            "                                                                 \n",
            " max_pooling1d (MaxPooling1  (None, 98, 32)            0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 92, 32)            7200      \n",
            "                                                                 \n",
            " global_max_pooling1d (Glob  (None, 32)                0         \n",
            " alMaxPooling1D)                                                 \n",
            "                                                                 \n",
            " dense_38 (Dense)            (None, 7)                 231       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1316135 (5.02 MB)\n",
            "Trainable params: 1316135 (5.02 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2txb3KJ2Xb9D",
        "outputId": "a984fbd1-b1b1-4481-d932-a785b3e85af2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "155/155 [==============================] - 23s 145ms/step - loss: 1.9371 - accuracy: 0.0862 - val_loss: 1.9204 - val_accuracy: 0.3961\n",
            "Epoch 2/20\n",
            "155/155 [==============================] - 19s 125ms/step - loss: 1.9039 - accuracy: 0.4320 - val_loss: 1.8831 - val_accuracy: 0.4406\n",
            "Epoch 3/20\n",
            "155/155 [==============================] - 19s 125ms/step - loss: 1.8585 - accuracy: 0.4395 - val_loss: 1.8278 - val_accuracy: 0.4406\n",
            "Epoch 4/20\n",
            "155/155 [==============================] - 21s 137ms/step - loss: 1.7915 - accuracy: 0.4395 - val_loss: 1.7459 - val_accuracy: 0.4406\n",
            "Epoch 5/20\n",
            "155/155 [==============================] - 19s 124ms/step - loss: 1.7056 - accuracy: 0.4395 - val_loss: 1.6532 - val_accuracy: 0.4406\n",
            "Epoch 6/20\n",
            "155/155 [==============================] - 20s 127ms/step - loss: 1.6177 - accuracy: 0.4395 - val_loss: 1.5653 - val_accuracy: 0.4406\n",
            "Epoch 7/20\n",
            "155/155 [==============================] - 22s 141ms/step - loss: 1.5398 - accuracy: 0.4395 - val_loss: 1.4903 - val_accuracy: 0.4406\n",
            "Epoch 8/20\n",
            "155/155 [==============================] - 21s 138ms/step - loss: 1.4778 - accuracy: 0.4395 - val_loss: 1.4339 - val_accuracy: 0.4406\n",
            "Epoch 9/20\n",
            "155/155 [==============================] - 21s 135ms/step - loss: 1.4356 - accuracy: 0.4395 - val_loss: 1.3972 - val_accuracy: 0.4406\n",
            "Epoch 10/20\n",
            "155/155 [==============================] - 19s 123ms/step - loss: 1.4086 - accuracy: 0.4395 - val_loss: 1.3728 - val_accuracy: 0.4406\n",
            "Epoch 11/20\n",
            "155/155 [==============================] - 19s 123ms/step - loss: 1.3911 - accuracy: 0.4395 - val_loss: 1.3568 - val_accuracy: 0.4406\n",
            "Epoch 12/20\n",
            "155/155 [==============================] - 22s 140ms/step - loss: 1.3784 - accuracy: 0.4395 - val_loss: 1.3421 - val_accuracy: 0.4406\n",
            "Epoch 13/20\n",
            "155/155 [==============================] - 19s 124ms/step - loss: 1.3647 - accuracy: 0.4395 - val_loss: 1.3302 - val_accuracy: 0.4406\n",
            "Epoch 14/20\n",
            "155/155 [==============================] - 19s 124ms/step - loss: 1.3553 - accuracy: 0.4395 - val_loss: 1.3221 - val_accuracy: 0.4406\n",
            "Epoch 15/20\n",
            "155/155 [==============================] - 21s 139ms/step - loss: 1.3487 - accuracy: 0.4395 - val_loss: 1.3163 - val_accuracy: 0.4406\n",
            "Epoch 16/20\n",
            "155/155 [==============================] - 19s 125ms/step - loss: 1.3434 - accuracy: 0.4395 - val_loss: 1.3118 - val_accuracy: 0.4406\n",
            "Epoch 17/20\n",
            "155/155 [==============================] - 19s 124ms/step - loss: 1.3390 - accuracy: 0.4395 - val_loss: 1.3081 - val_accuracy: 0.4406\n",
            "Epoch 18/20\n",
            "155/155 [==============================] - 21s 134ms/step - loss: 1.3352 - accuracy: 0.4395 - val_loss: 1.3052 - val_accuracy: 0.4406\n",
            "Epoch 19/20\n",
            "155/155 [==============================] - 19s 124ms/step - loss: 1.3319 - accuracy: 0.4395 - val_loss: 1.3026 - val_accuracy: 0.4406\n",
            "Epoch 20/20\n",
            "155/155 [==============================] - 19s 126ms/step - loss: 1.3290 - accuracy: 0.4395 - val_loss: 1.3006 - val_accuracy: 0.4406\n",
            "83/83 [==============================] - 3s 31ms/step - loss: 1.3349 - accuracy: 0.4300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss (mean squared error): [1.3348604440689087, 0.4299735724925995]\n"
          ]
        }
      ],
      "source": [
        "fit_evaluate_model(model_embedding_2, 10E-6, 'categorical_crossentropy', ['accuracy'], 20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 926
        },
        "id": "89pK7ifkX_qL",
        "outputId": "9c362f5b-6c37-4664-9467-76648d9f1db1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "83/83 [==============================] - 3s 40ms/step\n",
            "Confusion Matrix:\n",
            " [[   0    0    0  931    0    0    0]\n",
            " [   0    0    0   26    0    0    0]\n",
            " [   0    0    0   93    0    0    0]\n",
            " [   0    0    0 1139    0    0    0]\n",
            " [   0    0    0  171    0    0    0]\n",
            " [   0    0    0    1    0    0    0]\n",
            " [   0    0    0  288    0    0    0]]\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.00      0.00       931\n",
            "           1       1.00      0.00      0.00        26\n",
            "           2       1.00      0.00      0.00        93\n",
            "           3       0.43      1.00      0.60      1139\n",
            "           4       1.00      0.00      0.00       171\n",
            "           5       1.00      0.00      0.00         1\n",
            "           6       1.00      0.00      0.00       288\n",
            "\n",
            "    accuracy                           0.43      2649\n",
            "   macro avg       0.92      0.14      0.09      2649\n",
            "weighted avg       0.75      0.43      0.26      2649\n",
            "\n",
            "Accuracy: 0.42997357493393734\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "predict_from_model_classification(model_embedding_2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "id": "r6SVFF1_kUux"
      },
      "outputs": [],
      "source": [
        "max_len = max_len_overview\n",
        "model_embedding_3 = build_model_embedding_3()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "id": "oHsATtgXkVWk"
      },
      "outputs": [],
      "source": [
        "max_len = max_len_overview\n",
        "model_embedding_4 = build_model_embedding_4()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_embedding_4.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fYxQBHEZZhgC",
        "outputId": "7d65f436-1564-4778-9529-70cf3f7c5576"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_11\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_3 (Embedding)     (None, 500, 64)           640000    \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 32000)             0         \n",
            "                                                                 \n",
            " dense_40 (Dense)            (None, 32)                1024032   \n",
            "                                                                 \n",
            " dropout_11 (Dropout)        (None, 32)                0         \n",
            "                                                                 \n",
            " dense_41 (Dense)            (None, 32)                1056      \n",
            "                                                                 \n",
            " dropout_12 (Dropout)        (None, 32)                0         \n",
            "                                                                 \n",
            " dense_42 (Dense)            (None, 32)                1056      \n",
            "                                                                 \n",
            " dense_43 (Dense)            (None, 7)                 231       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1666375 (6.36 MB)\n",
            "Trainable params: 1666375 (6.36 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 831
        },
        "id": "h60J3k1op1zL",
        "outputId": "0843c795-6bd5-4aa3-cf9d-d97d36a20a8f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "155/155 [==============================] - 5s 26ms/step - loss: 1.4123 - accuracy: 0.4246 - val_loss: 1.2881 - val_accuracy: 0.4406\n",
            "Epoch 2/10\n",
            "155/155 [==============================] - 4s 28ms/step - loss: 1.3195 - accuracy: 0.4387 - val_loss: 1.2848 - val_accuracy: 0.4406\n",
            "Epoch 3/10\n",
            "155/155 [==============================] - 5s 31ms/step - loss: 1.3156 - accuracy: 0.4397 - val_loss: 1.2879 - val_accuracy: 0.4406\n",
            "Epoch 4/10\n",
            "155/155 [==============================] - 4s 25ms/step - loss: 1.3157 - accuracy: 0.4361 - val_loss: 1.2831 - val_accuracy: 0.4406\n",
            "Epoch 5/10\n",
            "155/155 [==============================] - 4s 26ms/step - loss: 1.3161 - accuracy: 0.4302 - val_loss: 1.2853 - val_accuracy: 0.4406\n",
            "Epoch 6/10\n",
            "155/155 [==============================] - 5s 34ms/step - loss: 1.3167 - accuracy: 0.4345 - val_loss: 1.2855 - val_accuracy: 0.4406\n",
            "Epoch 7/10\n",
            "155/155 [==============================] - 4s 26ms/step - loss: 1.3177 - accuracy: 0.4395 - val_loss: 1.2855 - val_accuracy: 0.4406\n",
            "Epoch 8/10\n",
            "155/155 [==============================] - 4s 26ms/step - loss: 1.3164 - accuracy: 0.4379 - val_loss: 1.2911 - val_accuracy: 0.4406\n",
            "Epoch 9/10\n",
            "155/155 [==============================] - 5s 31ms/step - loss: 1.3155 - accuracy: 0.4397 - val_loss: 1.2826 - val_accuracy: 0.4406\n",
            "Epoch 10/10\n",
            "155/155 [==============================] - 4s 28ms/step - loss: 1.3159 - accuracy: 0.4395 - val_loss: 1.2852 - val_accuracy: 0.4406\n",
            "83/83 [==============================] - 0s 5ms/step - loss: 1.3184 - accuracy: 0.4300\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss (mean squared error): [1.31843101978302, 0.4299735724925995]\n"
          ]
        }
      ],
      "source": [
        "fit_evaluate_model(model_embedding_4, 0.01, 'categorical_crossentropy', ['accuracy'], 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 160,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 926
        },
        "id": "R99OSgutkXjo",
        "outputId": "f1c5559b-f918-4718-c542-48371c1e5596"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "83/83 [==============================] - 1s 8ms/step\n",
            "Confusion Matrix:\n",
            " [[   0    0    0  931    0    0    0]\n",
            " [   0    0    0   26    0    0    0]\n",
            " [   0    0    0   93    0    0    0]\n",
            " [   0    0    0 1139    0    0    0]\n",
            " [   0    0    0  171    0    0    0]\n",
            " [   0    0    0    1    0    0    0]\n",
            " [   0    0    0  288    0    0    0]]\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.00      0.00       931\n",
            "           1       1.00      0.00      0.00        26\n",
            "           2       1.00      0.00      0.00        93\n",
            "           3       0.43      1.00      0.60      1139\n",
            "           4       1.00      0.00      0.00       171\n",
            "           5       1.00      0.00      0.00         1\n",
            "           6       1.00      0.00      0.00       288\n",
            "\n",
            "    accuracy                           0.43      2649\n",
            "   macro avg       0.92      0.14      0.09      2649\n",
            "weighted avg       0.75      0.43      0.26      2649\n",
            "\n",
            "Accuracy: 0.42997357493393734\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "predict_from_model_classification(model_embedding_4)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_lstm = build_lstm_model()"
      ],
      "metadata": {
        "id": "Ktbfv-GIp-wb"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_lstm.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PP_ziyi2aeUU",
        "outputId": "d4d02860-51bf-4f09-a2d5-5e9fc7921d36"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_12\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_4 (Embedding)     (None, 500, 128)          1280000   \n",
            "                                                                 \n",
            " spatial_dropout1d (Spatial  (None, 500, 128)          0         \n",
            " Dropout1D)                                                      \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 50)                35800     \n",
            "                                                                 \n",
            " dense_44 (Dense)            (None, 7)                 357       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1316157 (5.02 MB)\n",
            "Trainable params: 1316157 (5.02 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fit_evaluate_model(model_lstm, 0.01, 'categorical_crossentropy', ['accuracy'], 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 831
        },
        "id": "u4m6Lb7bqDkr",
        "outputId": "bfe09395-e76f-42c5-95d2-02a3c069aa2b"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "155/155 [==============================] - 164s 1s/step - loss: 1.3446 - accuracy: 0.4246 - val_loss: 1.2968 - val_accuracy: 0.4422\n",
            "Epoch 2/10\n",
            "155/155 [==============================] - 148s 955ms/step - loss: 1.1060 - accuracy: 0.5696 - val_loss: 1.4222 - val_accuracy: 0.4196\n",
            "Epoch 3/10\n",
            "155/155 [==============================] - 147s 950ms/step - loss: 0.5871 - accuracy: 0.7989 - val_loss: 1.9446 - val_accuracy: 0.3977\n",
            "Epoch 4/10\n",
            "155/155 [==============================] - 155s 1s/step - loss: 0.2718 - accuracy: 0.9098 - val_loss: 2.5612 - val_accuracy: 0.3864\n",
            "Epoch 5/10\n",
            "155/155 [==============================] - 149s 959ms/step - loss: 0.1259 - accuracy: 0.9602 - val_loss: 3.2181 - val_accuracy: 0.3743\n",
            "Epoch 6/10\n",
            "155/155 [==============================] - 152s 978ms/step - loss: 0.0748 - accuracy: 0.9763 - val_loss: 3.5736 - val_accuracy: 0.3840\n",
            "Epoch 7/10\n",
            "155/155 [==============================] - 146s 942ms/step - loss: 0.0453 - accuracy: 0.9850 - val_loss: 3.9687 - val_accuracy: 0.3864\n",
            "Epoch 8/10\n",
            "155/155 [==============================] - 151s 974ms/step - loss: 0.0316 - accuracy: 0.9903 - val_loss: 4.1975 - val_accuracy: 0.3921\n",
            "Epoch 9/10\n",
            "155/155 [==============================] - 153s 989ms/step - loss: 0.0267 - accuracy: 0.9917 - val_loss: 4.5611 - val_accuracy: 0.3985\n",
            "Epoch 10/10\n",
            "155/155 [==============================] - 165s 1s/step - loss: 0.0282 - accuracy: 0.9917 - val_loss: 4.8507 - val_accuracy: 0.3921\n",
            "83/83 [==============================] - 10s 124ms/step - loss: 5.0199 - accuracy: 0.3620\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss (mean squared error): [5.019917011260986, 0.36202341318130493]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict_from_model_classification(model_lstm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 926
        },
        "id": "_n92mQ4iqL_R",
        "outputId": "de7f29fe-9672-42a3-9006-7689ad0a9273"
      },
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "83/83 [==============================] - 16s 197ms/step\n",
            "Confusion Matrix:\n",
            " [[296   3  24 449 104   0  55]\n",
            " [ 13   1   0   8   3   0   1]\n",
            " [ 20   0   3  54   9   0   7]\n",
            " [344   2  27 619  73   0  74]\n",
            " [ 66   3   1  73  18   0  10]\n",
            " [  0   0   0   1   0   0   0]\n",
            " [ 81   1  11 154  19   0  22]]\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.36      0.32      0.34       931\n",
            "           1       0.10      0.04      0.06        26\n",
            "           2       0.05      0.03      0.04        93\n",
            "           3       0.46      0.54      0.50      1139\n",
            "           4       0.08      0.11      0.09       171\n",
            "           5       1.00      0.00      0.00         1\n",
            "           6       0.13      0.08      0.10       288\n",
            "\n",
            "    accuracy                           0.36      2649\n",
            "   macro avg       0.31      0.16      0.16      2649\n",
            "weighted avg       0.35      0.36      0.35      2649\n",
            "\n",
            "Accuracy: 0.36202340505851266\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-M9Sj1X8vjFR"
      },
      "source": [
        "#### 🔠 **Title: Text features**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 165,
      "metadata": {
        "id": "6bXAaUhdvwav"
      },
      "outputs": [],
      "source": [
        "y_classification_encoded = to_categorical(y_classification, num_classes=7)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 166,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NQI4qaHtv_gM",
        "outputId": "fc02b8e3-e2fc-4257-b624-399dd4ac181e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set shape: (6181, 90) (6181, 7)\n",
            "Testing set shape: (2649, 90) (2649, 7)\n"
          ]
        }
      ],
      "source": [
        "X_train, X_test, y_train, y_test = split_dataset(X_text_vectorized_title, y_classification_encoded)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 167,
      "metadata": {
        "id": "8fdV-dVjwChs"
      },
      "outputs": [],
      "source": [
        "max_len = max_len_title\n",
        "model_embedding = build_model_embedding()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_embedding.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rpeYgWQea1Gn",
        "outputId": "c9bed844-b1dd-4020-f0f4-f4e7e2b50dcb"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_19\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_7 (Embedding)     (None, 90, 64)            640000    \n",
            "                                                                 \n",
            " flatten_6 (Flatten)         (None, 5760)              0         \n",
            "                                                                 \n",
            " dense_67 (Dense)            (None, 128)               737408    \n",
            "                                                                 \n",
            " dense_68 (Dense)            (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_69 (Dense)            (None, 7)                 455       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1386119 (5.29 MB)\n",
            "Trainable params: 1386119 (5.29 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 831
        },
        "id": "-INOeBOUwFh_",
        "outputId": "35a861f8-0714-4135-da12-572b37396902"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "155/155 [==============================] - 4s 20ms/step - loss: 1.4469 - accuracy: 0.4217 - val_loss: 1.2918 - val_accuracy: 0.4406\n",
            "Epoch 2/10\n",
            "155/155 [==============================] - 3s 19ms/step - loss: 1.1061 - accuracy: 0.5722 - val_loss: 1.4631 - val_accuracy: 0.4058\n",
            "Epoch 3/10\n",
            "155/155 [==============================] - 3s 19ms/step - loss: 0.6215 - accuracy: 0.7921 - val_loss: 1.8908 - val_accuracy: 0.3452\n",
            "Epoch 4/10\n",
            "155/155 [==============================] - 6s 37ms/step - loss: 0.4140 - accuracy: 0.8643 - val_loss: 2.0036 - val_accuracy: 0.3088\n",
            "Epoch 5/10\n",
            "155/155 [==============================] - 4s 23ms/step - loss: 0.3082 - accuracy: 0.8987 - val_loss: 2.3933 - val_accuracy: 0.3670\n",
            "Epoch 6/10\n",
            "155/155 [==============================] - 3s 19ms/step - loss: 0.2221 - accuracy: 0.9213 - val_loss: 2.4901 - val_accuracy: 0.3678\n",
            "Epoch 7/10\n",
            "155/155 [==============================] - 5s 34ms/step - loss: 0.1929 - accuracy: 0.9345 - val_loss: 2.7963 - val_accuracy: 0.3452\n",
            "Epoch 8/10\n",
            "155/155 [==============================] - 4s 25ms/step - loss: 0.1584 - accuracy: 0.9438 - val_loss: 2.8815 - val_accuracy: 0.3177\n",
            "Epoch 9/10\n",
            "155/155 [==============================] - 3s 19ms/step - loss: 0.1507 - accuracy: 0.9486 - val_loss: 3.3774 - val_accuracy: 0.3678\n",
            "Epoch 10/10\n",
            "155/155 [==============================] - 3s 18ms/step - loss: 0.1329 - accuracy: 0.9527 - val_loss: 3.1967 - val_accuracy: 0.3605\n",
            "83/83 [==============================] - 1s 6ms/step - loss: 3.2587 - accuracy: 0.3722\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Loss (mean squared error): [3.258723020553589, 0.3722159266471863]\n"
          ]
        }
      ],
      "source": [
        "fit_evaluate_model(model_embedding, 0.01, 'categorical_crossentropy', ['accuracy'], 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 169,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 926
        },
        "id": "NPyKDYF0wFkb",
        "outputId": "ccb7cef7-3dec-4591-e01e-cc907ba9fa56"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "83/83 [==============================] - 0s 3ms/step\n",
            "Confusion Matrix:\n",
            " [[   0    0   83    0  848    0    0]\n",
            " [   0    0    1    0   25    0    0]\n",
            " [   0    0   16    0   76    0    1]\n",
            " [   0    0  123    0 1015    0    1]\n",
            " [   0    0   19    0  152    0    0]\n",
            " [   0    0    0    0    1    0    0]\n",
            " [   0    0   28    0  260    0    0]]\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.00      0.00       931\n",
            "           1       1.00      0.00      0.00        26\n",
            "           2       0.06      0.17      0.09        93\n",
            "           3       1.00      0.00      0.00      1139\n",
            "           4       0.06      0.89      0.12       171\n",
            "           5       1.00      0.00      0.00         1\n",
            "           6       0.00      0.00      0.00       288\n",
            "\n",
            "    accuracy                           0.06      2649\n",
            "   macro avg       0.59      0.15      0.03      2649\n",
            "weighted avg       0.80      0.06      0.01      2649\n",
            "\n",
            "Accuracy: 0.06342015855039637\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "predict_from_model_classification(model_embedding)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "wjJiRJ3x4GSV",
        "yfNyi-BE4OI-",
        "qTO9_9do7RPz",
        "n_VH6Byl5dxI",
        "nXwVZRnOEhRE",
        "sjF3AEcxeqox",
        "MX8VF4Ls6G_8",
        "JwK3btE7CpVc",
        "ViAsnzKBsPMO",
        "UaCpBYqkYN-P",
        "3aVYaNwhsubJ"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}