ml_titanic.ipynb

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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/kmendesDev/project_titanic/blob/main/ml_titanic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "#Projeto Titanic - Machine Learning"
      ],
      "metadata": {
        "id": "cna1SO30tCaD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Introdução"
      ],
      "metadata": {
        "id": "zq_GU5fMDro3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "👋🛳️ Olá, seja bem-vindo ao Desafio Titanic Kaggle! Esta é a lendária competição de Machine Learning do Titanic - o melhor e primeiro desafio de ML no Kaggle.\n",
        "\n",
        "Esse projeto foi desenvolvido em colaboração com os Cientistas de Dados:\n",
        "\n",
        ":iphone: Developers contacts\n",
        "\n",
        "Kaio Mendes - [![General badge](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white<SUBJECT>-<STATUS>-<COLOR>.svg)](https://www.linkedin.com/in/kaio-mendes-bba196150)  -  [![General badge](https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white<SUBJECT>-<STATUS>-<COLOR>.svg)](https://github.com/kmendesDev)\n",
        "\n",
        "Cassio Viana - [![General badge](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white<SUBJECT>-<STATUS>-<COLOR>.svg)](https://www.linkedin.com/in/cvs1987)  -  [![General badge](https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white<SUBJECT>-<STATUS>-<COLOR>.svg)](https://github.com/cvs2010)\n",
        "\n",
        "\n",
        "O objetivo desse projeto é utilizar aprendizado de máquina para criar um modelo que preveja quais passageiros sobreviveram ao naufrágio do Titanic.\n",
        "\n",
        "O Desafio: O naufrágio do Titanic é um dos naufrágios mais infames da história. Em 15 de abril de 1912, durante sua viagem inaugural, o RMS Titanic, amplamente considerado \"inafundável\", afundou após colidir com um iceberg. Infelizmente, não havia botes salva-vidas suficientes para todos a bordo, resultando na morte de 1502 dos 2224 passageiros e tripulantes.\n",
        "\n",
        "Embora tenha havido algum elemento de sorte envolvido na sobrevivência, parece que alguns grupos de pessoas tinham mais probabilidade de sobreviver do que outros.\n",
        "\n",
        "Neste projeto, iremos desenvolver um modelo preditivo de Machine Learning que responda à pergunta: \"que tipo de pessoas tinham mais probabilidade de sobreviver?\" usando dados dos passageiros (como nome, idade, gênero, classe socioeconômica, etc).\n",
        "\n",
        "Quais Dados foram usados?\n",
        "Dois conjuntos de dados semelhantes que incluem informações dos passageiros, como nome, idade, gênero, classe socioeconômica, etc. Um conjunto de dados é intitulado train.csv e o outro é intitulado test.csv.\n",
        "\n",
        "O train.csv contém os detalhes de um subconjunto dos passageiros a bordo (exatamente 891) e, o que é mais importante, revelará se eles sobreviveram ou não, também conhecido como \"verdade básica\". Já o conjunto de dados test.csv contém informações semelhantes, mas não revela a \"verdade básica\" para cada passageiro. Sua tarefa é prever esses resultados.\n",
        "\n",
        "Usando os padrões encontrados nos dados do train.csv, prevemos se os 418 passageiros a bordo (encontrados no test.csv) sobreviveram.\n",
        "\n",
        "Confira a pasta \"data\" para explorar ainda mais os conjuntos de dados.\n",
        "\n",
        "https://www.kaggle.com/competitions/titanic/overview/description\n"
      ],
      "metadata": {
        "id": "bzi6DMY8Pe2A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Importação de bibliotecas"
      ],
      "metadata": {
        "id": "lyJOI1yqtJsi"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EkSX0CM0scnO"
      },
      "outputs": [],
      "source": [
        "# Importando bibliotecas para manuseio de dados e visualização\n",
        "import pandas as pd  # Para manusear dados tabulares\n",
        "import seaborn as srn  # Para gráficos estatísticos\n",
        "import matplotlib.pyplot as plt  # Para criação de gráficos\n",
        "\n",
        "# Importando bibliotecas para cálculos matemáticos e estatísticos\n",
        "import statistics as sts  # Para cálculos matemáticos\n",
        "import numpy as np  # Para operações numéricas\n",
        "\n",
        "# Importando bibliotecas para pré-processamento de dados\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "from sklearn.pipeline import make_pipeline\n",
        "\n",
        "# Importando bibliotecas para balanceamento de classes\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "\n",
        "# Importando bibliotecas para avaliação de modelos\n",
        "from sklearn.metrics import (\n",
        "    roc_auc_score,\n",
        "    accuracy_score,\n",
        "    f1_score,\n",
        "    confusion_matrix,\n",
        "    classification_report,\n",
        "    recall_score,\n",
        "    precision_score,\n",
        "    fbeta_score\n",
        ")\n",
        "\n",
        "# Importando bibliotecas para treinamento de modelos\n",
        "from sklearn.model_selection import (\n",
        "    train_test_split,\n",
        "    cross_val_score,\n",
        "    StratifiedKFold,\n",
        "    GridSearchCV,\n",
        ")\n",
        "\n",
        "# Importando algoritmos de aprendizado de máquina\n",
        "from sklearn.svm import SVC, SVR\n",
        "from lightgbm import LGBMClassifier\n",
        "from sklearn.linear_model import SGDClassifier, LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.cluster import KMeans"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Importação dos dados após a limpeza e tratamento"
      ],
      "metadata": {
        "id": "EibQo_avtNM_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Baixar arquivos salvos no github\n",
        "!wget https://github.com/kmendesDev/project_titanic/raw/main/data/training_data_cleaned.csv\n",
        "!wget https://github.com/kmendesDev/project_titanic/raw/main/data/testing_data_cleaned.csv\n",
        "dataset_test = pd.read_csv(\"/content/testing_data_cleaned.csv\")\n",
        "dataset_train = pd.read_csv(\"/content/training_data_cleaned.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VNtBczyIskJW",
        "outputId": "440a85d6-3e30-4541-d31a-94d00355be1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-09-02 14:34:59--  https://github.com/kmendesDev/project_titanic/raw/main/data/training_data_cleaned.csv\n",
            "Resolving github.com (github.com)... 20.27.177.113\n",
            "Connecting to github.com (github.com)|20.27.177.113|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/kmendesDev/project_titanic/main/data/training_data_cleaned.csv [following]\n",
            "--2023-09-02 14:35:00--  https://raw.githubusercontent.com/kmendesDev/project_titanic/main/data/training_data_cleaned.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 49724 (49K) [text/plain]\n",
            "Saving to: ‘training_data_cleaned.csv.2’\n",
            "\n",
            "training_data_clean 100%[===================>]  48.56K  --.-KB/s    in 0.04s   \n",
            "\n",
            "2023-09-02 14:35:00 (1.24 MB/s) - ‘training_data_cleaned.csv.2’ saved [49724/49724]\n",
            "\n",
            "--2023-09-02 14:35:00--  https://github.com/kmendesDev/project_titanic/raw/main/data/testing_data_cleaned.csv\n",
            "Resolving github.com (github.com)... 20.27.177.113\n",
            "Connecting to github.com (github.com)|20.27.177.113|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/kmendesDev/project_titanic/main/data/testing_data_cleaned.csv [following]\n",
            "--2023-09-02 14:35:01--  https://raw.githubusercontent.com/kmendesDev/project_titanic/main/data/testing_data_cleaned.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 22713 (22K) [text/plain]\n",
            "Saving to: ‘testing_data_cleaned.csv.2’\n",
            "\n",
            "testing_data_cleane 100%[===================>]  22.18K  --.-KB/s    in 0.005s  \n",
            "\n",
            "2023-09-02 14:35:01 (4.46 MB/s) - ‘testing_data_cleaned.csv.2’ saved [22713/22713]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S_AW0BY3sxEH",
        "outputId": "555445f8-505f-4ecc-908f-d80c2fda290f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  Sex_female  \\\n",
              "0              1         0       3  22.0      1      0   7.2500           0   \n",
              "1              2         1       1  38.0      1      0  71.2833           1   \n",
              "2              3         1       3  26.0      0      0   7.9250           1   \n",
              "3              4         1       1  35.0      1      0  53.1000           1   \n",
              "4              5         0       3  35.0      0      0   8.0500           0   \n",
              "..           ...       ...     ...   ...    ...    ...      ...         ...   \n",
              "886          887         0       2  27.0      0      0  13.0000           0   \n",
              "887          888         1       1  19.0      0      0  30.0000           1   \n",
              "888          889         0       3  29.0      1      2  23.4500           1   \n",
              "889          890         1       1  26.0      0      0  30.0000           0   \n",
              "890          891         0       3  32.0      0      0   7.7500           0   \n",
              "\n",
              "     Sex_male  Embarked_C  Embarked_Q  Embarked_S  Ticket_number  \\\n",
              "0           1           0           0           1          21171   \n",
              "1           0           1           0           0          17599   \n",
              "2           0           0           0           1        3101282   \n",
              "3           0           0           0           1         113803   \n",
              "4           1           0           0           1         373450   \n",
              "..        ...         ...         ...         ...            ...   \n",
              "886         1           0           0           1         211536   \n",
              "887         0           0           0           1         112053   \n",
              "888         0           0           0           1           6607   \n",
              "889         1           1           0           0         111369   \n",
              "890         1           0           1           0         370376   \n",
              "\n",
              "     Ticket_value_encoded  Ticket_final  Cabin_encoded  Cabin_number  \\\n",
              "0                       1        121171              0             0   \n",
              "1                       2        217599              3            85   \n",
              "2                       3      33101282              0             0   \n",
              "3                       4       4113803              3           123   \n",
              "4                       4       4373450              0             0   \n",
              "..                    ...           ...            ...           ...   \n",
              "886                     4       4211536              0             0   \n",
              "887                     4       4112053              2            42   \n",
              "888                    16        166607              0             0   \n",
              "889                     4       4111369              3           148   \n",
              "890                     4       4370376              0             0   \n",
              "\n",
              "     Cabin_combined  \n",
              "0                 0  \n",
              "1               385  \n",
              "2                 0  \n",
              "3              3123  \n",
              "4                 0  \n",
              "..              ...  \n",
              "886               0  \n",
              "887             242  \n",
              "888               0  \n",
              "889            3148  \n",
              "890               0  \n",
              "\n",
              "[891 rows x 18 columns]"
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              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
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              "      <td>2</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6607</td>\n",
              "      <td>16</td>\n",
              "      <td>166607</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>4</td>\n",
              "      <td>4111369</td>\n",
              "      <td>3</td>\n",
              "      <td>148</td>\n",
              "      <td>3148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>4</td>\n",
              "      <td>4370376</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 18 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0720596b-2284-4d84-b76d-2a7c954e3dd2')\"\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",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0720596b-2284-4d84-b76d-2a7c954e3dd2 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-0720596b-2284-4d84-b76d-2a7c954e3dd2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3e447642-f2ab-4f38-bd0c-e7a5ffd0d2f3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3e447642-f2ab-4f38-bd0c-e7a5ffd0d2f3')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3e447642-f2ab-4f38-bd0c-e7a5ffd0d2f3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_train.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SqkYTIh7xPil",
        "outputId": "ac6364f6-f177-43fe-9947-fd8a2b245b12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass   Age  SibSp  Parch   Fare  Sex_female  \\\n",
              "886          887         0       2  27.0      0      0  13.00           0   \n",
              "887          888         1       1  19.0      0      0  30.00           1   \n",
              "888          889         0       3  29.0      1      2  23.45           1   \n",
              "889          890         1       1  26.0      0      0  30.00           0   \n",
              "890          891         0       3  32.0      0      0   7.75           0   \n",
              "\n",
              "     Sex_male  Embarked_C  Embarked_Q  Embarked_S  Ticket_number  \\\n",
              "886         1           0           0           1         211536   \n",
              "887         0           0           0           1         112053   \n",
              "888         0           0           0           1           6607   \n",
              "889         1           1           0           0         111369   \n",
              "890         1           0           1           0         370376   \n",
              "\n",
              "     Ticket_value_encoded  Ticket_final  Cabin_encoded  Cabin_number  \\\n",
              "886                     4       4211536              0             0   \n",
              "887                     4       4112053              2            42   \n",
              "888                    16        166607              0             0   \n",
              "889                     4       4111369              3           148   \n",
              "890                     4       4370376              0             0   \n",
              "\n",
              "     Cabin_combined  \n",
              "886               0  \n",
              "887             242  \n",
              "888               0  \n",
              "889            3148  \n",
              "890               0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c79ccf18-38b8-4a10-a087-9ceddbd0c955\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
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              "\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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Sex_female</th>\n",
              "      <th>Sex_male</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "      <th>Ticket_number</th>\n",
              "      <th>Ticket_value_encoded</th>\n",
              "      <th>Ticket_final</th>\n",
              "      <th>Cabin_encoded</th>\n",
              "      <th>Cabin_number</th>\n",
              "      <th>Cabin_combined</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>211536</td>\n",
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              "      <td>4211536</td>\n",
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              "      <td>19.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>30.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>112053</td>\n",
              "      <td>4</td>\n",
              "      <td>4112053</td>\n",
              "      <td>2</td>\n",
              "      <td>42</td>\n",
              "      <td>242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>29.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>23.45</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6607</td>\n",
              "      <td>16</td>\n",
              "      <td>166607</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>111369</td>\n",
              "      <td>4</td>\n",
              "      <td>4111369</td>\n",
              "      <td>3</td>\n",
              "      <td>148</td>\n",
              "      <td>3148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.75</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>4</td>\n",
              "      <td>4370376</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\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",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
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              "      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-c79ccf18-38b8-4a10-a087-9ceddbd0c955 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-c79ccf18-38b8-4a10-a087-9ceddbd0c955');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1dbb4f8d-4f13-4f47-8ab5-b719d784df10\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1dbb4f8d-4f13-4f47-8ab5-b719d784df10')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1dbb4f8d-4f13-4f47-8ab5-b719d784df10 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ek7qy-8v-l78",
        "outputId": "663fc428-c72c-4695-c477-87d966a8e77f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_train.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UFOrE9aH-rFG",
        "outputId": "862a0c0c-6fa4-40bf-f20a-fd85710fe4b6"
      },
      "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 18 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   PassengerId           891 non-null    int64  \n",
            " 1   Survived              891 non-null    int64  \n",
            " 2   Pclass                891 non-null    int64  \n",
            " 3   Age                   891 non-null    float64\n",
            " 4   SibSp                 891 non-null    int64  \n",
            " 5   Parch                 891 non-null    int64  \n",
            " 6   Fare                  891 non-null    float64\n",
            " 7   Sex_female            891 non-null    int64  \n",
            " 8   Sex_male              891 non-null    int64  \n",
            " 9   Embarked_C            891 non-null    int64  \n",
            " 10  Embarked_Q            891 non-null    int64  \n",
            " 11  Embarked_S            891 non-null    int64  \n",
            " 12  Ticket_number         891 non-null    int64  \n",
            " 13  Ticket_value_encoded  891 non-null    int64  \n",
            " 14  Ticket_final          891 non-null    int64  \n",
            " 15  Cabin_encoded         891 non-null    int64  \n",
            " 16  Cabin_number          891 non-null    int64  \n",
            " 17  Cabin_combined        891 non-null    int64  \n",
            "dtypes: float64(2), int64(16)\n",
            "memory usage: 125.4 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Vm3j5W3-uXL",
        "outputId": "8c508763-af69-4572-cc41-049fd268ca5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
              "count   891.000000  891.000000  891.000000  891.000000  891.000000   \n",
              "mean    446.000000    0.383838    2.308642   29.560236    0.523008   \n",
              "std     257.353842    0.486592    0.836071   13.005010    1.102743   \n",
              "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
              "25%     223.500000    0.000000    2.000000   22.000000    0.000000   \n",
              "50%     446.000000    0.000000    3.000000   29.000000    0.000000   \n",
              "75%     668.500000    1.000000    3.000000   35.000000    1.000000   \n",
              "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
              "\n",
              "            Parch        Fare  Sex_female    Sex_male  Embarked_C  Embarked_Q  \\\n",
              "count  891.000000  891.000000  891.000000  891.000000  891.000000  891.000000   \n",
              "mean     0.381594   32.204208    0.352413    0.647587    0.188552    0.086420   \n",
              "std      0.806057   49.693429    0.477990    0.477990    0.391372    0.281141   \n",
              "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      0.000000    7.910400    0.000000    0.000000    0.000000    0.000000   \n",
              "50%      0.000000   14.454200    0.000000    1.000000    0.000000    0.000000   \n",
              "75%      0.000000   31.000000    1.000000    1.000000    0.000000    0.000000   \n",
              "max      6.000000  512.329200    1.000000    1.000000    1.000000    1.000000   \n",
              "\n",
              "       Embarked_S  Ticket_number  Ticket_value_encoded  Ticket_final  \\\n",
              "count  891.000000   8.910000e+02            891.000000  8.910000e+02   \n",
              "mean     0.725028   2.552204e+05              6.437710  6.200213e+06   \n",
              "std      0.446751   5.694927e+05              7.335101  2.863388e+07   \n",
              "min      0.000000   0.000000e+00              1.000000  1.900000e+02   \n",
              "25%      0.000000   1.351850e+04              4.000000  2.177580e+05   \n",
              "50%      1.000000   1.108130e+05              4.000000  4.111320e+06   \n",
              "75%      1.000000   3.470735e+05              4.000000  4.347076e+06   \n",
              "max      1.000000   3.101317e+06             44.000000  4.231013e+08   \n",
              "\n",
              "       Cabin_encoded  Cabin_number  Cabin_combined  \n",
              "count     891.000000  8.910000e+02    8.910000e+02  \n",
              "mean        8.343434  1.315773e+05    5.008941e+08  \n",
              "std       108.018688  2.727308e+06    1.052412e+10  \n",
              "min         0.000000  0.000000e+00    0.000000e+00  \n",
              "25%         0.000000  0.000000e+00    0.000000e+00  \n",
              "50%         0.000000  0.000000e+00    0.000000e+00  \n",
              "75%         0.000000  0.000000e+00    0.000000e+00  \n",
              "max      2222.000000  5.759637e+07    2.222576e+11  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4134857a-0a3f-4b3d-824b-3664237a7bcb\" 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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Sex_female</th>\n",
              "      <th>Sex_male</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "      <th>Ticket_number</th>\n",
              "      <th>Ticket_value_encoded</th>\n",
              "      <th>Ticket_final</th>\n",
              "      <th>Cabin_encoded</th>\n",
              "      <th>Cabin_number</th>\n",
              "      <th>Cabin_combined</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",
              "      <td>891.000000</td>\n",
              "      <td>8.910000e+02</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>8.910000e+02</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>8.910000e+02</td>\n",
              "      <td>8.910000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.560236</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "      <td>0.352413</td>\n",
              "      <td>0.647587</td>\n",
              "      <td>0.188552</td>\n",
              "      <td>0.086420</td>\n",
              "      <td>0.725028</td>\n",
              "      <td>2.552204e+05</td>\n",
              "      <td>6.437710</td>\n",
              "      <td>6.200213e+06</td>\n",
              "      <td>8.343434</td>\n",
              "      <td>1.315773e+05</td>\n",
              "      <td>5.008941e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>13.005010</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "      <td>0.477990</td>\n",
              "      <td>0.477990</td>\n",
              "      <td>0.391372</td>\n",
              "      <td>0.281141</td>\n",
              "      <td>0.446751</td>\n",
              "      <td>5.694927e+05</td>\n",
              "      <td>7.335101</td>\n",
              "      <td>2.863388e+07</td>\n",
              "      <td>108.018688</td>\n",
              "      <td>2.727308e+06</td>\n",
              "      <td>1.052412e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</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",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.900000e+02</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</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",
              "      <td>0.000000</td>\n",
              "      <td>1.351850e+04</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.177580e+05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.108130e+05</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.111320e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.470735e+05</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.347076e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</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",
              "      <td>1.000000</td>\n",
              "      <td>3.101317e+06</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>4.231013e+08</td>\n",
              "      <td>2222.000000</td>\n",
              "      <td>5.759637e+07</td>\n",
              "      <td>2.222576e+11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    .colab-df-buttons div {\n",
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              "    [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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4134857a-0a3f-4b3d-824b-3664237a7bcb 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-4134857a-0a3f-4b3d-824b-3664237a7bcb');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-07fecfac-c751-4585-a2ea-369d39595d32')\"\n",
              "            title=\"Suggest charts.\"\n",
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              "\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-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-07fecfac-c751-4585-a2ea-369d39595d32 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6AyHDfrYD4tT",
        "outputId": "a3f0114d-f34c-41a3-8df1-8cf436510f86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Pclass   Age  SibSp  Parch      Fare  Sex_female  Sex_male  \\\n",
              "0            892       3  34.5      0      0    7.8292           0         1   \n",
              "1            893       3  47.0      1      0    7.0000           1         0   \n",
              "2            894       2  62.0      0      0    9.6875           0         1   \n",
              "3            895       3  27.0      0      0    8.6625           0         1   \n",
              "4            896       3  22.0      1      1   12.2875           1         0   \n",
              "..           ...     ...   ...    ...    ...       ...         ...       ...   \n",
              "413         1305       3  30.0      0      0    8.0500           0         1   \n",
              "414         1306       1  39.0      0      0  108.9000           1         0   \n",
              "415         1307       3  38.5      0      0    7.2500           0         1   \n",
              "416         1308       3  30.0      0      0    8.0500           0         1   \n",
              "417         1309       3  30.0      1      1   22.3583           0         1   \n",
              "\n",
              "     Embarked_C  Embarked_Q  Embarked_S  Ticket_number  Ticket_value_encoded  \\\n",
              "0             0           1           0         330911                     0   \n",
              "1             0           0           1         363272                     0   \n",
              "2             0           1           0         240276                     0   \n",
              "3             0           0           1         315154                     0   \n",
              "4             0           0           1        3101298                     0   \n",
              "..          ...         ...         ...            ...                   ...   \n",
              "413           0           0           1           3236                    25   \n",
              "414           1           0           0          17758                     2   \n",
              "415           0           0           1        3101262                    22   \n",
              "416           0           0           1         359309                     0   \n",
              "417           1           0           0           2668                     0   \n",
              "\n",
              "     Ticket_final  Cabin_encoded  Cabin_number  Cabin_combined  \n",
              "0          330911              0             0               0  \n",
              "1          363272              0             0               0  \n",
              "2          240276              0             0               0  \n",
              "3          315154              0             0               0  \n",
              "4         3101298              0             0               0  \n",
              "..            ...            ...           ...             ...  \n",
              "413        253236              0             0               0  \n",
              "414        217758              3           105            3105  \n",
              "415     223101262              0             0               0  \n",
              "416        359309              0             0               0  \n",
              "417          2668              0             0               0  \n",
              "\n",
              "[418 rows x 17 columns]"
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
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              "</table>\n",
              "<p>418 rows × 17 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e57fe76-1a0d-46b1-8bdc-25b5175b6303')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1e57fe76-1a0d-46b1-8bdc-25b5175b6303 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-1e57fe76-1a0d-46b1-8bdc-25b5175b6303');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-41082357-b2c9-4fd4-9daf-6ddf9bf8fa4d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-41082357-b2c9-4fd4-9daf-6ddf9bf8fa4d')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-41082357-b2c9-4fd4-9daf-6ddf9bf8fa4d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nJ5dZTKPD9V4",
        "outputId": "0b5b1f6b-2ad5-4285-e499-e98a4be6b331"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Pclass   Age  SibSp  Parch      Fare  Sex_female  Sex_male  \\\n",
              "413         1305       3  30.0      0      0    8.0500           0         1   \n",
              "414         1306       1  39.0      0      0  108.9000           1         0   \n",
              "415         1307       3  38.5      0      0    7.2500           0         1   \n",
              "416         1308       3  30.0      0      0    8.0500           0         1   \n",
              "417         1309       3  30.0      1      1   22.3583           0         1   \n",
              "\n",
              "     Embarked_C  Embarked_Q  Embarked_S  Ticket_number  Ticket_value_encoded  \\\n",
              "413           0           0           1           3236                    25   \n",
              "414           1           0           0          17758                     2   \n",
              "415           0           0           1        3101262                    22   \n",
              "416           0           0           1         359309                     0   \n",
              "417           1           0           0           2668                     0   \n",
              "\n",
              "     Ticket_final  Cabin_encoded  Cabin_number  Cabin_combined  \n",
              "413        253236              0             0               0  \n",
              "414        217758              3           105            3105  \n",
              "415     223101262              0             0               0  \n",
              "416        359309              0             0               0  \n",
              "417          2668              0             0               0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bfa37313-7a71-42bd-8ca5-4db6377c0362\" 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>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Sex_female</th>\n",
              "      <th>Sex_male</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "      <th>Ticket_number</th>\n",
              "      <th>Ticket_value_encoded</th>\n",
              "      <th>Ticket_final</th>\n",
              "      <th>Cabin_encoded</th>\n",
              "      <th>Cabin_number</th>\n",
              "      <th>Cabin_combined</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>1305</td>\n",
              "      <td>3</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3236</td>\n",
              "      <td>25</td>\n",
              "      <td>253236</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1306</td>\n",
              "      <td>1</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>108.9000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17758</td>\n",
              "      <td>2</td>\n",
              "      <td>217758</td>\n",
              "      <td>3</td>\n",
              "      <td>105</td>\n",
              "      <td>3105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>1307</td>\n",
              "      <td>3</td>\n",
              "      <td>38.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3101262</td>\n",
              "      <td>22</td>\n",
              "      <td>223101262</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>1308</td>\n",
              "      <td>3</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>359309</td>\n",
              "      <td>0</td>\n",
              "      <td>359309</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>1309</td>\n",
              "      <td>3</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>22.3583</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2668</td>\n",
              "      <td>0</td>\n",
              "      <td>2668</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bfa37313-7a71-42bd-8ca5-4db6377c0362')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bfa37313-7a71-42bd-8ca5-4db6377c0362 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-bfa37313-7a71-42bd-8ca5-4db6377c0362');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e668a170-ee3c-4b1a-800e-ab01da4a53a8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e668a170-ee3c-4b1a-800e-ab01da4a53a8')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e668a170-ee3c-4b1a-800e-ab01da4a53a8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oM0FTQUcEAMy",
        "outputId": "5c2de6df-94fa-46e4-cb63-e3ad35ef63f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(418, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1VUyaFcJEBvL",
        "outputId": "20d6f33e-0c31-4044-ba77-1322f2ae2c91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 17 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   PassengerId           418 non-null    int64  \n",
            " 1   Pclass                418 non-null    int64  \n",
            " 2   Age                   418 non-null    float64\n",
            " 3   SibSp                 418 non-null    int64  \n",
            " 4   Parch                 418 non-null    int64  \n",
            " 5   Fare                  418 non-null    float64\n",
            " 6   Sex_female            418 non-null    int64  \n",
            " 7   Sex_male              418 non-null    int64  \n",
            " 8   Embarked_C            418 non-null    int64  \n",
            " 9   Embarked_Q            418 non-null    int64  \n",
            " 10  Embarked_S            418 non-null    int64  \n",
            " 11  Ticket_number         418 non-null    int64  \n",
            " 12  Ticket_value_encoded  418 non-null    int64  \n",
            " 13  Ticket_final          418 non-null    int64  \n",
            " 14  Cabin_encoded         418 non-null    int64  \n",
            " 15  Cabin_number          418 non-null    int64  \n",
            " 16  Cabin_combined        418 non-null    int64  \n",
            "dtypes: float64(2), int64(15)\n",
            "memory usage: 55.6 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hNY8fS1wED16",
        "outputId": "d94c192c-94c7-4c7e-da3c-0819d8b0c9b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PassengerId      Pclass         Age       SibSp       Parch  \\\n",
              "count   418.000000  418.000000  418.000000  418.000000  418.000000   \n",
              "mean   1100.500000    2.265550   30.216507    0.447368    0.392344   \n",
              "std     120.810458    0.841838   12.635016    0.896760    0.981429   \n",
              "min     892.000000    1.000000    0.170000    0.000000    0.000000   \n",
              "25%     996.250000    1.000000   23.000000    0.000000    0.000000   \n",
              "50%    1100.500000    3.000000   30.000000    0.000000    0.000000   \n",
              "75%    1204.750000    3.000000   35.750000    1.000000    0.000000   \n",
              "max    1309.000000    3.000000   76.000000    8.000000    9.000000   \n",
              "\n",
              "             Fare  Sex_female    Sex_male  Embarked_C  Embarked_Q  Embarked_S  \\\n",
              "count  418.000000  418.000000  418.000000  418.000000  418.000000  418.000000   \n",
              "mean    35.625688    0.363636    0.636364    0.244019    0.110048    0.645933   \n",
              "std     55.840509    0.481622    0.481622    0.430019    0.313324    0.478803   \n",
              "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      7.895800    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "50%     14.454200    0.000000    1.000000    0.000000    0.000000    1.000000   \n",
              "75%     31.500000    1.000000    1.000000    0.000000    0.000000    1.000000   \n",
              "max    512.329200    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
              "\n",
              "       Ticket_number  Ticket_value_encoded  Ticket_final  Cabin_encoded  \\\n",
              "count   4.180000e+02            418.000000  4.180000e+02     418.000000   \n",
              "mean    2.384720e+05              3.921053  5.735537e+06      20.260766   \n",
              "std     5.537201e+05              9.143985  3.681155e+07     189.561444   \n",
              "min     0.000000e+00              0.000000  0.000000e+00       0.000000   \n",
              "25%     1.309650e+04              0.000000  2.815475e+04       0.000000   \n",
              "50%     3.384400e+04              0.000000  2.238600e+05       0.000000   \n",
              "75%     3.431312e+05              2.000000  3.492342e+05       0.000000   \n",
              "max     3.101315e+06             43.000000  4.231013e+08    2222.000000   \n",
              "\n",
              "       Cabin_number  Cabin_combined  \n",
              "count  4.180000e+02    4.180000e+02  \n",
              "mean   4.170550e+05    1.597816e+09  \n",
              "std    4.867543e+06    1.878367e+10  \n",
              "min    0.000000e+00    0.000000e+00  \n",
              "25%    0.000000e+00    0.000000e+00  \n",
              "50%    0.000000e+00    0.000000e+00  \n",
              "75%    0.000000e+00    0.000000e+00  \n",
              "max    5.759637e+07    2.222576e+11  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4060dd04-0798-4b77-95f8-4ea1da12bb5f\" 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>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Sex_female</th>\n",
              "      <th>Sex_male</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "      <th>Ticket_number</th>\n",
              "      <th>Ticket_value_encoded</th>\n",
              "      <th>Ticket_final</th>\n",
              "      <th>Cabin_encoded</th>\n",
              "      <th>Cabin_number</th>\n",
              "      <th>Cabin_combined</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>4.180000e+02</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>4.180000e+02</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>4.180000e+02</td>\n",
              "      <td>4.180000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1100.500000</td>\n",
              "      <td>2.265550</td>\n",
              "      <td>30.216507</td>\n",
              "      <td>0.447368</td>\n",
              "      <td>0.392344</td>\n",
              "      <td>35.625688</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.636364</td>\n",
              "      <td>0.244019</td>\n",
              "      <td>0.110048</td>\n",
              "      <td>0.645933</td>\n",
              "      <td>2.384720e+05</td>\n",
              "      <td>3.921053</td>\n",
              "      <td>5.735537e+06</td>\n",
              "      <td>20.260766</td>\n",
              "      <td>4.170550e+05</td>\n",
              "      <td>1.597816e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>120.810458</td>\n",
              "      <td>0.841838</td>\n",
              "      <td>12.635016</td>\n",
              "      <td>0.896760</td>\n",
              "      <td>0.981429</td>\n",
              "      <td>55.840509</td>\n",
              "      <td>0.481622</td>\n",
              "      <td>0.481622</td>\n",
              "      <td>0.430019</td>\n",
              "      <td>0.313324</td>\n",
              "      <td>0.478803</td>\n",
              "      <td>5.537201e+05</td>\n",
              "      <td>9.143985</td>\n",
              "      <td>3.681155e+07</td>\n",
              "      <td>189.561444</td>\n",
              "      <td>4.867543e+06</td>\n",
              "      <td>1.878367e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>892.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.170000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>996.250000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.895800</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>1.309650e+04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.815475e+04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1100.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.384400e+04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.238600e+05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1204.750000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>35.750000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.431312e+05</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.492342e+05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1309.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</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",
              "      <td>1.000000</td>\n",
              "      <td>3.101315e+06</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>4.231013e+08</td>\n",
              "      <td>2222.000000</td>\n",
              "      <td>5.759637e+07</td>\n",
              "      <td>2.222576e+11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4060dd04-0798-4b77-95f8-4ea1da12bb5f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\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",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4060dd04-0798-4b77-95f8-4ea1da12bb5f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0850fc4c-4819-4bbd-9ce1-7900b3768e59\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0850fc4c-4819-4bbd-9ce1-7900b3768e59')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0850fc4c-4819-4bbd-9ce1-7900b3768e59 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modelo de Machine Learning"
      ],
      "metadata": {
        "id": "CY3-gR2hL3w4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checando os dados antes de instanciar o modelo\n",
        "- Verificando valores NaN\n",
        "- Verificando valores numéricos\n",
        "- Verificando se ainda há dados faltantes\n",
        "- Verificando se ainda há dados categóricos\n",
        "- Verificando nomes de colunas corretos nos datasets de treinamento e teste"
      ],
      "metadata": {
        "id": "-IyEWO6YMMgB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_train_ml = dataset_train\n",
        "dataset_test_ml = dataset_test"
      ],
      "metadata": {
        "id": "28lkU5L3L6g3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_train_ml"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H2xLWRdBMGQr",
        "outputId": "dfb03d6b-ecd2-4982-f08f-29b274e92b90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  Sex_female  \\\n",
              "0              1         0       3  22.0      1      0   7.2500           0   \n",
              "1              2         1       1  38.0      1      0  71.2833           1   \n",
              "2              3         1       3  26.0      0      0   7.9250           1   \n",
              "3              4         1       1  35.0      1      0  53.1000           1   \n",
              "4              5         0       3  35.0      0      0   8.0500           0   \n",
              "..           ...       ...     ...   ...    ...    ...      ...         ...   \n",
              "886          887         0       2  27.0      0      0  13.0000           0   \n",
              "887          888         1       1  19.0      0      0  30.0000           1   \n",
              "888          889         0       3  29.0      1      2  23.4500           1   \n",
              "889          890         1       1  26.0      0      0  30.0000           0   \n",
              "890          891         0       3  32.0      0      0   7.7500           0   \n",
              "\n",
              "     Sex_male  Embarked_C  Embarked_Q  Embarked_S  Ticket_number  \\\n",
              "0           1           0           0           1          21171   \n",
              "1           0           1           0           0          17599   \n",
              "2           0           0           0           1        3101282   \n",
              "3           0           0           0           1         113803   \n",
              "4           1           0           0           1         373450   \n",
              "..        ...         ...         ...         ...            ...   \n",
              "886         1           0           0           1         211536   \n",
              "887         0           0           0           1         112053   \n",
              "888         0           0           0           1           6607   \n",
              "889         1           1           0           0         111369   \n",
              "890         1           0           1           0         370376   \n",
              "\n",
              "     Ticket_value_encoded  Ticket_final  Cabin_encoded  Cabin_number  \\\n",
              "0                       1        121171              0             0   \n",
              "1                       2        217599              3            85   \n",
              "2                       3      33101282              0             0   \n",
              "3                       4       4113803              3           123   \n",
              "4                       4       4373450              0             0   \n",
              "..                    ...           ...            ...           ...   \n",
              "886                     4       4211536              0             0   \n",
              "887                     4       4112053              2            42   \n",
              "888                    16        166607              0             0   \n",
              "889                     4       4111369              3           148   \n",
              "890                     4       4370376              0             0   \n",
              "\n",
              "     Cabin_combined  \n",
              "0                 0  \n",
              "1               385  \n",
              "2                 0  \n",
              "3              3123  \n",
              "4                 0  \n",
              "..              ...  \n",
              "886               0  \n",
              "887             242  \n",
              "888               0  \n",
              "889            3148  \n",
              "890               0  \n",
              "\n",
              "[891 rows x 18 columns]"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
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              "      <th>Embarked_Q</th>\n",
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              "      <th>Ticket_final</th>\n",
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              "      <th>Cabin_number</th>\n",
              "      <th>Cabin_combined</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>0</th>\n",
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              "      <td>38.0</td>\n",
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              "      <th>3</th>\n",
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              "      <td>113803</td>\n",
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              "      <td>3</td>\n",
              "      <td>123</td>\n",
              "      <td>3123</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>886</th>\n",
              "      <td>887</td>\n",
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              "      <td>242</td>\n",
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              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
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              "      <th>889</th>\n",
              "      <td>890</td>\n",
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              "      <td>0</td>\n",
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              "      <td>3148</td>\n",
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              "      <th>890</th>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 18 columns</p>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0705c2bf-8855-41b8-bfc1-b9c6eda4d558 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-0705c2bf-8855-41b8-bfc1-b9c6eda4d558');\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",
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              "\n",
              "\n",
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              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      --bg-color: #E8F0FE;\n",
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              "    display: none;\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a8e71ae1-9a8b-4a12-97a1-ab84601d3030 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test_ml"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IhICPpVzMH_g",
        "outputId": "8658328a-40d8-432d-b21f-971912bb8fab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Pclass   Age  SibSp  Parch      Fare  Sex_female  Sex_male  \\\n",
              "0            892       3  34.5      0      0    7.8292           0         1   \n",
              "1            893       3  47.0      1      0    7.0000           1         0   \n",
              "2            894       2  62.0      0      0    9.6875           0         1   \n",
              "3            895       3  27.0      0      0    8.6625           0         1   \n",
              "4            896       3  22.0      1      1   12.2875           1         0   \n",
              "..           ...     ...   ...    ...    ...       ...         ...       ...   \n",
              "413         1305       3  30.0      0      0    8.0500           0         1   \n",
              "414         1306       1  39.0      0      0  108.9000           1         0   \n",
              "415         1307       3  38.5      0      0    7.2500           0         1   \n",
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              "417         1309       3  30.0      1      1   22.3583           0         1   \n",
              "\n",
              "     Embarked_C  Embarked_Q  Embarked_S  Ticket_number  Ticket_value_encoded  \\\n",
              "0             0           1           0         330911                     0   \n",
              "1             0           0           1         363272                     0   \n",
              "2             0           1           0         240276                     0   \n",
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              "..          ...         ...         ...            ...                   ...   \n",
              "413           0           0           1           3236                    25   \n",
              "414           1           0           0          17758                     2   \n",
              "415           0           0           1        3101262                    22   \n",
              "416           0           0           1         359309                     0   \n",
              "417           1           0           0           2668                     0   \n",
              "\n",
              "     Ticket_final  Cabin_encoded  Cabin_number  Cabin_combined  \n",
              "0          330911              0             0               0  \n",
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              "..            ...            ...           ...             ...  \n",
              "413        253236              0             0               0  \n",
              "414        217758              3           105            3105  \n",
              "415     223101262              0             0               0  \n",
              "416        359309              0             0               0  \n",
              "417          2668              0             0               0  \n",
              "\n",
              "[418 rows x 17 columns]"
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-61128bb4-55ee-4d28-9a57-ecfcd29ceca7 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-61128bb4-55ee-4d28-9a57-ecfcd29ceca7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-07157650-a3a5-4f97-9f6b-bbe797588f41\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-07157650-a3a5-4f97-9f6b-bbe797588f41')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-07157650-a3a5-4f97-9f6b-bbe797588f41 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test_ml.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxBMfSKcHNId",
        "outputId": "4df6521c-b5df-41f0-9b23-696fcc9c4f92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(418, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_train_ml.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Upo60sgmHPiC",
        "outputId": "30af46cd-c3f2-41bd-8c7b-6a426bea3fdc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test_ml.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y8ZC2bHRHRBq",
        "outputId": "6d48dfc5-1b34-4836-8a3e-d3a879bbd958"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 17 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   PassengerId           418 non-null    int64  \n",
            " 1   Pclass                418 non-null    int64  \n",
            " 2   Age                   418 non-null    float64\n",
            " 3   SibSp                 418 non-null    int64  \n",
            " 4   Parch                 418 non-null    int64  \n",
            " 5   Fare                  418 non-null    float64\n",
            " 6   Sex_female            418 non-null    int64  \n",
            " 7   Sex_male              418 non-null    int64  \n",
            " 8   Embarked_C            418 non-null    int64  \n",
            " 9   Embarked_Q            418 non-null    int64  \n",
            " 10  Embarked_S            418 non-null    int64  \n",
            " 11  Ticket_number         418 non-null    int64  \n",
            " 12  Ticket_value_encoded  418 non-null    int64  \n",
            " 13  Ticket_final          418 non-null    int64  \n",
            " 14  Cabin_encoded         418 non-null    int64  \n",
            " 15  Cabin_number          418 non-null    int64  \n",
            " 16  Cabin_combined        418 non-null    int64  \n",
            "dtypes: float64(2), int64(15)\n",
            "memory usage: 55.6 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_train_ml.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70LnePOZHTrM",
        "outputId": "6d39fa85-afe7-4b74-9a0a-08ae0e82d928"
      },
      "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 18 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   PassengerId           891 non-null    int64  \n",
            " 1   Survived              891 non-null    int64  \n",
            " 2   Pclass                891 non-null    int64  \n",
            " 3   Age                   891 non-null    float64\n",
            " 4   SibSp                 891 non-null    int64  \n",
            " 5   Parch                 891 non-null    int64  \n",
            " 6   Fare                  891 non-null    float64\n",
            " 7   Sex_female            891 non-null    int64  \n",
            " 8   Sex_male              891 non-null    int64  \n",
            " 9   Embarked_C            891 non-null    int64  \n",
            " 10  Embarked_Q            891 non-null    int64  \n",
            " 11  Embarked_S            891 non-null    int64  \n",
            " 12  Ticket_number         891 non-null    int64  \n",
            " 13  Ticket_value_encoded  891 non-null    int64  \n",
            " 14  Ticket_final          891 non-null    int64  \n",
            " 15  Cabin_encoded         891 non-null    int64  \n",
            " 16  Cabin_number          891 non-null    int64  \n",
            " 17  Cabin_combined        891 non-null    int64  \n",
            "dtypes: float64(2), int64(16)\n",
            "memory usage: 125.4 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificando valores NaN no dataset_training_ml\n",
        "dataset_train_ml.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v-vvaECaJ4s0",
        "outputId": "baa96bbc-1b01-4732-e79c-117999a2793c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId             0\n",
              "Survived                0\n",
              "Pclass                  0\n",
              "Age                     0\n",
              "SibSp                   0\n",
              "Parch                   0\n",
              "Fare                    0\n",
              "Sex_female              0\n",
              "Sex_male                0\n",
              "Embarked_C              0\n",
              "Embarked_Q              0\n",
              "Embarked_S              0\n",
              "Ticket_number           0\n",
              "Ticket_value_encoded    0\n",
              "Ticket_final            0\n",
              "Cabin_encoded           0\n",
              "Cabin_number            0\n",
              "Cabin_combined          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificando valores NaN no dataset_training_ml\n",
        "dataset_test_ml.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1TPwEYqJ5cD",
        "outputId": "ae65df9c-9298-4a4e-a5cf-1046c749d999"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId             0\n",
              "Pclass                  0\n",
              "Age                     0\n",
              "SibSp                   0\n",
              "Parch                   0\n",
              "Fare                    0\n",
              "Sex_female              0\n",
              "Sex_male                0\n",
              "Embarked_C              0\n",
              "Embarked_Q              0\n",
              "Embarked_S              0\n",
              "Ticket_number           0\n",
              "Ticket_value_encoded    0\n",
              "Ticket_final            0\n",
              "Cabin_encoded           0\n",
              "Cabin_number            0\n",
              "Cabin_combined          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checando os nomes das colunas nos datasets de treino e teste\n",
        "print('Colunas do dataset_train_ml: ', dataset_train_ml.columns)\n",
        "print('/n')\n",
        "print('Colunas do dataset_test_ml: ', dataset_test_ml.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mCZR-CTEQpSS",
        "outputId": "371076e1-337c-46e8-a372-7ce0f9b29c94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colunas do dataset_train_ml:  Index(['PassengerId', 'Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare',\n",
            "       'Sex_female', 'Sex_male', 'Embarked_C', 'Embarked_Q', 'Embarked_S',\n",
            "       'Ticket_number', 'Ticket_value_encoded', 'Ticket_final',\n",
            "       'Cabin_encoded', 'Cabin_number', 'Cabin_combined'],\n",
            "      dtype='object')\n",
            "/n\n",
            "Colunas do dataset_test_ml:  Index(['PassengerId', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'Sex_female',\n",
            "       'Sex_male', 'Embarked_C', 'Embarked_Q', 'Embarked_S', 'Ticket_number',\n",
            "       'Ticket_value_encoded', 'Ticket_final', 'Cabin_encoded', 'Cabin_number',\n",
            "       'Cabin_combined'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tudo ok\n",
        "- Mesma quantidade de colunas (training tem uma coluna a mais 'Survived')\n",
        "- Mesmas etiquetas nas colunas dos datasets de treino e teste\n",
        "- Todos os valores numéricos com o mesmo tipo em cada coluna\n",
        "- Não há valores faltantes"
      ],
      "metadata": {
        "id": "mfntUJ2aJfNU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Criando o protótipo do modelo"
      ],
      "metadata": {
        "id": "xvySGy-BMPZE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Definindo qual modelo de machine learning utilizar"
      ],
      "metadata": {
        "id": "9iD-BzS-6nTz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Breve explicação a cerca de cada modelo:\n",
        "\n",
        "1. **RandomForestClassifier (rf)**:\n",
        "   - Tipo: Ensemble de árvores de decisão.\n",
        "   - Funcionamento: Cria um conjunto de árvores de decisão e combina suas previsões para obter uma decisão final.\n",
        "   - Uso: Classificação e também pode ser usado para tarefas de regressão.\n",
        "\n",
        "2. **DecisionTreeClassifier (dt)**:\n",
        "   - Tipo: Árvore de decisão.\n",
        "   - Funcionamento: Constrói uma árvore de decisão binária que divide o espaço de características em regiões para tomar decisões de classificação.\n",
        "   - Uso: Classificação.\n",
        "\n",
        "3. **SGDClassifier (sgdc)**:\n",
        "   - Tipo: Gradiente Descendente Estocástico (SGD) Classifier.\n",
        "   - Funcionamento: Usa o SGD para treinar modelos lineares, incluindo SVMs lineares e regressão logística.\n",
        "   - Uso: Classificação.\n",
        "\n",
        "4. **SVC (svc)**:\n",
        "   - Tipo: Support Vector Classifier (SVC).\n",
        "   - Funcionamento: Usa vetores de suporte para encontrar um hiperplano que melhor separa as classes em espaços de alta dimensão.\n",
        "   - Uso: Classificação.\n",
        "\n",
        "5. **LogisticRegression (lr)**:\n",
        "   - Tipo: Regressão Logística.\n",
        "   - Funcionamento: Modela a probabilidade de uma classe usando uma função logística.\n",
        "   - Uso: Classificação.\n",
        "\n",
        "6. **XGBClassifier (xgb)**:\n",
        "   - Tipo: Gradient Boosting Classifier.\n",
        "   - Funcionamento: Usa o método de impulso para melhorar o desempenho dos modelos fracos, geralmente árvores de decisão.\n",
        "   - Uso: Classificação.\n",
        "\n",
        "7. **LGBMClassifier (lgbm)**:\n",
        "   - Tipo: Gradient Boosting Classifier, com otimização para eficiência.\n",
        "   - Funcionamento: Semelhante ao XGBoost, mas otimizado para velocidade e eficiência computacional.\n",
        "   - Uso: Classificação.\n",
        "\n",
        "8. **GaussianNB (nb)**:\n",
        "   - Tipo: Naive Bayes.\n",
        "   - Funcionamento: Baseado no teorema de Bayes, assume independência condicional entre as características.\n",
        "   - Uso: Classificação, especialmente quando as premissas ingênuas de independência são atendidas.\n",
        "\n",
        "Cada um desses modelos é adequado para diferentes tipos de problemas de classificação, e a escolha depende da natureza dos dados e dos requisitos de desempenho.\n",
        "\n",
        "Os modelos K-means e SVR (support vector regression) não são adequados para problemas de classificação.\n",
        "\n",
        "  9. **K-Means:**\n",
        "\n",
        "  Tipo de Problema:\n",
        "\n",
        "  O K-Means é usado para clusterização, onde o objetivo é agrupar dados sem rótulos em grupos com base em similaridade.\n",
        "  \n",
        "  Supervisão:\n",
        "\n",
        "    - Os modelos de classificação requerem dados de treinamento rotulados para aprender a fazer previsões.\n",
        "    - O K-Means não requer rótulos; ele encontra agrupamentos com base apenas na similaridade entre os dados.\n",
        "    Saída:\n",
        "\n",
        "  Os modelos de classificação produzem uma classe como saída (por exemplo, \"spam\" ou \"não spam\").\n",
        "  O K-Means produz rótulos de clusters para cada observação.\n",
        "\n",
        "  10. **SVR (Support Vector Regression):**\n",
        "\n",
        "  Tipo de Problema:\n",
        "\n",
        "  O SVR é usado para regressão, onde o objetivo é prever um valor contínuo (por exemplo, prever o preço de uma casa).\n",
        "  Saída:\n",
        "\n",
        "    - Os modelos de classificação produzem classes categóricas como saída (por exemplo, \"sim\" ou \"não\").\n",
        "    - O SVR produz valores numéricos como saída, que representam uma estimativa contínua.\n",
        "\n",
        "\n",
        "Os modelos de classificação usam algoritmos que se concentram na separação de classes, como árvores de decisão, regressão logística, entre outros.\n",
        "O SVR utiliza a técnica de Máquinas de Vetores de Suporte para realizar a regressão, encontrando um hiperplano que melhor se ajusta aos dados de treinamento. Em resumo, a principal diferença está no tipo de problema que eles resolvem e na natureza de suas saídas. Os outros modelos são amplamente usados para classificação, enquanto o K-Means é usado para clusterização e o SVR para regressão."
      ],
      "metadata": {
        "id": "b8OcNotABHkP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def val_model(X, y, clf):\n",
        "    \"\"\"\n",
        "    Cross Validation Function.\n",
        "\n",
        "    # Arguments\n",
        "        X: DataFrame, independent variables.\n",
        "        y: Series, vector, target variable.\n",
        "        clf: Scikit-learn Classifier.\n",
        "        quiet: bool, indicating whether the function should print the results.\n",
        "\n",
        "    # Returns\n",
        "        tuple, containing cross-validation average accuracy, ROC AUC, Recall, Precision, F1-Score\n",
        "    \"\"\"\n",
        "    # Transforming X and y to numpy arrays\n",
        "    X = np.array(X)\n",
        "    y = np.array(y)\n",
        "    # Creating a pipeline with StandardScaler and clf (classifier model)\n",
        "    pipeline = make_pipeline(StandardScaler(), clf)\n",
        "    # Performing cross validation and scoring metrics\n",
        "    scores = cross_val_score(pipeline, X, y, scoring='accuracy')\n",
        "    roc_auc_scores = cross_val_score(pipeline, X, y, scoring='roc_auc')\n",
        "    recall_scores = cross_val_score(pipeline, X, y, scoring='recall')\n",
        "    precision_scores = cross_val_score(pipeline, X, y, scoring='precision')\n",
        "    f1_scores = cross_val_score(pipeline, X, y, scoring='f1')\n",
        "    # Returning results\n",
        "    return scores.mean(), roc_auc_scores.mean(), recall_scores.mean(), precision_scores.mean(), f1_scores.mean()\n",
        "\n",
        "# Instantiating the variables X and y\n",
        "X = dataset_train_ml.drop(columns=['Survived'])\n",
        "y = dataset_train_ml['Survived']\n",
        "\n",
        "# Splitting the dataset into the Training set and Test set\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Standardizing the data (Centering: The mean (average) of the data and Scaling: The standard deviation)\n",
        "scaler = StandardScaler().fit(X_train)\n",
        "X_train = scaler.transform(X_train)\n",
        "\n",
        "# X_train columns names\n",
        "X_train = pd.DataFrame(X_train, columns=X.columns)\n",
        "\n",
        "# Undersampling (data preprocessing technique used to balance class distributions in imbalanced datasets)\n",
        "rus = RandomUnderSampler(random_state=42)\n",
        "X_train_rus, y_train_rus = rus.fit_resample(X_train, y_train)\n",
        "\n",
        "# Instantiating the models\n",
        "rf = RandomForestClassifier(random_state=42)\n",
        "dt = DecisionTreeClassifier(random_state=42)\n",
        "sgdc = SGDClassifier(random_state=42)\n",
        "svc = SVC(random_state=42)\n",
        "lr = LogisticRegression(random_state=42)\n",
        "xgb = XGBClassifier(random_state=42)\n",
        "lgbm = LGBMClassifier(random_state=42, verbose=-1)\n",
        "nb = GaussianNB()\n",
        "\n",
        "# Creating the lists\n",
        "model = []\n",
        "accuracy = []\n",
        "roc_auc = []\n",
        "recall = []\n",
        "precision = []\n",
        "f1 = []\n",
        "confusion_matrices = []\n",
        "\n",
        "# Fitting the models\n",
        "rf_fit = rf.fit(X_train_rus, y_train_rus)\n",
        "dt_fit = dt.fit(X_train_rus, y_train_rus)\n",
        "sgdc_fit = sgdc.fit(X_train_rus, y_train_rus)\n",
        "svc_fit = svc.fit(X_train_rus, y_train_rus)\n",
        "lr_fit = lr.fit(X_train_rus, y_train_rus)\n",
        "xgb_fit = xgb.fit(X_train_rus, y_train_rus)\n",
        "lgbm_fit = lgbm.fit(X_train_rus, y_train_rus)\n",
        "nb_fit = nb.fit(X_train_rus, y_train_rus)\n",
        "\n",
        "# Evaluating results (accuracy, roc_auc, recall, precision, f1, confusion matrix)\n",
        "# This loop iterates through each of the listed models, collects the name of the classifier's class,\n",
        "# and calculates performance metrics using the val_model function.\n",
        "for clf in (rf, dt, sgdc, svc, lr, xgb, lgbm, nb):\n",
        "    model.append(clf.__class__.__name__)\n",
        "    acc, roc_auc_score, recall_score, precision_score_value, f1_score_value = val_model(X_train_rus, y_train_rus, clf)\n",
        "    # Calculating confusion matrix\n",
        "    y_pred = clf.predict(X_test)\n",
        "    confusion_matrix_value = confusion_matrix(y_test, y_pred)\n",
        "    # Appending the scores of each model\n",
        "    accuracy.append(acc)\n",
        "    roc_auc.append(roc_auc_score)\n",
        "    recall.append(recall_score)\n",
        "    precision.append(precision_score_value)\n",
        "    f1.append(f1_score_value)\n",
        "    confusion_matrices.append(confusion_matrix_value)\n",
        "\n",
        "# Creating a DataFrame to store results\n",
        "results_df = pd.DataFrame({\n",
        "    'Model': model,\n",
        "    'Accuracy': accuracy,\n",
        "    'ROC AUC': roc_auc,\n",
        "    'Recall': recall,\n",
        "    'Precision': precision,\n",
        "    'F1-Score': f1,\n",
        "    'Confusion Matrix': confusion_matrices\n",
        "})\n",
        "\n",
        "# Calculating the mean of metrics\n",
        "results_df['Mean Score'] = results_df[['Accuracy', 'ROC AUC', 'Recall', 'Precision', 'F1-Score']].mean(axis=1)\n",
        "\n",
        "# Starting the index from 1\n",
        "results_df.index = results_df.index + 1\n",
        "\n",
        "# Showing the results\n",
        "results_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "SWN-3ud90MoL",
        "outputId": "a7dd3145-c8c1-4087-cb87-dc3552f8a7da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Model  Accuracy   ROC AUC    Recall  Precision  F1-Score  \\\n",
              "1  RandomForestClassifier  0.783610  0.850151  0.746331   0.808187  0.775789   \n",
              "2  DecisionTreeClassifier  0.699567  0.699476  0.686443   0.709494  0.696068   \n",
              "3           SGDClassifier  0.705227  0.742181  0.698043   0.709487  0.697864   \n",
              "4                     SVC  0.785445  0.839810  0.724039   0.827842  0.770538   \n",
              "5      LogisticRegression  0.764849  0.840512  0.753529   0.770934  0.761292   \n",
              "6           XGBClassifier  0.763067  0.845762  0.753739   0.768507  0.760370   \n",
              "7          LGBMClassifier  0.791121  0.859413  0.787491   0.796234  0.791120   \n",
              "8              GaussianNB  0.518640  0.784287  0.067086   0.726667  0.121017   \n",
              "\n",
              "       Confusion Matrix  Mean Score  \n",
              "1    [[7, 98], [8, 66]]    0.792814  \n",
              "2   [[105, 0], [74, 0]]    0.698210  \n",
              "3   [[105, 0], [73, 1]]    0.710560  \n",
              "4   [[105, 0], [74, 0]]    0.789535  \n",
              "5   [[105, 0], [73, 1]]    0.778223  \n",
              "6   [[105, 0], [71, 3]]    0.778289  \n",
              "7  [[88, 17], [22, 52]]    0.805076  \n",
              "8  [[95, 10], [45, 29]]    0.443539  "
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
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              "\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>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>ROC AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Precision</th>\n",
              "      <th>F1-Score</th>\n",
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              "      <th>Mean Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>0.783610</td>\n",
              "      <td>0.850151</td>\n",
              "      <td>0.746331</td>\n",
              "      <td>0.808187</td>\n",
              "      <td>0.775789</td>\n",
              "      <td>[[7, 98], [8, 66]]</td>\n",
              "      <td>0.792814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>DecisionTreeClassifier</td>\n",
              "      <td>0.699567</td>\n",
              "      <td>0.699476</td>\n",
              "      <td>0.686443</td>\n",
              "      <td>0.709494</td>\n",
              "      <td>0.696068</td>\n",
              "      <td>[[105, 0], [74, 0]]</td>\n",
              "      <td>0.698210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SGDClassifier</td>\n",
              "      <td>0.705227</td>\n",
              "      <td>0.742181</td>\n",
              "      <td>0.698043</td>\n",
              "      <td>0.709487</td>\n",
              "      <td>0.697864</td>\n",
              "      <td>[[105, 0], [73, 1]]</td>\n",
              "      <td>0.710560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SVC</td>\n",
              "      <td>0.785445</td>\n",
              "      <td>0.839810</td>\n",
              "      <td>0.724039</td>\n",
              "      <td>0.827842</td>\n",
              "      <td>0.770538</td>\n",
              "      <td>[[105, 0], [74, 0]]</td>\n",
              "      <td>0.789535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>0.764849</td>\n",
              "      <td>0.840512</td>\n",
              "      <td>0.753529</td>\n",
              "      <td>0.770934</td>\n",
              "      <td>0.761292</td>\n",
              "      <td>[[105, 0], [73, 1]]</td>\n",
              "      <td>0.778223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>XGBClassifier</td>\n",
              "      <td>0.763067</td>\n",
              "      <td>0.845762</td>\n",
              "      <td>0.753739</td>\n",
              "      <td>0.768507</td>\n",
              "      <td>0.760370</td>\n",
              "      <td>[[105, 0], [71, 3]]</td>\n",
              "      <td>0.778289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>LGBMClassifier</td>\n",
              "      <td>0.791121</td>\n",
              "      <td>0.859413</td>\n",
              "      <td>0.787491</td>\n",
              "      <td>0.796234</td>\n",
              "      <td>0.791120</td>\n",
              "      <td>[[88, 17], [22, 52]]</td>\n",
              "      <td>0.805076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>GaussianNB</td>\n",
              "      <td>0.518640</td>\n",
              "      <td>0.784287</td>\n",
              "      <td>0.067086</td>\n",
              "      <td>0.726667</td>\n",
              "      <td>0.121017</td>\n",
              "      <td>[[95, 10], [45, 29]]</td>\n",
              "      <td>0.443539</td>\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fa13c798-82a9-4002-9d0a-8c5402bf4b7d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Métricas de Desempenho de Modelos de Machine Learning:**\n",
        "\n",
        "**1. Accuracy (Acurácia):**\n",
        "   - É a métrica mais comum para avaliar o desempenho de um modelo.\n",
        "   - Calcula a proporção de previsões corretas em relação ao total de previsões.\n",
        "   - A fórmula é: `(Número de previsões corretas) / (Número total de previsões)`.\n",
        "\n",
        "**2. ROC AUC (Área sob a Curva ROC):**\n",
        "   - Avalia a capacidade de classificação de um modelo de classificação binária.\n",
        "   - A Curva ROC (Receiver Operating Characteristic) mostra a taxa de verdadeiros positivos em relação à taxa de falsos positivos em diferentes limiares de classificação.\n",
        "   - O ROC AUC é a área sob essa curva. Quanto maior, melhor.\n",
        "   - É útil quando os dados estão desequilibrados.\n",
        "\n",
        "**3. Recall (Revocação):**\n",
        "   - Também conhecido como Sensibilidade ou Taxa de Verdadeiros Positivos.\n",
        "   - Mede a proporção de verdadeiros positivos (instâncias relevantes corretamente previstas) em relação ao total de instâncias relevantes no conjunto de dados.\n",
        "   - É importante quando o custo de falsos negativos é alto, como em diagnósticos médicos.\n",
        "\n",
        "**4. Precision (Precisão):**\n",
        "   - Também conhecida como Valor Preditivo Positivo.\n",
        "   - Mede a proporção de verdadeiros positivos em relação ao total de previsões positivas feitas pelo modelo.\n",
        "   - É importante quando o custo de falsos positivos é alto, como em sistemas de detecção de spam.\n",
        "\n",
        "**5. F1-Score:**\n",
        "   - É a média harmônica entre Recall e Precision.\n",
        "   - Oferece um equilíbrio entre Recall e Precision.\n",
        "   - É útil quando você deseja levar em consideração tanto os falsos positivos quanto os falsos negativos.\n",
        "   - A fórmula é: `2 * (Precision * Recall) / (Precision + Recall)`.\n",
        "\n",
        "**6. Confusion Matrix (Matriz de Confusão):**\n",
        "   - É uma tabela que descreve o desempenho de um modelo de classificação em um conjunto de dados de teste.\n",
        "   - Possui quatro elementos:\n",
        "     - Verdadeiros Positivos (TP): Instâncias positivas corretamente classificadas.\n",
        "     - Verdadeiros Negativos (TN): Instâncias negativas corretamente classificadas.\n",
        "     - Falsos Positivos (FP): Instâncias negativas erroneamente classificadas como positivas.\n",
        "     - Falsos Negativos (FN): Instâncias positivas erroneamente classificadas como negativas.\n",
        "   - É uma ferramenta poderosa para entender onde o modelo está errando e acertando.\n",
        "\n",
        "Essas métricas são usadas para avaliar o desempenho de modelos de machine learning e ajudam a tomar decisões sobre a escolha do modelo, ajustes de hiperparâmetros e otimização do desempenho. A escolha da métrica certa depende do problema e das consequências de falsos positivos e falsos negativos."
      ],
      "metadata": {
        "id": "xgBKa_0p5B5z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Modelo de Random Forest"
      ],
      "metadata": {
        "id": "ZOVoL1Kf6HTF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Resetando o dataset_test_ml\n",
        "dataset_test_ml = dataset_test"
      ],
      "metadata": {
        "id": "RV_VKfU4Wkkf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset_train_ml.drop(columns=['Survived'])\n",
        "y = dataset_train_ml['Survived']\n",
        "\n",
        "# Dividindo o conjunto de dados em treinamento e teste\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Criando o modelo de Random Forest\n",
        "random_forest = RandomForestClassifier(random_state=42)\n",
        "\n",
        "# Treinando o modelo\n",
        "random_forest.fit(X_train, y_train)\n",
        "\n",
        "# Fazendo previsões no conjunto de teste\n",
        "y_pred = random_forest.predict(X_test)\n",
        "\n",
        "# Calculando a acurácia do modelo\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f'Acurácia do modelo: {accuracy:.5f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_7At5cvMKuJ",
        "outputId": "cbc355ac-e7e1-4a5e-82bd-bfa14c819130"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acurácia do modelo: 0.82123\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Resetando o nome da variável classification_report\n",
        "from sklearn.metrics import classification_report # Não entendi porque está acontecendo isso??? Se retirar essa linha dá erro\n",
        "class_report = classification_report(y_test, y_pred)\n",
        "print(\"Relatório de Classificação:\")\n",
        "print(class_report)\n",
        "# Converter o relatório em um DataFrame\n",
        "classification_report = pd.DataFrame(classification_report(y_test, y_pred, output_dict=True)).transpose()\n",
        "# Calcular a média das métricas\n",
        "mean_metrics = classification_report.mean()\n",
        "# Excluir a coluna \"support\" das médias, se necessário\n",
        "mean_metrics = mean_metrics.drop(\"support\")\n",
        "# Calcular a média entre as métricas\n",
        "mean_average = mean_metrics.mean()\n",
        "# Exibir a média entre as métricas\n",
        "print(f'Média das Métricas {mean_average:.5f}')\n",
        "# Exibindo o F2-score no conjunto de teste\n",
        "# Calculando o F2-score no conjunto de teste\n",
        "f2_test_score = fbeta_score(y_test, y_pred, beta=2)\n",
        "print(f'F2-score no conjunto de teste: {f2_test_score:.5f}')\n",
        "# Calculando a acurácia do modelo\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f'Acurácia: {accuracy:.5f} \\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yRoZZCZcqwU0",
        "outputId": "a1aa907c-c0ea-4491-de21-629228dcb764"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Relatório de Classificação:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.90      0.85       105\n",
            "           1       0.83      0.72      0.77        74\n",
            "\n",
            "    accuracy                           0.82       179\n",
            "   macro avg       0.82      0.81      0.81       179\n",
            "weighted avg       0.82      0.82      0.82       179\n",
            "\n",
            "Média das Métricas 0.81633\n",
            "F2-score no conjunto de teste: 0.73611\n",
            "Acurácia: 0.82123 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prevendo a sobrevivência para o conjunto de teste\n",
        "\n",
        "# Excluindo a coluna 'Survived'\n",
        "dataset_test_ml = dataset_test_ml.drop(columns=['Survived'])\n",
        "\n",
        "# Separando a característica \"Survived\"\n",
        "dataset_test_ml['Survived'] = random_forest.predict(dataset_test_ml)\n",
        "\n",
        "# Movendo a coluna \"Survived\" para a segunda posição\n",
        "dataset_test_ml = dataset_test_ml[['PassengerId', 'Survived']].copy()"
      ],
      "metadata": {
        "id": "9Q7R7oU4Wxws"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test_ml"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "gdVsxMrTXIds",
        "outputId": "83f5489e-8782-41d5-e15a-e5061b069373"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived\n",
              "0            892         0\n",
              "1            893         0\n",
              "2            894         0\n",
              "3            895         0\n",
              "4            896         1\n",
              "..           ...       ...\n",
              "413         1305         0\n",
              "414         1306         1\n",
              "415         1307         0\n",
              "416         1308         0\n",
              "417         1309         0\n",
              "\n",
              "[418 rows x 2 columns]"
            ],
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              "\n",
              "  <div id=\"df-ff22168c-a7fa-4df6-8cd5-5420313fc018\" 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",
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              "    .dataframe thead th {\n",
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              "      <th>0</th>\n",
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              "      <td>895</td>\n",
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              "<p>418 rows × 2 columns</p>\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "        document.querySelector('#df-ff22168c-a7fa-4df6-8cd5-5420313fc018 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-ff22168c-a7fa-4df6-8cd5-5420313fc018');\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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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-16182c26-10d4-4b24-9a17-654bb2dfad21 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Contagem de sobreviventes\n",
        "dataset_test_ml['Survived'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9GVM0PzzX2wT",
        "outputId": "157d7d8f-a18d-439e-d9df-3ab538fcca03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    278\n",
              "1    140\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
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    {
      "cell_type": "code",
      "source": [
        "# Selecionando apenas as colunas PassengerId e Survived do dataset_test_ml\n",
        "dataset_test_submission = dataset_test_ml[['PassengerId', 'Survived']].copy()\n",
        "\n",
        "# Transformando a coluna \"Survived\" em valores numéricos inteiros usando .loc[]\n",
        "dataset_test_submission['Survived'] = dataset_test_submission['Survived'].astype(int)\n",
        "\n",
        "# Resetando o índice\n",
        "dataset_test_submission.reset_index(drop=True, inplace=True)\n",
        "dataset_test_submission"
      ],
      "metadata": {
        "colab": {
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      "execution_count": null,
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              "     PassengerId  Survived\n",
              "0            892         0\n",
              "1            893         0\n",
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              "3            895         0\n",
              "4            896         1\n",
              "..           ...       ...\n",
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              "414         1306         1\n",
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          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificando o shape do dataset_test_submission antes de salvar o arquivo .csv\n",
        "dataset_test_submission.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y76R7uRaYVFJ",
        "outputId": "19a76a11-e2cd-4d3c-a1db-a746895de2a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(418, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Salvando o dataset_test_submission em um arquivo .csv\n",
        "dataset_test_submission.to_csv('dataset_test_submission.csv', index=False)"
      ],
      "metadata": {
        "id": "V7rZPaVPX_Yj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prevendo a sobrevivência de cada passageiro em taxa (%)"
      ],
      "metadata": {
        "id": "3MdVcH5AWFkv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Carregue seus dados e prepare X e y\n",
        "X = dataset_train_ml.drop(columns=['Survived'])\n",
        "y = dataset_train_ml['Survived']\n",
        "\n",
        "# Divida o conjunto de dados em treinamento e teste\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Crie o modelo XGBoost\n",
        "random_forest = RandomForestClassifier(random_state=42)\n",
        "\n",
        "# Treine o modelo\n",
        "random_forest.fit(X_train, y_train)\n",
        "\n",
        "# Faça previsões de probabilidades no conjunto de teste\n",
        "y_probabilities = random_forest.predict_proba(X_test)\n",
        "\n",
        "# Obtenha as probabilidades de sobrevivência (classe 1)\n",
        "survival_probabilities = y_probabilities[:, 1]\n",
        "\n",
        "# Arredonde as probabilidades de sobrevivência para duas casas decimais\n",
        "survival_probabilities_rounded = [round(prob, 3) for prob in survival_probabilities]\n",
        "\n",
        "# Crie um DataFrame com as probabilidades arredondadas e a coluna 'Sobreviveu' baseada em y_pred\n",
        "result_df = pd.DataFrame({'Taxa de Sobrevivência': survival_probabilities_rounded,\n",
        "                          'Sobreviveu': y_pred})\n",
        "\n",
        "# Exiba o DataFrame resultante\n",
        "result_df"
      ],
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          "height": 423
        },
        "outputId": "92cedee1-de2d-48da-96dd-c39f9ea26e70",
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      "execution_count": null,
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            "text/plain": [
              "     Taxa de Sobrevivência  Sobreviveu\n",
              "0                     0.35           0\n",
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              "..                     ...         ...\n",
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              "175                   0.09           0\n",
              "176                   0.22           0\n",
              "177                   0.88           1\n",
              "178                   0.56           1\n",
              "\n",
              "[179 rows x 2 columns]"
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-85a6fb87-3418-4682-9cc7-1b60db34d24e 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-85a6fb87-3418-4682-9cc7-1b60db34d24e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-61097384-4748-49d1-b23e-ef157890a0cb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-61097384-4748-49d1-b23e-ef157890a0cb')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-61097384-4748-49d1-b23e-ef157890a0cb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    }
  ]
}