14_Feature_Engineering.ipynb

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        "# Feature Engineering\n",
        "- Definição: Feature Engineering é o processo de criar, transformar e selecionar variáveis (ou features) relevantes a partir de dados brutos para melhorar o desempenho de modelos de machine learning.\n",
        "-Ele é fundamental para extrair insights e informações úteis dos dados, aumentando a eficácia da modelagem."
      ],
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      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Montar Drive Google\n"
      ],
      "metadata": {
        "id": "PAYdSHy2aK8Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Mount Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8aVoL77sZoJ_",
        "outputId": "584e16d3-e7bb-4988-de06-ddf0043ab997"
      },
      "execution_count": 28,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dataset : Titanic\n",
        "- Normalizacao dos dados\n",
        "\n",
        "\n",
        "```\n",
        "# Isto está formatado como código\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "y1XNoZ47aTfh"
      }
    },
    {
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/.Dataset/test.csv', sep=',', encoding='UTF-8', header=0)\n",
        "df"
      ],
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        "outputId": "0fe2d1ba-7d81-47d8-ecaa-a27ba94a742d"
      },
      "execution_count": 29,
      "outputs": [
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              "        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",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
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              "        background-color: #434B5C;\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 418,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 120,\n        \"min\": 892,\n        \"max\": 1309,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          1213,\n          1216,\n          1280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 418,\n        \"samples\": [\n          \"Krekorian, Mr. Neshan\",\n          \"Kreuchen, Miss. Emilie\",\n          \"Canavan, Mr. Patrick\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.18120923562442,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          10.0,\n          34.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 363,\n        \"samples\": [\n          \"2673\",\n          \"W./C. 6607\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.90757617997383,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 169,\n        \"samples\": [\n          41.5792,\n          57.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 76,\n        \"samples\": [\n          \"A21\",\n          \"E45\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Q\",\n          \"S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Contando valores nulos\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "8C7NFuPcgVX1",
        "outputId": "afe2e6f2-a8fa-48f6-946a-2cd24a0726cb"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId     0\n",
              "Pclass          0\n",
              "Name            0\n",
              "Sex             0\n",
              "Age            86\n",
              "SibSp           0\n",
              "Parch           0\n",
              "Ticket          0\n",
              "Fare            1\n",
              "Cabin           0\n",
              "Embarked        0\n",
              "dtype: int64"
            ],
            "text/html": [
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <th>Name</th>\n",
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              "      <th>Age</th>\n",
              "      <td>86</td>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Describe\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "5YamdUuugXbj",
        "outputId": "39613777-107e-43a4-d569-28b11a90c3d3"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PassengerId      Pclass        Name         Sex         Age  \\\n",
              "count   418.000000  418.000000  418.000000  418.000000  332.000000   \n",
              "mean   1100.500000    2.265550  208.500000    0.636364   30.272590   \n",
              "std     120.810458    0.841838  120.810458    0.481622   14.181209   \n",
              "min     892.000000    1.000000    0.000000    0.000000    0.170000   \n",
              "25%     996.250000    1.000000  104.250000    0.000000   21.000000   \n",
              "50%    1100.500000    3.000000  208.500000    1.000000   27.000000   \n",
              "75%    1204.750000    3.000000  312.750000    1.000000   39.000000   \n",
              "max    1309.000000    3.000000  417.000000    1.000000   76.000000   \n",
              "\n",
              "            SibSp       Parch      Ticket        Fare       Cabin    Embarked  \n",
              "count  418.000000  418.000000  418.000000  417.000000  418.000000  418.000000  \n",
              "mean     0.447368    0.392344  180.944976   35.627188   67.437799    1.401914  \n",
              "std      0.896760    0.981429  107.533763   55.907576   19.091405    0.854496  \n",
              "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n",
              "25%      0.000000    0.000000   85.250000    7.895800   76.000000    1.000000  \n",
              "50%      0.000000    0.000000  181.000000   14.454200   76.000000    2.000000  \n",
              "75%      1.000000    0.000000  279.750000   31.500000   76.000000    2.000000  \n",
              "max      8.000000    9.000000  362.000000  512.329200   76.000000    2.000000  "
            ],
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              "\n",
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              "      <th>PassengerId</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>332.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>417.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1100.500000</td>\n",
              "      <td>2.265550</td>\n",
              "      <td>208.500000</td>\n",
              "      <td>0.636364</td>\n",
              "      <td>30.272590</td>\n",
              "      <td>0.447368</td>\n",
              "      <td>0.392344</td>\n",
              "      <td>180.944976</td>\n",
              "      <td>35.627188</td>\n",
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              "      <td>1.401914</td>\n",
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              "      <th>std</th>\n",
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              "      <td>0.841838</td>\n",
              "      <td>120.810458</td>\n",
              "      <td>0.481622</td>\n",
              "      <td>14.181209</td>\n",
              "      <td>0.896760</td>\n",
              "      <td>0.981429</td>\n",
              "      <td>107.533763</td>\n",
              "      <td>55.907576</td>\n",
              "      <td>19.091405</td>\n",
              "      <td>0.854496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>892.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.170000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>996.250000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>104.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>21.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>85.250000</td>\n",
              "      <td>7.895800</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1100.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>208.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>181.000000</td>\n",
              "      <td>14.454200</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1204.750000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>312.750000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>279.750000</td>\n",
              "      <td>31.500000</td>\n",
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              "      <td>2.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
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              "      <td>3.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>362.000000</td>\n",
              "      <td>512.329200</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>2.000000</td>\n",
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              "        <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",
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              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --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",
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              "    20% {\n",
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              "    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",
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              "    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-4d537733-6920-4e8b-b22e-6cff9452aba1 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",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 412.1232851470217,\n        \"min\": 120.81045760473994,\n        \"max\": 1309.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          418.0,\n          1100.5,\n          1204.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 147.0758997861715,\n        \"min\": 0.8418375519640519,\n        \"max\": 418.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.2655502392344498,\n          3.0,\n          0.8418375519640519\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 150.43062349599452,\n        \"min\": 0.0,\n        \"max\": 418.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          418.0,\n          208.5,\n          312.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 147.57791534303846,\n        \"min\": 0.0,\n        \"max\": 418.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.6363636363636364,\n          1.0,\n          0.48162214093223055\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 109.15868834351015,\n        \"min\": 0.17,\n        \"max\": 332.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          30.272590361445783,\n          27.0,\n          332.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 147.2874584027116,\n        \"min\": 0.0,\n        \"max\": 418.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          418.0,\n          0.4473684210526316,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 147.29373273558994,\n        \"min\": 0.0,\n        \"max\": 418.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3923444976076555,\n          9.0,\n          0.9814288785371684\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 142.70024512575088,\n        \"min\": 0.0,\n        \"max\": 418.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          180.94497607655504,\n          181.0,\n          418.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 206.20808392426468,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          35.627188489208635,\n          14.4542,\n          417.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 131.48894810191825,\n        \"min\": 0.0,\n        \"max\": 418.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          67.4377990430622,\n          76.0,\n          19.091404696188977\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 147.31947330570162,\n        \"min\": 0.0,\n        \"max\": 418.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          418.0,\n          1.4019138755980862,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(df)"
      ],
      "metadata": {
        "id": "h_Ye5QZ3aurp"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Cardinalidade\n",
        "df.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "MdvYd0LFgbhI",
        "outputId": "ef4a33b3-4654-4940-a503-a92704f88dbb"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    418\n",
              "Pclass           3\n",
              "Name           418\n",
              "Sex              2\n",
              "Age             79\n",
              "SibSp            7\n",
              "Parch            8\n",
              "Ticket         363\n",
              "Fare           169\n",
              "Cabin           77\n",
              "Embarked         3\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <td>418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <td>418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket</th>\n",
              "      <td>363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cabin</th>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vTy5xNCYazw3",
        "outputId": "c3b6d2eb-a952-4bb8-fc73-cb51e1069284"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PassengerId      int64\n",
            "Pclass           int64\n",
            "Name            object\n",
            "Sex             object\n",
            "Age            float64\n",
            "SibSp            int64\n",
            "Parch            int64\n",
            "Ticket          object\n",
            "Fare           float64\n",
            "Cabin           object\n",
            "Embarked        object\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizando a tabela\n",
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "uALpFx90erl9",
        "outputId": "57e40a92-6697-49b0-c7ae-59add3b86543"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Pclass                              Name     Sex   Age  SibSp  \\\n",
              "0          892       3                  Kelly, Mr. James    male  34.5      0   \n",
              "1          893       3  Wilkes, Mrs. James (Ellen Needs)  female  47.0      1   \n",
              "\n",
              "   Parch  Ticket    Fare Cabin Embarked  \n",
              "0      0  330911  7.8292   NaN        Q  \n",
              "1      0  363272  7.0000   NaN        S  "
            ],
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              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
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              "\n",
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              "\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",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "    [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",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-a11588fb-c2c4-4936-8e65-c09a41679f55 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-a11588fb-c2c4-4936-8e65-c09a41679f55');\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",
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              "  }\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-58aa3227-871a-42bc-85dd-0c0f46e066c3 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\": 418,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 120,\n        \"min\": 892,\n        \"max\": 1309,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          1213,\n          1216,\n          1280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 418,\n        \"samples\": [\n          \"Krekorian, Mr. Neshan\",\n          \"Kreuchen, Miss. Emilie\",\n          \"Canavan, Mr. Patrick\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.18120923562442,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          10.0,\n          34.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 363,\n        \"samples\": [\n          \"2673\",\n          \"W./C. 6607\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.90757617997383,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 169,\n        \"samples\": [\n          41.5792,\n          57.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 76,\n        \"samples\": [\n          \"A21\",\n          \"E45\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Q\",\n          \"S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Valores vazios\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "X_3sD_5XgjSF",
        "outputId": "416a6c75-6a5c-4663-e690-78976b4ac8bc"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId     0\n",
              "Pclass          0\n",
              "Name            0\n",
              "Sex             0\n",
              "Age            86\n",
              "SibSp           0\n",
              "Parch           0\n",
              "Ticket          0\n",
              "Fare            1\n",
              "Cabin           0\n",
              "Embarked        0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cabin</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificando os registros com embarque vazio\n",
        "df[df.Embarked.isnull()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "ETnL5vl4gm3D",
        "outputId": "ecc51cec-0370-4e92-cb9e-07b9e588c6d3"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [PassengerId, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\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-950c276d-e7ae-4e88-8948-4876fa62f4b5')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  <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-950c276d-e7ae-4e88-8948-4876fa62f4b5 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-950c276d-e7ae-4e88-8948-4876fa62f4b5');\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>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[df\",\n  \"rows\": 0,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Atualizando o valor para as 2 linhas\n",
        "df.loc[df.Embarked.isnull(),'Embarked'] = 'S'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BwFfjTvKgwbA",
        "outputId": "97f8cb90-646e-4caa-cce6-7a812b36cd21"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-46-d744cafc6db3>:2: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value 'S' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
            "  df.loc[df.Embarked.isnull(),'Embarked'] = 'S'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "jPzdQx2Eg1dn",
        "outputId": "19bcd3be-74ac-4a47-8c5e-22dc0f672f37"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Pclass  Name  Sex   Age  SibSp  Parch  Ticket     Fare  Cabin  \\\n",
              "0          892       3   206    1  34.5      0      0     152   7.8292     76   \n",
              "1          893       3   403    0  47.0      1      0     221   7.0000     76   \n",
              "2          894       2   269    1  62.0      0      0      73   9.6875     76   \n",
              "3          895       3   408    1  27.0      0      0     147   8.6625     76   \n",
              "4          896       3   178    0  22.0      1      1     138  12.2875     76   \n",
              "5          897       3   366    1  14.0      0      0     261   9.2250     76   \n",
              "\n",
              "  Embarked  \n",
              "0        1  \n",
              "1        2  \n",
              "2        1  \n",
              "3        2  \n",
              "4        2  \n",
              "5        2  "
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 418,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 120,\n        \"min\": 892,\n        \"max\": 1309,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          1213,\n          1216,\n          1280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 120,\n        \"min\": 0,\n        \"max\": 417,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          218,\n          219,\n          58\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\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      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.18120923562442,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          10.0,\n          34.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 107,\n        \"min\": 0,\n        \"max\": 362,\n        \"num_unique_values\": 363,\n        \"samples\": [\n          108,\n          360\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.90757617997383,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 169,\n        \"samples\": [\n          41.5792,\n          57.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 76,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          10,\n          74\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificando valores nulos na idade\n",
        "df[df.Age.isnull()].head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "cFzW86xRg8zR",
        "outputId": "47b8c9e0-d47c-4501-b6c7-61a2e776798b"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    PassengerId  Pclass  Name  Sex  Age  SibSp  Parch  Ticket     Fare  Cabin  \\\n",
              "10          902       3   187    1  NaN      0      0     195   7.8958     76   \n",
              "22          914       1   138    0  NaN      0      0     314  31.6833     76   \n",
              "29          921       3   339    1  NaN      2      0     104  21.6792     76   \n",
              "\n",
              "   Embarked  \n",
              "10        2  \n",
              "22        2  \n",
              "29        0  "
            ],
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              "\n",
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              "    .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-644efd24-7704-443c-8de1-edd549736f62 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-644efd24-7704-443c-8de1-edd549736f62');\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-ddbacccd-79d3-44d4-82b0-765f0e3f58d2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ddbacccd-79d3-44d4-82b0-765f0e3f58d2')\"\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-ddbacccd-79d3-44d4-82b0-765f0e3f58d2 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",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Podemos verificar o describe para a idade\n",
        "df.Age.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "Gs65j8AchCfc",
        "outputId": "e164809f-6f6d-4bee-acfe-049465b0bae4"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    332.000000\n",
              "mean      30.272590\n",
              "std       14.181209\n",
              "min        0.170000\n",
              "25%       21.000000\n",
              "50%       27.000000\n",
              "75%       39.000000\n",
              "max       76.000000\n",
              "Name: Age, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>332.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>30.272590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>14.181209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.170000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>21.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>27.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>39.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>76.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# E podemos visualizar isso graficamente no boxplot\n",
        "df.Age.plot.box();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "qjeLIWB7hHRS",
        "outputId": "e6495185-2f82-4d56-b25f-446e039725a3"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Age.plot.hist();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "I6HZTlk7hMMC",
        "outputId": "1e089427-3459-482a-e2c9-731afb534d5f"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Uma opção é usar a mediana, ou a média das idades\n",
        "df.Age.median()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fR719uYJhQr0",
        "outputId": "7660b6fc-2d5e-42b3-9c3b-981426dd17eb"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27.0"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ou podemos, por exemplo, tirar a média de idade por alguns grupos como o Pclass\n",
        "df.groupby('Pclass')['Age'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "id": "b8hiSRZihSeT",
        "outputId": "5599aeb0-44a6-4385-b288-3e7e6cdb676d"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass\n",
              "1    40.918367\n",
              "2    28.777500\n",
              "3    24.027945\n",
              "Name: Age, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>40.918367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28.777500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>24.027945</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Iniciando A Classificacao dos dados"
      ],
      "metadata": {
        "id": "2B7kgihwhWrr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importando o DecisionTreeClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "4TR27skVet84"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "metadata": {
        "id": "MGLMVGUNbTTq"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encoder = LabelEncoder()"
      ],
      "metadata": {
        "id": "Z6jNo9XibVsC"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for column in df.columns:\n",
        "    if df[column].dtype == 'object':  # Check if column is of type object (string)\n",
        "        df[column] = encoder.fit_transform(df[column])  # Encode the column"
      ],
      "metadata": {
        "id": "8Now5s--a3Ok"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()\n",
        "df_normalized = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)\n",
        "print(df_normalized)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oCTBrGVUa-H9",
        "outputId": "682e02a1-df99-45a7-ed7b-737a6810c949"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     PassengerId  Pclass      Name  Sex       Age  SibSp     Parch    Ticket  \\\n",
            "0       0.000000     1.0  0.494005  1.0  0.452723  0.000  0.000000  0.419890   \n",
            "1       0.002398     1.0  0.966427  0.0  0.617566  0.125  0.000000  0.610497   \n",
            "2       0.004796     0.5  0.645084  1.0  0.815377  0.000  0.000000  0.201657   \n",
            "3       0.007194     1.0  0.978417  1.0  0.353818  0.000  0.000000  0.406077   \n",
            "4       0.009592     1.0  0.426859  0.0  0.287881  0.125  0.111111  0.381215   \n",
            "..           ...     ...       ...  ...       ...    ...       ...       ...   \n",
            "413     0.990408     1.0  0.846523  1.0       NaN  0.000  0.000000  0.737569   \n",
            "414     0.992806     0.0  0.678657  0.0  0.512066  0.000  0.000000  0.895028   \n",
            "415     0.995204     1.0  0.796163  1.0  0.505473  0.000  0.000000  0.955801   \n",
            "416     0.997602     1.0  0.920863  1.0       NaN  0.000  0.000000  0.607735   \n",
            "417     1.000000     1.0  0.724221  1.0       NaN  0.125  0.111111  0.290055   \n",
            "\n",
            "         Fare     Cabin  Embarked  \n",
            "0    0.015282  1.000000       0.5  \n",
            "1    0.013663  1.000000       1.0  \n",
            "2    0.018909  1.000000       0.5  \n",
            "3    0.016908  1.000000       1.0  \n",
            "4    0.023984  1.000000       1.0  \n",
            "..        ...       ...       ...  \n",
            "413  0.015713  1.000000       1.0  \n",
            "414  0.212559  0.289474       0.0  \n",
            "415  0.014151  1.000000       1.0  \n",
            "416  0.015713  1.000000       1.0  \n",
            "417  0.043640  1.000000       0.0  \n",
            "\n",
            "[418 rows x 11 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_normalized)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "27ekE0YVbk_k",
        "outputId": "260b0471-54e9-42e4-a4cd-a66b2a59bf6f"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     PassengerId  Pclass      Name  Sex       Age  SibSp     Parch    Ticket  \\\n",
            "0       0.000000     1.0  0.494005  1.0  0.452723  0.000  0.000000  0.419890   \n",
            "1       0.002398     1.0  0.966427  0.0  0.617566  0.125  0.000000  0.610497   \n",
            "2       0.004796     0.5  0.645084  1.0  0.815377  0.000  0.000000  0.201657   \n",
            "3       0.007194     1.0  0.978417  1.0  0.353818  0.000  0.000000  0.406077   \n",
            "4       0.009592     1.0  0.426859  0.0  0.287881  0.125  0.111111  0.381215   \n",
            "..           ...     ...       ...  ...       ...    ...       ...       ...   \n",
            "413     0.990408     1.0  0.846523  1.0       NaN  0.000  0.000000  0.737569   \n",
            "414     0.992806     0.0  0.678657  0.0  0.512066  0.000  0.000000  0.895028   \n",
            "415     0.995204     1.0  0.796163  1.0  0.505473  0.000  0.000000  0.955801   \n",
            "416     0.997602     1.0  0.920863  1.0       NaN  0.000  0.000000  0.607735   \n",
            "417     1.000000     1.0  0.724221  1.0       NaN  0.125  0.111111  0.290055   \n",
            "\n",
            "         Fare     Cabin  Embarked  \n",
            "0    0.015282  1.000000       0.5  \n",
            "1    0.013663  1.000000       1.0  \n",
            "2    0.018909  1.000000       0.5  \n",
            "3    0.016908  1.000000       1.0  \n",
            "4    0.023984  1.000000       1.0  \n",
            "..        ...       ...       ...  \n",
            "413  0.015713  1.000000       1.0  \n",
            "414  0.212559  0.289474       0.0  \n",
            "415  0.014151  1.000000       1.0  \n",
            "416  0.015713  1.000000       1.0  \n",
            "417  0.043640  1.000000       0.0  \n",
            "\n",
            "[418 rows x 11 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Verificando o tipo de dados"
      ],
      "metadata": {
        "id": "mzMbBFJxc1Wj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_normalized\n",
        "    .dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8tQ6Phr5cfBM",
        "outputId": "93b95ba9-a5d1-46a5-ff78-a5c34bba44ab"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PassengerId    float64\n",
            "Pclass         float64\n",
            "Name           float64\n",
            "Sex            float64\n",
            "Age            float64\n",
            "SibSp          float64\n",
            "Parch          float64\n",
            "Ticket         float64\n",
            "Fare           float64\n",
            "Cabin          float64\n",
            "Embarked       float64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hshwtmV9czYK"
      },
      "execution_count": 39,
      "outputs": []
    }
  ]
}