ml_cdcs_2_answers_revised.ipynb

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      "source": [
        "import pandas as pd\n",
        "import sklearn\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from numpy import math\n",
        "from sklearn import model_selection\n",
        "from sklearn import linear_model\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import metrics\n",
        "from sklearn.dummy import DummyClassifier\n",
        "from sklearn import tree\n",
        "from sklearn import ensemble\n",
        "from sklearn.tree import export_graphviz\n",
        "import graphviz\n",
        "from sklearn import svm\n",
        "from sklearn import neighbors"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_train = pd.read_csv(\"sample_data/titanic_train.csv\")\n",
        "titanic_test = pd.read_csv(\"sample_data/titanic_test.csv\")"
      ],
      "metadata": {
        "id": "jt4BuHtRiuBe"
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      "execution_count": 3,
      "outputs": []
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      "source": [
        "titanic_train.head(10)"
      ],
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        "outputId": "b013b7bd-8168-4e22-c945-e0688f4d25bf"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   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",
              "5            6         0       3   \n",
              "6            7         0       1   \n",
              "7            8         0       3   \n",
              "8            9         1       3   \n",
              "9           10         1       2   \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",
              "5                                   Moran, Mr. James    male   NaN      0   \n",
              "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
              "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
              "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
              "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \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  \n",
              "5      0            330877   8.4583   NaN        Q  \n",
              "6      0             17463  51.8625   E46        S  \n",
              "7      1            349909  21.0750   NaN        S  \n",
              "8      2            347742  11.1333   NaN        S  \n",
              "9      0            237736  30.0708   NaN        C  "
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              "summary": "{\n  \"name\": \"titanic_train\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_train_wo_label = titanic_train.drop(axis=1, labels='Survived')\n",
        "titanic_full_dataset = pd.concat([titanic_train_wo_label, titanic_test])"
      ],
      "metadata": {
        "id": "iZLRjC6ji1BD"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### EDA"
      ],
      "metadata": {
        "id": "mO0OFu6A18yL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_full_dataset.isna().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "FPM1kx__i7L0",
        "outputId": "92195e0c-a996-4517-f3f3-8021b6479ea0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    False\n",
              "Pclass         False\n",
              "Name           False\n",
              "Sex            False\n",
              "Age             True\n",
              "SibSp          False\n",
              "Parch          False\n",
              "Ticket         False\n",
              "Fare            True\n",
              "Cabin           True\n",
              "Embarked        True\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_full_dataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "qTKn7KPajUiR",
        "outputId": "cd3d8bb8-2162-4457-d7ed-ff802e120b95"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 1309 entries, 0 to 417\n",
            "Data columns (total 11 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  1309 non-null   int64  \n",
            " 1   Pclass       1309 non-null   int64  \n",
            " 2   Name         1309 non-null   object \n",
            " 3   Sex          1309 non-null   object \n",
            " 4   Age          1046 non-null   float64\n",
            " 5   SibSp        1309 non-null   int64  \n",
            " 6   Parch        1309 non-null   int64  \n",
            " 7   Ticket       1309 non-null   object \n",
            " 8   Fare         1308 non-null   float64\n",
            " 9   Cabin        295 non-null    object \n",
            " 10  Embarked     1307 non-null   object \n",
            "dtypes: float64(2), int64(4), object(5)\n",
            "memory usage: 122.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_full_dataset.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0EiC7H-9Sdfy",
        "outputId": "6170c8c4-776e-4a50-e2ae-fd55ce513011"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId       0\n",
              "Pclass            0\n",
              "Name              0\n",
              "Sex               0\n",
              "Age             263\n",
              "SibSp             0\n",
              "Parch             0\n",
              "Ticket            0\n",
              "Fare              1\n",
              "Cabin          1014\n",
              "Embarked          2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_full_dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "collapsed": true,
        "id": "qozQjyl5jp3K",
        "outputId": "d6d208c8-3541-43ed-8b0a-4286cd3de9ee"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Pclass                                               Name  \\\n",
              "0            1       3                            Braund, Mr. Owen Harris   \n",
              "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
              "2            3       3                             Heikkinen, Miss. Laina   \n",
              "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
              "4            5       3                           Allen, Mr. William Henry   \n",
              "\n",
              "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
              "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
              "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
              "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
              "4    male  35.0      0      0            373450   8.0500   NaN        S  "
            ],
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "titanic_full_dataset",
              "summary": "{\n  \"name\": \"titanic_full_dataset\",\n  \"rows\": 1309,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 378,\n        \"min\": 1,\n        \"max\": 1309,\n        \"num_unique_values\": 1309,\n        \"samples\": [\n          1149,\n          1050,\n          983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1307,\n        \"samples\": [\n          \"Bryhl, Miss. Dagmar Jenny Ingeborg \",\n          \"Borebank, Mr. John James\",\n          \"Hoyt, Mr. William Fisher\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.413493211271321,\n        \"min\": 0.17,\n        \"max\": 80.0,\n        \"num_unique_values\": 98,\n        \"samples\": [\n          45.5,\n          23.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 929,\n        \"samples\": [\n          \"PC 17531\",\n          \"345765\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.75866823917411,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 281,\n        \"samples\": [\n          11.2417,\n          35.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 186,\n        \"samples\": [\n          \"B71\",\n          \"C51\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_full_dataset.groupby(['Sex']).size().reset_index(name=\"Count\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133
        },
        "collapsed": true,
        "id": "UssQOcDb3bdO",
        "outputId": "fb199228-c145-4d31-a503-eb58b3d765ba"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Sex  Count\n",
              "0  female    466\n",
              "1    male    843"
            ],
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              "    .colab-df-container {\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-1ab2924b-3aad-4953-a478-f1b2878030df 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-1ab2924b-3aad-4953-a478-f1b2878030df');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\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",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      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",
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              "</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",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"titanic_full_dataset\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 266,\n        \"min\": 466,\n        \"max\": 843,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          843,\n          466\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_full_dataset.groupby(['Embarked']).size().reset_index(name=\"Count\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "collapsed": true,
        "id": "fccMlcCw35tA",
        "outputId": "4fdabd83-373f-4928-f33d-4c6be1fc4bab"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Embarked  Count\n",
              "0        C    270\n",
              "1        Q    123\n",
              "2        S    914"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4034c702-e1f0-414f-beb6-143f6b48f8a4\" class=\"colab-df-container\">\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Count</th>\n",
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              "      <td>914</td>\n",
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              "    </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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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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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",
              "    }\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",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4034c702-e1f0-414f-beb6-143f6b48f8a4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4034c702-e1f0-414f-beb6-143f6b48f8a4');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "            title=\"Suggest charts\"\n",
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              "</svg>\n",
              "  </button>\n",
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              "      --bg-color: #E8F0FE;\n",
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              "\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    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-0d143f7d-16a4-4868-bf4d-d8c1f258f733 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"titanic_full_dataset\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"C\",\n          \"Q\",\n          \"S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 420,\n        \"min\": 123,\n        \"max\": 914,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          270,\n          123,\n          914\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_full_dataset.groupby(['Pclass']).size().reset_index(name=\"Count\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "collapsed": true,
        "id": "T-Ii-AoS5OF4",
        "outputId": "b723295e-764f-4bfe-fc31-0ce3a39d3a09"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pclass  Count\n",
              "0       1    323\n",
              "1       2    277\n",
              "2       3    709"
            ],
            "text/html": [
              "\n",
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              "        document.querySelector('#df-6e6b5da5-3f7a-4f79-a93a-3814b6a11735 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-6e6b5da5-3f7a-4f79-a93a-3814b6a11735');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
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              "    background-color: var(--hover-bg-color);\n",
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              "\n",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      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",
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              "      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",
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              "      }\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        document.querySelector('#df-8130c751-ef8b-4aa9-854f-e5dd0be77446 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"titanic_full_dataset\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 237,\n        \"min\": 277,\n        \"max\": 709,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          323,\n          277,\n          709\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_full_dataset.groupby(['Pclass'])['Age'].mean().reset_index(name=\"Average age\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "collapsed": true,
        "id": "vwICzXD55ybk",
        "outputId": "37560b18-333d-402f-d1a9-89e68c428da3"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pclass  Average age\n",
              "0       1    39.159930\n",
              "1       2    29.506705\n",
              "2       3    24.816367"
            ],
            "text/html": [
              "\n",
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              "        vertical-align: middle;\n",
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              "    }\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-70a1ba4d-a9e1-4afb-a8d0-425150b9ac9f 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-70a1ba4d-a9e1-4afb-a8d0-425150b9ac9f');\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-bc801c74-da35-411e-ae7b-c9bf338e1c73\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bc801c74-da35-411e-ae7b-c9bf338e1c73')\"\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-bc801c74-da35-411e-ae7b-c9bf338e1c73 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"titanic_full_dataset\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Average age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.313478342475944,\n        \"min\": 24.81636726546906,\n        \"max\": 39.15992957746479,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          39.15992957746479,\n          29.506704980842912,\n          24.81636726546906\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Now, let's look only at the training data and do some EDA as well as pre-processing"
      ],
      "metadata": {
        "id": "czYl2HYl7nJ8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "collapsed": true,
        "id": "IbfUhhi49Hr3",
        "outputId": "a8c30a27-4c9c-4391-bb8c-a8741f05fc5a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
              "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
              "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
              "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
              "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
              "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
              "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
              "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
              "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
              "\n",
              "            Parch        Fare  \n",
              "count  891.000000  891.000000  \n",
              "mean     0.381594   32.204208  \n",
              "std      0.806057   49.693429  \n",
              "min      0.000000    0.000000  \n",
              "25%      0.000000    7.910400  \n",
              "50%      0.000000   14.454200  \n",
              "75%      0.000000   31.000000  \n",
              "max      6.000000  512.329200  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d9a68670-852f-48ef-a0ac-94cfc090e373\" 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",
              "    </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>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</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>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</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>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</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>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</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",
              "    </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-d9a68670-852f-48ef-a0ac-94cfc090e373')\"\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-d9a68670-852f-48ef-a0ac-94cfc090e373 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-d9a68670-852f-48ef-a0ac-94cfc090e373');\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-261c9762-1942-4fb0-bf49-d150fd9f2ba6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-261c9762-1942-4fb0-bf49-d150fd9f2ba6')\"\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",
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              "    </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",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"titanic_train\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 320.8159711429855,\n        \"min\": 1.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          446.0,\n          668.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.8713661874558,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3838383838383838,\n          1.0,\n          0.4865924542648575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.2523437079694,\n        \"min\": 0.836071240977049,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          2.308641975308642,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 242.9056731818781,\n        \"min\": 0.42,\n        \"max\": 714.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          29.69911764705882,\n          28.0,\n          714.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.4908277465442,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          0.5230078563411896,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.65971717879,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.38159371492704824,\n          6.0,\n          0.8060572211299483\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 330.6256632228578,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.204207968574636,\n          14.4542,\n          891.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_train.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iSc82UHkU1_s",
        "outputId": "db7323b8-ad9b-4508-ec4d-adba0ecda2fd"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 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   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_train.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "iRFmeBrrJkxG",
        "outputId": "4a24d426-268e-4c35-a728-b8ad047ba72f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_train.groupby(['Sex']).size().reset_index(name=\"Count\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133
        },
        "collapsed": true,
        "id": "uZavumdvCOVM",
        "outputId": "7f6bf2d6-97d2-439b-a218-6a16157167e3"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Sex  Count\n",
              "0  female    314\n",
              "1    male    577"
            ],
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              "        const element = document.querySelector('#df-9faf8764-8bf8-4052-aaca-52e63b13b970');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      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-71e46232-60f0-4e88-ba53-5fe4edc6ced5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"titanic_train\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 185,\n        \"min\": 314,\n        \"max\": 577,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          577,\n          314\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(\n",
        "    x=\"Sex\",\n",
        "    y=\"Survived\",\n",
        "    data=titanic_train,\n",
        "    estimator=sum,\n",
        "    ci=None,\n",
        "    color='#69b3a2');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "collapsed": true,
        "id": "JkLTIbYu7kkg",
        "outputId": "3d9e6f21-d0f2-4b0b-e4b7-a0299bcbdc39"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-19-0b9ca85414c2>:1: FutureWarning: \n",
            "\n",
            "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
            "\n",
            "  sns.barplot(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "age_bins= [0, 18, 25, 34, 54, 64, 100]\n",
        "age_labels= ['0-18', '19-25', '26-34', '35-54', '55-64', '65+']\n",
        "titanic_train['age_binned'] = pd.cut(titanic_train['Age'], bins=age_bins, labels=age_labels)"
      ],
      "metadata": {
        "id": "mtZq5oOy8p26"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "age_binned_group1 = titanic_train.groupby(['age_binned']).size().reset_index(name=\"Count\")\n",
        "age_binned_group2 = titanic_train[titanic_train['Survived'] == 1.0].groupby(['age_binned']).size().reset_index(name=\"Survived\")\n",
        "age_binned_pct = age_binned_group1.merge(age_binned_group2, on=\"age_binned\")\n",
        "age_binned_pct['survived_pct'] = age_binned_pct['Survived']/age_binned_pct['Count']\n",
        "age_binned_pct"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "collapsed": true,
        "id": "EYc_Mu-KFSd2",
        "outputId": "7706fd77-93fe-4116-a3ab-b321aebe6a7f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  age_binned  Count  Survived  survived_pct\n",
              "0       0-18    139        70      0.503597\n",
              "1      19-25    162        54      0.333333\n",
              "2      26-34    177        72      0.406780\n",
              "3      35-54    194        81      0.417526\n",
              "4      55-64     31        12      0.387097\n",
              "5        65+     11         1      0.090909"
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age_binned</th>\n",
              "      <th>Count</th>\n",
              "      <th>Survived</th>\n",
              "      <th>survived_pct</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0-18</td>\n",
              "      <td>139</td>\n",
              "      <td>70</td>\n",
              "      <td>0.503597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>19-25</td>\n",
              "      <td>162</td>\n",
              "      <td>54</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26-34</td>\n",
              "      <td>177</td>\n",
              "      <td>72</td>\n",
              "      <td>0.406780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>35-54</td>\n",
              "      <td>194</td>\n",
              "      <td>81</td>\n",
              "      <td>0.417526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>55-64</td>\n",
              "      <td>31</td>\n",
              "      <td>12</td>\n",
              "      <td>0.387097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>65+</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>0.090909</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-136c42fe-d9c8-4e22-a28a-d5169f4e6fa2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\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-136c42fe-d9c8-4e22-a28a-d5169f4e6fa2 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-136c42fe-d9c8-4e22-a28a-d5169f4e6fa2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
              "<div id=\"df-b5f16b25-9a88-4d8f-aacc-6ceaac0ea370\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b5f16b25-9a88-4d8f-aacc-6ceaac0ea370')\"\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-b5f16b25-9a88-4d8f-aacc-6ceaac0ea370 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "age_binned_pct",
              "summary": "{\n  \"name\": \"age_binned_pct\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"age_binned\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"0-18\",\n          \"19-25\",\n          \"65+\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78,\n        \"min\": 11,\n        \"max\": 194,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          139,\n          162,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33,\n        \"min\": 1,\n        \"max\": 81,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          70,\n          54,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"survived_pct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.141351427995639,\n        \"min\": 0.09090909090909091,\n        \"max\": 0.5035971223021583,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.5035971223021583,\n          0.3333333333333333,\n          0.09090909090909091\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_train['survived_label'] = titanic_train['Survived'].apply(lambda x: \"Survived\" if x ==1 else \"Died\")\n",
        "\n",
        "g = sns.catplot(\n",
        "    data=titanic_train, kind=\"bar\",\n",
        "    x=\"age_binned\", y=\"Fare\", hue=\"survived_label\",\n",
        "    palette=\"dark\", alpha=.6, height=6\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "collapsed": true,
        "id": "g8uEzvHqBlC5",
        "outputId": "3619a18a-877d-4364-c46e-36fe4168934c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 711.236x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_train.groupby('Survived')['PassengerId'].count().reset_index(name=\"count_uniqueID\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133
        },
        "collapsed": true,
        "id": "Bo9abGoAHgHQ",
        "outputId": "a161f847-f6bc-4fb9-c20e-ad2a9292e0c1"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived  count_uniqueID\n",
              "0         0             549\n",
              "1         1             342"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "\n",
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              "        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>Survived</th>\n",
              "      <th>count_uniqueID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>342</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-9049744b-9840-41ce-9a4b-51d57fddb47f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\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",
              "      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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9049744b-9840-41ce-9a4b-51d57fddb47f 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-9049744b-9840-41ce-9a4b-51d57fddb47f');\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-f6f332d3-0a2c-45c5-95ad-4efc953a3f4f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f6f332d3-0a2c-45c5-95ad-4efc953a3f4f')\"\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-f6f332d3-0a2c-45c5-95ad-4efc953a3f4f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"titanic_train\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count_uniqueID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 146,\n        \"min\": 342,\n        \"max\": 549,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          342,\n          549\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_train.groupby('Ticket').size().reset_index(name=\"Count\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "brwSuPKsi0i3",
        "outputId": "2f9a987f-2ae0-477f-cf12-c33da851f350"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Ticket  Count\n",
              "0         110152      3\n",
              "1         110413      3\n",
              "2         110465      2\n",
              "3         110564      1\n",
              "4         110813      1\n",
              "..           ...    ...\n",
              "676   W./C. 6608      4\n",
              "677   W./C. 6609      1\n",
              "678  W.E.P. 5734      1\n",
              "679    W/C 14208      1\n",
              "680    WE/P 5735      2\n",
              "\n",
              "[681 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5ed3ccda-9ee2-4a07-a76c-d489785df354\" 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>Ticket</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>110152</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>110413</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>110465</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>110564</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>110813</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>676</th>\n",
              "      <td>W./C. 6608</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>677</th>\n",
              "      <td>W./C. 6609</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>678</th>\n",
              "      <td>W.E.P. 5734</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>679</th>\n",
              "      <td>W/C 14208</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>680</th>\n",
              "      <td>WE/P 5735</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>681 rows × 2 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-5ed3ccda-9ee2-4a07-a76c-d489785df354')\"\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-5ed3ccda-9ee2-4a07-a76c-d489785df354 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-5ed3ccda-9ee2-4a07-a76c-d489785df354');\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-5b6eec04-0b80-47c4-88ba-9a324288df16\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5b6eec04-0b80-47c4-88ba-9a324288df16')\"\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-5b6eec04-0b80-47c4-88ba-9a324288df16 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"titanic_train\",\n  \"rows\": 681,\n  \"fields\": [\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"SOTON/OQ 3101316\",\n          \"343120\",\n          \"11813\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 7,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          3,\n          2,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_train[titanic_train['Ticket'] == \"W./C. 6608\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "FLr_P19Hi-b6",
        "outputId": "14c82174-c9a2-4515-aeea-6075ae908c19"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                                     Name  \\\n",
              "86            87         0       3                   Ford, Mr. William Neal   \n",
              "147          148         0       3         Ford, Miss. Robina Maggie \"Ruby\"   \n",
              "436          437         0       3     Ford, Miss. Doolina Margaret \"Daisy\"   \n",
              "736          737         0       3  Ford, Mrs. Edward (Margaret Ann Watson)   \n",
              "\n",
              "        Sex   Age  SibSp  Parch      Ticket    Fare Cabin Embarked age_binned  \\\n",
              "86     male  16.0      1      3  W./C. 6608  34.375   NaN        S       0-18   \n",
              "147  female   9.0      2      2  W./C. 6608  34.375   NaN        S       0-18   \n",
              "436  female  21.0      2      2  W./C. 6608  34.375   NaN        S      19-25   \n",
              "736  female  48.0      1      3  W./C. 6608  34.375   NaN        S      35-54   \n",
              "\n",
              "    survived_label  \n",
              "86            Died  \n",
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              "type": "dataframe",
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          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Finish pre-processing\n",
        "def pre_process(df, vars_to_dummy):\n",
        "    #dummies\n",
        "    df_d = df[df['Embarked'].notna()]\n",
        "    df_d_1 = df_d[df_d['Age'].notna()]\n",
        "    df_d_2 = df_d_1[df_d_1['Fare'].notna()]\n",
        "    df_d_3 = pd.get_dummies(df_d_2, columns=vars_to_dummy)\n",
        "    return df_d_3"
      ],
      "metadata": {
        "id": "qzYXCejZGKm2"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vars_to_dummy = ['Sex','Embarked']\n",
        "training_set_d = pre_process(titanic_train, vars_to_dummy)\n",
        "training_set_d.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "collapsed": true,
        "id": "EpQWhbTCHTsm",
        "outputId": "e38149d0-ed5e-4b0c-f7c2-6b3ce46fac1a"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
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              "\n",
              "                                                Name   Age  SibSp  Parch  \\\n",
              "0                            Braund, Mr. Owen Harris  22.0      1      0   \n",
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              "2                             Heikkinen, Miss. Laina  26.0      0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  35.0      1      0   \n",
              "4                           Allen, Mr. William Henry  35.0      0      0   \n",
              "\n",
              "             Ticket     Fare Cabin age_binned survived_label  Sex_female  \\\n",
              "0         A/5 21171   7.2500   NaN      19-25           Died       False   \n",
              "1          PC 17599  71.2833   C85      35-54       Survived        True   \n",
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              "4            373450   8.0500   NaN      35-54           Died       False   \n",
              "\n",
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              "0      True       False       False        True  \n",
              "1     False        True       False       False  \n",
              "2     False       False       False        True  \n",
              "3     False       False       False        True  \n",
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              "      <td>Died</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</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-7c84d5dc-f0e3-443c-913c-5b0d743a0929')\"\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-7c84d5dc-f0e3-443c-913c-5b0d743a0929 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-7c84d5dc-f0e3-443c-913c-5b0d743a0929');\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-80a83897-7c9b-42db-bc19-e12716ac3e0b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-80a83897-7c9b-42db-bc19-e12716ac3e0b')\"\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-80a83897-7c9b-42db-bc19-e12716ac3e0b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "training_set_d",
              "summary": "{\n  \"name\": \"training_set_d\",\n  \"rows\": 712,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 258,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 712,\n        \"samples\": [\n          642,\n          497,\n          263\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 712,\n        \"samples\": [\n          \"Sagesser, Mlle. Emma\",\n          \"Eustis, Miss. Elizabeth Mussey\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.49293290032352,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 541,\n        \"samples\": [\n          \"345364\",\n          \"244367\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.93864817471089,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 219,\n        \"samples\": [\n          7.875,\n          146.5208\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 133,\n        \"samples\": [\n          \"C7\",\n          \"C104\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_binned\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"19-25\",\n          \"35-54\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"survived_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Survived\",\n          \"Died\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex_female\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked_C\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked_Q\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked_S\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_set_d.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "OjrGn_J-MIDH",
        "outputId": "879d8eac-09ac-4a2a-96b1-fb5167ab0646"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Age', 'SibSp', 'Parch',\n",
              "       'Ticket', 'Fare', 'Cabin', 'age_binned', 'survived_label', 'Sex_female',\n",
              "       'Sex_male', 'Embarked_C', 'Embarked_Q', 'Embarked_S'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_set_d.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "x4CDskEQPLge",
        "outputId": "8421ad47-1d16-4cf9-8e23-d8ff60d2aef1"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId         0\n",
              "Survived            0\n",
              "Pclass              0\n",
              "Name                0\n",
              "Age                 0\n",
              "SibSp               0\n",
              "Parch               0\n",
              "Ticket              0\n",
              "Fare                0\n",
              "Cabin             529\n",
              "age_binned          0\n",
              "survived_label      0\n",
              "Sex_female          0\n",
              "Sex_male            0\n",
              "Embarked_C          0\n",
              "Embarked_Q          0\n",
              "Embarked_S          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#X = training_set_d[training_set_d.columns[~training_set_d.columns.isin(['Survived', 'survived_label', 'age_binned', 'Name', 'Cabin', 'PassengerId'])]]\n",
        "y = training_set_d['Survived']"
      ],
      "metadata": {
        "id": "bP-VIIitItUh"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = training_set_d.drop(axis=1, labels= ['Survived', 'survived_label', 'age_binned', 'Name', 'Cabin', 'PassengerId', 'Sex_male', 'Ticket'])"
      ],
      "metadata": {
        "id": "7cC19N55dIFh"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will take the training data and split it into training and validation sets"
      ],
      "metadata": {
        "id": "7aNCWJu5MSgV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size = .2)"
      ],
      "metadata": {
        "id": "IEW3f7I8MSBV"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Model functions\n",
        "\n"
      ],
      "metadata": {
        "id": "EQK_EpsANsm0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def fit_model(model, X_train, y_train, importance_flag=False):\n",
        "    if type(model) == sklearn.linear_model._logistic.LogisticRegression and importance_flag:\n",
        "        clf = model.fit(X_train, y_train)\n",
        "        df = pd.DataFrame({\"Feature\":X_train.columns.tolist(),\"Coefficients\":abs(clf.coef_[0])}).sort_values(by=\"Coefficients\", ascending=False)\n",
        "        print(df)\n",
        "        return clf\n",
        "    elif type(model) == sklearn.ensemble._forest.RandomForestClassifier and importance_flag:\n",
        "        clf = model.fit(X_train, y_train)\n",
        "        df = pd.DataFrame({\"Feature\":X_train.columns.tolist(),\"Coefficients\":abs(clf.feature_importances_)}).sort_values(by=\"Coefficients\", ascending=False)\n",
        "        print(df)\n",
        "        return clf\n",
        "    else:\n",
        "        clf = model.fit(X_train, y_train)\n",
        "        return clf\n",
        "\n",
        "\n",
        "def evaluate(model_list, X_train, y_train, X_test, y_test):\n",
        "    for model in model_list:\n",
        "        clf = fit_model(model, X_train, y_train)\n",
        "        y_pred = clf.predict(X_test)\n",
        "        cm = metrics.confusion_matrix(y_test, y_pred, labels=clf.classes_)\n",
        "        disp = metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf.classes_)\n",
        "        print(model)\n",
        "        print(metrics.classification_report(y_test, y_pred))\n",
        "        disp.plot()\n",
        "        plt.show()\n",
        "\n",
        "def predict_scores(clf, X_test):\n",
        "    y_pred = clf.predict_proba(X_test)[:,1]\n",
        "    return y_pred"
      ],
      "metadata": {
        "id": "VtFWaOeCNzAz"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model fitting and interpretation"
      ],
      "metadata": {
        "id": "hpYfAs_CTYmj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr_penalized = linear_model.LogisticRegression(penalty='l1', solver='liblinear')\n",
        "lr_penalized_model = fit_model(lr_penalized, X_train, y_train, importance_flag=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "6xROqa96Q_hv",
        "outputId": "c7936729-c3f0-4de8-c27b-8298b8add6d7"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      Feature  Coefficients\n",
            "5  Sex_female      2.454767\n",
            "0      Pclass      0.934949\n",
            "6  Embarked_C      0.685344\n",
            "2       SibSp      0.306095\n",
            "8  Embarked_S      0.149415\n",
            "1         Age      0.037992\n",
            "3       Parch      0.024407\n",
            "4        Fare      0.002260\n",
            "7  Embarked_Q      0.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf = ensemble.RandomForestClassifier()"
      ],
      "metadata": {
        "id": "NB-k8Y-3cw-E"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_model = fit_model(rf, X_train, y_train, importance_flag=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "iqC5DtK_RGqf",
        "outputId": "1f87c01f-41a4-4a87-a9c8-916884405ccc"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      Feature  Coefficients\n",
            "1         Age      0.295297\n",
            "4        Fare      0.258674\n",
            "5  Sex_female      0.240533\n",
            "0      Pclass      0.075341\n",
            "2       SibSp      0.053252\n",
            "3       Parch      0.040825\n",
            "6  Embarked_C      0.017297\n",
            "8  Embarked_S      0.013072\n",
            "7  Embarked_Q      0.005710\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dec_tree = fit_model(tree.DecisionTreeClassifier(max_depth=5), X_train, y_train)\n",
        "y_pred = dec_tree.predict(X_test)\n",
        "class_names = ['Survived', 'Died']\n",
        "export_graphviz(\n",
        "dec_tree, feature_names=X_train.columns, class_names=class_names, rounded=True, filled=True, out_file=\"tree.dot\")\n",
        "graphviz.render('dot', 'png', \"tree.dot\")\n",
        "graphviz.Source.from_file(\"tree.dot\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 936
        },
        "collapsed": true,
        "id": "f4glF-j9RKLA",
        "outputId": "c35859da-9c97-4992-b7e9-eb569336d6d3"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.43.0 (0)\n -->\n<!-- Title: Tree Pages: 1 -->\n<svg width=\"3026pt\" height=\"671pt\"\n viewBox=\"0.00 0.00 3026.00 671.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 667)\">\n<title>Tree</title>\n<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-667 3022,-667 3022,4 -4,4\"/>\n<!-- 0 -->\n<g id=\"node1\" class=\"node\">\n<title>0</title>\n<path fill=\"#f6d5bc\" stroke=\"black\" d=\"M1496.5,-663C1496.5,-663 1389.5,-663 1389.5,-663 1383.5,-663 1377.5,-657 1377.5,-651 1377.5,-651 1377.5,-592 1377.5,-592 1377.5,-586 1383.5,-580 1389.5,-580 1389.5,-580 1496.5,-580 1496.5,-580 1502.5,-580 1508.5,-586 1508.5,-592 1508.5,-592 1508.5,-651 1508.5,-651 1508.5,-657 1502.5,-663 1496.5,-663\"/>\n<text text-anchor=\"middle\" x=\"1443\" y=\"-647.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Sex_female &lt;= 0.5</text>\n<text text-anchor=\"middle\" x=\"1443\" y=\"-632.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.48</text>\n<text text-anchor=\"middle\" x=\"1443\" y=\"-617.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 569</text>\n<text text-anchor=\"middle\" x=\"1443\" y=\"-602.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [342, 227]</text>\n<text text-anchor=\"middle\" x=\"1443\" y=\"-587.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<path fill=\"#eca26c\" stroke=\"black\" d=\"M1051.5,-544C1051.5,-544 958.5,-544 958.5,-544 952.5,-544 946.5,-538 946.5,-532 946.5,-532 946.5,-473 946.5,-473 946.5,-467 952.5,-461 958.5,-461 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y=\"-498.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 204</text>\n<text text-anchor=\"middle\" x=\"1720\" y=\"-483.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [52, 152]</text>\n<text text-anchor=\"middle\" x=\"1720\" y=\"-468.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 0&#45;&gt;28 -->\n<g id=\"edge28\" class=\"edge\">\n<title>0&#45;&gt;28</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1508.65,-592.77C1551.87,-574.52 1608.33,-550.67 1652.02,-532.21\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1653.57,-535.36 1661.42,-528.24 1650.85,-528.91 1653.57,-535.36\"/>\n<text text-anchor=\"middle\" x=\"1652.01\" y=\"-547.71\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\n</g>\n<!-- 2 -->\n<g id=\"node3\" class=\"node\">\n<title>2</title>\n<path fill=\"#d3e9f9\" stroke=\"black\" d=\"M543,-425C543,-425 457,-425 457,-425 451,-425 445,-419 445,-413 445,-413 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564.96,-399.55\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"565.61,-396.11 555.08,-397.26 564.03,-402.93 565.61,-396.11\"/>\n</g>\n<!-- 13 -->\n<g id=\"node14\" class=\"node\">\n<title>13</title>\n<path fill=\"#ea9b62\" stroke=\"black\" d=\"M1051.5,-425C1051.5,-425 958.5,-425 958.5,-425 952.5,-425 946.5,-419 946.5,-413 946.5,-413 946.5,-354 946.5,-354 946.5,-348 952.5,-342 958.5,-342 958.5,-342 1051.5,-342 1051.5,-342 1057.5,-342 1063.5,-348 1063.5,-354 1063.5,-354 1063.5,-413 1063.5,-413 1063.5,-419 1057.5,-425 1051.5,-425\"/>\n<text text-anchor=\"middle\" x=\"1005\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Pclass &lt;= 1.5</text>\n<text text-anchor=\"middle\" x=\"1005\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.284</text>\n<text text-anchor=\"middle\" x=\"1005\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 333</text>\n<text text-anchor=\"middle\" x=\"1005\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [276, 57]</text>\n<text text-anchor=\"middle\" x=\"1005\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 1&#45;&gt;13 -->\n<g id=\"edge13\" class=\"edge\">\n<title>1&#45;&gt;13</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1005,-460.91C1005,-452.65 1005,-443.86 1005,-435.3\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1008.5,-435.02 1005,-425.02 1001.5,-435.02 1008.5,-435.02\"/>\n</g>\n<!-- 3 -->\n<g id=\"node4\" class=\"node\">\n<title>3</title>\n<path fill=\"#45a3e7\" stroke=\"black\" d=\"M345,-306C345,-306 267,-306 267,-306 261,-306 255,-300 255,-294 255,-294 255,-235 255,-235 255,-229 261,-223 267,-223 267,-223 345,-223 345,-223 351,-223 357,-229 357,-235 357,-235 357,-294 357,-294 357,-300 351,-306 345,-306\"/>\n<text text-anchor=\"middle\" x=\"306\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Parch &lt;= 0.5</text>\n<text text-anchor=\"middle\" x=\"306\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.105</text>\n<text text-anchor=\"middle\" x=\"306\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 18</text>\n<text text-anchor=\"middle\" x=\"306\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 17]</text>\n<text text-anchor=\"middle\" x=\"306\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 2&#45;&gt;3 -->\n<g id=\"edge3\" class=\"edge\">\n<title>2&#45;&gt;3</title>\n<path fill=\"none\" stroke=\"black\" d=\"M444.93,-349.29C420.28,-334.42 391.15,-316.85 365.95,-301.66\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"367.59,-298.56 357.22,-296.39 363.97,-304.55 367.59,-298.56\"/>\n</g>\n<!-- 8 -->\n<g id=\"node9\" class=\"node\">\n<title>8</title>\n<path fill=\"#e78b48\" stroke=\"black\" d=\"M546,-306C546,-306 454,-306 454,-306 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d=\"M500,-341.91C500,-333.65 500,-324.86 500,-316.3\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"503.5,-316.02 500,-306.02 496.5,-316.02 503.5,-316.02\"/>\n</g>\n<!-- 4 -->\n<g id=\"node5\" class=\"node\">\n<title>4</title>\n<path fill=\"#ffffff\" stroke=\"black\" d=\"M225,-187C225,-187 133,-187 133,-187 127,-187 121,-181 121,-175 121,-175 121,-116 121,-116 121,-110 127,-104 133,-104 133,-104 225,-104 225,-104 231,-104 237,-110 237,-116 237,-116 237,-175 237,-175 237,-181 231,-187 225,-187\"/>\n<text text-anchor=\"middle\" x=\"179\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">SibSp &lt;= 0.5</text>\n<text text-anchor=\"middle\" x=\"179\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n<text text-anchor=\"middle\" x=\"179\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n<text text-anchor=\"middle\" x=\"179\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" 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text-anchor=\"middle\" x=\"58\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"58\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n<text text-anchor=\"middle\" x=\"58\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 0]</text>\n<text text-anchor=\"middle\" x=\"58\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 4&#45;&gt;5 -->\n<g id=\"edge5\" class=\"edge\">\n<title>4&#45;&gt;5</title>\n<path fill=\"none\" stroke=\"black\" d=\"M133.94,-103.73C123.66,-94.42 112.75,-84.54 102.49,-75.26\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"104.56,-72.41 94.8,-68.3 99.86,-77.6 104.56,-72.41\"/>\n</g>\n<!-- 6 -->\n<g id=\"node7\" class=\"node\">\n<title>6</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M217.5,-68C217.5,-68 146.5,-68 146.5,-68 140.5,-68 134.5,-62 134.5,-56 134.5,-56 134.5,-12 134.5,-12 134.5,-6 140.5,0 146.5,0 146.5,0 217.5,0 217.5,0 223.5,0 229.5,-6 229.5,-12 229.5,-12 229.5,-56 229.5,-56 229.5,-62 223.5,-68 217.5,-68\"/>\n<text text-anchor=\"middle\" x=\"182\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"182\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n<text text-anchor=\"middle\" x=\"182\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1]</text>\n<text text-anchor=\"middle\" x=\"182\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 4&#45;&gt;6 -->\n<g id=\"edge6\" class=\"edge\">\n<title>4&#45;&gt;6</title>\n<path fill=\"none\" stroke=\"black\" d=\"M180.12,-103.73C180.34,-95.52 180.58,-86.86 180.81,-78.56\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"184.31,-78.39 181.09,-68.3 177.32,-78.2 184.31,-78.39\"/>\n</g>\n<!-- 9 -->\n<g 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8&#45;&gt;9 -->\n<g id=\"edge9\" class=\"edge\">\n<title>8&#45;&gt;9</title>\n<path fill=\"none\" stroke=\"black\" d=\"M476.75,-222.91C471.77,-214.2 466.44,-204.9 461.29,-195.89\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"464.21,-193.96 456.2,-187.02 458.14,-197.44 464.21,-193.96\"/>\n</g>\n<!-- 12 -->\n<g id=\"node13\" class=\"node\">\n<title>12</title>\n<path fill=\"#e58139\" stroke=\"black\" d=\"M613,-179.5C613,-179.5 521,-179.5 521,-179.5 515,-179.5 509,-173.5 509,-167.5 509,-167.5 509,-123.5 509,-123.5 509,-117.5 515,-111.5 521,-111.5 521,-111.5 613,-111.5 613,-111.5 619,-111.5 625,-117.5 625,-123.5 625,-123.5 625,-167.5 625,-167.5 625,-173.5 619,-179.5 613,-179.5\"/>\n<text text-anchor=\"middle\" x=\"567\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"567\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 8</text>\n<text text-anchor=\"middle\" x=\"567\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [8, 0]</text>\n<text text-anchor=\"middle\" x=\"567\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 8&#45;&gt;12 -->\n<g id=\"edge12\" class=\"edge\">\n<title>8&#45;&gt;12</title>\n<path fill=\"none\" stroke=\"black\" d=\"M523.25,-222.91C529.63,-211.76 536.56,-199.66 542.98,-188.44\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"546.07,-190.08 548.01,-179.67 540,-186.61 546.07,-190.08\"/>\n</g>\n<!-- 10 -->\n<g id=\"node11\" class=\"node\">\n<title>10</title>\n<path fill=\"#e58139\" stroke=\"black\" d=\"M359,-68C359,-68 267,-68 267,-68 261,-68 255,-62 255,-56 255,-56 255,-12 255,-12 255,-6 261,0 267,0 267,0 359,0 359,0 365,0 371,-6 371,-12 371,-12 371,-56 371,-56 371,-62 365,-68 359,-68\"/>\n<text text-anchor=\"middle\" x=\"313\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"313\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\n<text text-anchor=\"middle\" x=\"313\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [5, 0]</text>\n<text text-anchor=\"middle\" x=\"313\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 9&#45;&gt;10 -->\n<g id=\"edge10\" class=\"edge\">\n<title>9&#45;&gt;10</title>\n<path fill=\"none\" stroke=\"black\" d=\"M388.32,-103.73C378.12,-94.42 367.29,-84.54 357.12,-75.26\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"359.24,-72.45 349.49,-68.3 354.52,-77.63 359.24,-72.45\"/>\n</g>\n<!-- 11 -->\n<g id=\"node12\" class=\"node\">\n<title>11</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M472.5,-68C472.5,-68 401.5,-68 401.5,-68 395.5,-68 389.5,-62 389.5,-56 389.5,-56 389.5,-12 389.5,-12 389.5,-6 395.5,0 401.5,0 401.5,0 472.5,0 472.5,0 478.5,0 484.5,-6 484.5,-12 484.5,-12 484.5,-56 484.5,-56 484.5,-62 478.5,-68 472.5,-68\"/>\n<text text-anchor=\"middle\" x=\"437\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"437\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n<text text-anchor=\"middle\" x=\"437\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1]</text>\n<text text-anchor=\"middle\" x=\"437\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 9&#45;&gt;11 -->\n<g id=\"edge11\" class=\"edge\">\n<title>9&#45;&gt;11</title>\n<path fill=\"none\" stroke=\"black\" d=\"M434.49,-103.73C434.79,-95.52 435.11,-86.86 435.41,-78.56\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"438.92,-78.42 435.78,-68.3 431.92,-78.17 438.92,-78.42\"/>\n</g>\n<!-- 14 -->\n<g id=\"node15\" class=\"node\">\n<title>14</title>\n<path fill=\"#f3c5a4\" stroke=\"black\" d=\"M929,-306C929,-306 837,-306 837,-306 831,-306 825,-300 825,-294 825,-294 825,-235 825,-235 825,-229 831,-223 837,-223 837,-223 929,-223 929,-223 935,-223 941,-229 941,-235 941,-235 941,-294 941,-294 941,-300 935,-306 929,-306\"/>\n<text text-anchor=\"middle\" x=\"883\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Age &lt;= 36.5</text>\n<text text-anchor=\"middle\" x=\"883\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.456</text>\n<text text-anchor=\"middle\" x=\"883\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 74</text>\n<text text-anchor=\"middle\" x=\"883\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [48, 26]</text>\n<text text-anchor=\"middle\" x=\"883\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 13&#45;&gt;14 -->\n<g id=\"edge14\" class=\"edge\">\n<title>13&#45;&gt;14</title>\n<path fill=\"none\" stroke=\"black\" d=\"M962.67,-341.91C953.02,-332.65 942.68,-322.73 932.75,-313.21\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"934.89,-310.42 925.25,-306.02 930.05,-315.47 934.89,-310.42\"/>\n</g>\n<!-- 21 -->\n<g id=\"node22\" class=\"node\">\n<title>21</title>\n<path fill=\"#e99254\" stroke=\"black\" d=\"M1178.5,-306C1178.5,-306 1085.5,-306 1085.5,-306 1079.5,-306 1073.5,-300 1073.5,-294 1073.5,-294 1073.5,-235 1073.5,-235 1073.5,-229 1079.5,-223 1085.5,-223 1085.5,-223 1178.5,-223 1178.5,-223 1184.5,-223 1190.5,-229 1190.5,-235 1190.5,-235 1190.5,-294 1190.5,-294 1190.5,-300 1184.5,-306 1178.5,-306\"/>\n<text text-anchor=\"middle\" x=\"1132\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Age &lt;= 32.5</text>\n<text text-anchor=\"middle\" x=\"1132\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.211</text>\n<text text-anchor=\"middle\" x=\"1132\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 259</text>\n<text text-anchor=\"middle\" x=\"1132\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [228, 31]</text>\n<text text-anchor=\"middle\" x=\"1132\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 13&#45;&gt;21 -->\n<g id=\"edge21\" class=\"edge\">\n<title>13&#45;&gt;21</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1049.06,-341.91C1059.21,-332.56 1070.09,-322.54 1080.52,-312.93\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1083.03,-315.37 1088.02,-306.02 1078.29,-310.22 1083.03,-315.37\"/>\n</g>\n<!-- 15 -->\n<g id=\"node16\" class=\"node\">\n<title>15</title>\n<path fill=\"#f2f8fd\" stroke=\"black\" d=\"M741,-187C741,-187 655,-187 655,-187 649,-187 643,-181 643,-175 643,-175 643,-116 643,-116 643,-110 649,-104 655,-104 655,-104 741,-104 741,-104 747,-104 753,-110 753,-116 753,-116 753,-175 753,-175 753,-181 747,-187 741,-187\"/>\n<text text-anchor=\"middle\" x=\"698\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Age &lt;= 31.5</text>\n<text text-anchor=\"middle\" x=\"698\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.499</text>\n<text text-anchor=\"middle\" x=\"698\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 29</text>\n<text text-anchor=\"middle\" x=\"698\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [14, 15]</text>\n<text text-anchor=\"middle\" x=\"698\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 14&#45;&gt;15 -->\n<g id=\"edge15\" class=\"edge\">\n<title>14&#45;&gt;15</title>\n<path fill=\"none\" stroke=\"black\" d=\"M824.97,-226.8C804.94,-214.14 782.4,-199.88 761.93,-186.93\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"763.62,-183.86 753.29,-181.47 759.88,-189.77 763.62,-183.86\"/>\n</g>\n<!-- 18 -->\n<g id=\"node19\" class=\"node\">\n<title>18</title>\n<path fill=\"#edaa79\" stroke=\"black\" d=\"M929,-187C929,-187 837,-187 837,-187 831,-187 825,-181 825,-175 825,-175 825,-116 825,-116 825,-110 831,-104 837,-104 837,-104 929,-104 929,-104 935,-104 941,-110 941,-116 941,-116 941,-175 941,-175 941,-181 935,-187 929,-187\"/>\n<text text-anchor=\"middle\" x=\"883\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Age &lt;= 75.5</text>\n<text text-anchor=\"middle\" x=\"883\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.369</text>\n<text text-anchor=\"middle\" x=\"883\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 45</text>\n<text text-anchor=\"middle\" x=\"883\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [34, 11]</text>\n<text text-anchor=\"middle\" x=\"883\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 14&#45;&gt;18 -->\n<g id=\"edge18\" class=\"edge\">\n<title>14&#45;&gt;18</title>\n<path fill=\"none\" stroke=\"black\" d=\"M883,-222.91C883,-214.65 883,-205.86 883,-197.3\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"886.5,-197.02 883,-187.02 879.5,-197.02 886.5,-197.02\"/>\n</g>\n<!-- 16 -->\n<g id=\"node17\" class=\"node\">\n<title>16</title>\n<path fill=\"#f6d2b8\" stroke=\"black\" d=\"M615,-68C615,-68 523,-68 523,-68 517,-68 511,-62 511,-56 511,-56 511,-12 511,-12 511,-6 517,0 523,0 523,0 615,0 615,0 621,0 627,-6 627,-12 627,-12 627,-56 627,-56 627,-62 621,-68 615,-68\"/>\n<text text-anchor=\"middle\" x=\"569\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.476</text>\n<text text-anchor=\"middle\" x=\"569\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 23</text>\n<text text-anchor=\"middle\" x=\"569\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [14, 9]</text>\n<text text-anchor=\"middle\" x=\"569\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 15&#45;&gt;16 -->\n<g id=\"edge16\" class=\"edge\">\n<title>15&#45;&gt;16</title>\n<path fill=\"none\" stroke=\"black\" d=\"M649.97,-103.73C638.9,-94.33 627.14,-84.35 616.11,-74.99\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"618.12,-72.1 608.23,-68.3 613.59,-77.44 618.12,-72.1\"/>\n</g>\n<!-- 17 -->\n<g id=\"node18\" class=\"node\">\n<title>17</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M728.5,-68C728.5,-68 657.5,-68 657.5,-68 651.5,-68 645.5,-62 645.5,-56 645.5,-56 645.5,-12 645.5,-12 645.5,-6 651.5,0 657.5,0 657.5,0 728.5,0 728.5,0 734.5,0 740.5,-6 740.5,-12 740.5,-12 740.5,-56 740.5,-56 740.5,-62 734.5,-68 728.5,-68\"/>\n<text text-anchor=\"middle\" x=\"693\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"693\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6</text>\n<text text-anchor=\"middle\" x=\"693\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 6]</text>\n<text text-anchor=\"middle\" x=\"693\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 15&#45;&gt;17 -->\n<g id=\"edge17\" class=\"edge\">\n<title>15&#45;&gt;17</title>\n<path fill=\"none\" stroke=\"black\" d=\"M696.14,-103.73C695.76,-95.52 695.37,-86.86 694.99,-78.56\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"698.47,-78.13 694.52,-68.3 691.48,-78.45 698.47,-78.13\"/>\n</g>\n<!-- 19 -->\n<g id=\"node20\" class=\"node\">\n<title>19</title>\n<path fill=\"#eda673\" stroke=\"black\" d=\"M863,-68C863,-68 771,-68 771,-68 765,-68 759,-62 759,-56 759,-56 759,-12 759,-12 759,-6 765,0 771,0 771,0 863,0 863,0 869,0 875,-6 875,-12 875,-12 875,-56 875,-56 875,-62 869,-68 863,-68\"/>\n<text text-anchor=\"middle\" x=\"817\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.351</text>\n<text text-anchor=\"middle\" x=\"817\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 44</text>\n<text text-anchor=\"middle\" x=\"817\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [34, 10]</text>\n<text text-anchor=\"middle\" x=\"817\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 18&#45;&gt;19 -->\n<g id=\"edge19\" class=\"edge\">\n<title>18&#45;&gt;19</title>\n<path fill=\"none\" stroke=\"black\" d=\"M858.42,-103.73C853.15,-94.97 847.56,-85.7 842.26,-76.91\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"845.23,-75.06 837.07,-68.3 839.24,-78.67 845.23,-75.06\"/>\n</g>\n<!-- 20 -->\n<g id=\"node21\" class=\"node\">\n<title>20</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M976.5,-68C976.5,-68 905.5,-68 905.5,-68 899.5,-68 893.5,-62 893.5,-56 893.5,-56 893.5,-12 893.5,-12 893.5,-6 899.5,0 905.5,0 905.5,0 976.5,0 976.5,0 982.5,0 988.5,-6 988.5,-12 988.5,-12 988.5,-56 988.5,-56 988.5,-62 982.5,-68 976.5,-68\"/>\n<text text-anchor=\"middle\" x=\"941\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"941\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n<text text-anchor=\"middle\" x=\"941\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1]</text>\n<text text-anchor=\"middle\" x=\"941\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 18&#45;&gt;20 -->\n<g id=\"edge20\" class=\"edge\">\n<title>18&#45;&gt;20</title>\n<path fill=\"none\" stroke=\"black\" d=\"M904.6,-103.73C909.19,-95.06 914.04,-85.9 918.66,-77.18\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"921.77,-78.77 923.36,-68.3 915.59,-75.5 921.77,-78.77\"/>\n</g>\n<!-- 22 -->\n<g id=\"node23\" class=\"node\">\n<title>22</title>\n<path fill=\"#ea985d\" stroke=\"black\" d=\"M1178.5,-187C1178.5,-187 1085.5,-187 1085.5,-187 1079.5,-187 1073.5,-181 1073.5,-175 1073.5,-175 1073.5,-116 1073.5,-116 1073.5,-110 1079.5,-104 1085.5,-104 1085.5,-104 1178.5,-104 1178.5,-104 1184.5,-104 1190.5,-110 1190.5,-116 1190.5,-116 1190.5,-175 1190.5,-175 1190.5,-181 1184.5,-187 1178.5,-187\"/>\n<text text-anchor=\"middle\" x=\"1132\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Age &lt;= 30.75</text>\n<text text-anchor=\"middle\" x=\"1132\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.261</text>\n<text text-anchor=\"middle\" x=\"1132\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 175</text>\n<text text-anchor=\"middle\" x=\"1132\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [148, 27]</text>\n<text text-anchor=\"middle\" x=\"1132\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 21&#45;&gt;22 -->\n<g id=\"edge22\" class=\"edge\">\n<title>21&#45;&gt;22</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1132,-222.91C1132,-214.65 1132,-205.86 1132,-197.3\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1135.5,-197.02 1132,-187.02 1128.5,-197.02 1135.5,-197.02\"/>\n</g>\n<!-- 25 -->\n<g id=\"node26\" class=\"node\">\n<title>25</title>\n<path fill=\"#e68743\" stroke=\"black\" d=\"M1380,-187C1380,-187 1288,-187 1288,-187 1282,-187 1276,-181 1276,-175 1276,-175 1276,-116 1276,-116 1276,-110 1282,-104 1288,-104 1288,-104 1380,-104 1380,-104 1386,-104 1392,-110 1392,-116 1392,-116 1392,-175 1392,-175 1392,-181 1386,-187 1380,-187\"/>\n<text text-anchor=\"middle\" x=\"1334\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fare &lt;= 11.387</text>\n<text text-anchor=\"middle\" x=\"1334\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.091</text>\n<text text-anchor=\"middle\" x=\"1334\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 84</text>\n<text text-anchor=\"middle\" x=\"1334\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [80, 4]</text>\n<text text-anchor=\"middle\" x=\"1334\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 21&#45;&gt;25 -->\n<g id=\"edge25\" class=\"edge\">\n<title>21&#45;&gt;25</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1190.7,-229.5C1214.56,-215.68 1242.27,-199.63 1266.92,-185.35\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1268.95,-188.22 1275.85,-180.18 1265.45,-182.16 1268.95,-188.22\"/>\n</g>\n<!-- 23 -->\n<g id=\"node24\" class=\"node\">\n<title>23</title>\n<path fill=\"#e99356\" stroke=\"black\" d=\"M1111.5,-68C1111.5,-68 1018.5,-68 1018.5,-68 1012.5,-68 1006.5,-62 1006.5,-56 1006.5,-56 1006.5,-12 1006.5,-12 1006.5,-6 1012.5,0 1018.5,0 1018.5,0 1111.5,0 1111.5,0 1117.5,0 1123.5,-6 1123.5,-12 1123.5,-12 1123.5,-56 1123.5,-56 1123.5,-62 1117.5,-68 1111.5,-68\"/>\n<text text-anchor=\"middle\" x=\"1065\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.221</text>\n<text text-anchor=\"middle\" x=\"1065\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 158</text>\n<text text-anchor=\"middle\" x=\"1065\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [138, 20]</text>\n<text text-anchor=\"middle\" x=\"1065\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 22&#45;&gt;23 -->\n<g id=\"edge23\" class=\"edge\">\n<title>22&#45;&gt;23</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1107.05,-103.73C1101.69,-94.97 1096.02,-85.7 1090.64,-76.91\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1093.58,-75 1085.38,-68.3 1087.61,-78.66 1093.58,-75\"/>\n</g>\n<!-- 24 -->\n<g id=\"node25\" class=\"node\">\n<title>24</title>\n<path fill=\"#f7d9c4\" stroke=\"black\" d=\"M1246,-68C1246,-68 1154,-68 1154,-68 1148,-68 1142,-62 1142,-56 1142,-56 1142,-12 1142,-12 1142,-6 1148,0 1154,0 1154,0 1246,0 1246,0 1252,0 1258,-6 1258,-12 1258,-12 1258,-56 1258,-56 1258,-62 1252,-68 1246,-68\"/>\n<text text-anchor=\"middle\" x=\"1200\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.484</text>\n<text text-anchor=\"middle\" x=\"1200\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 17</text>\n<text text-anchor=\"middle\" x=\"1200\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [10, 7]</text>\n<text text-anchor=\"middle\" x=\"1200\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 22&#45;&gt;24 -->\n<g id=\"edge24\" class=\"edge\">\n<title>22&#45;&gt;24</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1157.32,-103.73C1162.76,-94.97 1168.51,-85.7 1173.98,-76.91\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1177.02,-78.64 1179.32,-68.3 1171.07,-74.95 1177.02,-78.64\"/>\n</g>\n<!-- 26 -->\n<g id=\"node27\" class=\"node\">\n<title>26</title>\n<path fill=\"#e78d4b\" stroke=\"black\" d=\"M1380,-68C1380,-68 1288,-68 1288,-68 1282,-68 1276,-62 1276,-56 1276,-56 1276,-12 1276,-12 1276,-6 1282,0 1288,0 1288,0 1380,0 1380,0 1386,0 1392,-6 1392,-12 1392,-12 1392,-56 1392,-56 1392,-62 1386,-68 1380,-68\"/>\n<text text-anchor=\"middle\" x=\"1334\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.156</text>\n<text text-anchor=\"middle\" x=\"1334\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 47</text>\n<text text-anchor=\"middle\" x=\"1334\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [43, 4]</text>\n<text text-anchor=\"middle\" x=\"1334\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 25&#45;&gt;26 -->\n<g id=\"edge26\" class=\"edge\">\n<title>25&#45;&gt;26</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1334,-103.73C1334,-95.52 1334,-86.86 1334,-78.56\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1337.5,-78.3 1334,-68.3 1330.5,-78.3 1337.5,-78.3\"/>\n</g>\n<!-- 27 -->\n<g id=\"node28\" class=\"node\">\n<title>27</title>\n<path fill=\"#e58139\" stroke=\"black\" d=\"M1514,-68C1514,-68 1422,-68 1422,-68 1416,-68 1410,-62 1410,-56 1410,-56 1410,-12 1410,-12 1410,-6 1416,0 1422,0 1422,0 1514,0 1514,0 1520,0 1526,-6 1526,-12 1526,-12 1526,-56 1526,-56 1526,-62 1520,-68 1514,-68\"/>\n<text text-anchor=\"middle\" x=\"1468\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"1468\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 37</text>\n<text text-anchor=\"middle\" x=\"1468\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [37, 0]</text>\n<text text-anchor=\"middle\" x=\"1468\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 25&#45;&gt;27 -->\n<g id=\"edge27\" class=\"edge\">\n<title>25&#45;&gt;27</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1383.9,-103.73C1395.5,-94.24 1407.85,-84.16 1419.4,-74.72\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1421.72,-77.34 1427.25,-68.3 1417.29,-71.92 1421.72,-77.34\"/>\n</g>\n<!-- 29 -->\n<g id=\"node30\" class=\"node\">\n<title>29</title>\n<path fill=\"#46a3e7\" stroke=\"black\" d=\"M1763,-425C1763,-425 1677,-425 1677,-425 1671,-425 1665,-419 1665,-413 1665,-413 1665,-354 1665,-354 1665,-348 1671,-342 1677,-342 1677,-342 1763,-342 1763,-342 1769,-342 1775,-348 1775,-354 1775,-354 1775,-413 1775,-413 1775,-419 1769,-425 1763,-425\"/>\n<text text-anchor=\"middle\" x=\"1720\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Age &lt;= 3.0</text>\n<text text-anchor=\"middle\" x=\"1720\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.112</text>\n<text text-anchor=\"middle\" x=\"1720\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 117</text>\n<text text-anchor=\"middle\" x=\"1720\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [7, 110]</text>\n<text text-anchor=\"middle\" x=\"1720\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 28&#45;&gt;29 -->\n<g id=\"edge29\" class=\"edge\">\n<title>28&#45;&gt;29</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1720,-460.91C1720,-452.65 1720,-443.86 1720,-435.3\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1723.5,-435.02 1720,-425.02 1716.5,-435.02 1723.5,-435.02\"/>\n</g>\n<!-- 38 -->\n<g id=\"node39\" class=\"node\">\n<title>38</title>\n<path fill=\"#fdf7f2\" stroke=\"black\" d=\"M2313,-425C2313,-425 2221,-425 2221,-425 2215,-425 2209,-419 2209,-413 2209,-413 2209,-354 2209,-354 2209,-348 2215,-342 2221,-342 2221,-342 2313,-342 2313,-342 2319,-342 2325,-348 2325,-354 2325,-354 2325,-413 2325,-413 2325,-419 2319,-425 2313,-425\"/>\n<text text-anchor=\"middle\" x=\"2267\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fare &lt;= 20.663</text>\n<text text-anchor=\"middle\" x=\"2267\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.499</text>\n<text text-anchor=\"middle\" x=\"2267\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 87</text>\n<text text-anchor=\"middle\" x=\"2267\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [45, 42]</text>\n<text text-anchor=\"middle\" x=\"2267\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 28&#45;&gt;38 -->\n<g id=\"edge38\" class=\"edge\">\n<title>28&#45;&gt;38</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1778.54,-488.98C1880.63,-467.14 2090.69,-422.21 2199.04,-399.04\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"2199.84,-402.45 2208.88,-396.93 2198.37,-395.6 2199.84,-402.45\"/>\n</g>\n<!-- 30 -->\n<g id=\"node31\" class=\"node\">\n<title>30</title>\n<path fill=\"#e58139\" stroke=\"black\" d=\"M1701,-298.5C1701,-298.5 1609,-298.5 1609,-298.5 1603,-298.5 1597,-292.5 1597,-286.5 1597,-286.5 1597,-242.5 1597,-242.5 1597,-236.5 1603,-230.5 1609,-230.5 1609,-230.5 1701,-230.5 1701,-230.5 1707,-230.5 1713,-236.5 1713,-242.5 1713,-242.5 1713,-286.5 1713,-286.5 1713,-292.5 1707,-298.5 1701,-298.5\"/>\n<text text-anchor=\"middle\" x=\"1655\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"1655\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n<text text-anchor=\"middle\" x=\"1655\" y=\"-253.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 0]</text>\n<text text-anchor=\"middle\" x=\"1655\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 29&#45;&gt;30 -->\n<g id=\"edge30\" class=\"edge\">\n<title>29&#45;&gt;30</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1697.45,-341.91C1691.26,-330.76 1684.53,-318.66 1678.3,-307.44\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1681.34,-305.71 1673.43,-298.67 1675.22,-309.11 1681.34,-305.71\"/>\n</g>\n<!-- 31 -->\n<g id=\"node32\" class=\"node\">\n<title>31</title>\n<path fill=\"#44a2e6\" stroke=\"black\" d=\"M1829,-306C1829,-306 1743,-306 1743,-306 1737,-306 1731,-300 1731,-294 1731,-294 1731,-235 1731,-235 1731,-229 1737,-223 1743,-223 1743,-223 1829,-223 1829,-223 1835,-223 1841,-229 1841,-235 1841,-235 1841,-294 1841,-294 1841,-300 1835,-306 1829,-306\"/>\n<text text-anchor=\"middle\" x=\"1786\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Fare &lt;= 13.25</text>\n<text text-anchor=\"middle\" x=\"1786\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.098</text>\n<text text-anchor=\"middle\" x=\"1786\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 116</text>\n<text text-anchor=\"middle\" x=\"1786\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [6, 110]</text>\n<text text-anchor=\"middle\" x=\"1786\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 29&#45;&gt;31 -->\n<g id=\"edge31\" class=\"edge\">\n<title>29&#45;&gt;31</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1742.9,-341.91C1747.81,-333.2 1753.06,-323.9 1758.14,-314.89\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1761.28,-316.45 1763.14,-306.02 1755.18,-313.01 1761.28,-316.45\"/>\n</g>\n<!-- 32 -->\n<g id=\"node33\" class=\"node\">\n<title>32</title>\n<path fill=\"#5eafea\" stroke=\"black\" d=\"M1761,-187C1761,-187 1683,-187 1683,-187 1677,-187 1671,-181 1671,-175 1671,-175 1671,-116 1671,-116 1671,-110 1677,-104 1683,-104 1683,-104 1761,-104 1761,-104 1767,-104 1773,-110 1773,-116 1773,-116 1773,-175 1773,-175 1773,-181 1767,-187 1761,-187\"/>\n<text text-anchor=\"middle\" x=\"1722\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Age &lt;= 53.5</text>\n<text text-anchor=\"middle\" x=\"1722\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.266</text>\n<text text-anchor=\"middle\" x=\"1722\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 19</text>\n<text text-anchor=\"middle\" x=\"1722\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3, 16]</text>\n<text text-anchor=\"middle\" x=\"1722\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 31&#45;&gt;32 -->\n<g id=\"edge32\" class=\"edge\">\n<title>31&#45;&gt;32</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1763.8,-222.91C1759.03,-214.2 1753.94,-204.9 1749.02,-195.89\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1752.03,-194.11 1744.17,-187.02 1745.89,-197.47 1752.03,-194.11\"/>\n</g>\n<!-- 35 -->\n<g id=\"node36\" class=\"node\">\n<title>35</title>\n<path fill=\"#3fa0e6\" stroke=\"black\" d=\"M1888,-187C1888,-187 1810,-187 1810,-187 1804,-187 1798,-181 1798,-175 1798,-175 1798,-116 1798,-116 1798,-110 1804,-104 1810,-104 1810,-104 1888,-104 1888,-104 1894,-104 1900,-110 1900,-116 1900,-116 1900,-175 1900,-175 1900,-181 1894,-187 1888,-187\"/>\n<text text-anchor=\"middle\" x=\"1849\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">SibSp &lt;= 0.5</text>\n<text text-anchor=\"middle\" x=\"1849\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.06</text>\n<text text-anchor=\"middle\" x=\"1849\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 97</text>\n<text text-anchor=\"middle\" x=\"1849\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3, 94]</text>\n<text text-anchor=\"middle\" x=\"1849\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 31&#45;&gt;35 -->\n<g id=\"edge35\" class=\"edge\">\n<title>31&#45;&gt;35</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1807.86,-222.91C1812.55,-214.2 1817.56,-204.9 1822.4,-195.89\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1825.52,-197.48 1827.18,-187.02 1819.36,-194.17 1825.52,-197.48\"/>\n</g>\n<!-- 33 -->\n<g id=\"node34\" class=\"node\">\n<title>33</title>\n<path fill=\"#52a9e8\" stroke=\"black\" d=\"M1634,-68C1634,-68 1556,-68 1556,-68 1550,-68 1544,-62 1544,-56 1544,-56 1544,-12 1544,-12 1544,-6 1550,0 1556,0 1556,0 1634,0 1634,0 1640,0 1646,-6 1646,-12 1646,-12 1646,-56 1646,-56 1646,-62 1640,-68 1634,-68\"/>\n<text text-anchor=\"middle\" x=\"1595\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.198</text>\n<text text-anchor=\"middle\" x=\"1595\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 18</text>\n<text text-anchor=\"middle\" x=\"1595\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 16]</text>\n<text text-anchor=\"middle\" x=\"1595\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 32&#45;&gt;33 -->\n<g id=\"edge33\" class=\"edge\">\n<title>32&#45;&gt;33</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1674.71,-103.73C1663.81,-94.33 1652.24,-84.35 1641.38,-74.99\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1643.48,-72.18 1633.62,-68.3 1638.91,-77.48 1643.48,-72.18\"/>\n</g>\n<!-- 34 -->\n<g id=\"node35\" class=\"node\">\n<title>34</title>\n<path fill=\"#e58139\" stroke=\"black\" d=\"M1768,-68C1768,-68 1676,-68 1676,-68 1670,-68 1664,-62 1664,-56 1664,-56 1664,-12 1664,-12 1664,-6 1670,0 1676,0 1676,0 1768,0 1768,0 1774,0 1780,-6 1780,-12 1780,-12 1780,-56 1780,-56 1780,-62 1774,-68 1768,-68\"/>\n<text text-anchor=\"middle\" x=\"1722\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"1722\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n<text text-anchor=\"middle\" x=\"1722\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 0]</text>\n<text text-anchor=\"middle\" x=\"1722\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 32&#45;&gt;34 -->\n<g id=\"edge34\" class=\"edge\">\n<title>32&#45;&gt;34</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1722,-103.73C1722,-95.52 1722,-86.86 1722,-78.56\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1725.5,-78.3 1722,-68.3 1718.5,-78.3 1725.5,-78.3\"/>\n</g>\n<!-- 36 -->\n<g id=\"node37\" class=\"node\">\n<title>36</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M1888,-68C1888,-68 1810,-68 1810,-68 1804,-68 1798,-62 1798,-56 1798,-56 1798,-12 1798,-12 1798,-6 1804,0 1810,0 1810,0 1888,0 1888,0 1894,0 1900,-6 1900,-12 1900,-12 1900,-56 1900,-56 1900,-62 1894,-68 1888,-68\"/>\n<text text-anchor=\"middle\" x=\"1849\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"1849\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 46</text>\n<text text-anchor=\"middle\" x=\"1849\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 46]</text>\n<text text-anchor=\"middle\" x=\"1849\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 35&#45;&gt;36 -->\n<g id=\"edge36\" class=\"edge\">\n<title>35&#45;&gt;36</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1849,-103.73C1849,-95.52 1849,-86.86 1849,-78.56\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1852.5,-78.3 1849,-68.3 1845.5,-78.3 1852.5,-78.3\"/>\n</g>\n<!-- 37 -->\n<g id=\"node38\" class=\"node\">\n<title>37</title>\n<path fill=\"#45a3e7\" stroke=\"black\" d=\"M2008,-68C2008,-68 1930,-68 1930,-68 1924,-68 1918,-62 1918,-56 1918,-56 1918,-12 1918,-12 1918,-6 1924,0 1930,0 1930,0 2008,0 2008,0 2014,0 2020,-6 2020,-12 2020,-12 2020,-56 2020,-56 2020,-62 2014,-68 2008,-68\"/>\n<text text-anchor=\"middle\" x=\"1969\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.111</text>\n<text text-anchor=\"middle\" x=\"1969\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 51</text>\n<text text-anchor=\"middle\" x=\"1969\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3, 48]</text>\n<text text-anchor=\"middle\" x=\"1969\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 35&#45;&gt;37 -->\n<g id=\"edge37\" class=\"edge\">\n<title>35&#45;&gt;37</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1893.68,-103.73C1903.88,-94.42 1914.71,-84.54 1924.88,-75.26\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1927.48,-77.63 1932.51,-68.3 1922.76,-72.45 1927.48,-77.63\"/>\n</g>\n<!-- 39 -->\n<g id=\"node40\" class=\"node\">\n<title>39</title>\n<path fill=\"#d6ebfa\" stroke=\"black\" d=\"M2310,-306C2310,-306 2224,-306 2224,-306 2218,-306 2212,-300 2212,-294 2212,-294 2212,-235 2212,-235 2212,-229 2218,-223 2224,-223 2224,-223 2310,-223 2310,-223 2316,-223 2322,-229 2322,-235 2322,-235 2322,-294 2322,-294 2322,-300 2316,-306 2310,-306\"/>\n<text text-anchor=\"middle\" x=\"2267\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Age &lt;= 17.5</text>\n<text text-anchor=\"middle\" x=\"2267\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.493</text>\n<text text-anchor=\"middle\" x=\"2267\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 70</text>\n<text text-anchor=\"middle\" x=\"2267\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [31, 39]</text>\n<text text-anchor=\"middle\" x=\"2267\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 38&#45;&gt;39 -->\n<g id=\"edge39\" class=\"edge\">\n<title>38&#45;&gt;39</title>\n<path fill=\"none\" stroke=\"black\" d=\"M2267,-341.91C2267,-333.65 2267,-324.86 2267,-316.3\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"2270.5,-316.02 2267,-306.02 2263.5,-316.02 2270.5,-316.02\"/>\n</g>\n<!-- 46 -->\n<g id=\"node47\" class=\"node\">\n<title>46</title>\n<path fill=\"#eb9c63\" stroke=\"black\" d=\"M2686,-306C2686,-306 2594,-306 2594,-306 2588,-306 2582,-300 2582,-294 2582,-294 2582,-235 2582,-235 2582,-229 2588,-223 2594,-223 2594,-223 2686,-223 2686,-223 2692,-223 2698,-229 2698,-235 2698,-235 2698,-294 2698,-294 2698,-300 2692,-306 2686,-306\"/>\n<text text-anchor=\"middle\" x=\"2640\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Age &lt;= 5.5</text>\n<text text-anchor=\"middle\" x=\"2640\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.291</text>\n<text text-anchor=\"middle\" x=\"2640\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 17</text>\n<text text-anchor=\"middle\" x=\"2640\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [14, 3]</text>\n<text text-anchor=\"middle\" x=\"2640\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 38&#45;&gt;46 -->\n<g id=\"edge46\" class=\"edge\">\n<title>38&#45;&gt;46</title>\n<path fill=\"none\" stroke=\"black\" d=\"M2325.28,-364.22C2392.11,-343.26 2501.51,-308.94 2572.12,-286.79\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"2573.33,-290.08 2581.82,-283.75 2571.23,-283.4 2573.33,-290.08\"/>\n</g>\n<!-- 40 -->\n<g id=\"node41\" class=\"node\">\n<title>40</title>\n<path fill=\"#6eb7ec\" stroke=\"black\" d=\"M2243,-187C2243,-187 2165,-187 2165,-187 2159,-187 2153,-181 2153,-175 2153,-175 2153,-116 2153,-116 2153,-110 2159,-104 2165,-104 2165,-104 2243,-104 2243,-104 2249,-104 2255,-110 2255,-116 2255,-116 2255,-175 2255,-175 2255,-181 2249,-187 2243,-187\"/>\n<text text-anchor=\"middle\" x=\"2204\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Age &lt;= 14.75</text>\n<text text-anchor=\"middle\" x=\"2204\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.332</text>\n<text text-anchor=\"middle\" x=\"2204\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 19</text>\n<text text-anchor=\"middle\" x=\"2204\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [4, 15]</text>\n<text text-anchor=\"middle\" x=\"2204\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 39&#45;&gt;40 -->\n<g id=\"edge40\" class=\"edge\">\n<title>39&#45;&gt;40</title>\n<path fill=\"none\" stroke=\"black\" d=\"M2245.14,-222.91C2240.45,-214.2 2235.44,-204.9 2230.6,-195.89\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"2233.64,-194.17 2225.82,-187.02 2227.48,-197.48 2233.64,-194.17\"/>\n</g>\n<!-- 43 -->\n<g id=\"node44\" class=\"node\">\n<title>43</title>\n<path fill=\"#fcf1e9\" stroke=\"black\" d=\"M2377,-187C2377,-187 2285,-187 2285,-187 2279,-187 2273,-181 2273,-175 2273,-175 2273,-116 2273,-116 2273,-110 2279,-104 2285,-104 2285,-104 2377,-104 2377,-104 2383,-104 2389,-110 2389,-116 2389,-116 2389,-175 2389,-175 2389,-181 2383,-187 2377,-187\"/>\n<text text-anchor=\"middle\" x=\"2331\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Parch &lt;= 1.5</text>\n<text text-anchor=\"middle\" x=\"2331\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.498</text>\n<text text-anchor=\"middle\" x=\"2331\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 51</text>\n<text text-anchor=\"middle\" x=\"2331\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [27, 24]</text>\n<text text-anchor=\"middle\" x=\"2331\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 39&#45;&gt;43 -->\n<g id=\"edge43\" class=\"edge\">\n<title>39&#45;&gt;43</title>\n<path fill=\"none\" stroke=\"black\" d=\"M2289.2,-222.91C2293.97,-214.2 2299.06,-204.9 2303.98,-195.89\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"2307.11,-197.47 2308.83,-187.02 2300.97,-194.11 2307.11,-197.47\"/>\n</g>\n<!-- 41 -->\n<g id=\"node42\" class=\"node\">\n<title>41</title>\n<path fill=\"#88c4ef\" stroke=\"black\" d=\"M2128,-68C2128,-68 2050,-68 2050,-68 2044,-68 2038,-62 2038,-56 2038,-56 2038,-12 2038,-12 2038,-6 2044,0 2050,0 2050,0 2128,0 2128,0 2134,0 2140,-6 2140,-12 2140,-12 2140,-56 2140,-56 2140,-62 2134,-68 2128,-68\"/>\n<text text-anchor=\"middle\" x=\"2089\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.408</text>\n<text text-anchor=\"middle\" x=\"2089\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 14</text>\n<text text-anchor=\"middle\" x=\"2089\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [4, 10]</text>\n<text text-anchor=\"middle\" x=\"2089\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 40&#45;&gt;41 -->\n<g id=\"edge41\" class=\"edge\">\n<title>40&#45;&gt;41</title>\n<path fill=\"none\" stroke=\"black\" d=\"M2161.18,-103.73C2151.41,-94.42 2141.03,-84.54 2131.28,-75.26\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"2133.63,-72.66 2123.97,-68.3 2128.8,-77.73 2133.63,-72.66\"/>\n</g>\n<!-- 42 -->\n<g id=\"node43\" class=\"node\">\n<title>42</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M2241.5,-68C2241.5,-68 2170.5,-68 2170.5,-68 2164.5,-68 2158.5,-62 2158.5,-56 2158.5,-56 2158.5,-12 2158.5,-12 2158.5,-6 2164.5,0 2170.5,0 2170.5,0 2241.5,0 2241.5,0 2247.5,0 2253.5,-6 2253.5,-12 2253.5,-12 2253.5,-56 2253.5,-56 2253.5,-62 2247.5,-68 2241.5,-68\"/>\n<text text-anchor=\"middle\" x=\"2206\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"2206\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\n<text text-anchor=\"middle\" x=\"2206\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 5]</text>\n<text text-anchor=\"middle\" x=\"2206\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 40&#45;&gt;42 -->\n<g id=\"edge42\" class=\"edge\">\n<title>40&#45;&gt;42</title>\n<path fill=\"none\" stroke=\"black\" d=\"M2204.74,-103.73C2204.89,-95.52 2205.05,-86.86 2205.2,-78.56\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"2208.71,-78.36 2205.39,-68.3 2201.71,-78.23 2208.71,-78.36\"/>\n</g>\n<!-- 44 -->\n<g id=\"node45\" class=\"node\">\n<title>44</title>\n<path fill=\"#f8decc\" stroke=\"black\" d=\"M2376,-68C2376,-68 2284,-68 2284,-68 2278,-68 2272,-62 2272,-56 2272,-56 2272,-12 2272,-12 2272,-6 2278,0 2284,0 2284,0 2376,0 2376,0 2382,0 2388,-6 2388,-12 2388,-12 2388,-56 2388,-56 2388,-62 2382,-68 2376,-68\"/>\n<text text-anchor=\"middle\" x=\"2330\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.489</text>\n<text text-anchor=\"middle\" x=\"2330\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 47</text>\n<text text-anchor=\"middle\" x=\"2330\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [27, 20]</text>\n<text text-anchor=\"middle\" x=\"2330\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 43&#45;&gt;44 -->\n<g id=\"edge44\" class=\"edge\">\n<title>43&#45;&gt;44</title>\n<path fill=\"none\" stroke=\"black\" d=\"M2330.63,-103.73C2330.55,-95.52 2330.47,-86.86 2330.4,-78.56\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"2333.9,-78.27 2330.3,-68.3 2326.9,-78.33 2333.9,-78.27\"/>\n</g>\n<!-- 45 -->\n<g id=\"node46\" class=\"node\">\n<title>45</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M2489.5,-68C2489.5,-68 2418.5,-68 2418.5,-68 2412.5,-68 2406.5,-62 2406.5,-56 2406.5,-56 2406.5,-12 2406.5,-12 2406.5,-6 2412.5,0 2418.5,0 2418.5,0 2489.5,0 2489.5,0 2495.5,0 2501.5,-6 2501.5,-12 2501.5,-12 2501.5,-56 2501.5,-56 2501.5,-62 2495.5,-68 2489.5,-68\"/>\n<text text-anchor=\"middle\" x=\"2454\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"2454\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n<text text-anchor=\"middle\" x=\"2454\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 4]</text>\n<text text-anchor=\"middle\" x=\"2454\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 43&#45;&gt;45 -->\n<g id=\"edge45\" class=\"edge\">\n<title>43&#45;&gt;45</title>\n<path fill=\"none\" stroke=\"black\" d=\"M2376.8,-103.73C2387.35,-94.33 2398.57,-84.35 2409.08,-74.99\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"2411.45,-77.56 2416.59,-68.3 2406.8,-72.33 2411.45,-77.56\"/>\n</g>\n<!-- 47 -->\n<g id=\"node48\" class=\"node\">\n<title>47</title>\n<path fill=\"#9ccef2\" stroke=\"black\" d=\"M2675.5,-187C2675.5,-187 2604.5,-187 2604.5,-187 2598.5,-187 2592.5,-181 2592.5,-175 2592.5,-175 2592.5,-116 2592.5,-116 2592.5,-110 2598.5,-104 2604.5,-104 2604.5,-104 2675.5,-104 2675.5,-104 2681.5,-104 2687.5,-110 2687.5,-116 2687.5,-116 2687.5,-175 2687.5,-175 2687.5,-181 2681.5,-187 2675.5,-187\"/>\n<text text-anchor=\"middle\" x=\"2640\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Age &lt;= 3.0</text>\n<text text-anchor=\"middle\" x=\"2640\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n<text text-anchor=\"middle\" x=\"2640\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n<text text-anchor=\"middle\" x=\"2640\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 2]</text>\n<text text-anchor=\"middle\" x=\"2640\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 46&#45;&gt;47 -->\n<g id=\"edge47\" class=\"edge\">\n<title>46&#45;&gt;47</title>\n<path fill=\"none\" stroke=\"black\" d=\"M2640,-222.91C2640,-214.65 2640,-205.86 2640,-197.3\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"2643.5,-197.02 2640,-187.02 2636.5,-197.02 2643.5,-197.02\"/>\n</g>\n<!-- 50 -->\n<g id=\"node51\" class=\"node\">\n<title>50</title>\n<path fill=\"#e78b48\" stroke=\"black\" d=\"M2872,-187C2872,-187 2780,-187 2780,-187 2774,-187 2768,-181 2768,-175 2768,-175 2768,-116 2768,-116 2768,-110 2774,-104 2780,-104 2780,-104 2872,-104 2872,-104 2878,-104 2884,-110 2884,-116 2884,-116 2884,-175 2884,-175 2884,-181 2878,-187 2872,-187\"/>\n<text text-anchor=\"middle\" x=\"2826\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Parch &lt;= 4.5</text>\n<text text-anchor=\"middle\" x=\"2826\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.133</text>\n<text text-anchor=\"middle\" x=\"2826\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 14</text>\n<text text-anchor=\"middle\" x=\"2826\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [13, 1]</text>\n<text text-anchor=\"middle\" x=\"2826\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 46&#45;&gt;50 -->\n<g id=\"edge50\" class=\"edge\">\n<title>46&#45;&gt;50</title>\n<path fill=\"none\" stroke=\"black\" d=\"M2698.09,-226.96C2717.4,-214.81 2739.06,-201.19 2758.99,-188.65\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"2761.11,-191.45 2767.71,-183.16 2757.39,-185.53 2761.11,-191.45\"/>\n</g>\n<!-- 48 -->\n<g id=\"node49\" class=\"node\">\n<title>48</title>\n<path fill=\"#e58139\" stroke=\"black\" d=\"M2624,-68C2624,-68 2532,-68 2532,-68 2526,-68 2520,-62 2520,-56 2520,-56 2520,-12 2520,-12 2520,-6 2526,0 2532,0 2532,0 2624,0 2624,0 2630,0 2636,-6 2636,-12 2636,-12 2636,-56 2636,-56 2636,-62 2630,-68 2624,-68\"/>\n<text text-anchor=\"middle\" x=\"2578\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"2578\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n<text text-anchor=\"middle\" x=\"2578\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 0]</text>\n<text text-anchor=\"middle\" x=\"2578\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 47&#45;&gt;48 -->\n<g id=\"edge48\" class=\"edge\">\n<title>47&#45;&gt;48</title>\n<path fill=\"none\" stroke=\"black\" d=\"M2616.91,-103.73C2612.01,-95.06 2606.82,-85.9 2601.88,-77.18\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"2604.83,-75.28 2596.85,-68.3 2598.74,-78.73 2604.83,-75.28\"/>\n</g>\n<!-- 49 -->\n<g id=\"node50\" class=\"node\">\n<title>49</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M2737.5,-68C2737.5,-68 2666.5,-68 2666.5,-68 2660.5,-68 2654.5,-62 2654.5,-56 2654.5,-56 2654.5,-12 2654.5,-12 2654.5,-6 2660.5,0 2666.5,0 2666.5,0 2737.5,0 2737.5,0 2743.5,0 2749.5,-6 2749.5,-12 2749.5,-12 2749.5,-56 2749.5,-56 2749.5,-62 2743.5,-68 2737.5,-68\"/>\n<text text-anchor=\"middle\" x=\"2702\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"2702\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n<text text-anchor=\"middle\" x=\"2702\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 2]</text>\n<text text-anchor=\"middle\" x=\"2702\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Died</text>\n</g>\n<!-- 47&#45;&gt;49 -->\n<g id=\"edge49\" class=\"edge\">\n<title>47&#45;&gt;49</title>\n<path fill=\"none\" stroke=\"black\" d=\"M2663.09,-103.73C2667.99,-95.06 2673.18,-85.9 2678.12,-77.18\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"2681.26,-78.73 2683.15,-68.3 2675.17,-75.28 2681.26,-78.73\"/>\n</g>\n<!-- 51 -->\n<g id=\"node52\" class=\"node\">\n<title>51</title>\n<path fill=\"#e58139\" stroke=\"black\" d=\"M2872,-68C2872,-68 2780,-68 2780,-68 2774,-68 2768,-62 2768,-56 2768,-56 2768,-12 2768,-12 2768,-6 2774,0 2780,0 2780,0 2872,0 2872,0 2878,0 2884,-6 2884,-12 2884,-12 2884,-56 2884,-56 2884,-62 2878,-68 2872,-68\"/>\n<text text-anchor=\"middle\" x=\"2826\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"2826\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 9</text>\n<text text-anchor=\"middle\" x=\"2826\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [9, 0]</text>\n<text text-anchor=\"middle\" x=\"2826\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 50&#45;&gt;51 -->\n<g id=\"edge51\" class=\"edge\">\n<title>50&#45;&gt;51</title>\n<path fill=\"none\" stroke=\"black\" d=\"M2826,-103.73C2826,-95.52 2826,-86.86 2826,-78.56\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"2829.5,-78.3 2826,-68.3 2822.5,-78.3 2829.5,-78.3\"/>\n</g>\n<!-- 52 -->\n<g id=\"node53\" class=\"node\">\n<title>52</title>\n<path fill=\"#eca06a\" stroke=\"black\" d=\"M3006,-68C3006,-68 2914,-68 2914,-68 2908,-68 2902,-62 2902,-56 2902,-56 2902,-12 2902,-12 2902,-6 2908,0 2914,0 2914,0 3006,0 3006,0 3012,0 3018,-6 3018,-12 3018,-12 3018,-56 3018,-56 3018,-62 3012,-68 3006,-68\"/>\n<text text-anchor=\"middle\" x=\"2960\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.32</text>\n<text text-anchor=\"middle\" x=\"2960\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\n<text text-anchor=\"middle\" x=\"2960\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [4, 1]</text>\n<text text-anchor=\"middle\" x=\"2960\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = Survived</text>\n</g>\n<!-- 50&#45;&gt;52 -->\n<g id=\"edge52\" class=\"edge\">\n<title>50&#45;&gt;52</title>\n<path fill=\"none\" stroke=\"black\" d=\"M2875.9,-103.73C2887.5,-94.24 2899.85,-84.16 2911.4,-74.72\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"2913.72,-77.34 2919.25,-68.3 2909.29,-71.92 2913.72,-77.34\"/>\n</g>\n</g>\n</svg>\n",
            "text/plain": [
              "<graphviz.sources.Source at 0x7a9ffaaa6440>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## And now let's compare results for all the different models and choose our best model"
      ],
      "metadata": {
        "id": "sYb74WVmTdWc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dummy = DummyClassifier(strategy=\"most_frequent\")\n",
        "logistic = LogisticRegression(max_iter=5000)\n",
        "model_list = [dummy, logistic]\n",
        "evaluate(model_list, X_train, y_train, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "collapsed": true,
        "id": "8ha5K7c0Oi-k",
        "outputId": "e38b6e15-7072-4e70-a004-a6c823e54cac"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DummyClassifier(strategy='most_frequent')\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.57      1.00      0.73        82\n",
            "           1       0.00      0.00      0.00        61\n",
            "\n",
            "    accuracy                           0.57       143\n",
            "   macro avg       0.29      0.50      0.36       143\n",
            "weighted avg       0.33      0.57      0.42       143\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LogisticRegression(max_iter=5000)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.88      0.84        82\n",
            "           1       0.81      0.72      0.77        61\n",
            "\n",
            "    accuracy                           0.81       143\n",
            "   macro avg       0.81      0.80      0.80       143\n",
            "weighted avg       0.81      0.81      0.81       143\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_list = [lr_penalized]\n",
        "evaluate(model_list, X_train, y_train, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "collapsed": true,
        "id": "Jd5dEAsLQzAw",
        "outputId": "29305649-745f-4ea6-b775-a4443fd06785"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LogisticRegression(penalty='l1', solver='liblinear')\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.87      0.84        82\n",
            "           1       0.80      0.72      0.76        61\n",
            "\n",
            "    accuracy                           0.80       143\n",
            "   macro avg       0.80      0.79      0.80       143\n",
            "weighted avg       0.80      0.80      0.80       143\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt = tree.DecisionTreeClassifier()\n",
        "model_list = [dt, rf]\n",
        "evaluate(model_list, X_train, y_train, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "collapsed": true,
        "id": "g1_M5pVDQ278",
        "outputId": "7a9db364-a118-4f06-becb-a6f2ad30f987"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DecisionTreeClassifier()\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.77      0.82        82\n",
            "           1       0.73      0.85      0.79        61\n",
            "\n",
            "    accuracy                           0.80       143\n",
            "   macro avg       0.80      0.81      0.80       143\n",
            "weighted avg       0.81      0.80      0.81       143\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RandomForestClassifier()\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.78      0.82        82\n",
            "           1       0.74      0.82      0.78        61\n",
            "\n",
            "    accuracy                           0.80       143\n",
            "   macro avg       0.79      0.80      0.80       143\n",
            "weighted avg       0.80      0.80      0.80       143\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Now for some less interpretable models"
      ],
      "metadata": {
        "id": "iLRlRKQoTnkn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ab =  ensemble.AdaBoostClassifier(tree.DecisionTreeClassifier(max_depth=1), algorithm=\"SAMME\", n_estimators=200)\n",
        "knn = neighbors.KNeighborsClassifier(n_neighbors=10, n_jobs=-1)\n",
        "gb = ensemble.GradientBoostingClassifier(learning_rate=0.05, subsample=0.5, max_depth=6, n_estimators=10)\n",
        "bg = ensemble.BaggingClassifier(linear_model.LogisticRegression(penalty='l2', C=1e5, n_jobs=-1))\n",
        "model_list = [ab, knn, gb, bg]\n",
        "evaluate(model_list, X_train, y_train, X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "collapsed": true,
        "id": "dORnZGTrTrqT",
        "outputId": "1b72db6d-317c-45b1-bb3d-a350dda8bb44"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AdaBoostClassifier(algorithm='SAMME',\n",
            "                   estimator=DecisionTreeClassifier(max_depth=1),\n",
            "                   n_estimators=200)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.80      0.84        82\n",
            "           1       0.76      0.84      0.80        61\n",
            "\n",
            "    accuracy                           0.82       143\n",
            "   macro avg       0.81      0.82      0.82       143\n",
            "weighted avg       0.82      0.82      0.82       143\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNeighborsClassifier(n_jobs=-1, n_neighbors=10)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.69      0.79      0.74        82\n",
            "           1       0.65      0.52      0.58        61\n",
            "\n",
            "    accuracy                           0.68       143\n",
            "   macro avg       0.67      0.66      0.66       143\n",
            "weighted avg       0.68      0.68      0.67       143\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GradientBoostingClassifier(learning_rate=0.05, max_depth=6, n_estimators=10,\n",
            "                           subsample=0.5)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.95      0.85        82\n",
            "           1       0.90      0.62      0.74        61\n",
            "\n",
            "    accuracy                           0.81       143\n",
            "   macro avg       0.84      0.79      0.80       143\n",
            "weighted avg       0.83      0.81      0.80       143\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BaggingClassifier(estimator=LogisticRegression(C=100000.0, n_jobs=-1))\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.84      0.83        82\n",
            "           1       0.78      0.74      0.76        61\n",
            "\n",
            "    accuracy                           0.80       143\n",
            "   macro avg       0.79      0.79      0.79       143\n",
            "weighted avg       0.80      0.80      0.80       143\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Another form of evaluation is K-fold evaluation"
      ],
      "metadata": {
        "id": "JvRq0WpAZhse"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def kfold_evaluate(X, y, model_list, splits):\n",
        "    kf = model_selection.KFold(n_splits=splits)\n",
        "    X_k = np.array(X)\n",
        "    y_k = np.array(y)\n",
        "    for train_index, test_index in kf.split(X_k):\n",
        "        #print(train_index)\n",
        "        #print(test_index)\n",
        "        X_train, X_test = X_k[train_index], X_k[test_index]\n",
        "        y_train, y_test = y_k[train_index], y_k[test_index]\n",
        "        for model in model_list:\n",
        "            print(model)\n",
        "            clf = model.fit(X_train, y_train)\n",
        "            y_pred = clf.predict(X_test)\n",
        "            print(metrics.classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "id": "PZVklUzMZg43"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_list = [dummy, lr_penalized, logistic, dt, rf, ab, knn, gb, bg]\n",
        "kfold_evaluate(X, y, model_list, 4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "NIWcZO_qaT2b",
        "outputId": "0a3b46b9-68a9-4ee1-d242-4ce1484deec3"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DummyClassifier(strategy='most_frequent')\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.66      1.00      0.79       117\n",
            "           1       0.00      0.00      0.00        61\n",
            "\n",
            "    accuracy                           0.66       178\n",
            "   macro avg       0.33      0.50      0.40       178\n",
            "weighted avg       0.43      0.66      0.52       178\n",
            "\n",
            "LogisticRegression(penalty='l1', solver='liblinear')\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.81      0.83       117\n",
            "           1       0.67      0.72      0.69        61\n",
            "\n",
            "    accuracy                           0.78       178\n",
            "   macro avg       0.76      0.77      0.76       178\n",
            "weighted avg       0.79      0.78      0.78       178\n",
            "\n",
            "LogisticRegression(max_iter=5000)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.82      0.83       117\n",
            "           1       0.67      0.70      0.69        61\n",
            "\n",
            "    accuracy                           0.78       178\n",
            "   macro avg       0.76      0.76      0.76       178\n",
            "weighted avg       0.78      0.78      0.78       178\n",
            "\n",
            "DecisionTreeClassifier()\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.77      0.80       117\n",
            "           1       0.61      0.69      0.65        61\n",
            "\n",
            "    accuracy                           0.74       178\n",
            "   macro avg       0.72      0.73      0.72       178\n",
            "weighted avg       0.75      0.74      0.74       178\n",
            "\n",
            "RandomForestClassifier()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.85      0.83       117\n",
            "           1       0.68      0.64      0.66        61\n",
            "\n",
            "    accuracy                           0.78       178\n",
            "   macro avg       0.75      0.74      0.75       178\n",
            "weighted avg       0.77      0.78      0.77       178\n",
            "\n",
            "AdaBoostClassifier(algorithm='SAMME',\n",
            "                   estimator=DecisionTreeClassifier(max_depth=1),\n",
            "                   n_estimators=200)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.84      0.84       117\n",
            "           1       0.69      0.70      0.70        61\n",
            "\n",
            "    accuracy                           0.79       178\n",
            "   macro avg       0.77      0.77      0.77       178\n",
            "weighted avg       0.79      0.79      0.79       178\n",
            "\n",
            "KNeighborsClassifier(n_jobs=-1, n_neighbors=10)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.69      0.75      0.72       117\n",
            "           1       0.43      0.36      0.39        61\n",
            "\n",
            "    accuracy                           0.62       178\n",
            "   macro avg       0.56      0.56      0.56       178\n",
            "weighted avg       0.60      0.62      0.61       178\n",
            "\n",
            "GradientBoostingClassifier(learning_rate=0.05, max_depth=6, n_estimators=10,\n",
            "                           subsample=0.5)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.94      0.86       117\n",
            "           1       0.82      0.54      0.65        61\n",
            "\n",
            "    accuracy                           0.80       178\n",
            "   macro avg       0.81      0.74      0.76       178\n",
            "weighted avg       0.81      0.80      0.79       178\n",
            "\n",
            "BaggingClassifier(estimator=LogisticRegression(C=100000.0, n_jobs=-1))\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.81      0.82       117\n",
            "           1       0.65      0.67      0.66        61\n",
            "\n",
            "    accuracy                           0.76       178\n",
            "   macro avg       0.74      0.74      0.74       178\n",
            "weighted avg       0.77      0.76      0.76       178\n",
            "\n",
            "DummyClassifier(strategy='most_frequent')\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.56      1.00      0.72       100\n",
            "           1       0.00      0.00      0.00        78\n",
            "\n",
            "    accuracy                           0.56       178\n",
            "   macro avg       0.28      0.50      0.36       178\n",
            "weighted avg       0.32      0.56      0.40       178\n",
            "\n",
            "LogisticRegression(penalty='l1', solver='liblinear')\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.85      0.83       100\n",
            "           1       0.79      0.73      0.76        78\n",
            "\n",
            "    accuracy                           0.80       178\n",
            "   macro avg       0.80      0.79      0.79       178\n",
            "weighted avg       0.80      0.80      0.80       178\n",
            "\n",
            "LogisticRegression(max_iter=5000)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.85      0.83       100\n",
            "           1       0.79      0.73      0.76        78\n",
            "\n",
            "    accuracy                           0.80       178\n",
            "   macro avg       0.80      0.79      0.79       178\n",
            "weighted avg       0.80      0.80      0.80       178\n",
            "\n",
            "DecisionTreeClassifier()\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.78      0.78       100\n",
            "           1       0.72      0.73      0.73        78\n",
            "\n",
            "    accuracy                           0.76       178\n",
            "   macro avg       0.75      0.76      0.76       178\n",
            "weighted avg       0.76      0.76      0.76       178\n",
            "\n",
            "RandomForestClassifier()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.93      0.87       100\n",
            "           1       0.89      0.73      0.80        78\n",
            "\n",
            "    accuracy                           0.84       178\n",
            "   macro avg       0.85      0.83      0.84       178\n",
            "weighted avg       0.85      0.84      0.84       178\n",
            "\n",
            "AdaBoostClassifier(algorithm='SAMME',\n",
            "                   estimator=DecisionTreeClassifier(max_depth=1),\n",
            "                   n_estimators=200)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.88      0.86       100\n",
            "           1       0.84      0.79      0.82        78\n",
            "\n",
            "    accuracy                           0.84       178\n",
            "   macro avg       0.84      0.84      0.84       178\n",
            "weighted avg       0.84      0.84      0.84       178\n",
            "\n",
            "KNeighborsClassifier(n_jobs=-1, n_neighbors=10)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.68      0.80      0.74       100\n",
            "           1       0.67      0.53      0.59        78\n",
            "\n",
            "    accuracy                           0.68       178\n",
            "   macro avg       0.68      0.66      0.66       178\n",
            "weighted avg       0.68      0.68      0.67       178\n",
            "\n",
            "GradientBoostingClassifier(learning_rate=0.05, max_depth=6, n_estimators=10,\n",
            "                           subsample=0.5)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.93      0.85       100\n",
            "           1       0.88      0.67      0.76        78\n",
            "\n",
            "    accuracy                           0.81       178\n",
            "   macro avg       0.83      0.80      0.80       178\n",
            "weighted avg       0.83      0.81      0.81       178\n",
            "\n",
            "BaggingClassifier(estimator=LogisticRegression(C=100000.0, n_jobs=-1))\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.84      0.82       100\n",
            "           1       0.78      0.72      0.75        78\n",
            "\n",
            "    accuracy                           0.79       178\n",
            "   macro avg       0.79      0.78      0.78       178\n",
            "weighted avg       0.79      0.79      0.79       178\n",
            "\n",
            "DummyClassifier(strategy='most_frequent')\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.56      1.00      0.71        99\n",
            "           1       0.00      0.00      0.00        79\n",
            "\n",
            "    accuracy                           0.56       178\n",
            "   macro avg       0.28      0.50      0.36       178\n",
            "weighted avg       0.31      0.56      0.40       178\n",
            "\n",
            "LogisticRegression(penalty='l1', solver='liblinear')\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      0.88      0.80        99\n",
            "           1       0.80      0.59      0.68        79\n",
            "\n",
            "    accuracy                           0.75       178\n",
            "   macro avg       0.76      0.74      0.74       178\n",
            "weighted avg       0.76      0.75      0.75       178\n",
            "\n",
            "LogisticRegression(max_iter=5000)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.74      0.89      0.81        99\n",
            "           1       0.81      0.61      0.70        79\n",
            "\n",
            "    accuracy                           0.76       178\n",
            "   macro avg       0.78      0.75      0.75       178\n",
            "weighted avg       0.77      0.76      0.76       178\n",
            "\n",
            "DecisionTreeClassifier()\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.84      0.80        99\n",
            "           1       0.77      0.68      0.72        79\n",
            "\n",
            "    accuracy                           0.77       178\n",
            "   macro avg       0.77      0.76      0.76       178\n",
            "weighted avg       0.77      0.77      0.77       178\n",
            "\n",
            "RandomForestClassifier()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.87      0.81        99\n",
            "           1       0.80      0.65      0.71        79\n",
            "\n",
            "    accuracy                           0.77       178\n",
            "   macro avg       0.78      0.76      0.76       178\n",
            "weighted avg       0.77      0.77      0.77       178\n",
            "\n",
            "AdaBoostClassifier(algorithm='SAMME',\n",
            "                   estimator=DecisionTreeClassifier(max_depth=1),\n",
            "                   n_estimators=200)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.90      0.83        99\n",
            "           1       0.84      0.66      0.74        79\n",
            "\n",
            "    accuracy                           0.79       178\n",
            "   macro avg       0.80      0.78      0.78       178\n",
            "weighted avg       0.80      0.79      0.79       178\n",
            "\n",
            "KNeighborsClassifier(n_jobs=-1, n_neighbors=10)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.64      0.89      0.74        99\n",
            "           1       0.72      0.37      0.49        79\n",
            "\n",
            "    accuracy                           0.66       178\n",
            "   macro avg       0.68      0.63      0.62       178\n",
            "weighted avg       0.68      0.66      0.63       178\n",
            "\n",
            "GradientBoostingClassifier(learning_rate=0.05, max_depth=6, n_estimators=10,\n",
            "                           subsample=0.5)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.70      0.96      0.81        99\n",
            "           1       0.90      0.48      0.63        79\n",
            "\n",
            "    accuracy                           0.75       178\n",
            "   macro avg       0.80      0.72      0.72       178\n",
            "weighted avg       0.79      0.75      0.73       178\n",
            "\n",
            "BaggingClassifier(estimator=LogisticRegression(C=100000.0, n_jobs=-1))\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.74      0.88      0.80        99\n",
            "           1       0.80      0.61      0.69        79\n",
            "\n",
            "    accuracy                           0.76       178\n",
            "   macro avg       0.77      0.74      0.75       178\n",
            "weighted avg       0.77      0.76      0.75       178\n",
            "\n",
            "DummyClassifier(strategy='most_frequent')\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.61      1.00      0.76       108\n",
            "           1       0.00      0.00      0.00        70\n",
            "\n",
            "    accuracy                           0.61       178\n",
            "   macro avg       0.30      0.50      0.38       178\n",
            "weighted avg       0.37      0.61      0.46       178\n",
            "\n",
            "LogisticRegression(penalty='l1', solver='liblinear')\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.89      0.86       108\n",
            "           1       0.81      0.73      0.77        70\n",
            "\n",
            "    accuracy                           0.83       178\n",
            "   macro avg       0.82      0.81      0.81       178\n",
            "weighted avg       0.82      0.83      0.82       178\n",
            "\n",
            "LogisticRegression(max_iter=5000)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.88      0.86       108\n",
            "           1       0.80      0.73      0.76        70\n",
            "\n",
            "    accuracy                           0.82       178\n",
            "   macro avg       0.82      0.80      0.81       178\n",
            "weighted avg       0.82      0.82      0.82       178\n",
            "\n",
            "DecisionTreeClassifier()\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.78      0.80       108\n",
            "           1       0.68      0.73      0.70        70\n",
            "\n",
            "    accuracy                           0.76       178\n",
            "   macro avg       0.75      0.75      0.75       178\n",
            "weighted avg       0.76      0.76      0.76       178\n",
            "\n",
            "RandomForestClassifier()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.87      0.87       108\n",
            "           1       0.80      0.79      0.79        70\n",
            "\n",
            "    accuracy                           0.84       178\n",
            "   macro avg       0.83      0.83      0.83       178\n",
            "weighted avg       0.84      0.84      0.84       178\n",
            "\n",
            "AdaBoostClassifier(algorithm='SAMME',\n",
            "                   estimator=DecisionTreeClassifier(max_depth=1),\n",
            "                   n_estimators=200)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.88      0.87       108\n",
            "           1       0.81      0.79      0.80        70\n",
            "\n",
            "    accuracy                           0.84       178\n",
            "   macro avg       0.84      0.83      0.83       178\n",
            "weighted avg       0.84      0.84      0.84       178\n",
            "\n",
            "KNeighborsClassifier(n_jobs=-1, n_neighbors=10)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.85      0.80       108\n",
            "           1       0.71      0.56      0.62        70\n",
            "\n",
            "    accuracy                           0.74       178\n",
            "   macro avg       0.73      0.70      0.71       178\n",
            "weighted avg       0.73      0.74      0.73       178\n",
            "\n",
            "GradientBoostingClassifier(learning_rate=0.05, max_depth=6, n_estimators=10,\n",
            "                           subsample=0.5)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.94      0.88       108\n",
            "           1       0.89      0.69      0.77        70\n",
            "\n",
            "    accuracy                           0.84       178\n",
            "   macro avg       0.86      0.82      0.83       178\n",
            "weighted avg       0.85      0.84      0.84       178\n",
            "\n",
            "BaggingClassifier(estimator=LogisticRegression(C=100000.0, n_jobs=-1))\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.87      0.85       108\n",
            "           1       0.78      0.73      0.76        70\n",
            "\n",
            "    accuracy                           0.81       178\n",
            "   macro avg       0.81      0.80      0.80       178\n",
            "weighted avg       0.81      0.81      0.81       178\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Now, let's assume that our best model is the AdaBoost model."
      ],
      "metadata": {
        "id": "T7PXSU4ZpuG3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Draw precision/recall curves and AUC-ROC curves here"
      ],
      "metadata": {
        "id": "vHlFe5dO4pCT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Insert your code here!"
      ],
      "metadata": {
        "id": "wBYS9uTo4ml4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unlabeled_filename = \"sample_data/titanic_test.csv\"\n",
        "unlabeled_df = pd.read_csv(unlabeled_filename)\n",
        "unlabeled_set_d = pre_process(unlabeled_df, vars_to_dummy)\n",
        "unlabeled_set_d.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "eHFsFr9y9kbu",
        "outputId": "52dd9a72-88b5-49e9-9764-b3ccd28fc678"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Pclass                                          Name   Age  \\\n",
              "0          892       3                              Kelly, Mr. James  34.5   \n",
              "1          893       3              Wilkes, Mrs. James (Ellen Needs)  47.0   \n",
              "2          894       2                     Myles, Mr. Thomas Francis  62.0   \n",
              "3          895       3                              Wirz, Mr. Albert  27.0   \n",
              "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  22.0   \n",
              "\n",
              "   SibSp  Parch   Ticket     Fare Cabin  Sex_female  Sex_male  Embarked_C  \\\n",
              "0      0      0   330911   7.8292   NaN       False      True       False   \n",
              "1      1      0   363272   7.0000   NaN        True     False       False   \n",
              "2      0      0   240276   9.6875   NaN       False      True       False   \n",
              "3      0      0   315154   8.6625   NaN       False      True       False   \n",
              "4      1      1  3101298  12.2875   NaN        True     False       False   \n",
              "\n",
              "   Embarked_Q  Embarked_S  \n",
              "0        True       False  \n",
              "1       False        True  \n",
              "2        True       False  \n",
              "3       False        True  \n",
              "4       False        True  "
            ],
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              "\n",
              "  <div id=\"df-05f7dd00-98dc-446c-a61a-91c05657fdfe\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>62.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>False</td>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
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              "      <td>12.2875</td>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "        const element = document.querySelector('#df-05f7dd00-98dc-446c-a61a-91c05657fdfe');\n",
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              "                                                    [key], {});\n",
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              "\n",
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              "  }\n",
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              "  .colab-df-quickchart:hover {\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "unlabeled_set_d",
              "summary": "{\n  \"name\": \"unlabeled_set_d\",\n  \"rows\": 331,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 122,\n        \"min\": 892,\n        \"max\": 1307,\n        \"num_unique_values\": 331,\n        \"samples\": [\n          919,\n          1281,\n          981\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 331,\n        \"samples\": [\n          \"Daher, Mr. Shedid\",\n          \"Palsson, Master. Paul Folke\",\n          \"Wells, Master. Ralph Lester\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.104572594801613,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 78,\n        \"samples\": [\n          13.0,\n          34.5,\n          31.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 284,\n        \"samples\": [\n          \"A/4 48871\",\n          \"3101295\",\n          \"36928\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61.22855822554925,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 148,\n        \"samples\": [\n          146.5208,\n          221.7792,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 72,\n        \"samples\": [\n          \"A21\",\n          \"E39 E41\",\n          \"C55 C57\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex_female\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked_C\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked_Q\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked_S\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def modify_unlabeled(unlabeled_set_d, training_set_d):\n",
        "    set1 = set(unlabeled_set_d.columns)\n",
        "    set2 = set(training_set_d.columns)\n",
        "    set2.remove('Survived')\n",
        "    set_diff = set2 - set1\n",
        "    set_diff2 = set1 - set2\n",
        "    for col in set_diff:\n",
        "        unlabeled_set_d[col] = 0\n",
        "    for col in set_diff2:\n",
        "        unlabeled_set_d.drop(col, axis=1, inplace=True)\n",
        "    return unlabeled_set_d"
      ],
      "metadata": {
        "id": "-1w8KrD3-lEt"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unlabeled_set_d_use = modify_unlabeled(unlabeled_set_d, training_set_d)"
      ],
      "metadata": {
        "id": "vJXGa26N-mC2"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unlabeled_set_d_use.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "4IvC51GRFCjn",
        "outputId": "1061f5c4-4f9e-4ff2-86a2-15918c623715"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Pclass                                          Name   Age  \\\n",
              "0          892       3                              Kelly, Mr. James  34.5   \n",
              "1          893       3              Wilkes, Mrs. James (Ellen Needs)  47.0   \n",
              "2          894       2                     Myles, Mr. Thomas Francis  62.0   \n",
              "3          895       3                              Wirz, Mr. Albert  27.0   \n",
              "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  22.0   \n",
              "\n",
              "   SibSp  Parch   Ticket     Fare Cabin  Sex_female  Sex_male  Embarked_C  \\\n",
              "0      0      0   330911   7.8292   NaN       False      True       False   \n",
              "1      1      0   363272   7.0000   NaN        True     False       False   \n",
              "2      0      0   240276   9.6875   NaN       False      True       False   \n",
              "3      0      0   315154   8.6625   NaN       False      True       False   \n",
              "4      1      1  3101298  12.2875   NaN        True     False       False   \n",
              "\n",
              "   Embarked_Q  Embarked_S  age_binned  survived_label  \n",
              "0        True       False           0               0  \n",
              "1       False        True           0               0  \n",
              "2        True       False           0               0  \n",
              "3       False        True           0               0  \n",
              "4       False        True           0               0  "
            ],
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              "      <td>Myles, Mr. Thomas Francis</td>\n",
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              "    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",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "unlabeled_set_d_use",
              "summary": "{\n  \"name\": \"unlabeled_set_d_use\",\n  \"rows\": 331,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 122,\n        \"min\": 892,\n        \"max\": 1307,\n        \"num_unique_values\": 331,\n        \"samples\": [\n          919,\n          1281,\n          981\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 331,\n        \"samples\": [\n          \"Daher, Mr. Shedid\",\n          \"Palsson, Master. Paul Folke\",\n          \"Wells, Master. Ralph Lester\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.104572594801613,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 78,\n        \"samples\": [\n          13.0,\n          34.5,\n          31.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 284,\n        \"samples\": [\n          \"A/4 48871\",\n          \"3101295\",\n          \"36928\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61.22855822554925,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 148,\n        \"samples\": [\n          146.5208,\n          221.7792,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 72,\n        \"samples\": [\n          \"A21\",\n          \"E39 E41\",\n          \"C55 C57\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex_female\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked_C\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked_Q\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked_S\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_binned\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"survived_label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = training_set_d.drop(axis=1, labels= ['Survived', 'survived_label', 'age_binned', 'Name', 'Cabin', 'PassengerId', 'Sex_male', 'Ticket'])\n",
        "y_train = training_set_d['Survived']\n",
        "X_test = unlabeled_set_d_use.drop(axis=1, labels= ['survived_label', 'age_binned', 'Name', 'Cabin', 'PassengerId', 'Sex_male', 'Ticket'])"
      ],
      "metadata": {
        "id": "-0rJtVqzH-QE"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ab_final =  ensemble.AdaBoostClassifier(tree.DecisionTreeClassifier(max_depth=1), algorithm=\"SAMME\", n_estimators=200)"
      ],
      "metadata": {
        "id": "S_Rhn3QfK29H"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf = fit_model(ab_final, X_train, y_train)\n",
        "y_pred_scores_final = predict_scores(clf, X_test)"
      ],
      "metadata": {
        "id": "ljSTEMloNDFg"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_df = pd.DataFrame(unlabeled_set_d['PassengerId'])\n",
        "final_df['pred_scores'] = y_pred_scores_final"
      ],
      "metadata": {
        "id": "cHNvuKGVNMmE"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "LIKfgiP0NbGe",
        "outputId": "776bb133-a5bd-4148-a399-18f3679a92ca"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  pred_scores\n",
              "0            892     0.456916\n",
              "1            893     0.494326\n",
              "2            894     0.460308\n",
              "3            895     0.448938\n",
              "4            896     0.502740\n",
              "..           ...          ...\n",
              "409         1301     0.531568\n",
              "411         1303     0.549542\n",
              "412         1304     0.516172\n",
              "414         1306     0.559914\n",
              "415         1307     0.440850\n",
              "\n",
              "[331 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
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              "\n",
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              "      <th>PassengerId</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>892</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>0.494326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>0.460308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>0.448938</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>0.502740</td>\n",
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              "      <td>1301</td>\n",
              "      <td>0.531568</td>\n",
              "    </tr>\n",
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              "      <th>411</th>\n",
              "      <td>1303</td>\n",
              "      <td>0.549542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>412</th>\n",
              "      <td>1304</td>\n",
              "      <td>0.516172</td>\n",
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              "      <th>414</th>\n",
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              "      <td>0.559914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>1307</td>\n",
              "      <td>0.440850</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>331 rows × 2 columns</p>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      spin 1s steps(1) infinite;\n",
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              "  @keyframes spin {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7bb202ec-be29-439e-8dbd-990d6e8da202 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "final_df",
              "summary": "{\n  \"name\": \"final_df\",\n  \"rows\": 331,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 122,\n        \"min\": 892,\n        \"max\": 1307,\n        \"num_unique_values\": 331,\n        \"samples\": [\n          919,\n          1281,\n          981\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pred_scores\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03971873237419077,\n        \"min\": 0.4189719551163389,\n        \"max\": 0.6227188921226416,\n        \"num_unique_values\": 151,\n        \"samples\": [\n          0.48232481914141145,\n          0.5104379378296564,\n          0.46659359268088835\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Additional evaluations: expert and ethics-based evaluations"
      ],
      "metadata": {
        "id": "wT41_6i5OizZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Fairness toolkits\n",
        "https://aif360.readthedocs.io/en/latest/modules/generated/aif360.metrics.ClassificationMetric.html#aif360.metrics.ClassificationMetric.statistical_parity_difference"
      ],
      "metadata": {
        "id": "jLMS45T2T6YE"
      }
    }
  ]
}