Titanic_CRISP_DS.ipynb

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        "# Business Problem:\n",
        "O naufrágio do Titanic aconteceu no dia 15 de abril de 1912, nas águas do Oceano Atlântico, após a colisão em um *iceberg* da embarcação RMS Titanic que saíra de Southampton em direção à Nova York no dia 10. Ao todo, 1502 das 2224 pessoas, entre passageiros e tripulação, vieram a falecer. \n",
        "\n",
        "\n",
        "# Business Understanding:\n",
        "O objetivo da construção do RMS Titanic era fazer com que este navio fosse a maior referência de sua época, especialmente no aspecto do luxo, segurança e nível de engenharia aportado. Foram 4 anos de projeto para idealização, com a promessa de ser \"inafundável\". Porém, ocorreram relatos de falhas técnicas pela tripulação, além do número de botes salva-vidas ser menor que o número de pessoas, mostrando que detalhes muito importantes sob a questão da segurança passaram despercebidos pela *White Star Line*, empresa operadora do navio.\n",
        "\n",
        "No que tange às pessoas, é importante trazer à tona o levantamento de seus dados e verificarmos se há alguma relação que seja possível inferir, e que mostre se há um padrão no perfil dos sobreviventes e não-sobreviventes. A seguir serão apresentadas algumas hipóteses, cujas confirmações ou rejeições serão determinadas com o auxílio das ferramentas de Ciência de Dados:\n",
        "\n",
        "1) Pessoas idosas tiveram menor chance de sobrevivência, dada a ocorrência do naufrágio?\n",
        "\n",
        "2) A porcentagem de homens sobreviventes é inferior à de mulheres sobreviventes? (Considerando a porcentagem em relação ao número total de homens e ao total de mulheres)\n",
        "\n",
        "3) O fato de uma pessoa ter parentes/pessoas próximas foi importante para a sua sobrevivência, dado que espera-se um auxílio maior entre as pessoas pela questão sentimental?\n",
        "\n",
        "4) Pessoas que estavam numa classe menos favorecida dentro da embarcação tiveram menor porcentagem de sobrevivência em relação à quem estava numa classe mais favorecida?\n",
        "\n",
        "Além de responder estas perguntas, o objetivo deste trabalho é obter um modelo que consiga prever se um passageiro sobreviveu ou não à tragédia, cujos dados nunca foram \"vistos\" por este modelo.\n",
        "\n",
        "# Data Extraction:\n",
        "A base de dados da competição \"Titanic - Machine Learning from Disaster\" do Kaggle contém os dados de passageiros à bordo do RMS Titanic. Nesta base, há a separação dos dados de treino e de teste, contempladas em dois diferentes arquivos xlsx. A próxima linha de código irá conter o carregamento do pacote \"*pandas*\" para realização da leitura dos dados e nas duas linhas abaixo serão visualizadas as 5 primeiras linhas dos DataFrames com o comando \"*head*\".\n",
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        "import pandas as pd\n",
        "dados_treino = pd.read_excel('/content/Train.xlsx')\n",
        "dados_teste = pd.read_excel('/content/Test.xlsx')"
      ]
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        "dados_treino\n",
        "inicio_dados_treino = dados_treino.copy()"
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        "dados_teste.head()"
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              "   Kaggle_Pass_Id  pclass  ...      home.dest orig_seq\n",
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        "# Data Cleaning\n",
        "Vamos agora entender quais são as colunas do dataset:\n",
        "\n",
        "- **Kaggle_Pass_Id:** número da linha no *dataset*.\n",
        "- **pclass:** classe da passagem (1, 2 ou 3).\n",
        "- **survived:** indica se o passageiro sobreviveu (índice 1) ou não (índice 0).\n",
        "- **name:** nome do passageiro.\n",
        "- **sex:** sexo do passageiro.\n",
        "- **age:** idade do passageiro.\n",
        "- **sibsp:** número de cônjuges e/ou irmãos a bordo.\n",
        "- **parch:** número de pais e filhos a bordo.\n",
        "- **ticket:** número/código da passagem.\n",
        "- **fare:** preço da passagem.\n",
        "- **cabin:** número da cabine do passageiro.\n",
        "- **embarked:** porto de embarque: (C = Cherbourg; Q = Queenstown; S = Southampton).\n",
        "- **boat:** identificação do bote salva-vidas (em caso de sobrevivência).\n",
        "- **body:** número do corpo (pessoa não-sobrevivente, corpo recuperado).\n",
        "- **home.dest:** local de moradia do passageiro.\n",
        "- **orig_seq:** outro código de identificação do passageiro.\n",
        "\n",
        "Sabendo-se que o objetivo é avaliar as hipóteses e obter um modelo que consiga prever a sobrevivência ou não de passageiros, precisamos avaliar quais *features* são as mais relevantes para auxiliar o modelo no aprendizado, e quais devem ser descartadas pois auxiliam em menor peso e ainda aumentam o gasto computacional.\n",
        "\n",
        "- A *feature* 'orig_seq' será descartada pois é apenas mais uma identificação do passageiro no *dataset*;\n",
        "- *Features* com valores faltantes acima de 40% serão descartadas;\n",
        "\n",
        "Para aplicação conjunta destas mudanças nos dois conjuntos de dados, será realizada uma concatenação.\n",
        "\n",
        "A variável alvo 'survived' será guardada em um objeto com uma cópia antes das transformações."
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        "dados_treino_id = dados_treino.shape[0]\n",
        "dados_teste_id = dados_teste.shape[0]\n",
        "alvo = dados_treino['survived'].copy()\n",
        "dados_treino.drop(['survived'], axis = 1, inplace = True)\n",
        "concatenado = pd.concat(objs = [dados_treino, dados_teste], axis = 0).reset_index(drop = True)"
      ],
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        "concatenado.head()"
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Lower Clapton, Middlesex or Erdington, Birmingham</td>\n",
              "      <td>619</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fef3d780-b92d-4296-9744-814b49d7ac16')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-fef3d780-b92d-4296-9744-814b49d7ac16 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-fef3d780-b92d-4296-9744-814b49d7ac16');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Kaggle_Pass_Id  ...  orig_seq\n",
              "0               1  ...       690\n",
              "1               2  ...        86\n",
              "2               3  ...       861\n",
              "3               4  ...       128\n",
              "4               5  ...       619\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ao final deste trabalho foram gerados modelos que foram submetidos à competição \"Titanic - Machine Learning from Disaster\", e uma exigência é que o nome da coluna de identificação do ID seja \"PassengerId\". Portanto, ocorreu a renomeação da coluna \"Kaggle_Pass_Id\"."
      ],
      "metadata": {
        "id": "fXesXKSPVhx0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "concatenado.rename(columns={'Kaggle_Pass_Id': 'PassengerId'}, inplace = True)\n",
        "concatenado.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "7040Flwx38ou",
        "outputId": "84e2aa4f-b3ce-4202-ef7c-8bc105474cca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "      <th>1</th>\n",
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              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>Lower Clapton, Middlesex or Erdington, Birmingham</td>\n",
              "      <td>619</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5b29ffe9-7ba2-4a00-a3f4-365a8e00de3e')\"\n",
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              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5b29ffe9-7ba2-4a00-a3f4-365a8e00de3e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   PassengerId  ...  orig_seq\n",
              "0            1  ...       690\n",
              "1            2  ...        86\n",
              "2            3  ...       861\n",
              "3            4  ...       128\n",
              "4            5  ...       619\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verificação de valores faltantes:"
      ],
      "metadata": {
        "id": "GUwrnTv9YA--"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(concatenado.isna().mean())*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P_vAkeUrm_Z_",
        "outputId": "e89876f2-a17f-4858-b8fe-4c19ef4eb62a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId     0.000000\n",
              "pclass          0.000000\n",
              "name            0.000000\n",
              "sex             0.000000\n",
              "age            20.091673\n",
              "sibsp           0.000000\n",
              "parch           0.000000\n",
              "ticket          0.000000\n",
              "fare            0.076394\n",
              "cabin          77.463713\n",
              "embarked        0.152788\n",
              "boat           62.872422\n",
              "body           90.756303\n",
              "home.dest      43.086325\n",
              "orig_seq        0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Conforme dito acima, todas as *features* com 40% de valores faltantes serão descartadas, além da *feature* 'orig_seq'."
      ],
      "metadata": {
        "id": "S-6NjjWEWVPN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "concatenado.drop(['cabin', 'boat', 'body', 'home.dest', 'orig_seq'], axis = 1, inplace = True)\n",
        "concatenado.head()"
      ],
      "metadata": {
        "id": "49gVdarvm_X6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "8219835c-1be3-4b02-bbc8-c3cb3148ed2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d0ce3d1-6bd7-4459-a8b5-eb2633e589e8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      display: none;\n",
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              "\n",
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              "\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-0d0ce3d1-6bd7-4459-a8b5-eb2633e589e8 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-0d0ce3d1-6bd7-4459-a8b5-eb2633e589e8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   PassengerId  pclass  ...     fare embarked\n",
              "0            1       3  ...   7.2500        S\n",
              "1            2       1  ...  71.2833        C\n",
              "2            3       3  ...   7.9250        S\n",
              "3            4       1  ...  53.1000        S\n",
              "4            5       3  ...   8.0500        S\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "concatenado"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "6TjPnoFJvOkR",
        "outputId": "50fc1788-e6a5-4d4f-8dcf-972152f62f1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
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              "    <tr>\n",
              "      <th>1307</th>\n",
              "      <td>1308</td>\n",
              "      <td>3</td>\n",
              "      <td>Ware, Mr. Frederick</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0</td>\n",
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              "      <th>1308</th>\n",
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              "      <td>male</td>\n",
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              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1309 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6c1eec7f-dcc8-4e42-a278-0527168dd853')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6c1eec7f-dcc8-4e42-a278-0527168dd853 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-6c1eec7f-dcc8-4e42-a278-0527168dd853');\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      PassengerId  pclass  ...      fare embarked\n",
              "0               1       3  ...    7.2500        S\n",
              "1               2       1  ...   71.2833        C\n",
              "2               3       3  ...    7.9250        S\n",
              "3               4       1  ...   53.1000        S\n",
              "4               5       3  ...    8.0500        S\n",
              "...           ...     ...  ...       ...      ...\n",
              "1304         1305       3  ...    8.0500        S\n",
              "1305         1306       1  ...  108.9000        C\n",
              "1306         1307       3  ...    7.2500        S\n",
              "1307         1308       3  ...    8.0500        S\n",
              "1308         1309       3  ...   22.3583        C\n",
              "\n",
              "[1309 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para a *feature* 'embarked', foi aplicada a técnica \"*one-hot encoding*\": "
      ],
      "metadata": {
        "id": "UgKKgsgdWonv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "encoding_embarked = pd.get_dummies(concatenado['embarked'])"
      ],
      "metadata": {
        "id": "1wwNGc0Gt11W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encoding_embarked"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "U0kxolCgueTZ",
        "outputId": "b07f5249-5fb0-4400-efe7-5d7aff7f1f7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<p>1309 rows × 3 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4a6f263e-b111-4fe0-8082-3d937e2cbaf6')\"\n",
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    {
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        "encoding_embarked.rename(columns = {'C' : 'embarked_C', 'Q' : 'embarked_Q', 'S' : 'embarked_S'}, inplace = True)"
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      "metadata": {
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    {
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        "concatenado = pd.concat(objs = [concatenado, encoding_embarked], axis = 1).reset_index(drop = True)"
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      "metadata": {
        "id": "YYwpQnE2v_24"
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              "1307         1308       3  ...          0          1\n",
              "1308         1309       3  ...          0          0\n",
              "\n",
              "[1309 rows x 13 columns]"
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      "execution_count": null,
      "outputs": []
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              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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              "      <td>Allen, Mr. William Henry</td>\n",
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              "    <tr>\n",
              "      <th>1308</th>\n",
              "      <td>1309</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "<p>1309 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-02cc47d0-b53c-40da-870f-dff980f9d388')\"\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "          const element = document.querySelector('#df-02cc47d0-b53c-40da-870f-dff980f9d388');\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",
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              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      PassengerId  pclass  ... embarked_Q embarked_S\n",
              "0               1       3  ...          0          1\n",
              "1               2       1  ...          0          0\n",
              "2               3       3  ...          0          1\n",
              "3               4       1  ...          0          1\n",
              "4               5       3  ...          0          1\n",
              "...           ...     ...  ...        ...        ...\n",
              "1304         1305       3  ...          0          1\n",
              "1305         1306       1  ...          0          0\n",
              "1306         1307       3  ...          0          1\n",
              "1307         1308       3  ...          0          1\n",
              "1308         1309       3  ...          0          0\n",
              "\n",
              "[1309 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tratamento dos dados faltantes e substituição de variáveis nominais em numéricas. Para as *features* 'age' e 'fare', a substituição foi realizada pela mediana."
      ],
      "metadata": {
        "id": "M3q_Fnf_KK_c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "concatenado['sex'] = concatenado['sex'].map({'male': 0, 'female': 1})\n",
        "\n",
        "age_median = concatenado['age'].median()\n",
        "concatenado['age'].fillna(age_median, inplace = True)\n",
        "\n",
        "fare_median = concatenado['fare'].median()\n",
        "concatenado['fare'].fillna(fare_median, inplace = True)\n",
        "\n",
        "concatenado"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "0gIccsOBMJAO",
        "outputId": "d626f333-4aa7-4e9a-f1fd-d67ff83db714"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "          const element = document.querySelector('#df-db27c273-5194-4f2f-afdb-f047fa00f61a');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      PassengerId  pclass  ... embarked_Q  embarked_S\n",
              "0               1       3  ...          0           1\n",
              "1               2       1  ...          0           0\n",
              "2               3       3  ...          0           1\n",
              "3               4       1  ...          0           1\n",
              "4               5       3  ...          0           1\n",
              "...           ...     ...  ...        ...         ...\n",
              "1304         1305       3  ...          0           1\n",
              "1305         1306       1  ...          0           0\n",
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              "1307         1308       3  ...          0           1\n",
              "1308         1309       3  ...          0           0\n",
              "\n",
              "[1309 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dados faltantes tratados, *features* totalmente preenchidas:"
      ],
      "metadata": {
        "id": "T8Z247FpXyku"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(concatenado.isna().mean())*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6hUKPjkZcKTk",
        "outputId": "e226c4b7-33dd-47c8-f833-a15a1b6d0c46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    0.0\n",
              "pclass         0.0\n",
              "name           0.0\n",
              "sex            0.0\n",
              "age            0.0\n",
              "sibsp          0.0\n",
              "parch          0.0\n",
              "ticket         0.0\n",
              "fare           0.0\n",
              "embarked_C     0.0\n",
              "embarked_Q     0.0\n",
              "embarked_S     0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Após realização das alterações no *dataframe* concatenado, faz-se necessária a separação entre dados de treino e teste novamente: "
      ],
      "metadata": {
        "id": "ulmbvcXsYa1C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados_treino = concatenado.iloc[:dados_treino_id]\n",
        "dados_teste = concatenado.iloc[dados_treino_id:]"
      ],
      "metadata": {
        "id": "E52y0lDTK2Ez"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reinserção da variável alvo ao *dataframe* de treino:"
      ],
      "metadata": {
        "id": "I9UGKWH0YqvL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados_treino['survived'] = alvo\n",
        "dados_treino"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "Z8lr2ALMUhmg",
        "outputId": "60953ee1-0226-48b7-be7f-1c54fe68bb3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "execute_result",
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              "          const element = document.querySelector('#df-cf23951d-1301-4c8a-8737-d47ca9c5ac08');\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",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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            ],
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              "     PassengerId  pclass  ... embarked_S  survived\n",
              "0              1       3  ...          1         0\n",
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              "2              3       3  ...          1         1\n",
              "3              4       1  ...          1         1\n",
              "4              5       3  ...          1         0\n",
              "..           ...     ...  ...        ...       ...\n",
              "886          887       2  ...          1         0\n",
              "887          888       1  ...          1         1\n",
              "888          889       3  ...          1         0\n",
              "889          890       1  ...          0         1\n",
              "890          891       3  ...          0         0\n",
              "\n",
              "[891 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_teste"
      ],
      "metadata": {
        "id": "Xr5KVEV9UkvJ",
        "outputId": "9589afd4-99a6-4db2-d835-3e5121d87cb8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
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      "execution_count": null,
      "outputs": [
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>893</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>894</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>895</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1304</th>\n",
              "      <td>1305</td>\n",
              "      <td>3</td>\n",
              "      <td>Spector, Mr. Woolf</td>\n",
              "      <td>0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A.5. 3236</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1305</th>\n",
              "      <td>1306</td>\n",
              "      <td>1</td>\n",
              "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
              "      <td>1</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17758</td>\n",
              "      <td>108.9000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1306</th>\n",
              "      <td>1307</td>\n",
              "      <td>3</td>\n",
              "      <td>Saether, Mr. Simon Sivertsen</td>\n",
              "      <td>0</td>\n",
              "      <td>38.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>SOTON/O.Q. 3101262</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1307</th>\n",
              "      <td>1308</td>\n",
              "      <td>3</td>\n",
              "      <td>Ware, Mr. Frederick</td>\n",
              "      <td>0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>359309</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1308</th>\n",
              "      <td>1309</td>\n",
              "      <td>3</td>\n",
              "      <td>Peter, Master. Michael J</td>\n",
              "      <td>0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2668</td>\n",
              "      <td>22.3583</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>418 rows × 12 columns</p>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
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              "  "
            ],
            "text/plain": [
              "      PassengerId  pclass  ... embarked_Q  embarked_S\n",
              "891           892       3  ...          1           0\n",
              "892           893       3  ...          0           1\n",
              "893           894       2  ...          1           0\n",
              "894           895       3  ...          0           1\n",
              "895           896       3  ...          0           1\n",
              "...           ...     ...  ...        ...         ...\n",
              "1304         1305       3  ...          0           1\n",
              "1305         1306       1  ...          0           0\n",
              "1306         1307       3  ...          0           1\n",
              "1307         1308       3  ...          0           1\n",
              "1308         1309       3  ...          0           0\n",
              "\n",
              "[418 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EDA - Análise Exploratória de Dados\n",
        "\n",
        "Nesta etapa, nos aprofundaremos sobre os dados, a fim de extrair informações que permitam responder as hipóteses e descobrirmos novos fatos a respeito das *features*.\n",
        "\n"
      ],
      "metadata": {
        "id": "g7-6JnBVPyaR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "jByOwXh5pxT2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados_treino.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f0PJAIanQETi",
        "outputId": "31f84fab-c8f1-407e-f141-4c991fba0506"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 13 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   pclass       891 non-null    int64  \n",
            " 2   name         891 non-null    object \n",
            " 3   sex          891 non-null    int64  \n",
            " 4   age          891 non-null    float64\n",
            " 5   sibsp        891 non-null    int64  \n",
            " 6   parch        891 non-null    int64  \n",
            " 7   ticket       891 non-null    object \n",
            " 8   fare         891 non-null    float64\n",
            " 9   embarked_C   891 non-null    uint8  \n",
            " 10  embarked_Q   891 non-null    uint8  \n",
            " 11  embarked_S   891 non-null    uint8  \n",
            " 12  survived     891 non-null    int64  \n",
            "dtypes: float64(2), int64(6), object(2), uint8(3)\n",
            "memory usage: 72.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vamos explorar algumas estatísticas a respeito dos dados. Primeiro, iremos verificar como eram os dados no início dos dados de treino, com ênfase na *feature* 'age' pois possuía cerca de 20% de dados faltantes. O objetivo é notar se a escolha de substituição de faltantes pela mediana foi uma escolha acertada."
      ],
      "metadata": {
        "id": "M16aHl_o6ugY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inicio_dados_treino.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ixZLJed98PCy",
        "outputId": "e3c7a75c-f95d-45e4-97c9-a0dddfae4072"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Kaggle_Pass_Id</th>\n",
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              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
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              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "      <td>162.843373</td>\n",
              "      <td>662.304153</td>\n",
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              "      <td>0.836071</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>14.526507</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
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              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.416700</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "      <td>73.500000</td>\n",
              "      <td>335.500000</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "      <td>169.000000</td>\n",
              "      <td>666.000000</td>\n",
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              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>252.000000</td>\n",
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              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "      <td>328.000000</td>\n",
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              "          document.querySelector('#df-385aaa71-9a66-4f29-bbb5-c7d4e1453d9f 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-385aaa71-9a66-4f29-bbb5-c7d4e1453d9f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       Kaggle_Pass_Id      pclass  ...        body     orig_seq\n",
              "count      891.000000  891.000000  ...   83.000000   891.000000\n",
              "mean       446.000000    2.308642  ...  162.843373   662.304153\n",
              "std        257.353842    0.836071  ...   96.945356   378.282967\n",
              "min          1.000000    1.000000  ...    4.000000     1.000000\n",
              "25%        223.500000    2.000000  ...   73.500000   335.500000\n",
              "50%        446.000000    3.000000  ...  169.000000   666.000000\n",
              "75%        668.500000    3.000000  ...  252.000000   993.500000\n",
              "max        891.000000    3.000000  ...  328.000000  1309.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agora, verificaremos os dados de treino atualizados após as modificações:"
      ],
      "metadata": {
        "id": "xVBbYkoj-YYT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados_treino.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "UqgWRHd1pkiG",
        "outputId": "086d3c77-40a7-4b55-e8f5-a0e78ecd8985"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked_C</th>\n",
              "      <th>embarked_Q</th>\n",
              "      <th>embarked_S</th>\n",
              "      <th>survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>0.352413</td>\n",
              "      <td>29.361579</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "      <td>0.188552</td>\n",
              "      <td>0.086420</td>\n",
              "      <td>0.722783</td>\n",
              "      <td>0.383838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>0.477990</td>\n",
              "      <td>13.019705</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "      <td>0.391372</td>\n",
              "      <td>0.281141</td>\n",
              "      <td>0.447876</td>\n",
              "      <td>0.486592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.416700</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",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</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>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "      padding: 0 0 0 0;\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-11c43372-5055-4a98-a958-570d1ce99503 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-11c43372-5055-4a98-a958-570d1ce99503');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       PassengerId      pclass         sex  ...  embarked_Q  embarked_S    survived\n",
              "count   891.000000  891.000000  891.000000  ...  891.000000  891.000000  891.000000\n",
              "mean    446.000000    2.308642    0.352413  ...    0.086420    0.722783    0.383838\n",
              "std     257.353842    0.836071    0.477990  ...    0.281141    0.447876    0.486592\n",
              "min       1.000000    1.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     223.500000    2.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "50%     446.000000    3.000000    0.000000  ...    0.000000    1.000000    0.000000\n",
              "75%     668.500000    3.000000    1.000000  ...    0.000000    1.000000    1.000000\n",
              "max     891.000000    3.000000    1.000000  ...    1.000000    1.000000    1.000000\n",
              "\n",
              "[8 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Após tratamento de dados faltantes, nota-se que a mediana não foi alterada, a média diminuiu de 29.70 para 29.36 e o desvio-padrão teve uma redução de 14.53 para 13.02. Portanto, não ocorreram grandes impactos nesta *feature* e a substituição pela mediana foi adequada.\n",
        "\n",
        "Um primeiro indicativo a respeito da *feature* 'fare' é a existência de *outliers* pois a média é maior que o 3º quartil e o desvio-padrão igual a 49.69, valor muito elevado para esta distribuição.\n",
        "\n",
        "A seguir, verificaremos graficamente estes fatos por *boxplots*. Porém, acrescentaremos os *boxplots* de teste para identificar se há o mesmo comportamento."
      ],
      "metadata": {
        "id": "9qtlrA277KxB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados_treino.boxplot(column='age', vert=False )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "3LxnEmPNpqaF",
        "outputId": "117820e6-f0a8-4fe4-a3ee-cf9ef77b3d31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f82224b9a50>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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s6Ydm9pbkpu2Snk57rhl26PXTMFL6s/1A0s1mdknyGq09Z+0/xtL8wcYifgjxbknfU/Xc7J+mPMuoqufLXlP1Xczdqp6bPSLpWUn/JalvmWd6h6rfZn5b0rHk8u6050pm2yDpW8lsJyT9eXL7tZKekPR9Vb91zqW4T7dKeiwLcyXbP55cnqod7xnZlxslHU325RclXZ6FuZLZuiX9RFJvw22pzybpfknfTY79f5aUW4pjjF8pAADBrMRTMQCA8yDsABAMYQeAYAg7AARD2AEgGMIOAMEQdgAI5v8BdbQxQeP1s7wAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_teste.boxplot(column='age', vert=False )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "8lBG3VrMcoMR",
        "outputId": "50a7cb58-a950-4997-b07f-494efa53cd4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8221be74d0>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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b2XXJxy3Kn//PKR/4HfWez90fcfdV7p5R/utt1N13pmU+STKzVjNbWvhY+fPEJ5WSfezuP5V01sw+kKy6S9L3lJL5SvTr3dMwUvrmm1m9T/JX8cONbZJ+oPx52MEUzDMs6SeS3lH+mckDyp+DfU7SDyX9m6TldZzvo8p/C/ldSceSy7a0zChpg6TvJPOdlPRnyfpfk/SipB8p/61xUwr29VZJz6RtvmSW48nl5cJxkZZ9nMyyUdKRZD9/XdL1KZuvVdIbkpaVrEvNfLO98JYCABDMQj0VAwC4CsIOAMEQdgAIhrADQDCEHQCCIewAEAxhB4Bg/g/F8RJgqsuZTAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_treino.boxplot(column='fare', vert=False )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "Ku6kDRIZp4N5",
        "outputId": "77d31a5c-c4c6-467d-ecf5-8e3fe98e849d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8221722c50>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_teste.boxplot(column='fare', vert=False )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "j3iWdeRPe3dV",
        "outputId": "91d74c39-e087-41fd-96e2-c615f83feb1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f82216969d0>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "O comportamento entre os dados de treino e teste para estas duas *features* são semelhantes, no entanto somente na *feature* 'fare' serão tratados os *outliers*. Será guardada uma cópia do *dataset* de treino e teste caso seja necessária uma aplicação futura. "
      ],
      "metadata": {
        "id": "dsUjczO6eP8A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Vamos guardar uma cópia da feature 'fare' antes das transformações\n",
        "guardando_treino = dados_treino.copy()\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "# Distância interquartil:\n",
        "\n",
        "Q1_treino = dados_treino['fare'].quantile(0.25)\n",
        "Q3_treino = dados_treino['fare'].quantile(0.75)\n",
        "dist_iqt_treino = Q3_treino - Q1_treino\n",
        "\n",
        "largura_whisker = 1.5\n",
        "whisker_inferior_treino = Q1_treino - (largura_whisker * dist_iqt_treino)\n",
        "whisker_superior_treino = Q3_treino + (largura_whisker * dist_iqt_treino)\n",
        "dados_treino['fare'] = np.where(dados_treino['fare'] > whisker_superior_treino, whisker_superior_treino, np.where(dados_treino['fare'] < whisker_inferior_treino,\n",
        "                                                                                                                  whisker_inferior_treino, dados_treino['fare']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vIFmIAkvPHNq",
        "outputId": "9ff5a5be-e371-41fe-8e52-7bc7a5e2f5c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  app.launch_new_instance()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Vamos guardar uma cópia da feature 'fare' antes das transformações\n",
        "guardando_teste = dados_teste.copy()\n",
        "\n",
        "# Distância interquartil:\n",
        "\n",
        "Q1_teste = dados_teste['fare'].quantile(0.25)\n",
        "Q3_teste = dados_teste['fare'].quantile(0.75)\n",
        "dist_iqt_teste = Q3_teste - Q1_teste\n",
        "\n",
        "largura_whisker = 1.5\n",
        "whisker_inferior_teste = Q1_teste - (largura_whisker * dist_iqt_teste)\n",
        "whisker_superior_teste = Q3_teste + (largura_whisker * dist_iqt_teste)\n",
        "dados_teste['fare'] = np.where(dados_teste['fare'] > whisker_superior_teste, whisker_superior_teste, np.where(dados_teste['fare'] < whisker_inferior_teste,\n",
        "                                                                                                              whisker_inferior_teste, dados_teste['fare']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H9XA8PUarmbl",
        "outputId": "0e880617-d686-4415-d7a1-6085e0bb520a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Boxplots* representando as *features* agora sem *outliers*:"
      ],
      "metadata": {
        "id": "ocLHFEDEfZG7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados_treino.boxplot(column='fare', vert=False )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "OqoE8qrvsz-e",
        "outputId": "50c0ba22-ee38-4d64-d0c4-46e11652efba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f822160ced0>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_teste.boxplot(column='fare', vert=False )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "GqYsae0Fs3V7",
        "outputId": "ccf99ca2-6e27-49ea-f175-4a55c125a7cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f82215841d0>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verificando a correlação de Pearson entre as *features* do *dataset* de treino:"
      ],
      "metadata": {
        "id": "55R3x2GZfjGU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(10,10))         \n",
        "sns.heatmap(dados_treino.corr(), cmap = \"Blues\", annot=True, linewidths=.5, ax=ax)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "8XE9rcPISsyP",
        "outputId": "75fb36dd-cdb9-48df-e023-921f2c7ab545"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agora, vamos verificar os histogramas das *features* do *dataset* de treino:"
      ],
      "metadata": {
        "id": "TLkdeoA8f57R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados_treino.hist(figsize = (10,10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 845
        },
        "id": "VvpQNtn6IXl0",
        "outputId": "8e5ec0f6-9612-448b-de73-15ec37da2cf7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8218b30b90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8221722210>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8221623e10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f8218ba3710>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8218c4fc90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8217256e50>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f8217218490>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f82171cea90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f82171d8e10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f82171954d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8217102fd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f821713a310>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 12 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agora, vamos realizar a extração dos títulos dos nomes dos passageiros, a fim de buscar uma associação entre o nome e a sobrevivência do passageiro:"
      ],
      "metadata": {
        "id": "vCsM2t7GvmIH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def extracao_titulo(df):\n",
        "    df['titulo'] = df.name.apply(lambda x: x.split(',')[1].split('.')[0].strip().lower())\n",
        "extracao_titulo(dados_treino)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lXNwlABovi8e",
        "outputId": "40d43bef-6945-407b-c869-b23d77c71d4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_treino"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "OH40vBwZxJBb",
        "outputId": "a141824d-c65a-4dfc-f1ba-aae3df7bf748"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-669f098e-623d-4e1b-a967-63a520be635a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>pclass</th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>ticket</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked_C</th>\n",
              "      <th>embarked_Q</th>\n",
              "      <th>embarked_S</th>\n",
              "      <th>survived</th>\n",
              "      <th>titulo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>65.6344</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>rev</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>1</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>mr</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-669f098e-623d-4e1b-a967-63a520be635a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-669f098e-623d-4e1b-a967-63a520be635a');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     PassengerId  pclass  ... survived  titulo\n",
              "0              1       3  ...        0      mr\n",
              "1              2       1  ...        1     mrs\n",
              "2              3       3  ...        1    miss\n",
              "3              4       1  ...        1     mrs\n",
              "4              5       3  ...        0      mr\n",
              "..           ...     ...  ...      ...     ...\n",
              "886          887       2  ...        0     rev\n",
              "887          888       1  ...        1    miss\n",
              "888          889       3  ...        0    miss\n",
              "889          890       1  ...        1      mr\n",
              "890          891       3  ...        0      mr\n",
              "\n",
              "[891 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verificando quantas vezes cada título se repete:"
      ],
      "metadata": {
        "id": "nsynP5h77B2s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "contagem = dados_treino['titulo'].value_counts()"
      ],
      "metadata": {
        "id": "Y3r4ukWIxRd_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "contagem"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r4_ewqMMxk5E",
        "outputId": "573ce88a-2f96-462b-808a-52aacc02fbe0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mr              517\n",
              "miss            182\n",
              "mrs             125\n",
              "master           40\n",
              "dr                7\n",
              "rev               6\n",
              "mlle              2\n",
              "major             2\n",
              "col               2\n",
              "mme               1\n",
              "lady              1\n",
              "ms                1\n",
              "the countess      1\n",
              "don               1\n",
              "capt              1\n",
              "jonkheer          1\n",
              "sir               1\n",
              "Name: titulo, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vamos verificar qual a porcentagem somada dos 4 primeiros títulos em todo o *dataset* de treino, a fim de desconsiderar os outros títulos caso o valor seja acima de 90%."
      ],
      "metadata": {
        "id": "2iBy3RFphOjF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "soma_4_primeiros = 0\n",
        "for i in range(0,4,1):\n",
        "    soma_4_primeiros += contagem[i]\n",
        "print(soma_4_primeiros)\n",
        "\n",
        "percentual_4_primeiros = (soma_4_primeiros/contagem.sum())*100\n",
        "print(percentual_4_primeiros)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V9RyI48e7psJ",
        "outputId": "5600a82a-63e1-4d97-ac1d-dd738c35eff2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "864\n",
            "96.96969696969697\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Com o valor próximo a 97%, consideraremos apenas os 4 primeiros títulos com mais aparições. A seguir, foram criadas variáveis para filtrar o *dataframe* a estes títulos:"
      ],
      "metadata": {
        "id": "13fPkGllhxjx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "contem_mr = dados_treino.loc[dados_treino['titulo'] == 'mr']\n",
        "contem_miss = dados_treino.loc[dados_treino['titulo'] == 'miss']\n",
        "contem_mrs = dados_treino.loc[dados_treino['titulo'] == 'mrs']\n",
        "contem_master = dados_treino.loc[dados_treino['titulo'] == 'master']"
      ],
      "metadata": {
        "id": "AYxgU0zLz2NY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verificando a porcentagem de sobrevivência para os 4 títulos:"
      ],
      "metadata": {
        "id": "66MhBSFaZ3Kl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "contem_mr_sobreviventes = len(contem_mr.loc[contem_mr['survived'] == 1])\n",
        "percentual_mr_sobreviventes = (contem_mr_sobreviventes/len(contem_mr))*100\n",
        "print('O percentual de pessoas sobreviventes com \"Mr\" no nome é: {0:.2f}%'.format(percentual_mr_sobreviventes))\n",
        "\n",
        "contem_miss_sobreviventes = len(contem_miss.loc[contem_miss['survived'] == 1])\n",
        "percentual_miss_sobreviventes = (contem_miss_sobreviventes/len(contem_miss))*100\n",
        "print('O percentual de pessoas sobreviventes com \"Miss\" no nome é: {0:.2f}%'.format(percentual_miss_sobreviventes))\n",
        "\n",
        "contem_mrs_sobreviventes = len(contem_mrs.loc[contem_mrs['survived'] == 1])\n",
        "percentual_mrs_sobreviventes = (contem_mrs_sobreviventes/len(contem_mrs))*100\n",
        "print('O percentual de pessoas sobreviventes com \"Mrs\" no nome é: {0:.2f}%'.format(percentual_mrs_sobreviventes))\n",
        "\n",
        "contem_master_sobreviventes = len(contem_master.loc[contem_master['survived'] == 1])\n",
        "percentual_master_sobreviventes = (contem_master_sobreviventes/len(contem_master))*100\n",
        "print('O percentual de pessoas sobreviventes com \"Master\" no nome é: {0:.2f}%'.format(percentual_master_sobreviventes))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QnUIKhvi1jwk",
        "outputId": "816c01d9-1150-4849-8f16-8e04d1c22e43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O percentual de pessoas sobreviventes com \"Mr\" no nome é: 15.67%\n",
            "O percentual de pessoas sobreviventes com \"Miss\" no nome é: 69.78%\n",
            "O percentual de pessoas sobreviventes com \"Mrs\" no nome é: 79.20%\n",
            "O percentual de pessoas sobreviventes com \"Master\" no nome é: 57.50%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Estes percentuais acima indicam que a porcentagem de homens sobreviventes é bem abaixo da porcentagem de sobrevivência de mulheres. Buscaremos ampliar esta discussão posteriormente."
      ],
      "metadata": {
        "id": "bqeTfcPtiKNQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vamos explorar a *feature* 'age'. Primeiro, verificaremos a porcentagem de crianças sobreviventes ao naufrágio (considerando como criança, o ser humano abaixo dos 12 anos de idade). Em seguida, serão verificados a respeito dos idosos, buscando responder uma das hipóteses apresentadas em \"*Business Understanding*\". Importante registrar que em 1912, a expectativa de vida nos Estados Unidos era de 51,5 anos para homens e 55,9 para mulheres, e portanto será considerada a idade para idosos igual ou acima dos 45 anos."
      ],
      "metadata": {
        "id": "U8jYMgx8jKjD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "criancas = dados_treino.loc[dados_treino['age'] < 12]\n",
        "criancas_mortes = criancas.loc[criancas['survived'] == 0].value_counts().sum()\n",
        "criancas_sobreviventes = criancas.loc[criancas['survived'] == 1].value_counts().sum()\n",
        "print('O percentual de crianças sobreviventes foi de: {0:.2f}%'.format((criancas_sobreviventes/len(criancas)) *100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XF8sPj-yiK8g",
        "outputId": "0d310f4d-3d3f-41b7-aaa1-df4ae5fe35c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O percentual de crianças sobreviventes foi de: 57.35%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idosos = dados_treino.loc[dados_treino['age'] >= 45]\n",
        "percentual_idosos = (len(idosos)/len(dados_treino['sex']))*100\n",
        "idosos_sobreviventes = idosos.loc[idosos['survived'] == 1].value_counts().sum()\n",
        "percentual_idosos_sobreviventes = (idosos_sobreviventes/len(idosos))*100\n",
        "\n",
        "print('A quantidade de idosos no RMS Titanic era: ', len(idosos))\n",
        "print('O percentual de idosos no RMS Titanic era: {0:.2f}%'.format(percentual_idosos))\n",
        "print('O percentual do total de idosos no RMS Titanic que sobreviveram foi de: {0:.2f}%'.format(percentual_idosos_sobreviventes))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NjHIp4leWZpB",
        "outputId": "5ec0130f-7b08-4566-9137-596b02c16493"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A quantidade de idosos no RMS Titanic era:  115\n",
            "O percentual de idosos no RMS Titanic era: 12.91%\n",
            "O percentual do total de idosos no RMS Titanic que sobreviveram foi de: 37.39%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Os percentuais indicam que as crianças tiveram maior probabilidade de sobrevivência, enquanto o fato de ser idoso indica menor probabilidade, confirmando assim a hipótese 1."
      ],
      "metadata": {
        "id": "fCTQsiEIkkme"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agora, nos aprofundaremos nas *features* 'sex' e 'pclass'. Buscaremos entender o quanto que o gênero do passageiro e a classe no RMS Titanic influenciaram para a sua sobrevivência, a fim de respondermos as hipóteses 2 e 4. Calcularemos os percentuais em relação à todos os passageiros de um respectivo gênero ou classe. Posteriormente serão plotados gráficos para a discussão."
      ],
      "metadata": {
        "id": "FGgZ7dWYnUkN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "total_homens = dados_treino.loc[dados_treino['sex'] == 0].value_counts().sum()\n",
        "total_mulheres = dados_treino.loc[dados_treino['sex'] == 1].value_counts().sum()\n",
        "sobreviventes = dados_treino.loc[dados_treino['survived'] == 1]\n",
        "homens_sobreviventes = sobreviventes.loc[sobreviventes['sex'] == 0].value_counts().sum()\n",
        "mulheres_sobreviventes = sobreviventes.loc[sobreviventes['sex'] == 1].value_counts().sum()\n",
        "percentual_homens_sobreviventes = np.around(((homens_sobreviventes/total_homens)*100), decimals = 2) \n",
        "percentual_mulheres_sobreviventes = np.around(((mulheres_sobreviventes/total_mulheres)*100), decimals = 2)\n",
        "lista_sex = [0,1]\n",
        "lista_sex_sobreviventes = [percentual_homens_sobreviventes, percentual_mulheres_sobreviventes]\n",
        "\n",
        "total_classe1 = dados_treino.loc[dados_treino['pclass'] == 1].value_counts().sum()\n",
        "total_classe2 = dados_treino.loc[dados_treino['pclass'] == 2].value_counts().sum()\n",
        "total_classe3 = dados_treino.loc[dados_treino['pclass'] == 3].value_counts().sum()\n",
        "classe1_sobreviventes = sobreviventes.loc[sobreviventes['pclass'] == 1].value_counts().sum()\n",
        "classe2_sobreviventes = sobreviventes.loc[sobreviventes['pclass'] == 2].value_counts().sum()\n",
        "classe3_sobreviventes = sobreviventes.loc[sobreviventes['pclass'] == 3].value_counts().sum()\n",
        "percentual_classe1_sobreviventes = np.around(((classe1_sobreviventes/total_classe1)*100), decimals = 2) \n",
        "percentual_classe2_sobreviventes = np.around(((classe2_sobreviventes/total_classe2)*100), decimals = 2)\n",
        "percentual_classe3_sobreviventes = np.around(((classe3_sobreviventes/total_classe3)*100), decimals = 2)\n",
        "lista_classe = [1,2,3]\n",
        "lista_classe_sobreviventes = [percentual_classe1_sobreviventes, percentual_classe2_sobreviventes, percentual_classe3_sobreviventes]\n"
      ],
      "metadata": {
        "id": "uWdQ6p0329Xe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pela hipótese 3, desejamos compreender o quanto que o fato de uma pessoa possuir parentes/pessoas próximas auxiliou em sua sobrevivência ou não. Para obter a resposta, tomaremos uma cópia dos dados de treino e guardaremos em um novo objeto. Após, será criada uma coluna que contém a soma das colunas 'sibsp' e 'parch':"
      ],
      "metadata": {
        "id": "NFrqlo5j95t0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sibparch = dados_treino.copy()"
      ],
      "metadata": {
        "id": "m6KrkwOKadxf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sibparch['sibsp + parch'] = sibparch['sibsp'] + sibparch['parch']\n",
        "sibparch.drop(['sibsp', 'parch', 'titulo'], axis = 1, inplace = True)\n",
        "sibparch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "mSA5ph1xaehI",
        "outputId": "ce61e85d-d329-42e7-de11-b12bc16e7a6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>pclass</th>\n",
              "      <th>name</th>\n",
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              "                                                     [key], {});\n",
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              "  "
            ],
            "text/plain": [
              "     PassengerId  pclass  ... survived  sibsp + parch\n",
              "0              1       3  ...        0              1\n",
              "1              2       1  ...        1              1\n",
              "2              3       3  ...        1              0\n",
              "3              4       1  ...        1              1\n",
              "4              5       3  ...        0              0\n",
              "..           ...     ...  ...      ...            ...\n",
              "886          887       2  ...        0              0\n",
              "887          888       1  ...        1              0\n",
              "888          889       3  ...        0              3\n",
              "889          890       1  ...        1              0\n",
              "890          891       3  ...        0              0\n",
              "\n",
              "[891 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agora, coletaremos as informações do total de passageiros que tinham um número x de parentes/pessoas próximas:"
      ],
      "metadata": {
        "id": "qd3MoblS_ShS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "soma_sibparch = sibparch['sibsp + parch'].value_counts()"
      ],
      "metadata": {
        "id": "MC0m7jE7C3CT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "soma_sibparch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Hz_vIlBDJIz",
        "outputId": "03fc60bf-141c-405a-ae73-5785f964fc0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     537\n",
              "1     161\n",
              "2     102\n",
              "3      29\n",
              "5      22\n",
              "4      15\n",
              "6      12\n",
              "10      7\n",
              "7       6\n",
              "Name: sibsp + parch, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Coletaremos também o total de sobreviventes para cada total de parentes/pessoas próximas, a fim de obtermos o percentual de sobreviventes para esta situação. Em suma, como exemplo, estamos respondendo a pergunta: \"Se um passageiro tem 3 parentes/pessoas próximas, qual a porcentagem de sobreviventes dentre todos os que possuem 3 parentes/pessoas próximas?\" Posteriormente será plotado um gráfico para a resposta."
      ],
      "metadata": {
        "id": "z21dXHJdFLIo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "total_0 = soma_sibparch[0]\n",
        "total_1 = soma_sibparch[1]\n",
        "total_2 = soma_sibparch[2]\n",
        "total_3 = soma_sibparch[3]\n",
        "total_4 = soma_sibparch[4]\n",
        "total_5 = soma_sibparch[5]\n",
        "total_6 = soma_sibparch[6]\n",
        "total_7 = soma_sibparch[7]\n",
        "total_10 = soma_sibparch[10]\n",
        "\n",
        "sobreviventes2 = sibparch.loc[sibparch['survived'] == 1]\n",
        "total_0_sobreviventes = sobreviventes2.loc[sobreviventes2['sibsp + parch'] == 0].value_counts().sum()\n",
        "total_1_sobreviventes = sobreviventes2.loc[sobreviventes2['sibsp + parch'] == 1].value_counts().sum()\n",
        "total_2_sobreviventes = sobreviventes2.loc[sobreviventes2['sibsp + parch'] == 2].value_counts().sum()\n",
        "total_3_sobreviventes = sobreviventes2.loc[sobreviventes2['sibsp + parch'] == 3].value_counts().sum()\n",
        "total_4_sobreviventes = sobreviventes2.loc[sobreviventes2['sibsp + parch'] == 4].value_counts().sum()\n",
        "total_5_sobreviventes = sobreviventes2.loc[sobreviventes2['sibsp + parch'] == 5].value_counts().sum()\n",
        "total_6_sobreviventes = sobreviventes2.loc[sobreviventes2['sibsp + parch'] == 6].value_counts().sum()\n",
        "total_7_sobreviventes = sobreviventes2.loc[sobreviventes2['sibsp + parch'] == 7].value_counts().sum()\n",
        "total_10_sobreviventes = sobreviventes2.loc[sobreviventes2['sibsp + parch'] == 10].value_counts().sum()\n",
        "\n",
        "percentual_total_0_sobreviventes = np.around(((total_0_sobreviventes/total_0)*100), decimals = 2)\n",
        "percentual_total_1_sobreviventes = np.around(((total_1_sobreviventes/total_1)*100), decimals = 2)\n",
        "percentual_total_2_sobreviventes = np.around(((total_2_sobreviventes/total_2)*100), decimals = 2)\n",
        "percentual_total_3_sobreviventes = np.around(((total_3_sobreviventes/total_3)*100), decimals = 2)\n",
        "percentual_total_4_sobreviventes = np.around(((total_4_sobreviventes/total_4)*100), decimals = 2)\n",
        "percentual_total_5_sobreviventes = np.around(((total_5_sobreviventes/total_5)*100), decimals = 2)\n",
        "percentual_total_6_sobreviventes = np.around(((total_6_sobreviventes/total_6)*100), decimals = 2)\n",
        "percentual_total_7_sobreviventes = np.around(((total_7_sobreviventes/total_7)*100), decimals = 2)\n",
        "percentual_total_10_sobreviventes = np.around(((total_10_sobreviventes/total_10)*100), decimals = 2)\n",
        "\n",
        "lista_sibparch = [0,1,2,3,4,5,6,7,10]\n",
        "lista_sibparch_sobreviventes = [percentual_total_0_sobreviventes, percentual_total_1_sobreviventes, percentual_total_2_sobreviventes, percentual_total_3_sobreviventes,\n",
        "                                percentual_total_4_sobreviventes, percentual_total_5_sobreviventes, percentual_total_6_sobreviventes, percentual_total_7_sobreviventes,\n",
        "                                percentual_total_10_sobreviventes]"
      ],
      "metadata": {
        "id": "W9cpQxc3DEmT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "A seguir serão plotados os gráficos a fim de respondermos as hipóteses 2,3 e 4:"
      ],
      "metadata": {
        "id": "uQtgucEwGfIr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib as mpl\n",
        "\n",
        "mpl.rcParams['axes.spines.right'] = False\n",
        "mpl.rcParams['axes.spines.top'] = False\n",
        "\n",
        "fig, (axis1, axis2, axis3) = plt.subplots(1,3, figsize = (25,7))\n",
        "\n",
        "plot1 = sns.barplot(x = lista_sex, y = lista_sex_sobreviventes, data = dados_treino, ax = axis1, ci = None)\n",
        "plot2 = sns.barplot(x = lista_classe, y = lista_classe_sobreviventes, data = dados_treino, ax = axis2, ci = None)\n",
        "plot3 = sns.barplot(x = lista_sibparch, y = lista_sibparch_sobreviventes, data = sibparch, ax = axis3, ci = None)\n",
        "\n",
        "plot1.set(xlabel='Sexo', ylabel='Porcentagem de sobrevivência (%)')\n",
        "plot2.set(xlabel='Classe', ylabel='Porcentagem de sobrevivência (%)')\n",
        "plot3.set(xlabel='Número de parentes somados (sibsp + parch)', ylabel='Porcentagem de sobrevivência (%)')\n",
        "\n",
        "for i in plot1.patches:\n",
        "    plot1.annotate(i.get_height(), (i.get_x() + i.get_width()/2, i.get_height()),\n",
        "                 ha = 'center', va = 'baseline', fontsize = 10, color = 'black', xytext = (0,3), textcoords = 'offset points')\n",
        "    \n",
        "for i in plot2.patches:\n",
        "    plot2.annotate(i.get_height(), (i.get_x() + i.get_width()/2, i.get_height()),\n",
        "                 ha = 'center', va = 'baseline', fontsize = 10, color = 'black', xytext = (0,3), textcoords = 'offset points')\n",
        "    \n",
        "for i in plot3.patches:\n",
        "    plot3.annotate(i.get_height(), (i.get_x() + i.get_width()/2, i.get_height()),\n",
        "                 ha = 'center', va = 'baseline', fontsize = 10, color = 'black', xytext = (0,3), textcoords = 'offset points')\n",
        "    \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "7GkRZSAyIXj7",
        "outputId": "aaa1ea30-384b-4bad-9ccd-cab579918ff1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x504 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Os gráficos acima contém alguns fatos:\n",
        "- Conforme notado anteriormente na análise por títulos, realmente a porcentagem de homens sobreviventes é bem abaixo da porcentagem de mulheres sobreviventes, confirmando a hipótese 2;\n",
        "- A classe do passageiro pesou para a sua sobrevivência. Quanto menos favorecida a classe, menor a probabilidade de sobrevivência, confirmando a hipótese 4;\n",
        "- A porcentagem de sobreviventes em relação ao número de parentes/pessoas próximas não contém um padrão notório, e pelas informações contidas no objeto 'soma_sibparch', tem-se uma concentração maior de passageiros com 1,2 ou 3 parentes/pessoas próximas, o que gera um desbalanceamento na análise. Portanto não é possível afirmar se esta *feature* nos fornece uma informação precisa para confirmar ou rejeitar a hipótese 3.  "
      ],
      "metadata": {
        "id": "lY4DCHoUIljW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modeling\n",
        "\n",
        "Vamos para a etapa de modelagem, onde faremos os últimos ajustes antes da aplicação dos algoritmos de *machine learning*. Guardaremos uma cópia dos dados de treino e teste da *feature* 'PassengerId' e excluiremos as *features*: 'name', 'ticket', 'titulo', 'embarked_C', 'embarked_Q' e 'embarked_S'. "
      ],
      "metadata": {
        "id": "yT52rnssRdB3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "passenger_id_treino = dados_treino['PassengerId'].copy()\n",
        "passenger_id_teste = dados_teste['PassengerId'].copy()\n",
        "dados_treino.drop(['PassengerId', 'name', 'ticket', 'titulo', 'embarked_C','embarked_Q','embarked_S'], axis = 1, inplace = True)\n",
        "dados_teste.drop(['PassengerId', 'name', 'ticket', 'embarked_C','embarked_Q','embarked_S'], axis = 1, inplace = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bB2J1t2RrIrM",
        "outputId": "0d383e79-8130-418d-9c9c-b4d10bd5f412"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4174: 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",
            "  errors=errors,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ML Algorithms\n",
        "\n",
        "Vamos aplicar dois algoritmos neste trabalho: Random Forest Classifier e Gradient Boosting Classifier. Faremos a divisão da variável alvo 'survived' das preditoras logo abaixo:"
      ],
      "metadata": {
        "id": "bCIyRki4RpLd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier"
      ],
      "metadata": {
        "id": "9M-cthRnRxk1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = dados_treino.drop(['survived'], axis = 1)\n",
        "y_train = dados_treino['survived']\n",
        "\n",
        "x_test = dados_teste"
      ],
      "metadata": {
        "id": "O1aeAJIrUaKV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "w8vzBxa2Y9WF",
        "outputId": "0c286ae9-bec9-4c00-b204-8529b1980047"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-02fd61e3-8f96-498c-a3d4-0f84aa790c2a 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-02fd61e3-8f96-498c-a3d4-0f84aa790c2a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   pclass  sex   age  sibsp  parch     fare\n",
              "0       3    0  22.0      1      0   7.2500\n",
              "1       1    1  38.0      1      0  65.6344\n",
              "2       3    1  26.0      0      0   7.9250\n",
              "3       1    1  35.0      1      0  53.1000\n",
              "4       3    0  35.0      0      0   8.0500"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dPDWzeC8ZC9G",
        "outputId": "b96c6aba-6858-46be-a9da-89862c1d8704"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5d33a8a8-ba2a-4781-92f9-b83097ba494b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>891</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
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              "      <td>7.8292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>892</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0000</td>\n",
              "    </tr>\n",
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              "      <th>893</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.6875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>894</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>895</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d33a8a8-ba2a-4781-92f9-b83097ba494b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
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              "\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5d33a8a8-ba2a-4781-92f9-b83097ba494b 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-5d33a8a8-ba2a-4781-92f9-b83097ba494b');\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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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     pclass  sex   age  sibsp  parch     fare\n",
              "891       3    0  34.5      0      0   7.8292\n",
              "892       3    1  47.0      1      0   7.0000\n",
              "893       2    0  62.0      0      0   9.6875\n",
              "894       3    0  27.0      0      0   8.6625\n",
              "895       3    1  22.0      1      1  12.2875"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aplicando os modelos Random Forest Classifier e Gradient Boosting Classifier aos dados de treino apenas com a definição de 100 estimadores e realizando previsões com os dados de teste: "
      ],
      "metadata": {
        "id": "A7fQ2anFsabd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "modeloRF = RandomForestClassifier(n_estimators = 100, random_state = 0)\n",
        "modeloRF.fit(x_train,y_train)\n",
        "\n",
        "pred_rf = modeloRF.predict(x_test)\n",
        "\n",
        "modeloGB = GradientBoostingClassifier(n_estimators = 100, random_state = 0)\n",
        "modeloGB.fit(x_train,y_train)\n",
        "\n",
        "pred_gb = modeloGB.predict(x_test)"
      ],
      "metadata": {
        "id": "gelQ_iL2UGz0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "A seguir, veremos as importâncias de cada *feature* para os treinamentos dos modelos escolhidos:"
      ],
      "metadata": {
        "id": "TyLJ6eIE4mJx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "feat_import_rf = np.around(modeloRF.feature_importances_, decimals = 4)\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "plot = sns.barplot(x = x_train.columns, y= feat_import_rf)\n",
        "for i in plot.patches:\n",
        "    plot.annotate(i.get_height(), (i.get_x() + i.get_width()/2, i.get_height()),\n",
        "                 ha = 'center', va = 'baseline', fontsize = 10, color = 'black', xytext = (0,3), textcoords = 'offset points')\n",
        "plt.title('Random Forest Classifier', fontsize = 14)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "Feo5fT6_qTiv",
        "outputId": "af35d0a5-7daa-44e4-eac9-8c224457ce26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feat_import_gb = np.around(modeloGB.feature_importances_, decimals = 4)\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "plot = sns.barplot(x = x_train.columns, y= feat_import_gb)\n",
        "for i in plot.patches:\n",
        "    plot.annotate(i.get_height(), (i.get_x() + i.get_width()/2, i.get_height()),\n",
        "                 ha = 'center', va = 'baseline', fontsize = 10, color = 'black', xytext = (0,3), textcoords = 'offset points')\n",
        "plt.title('Gradient Boosting Classifier', fontsize = 14)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "MBT3U2f_0cuF",
        "outputId": "5657463f-5d1c-4eba-bdcd-fcfe177761d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vamos agora realizar a submeção destas previsões na competição do *Kaggle*, transformando em um arquivo csv:"
      ],
      "metadata": {
        "id": "I8c_Y_qQ5Qw4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "submit_kaggle_rf = pd.Series(pred_rf, index = passenger_id_teste, name = 'survived')\n",
        "submit_kaggle_rf.to_csv(\"modelo_rf_Igor.csv\", header = True)"
      ],
      "metadata": {
        "id": "0EOanSEZUGyP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verificando as 10 primeiras linhas das previsões do 1º modelo Random Forest Classifier:"
      ],
      "metadata": {
        "id": "mgTc0Bl85l4C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!head -n10 modelo_rf_Igor.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qb2La6jMUGnE",
        "outputId": "05d922bb-5473-4e6c-83dc-77be4e7d4677"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PassengerId,survived\n",
            "892,0\n",
            "893,0\n",
            "894,1\n",
            "895,1\n",
            "896,0\n",
            "897,0\n",
            "898,0\n",
            "899,0\n",
            "900,1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "from google.colab.patches import cv2_imshow"
      ],
      "metadata": {
        "id": "i6PnuMW0ekl4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "A seguir, a demonstração do *score* na competição e logo abaixo a posição na classificação geral:"
      ],
      "metadata": {
        "id": "y8cbdaIr5x20"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "score_rf = cv2.imread('/content/ScoreRF.PNG')\n",
        "cv2_imshow(score_rf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "o9n9LVBNfalT",
        "outputId": "8472f9c2-41ec-4c2b-9a8d-602f25b12bf1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=954x186 at 0x7F8210252C90>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "position_rf = cv2.imread('/content/PositionRF.PNG')\n",
        "cv2_imshow(position_rf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "id": "lDFtjTpHfqN8",
        "outputId": "7b551944-2d45-425c-bdc9-e0d0b4b40939"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=962x50 at 0x7F8206828510>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Procedimento idêntico foi realizado para o 1º modelo Gradient Boosting Classifier:"
      ],
      "metadata": {
        "id": "ntPh3mXf6FV8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "submit_kaggle_gb = pd.Series(pred_gb, index = passenger_id_teste, name = 'survived')\n",
        "submit_kaggle_gb.to_csv(\"modelo_gb_Igor.csv\", header = True)"
      ],
      "metadata": {
        "id": "T6iiVcwxf6gh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score_gb = cv2.imread('/content/ScoreGB.PNG')\n",
        "cv2_imshow(score_gb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "2Pa-tD4ff6O-",
        "outputId": "ce7326b1-6b94-4119-9612-12714f9b4bfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=930x185 at 0x7F8206828F90>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "position_gb = cv2.imread('/content/PositionGB.PNG')\n",
        "cv2_imshow(position_gb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 64
        },
        "id": "a3ZuqhPxjSle",
        "outputId": "06e712e3-a9e7-4cc5-ccb4-f35a8ce76f74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=957x47 at 0x7F8206831F10>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vamos buscar uma melhora da performance do modelo, incluindo o *split* dos dados em 5 *folds*, aplicando validação cruzada e aumentando o número de estimadores para 200. Também, especificaremos mais 3 parâmetros: 'min_samples_split', 'max_depth' e min_samples_leaf'. Todo o procedimento para submeção dos resultados na competição foi realizada igualmente aos modelos anteriores."
      ],
      "metadata": {
        "id": "_bsFP1d275Wg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.model_selection import cross_val_score"
      ],
      "metadata": {
        "id": "QGrh09ybubF9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "modeloRF2 = RandomForestClassifier(n_estimators = 200, min_samples_split = 2, max_depth = 3, min_samples_leaf = 2, random_state = 0)\n",
        "Skfold = StratifiedKFold(n_splits = 5, shuffle = True)\n",
        "resultadoRF = cross_val_score(modeloRF2,x_train,y_train,cv = Skfold, n_jobs = -1)\n",
        "print(resultadoRF.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HkMfWt05C8-a",
        "outputId": "d5a16e88-a490-4568-a140-ceac885d254c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8137530600715586\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modeloRF2.fit(x_train,y_train)\n",
        "pred_rf2 = modeloRF2.predict(x_test)"
      ],
      "metadata": {
        "id": "vLmxyW9JEKLe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "modeloGB2 = GradientBoostingClassifier(n_estimators = 200, min_samples_split = 2, max_depth = 3, min_samples_leaf = 2, random_state = 0)\n",
        "SkfoldGB = StratifiedKFold(n_splits = 5, shuffle = True)\n",
        "resultadoGB = cross_val_score(modeloGB2,x_train,y_train,cv = Skfold, n_jobs = -1)\n",
        "print(resultadoGB.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bZ_ft1IP6mFG",
        "outputId": "c411ae63-b6c4-446d-d875-63019615caca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8204318624066286\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modeloGB2.fit(x_train,y_train)\n",
        "pred_gb2 = modeloGB2.predict(x_test)"
      ],
      "metadata": {
        "id": "DK1gTZNLK_CT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submit_kaggle_rf2 = pd.Series(pred_rf2, index = passenger_id_teste, name = 'survived')\n",
        "submit_kaggle_rf2.to_csv(\"modelo_rf2_Igor.csv\", header = True)\n",
        "\n",
        "submit_kaggle_gb2 = pd.Series(pred_gb2, index = passenger_id_teste, name = 'survived')\n",
        "submit_kaggle_gb2.to_csv(\"modelo_gb2_Igor.csv\", header = True)"
      ],
      "metadata": {
        "id": "7npDHJStNlVU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score_rf2 = cv2.imread('/content/ScoreRF2.PNG')\n",
        "cv2_imshow(score_rf2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "yXsJtLnrTAhy",
        "outputId": "7c29301e-39e1-4c05-b99e-c9201f8b86bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=936x156 at 0x7F8206848490>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "position_rf2 = cv2.imread('/content/PositionRF2.PNG')\n",
        "cv2_imshow(position_rf2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "id": "1K1gZ0VMTK_T",
        "outputId": "437a6631-642c-4c45-8f14-2337d6903b2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=958x51 at 0x7F82068629D0>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_gb2 = cv2.imread('/content/ScoreGB2.PNG')\n",
        "cv2_imshow(score_gb2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "AHCqwJi_UbNQ",
        "outputId": "25115123-8e2f-4b9a-9c96-7ebdfb95e23d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=924x148 at 0x7F8205FB4610>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Após acréscimo de mais atributos, separando os dados em 5 *folds* e aplicando validação cruzada, foi obtida uma leve melhora nos *scores*. Agora, vamos buscar melhorar nossos modelos pela variação dos parâmetros: "
      ],
      "metadata": {
        "id": "4UvYPdcm8aby"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "minimos_splitRF = np.array([2,3,4,5,6])\n",
        "maximo_nivelRF = np.array([3,4,5,6,7])\n",
        "minimo_leafRF = np.array([2,3,4,5,6])\n",
        "valores_gridRF = {'min_samples_split': minimos_splitRF, 'max_depth': maximo_nivelRF, 'min_samples_leaf': minimo_leafRF}\n",
        "\n",
        "modeloRF3 = RandomForestClassifier(n_estimators = 200, n_jobs = -1, random_state = 0)\n",
        "\n",
        "gridRandomForest = GridSearchCV(estimator = modeloRF3, param_grid = valores_gridRF, cv = 5, n_jobs = -1)\n",
        "gridRandomForest.fit(x_train,y_train)\n",
        "\n",
        "print('Mínimo split: ', gridRandomForest.best_estimator_.min_samples_split)\n",
        "print('Máxima profundidade: ', gridRandomForest.best_estimator_.max_depth)\n",
        "print('Mínimo leaf: ', gridRandomForest.best_estimator_.min_samples_leaf)\n",
        "\n",
        "\n",
        "minimos_splitGB = np.array([2,3,4,5,6])\n",
        "maximo_nivelGB = np.array([3,4,5,6,7])\n",
        "minimo_leafGB = np.array([2,3,4,5,6])\n",
        "valores_gridGB = {'min_samples_split': minimos_splitGB, 'max_depth': maximo_nivelGB, 'min_samples_leaf': minimo_leafGB}\n",
        "\n",
        "modeloGB3 = GradientBoostingClassifier(n_estimators = 200, random_state = 0)\n",
        "\n",
        "gridGB = GridSearchCV(estimator = modeloGB3, param_grid = valores_gridGB, cv = 5, n_jobs = -1)\n",
        "gridGB.fit(x_train,y_train)\n",
        "\n",
        "print('Mínimo split: ', gridGB.best_estimator_.min_samples_split)\n",
        "print('Máxima profundidade: ', gridGB.best_estimator_.max_depth)\n",
        "print('Mínimo leaf: ', gridGB.best_estimator_.min_samples_leaf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NqtNdh2HaczK",
        "outputId": "416543c4-99ef-46d2-b1fe-1af9c6dafe7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mínimo split:  2\n",
            "Máxima profundidade:  7\n",
            "Mínimo leaf:  2\n",
            "Mínimo split:  2\n",
            "Máxima profundidade:  3\n",
            "Mínimo leaf:  2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modeloRF4 = RandomForestClassifier(n_estimators = 200, min_samples_split = 2, max_depth = 7, min_samples_leaf = 2, random_state = 0)\n",
        "SkfoldRF4 = StratifiedKFold(n_splits = 5, shuffle = True)\n",
        "resultadoRF4 = cross_val_score(modeloRF4, x_train, y_train, cv = SkfoldRF4, n_jobs = -1)\n",
        "print(resultadoRF4.mean())\n",
        "\n",
        "modeloRF4.fit(x_train,y_train)\n",
        "pred_rf4 = modeloRF4.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ErVHzG0hdCWr",
        "outputId": "19665be6-0f93-4c86-a5ed-91e2ae074556"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8293704098926622\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modeloGB4 = GradientBoostingClassifier(n_estimators = 200, min_samples_split = 2, max_depth = 3, min_samples_leaf = 2)\n",
        "SkfoldGB4 = StratifiedKFold(n_splits = 5, shuffle = True)\n",
        "resultadoGB4 = cross_val_score(modeloGB4, x_train, y_train, cv = SkfoldGB4, n_jobs = -1)\n",
        "print(resultadoGB4.mean())\n",
        "\n",
        "modeloGB4.fit(x_train,y_train)\n",
        "pred_gb4 = modeloGB4.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b_LOsdmyeFP6",
        "outputId": "103bfaa1-404f-4df9-8467-ebdc2a918048"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8361433682756889\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submit_kaggle_rf4 = pd.Series(pred_rf4, index = passenger_id_teste, name = 'survived')\n",
        "submit_kaggle_rf4.to_csv(\"modelo_rf4_Igor.csv\", header = True)\n",
        "\n",
        "submit_kaggle_gb4 = pd.Series(pred_gb4, index = passenger_id_teste, name = 'survived')\n",
        "submit_kaggle_gb4.to_csv(\"modelo_gb4_Igor.csv\", header = True)"
      ],
      "metadata": {
        "id": "HNq00va0lbfs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score_rf4 = cv2.imread('/content/ScoreRF4.PNG')\n",
        "cv2_imshow(score_rf4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171
        },
        "id": "SmpwDV7Zmi66",
        "outputId": "42f48afb-1928-44a9-b0c4-e18aaa9caf71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=934x154 at 0x7F8205F87590>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "position_rf4 = cv2.imread('/content/PositionRF4.PNG')\n",
        "cv2_imshow(position_rf4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "id": "rdPflwTUoJ8v",
        "outputId": "9c2c7ddd-3c3d-489f-e560-6915999362de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=960x50 at 0x7F8205F87690>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_gb4 = cv2.imread('/content/ScoreGB4.PNG')\n",
        "cv2_imshow(score_gb4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 169
        },
        "id": "ZIt5vgWaoJzw",
        "outputId": "7e028f43-7c7d-47f8-8282-93e859754e54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=923x152 at 0x7F8203701890>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Após escolha dos melhores parâmetros foi obtido o melhor resultado, com o *score* de 0.78229 para o modelo utilizando Random Forest Classifier. Porém, para o modelo utilizando Gradient Boosting Classifier o melhor resultado foi 0.77033 alcançado no teste anterior."
      ],
      "metadata": {
        "id": "LtI8j0oiBLOf"
      }
    }
  ]
}