Kaggle_Titanic.ipynb

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      "name": "Kaggle Titanic.ipynb",
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      "cell_type": "markdown",
      "source": [
        "## Imports iniciais"
      ],
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      "source": [
        "# https://www.kaggle.com/c/titanic\n",
        "import pandas as pd\n",
        "\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "import sklearn\n",
        "import sklearn.model_selection\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import KFold\n",
        "\n",
        "# Algoritmos de aprendizados\n",
        "from sklearn import tree\n",
        "\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn import svm\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "from sklearn.model_selection import cross_val_score"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Análise exploratória"
      ],
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        "id": "yPjn378OT42d"
      }
    },
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      "cell_type": "code",
      "source": [
        "data = pd.read_csv('train.csv')\n",
        "test_data = pd.read_csv('test.csv')\n",
        "data.head() # Conhecendo na prática as colunas"
      ],
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              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
              "4      0            373450   8.0500   NaN        S  "
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        "data.shape"
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            "text/plain": [
              "(891, 12)"
            ]
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          "execution_count": 398
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    {
      "cell_type": "code",
      "source": [
        "# Testando agrupamentos por classe e sexo\n",
        "data[['Age', 'Survived', 'Fare', 'Pclass', 'Sex']].groupby(['Pclass', 'Sex']).mean()\n",
        "\n",
        "# Temos agora a noção de qual grupo teve uma taxa maior de sobrevivência. A idade média será útil para a imputação"
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            "text/plain": [
              "                     Age  Survived        Fare\n",
              "Pclass Sex                                    \n",
              "1      female  34.611765  0.968085  106.125798\n",
              "       male    41.281386  0.368852   67.226127\n",
              "2      female  28.722973  0.921053   21.970121\n",
              "       male    30.740707  0.157407   19.741782\n",
              "3      female  21.750000  0.500000   16.118810\n",
              "       male    26.507589  0.135447   12.661633"
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      "cell_type": "code",
      "source": [
        "# O número de ticket tem uma notação diferente, vamos investigar\n",
        "data['Ticket'].unique()[:4]\n",
        "# Com os 5 primeiros, vemos que a notação é estranha. Pode ter alguma informação importante nisso\n",
        "# Com certa análise, os prefixos possuem relação com a acomodação e a numeração ajuda a reconhecer parentes(números próximos)\n",
        "# Porém por conta da natureza qualitativa, essa coluna não será usada"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sEm1Wu2nR_Vq",
        "outputId": "92fca796-db54-46c0-bf87-2bea4c35d176"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['A/5 21171', 'PC 17599', 'STON/O2. 3101282', '113803'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 400
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificando nulos nas colunas\n",
        "for coluna in data:\n",
        "  print(coluna, data[coluna].isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i253fuQ3ShL2",
        "outputId": "a15529fc-1f53-4267-afa6-7de0fb469521"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PassengerId 0\n",
            "Survived 0\n",
            "Pclass 0\n",
            "Name 0\n",
            "Sex 0\n",
            "Age 177\n",
            "SibSp 0\n",
            "Parch 0\n",
            "Ticket 0\n",
            "Fare 0\n",
            "Cabin 687\n",
            "Embarked 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Arrumando valores faltantes / errôneos"
      ],
      "metadata": {
        "id": "L4rXdh5oXfXr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Mais de 75% dos elementos de Cabin são nulos, não compensa manter a coluna pois a imputação seria inconsistente\n",
        "data = data.drop(columns='Cabin')\n",
        "\n",
        "# ========================================\n",
        "test_data = test_data.drop(columns='Cabin') # OBS: todas as alterações serão replicadas nos dados da submissão"
      ],
      "metadata": {
        "id": "75yiad8wS5jg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# A coluna de ticket não será usada por ser muito variada(a ponto da numeração quase servir como identificação por ser única)\n",
        "data = data.drop(columns='Ticket')\n",
        "test_data = test_data.drop(columns='Ticket')"
      ],
      "metadata": {
        "id": "6s5D9gbDTDY9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Embarked possui apenas dois valores nulos, vamos completá-los usando a moda\n",
        "moda = data.loc[data['Embarked'].notnull(), 'Embarked'].mode()\n",
        "print('Moda da coluna Embarked:', moda[0])\n",
        "print(data['Embarked'].unique())\n",
        "data['Embarked'] = data['Embarked'].fillna(moda[0])\n",
        "print('Elementos únicos após imputação:')\n",
        "print(data['Embarked'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5H0EBbijVdpX",
        "outputId": "731502cd-771e-4960-b07d-fe242c598789"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moda da coluna Embarked: S\n",
            "['S' 'C' 'Q' nan]\n",
            "Elementos únicos após imputação:\n",
            "['S' 'C' 'Q']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Idade possui 177 valores nulos (20%). Pode valer a pena ainda fazer imputação\n",
        "# Vamos preencher cada valor faltante com a média de seu agrupamento(mesma classe, mesmo sexo)\n",
        "\n",
        "data['Age'] = data['Age'].fillna(data.groupby(['Pclass', 'Sex'])['Age'].transform('mean'))"
      ],
      "metadata": {
        "id": "R98nnxi3YBsF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Não há mais elementos nulos no dataframe\n",
        "for coluna in data:\n",
        "  print(coluna, data[coluna].isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vMp2zH2ZRsOH",
        "outputId": "6fe499d1-a662-4404-dbba-50eea76a4040"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PassengerId 0\n",
            "Survived 0\n",
            "Pclass 0\n",
            "Name 0\n",
            "Sex 0\n",
            "Age 0\n",
            "SibSp 0\n",
            "Parch 0\n",
            "Fare 0\n",
            "Embarked 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Testando novas features"
      ],
      "metadata": {
        "id": "aQZoiY0HXkSO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Verifica se há relação entre pronome de tratamento e classe social\n",
        "\n",
        "# Retorna o pronome de tratamento da pessoa ou erro se não possuir\n",
        "def obtemPronome(linha):\n",
        "  nomeSplitado = linha['Name'].split()\n",
        "  for palavra in nomeSplitado:\n",
        "    if(palavra[-1] == '.'):\n",
        "      return palavra\n",
        "\n",
        "  return 'ERRO'\n",
        "\n",
        "\n",
        "data['Pronome'] = data.apply(obtemPronome, axis=1)\n",
        "test_data['Pronome'] = test_data.apply(obtemPronome, axis=1)\n",
        "print(data['Pronome'].unique())\n",
        "\n",
        "# Já podemos apagar a coluna de nomes, pois o foco nela são os pronomes\n",
        "data = data.drop(columns='Name')\n",
        "test_data = test_data.drop(columns='Name')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Z1ySLMyfchZ",
        "outputId": "47c577e9-640e-4d7c-bb5f-134d558fa309"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Mr.' 'Mrs.' 'Miss.' 'Master.' 'Don.' 'Rev.' 'Dr.' 'Mme.' 'Ms.' 'Major.'\n",
            " 'Lady.' 'Sir.' 'Mlle.' 'Col.' 'Capt.' 'Countess.' 'Jonkheer.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convertendo a coluna sexo de string para numerico\n",
        "data['Sex'] = np.where(data['Sex'] == 'female', 1, 0)\n",
        "test_data['Sex'] = np.where(test_data['Sex'] == 'female', 1, 0)"
      ],
      "metadata": {
        "id": "M-dgKxZvhqJi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[['Pronome', 'Age', 'Fare', 'Survived', 'Pclass', 'Sex']].groupby(['Pclass', 'Pronome']).mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "id": "eJkETplADqxP",
        "outputId": "7051c447-1555-469b-d941-d47109d14e9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Age        Fare  Survived  Sex\n",
              "Pclass Pronome                                        \n",
              "1      Capt.      70.000000   71.000000  0.000000  0.0\n",
              "       Col.       58.000000   31.025000  0.500000  0.0\n",
              "       Countess.  33.000000   86.500000  1.000000  1.0\n",
              "       Don.       40.000000   27.720800  0.000000  0.0\n",
              "       Dr.        43.256277   63.935840  0.600000  0.2\n",
              "       Jonkheer.  38.000000    0.000000  0.000000  0.0\n",
              "       Lady.      48.000000   39.600000  1.000000  1.0\n",
              "       Major.     48.500000   28.525000  0.500000  0.0\n",
              "       Master.     5.306667  117.802767  1.000000  0.0\n",
              "       Miss.      30.100256  124.402715  0.956522  1.0\n",
              "       Mlle.      24.000000   59.402100  1.000000  1.0\n",
              "       Mme.       24.000000   69.300000  1.000000  1.0\n",
              "       Mr.        41.524558   68.034385  0.345794  0.0\n",
              "       Mrs.       39.687955   93.170636  0.976190  1.0\n",
              "       Sir.       49.000000   56.929200  1.000000  0.0\n",
              "2      Dr.        38.500000   12.250000  0.000000  0.0\n",
              "       Master.     2.258889   27.306022  1.000000  0.0\n",
              "       Miss.      22.763116   20.393750  0.941176  1.0\n",
              "       Mr.        32.567762   19.252564  0.087912  0.0\n",
              "       Mrs.       33.682927   23.496139  0.902439  1.0\n",
              "       Ms.        28.000000   13.000000  1.000000  1.0\n",
              "       Rev.       43.166667   18.312500  0.000000  0.0\n",
              "3      Master.     8.373227   28.177232  0.392857  0.0\n",
              "       Miss.      17.943627   15.248043  0.500000  1.0\n",
              "       Mr.        28.099320   11.299762  0.112853  0.0\n",
              "       Mrs.       30.994048   18.233529  0.500000  1.0"
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
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              "      <th>Survived</th>\n",
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              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <th>Pronome</th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"15\" valign=\"top\">1</th>\n",
              "      <th>Capt.</th>\n",
              "      <td>70.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Col.</th>\n",
              "      <td>58.000000</td>\n",
              "      <td>31.025000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Countess.</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>86.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Don.</th>\n",
              "      <td>40.000000</td>\n",
              "      <td>27.720800</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>Dr.</th>\n",
              "      <td>43.256277</td>\n",
              "      <td>63.935840</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jonkheer.</th>\n",
              "      <td>38.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lady.</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>39.600000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Major.</th>\n",
              "      <td>48.500000</td>\n",
              "      <td>28.525000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Master.</th>\n",
              "      <td>5.306667</td>\n",
              "      <td>117.802767</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Miss.</th>\n",
              "      <td>30.100256</td>\n",
              "      <td>124.402715</td>\n",
              "      <td>0.956522</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mlle.</th>\n",
              "      <td>24.000000</td>\n",
              "      <td>59.402100</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mme.</th>\n",
              "      <td>24.000000</td>\n",
              "      <td>69.300000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mr.</th>\n",
              "      <td>41.524558</td>\n",
              "      <td>68.034385</td>\n",
              "      <td>0.345794</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mrs.</th>\n",
              "      <td>39.687955</td>\n",
              "      <td>93.170636</td>\n",
              "      <td>0.976190</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sir.</th>\n",
              "      <td>49.000000</td>\n",
              "      <td>56.929200</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"7\" valign=\"top\">2</th>\n",
              "      <th>Dr.</th>\n",
              "      <td>38.500000</td>\n",
              "      <td>12.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Master.</th>\n",
              "      <td>2.258889</td>\n",
              "      <td>27.306022</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Miss.</th>\n",
              "      <td>22.763116</td>\n",
              "      <td>20.393750</td>\n",
              "      <td>0.941176</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>Mr.</th>\n",
              "      <td>32.567762</td>\n",
              "      <td>19.252564</td>\n",
              "      <td>0.087912</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>33.682927</td>\n",
              "      <td>23.496139</td>\n",
              "      <td>0.902439</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>Ms.</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
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              "      <td>18.312500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
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              "      <th rowspan=\"4\" valign=\"top\">3</th>\n",
              "      <th>Master.</th>\n",
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              "          const element = document.querySelector('#df-9047eb3a-d7fb-4ad7-b529-91be673e9fae');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 410
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Relação entre classe de viagem e custo do ingresso\n",
        "sns.displot(data=data, x=\"Fare\", hue=\"Pclass\", kind=\"kde\", height=7, aspect=1.5)\n",
        "\n",
        "plt.show()\n",
        "# Percebemos que existem outliers, pois o gráfico está alcançando taxas altas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "S_GsSrxxHzDp",
        "outputId": "6b592836-b414-44f9-955e-6e155a2a707b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 798.375x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Remoção de outliers"
      ],
      "metadata": {
        "id": "_Q4cUc99Sys7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Apagando os outliers(taxas extremas)\n",
        "Q1 = data['Fare'].quantile(0.25)\n",
        "Q3 = data['Fare'].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "# Encontra os outliers\n",
        "print('Entrada média com outliers:', data['Fare'].mean())\n",
        "tamanho_antigo = len(data)\n",
        "data = data[(data['Fare'] <= Q3 + (IQR*4)) & (data['Fare'] >= Q1 - (IQR*4))] \n",
        "print('Quantidade de outliers removidos:', tamanho_antigo - len(data))\n",
        "print('Entrada média sem outliers:', data['Fare'].mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4eVd9rcOLzLl",
        "outputId": "e8911c83-2cb3-4f51-93db-bcfd8592b904"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Entrada média com outliers: 32.2042079685746\n",
            "Quantidade de outliers removidos: 38\n",
            "Entrada média sem outliers: 24.0037702227432\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(data=data, x=\"Fare\", hue=\"Pclass\", kind=\"kde\", height=7, aspect=1.5)\n",
        "\n",
        "plt.show()\n",
        "# OBS: a remoção de outliers ocorreu após a imputação de dados pois ela não afeta muito a distribuição de idades"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "31dzolaUNRh4",
        "outputId": "c2f9bc64-dd35-4287-8b1a-f2fb7f3ceac5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 798.375x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Convertendo todos os dados em quantitativos"
      ],
      "metadata": {
        "id": "26P0uxi5TNhK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Pclass é numérico, mas ele representa uma hierarquia. Precisamos passá-lo para o get dummies\n",
        "data['Pclass'] = data['Pclass'].apply(str)\n",
        "test_data['Pclass'] = test_data['Pclass'].apply(str)"
      ],
      "metadata": {
        "id": "wiDQcplHlrlM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HXOA6vIi3R-b",
        "outputId": "17a980b7-717c-4cc8-99f8-ba6aad5de65a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare',\n",
              "       'Embarked', 'Pronome'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 415
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# A coluna pronome acabou se mostrando inconsistente no get dummies, pois os dados base não necessariamente possuem todos os pronomes dos\n",
        "# dados da submissão, fazendo com que o número de colunas seja diferente\n",
        "# Dessa forma, a coluna de pronome será desconsiderada\n",
        "data = data.drop(columns='Pronome')\n",
        "test_data = test_data.drop(columns='Pronome')\n",
        "\n",
        "# Aplica o get dummies nas demais colunas categóricas\n",
        "data = pd.get_dummies(data, prefix=['Pclass', 'Embarked'])\n",
        "test_data = pd.get_dummies(test_data, prefix=['Pclass', 'Embarked'])"
      ],
      "metadata": {
        "id": "LN2FkVE2UiAw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Disposição dos dados atualmente:\n",
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aETObpV4S2FA",
        "outputId": "601461e7-eb4d-4a82-b1e9-f389fdee4d04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(853, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 417
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "SzIaH_TZS7Nx",
        "outputId": "1822394d-f153-456f-b7a3-a102ec1d87b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Sex   Age  SibSp  Parch     Fare  Pclass_1  \\\n",
              "0            1         0    0  22.0      1      0   7.2500         0   \n",
              "1            2         1    1  38.0      1      0  71.2833         1   \n",
              "2            3         1    1  26.0      0      0   7.9250         0   \n",
              "3            4         1    1  35.0      1      0  53.1000         1   \n",
              "4            5         0    0  35.0      0      0   8.0500         0   \n",
              "\n",
              "   Pclass_2  Pclass_3  Embarked_C  Embarked_Q  Embarked_S  \n",
              "0         0         1           0           0           1  \n",
              "1         0         0           1           0           0  \n",
              "2         0         1           0           0           1  \n",
              "3         0         0           0           0           1  \n",
              "4         0         1           0           0           1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7b207db5-c7a5-4f1c-909c-e2c8a6cc6334\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Pclass_1</th>\n",
              "      <th>Pclass_2</th>\n",
              "      <th>Pclass_3</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7b207db5-c7a5-4f1c-909c-e2c8a6cc6334')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-7b207db5-c7a5-4f1c-909c-e2c8a6cc6334 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-7b207db5-c7a5-4f1c-909c-e2c8a6cc6334');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 418
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Aplicando o aprendizado de máquina"
      ],
      "metadata": {
        "id": "sVveT2meUstb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Separando dados de treino e de teste\n",
        "X = data.drop(columns=['Survived', 'PassengerId'])\n",
        "y = data['Survived']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
        "print(\"Tamanho treino:\", X_train.shape)\n",
        "print(\"Tamanho teste:\", X_test.shape)\n",
        "\n",
        "# Aplicando uma árvore de decisão inicial para entender a lógica mais simples de classificação\n",
        "clf = tree.DecisionTreeClassifier(max_depth=3)\n",
        "clf = clf.fit(X_train, y_train)\n",
        "acc = accuracy_score(y_test, clf.predict(X_test))\n",
        "print('Acuracia:', acc)\n",
        "\n",
        "print(X_train.columns)\n",
        "\n",
        "plt.figure(figsize=(15,10))\n",
        "tree.plot_tree(clf, filled=True) \n",
        "plt.show(True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "Fo-l408UUsZb",
        "outputId": "47314b57-0768-430f-c097-6bc84dfb1469"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tamanho treino: (682, 11)\n",
            "Tamanho teste: (171, 11)\n",
            "Acuracia: 0.8304093567251462\n",
            "Index(['Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Pclass_1', 'Pclass_2',\n",
            "       'Pclass_3', 'Embarked_C', 'Embarked_Q', 'Embarked_S'],\n",
            "      dtype='object')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Aplicação massiva de algoritmos para ver os que mais se destacam\n",
        "\n",
        "# Normalizando os dados para o knn e SVM\n",
        "y = data['Survived']\n",
        "min_max_scaler = preprocessing.MinMaxScaler()\n",
        "escalado = min_max_scaler.fit_transform(data.drop(columns=['Survived', 'PassengerId']).values)\n",
        "\n",
        "X_norm = pd.DataFrame(escalado)\n",
        "X = data.drop(columns=['Survived', 'PassengerId'])\n",
        "\n",
        "# Definição inicial da validação cruzada\n",
        "numeroFolds = 5\n",
        "kf = KFold(n_splits=numeroFolds, shuffle=True)\n",
        "\n",
        "# Calcula as scores dos algoritmos\n",
        "knn = KNeighborsClassifier(n_neighbors=1)\n",
        "scores = cross_val_score(knn, X_norm, y, cv=kf)\n",
        "print('Acurácia com 1 K-NN: %0.4f' % (scores.mean()))\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=3)\n",
        "scores = cross_val_score(knn, X_norm, y, cv=kf)\n",
        "print('Acurácia com 3 K-NN: %0.4f' % (scores.mean()))\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "scores = cross_val_score(knn, X_norm, y, cv=kf)\n",
        "print('Acurácia com 5 K-NN: %0.4f' % (scores.mean()))\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=7)\n",
        "scores = cross_val_score(knn, X_norm, y, cv=kf)\n",
        "print('Acurácia com 7 K-NN: %0.4f' % (scores.mean()))\n",
        "\n",
        "clf = tree.DecisionTreeClassifier(max_depth=3)\n",
        "scores = cross_val_score(clf, X, y, cv=kf)\n",
        "print('Acurácia para árvore com profundidade 3: %0.4f' % (scores.mean()))\n",
        "\n",
        "clf = tree.DecisionTreeClassifier(max_depth=4)\n",
        "scores = cross_val_score(clf, X, y, cv=kf)\n",
        "print('Acurácia para árvore com profundidade 4: %0.4f' % (scores.mean()))\n",
        "\n",
        "clf = tree.DecisionTreeClassifier(max_depth=5)\n",
        "scores = cross_val_score(clf, X, y, cv=kf)\n",
        "print('Acurácia para árvore com profundidade 5: %0.4f' % (scores.mean()))\n",
        "\n",
        "clf = RandomForestClassifier()\n",
        "scores = cross_val_score(clf, X, y, cv=kf)\n",
        "print('Acurácia Random Forest: %0.4f' % (scores.mean()))\n",
        "\n",
        "clf = XGBClassifier() # O XGBoost aparentou ser ligeiramente melhor\n",
        "scores = cross_val_score(clf, X, y, cv=kf)\n",
        "print('Acurácia XGBoost: %0.4f' % (scores.mean()))\n",
        "\n",
        "clf = SVC()\n",
        "scores = cross_val_score(clf, X_norm, y, cv=kf)\n",
        "print('Acurácia SVC: %0.4f' % (scores.mean()))\n",
        "\n",
        "clf = GaussianNB()\n",
        "scores = cross_val_score(clf, X, y, cv=kf)\n",
        "print('Acurácia Naive Bayes: %0.4f' % (scores.mean()))\n",
        "\n",
        "clf = AdaBoostClassifier()\n",
        "scores = cross_val_score(clf, X, y, cv=kf)\n",
        "print('Acurácia AdaBoost: %0.4f' % (scores.mean()))\n",
        "\n",
        "clf = LogisticRegression(max_iter=1000)\n",
        "scores = cross_val_score(clf, X, y, cv=kf)\n",
        "print('Acurácia RegLog até 1000 iter: %0.4f' % (scores.mean()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oYj1MwKesY9S",
        "outputId": "7f9effa0-0fae-4940-f584-8728436f5016"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acurácia com 1 K-NN: 0.7503\n",
            "Acurácia com 3 K-NN: 0.8008\n",
            "Acurácia com 5 K-NN: 0.8078\n",
            "Acurácia com 7 K-NN: 0.8078\n",
            "Acurácia para árvore com profundidade 3: 0.8136\n",
            "Acurácia para árvore com profundidade 4: 0.8077\n",
            "Acurácia para árvore com profundidade 5: 0.8112\n",
            "Acurácia Random Forest: 0.8125\n",
            "Acurácia XGBoost: 0.8160\n",
            "Acurácia SVC: 0.8100\n",
            "Acurácia Naive Bayes: 0.7620\n",
            "Acurácia AdaBoost: 0.8101\n",
            "Acurácia RegLog até 1000 iter: 0.8043\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ajustes finais no dataframe da submissão"
      ],
      "metadata": {
        "id": "pS1QQVWicYn4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Tratamento de nulos em teste\n",
        "for coluna in test_data:\n",
        "  print(coluna, test_data[coluna].isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ujygbiwo85kH",
        "outputId": "5ad83cb1-1fe1-4f6c-eb3c-1998177c032b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PassengerId 0\n",
            "Sex 0\n",
            "Age 86\n",
            "SibSp 0\n",
            "Parch 0\n",
            "Fare 1\n",
            "Pclass_1 0\n",
            "Pclass_2 0\n",
            "Pclass_3 0\n",
            "Embarked_C 0\n",
            "Embarked_Q 0\n",
            "Embarked_S 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 86 elementos nulos na coluna Age\n",
        "media = test_data.loc[test_data['Age'].notnull(), 'Age'].mean()\n",
        "print('Media da coluna Age:', media)\n",
        "test_data['Age'] = test_data['Age'].fillna(media)\n",
        "\n",
        "# 1 elemento nulo na coluna Fare\n",
        "media = test_data.loc[test_data['Fare'].notnull(), 'Fare'].mean()\n",
        "print('Media da coluna Fare:', media)\n",
        "test_data['Fare'] = test_data['Fare'].fillna(media)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KPH_onbP66cy",
        "outputId": "5dd850a0-3454-4ede-a101-55b7a97caa51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Media da coluna Age: 30.272590361445783\n",
            "Media da coluna Fare: 35.6271884892086\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tratamento de nulos em teste\n",
        "for coluna in test_data:\n",
        "  print(coluna, test_data[coluna].isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BDNbITKlInqa",
        "outputId": "ae9a25bc-9f32-40fd-b91f-451835ef6874"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PassengerId 0\n",
            "Sex 0\n",
            "Age 0\n",
            "SibSp 0\n",
            "Parch 0\n",
            "Fare 0\n",
            "Pclass_1 0\n",
            "Pclass_2 0\n",
            "Pclass_3 0\n",
            "Embarked_C 0\n",
            "Embarked_Q 0\n",
            "Embarked_S 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_data.shape)\n",
        "test_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "S2LymLuNcill",
        "outputId": "00e3d264-c109-4b82-aa25-f70e3fb5b4a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(418, 12)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Sex   Age  SibSp  Parch     Fare  Pclass_1  Pclass_2  \\\n",
              "0          892    0  34.5      0      0   7.8292         0         0   \n",
              "1          893    1  47.0      1      0   7.0000         0         0   \n",
              "2          894    0  62.0      0      0   9.6875         0         1   \n",
              "3          895    0  27.0      0      0   8.6625         0         0   \n",
              "4          896    1  22.0      1      1  12.2875         0         0   \n",
              "\n",
              "   Pclass_3  Embarked_C  Embarked_Q  Embarked_S  \n",
              "0         1           0           1           0  \n",
              "1         1           0           0           1  \n",
              "2         0           0           1           0  \n",
              "3         1           0           0           1  \n",
              "4         1           0           0           1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-328d9e60-b37a-48e9-8b1c-c6b17949e106\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Pclass_1</th>\n",
              "      <th>Pclass_2</th>\n",
              "      <th>Pclass_3</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>0</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>1</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-328d9e60-b37a-48e9-8b1c-c6b17949e106')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .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",
              "    [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-328d9e60-b37a-48e9-8b1c-c6b17949e106 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-328d9e60-b37a-48e9-8b1c-c6b17949e106');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 424
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Aplicando o aprendizado nos dados de submissão"
      ],
      "metadata": {
        "id": "JUQs11bAcSLy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# >>>>>>>>>> Dados de treino\n",
        "\n",
        "y_train = data['Survived']\n",
        "X_train = data.drop(columns=['Survived', 'PassengerId'])\n",
        "\n",
        "#min_max_scaler = preprocessing.MinMaxScaler()\n",
        "#escalado = min_max_scaler.fit_transform(data.drop(columns=['Survived', 'PassengerId']).values)\n",
        "#X_norm_train = pd.DataFrame(escalado)\n",
        "\n",
        "# >>>>>>>>>> Dados de teste\n",
        "\n",
        "X_test = test_data.drop(columns=['PassengerId'])\n",
        "\n",
        "#min_max_scaler = preprocessing.MinMaxScaler()\n",
        "#escalado = min_max_scaler.fit_transform(test_data.drop(columns=['PassengerId']).values)\n",
        "#X_norm_test = pd.DataFrame(escalado)\n",
        "\n",
        "# >>>>>>>>>> Aplicando o algoritmo\n",
        "clf = XGBClassifier() # Usaremos o XGBoost pois ele foi ligeiramente melhor\n",
        "clf = clf.fit(X_train, y_train)\n",
        "survived = pd.DataFrame(clf.predict(X_test))\n",
        "\n",
        "submission = pd.concat([test_data['PassengerId'], survived], axis=1)\n",
        "submission.columns = ['PassengerId', 'Survived']\n",
        "submission = submission.set_index('PassengerId')\n",
        "submission.to_csv('submission.csv')"
      ],
      "metadata": {
        "id": "0kdYc-aVz9Ss"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}