Tech_Challenge_5_Sugestão_de_indicação_de_bolsa_de_estudos.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Notebook: Criação de MLP - Sugestão de bolsa de estudos para alunos\n",
        "Este notebook tem por objetivo criar um Perceptron Multilayer (MLP), que é responsável por sugerir bolsas de estudos para alunos, com base em sua performance acadêmica. Vale notar que outras features também podem ser agregadas ao modelo para torná-lo mais rico."
      ],
      "metadata": {
        "id": "5EnSsEuyr_84"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1) Imports"
      ],
      "metadata": {
        "id": "Q_IkDNpnUTLY"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "--64gRgNpvRN"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.utils import shuffle\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras import models\n",
        "from keras import layers\n",
        "from keras import metrics\n",
        "from keras.optimizers import Adam\n",
        "from keras.metrics import Precision\n",
        "from keras.models import load_model\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from tqdm.keras import TqdmCallback\n",
        "import joblib\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2) Carregando o dataset preparado para a sugestão de bolsas de estudos"
      ],
      "metadata": {
        "id": "JGuddtR7UUnf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/processado_base_ml_bolsa.csv', sep=';')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "slKjIJyfO-U7",
        "outputId": "ad7efcdf-ac34-46d8-a9cd-e86c9f6d83e1"
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      "execution_count": null,
      "outputs": [
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              "        IAA       IEG  IPS       IDA       IPP       IPV   IAN      INDE  \\\n",
              "0  10.00002  7.581705  7.5  8.816667  5.625000  7.916665   5.0  7.675509   \n",
              "1  10.00002  8.071429  7.5  5.083333  6.250000  8.055553   5.0  7.117065   \n",
              "2   6.41668  8.557505  7.5  7.083333  6.458333  7.833337   5.0  7.232336   \n",
              "3   9.00002  9.333333  7.5  5.500000  6.250000  7.500000  10.0  7.741669   \n",
              "4  10.00002  9.314954  7.5  5.583333  5.937500  7.916670   5.0  7.406743   \n",
              "\n",
              "   INDICADO_BOLSA  \n",
              "0               1  \n",
              "1               0  \n",
              "2               0  \n",
              "3               0  \n",
              "4               0  "
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              "        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-d600eb0f-4aec-4b06-b65c-f5ab8e20294c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d600eb0f-4aec-4b06-b65c-f5ab8e20294c')\"\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-d600eb0f-4aec-4b06-b65c-f5ab8e20294c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 862,\n  \"fields\": [\n    {\n      \"column\": \"IAA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0802639670687553,\n        \"min\": 0.0,\n        \"max\": 10.00002,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          5.41666,\n          7.50001,\n          7.416679999999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IEG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6490228169292414,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 760,\n        \"samples\": [\n          6.116071428571428,\n          7.945767195767196,\n          8.436240842490841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0702021762472804,\n        \"min\": 2.5,\n        \"max\": 10.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          3.75,\n          10.0,\n          7.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IDA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.059346013993776,\n        \"min\": 0.0,\n        \"max\": 9.916666666666668,\n        \"num_unique_values\": 380,\n        \"samples\": [\n          8.583333333333334,\n          7.388888888888889,\n          7.833333333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1355096352539702,\n        \"min\": 0.0,\n        \"max\": 9.21875,\n        \"num_unique_values\": 58,\n        \"samples\": [\n          5.625,\n          3.75,\n          5.15625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0985482345160225,\n        \"min\": 2.50001,\n        \"max\": 10.00001,\n        \"num_unique_values\": 452,\n        \"samples\": [\n          5.54166,\n          3.8333325,\n          5.999996666666668\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.3878164744207337,\n        \"min\": 2.5,\n        \"max\": 10.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5.0,\n          10.0,\n          2.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INDE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0299117901917074,\n        \"min\": 3.031806222222222,\n        \"max\": 9.441521857142858,\n        \"num_unique_values\": 862,\n        \"samples\": [\n          6.555924462189958,\n          7.411457833333333,\n          8.27865517948718\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INDICADO_BOLSA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# executa um embaralhamento inicial, para garantir a aleatoriedade da amostra\n",
        "df = shuffle(df, random_state=777)\n",
        "\n",
        "# arredonda os indicadores\n",
        "df = df.round(2)"
      ],
      "metadata": {
        "id": "-uz7qkQDW1BX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "eH7EN5_eW_OX",
        "outputId": "a97d746a-8d06-4cad-cf87-8fb5eab2a521"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      IAA   IEG  IPS   IDA   IPP   IPV   IAN  INDE  INDICADO_BOLSA\n",
              "414  7.42  7.46  5.0  4.93  5.62  6.50   5.0  6.08               0\n",
              "232  8.75  6.77  7.5  4.67  7.19  7.37   5.0  6.61               1\n",
              "282  8.00  7.75  7.5  5.75  6.04  7.50   5.0  6.85               0\n",
              "511  7.00  8.24  7.5  3.67  6.25  6.83   5.0  6.32               0\n",
              "643  9.50  8.09  7.5  2.00  6.46  6.61  10.0  6.69               0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a8c835c3-1085-4c7c-a966-43dc996ea215\" 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>IAA</th>\n",
              "      <th>IEG</th>\n",
              "      <th>IPS</th>\n",
              "      <th>IDA</th>\n",
              "      <th>IPP</th>\n",
              "      <th>IPV</th>\n",
              "      <th>IAN</th>\n",
              "      <th>INDE</th>\n",
              "      <th>INDICADO_BOLSA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>7.42</td>\n",
              "      <td>7.46</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.93</td>\n",
              "      <td>5.62</td>\n",
              "      <td>6.50</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.08</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>8.75</td>\n",
              "      <td>6.77</td>\n",
              "      <td>7.5</td>\n",
              "      <td>4.67</td>\n",
              "      <td>7.19</td>\n",
              "      <td>7.37</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.61</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>282</th>\n",
              "      <td>8.00</td>\n",
              "      <td>7.75</td>\n",
              "      <td>7.5</td>\n",
              "      <td>5.75</td>\n",
              "      <td>6.04</td>\n",
              "      <td>7.50</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.85</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>511</th>\n",
              "      <td>7.00</td>\n",
              "      <td>8.24</td>\n",
              "      <td>7.5</td>\n",
              "      <td>3.67</td>\n",
              "      <td>6.25</td>\n",
              "      <td>6.83</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.32</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>643</th>\n",
              "      <td>9.50</td>\n",
              "      <td>8.09</td>\n",
              "      <td>7.5</td>\n",
              "      <td>2.00</td>\n",
              "      <td>6.46</td>\n",
              "      <td>6.61</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6.69</td>\n",
              "      <td>0</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-a8c835c3-1085-4c7c-a966-43dc996ea215')\"\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",
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              "  </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-a8c835c3-1085-4c7c-a966-43dc996ea215 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-a8c835c3-1085-4c7c-a966-43dc996ea215');\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-df6be5b4-37fc-4901-9fd7-3985cff9e588\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-df6be5b4-37fc-4901-9fd7-3985cff9e588')\"\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",
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              "    90% {\n",
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              "    }\n",
              "  }\n",
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              "\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",
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              "        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-df6be5b4-37fc-4901-9fd7-3985cff9e588 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 862,\n  \"fields\": [\n    {\n      \"column\": \"IAA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.080193165579477,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          4.58,\n          3.5,\n          9.58\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IEG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6490870067356755,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 414,\n        \"samples\": [\n          6.53,\n          3.33,\n          9.64\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0705319564404638,\n        \"min\": 2.5,\n        \"max\": 10.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          10.0,\n          3.75,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IDA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.059343295851339,\n        \"min\": 0.0,\n        \"max\": 9.92,\n        \"num_unique_values\": 308,\n        \"samples\": [\n          4.12,\n          9.08,\n          4.69\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1359188461532572,\n        \"min\": 0.0,\n        \"max\": 9.22,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          4.06,\n          6.41,\n          9.22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.098771295448479,\n        \"min\": 2.5,\n        \"max\": 10.0,\n        \"num_unique_values\": 178,\n        \"samples\": [\n          7.08,\n          6.08,\n          5.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.3878164744207337,\n        \"min\": 2.5,\n        \"max\": 10.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5.0,\n          10.0,\n          2.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INDE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.029892097528074,\n        \"min\": 3.03,\n        \"max\": 9.44,\n        \"num_unique_values\": 341,\n        \"samples\": [\n          9.35,\n          5.94,\n          7.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INDICADO_BOLSA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "set(df['INDICADO_BOLSA'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rRImct_MPCza",
        "outputId": "8760eddb-a2b1-45a1-c32b-7476071be3b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0, 1}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['INDICADO_BOLSA'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0X8SI96oPO9J",
        "outputId": "e102876b-205e-4bf5-b5d2-1cfe36ec8a58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "INDICADO_BOLSA\n",
              "0    730\n",
              "1    132\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3) Mockando e melhorando os dados de treinamento\n",
        "Nesta seção, adicionamos alguns dados mockados para incrementar o dataset de alunos.\n",
        "*   200 registros de alunos com notas >= 8 nos indicadores, que representam ótimos candidatos à indicação de bolsa\n",
        "*   100 registros de alunos notas >= 0 e <= 5 nos indicadores, que indicam que são alunos que não deveriam receber uma indicação de bolsa\n",
        "\n",
        "Tais dados mockados têm o intuíto de incrementar os dados de treino para melhorar a performance da rede neural."
      ],
      "metadata": {
        "id": "oeNmKSXTxrJi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "colunas = df.columns.difference(['INDICADO_BOLSA'])"
      ],
      "metadata": {
        "id": "0YI2CdLOxt0L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mock_alunos1 = np.random.uniform(8, 10, size=(200, len(colunas)))\n",
        "mock_alunos1 = pd.DataFrame(mock_alunos1, columns=colunas)\n",
        "mock_alunos1['INDICADO_BOLSA'] = 1\n",
        "mock_alunos1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "1vrrL1OA1cej",
        "outputId": "81b3ac84-d5a5-4b62-c862-b66c06f762b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          IAA       IAN       IDA       IEG      INDE       IPP       IPS  \\\n",
              "0    9.521019  9.967088  9.024905  9.936667  8.683658  9.427598  9.445297   \n",
              "1    9.997640  9.984019  8.648079  9.932633  9.681846  8.282324  8.019247   \n",
              "2    8.276829  9.040081  9.972715  9.862956  8.703181  9.044846  9.003231   \n",
              "3    9.273147  8.466438  9.784049  9.285377  8.411769  8.967772  8.496184   \n",
              "4    9.502296  9.414036  8.507668  9.952614  9.346656  9.739795  9.450776   \n",
              "..        ...       ...       ...       ...       ...       ...       ...   \n",
              "195  9.247600  8.581845  9.763602  8.226642  8.225955  8.509375  8.697065   \n",
              "196  8.617461  9.083300  9.688399  9.317922  8.195364  9.315300  9.556210   \n",
              "197  8.774446  9.237568  8.238316  9.736715  8.021516  8.938725  8.092539   \n",
              "198  9.915405  9.462377  9.454912  9.820830  8.935420  9.824980  9.407585   \n",
              "199  8.386484  9.889986  8.066325  9.565519  9.943032  9.072857  9.011945   \n",
              "\n",
              "          IPV  INDICADO_BOLSA  \n",
              "0    9.452823               1  \n",
              "1    9.855904               1  \n",
              "2    8.512760               1  \n",
              "3    9.609891               1  \n",
              "4    8.389091               1  \n",
              "..        ...             ...  \n",
              "195  9.372389               1  \n",
              "196  9.798771               1  \n",
              "197  8.134584               1  \n",
              "198  9.861736               1  \n",
              "199  8.980495               1  \n",
              "\n",
              "[200 rows x 9 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>IAA</th>\n",
              "      <th>IAN</th>\n",
              "      <th>IDA</th>\n",
              "      <th>IEG</th>\n",
              "      <th>INDE</th>\n",
              "      <th>IPP</th>\n",
              "      <th>IPS</th>\n",
              "      <th>IPV</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9.521019</td>\n",
              "      <td>9.967088</td>\n",
              "      <td>9.024905</td>\n",
              "      <td>9.936667</td>\n",
              "      <td>8.683658</td>\n",
              "      <td>9.427598</td>\n",
              "      <td>9.445297</td>\n",
              "      <td>9.452823</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.997640</td>\n",
              "      <td>9.984019</td>\n",
              "      <td>8.648079</td>\n",
              "      <td>9.932633</td>\n",
              "      <td>9.681846</td>\n",
              "      <td>8.282324</td>\n",
              "      <td>8.019247</td>\n",
              "      <td>9.855904</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.276829</td>\n",
              "      <td>9.040081</td>\n",
              "      <td>9.972715</td>\n",
              "      <td>9.862956</td>\n",
              "      <td>8.703181</td>\n",
              "      <td>9.044846</td>\n",
              "      <td>9.003231</td>\n",
              "      <td>8.512760</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.273147</td>\n",
              "      <td>8.466438</td>\n",
              "      <td>9.784049</td>\n",
              "      <td>9.285377</td>\n",
              "      <td>8.411769</td>\n",
              "      <td>8.967772</td>\n",
              "      <td>8.496184</td>\n",
              "      <td>9.609891</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9.502296</td>\n",
              "      <td>9.414036</td>\n",
              "      <td>8.507668</td>\n",
              "      <td>9.952614</td>\n",
              "      <td>9.346656</td>\n",
              "      <td>9.739795</td>\n",
              "      <td>9.450776</td>\n",
              "      <td>8.389091</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>9.247600</td>\n",
              "      <td>8.581845</td>\n",
              "      <td>9.763602</td>\n",
              "      <td>8.226642</td>\n",
              "      <td>8.225955</td>\n",
              "      <td>8.509375</td>\n",
              "      <td>8.697065</td>\n",
              "      <td>9.372389</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>8.617461</td>\n",
              "      <td>9.083300</td>\n",
              "      <td>9.688399</td>\n",
              "      <td>9.317922</td>\n",
              "      <td>8.195364</td>\n",
              "      <td>9.315300</td>\n",
              "      <td>9.556210</td>\n",
              "      <td>9.798771</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>8.774446</td>\n",
              "      <td>9.237568</td>\n",
              "      <td>8.238316</td>\n",
              "      <td>9.736715</td>\n",
              "      <td>8.021516</td>\n",
              "      <td>8.938725</td>\n",
              "      <td>8.092539</td>\n",
              "      <td>8.134584</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>9.915405</td>\n",
              "      <td>9.462377</td>\n",
              "      <td>9.454912</td>\n",
              "      <td>9.820830</td>\n",
              "      <td>8.935420</td>\n",
              "      <td>9.824980</td>\n",
              "      <td>9.407585</td>\n",
              "      <td>9.861736</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>8.386484</td>\n",
              "      <td>9.889986</td>\n",
              "      <td>8.066325</td>\n",
              "      <td>9.565519</td>\n",
              "      <td>9.943032</td>\n",
              "      <td>9.072857</td>\n",
              "      <td>9.011945</td>\n",
              "      <td>8.980495</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 9 columns</p>\n",
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              "      --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",
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              "  }\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",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      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-bca9684c-8afa-46f2-85f4-d53ff435f065 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_3445974e-65c6-46c0-8815-cbd8ac28d00e\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('mock_alunos1')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_3445974e-65c6-46c0-8815-cbd8ac28d00e button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('mock_alunos1');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "mock_alunos1",
              "summary": "{\n  \"name\": \"mock_alunos1\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"IAA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5676796222029203,\n        \"min\": 8.000799076977463,\n        \"max\": 9.997640425875094,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          9.416516677856942,\n          8.630219615138923,\n          9.124470581147706\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6006553927029026,\n        \"min\": 8.004117739074161,\n        \"max\": 9.9884068634624,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          8.108262549286314,\n          9.17148090445128,\n          8.128337552384398\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IDA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5819675143418289,\n        \"min\": 8.012431934986816,\n        \"max\": 9.997557154559932,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          8.14032746479633,\n          9.76593225411864,\n          8.459349598352384\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IEG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6250822958323613,\n        \"min\": 8.000029966290818,\n        \"max\": 9.987337630316796,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          9.57594671501068,\n          8.419792488372305,\n          8.825965008320953\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INDE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5562395933935298,\n        \"min\": 8.005394191139994,\n        \"max\": 9.991310631582454,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          8.811707228699971,\n          9.216488996863502,\n          9.875767525078953\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.520015202297572,\n        \"min\": 8.004713756361157,\n        \"max\": 9.989907621334504,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          8.366864763543818,\n          8.57285142967333,\n          9.25429875239738\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5706420772905144,\n        \"min\": 8.00069518523662,\n        \"max\": 9.994956703278605,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          9.625292862000732,\n          8.200907480293704,\n          9.390593617739302\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5754724790883027,\n        \"min\": 8.011218289005948,\n        \"max\": 9.99577757553888,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          9.956445593853235,\n          8.158218858296948,\n          9.324699044486096\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INDICADO_BOLSA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mock_alunos2 = np.random.uniform(0, 5, size=(400, len(colunas)))\n",
        "mock_alunos2 = pd.DataFrame(mock_alunos2, columns=colunas)\n",
        "mock_alunos2['INDICADO_BOLSA'] = 0\n",
        "mock_alunos2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "f31UuMnK1emT",
        "outputId": "514ab9ae-598c-43eb-d2be-b20353c4fc78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          IAA       IAN       IDA       IEG      INDE       IPP       IPS  \\\n",
              "0    0.036016  4.365024  3.132978  1.678878  3.624884  4.332691  4.232069   \n",
              "1    0.179587  1.858365  2.969177  3.899053  0.159550  3.948745  1.737357   \n",
              "2    4.284608  4.157443  0.119836  2.297599  3.108835  0.398467  3.167317   \n",
              "3    2.802324  2.474301  3.135784  3.932459  3.916078  3.228152  2.362145   \n",
              "4    4.869097  2.327434  2.249361  0.191704  4.388974  1.345666  1.203351   \n",
              "..        ...       ...       ...       ...       ...       ...       ...   \n",
              "395  2.029412  3.700622  0.288528  0.339369  1.984933  2.336563  1.788500   \n",
              "396  0.402934  1.730456  1.812391  4.934183  3.393399  0.585540  2.307930   \n",
              "397  4.248200  4.795696  1.113261  4.757185  1.974102  0.425372  3.784336   \n",
              "398  3.868237  3.181819  0.805875  0.103474  1.106290  1.785115  1.714961   \n",
              "399  2.002471  4.285741  1.003343  1.218296  4.102571  0.792873  2.342953   \n",
              "\n",
              "          IPV  INDICADO_BOLSA  \n",
              "0    1.304555               0  \n",
              "1    3.715811               0  \n",
              "2    2.339621               0  \n",
              "3    0.360358               0  \n",
              "4    1.104061               0  \n",
              "..        ...             ...  \n",
              "395  0.528664               0  \n",
              "396  0.669251               0  \n",
              "397  1.895203               0  \n",
              "398  3.878986               0  \n",
              "399  2.967310               0  \n",
              "\n",
              "[400 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>IAA</th>\n",
              "      <th>IAN</th>\n",
              "      <th>IDA</th>\n",
              "      <th>IEG</th>\n",
              "      <th>INDE</th>\n",
              "      <th>IPP</th>\n",
              "      <th>IPS</th>\n",
              "      <th>IPV</th>\n",
              "      <th>INDICADO_BOLSA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.036016</td>\n",
              "      <td>4.365024</td>\n",
              "      <td>3.132978</td>\n",
              "      <td>1.678878</td>\n",
              "      <td>3.624884</td>\n",
              "      <td>4.332691</td>\n",
              "      <td>4.232069</td>\n",
              "      <td>1.304555</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.179587</td>\n",
              "      <td>1.858365</td>\n",
              "      <td>2.969177</td>\n",
              "      <td>3.899053</td>\n",
              "      <td>0.159550</td>\n",
              "      <td>3.948745</td>\n",
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              "      <td>3.715811</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.284608</td>\n",
              "      <td>4.157443</td>\n",
              "      <td>0.119836</td>\n",
              "      <td>2.297599</td>\n",
              "      <td>3.108835</td>\n",
              "      <td>0.398467</td>\n",
              "      <td>3.167317</td>\n",
              "      <td>2.339621</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.802324</td>\n",
              "      <td>2.474301</td>\n",
              "      <td>3.135784</td>\n",
              "      <td>3.932459</td>\n",
              "      <td>3.916078</td>\n",
              "      <td>3.228152</td>\n",
              "      <td>2.362145</td>\n",
              "      <td>0.360358</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.869097</td>\n",
              "      <td>2.327434</td>\n",
              "      <td>2.249361</td>\n",
              "      <td>0.191704</td>\n",
              "      <td>4.388974</td>\n",
              "      <td>1.345666</td>\n",
              "      <td>1.203351</td>\n",
              "      <td>1.104061</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>2.029412</td>\n",
              "      <td>3.700622</td>\n",
              "      <td>0.288528</td>\n",
              "      <td>0.339369</td>\n",
              "      <td>1.984933</td>\n",
              "      <td>2.336563</td>\n",
              "      <td>1.788500</td>\n",
              "      <td>0.528664</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>0.402934</td>\n",
              "      <td>1.730456</td>\n",
              "      <td>1.812391</td>\n",
              "      <td>4.934183</td>\n",
              "      <td>3.393399</td>\n",
              "      <td>0.585540</td>\n",
              "      <td>2.307930</td>\n",
              "      <td>0.669251</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>4.248200</td>\n",
              "      <td>4.795696</td>\n",
              "      <td>1.113261</td>\n",
              "      <td>4.757185</td>\n",
              "      <td>1.974102</td>\n",
              "      <td>0.425372</td>\n",
              "      <td>3.784336</td>\n",
              "      <td>1.895203</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>398</th>\n",
              "      <td>3.868237</td>\n",
              "      <td>3.181819</td>\n",
              "      <td>0.805875</td>\n",
              "      <td>0.103474</td>\n",
              "      <td>1.106290</td>\n",
              "      <td>1.785115</td>\n",
              "      <td>1.714961</td>\n",
              "      <td>3.878986</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>2.002471</td>\n",
              "      <td>4.285741</td>\n",
              "      <td>1.003343</td>\n",
              "      <td>1.218296</td>\n",
              "      <td>4.102571</td>\n",
              "      <td>0.792873</td>\n",
              "      <td>2.342953</td>\n",
              "      <td>2.967310</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 9 columns</p>\n",
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c7e42da0-9c0b-4633-a9a0-9ead14ac4d13')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      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",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-c7e42da0-9c0b-4633-a9a0-9ead14ac4d13 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-c7e42da0-9c0b-4633-a9a0-9ead14ac4d13');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    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",
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              "    }\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-8a3ead28-63bd-404f-9921-b3feaff8672e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_2196d416-d381-49ea-bf42-ef443ba97eb8\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('mock_alunos2')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_2196d416-d381-49ea-bf42-ef443ba97eb8 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('mock_alunos2');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "mock_alunos2",
              "summary": "{\n  \"name\": \"mock_alunos2\",\n  \"rows\": 400,\n  \"fields\": [\n    {\n      \"column\": \"IAA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4698176170646349,\n        \"min\": 0.00048694482467914746,\n        \"max\": 4.998725477001492,\n        \"num_unique_values\": 400,\n        \"samples\": [\n          3.7903247711159143,\n          1.8969164856365628,\n          3.1715688163920857\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4460192827390288,\n        \"min\": 0.017603161870232142,\n        \"max\": 4.986996595084516,\n        \"num_unique_values\": 400,\n        \"samples\": [\n          3.5925320799576266,\n          3.7341122085971494,\n          0.9939413771553585\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IDA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4384781717186417,\n        \"min\": 0.010211834569352485,\n        \"max\": 4.995865269646387,\n        \"num_unique_values\": 400,\n        \"samples\": [\n          1.5468889136057495,\n          0.8228845552530467,\n          4.317977812734044\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IEG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.435448629054298,\n        \"min\": 0.0031471227715906824,\n        \"max\": 4.974553639989418,\n        \"num_unique_values\": 400,\n        \"samples\": [\n          4.1815863506637605,\n          3.4206313491721163,\n          0.06462676727164884\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INDE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4541497339599634,\n        \"min\": 0.007758865238363111,\n        \"max\": 4.983901367313034,\n        \"num_unique_values\": 400,\n        \"samples\": [\n          2.8387487248522274,\n          0.839182577952412,\n          1.9652542465309892\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4125568261350758,\n        \"min\": 0.004821271651129777,\n        \"max\": 4.998790501683922,\n        \"num_unique_values\": 400,\n        \"samples\": [\n          3.1369012316125993,\n          0.23651484164849468,\n          1.7852693349827076\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3860263429992026,\n        \"min\": 0.0007694635206351164,\n        \"max\": 4.990801942945594,\n        \"num_unique_values\": 400,\n        \"samples\": [\n          3.33759833243553,\n          1.5703964266808013,\n          3.0853122752135054\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3930999553995378,\n        \"min\": 0.012323576819411874,\n        \"max\": 4.995174620229107,\n        \"num_unique_values\": 400,\n        \"samples\": [\n          4.247171334441951,\n          3.288370657365446,\n          0.37077487085098004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INDICADO_BOLSA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([df, mock_alunos1, mock_alunos2], ignore_index=True)"
      ],
      "metadata": {
        "id": "1YDeR1jrybeT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4) Preparando o dataset para treinamento e validação da rede neural"
      ],
      "metadata": {
        "id": "qTNj7oelUXeP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# features\n",
        "X = df[df.columns.difference(['INDICADO_BOLSA'])]\n",
        "# targets\n",
        "y = df['INDICADO_BOLSA']"
      ],
      "metadata": {
        "id": "TA6ztdwFPtby"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "PfDqlnKiTYhX",
        "outputId": "2dcd056a-9cf1-4127-ac6f-e497d6123d6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           IAA        IAN       IDA       IEG      INDE       IPP       IPS  \\\n",
              "0     7.420000   5.000000  4.930000  7.460000  6.080000  5.620000  5.000000   \n",
              "1     8.750000   5.000000  4.670000  6.770000  6.610000  7.190000  7.500000   \n",
              "2     8.000000   5.000000  5.750000  7.750000  6.850000  6.040000  7.500000   \n",
              "3     7.000000   5.000000  3.670000  8.240000  6.320000  6.250000  7.500000   \n",
              "4     9.500000  10.000000  2.000000  8.090000  6.690000  6.460000  7.500000   \n",
              "...        ...        ...       ...       ...       ...       ...       ...   \n",
              "1457  2.029412   3.700622  0.288528  0.339369  1.984933  2.336563  1.788500   \n",
              "1458  0.402934   1.730456  1.812391  4.934183  3.393399  0.585540  2.307930   \n",
              "1459  4.248200   4.795696  1.113261  4.757185  1.974102  0.425372  3.784336   \n",
              "1460  3.868237   3.181819  0.805875  0.103474  1.106290  1.785115  1.714961   \n",
              "1461  2.002471   4.285741  1.003343  1.218296  4.102571  0.792873  2.342953   \n",
              "\n",
              "           IPV  \n",
              "0     6.500000  \n",
              "1     7.370000  \n",
              "2     7.500000  \n",
              "3     6.830000  \n",
              "4     6.610000  \n",
              "...        ...  \n",
              "1457  0.528664  \n",
              "1458  0.669251  \n",
              "1459  1.895203  \n",
              "1460  3.878986  \n",
              "1461  2.967310  \n",
              "\n",
              "[1462 rows x 8 columns]"
            ],
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>IAA</th>\n",
              "      <th>IAN</th>\n",
              "      <th>IDA</th>\n",
              "      <th>IEG</th>\n",
              "      <th>INDE</th>\n",
              "      <th>IPP</th>\n",
              "      <th>IPS</th>\n",
              "      <th>IPV</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.420000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.930000</td>\n",
              "      <td>7.460000</td>\n",
              "      <td>6.080000</td>\n",
              "      <td>5.620000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.750000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.670000</td>\n",
              "      <td>6.770000</td>\n",
              "      <td>6.610000</td>\n",
              "      <td>7.190000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>7.370000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.750000</td>\n",
              "      <td>7.750000</td>\n",
              "      <td>6.850000</td>\n",
              "      <td>6.040000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>7.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.670000</td>\n",
              "      <td>8.240000</td>\n",
              "      <td>6.320000</td>\n",
              "      <td>6.250000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>6.830000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9.500000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>8.090000</td>\n",
              "      <td>6.690000</td>\n",
              "      <td>6.460000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>6.610000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1457</th>\n",
              "      <td>2.029412</td>\n",
              "      <td>3.700622</td>\n",
              "      <td>0.288528</td>\n",
              "      <td>0.339369</td>\n",
              "      <td>1.984933</td>\n",
              "      <td>2.336563</td>\n",
              "      <td>1.788500</td>\n",
              "      <td>0.528664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458</th>\n",
              "      <td>0.402934</td>\n",
              "      <td>1.730456</td>\n",
              "      <td>1.812391</td>\n",
              "      <td>4.934183</td>\n",
              "      <td>3.393399</td>\n",
              "      <td>0.585540</td>\n",
              "      <td>2.307930</td>\n",
              "      <td>0.669251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1459</th>\n",
              "      <td>4.248200</td>\n",
              "      <td>4.795696</td>\n",
              "      <td>1.113261</td>\n",
              "      <td>4.757185</td>\n",
              "      <td>1.974102</td>\n",
              "      <td>0.425372</td>\n",
              "      <td>3.784336</td>\n",
              "      <td>1.895203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1460</th>\n",
              "      <td>3.868237</td>\n",
              "      <td>3.181819</td>\n",
              "      <td>0.805875</td>\n",
              "      <td>0.103474</td>\n",
              "      <td>1.106290</td>\n",
              "      <td>1.785115</td>\n",
              "      <td>1.714961</td>\n",
              "      <td>3.878986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1461</th>\n",
              "      <td>2.002471</td>\n",
              "      <td>4.285741</td>\n",
              "      <td>1.003343</td>\n",
              "      <td>1.218296</td>\n",
              "      <td>4.102571</td>\n",
              "      <td>0.792873</td>\n",
              "      <td>2.342953</td>\n",
              "      <td>2.967310</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1462 rows × 8 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-0ea00b05-fff6-4cb9-ad2f-b0d183fa0af9 button.colab-df-convert');\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X')\"\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        google.colab.notebook.generateWithVariable('X');\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 1462,\n  \"fields\": [\n    {\n      \"column\": \"IAA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.2211498534476437,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 632,\n        \"samples\": [\n          1.1015639167262377,\n          4.3156867192967745,\n          3.327474842756178\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.927043186310902,\n        \"min\": 0.017603161870232142,\n        \"max\": 10.0,\n        \"num_unique_values\": 603,\n        \"samples\": [\n          8.278966475438851,\n          0.08649064806686646,\n          0.9223148849912555\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IDA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7953187224130613,\n        \"min\": 0.0,\n        \"max\": 9.997557154559932,\n        \"num_unique_values\": 908,\n        \"samples\": [\n          0.9366273008713355,\n          8.343896891423988,\n          8.513686360566867\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IEG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.964841459109055,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 1014,\n        \"samples\": [\n          3.3492817160065895,\n          8.71511973456959,\n          8.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INDE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.537936948779774,\n        \"min\": 0.007758865238363111,\n        \"max\": 9.991310631582454,\n        \"num_unique_values\": 941,\n        \"samples\": [\n          5.58,\n          0.9980950652574244,\n          8.531385170530342\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.455581446343107,\n        \"min\": 0.0,\n        \"max\": 9.989907621334504,\n        \"num_unique_values\": 651,\n        \"samples\": [\n          3.8436413364477247,\n          1.4134310515867077,\n          0.08816451523521118\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.520092003951782,\n        \"min\": 0.0007694635206351164,\n        \"max\": 10.0,\n        \"num_unique_values\": 612,\n        \"samples\": [\n          9.018602824711941,\n          4.353073762849362,\n          9.749829822899942\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6257657885287715,\n        \"min\": 0.012323576819411874,\n        \"max\": 10.0,\n        \"num_unique_values\": 778,\n        \"samples\": [\n          0.8880461707444176,\n          4.247171334441951,\n          3.9447494395764338\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cIMrJS3WTZHH",
        "outputId": "09bb01b8-ab96-4f99-86b1-13a59852a8e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       0\n",
              "1       1\n",
              "2       0\n",
              "3       0\n",
              "4       0\n",
              "       ..\n",
              "1457    0\n",
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              "1459    0\n",
              "1460    0\n",
              "1461    0\n",
              "Name: INDICADO_BOLSA, Length: 1462, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, stratify=y)"
      ],
      "metadata": {
        "id": "NVVRaUe1TpHJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "scaler.fit(X_train)\n",
        "scaler.fit(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "ThVIODw9TsPC",
        "outputId": "bc1b86e7-6a94-4900-fe34-bc6341d2f139"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StandardScaler()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = scaler.transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "TcBsiw2qTzbR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HZFzQigaT1uX",
        "outputId": "53d224b9-4d41-47da-94bd-42992cccd0f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1169, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MnoAqSJIT4EX",
        "outputId": "c20c8413-3e39-4082-c9ba-1959133bd954"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(293, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# a soma do treino e do teste = shape original?\n",
        "X_train.shape[0] + X_test.shape[0] == df.shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NXmnFPcWT5Qf",
        "outputId": "7642f930-1156-4b27-b318-4f603d35a4f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = np.asarray(y_train).reshape((-1, 1))\n",
        "y_test = np.asarray(y_test).reshape((-1, 1))"
      ],
      "metadata": {
        "id": "KoGO4c4wV5Jn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v8ZgWWhaj0ll",
        "outputId": "619cc9a1-fadc-4fd5-f44f-099893dc77b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.01317423, -1.08384606,  0.42719365,  1.0851166 ,  0.48989932,\n",
              "         0.167774  ,  0.59584497,  0.3706894 ],\n",
              "       [ 1.01317423, -0.22217658,  0.83512473,  0.75899048,  0.54492153,\n",
              "        -0.63759808,  0.59584497,  0.4357704 ],\n",
              "       [ 0.83657926,  1.0067548 ,  1.23164985,  1.09172701,  1.55548715,\n",
              "         1.54877426,  1.26641601,  1.24771142],\n",
              "       [ 0.36395164, -1.08384606,  0.39906047,  1.12328029,  0.49775963,\n",
              "         1.03740449, -0.15759834,  0.76883196],\n",
              "       [ 0.43223333,  1.045565  ,  1.03033247,  0.99622943,  0.78743211,\n",
              "         1.30349094,  0.84025072,  1.43626345]])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FwGgALwRjvge",
        "outputId": "69336fa1-dde5-447b-b5bd-549b452c6b18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0],\n",
              "       [0],\n",
              "       [1],\n",
              "       [0],\n",
              "       [1]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4.1) Criando a rede neural Multilayer Perceptron"
      ],
      "metadata": {
        "id": "dLZUfgQcUcD_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Semente aleatótia para manter os mesmos dados\n",
        "tf.random.set_seed(777)\n",
        "\n",
        "# Definindo entradas da rede + tamanho da batch de processamento\n",
        "input_shape = X_train.shape[1]    # Variáveis de entrada\n",
        "output_shape = y_train.shape[1]   # Classe preditora\n",
        "batch_size = 32\n",
        "\n",
        "# Abrindo uma sequencia de neuronios\n",
        "model = models.Sequential()\n",
        "\n",
        "# input layer\n",
        "# Entrada da rede\n",
        "model.add(layers.Dense(batch_size,input_shape=(input_shape,),activation='relu'))\n",
        "\n",
        "# hidden layer\n",
        "# Camada oculta\n",
        "model.add(layers.Dense(8,activation='relu'))\n",
        "\n",
        "# dropout layer\n",
        "# Aplicando regularização\n",
        "model.add(layers.Dropout(0.5))\n",
        "\n",
        "# output layer\n",
        "# Camada de saída\n",
        "model.add(layers.Dense(output_shape, activation='sigmoid'))\n",
        "\n",
        "# Configurar o otimizador Adam com uma learning rate específica\n",
        "# Defina a learning rate desejada\n",
        "learning_rate = 0.000015\n",
        "otimizador = Adam(learning_rate=learning_rate)\n",
        "\n",
        "# Compilar o modelo com o otimizador configurado\n",
        "model.compile(loss='binary_crossentropy', optimizer=otimizador, metrics=['accuracy'])\n",
        "\n",
        "# Monitor para interromper o modelo antecipadamente quando a melhoria da perda de validação for mínima\n",
        "monitor = EarlyStopping(monitor='val_loss', patience=20, verbose=1, mode='auto') #Aqui utilizando early stopping\n",
        "\n",
        "# checkpointer = ModelCheckpoint(filepath=\"weights.best.hdf5\", verbose=0, save_best_only=True) # save best model\n",
        "\n",
        "# summmary\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "nrNTCLKOUiJn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b3b4691-f305-49fb-ab79-42c744a7220c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 32)                288       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 8)                 264       \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 8)                 0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 9         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 561 (2.19 KB)\n",
            "Trainable params: 561 (2.19 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Configurando as épocas de processamento para a convergência do erro da função de custo\n",
        "epoch = 2000\n",
        "\n",
        "hist = model.fit(X_train,y_train,epochs = epoch,batch_size=batch_size,shuffle=True,validation_data=(X_test, y_test),verbose=0,callbacks=[TqdmCallback(verbose=0)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "653bd3858f314fc08036f6547d8f21f9",
            "1b9a238b10894a8e8c30a53b62f55773",
            "c963db9750324569b7249b656e412a9c",
            "7ac38e0009e34e80a0bc02e5ec03af20",
            "f0da4dd595e947e7a1395bb65ea0a537",
            "afbe446ceb2a462e8378ef115c0fdc63",
            "8731c9d6219b498187842d68eb1ac6a5",
            "42366c6617244a5e80b59f65f3cc74e2",
            "92155cb5c5534a6eba480c5404425b80",
            "2f415e1b0246401eb091ca63edf99b55",
            "ea145a61c96845849e6f50498148a77c"
          ]
        },
        "id": "f_T897HZWbMl",
        "outputId": "c90b7ab9-836d-4757-dce8-f394f56f8627"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0epoch [00:00, ?epoch/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "653bd3858f314fc08036f6547d8f21f9"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = '{:.2%}'.format(hist.history['accuracy'][-1])\n",
        "print(f\"O modelo possui uma acurácia de {acc} com {epoch} epochs de processamento\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZwNzhQmFWd_J",
        "outputId": "2276901f-e736-4703-fecc-5bbb801eed12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O modelo possui uma acurácia de 88.71% com 2000 epochs de processamento\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4.2) Validando o resultado da rede neural"
      ],
      "metadata": {
        "id": "0AQ2vsSLWoaO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(model, to_file='model.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "J-bJjjL-_Yyh",
        "outputId": "134de69d-4c27-4be5-e8b6-e31d09d1b3d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_training_data_hist = pd.DataFrame(hist.history)"
      ],
      "metadata": {
        "id": "MHW2f1wV8bZ5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizando os resultados de treino\n",
        "train_acc = hist.history['accuracy']\n",
        "val_acc = hist.history['val_accuracy']\n",
        "\n",
        "train_loss = hist.history['loss']\n",
        "val_loss = hist.history['val_loss']\n",
        "\n",
        "epochs_range = range(epoch)\n",
        "\n",
        "# Plot Acurácia\n",
        "plt.figure(figsize=(20, 8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs_range, train_acc, label='Acurácia de Treinamento')\n",
        "plt.plot(epochs_range, val_acc, label='Acurácia de Validação')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Acurácia de treino e teste')\n",
        "\n",
        "# Plot Erro de treinamento\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs_range, train_loss, label='Erro de treinamento')\n",
        "plt.plot(epochs_range, val_loss, label='Erro de Validação')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Erro de treinamento vs validação')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "H4qerCELWqT2",
        "outputId": "74914274-3529-4e53-e217-ec8caf14d948"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predictions\n",
        "y_pred = model.predict(X_test)\n",
        "y_pred_class = [round(x[0]) for x in y_pred]\n",
        "y_test_class = y_test\n",
        "\n",
        "# classification report\n",
        "class_names = ['Não', 'Sim']\n",
        "\n",
        "print(classification_report(y_test_class, y_pred_class, target_names=class_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wYVFFjCJlInb",
        "outputId": "b9772464-8075-4f3b-dbec-ac80d5d8fc3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10/10 [==============================] - 0s 3ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         Não       0.91      0.96      0.94       226\n",
            "         Sim       0.85      0.67      0.75        67\n",
            "\n",
            "    accuracy                           0.90       293\n",
            "   macro avg       0.88      0.82      0.84       293\n",
            "weighted avg       0.89      0.90      0.89       293\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5) Exportação da rede neural e do scaler"
      ],
      "metadata": {
        "id": "urrK2VxrTwx4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('multilayer-perceptron')"
      ],
      "metadata": {
        "id": "y31N5qeDTu9y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_training_data_hist.to_csv('model_training_data_hist.csv', index=None, sep=';')"
      ],
      "metadata": {
        "id": "pO0Ni_ii8usi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(scaler, 'scaler.pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dsv5HZZUiwJk",
        "outputId": "e2d65f86-dcfb-4b80-e7d0-9810015500c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['scaler.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6) Reimportação e teste do modelo e do scaler"
      ],
      "metadata": {
        "id": "qmX9pnLmimsH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_recarregado = load_model('multilayer-perceptron')\n",
        "scaler_recarregado = joblib.load('scaler.pkl')"
      ],
      "metadata": {
        "id": "067FazCEh4Us"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "teste = df.iloc[0:5]\n",
        "teste.drop(columns=['INDICADO_BOLSA'], axis=1, inplace=True)\n",
        "teste = scaler_recarregado.fit_transform(teste)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OkY3qGH7itAh",
        "outputId": "4fddd256-ceec-4fcd-8355-2c5a5b8533f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-51-6ef362041268>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  teste.drop(columns=['INDICADO_BOLSA'], axis=1, inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = model_recarregado.predict(teste)\n",
        "pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1iZWYCJ80WP7",
        "outputId": "aec5c3f0-5d04-4b2d-a459-ce4b9de08e49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 36ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2.9863423e-07],\n",
              "       [6.6905499e-02],\n",
              "       [4.6237937e-01],\n",
              "       [9.0690525e-03],\n",
              "       [1.8020302e-02]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred.flatten().round()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MwrY22xK0eS6",
        "outputId": "63ed6119-495b-4f87-cf9f-386539593b05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0.], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aluno_bom = pd.DataFrame({\n",
        "  'IAA': [10.0],\n",
        "  'IAN': [10.0],\n",
        "  'IDA': [10.0],\n",
        "  'IEG': [10.0],\n",
        "  'INDE': [10.0],\n",
        "  'IPP': [10.0],\n",
        "  'IPS': [10.0],\n",
        "  'IPV': [10.0]\n",
        "})\n",
        "aluno_bom2 = pd.DataFrame({\n",
        "  'IAA': [8.0],\n",
        "  'IAN': [8.0],\n",
        "  'IDA': [7.0],\n",
        "  'IEG': [9.0],\n",
        "  'INDE': [7.0],\n",
        "  'IPP': [8.0],\n",
        "  'IPS': [8.0],\n",
        "  'IPV': [10.0]\n",
        "})\n",
        "aluno_ruim = pd.DataFrame({\n",
        "  'IAA': [0.0],\n",
        "  'IAN': [0.0],\n",
        "  'IDA': [0.0],\n",
        "  'IEG': [0.0],\n",
        "  'INDE': [0.0],\n",
        "  'IPP': [0.0],\n",
        "  'IPS': [0.0],\n",
        "  'IPV': [0.0]\n",
        "})\n",
        "aluno_ruim2 = pd.DataFrame({\n",
        "  'IAA': [2.0],\n",
        "  'IAN': [2.0],\n",
        "  'IDA': [2.0],\n",
        "  'IEG': [2.0],\n",
        "  'INDE': [0.0],\n",
        "  'IPP': [0.0],\n",
        "  'IPS': [0.0],\n",
        "  'IPV': [0.0]\n",
        "})\n",
        "aluno_ruim3 = pd.DataFrame({\n",
        "  'IAA': [5.0],\n",
        "  'IAN': [8.0],\n",
        "  'IDA': [6.0],\n",
        "  'IEG': [0.0],\n",
        "  'INDE': [2.0],\n",
        "  'IPP': [0.0],\n",
        "  'IPS': [1.0],\n",
        "  'IPV': [0.0]\n",
        "})\n",
        "aluno_ruim4 = pd.DataFrame({\n",
        "  'IAA': [10.0],\n",
        "  'IAN': [8.0],\n",
        "  'IDA': [6.0],\n",
        "  'IEG': [1.0],\n",
        "  'INDE': [2.0],\n",
        "  'IPP': [0.0],\n",
        "  'IPS': [5.0],\n",
        "  'IPV': [0.0]\n",
        "})\n",
        "aluno_mediano1 = pd.DataFrame({\n",
        "  'IAA': [4.0],\n",
        "  'IAN': [3.0],\n",
        "  'IDA': [6.0],\n",
        "  'IEG': [7.0],\n",
        "  'INDE': [6.0],\n",
        "  'IPP': [5.0],\n",
        "  'IPS': [7.0],\n",
        "  'IPV': [8.0]\n",
        "})\n",
        "aluno_mediano2 = pd.DataFrame({\n",
        "  'IAA': [8.0],\n",
        "  'IAN': [8.0],\n",
        "  'IDA': [9.0],\n",
        "  'IEG': [5.0],\n",
        "  'INDE': [4.0],\n",
        "  'IPP': [5.0],\n",
        "  'IPS': [7.0],\n",
        "  'IPV': [7.0]\n",
        "})\n",
        "aluno_mediano3 = pd.DataFrame({\n",
        "  'IAA': [5.0],\n",
        "  'IAN': [5.0],\n",
        "  'IDA': [5.0],\n",
        "  'IEG': [5.0],\n",
        "  'INDE': [5.0],\n",
        "  'IPP': [5.0],\n",
        "  'IPS': [5.0],\n",
        "  'IPV': [5.0]\n",
        "})\n",
        "aluno_mediano4 = pd.DataFrame({\n",
        "  'IAA': [4.0],\n",
        "  'IAN': [4.0],\n",
        "  'IDA': [4.0],\n",
        "  'IEG': [4.0],\n",
        "  'INDE': [3.0],\n",
        "  'IPP': [3.0],\n",
        "  'IPS': [3.0],\n",
        "  'IPV': [3.0]\n",
        "})\n",
        "teste = pd.DataFrame(columns=df.columns.difference(['INDICADO_BOLSA']))\n",
        "teste = pd.concat([teste, aluno_bom, aluno_bom2, aluno_mediano1, aluno_mediano2, aluno_mediano3, aluno_mediano4, aluno_ruim, aluno_ruim2, aluno_ruim3, aluno_ruim4], ignore_index=True)\n",
        "teste"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "PC3XdMYckTvt",
        "outputId": "a15eb281-2833-420c-b217-15b1de0d5a13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    IAA   IAN   IDA   IEG  INDE   IPP   IPS   IPV\n",
              "0  10.0  10.0  10.0  10.0  10.0  10.0  10.0  10.0\n",
              "1   8.0   8.0   7.0   9.0   7.0   8.0   8.0  10.0\n",
              "2   4.0   3.0   6.0   7.0   6.0   5.0   7.0   8.0\n",
              "3   8.0   8.0   9.0   5.0   4.0   5.0   7.0   7.0\n",
              "4   5.0   5.0   5.0   5.0   5.0   5.0   5.0   5.0\n",
              "5   4.0   4.0   4.0   4.0   3.0   3.0   3.0   3.0\n",
              "6   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0\n",
              "7   2.0   2.0   2.0   2.0   0.0   0.0   0.0   0.0\n",
              "8   5.0   8.0   6.0   0.0   2.0   0.0   1.0   0.0\n",
              "9  10.0   8.0   6.0   1.0   2.0   0.0   5.0   0.0"
            ],
            "text/html": [
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              "type": "dataframe",
              "variable_name": "teste",
              "summary": "{\n  \"name\": \"teste\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"IAA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.3399933466334266,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          10.0,\n          8.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IAN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.272783388968954,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          10.0,\n          8.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IDA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9907264074877267,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.0,\n          4.0,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IEG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.591656999213594,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9.0,\n          0.0,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INDE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.1780497164141406,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.0,\n          3.0,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.627058802329452,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8.0,\n          0.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.5023801430836525,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          10.0,\n          8.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IPV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.24394994210713,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          10.0,\n          8.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "teste_scaled = scaler_recarregado.fit_transform(teste)\n",
        "teste_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zskxJ0xUkVri",
        "outputId": "15e11d74-a068-4b1f-bea0-c47577c75bf1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.38862769,  1.41714458,  1.5860416 ,  1.67285672,  2.02324229,\n",
              "         1.85996222,  1.62520902,  1.4157395 ],\n",
              "       [ 0.75743328,  0.77298795,  0.52868053,  1.37937309,  1.0282051 ,\n",
              "         1.27872403,  1.02327975,  1.4157395 ],\n",
              "       [-0.50495552, -0.83740361,  0.17622684,  0.79240582,  0.69652603,\n",
              "         0.40686674,  0.72231512,  0.9189888 ],\n",
              "       [ 0.75743328,  0.77298795,  1.23358791,  0.20543854,  0.03316791,\n",
              "         0.40686674,  0.72231512,  0.67061345],\n",
              "       [-0.18935832, -0.19324699, -0.17622684,  0.20543854,  0.36484697,\n",
              "         0.40686674,  0.12038585,  0.17386275],\n",
              "       [-0.50495552, -0.5153253 , -0.52868053, -0.08804509, -0.29851116,\n",
              "        -0.17437146, -0.48154341, -0.32288796],\n",
              "       [-1.76734433, -1.80363855, -1.93849529, -1.26197963, -1.29354835,\n",
              "        -1.04622875, -1.38443731, -1.06801401],\n",
              "       [-1.13614993, -1.15948193, -1.23358791, -0.67501236, -1.29354835,\n",
              "        -1.04622875, -1.38443731, -1.06801401],\n",
              "       [-0.18935832,  0.77298795,  0.17622684, -1.26197963, -0.63019022,\n",
              "        -1.04622875, -1.08347268, -1.06801401],\n",
              "       [ 1.38862769,  0.77298795,  0.17622684, -0.968496  , -0.63019022,\n",
              "        -1.04622875,  0.12038585, -1.06801401]])"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = model_recarregado.predict(teste_scaled)\n",
        "pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SGUDayMRi-o_",
        "outputId": "b14ae098-073e-4081-c4dd-be8777cd11ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 36ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9.1881245e-01],\n",
              "       [7.3110640e-01],\n",
              "       [1.0495310e-01],\n",
              "       [5.4872859e-01],\n",
              "       [1.4150544e-01],\n",
              "       [1.6584473e-03],\n",
              "       [2.3146779e-07],\n",
              "       [1.0096496e-06],\n",
              "       [1.6665547e-04],\n",
              "       [1.4032723e-04]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred.flatten().round()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5-NO4JbLpL98",
        "outputId": "48a9736c-7444-46ac-b6a7-cc6ca26f9876"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 1., 0., 1., 0., 0., 0., 0., 0., 0.], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    }
  ]
}