Jun_ML_Classification_2_HW_6_2.ipynb

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      "name": "Jun ML Classification-2 HW  6.2.ipynb",
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        "<a href=\"https://colab.research.google.com/github/oObelix/DataScience/blob/master/Jun_ML_Classification_2_HW_6_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lRkA2eOKzXtO"
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      "source": [
        "# Урок 2. Метрики качества классификации ч.1\n",
        "\n",
        "В уроке мы разбирали матрицу ошибок и некоторые метрики, основанные на терминах матрицы ошибок. Здесь же посмотрим, как эти метрики применять на практике и что  по ним можно выяснить."
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ihmfQuGxzXtQ"
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      "source": [
        "### 1.1\n",
        "Выкачайте тренировочный датасет Titanic - train.csv - с сайта [kaggle](https://www.kaggle.com/c/titanic/data). С помощью функции pd.read_csv() загрузите данные в датафрейм. Выведите первые 20 строк и проанализируйте данные: какие колонки присутствуют (более конкретная информация по ним есть на сайте kaggle), каким образом в них обозначены данные и какие типы данных используются (используйте pandas.dtypes). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PV-jZFDRzXtR"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1-Zv8POVzXtU"
      },
      "source": [
        "### YOUR CODE HERE ###\n",
        "\n",
        "df = pd.read_csv('sample_data/train.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "ICURKbxPcOE2",
        "outputId": "c26c34ef-8ae7-48c7-f4fa-5d28971616c5"
      },
      "source": [
        "df.head(20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
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              "      <td>Braund, Mr. Owen Harris</td>\n",
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              "      <td>22.0</td>\n",
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              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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              "      <td>PC 17599</td>\n",
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              "      <td>C85</td>\n",
              "      <td>C</td>\n",
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              "      <td>3</td>\n",
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              "      <td>Heikkinen, Miss. Laina</td>\n",
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              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
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              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
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              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Moran, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330877</td>\n",
              "      <td>8.4583</td>\n",
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              "      <td>Q</td>\n",
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              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>McCarthy, Mr. Timothy J</td>\n",
              "      <td>male</td>\n",
              "      <td>54.0</td>\n",
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              "      <td>8</td>\n",
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              "      <td>Palsson, Master. Gosta Leonard</td>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>9</td>\n",
              "      <td>1</td>\n",
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              "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
              "      <td>female</td>\n",
              "      <td>27.0</td>\n",
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              "      <td>347742</td>\n",
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              "      <td>1</td>\n",
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              "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
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              "      <td>11</td>\n",
              "      <td>1</td>\n",
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              "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
              "      <td>female</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>16.7000</td>\n",
              "      <td>G6</td>\n",
              "      <td>S</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Bonnell, Miss. Elizabeth</td>\n",
              "      <td>female</td>\n",
              "      <td>58.0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>S</td>\n",
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              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Saundercock, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5. 2151</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
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              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Andersson, Mr. Anders Johan</td>\n",
              "      <td>male</td>\n",
              "      <td>39.0</td>\n",
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              "      <td>5</td>\n",
              "      <td>347082</td>\n",
              "      <td>31.2750</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>350406</td>\n",
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              "      <td>S</td>\n",
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              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
              "      <td>female</td>\n",
              "      <td>55.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>248706</td>\n",
              "      <td>16.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Rice, Master. Eugene</td>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>382652</td>\n",
              "      <td>29.1250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Williams, Mr. Charles Eugene</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>244373</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
              "      <td>female</td>\n",
              "      <td>31.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>345763</td>\n",
              "      <td>18.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Masselmani, Mrs. Fatima</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2649</td>\n",
              "      <td>7.2250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>"
            ],
            "text/plain": [
              "    PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0             1         0       3  ...   7.2500   NaN         S\n",
              "1             2         1       1  ...  71.2833   C85         C\n",
              "2             3         1       3  ...   7.9250   NaN         S\n",
              "3             4         1       1  ...  53.1000  C123         S\n",
              "4             5         0       3  ...   8.0500   NaN         S\n",
              "5             6         0       3  ...   8.4583   NaN         Q\n",
              "6             7         0       1  ...  51.8625   E46         S\n",
              "7             8         0       3  ...  21.0750   NaN         S\n",
              "8             9         1       3  ...  11.1333   NaN         S\n",
              "9            10         1       2  ...  30.0708   NaN         C\n",
              "10           11         1       3  ...  16.7000    G6         S\n",
              "11           12         1       1  ...  26.5500  C103         S\n",
              "12           13         0       3  ...   8.0500   NaN         S\n",
              "13           14         0       3  ...  31.2750   NaN         S\n",
              "14           15         0       3  ...   7.8542   NaN         S\n",
              "15           16         1       2  ...  16.0000   NaN         S\n",
              "16           17         0       3  ...  29.1250   NaN         Q\n",
              "17           18         1       2  ...  13.0000   NaN         S\n",
              "18           19         0       3  ...  18.0000   NaN         S\n",
              "19           20         1       3  ...   7.2250   NaN         C\n",
              "\n",
              "[20 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VCRXFsU8cOJQ",
        "outputId": "16187504-1b22-4cff-8bb3-201c8c642040"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      int64\n",
              "Survived         int64\n",
              "Pclass           int64\n",
              "Name            object\n",
              "Sex             object\n",
              "Age            float64\n",
              "SibSp            int64\n",
              "Parch            int64\n",
              "Ticket          object\n",
              "Fare           float64\n",
              "Cabin           object\n",
              "Embarked        object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2QI0ju6RcOQO"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W67hWJy1zXtX"
      },
      "source": [
        "### 1.2\n",
        "Проверьте, имеются ли пропущенные значения в колонках, и выведите сумму всех пропущенных значений в каждой из колонок."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UrWKo-nZzXtX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b7d8eb6-c37d-4822-c318-4cc2e1b8bc8a"
      },
      "source": [
        "### YOUR CODE HERE ###\n",
        "\n",
        "df.isnull().sum(axis=0)"
      ],
      "execution_count": null,
      "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": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "glEEE79aKHzT"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WEXNaPplzXtZ"
      },
      "source": [
        "### 1.3\n",
        "Замените все пропущенные значения колонки Age на медианы в зависимости от пола человека: т.е. если пол человека в строке с пропущенным значением \"male\", заменяете пропущенное значение возраста на медиану по всем известным возрастам мужчин, и наоборот. Выведите медианы возраста в зависимости от пола. Пропущенные значения колонок Cabin и Embarked замените на U (Unknown)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iN0qq_kAzXta"
      },
      "source": [
        "Примечание: для группировки по полу можно использовать метод df.groupby(), который имеет встроенные агрегатные функции (в т.ч. для вычисления медианы).  Для установки пропущенных значений в датасете воспользуйтесь функцией df.apply()."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X99uZ48yzXtb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e9a5651-f16f-4078-bc7d-369855d6138d"
      },
      "source": [
        "### YOUR CODE HERE ###\n",
        "\n",
        "age_median = df.groupby('Sex')['Age'].median()\n",
        "print(age_median['female'], age_median['male'])\n",
        "# for key, item in man_median:\n",
        "#     print(man_median.get_group(key), \"\\n\\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "27.0 29.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "lXpGTvIymF6N",
        "outputId": "20f26d94-ba80-45b2-f3b1-fc94a37cfd2f"
      },
      "source": [
        "# с пользовательской функцией без lambda\n",
        "def age_replace(row, age_median_female, age_median_male):\n",
        "  if pd.isnull(row['Cabin']):\n",
        "    row['Cabin'] = 'U'\n",
        "  if pd.isnull(row['Embarked']):\n",
        "    row['Embarked'] = 'U'\n",
        "  if pd.isnull(row['Age']):\n",
        "    if row['Sex'] == 'female':\n",
        "      row['Age'] = age_median_female\n",
        "    else:\n",
        "      row['Age'] = age_median_male\n",
        "  return row\n",
        "\n",
        "df_age_median = df.apply(age_replace, args = [age_median['female'], \n",
        "                                              age_median['male']], axis = 1)\n",
        "df_age_median.head(20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Moran, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330877</td>\n",
              "      <td>8.4583</td>\n",
              "      <td>U</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>McCarthy, Mr. Timothy J</td>\n",
              "      <td>male</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17463</td>\n",
              "      <td>51.8625</td>\n",
              "      <td>E46</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Palsson, Master. Gosta Leonard</td>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>349909</td>\n",
              "      <td>21.0750</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
              "      <td>female</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>347742</td>\n",
              "      <td>11.1333</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>237736</td>\n",
              "      <td>30.0708</td>\n",
              "      <td>U</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
              "      <td>female</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>PP 9549</td>\n",
              "      <td>16.7000</td>\n",
              "      <td>G6</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Bonnell, Miss. Elizabeth</td>\n",
              "      <td>female</td>\n",
              "      <td>58.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113783</td>\n",
              "      <td>26.5500</td>\n",
              "      <td>C103</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Saundercock, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5. 2151</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Andersson, Mr. Anders Johan</td>\n",
              "      <td>male</td>\n",
              "      <td>39.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>347082</td>\n",
              "      <td>31.2750</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>350406</td>\n",
              "      <td>7.8542</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
              "      <td>female</td>\n",
              "      <td>55.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>248706</td>\n",
              "      <td>16.0000</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Rice, Master. Eugene</td>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>382652</td>\n",
              "      <td>29.1250</td>\n",
              "      <td>U</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Williams, Mr. Charles Eugene</td>\n",
              "      <td>male</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>244373</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
              "      <td>female</td>\n",
              "      <td>31.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>345763</td>\n",
              "      <td>18.0000</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Masselmani, Mrs. Fatima</td>\n",
              "      <td>female</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2649</td>\n",
              "      <td>7.2250</td>\n",
              "      <td>U</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0             1         0       3  ...   7.2500     U         S\n",
              "1             2         1       1  ...  71.2833   C85         C\n",
              "2             3         1       3  ...   7.9250     U         S\n",
              "3             4         1       1  ...  53.1000  C123         S\n",
              "4             5         0       3  ...   8.0500     U         S\n",
              "5             6         0       3  ...   8.4583     U         Q\n",
              "6             7         0       1  ...  51.8625   E46         S\n",
              "7             8         0       3  ...  21.0750     U         S\n",
              "8             9         1       3  ...  11.1333     U         S\n",
              "9            10         1       2  ...  30.0708     U         C\n",
              "10           11         1       3  ...  16.7000    G6         S\n",
              "11           12         1       1  ...  26.5500  C103         S\n",
              "12           13         0       3  ...   8.0500     U         S\n",
              "13           14         0       3  ...  31.2750     U         S\n",
              "14           15         0       3  ...   7.8542     U         S\n",
              "15           16         1       2  ...  16.0000     U         S\n",
              "16           17         0       3  ...  29.1250     U         Q\n",
              "17           18         1       2  ...  13.0000     U         S\n",
              "18           19         0       3  ...  18.0000     U         S\n",
              "19           20         1       3  ...   7.2250     U         C\n",
              "\n",
              "[20 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n4y7HgtRzXtd"
      },
      "source": [
        "### 1.4\n",
        "Выведите возраст пассажиров с PassengerID = [6, 20]. Убедитесь, что заполнены все пропущенные значения (воспользуйтесь функцией df.isnull() )."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOb5XHuJzXte",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "08897766-30df-4714-cb39-9727e8109a5d"
      },
      "source": [
        "### YOUR CODE HERE ###\n",
        "\n",
        "df_age_median[df_age_median['PassengerId'].isin([6, 20])]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Moran, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330877</td>\n",
              "      <td>8.4583</td>\n",
              "      <td>U</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Masselmani, Mrs. Fatima</td>\n",
              "      <td>female</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2649</td>\n",
              "      <td>7.2250</td>\n",
              "      <td>U</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    PassengerId  Survived  Pclass  ...    Fare Cabin  Embarked\n",
              "5             6         0       3  ...  8.4583     U         Q\n",
              "19           20         1       3  ...  7.2250     U         C\n",
              "\n",
              "[2 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "V-O3c_iWyt9R",
        "outputId": "406c880f-1da8-49ff-9af2-97425b0f5da3"
      },
      "source": [
        "df_age_median[(df_age_median['PassengerId'] > 6) & (df_age_median['PassengerId'] < 20)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>McCarthy, Mr. Timothy J</td>\n",
              "      <td>male</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17463</td>\n",
              "      <td>51.8625</td>\n",
              "      <td>E46</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Palsson, Master. Gosta Leonard</td>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>349909</td>\n",
              "      <td>21.0750</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
              "      <td>female</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>347742</td>\n",
              "      <td>11.1333</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>237736</td>\n",
              "      <td>30.0708</td>\n",
              "      <td>U</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
              "      <td>female</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>PP 9549</td>\n",
              "      <td>16.7000</td>\n",
              "      <td>G6</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Bonnell, Miss. Elizabeth</td>\n",
              "      <td>female</td>\n",
              "      <td>58.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113783</td>\n",
              "      <td>26.5500</td>\n",
              "      <td>C103</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Saundercock, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5. 2151</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Andersson, Mr. Anders Johan</td>\n",
              "      <td>male</td>\n",
              "      <td>39.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>347082</td>\n",
              "      <td>31.2750</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>350406</td>\n",
              "      <td>7.8542</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
              "      <td>female</td>\n",
              "      <td>55.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>248706</td>\n",
              "      <td>16.0000</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Rice, Master. Eugene</td>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>382652</td>\n",
              "      <td>29.1250</td>\n",
              "      <td>U</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Williams, Mr. Charles Eugene</td>\n",
              "      <td>male</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>244373</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
              "      <td>female</td>\n",
              "      <td>31.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>345763</td>\n",
              "      <td>18.0000</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "6             7         0       1  ...  51.8625   E46         S\n",
              "7             8         0       3  ...  21.0750     U         S\n",
              "8             9         1       3  ...  11.1333     U         S\n",
              "9            10         1       2  ...  30.0708     U         C\n",
              "10           11         1       3  ...  16.7000    G6         S\n",
              "11           12         1       1  ...  26.5500  C103         S\n",
              "12           13         0       3  ...   8.0500     U         S\n",
              "13           14         0       3  ...  31.2750     U         S\n",
              "14           15         0       3  ...   7.8542     U         S\n",
              "15           16         1       2  ...  16.0000     U         S\n",
              "16           17         0       3  ...  29.1250     U         Q\n",
              "17           18         1       2  ...  13.0000     U         S\n",
              "18           19         0       3  ...  18.0000     U         S\n",
              "\n",
              "[13 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ayEbWnlzXth"
      },
      "source": [
        "Так как не все классификаторы могут работать с категориальными признаками, в данном случае необходимо заменить их на числовые.\n",
        "\n",
        "Также можно было бы выделить расширенные признаки, такие как статус пассажира (Mr., Mrs., Miss., Dr., Master. и т.д.), собственный индекс билета, индекс палубы и другие, но здесь ограничимся выбором более простых признаков."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FLO_ZmHiW6LT"
      },
      "source": [
        "# df_age_median.replace({'Cabin': {'A(.*)': '1\\\\1', 'B(.*)': '2\\\\1', \n",
        "#                                  'C(.*)': '2\\\\1', 'E(.*)': '4\\\\1'}}, regex=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yokvJp2dzXth"
      },
      "source": [
        "### 1.5\n",
        "В колонке Sex замените значения на 0, если пол \"male\", и на 1, если \"female\". В колонке Embarked замените параметры \"U\", \"S\", \"C\", \"Q\" на 0, 1, 2, 3 соответственно. Отбросьте колонки PassengerId, Name, Ticket, Cabin. Выведите первые 20 строк получившегося набора данных."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xf7r1L1SzXti",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "outputId": "b56097bb-0b09-44f5-9bb5-02e6fc79e7dc"
      },
      "source": [
        "### YOUR CODE HERE ###\n",
        "\n",
        "df_1 = df_age_median.replace({'Sex': {'male': 0, 'female': 1}, \n",
        "                       'Embarked': {\"U\": 0, \"S\": 1, \"C\": 2, \"Q\": 3}}) \\\n",
        "                       .drop(columns=['PassengerId', 'Name', 'Ticket', 'Cabin'])\n",
        "df_1.head(20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.4583</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>51.8625</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>21.0750</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>11.1333</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0708</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>16.7000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>58.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>26.5500</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>31.2750</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8542</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>55.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16.0000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>29.1250</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>31.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>18.0000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2250</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
              "0          0       3    0  22.0      1      0   7.2500         1\n",
              "1          1       1    1  38.0      1      0  71.2833         2\n",
              "2          1       3    1  26.0      0      0   7.9250         1\n",
              "3          1       1    1  35.0      1      0  53.1000         1\n",
              "4          0       3    0  35.0      0      0   8.0500         1\n",
              "5          0       3    0  29.0      0      0   8.4583         3\n",
              "6          0       1    0  54.0      0      0  51.8625         1\n",
              "7          0       3    0   2.0      3      1  21.0750         1\n",
              "8          1       3    1  27.0      0      2  11.1333         1\n",
              "9          1       2    1  14.0      1      0  30.0708         2\n",
              "10         1       3    1   4.0      1      1  16.7000         1\n",
              "11         1       1    1  58.0      0      0  26.5500         1\n",
              "12         0       3    0  20.0      0      0   8.0500         1\n",
              "13         0       3    0  39.0      1      5  31.2750         1\n",
              "14         0       3    1  14.0      0      0   7.8542         1\n",
              "15         1       2    1  55.0      0      0  16.0000         1\n",
              "16         0       3    0   2.0      4      1  29.1250         3\n",
              "17         1       2    0  29.0      0      0  13.0000         1\n",
              "18         0       3    1  31.0      1      0  18.0000         1\n",
              "19         1       3    1  27.0      0      0   7.2250         2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qMAW08A5zXtk"
      },
      "source": [
        "### 1.6\n",
        "Приступим к построению классификаторов. **Условимся, что, если функция или объект модели имеют параметр random_state, то устанавливаем его равным 17 в каждом из случаев.**\n",
        "\n",
        "Разделите данные на тренировочный и тестовый датасеты, установив размер тестового как 0.25 (первая колонка Survived является целевой, поэтому необходимо сначала ее отделить от признаков)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ld64gUCAzXtm"
      },
      "source": [
        "from sklearn.model_selection import train_test_split "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jWs-vIXdzXtr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e481fc1-a32c-468b-b4de-e4ad4b1e71aa"
      },
      "source": [
        "### YOUR CODE HERE ###\n",
        "\n",
        "df_survived = df_1['Survived']\n",
        "# df_survived\n",
        "df_2 = df_1.drop(columns=['Survived'])\n",
        "# df_2\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(df_2, df_survived, \n",
        "                                                    test_size=0.25, \n",
        "                                                    random_state=17)\n",
        "print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(668, 7) (223, 7) (668,) (223,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LRUJb1gozXtv"
      },
      "source": [
        "### 1.7\n",
        "Обучите на полученных выборках несколько классификаторов, которые импортированы ниже.\n",
        "\n",
        "**Примечание: в методе ближайших соседей используйте количество соседей, равное 5.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1szEVSTzXtw"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1pq3i9LnzXty"
      },
      "source": [
        "### YOUR CODE HERE ###\n",
        "\n",
        "model_knc = KNeighborsClassifier(n_neighbors=5).fit(x_train, y_train)\n",
        "model_gnb = GaussianNB().fit(x_train, y_train)\n",
        "model_dtc = DecisionTreeClassifier().fit(x_train, y_train)\n",
        "model_lrg = LogisticRegression().fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jv5Uy5UmzXt2"
      },
      "source": [
        "Очевидно, в этом случае использовать метрику accuracy, которую мы применяли до этого, не совсем правильно: она не даст верную оценку классификатору, потому как выживших явно меньше, чем погибших.\n",
        "\n",
        "Вспомним про такие метрики как точность и полнота. Здесь они подойдут хорошо, так как у нас имеются положительный и отрицательный классы, и определить корректно один из них может оказаться более важным ."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Opt43TOIzXt3"
      },
      "source": [
        "### 1.8\n",
        "В каждом из пунктов следующего теста выберите, что важнее максимизировать: точность (precision) или полноту (recall). Выпишите ответы.\n",
        "\n",
        "1. Вероятность того, что при определенной поломке самолета он сможет долететь до пункта назначения (1 - долетел, 0 - не долетел).\n",
        "2. Предсказание, представляет ли человек опасность, по анализу психического состояния (1 - представляет опасность, 0 - не представляет опасности).\n",
        "3. Предсказание ухода клиента (1 - клиент ушел, 0 - остался).\n",
        "4. Выявление рака на основе медицинских показателей (1 - болен раком, 0 - здоров).\n",
        "5. Предсказание летальности при наблюдаемой мутации (1 - выживание, 0 - летальный исход).\n",
        "6. Определение важности происшествия для экстренных служб (1 - важно, 0 - неважно).\n",
        "7. Окупятся ли вложения в бизнес (1 - окупятся, 0 - не окупятся)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1-v6SmGzXt3"
      },
      "source": [
        "Ваш ответ:\n",
        "\n",
        "1. precision<br>\n",
        "2. recall - это если мы не боимся ложно обвинить человека нездоровым и опасным!<br>\n",
        "3. precision<br>\n",
        "4. recall<br>\n",
        "5. precision<br>\n",
        "6. recall<br>\n",
        "7. precision<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FqrtMi-jzXt4"
      },
      "source": [
        "### 1.9\n",
        "Определите, что важнее использовать в случае датасета Titanic: полноту или точность. Объясните, почему.\n",
        "\n",
        "\n",
        "## Важно знать точность. Мы НЕ должны завышать колличество выживших. Правильнее в катастрофе считать человека погибшим, пока не доказанно обратное.\n",
        "\n",
        "<br>\n",
        "<br>\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "### 1.10\n",
        "Для каждого классификатора выведите матрицу ошибок и самостоятельно рассчитайте метрику recall (расчеты должны присутствовать). Проверьте расчеты, воспользовавшись встроенной метрикой recall_score."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cJ3N4TqEzXt5"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, recall_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bVukHkOgzXt7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b43f82f-422c-419a-82d8-84374298f6ac"
      },
      "source": [
        "### YOUR CODE HERE ###\n",
        "\n",
        "# 1) Что имеется ввиду под \"Для каждого классификатора\"?\n",
        "# 2) Какие данные использовать для проверки их recall расчётами и recall_score?\n",
        "\n",
        "print('KNeighborsClassifier')\n",
        "y_pred = model_knc.predict(x_test)\n",
        "cm_knc = confusion_matrix(y_test, y_pred)\n",
        "print(cm_knc)\n",
        "# матрица оказалась вывернута наизнанку относительно той, что мы изучали в лекциях\n",
        "# её структура имеет вид\n",
        "# [[TN, FP]\n",
        "#  [FN, TP]]\n",
        "# TP = cm_knc[1][1], FN = cm_knc[1][0]\n",
        "print('recall =       ', cm_knc[1][1] / (cm_knc[1][1] + cm_knc[1][0]))\n",
        "print('recall_score = ', recall_score(y_test, y_pred))\n",
        "print('-'*30)\n",
        "\n",
        "print('GaussianNB')\n",
        "y_pred = model_gnb.predict(x_test)\n",
        "cm_gnb = confusion_matrix(y_test, y_pred)\n",
        "print(cm_gnb)\n",
        "print('recall =       ', cm_gnb[1][1] / (cm_gnb[1][1] + cm_gnb[1][0]))\n",
        "print('recall_score = ', recall_score(y_test, y_pred))\n",
        "print('-'*30)\n",
        "\n",
        "print('DecisionTreeClassifier')\n",
        "y_pred = model_dtc.predict(x_test)\n",
        "cm_dtc = confusion_matrix(y_test, y_pred)\n",
        "print(cm_dtc)\n",
        "print('recall =       ', cm_dtc[1][1] / (cm_dtc[1][1] + cm_dtc[1][0]))\n",
        "print('recall_score = ', recall_score(y_test, y_pred))\n",
        "print('-'*30)\n",
        "\n",
        "print('LogisticRegression')\n",
        "y_pred = model_lrg.predict(x_test)\n",
        "cm_lrg = confusion_matrix(y_test, y_pred)\n",
        "print(cm_lrg)\n",
        "print('recall =       ', cm_lrg[1][1] / (cm_lrg[1][1] + cm_lrg[1][0]))\n",
        "print('recall_score = ', recall_score(y_test, y_pred))\n",
        "print('-'*30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KNeighborsClassifier\n",
            "[[110  21]\n",
            " [ 47  45]]\n",
            "recall =        0.4891304347826087\n",
            "recall_score =  0.4891304347826087\n",
            "------------------------------\n",
            "GaussianNB\n",
            "[[111  20]\n",
            " [ 27  65]]\n",
            "recall =        0.7065217391304348\n",
            "recall_score =  0.7065217391304348\n",
            "------------------------------\n",
            "DecisionTreeClassifier\n",
            "[[112  19]\n",
            " [ 27  65]]\n",
            "recall =        0.7065217391304348\n",
            "recall_score =  0.7065217391304348\n",
            "------------------------------\n",
            "LogisticRegression\n",
            "[[114  17]\n",
            " [ 28  64]]\n",
            "recall =        0.6956521739130435\n",
            "recall_score =  0.6956521739130435\n",
            "------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hvz-MxztzXt8"
      },
      "source": [
        "### 1.11\n",
        "Выберите ту модель, на которой метрика recall давала лучший ответ, и рассчитайте для нее precision, используя встроенную функцию."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UUdiUwlszXt9"
      },
      "source": [
        "from sklearn.metrics import precision_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4UcVLFdUzXt_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "103d06a7-425e-4cf9-bd3f-07e57b0314ee"
      },
      "source": [
        "### YOUR CODE HERE ###\n",
        "\n",
        "print('GaussianNB')\n",
        "y_pred = model_gnb.predict(x_test)\n",
        "print('precision_score = ', precision_score(y_test, y_pred))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GaussianNB\n",
            "precision_score =  0.7647058823529411\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JarakMepnI3A"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}