Titanic.ipynb

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      "name": "Titanic.ipynb",
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        "\n",
        "\n",
        " \n",
        "\n",
        "```sh\n",
        "!git clone https://github.com/OhyunHighSchoolML/dosoung.git\n",
        "```\n",
        "\n"
      ]
    },
    {
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      "source": [
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
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        "id": "AeD4oCbnXu3M",
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      "source": [
        "train_df = pd.read_csv(\"./titanic/data/train.csv\")\n",
        "test_df = pd.read_csv(\"./titanic/data/test.csv\")"
      ],
      "execution_count": 2,
      "outputs": []
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      "source": [
        "train_df"
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      "execution_count": 3,
      "outputs": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
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              "<p>891 rows × 12 columns</p>\n",
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              "     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",
              "..           ...       ...     ...  ...      ...   ...       ...\n",
              "886          887         0       2  ...  13.0000   NaN         S\n",
              "887          888         1       1  ...  30.0000   B42         S\n",
              "888          889         0       3  ...  23.4500   NaN         S\n",
              "889          890         1       1  ...  30.0000  C148         C\n",
              "890          891         0       3  ...   7.7500   NaN         Q\n",
              "\n",
              "[891 rows x 12 columns]"
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        "train_df.info()"
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            "<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",
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            " 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"
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              "      <th>416</th>\n",
              "      <td>1308</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>22.3583</td>\n",
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              "      <td>C</td>\n",
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            "text/plain": [
              "     PassengerId  Pclass  ... Cabin Embarked\n",
              "0            892       3  ...   NaN        Q\n",
              "1            893       3  ...   NaN        S\n",
              "2            894       2  ...   NaN        Q\n",
              "3            895       3  ...   NaN        S\n",
              "4            896       3  ...   NaN        S\n",
              "..           ...     ...  ...   ...      ...\n",
              "413         1305       3  ...   NaN        S\n",
              "414         1306       1  ...  C105        C\n",
              "415         1307       3  ...   NaN        S\n",
              "416         1308       3  ...   NaN        S\n",
              "417         1309       3  ...   NaN        C\n",
              "\n",
              "[418 rows x 11 columns]"
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      "source": [
        "test_df.info()"
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      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 11 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  418 non-null    int64  \n",
            " 1   Pclass       418 non-null    int64  \n",
            " 2   Name         418 non-null    object \n",
            " 3   Sex          418 non-null    object \n",
            " 4   Age          332 non-null    float64\n",
            " 5   SibSp        418 non-null    int64  \n",
            " 6   Parch        418 non-null    int64  \n",
            " 7   Ticket       418 non-null    object \n",
            " 8   Fare         417 non-null    float64\n",
            " 9   Cabin        91 non-null     object \n",
            " 10  Embarked     418 non-null    object \n",
            "dtypes: float64(2), int64(4), object(5)\n",
            "memory usage: 36.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mO7vTdf0Y8ys",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "2f72799f-fb43-4147-a5ee-baf70be26345"
      },
      "source": [
        "train_df.describe()"
      ],
      "execution_count": 7,
      "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>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>"
            ],
            "text/plain": [
              "       PassengerId    Survived      Pclass  ...       SibSp       Parch        Fare\n",
              "count   891.000000  891.000000  891.000000  ...  891.000000  891.000000  891.000000\n",
              "mean    446.000000    0.383838    2.308642  ...    0.523008    0.381594   32.204208\n",
              "std     257.353842    0.486592    0.836071  ...    1.102743    0.806057   49.693429\n",
              "min       1.000000    0.000000    1.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     223.500000    0.000000    2.000000  ...    0.000000    0.000000    7.910400\n",
              "50%     446.000000    0.000000    3.000000  ...    0.000000    0.000000   14.454200\n",
              "75%     668.500000    1.000000    3.000000  ...    1.000000    0.000000   31.000000\n",
              "max     891.000000    1.000000    3.000000  ...    8.000000    6.000000  512.329200\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B8GLyJ_0Y--E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "1907a124-365b-411e-f3bf-c1a31518c4c9"
      },
      "source": [
        "train_df.describe(include=['O']) # category에 관하여 describe를 해준다."
      ],
      "execution_count": 8,
      "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>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891</td>\n",
              "      <td>891</td>\n",
              "      <td>891</td>\n",
              "      <td>204</td>\n",
              "      <td>889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>891</td>\n",
              "      <td>2</td>\n",
              "      <td>681</td>\n",
              "      <td>147</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Greenberg, Mr. Samuel</td>\n",
              "      <td>male</td>\n",
              "      <td>1601</td>\n",
              "      <td>B96 B98</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>577</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>644</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Name   Sex Ticket    Cabin Embarked\n",
              "count                     891   891    891      204      889\n",
              "unique                    891     2    681      147        3\n",
              "top     Greenberg, Mr. Samuel  male   1601  B96 B98        S\n",
              "freq                        1   577      7        4      644"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mBfxt3KkZmyz",
        "colab_type": "text"
      },
      "source": [
        "# 데이터 분석"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RhNoOKeAZtBI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "143c72d0-e7bf-41b1-e199-185f299d6d61"
      },
      "source": [
        "train_df[['Pclass', 'Survived']].groupby('Pclass').mean()"
      ],
      "execution_count": 9,
      "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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.629630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.472826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.242363</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Survived\n",
              "Pclass          \n",
              "1       0.629630\n",
              "2       0.472826\n",
              "3       0.242363"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57LNIjXyaU_U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "f03c5b68-a3d8-4f0d-9631-aacc79f5b306"
      },
      "source": [
        "train_df[['Sex', 'Survived']].groupby('Sex').mean()"
      ],
      "execution_count": 10,
      "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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>0.742038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.188908</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Survived\n",
              "Sex             \n",
              "female  0.742038\n",
              "male    0.188908"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-TPN8-6Fa9mc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "e994173a-4947-4b4d-e679-f509d079614d"
      },
      "source": [
        "train_df[['Pclass', 'Survived']].groupby('Pclass').mean()"
      ],
      "execution_count": 11,
      "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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.629630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.472826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.242363</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Survived\n",
              "Pclass          \n",
              "1       0.629630\n",
              "2       0.472826\n",
              "3       0.242363"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FKoZ0-BQbLPk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "93d400cd-86d3-4776-9b1d-c8ec698e526d"
      },
      "source": [
        "train_df[['Embarked', 'Survived']].groupby('Embarked').mean()"
      ],
      "execution_count": 12,
      "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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>0.553571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Q</th>\n",
              "      <td>0.389610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S</th>\n",
              "      <td>0.336957</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Survived\n",
              "Embarked          \n",
              "C         0.553571\n",
              "Q         0.389610\n",
              "S         0.336957"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RlysPZEwbioU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "57ba7ed6-eb75-4613-f96e-e8351f274836"
      },
      "source": [
        "train_df[['SibSp', 'Survived']].groupby('SibSp').mean()"
      ],
      "execution_count": 13,
      "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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.345395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.535885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.464286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Survived\n",
              "SibSp          \n",
              "0      0.345395\n",
              "1      0.535885\n",
              "2      0.464286\n",
              "3      0.250000\n",
              "4      0.166667\n",
              "5      0.000000\n",
              "8      0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gS0Ak1TpbxVs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "0faecc06-167c-401b-bf92-a5f62d0a028c"
      },
      "source": [
        "train_df[['Parch', 'Survived']].groupby('Parch').mean()"
      ],
      "execution_count": 14,
      "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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.343658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.550847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Survived\n",
              "Parch          \n",
              "0      0.343658\n",
              "1      0.550847\n",
              "2      0.500000\n",
              "3      0.600000\n",
              "4      0.000000\n",
              "5      0.200000\n",
              "6      0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5w3cOycFb1EE",
        "colab_type": "text"
      },
      "source": [
        "# visualization data analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gPhiUR1ib_U0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "0358e96b-f848-456e-bea9-70a4c0d03f75"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jPNVSotlcKg8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "outputId": "0350c6c0-54c0-4a2a-ab84-c07520249f65"
      },
      "source": [
        "plt.hist(train_df['Age'],bins=30)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
            "  keep = (tmp_a >= first_edge)\n",
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
            "  keep &= (tmp_a <= last_edge)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([30., 14., 10., 14.,  3., 29., 39., 65., 73., 42., 65., 62., 33.,\n",
              "        47., 38., 21., 28., 21., 16., 14., 15.,  7.,  9.,  8.,  4.,  0.,\n",
              "         5.,  1.,  0.,  1.]),\n",
              " array([ 0.42      ,  3.07266667,  5.72533333,  8.378     , 11.03066667,\n",
              "        13.68333333, 16.336     , 18.98866667, 21.64133333, 24.294     ,\n",
              "        26.94666667, 29.59933333, 32.252     , 34.90466667, 37.55733333,\n",
              "        40.21      , 42.86266667, 45.51533333, 48.168     , 50.82066667,\n",
              "        53.47333333, 56.126     , 58.77866667, 61.43133333, 64.084     ,\n",
              "        66.73666667, 69.38933333, 72.042     , 74.69466667, 77.34733333,\n",
              "        80.        ]),\n",
              " <a list of 30 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hmBRMxaXcSpU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "63bd01ca-679b-4880-b413-f9ab6e9a3a39"
      },
      "source": [
        "grid = sns.FacetGrid(train_df, col='Survived', hue='Survived')\n",
        "grid.map(plt.hist,'Age', bins=20)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f471b1d5d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DDOYgyusc0Ic",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "outputId": "987e5fed-a1bc-40e7-8679-9257ddfe4009"
      },
      "source": [
        "grid = sns.FacetGrid(train_df, row='Pclass', col='Survived')\n",
        "grid.map(plt.hist, 'Age', bins=20)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f4718496470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JTTKgJElNMqAkSU0aqYBK8niSW5PckeTyJM94mnnfm+Ttc1nfLup4XZLbktye5CtJDt/FfOuSHDtJ+35JrkmyOcldSQZ193mSXJRk5QDWc3qSjwxgPUd22+meJH+aJLNd5yiynyz6fvKBJN9N8uhs1zVsIxVQwI+q6oiqOgz4MXDmfBfUh28D/1tV/UvgD5n+rev/E3BtVR1eVSuBs6ezcJIlu5pWVW+oqrumWc8w/TnwRuCQ7tHXz61rJ/aTxd1P/htw1HwX0Y9RC6jxvgQcDJDk9d3e1+YkH584Y5I3Jrmpm37FE3uUSX6728vcnOSLXduhSW7s9kBvS3LIbIqsqq9U1cPd6NeA5dNcxTJg67j13dbVeWySa8a9xo8kOb0bvi/JeUm+DrwjyY3j5luR5PZu+IYkq5KcmeSD4+Z5ck8vye+M2x5/8URHTvLvknyzW/cx03xNO0myDHh2VX2ter8h8zHgpNmuV/aTxdRPutf2tap6YBDrGraRDKgkS4HjgduTHAq8Bziuqg4HzppkkSur6le76VuAM7r23wde0bW/ums7E7igqo4AVjHuTT/u+Td0b8SJj9dPUfoZwH+f5sv9KHBxkuuTvDvJL/W53Peq6kVVdS6we5KDuvbXABsmzHsF8Fvjxl8DXJrkV7rhY7rt8Tjwui5M3kevw70MmPT0R5Jf38V2+soks+/PU7f11q5NM2Q/6ctC6ycLyqx+UXcB2jPJrd3wl4CLgTcBl1fVDoCq+v4kyx2W5P3A3sBe9H7mHuDvgHVJLgOu7Nq+Crw7yXJ6HfbuiSurqtdMt/Akv06v471sOstV1eeTPI/e6a7jgVuSHNbHouM712X0OtC53d+n1F9V25Pcm+Ro4G7gBfS2zZuBI4Gb0rsctCewDXgxcENVbe9e2wbgX0xS+/XAEf2/Wg2I/cR+0oRRC6gfdXsoT0p/19HXASdV1ebu8P5YgKo6M8mLgROAm5McWVWfTLKxa/tckjdV1RcmPOcG4PmTPM+HqupjExuT/CvgIuD4qvpePwWP1/1n8kngk93pil8DHuKpR9B7TFjsh+OGNwCXJ7myt7qd/zMBLgVOAf4e+ExVVXobd31VnTPh9fR16q37z+b8SSb9Y1W9dELb/Tz1tM7yrk3TZz9ZvP1kYamqkXkAj07SdijwTeC53fg+3d/3Am/vhncAvwDsBlwLrOvaf3ncem6itxfzPCBd258Ab51lzQcC9wAvnWK+dcCxk7QfBzyjG34WvVMvvwocANwH/Dy9Pd5vA6d3890H7DthPTcBHwfeOa7tBmBVN/wc4FvA9cBRXdtKenuKv/DEtgX+Ob3z/d8Bnttt0y8BHxnAv++NwNFA6J3ieeV8v+cW4sN+srj7ydP9O7f2GLUjqJ1U1Z1JPgD8bZLHgVuA0yfM9nvARmB79/dZXfsHu4u7Aa4DNgPvAk5L8hPgQeCPZlni79N7g/7Xbi/2sapaNY3ljwQ+kuQxenuCF1XVTQDdKZc76HW6W6ZYzwbgg8BBk02sqoeTbAFWVtWNXdtdSd4D/E2SnwN+Ary5qr6W5L30TvM8Atw62Tpn4N/T+w9oT3oBNd3rENoF+8ni6SdJ/jPwb4FnJNlK77W+dxDrHrQn9mC0wCVZR2+P9YZ5LkVqlv1kYRnJT/FJktpnQC0ef0XvnLikXbOfLCCe4pMkNWlOj6BWr15dgA8fo/KYEfuJjxF8TGpOA2rHjh1z+XTSgmQ/kXq8BiVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWpS3wGVZEmSW5Jc040flGRjknuSbEiy+/DKlCSNmukcQZ0FbBk3fh5wflUdDDwMnDHIwiRJo62vgEqyHDgBuKgbD3Ac8OlulvXAScMoUJI0mvo9gvow8E7gp934c4FHquqxbnwrsP9kCyZZm2RTkk3bt2+fVbHSYmU/kXY2ZUAleRWwrapunskTVNWFVbWqqlaNjY3NZBXSomc/kXa2tI95jgFeneSVwB7As4ELgL2TLO2OopYD9w+vTEnSqJnyCKqqzqmq5VW1Angt8IWqeh1wPXByN9sa4KqhVSlJGjmz+R7Uu4D/mOQeetekLh5MSZIk9XeK70lVdQNwQzd8L3DU4EuSJMk7SUiSGmVASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaNGVAJdkjyY1JNie5M8n7uvaDkmxMck+SDUl2H365kqRR0c8R1D8Bx1XV4cARwOokRwPnAedX1cHAw8AZwytTkjRqpgyo6nm0G92texRwHPDprn09cNJQKpQkjaS+rkElWZLkVmAbcC3wLeCRqnqsm2UrsP9wSpQkjaK+AqqqHq+qI4DlwFHAC/p9giRrk2xKsmn79u0zLFNa3Own0s6m9Sm+qnoEuB54CbB3kqXdpOXA/btY5sKqWlVVq8bGxmZVrLRY2U+knfXzKb6xJHt3w3sCvwFsoRdUJ3ezrQGuGlaRkqTRs3TqWVgGrE+yhF6gXVZV1yS5C7g0yfuBW4CLh1inJGnETBlQVXUb8MJJ2u+ldz1KkqSB804SkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJk0ZUEkOSHJ9kruS3JnkrK59nyTXJrm7+/uc4ZcrSRoV/RxBPQa8rapWAkcDb06yEjgbuK6qDgGu68YlSRqIKQOqqh6oqq93w/8AbAH2B04E1nezrQdOGlaRkqTRM61rUElWAC8ENgL7VdUD3aQHgf12sczaJJuSbNq+ffssSpUWL/uJtLO+AyrJXsAVwFur6gfjp1VVATXZclV1YVWtqqpVY2NjsypWWqzsJ9LO+gqoJLvRC6dPVNWVXfNDSZZ105cB24ZToiRpFPXzKb4AFwNbqupD4yZdDazphtcAVw2+PEnSqFraxzzHAKcBtye5tWv7XeBc4LIkZwDfAU4ZTomSpFE0ZUBV1ZeB7GLyywdbjiRJPd5JQpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktSkpVPNkOQS4FXAtqo6rGvbB9gArADuA06pqoeHV+biseLsz07aft+5J8xxJZLUtn6OoNYBqye0nQ1cV1WHANd145IkDcyUAVVVXwS+P6H5RGB9N7weOGnAdUmSRtyUp/h2Yb+qeqAbfhDYb1czJlkLrAU48MADZ/h07RrUKTtP/Y226faTXb1fYLTeM26HxW3WH5KoqgLqaaZfWFWrqmrV2NjYbJ9OWpTsJ9LOZhpQDyVZBtD93Ta4kiRJmnlAXQ2s6YbXAFcNphxJknr6+Zj5p4BjgX2TbAX+ADgXuCzJGcB3gFOGWaSkxcPrrerXlAFVVafuYtLLB1yLJElP8k4SkqQmGVCSpCbN9HtQmmeTncf3HL4Wq6f7vpMWL4+gJElNMqAkSU0yoCRJTfIa1JAM6py5597VqkHfB8/3uibyCEqS1CQDSpLUJANKktQkr0FNwnPhkjT/PIKSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yY+ZLyL+lLZg8LcgWqhmsh3cdm3xCEqS1CQDSpLUJANKktQkr0GNsOne0mk65+CHuW7N3Fxdp2z9dmFzVZ/XtGbHIyhJUpMMKElSkwwoSVKTvAY1Avz5ec2G/+49g75+5/cWp+YRlCSpSQaUJKlJBpQkqUleg1ITpnM+3u9YqSVzeY1u1K5bzeoIKsnqJN9Ick+SswdVlCRJMw6oJEuAjwLHAyuBU5OsHFRhkqTRNpsjqKOAe6rq3qr6MXApcOJgypIkjbpU1cwWTE4GVlfVG7rx04AXV9VbJsy3FljbjT4f+MYuVrkvsGNGxcw9ax2OhVQrTF3vjqpa3c+KptFP+nnelljrcCy2WiftK0P/kERVXQhcONV8STZV1aph1zMI1jocC6lWGGy9/faTQT/vsFnrcIxKrbM5xXc/cMC48eVdmyRJszabgLoJOCTJQUl2B14LXD2YsiRJo27Gp/iq6rEkbwE+DywBLqmqO2dRS1+nNxphrcOxkGqF+at3IW0nax2Okah1xh+SkCRpmLzVkSSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkjFVBJHk9ya5I7klye5BlPM+97k7x9LuvbRR0nJrmtq3tTkpftYr4bkqyYpP353bRbk2xJMrC7ICf5XJK9B7CegWzrJKuTfCPJPUnOnu36RpX9ZNH3k0uSbEtyx2zXNWwjFVDAj6rqiKo6DPgxcOZ8F9SH64DDq+oI4P8ELprm8n8KnN+97l8B/mw6CydZsqtpVfXKqnpkmvUMRVfnR4HjgZXAqUlWzm9VC5b9ZJH2k846YKefV2/RqAXUeF8CDgZI8vpu72tzko9PnDHJG5Pc1E2/4ok9yiS/3e1lbk7yxa7t0CQ3dntityU5ZDZFVtWj9bPfRHkmMN3fR1kGbB23vtu7Ok9P8pFxr/GaJMd2w48m+S9JNgPnJLl83HzHJrmmG74vyb5Jzk3y5nHzPLmnl+Qd3ba7Lcn7xs3z7iTfTPJl4PnTfE2TOQq4p6ruraofA5cCJw5gvaPOfrK4+glV9UXg+4NY17DN+AcLF7IkS+ntaf91kkOB9wAvraodSfaZZJErq+r/7pZ9P3AGvT2s3wdeUVX3jzuEPxO4oKo+kd4vDe+0Z5VkA5O/2T5UVR+bZP7fAv4Y+AXghGm+3POBLyT5CvA3wF/2sTf3TGBjVb2t21b3JnlmVf0QeA29//zH2wB8mN4RDMApwCuS/CZwCL3wCHB1kl8DfkjvF5iPoPce/Dpw88QikrwOeMck9d1TVSdPaNsf+O648a3Ai6d4nXoa9pNF2U8WlFELqD2T3NoNfwm4GHgTcHlV7QCoqsn2LA7rOtzewF70fkUY4O+AdUkuA67s2r4KvDvJcnod9u6JK6uq10yn6Kr6DPCZ7k37h8C/nsayf5nk8/QO6U8E3pTk8CkWexy4olv+sSR/DfzvST5Nr+O/c8Jz3JLkF5L8EjAGPFxV301yFvCbwC3drHvR64jPAj5TVf8IkOTqXdT+CeAT/b5WDYz9xH7ShFELqB9156iflKSf5dYBJ1XV5iSnA8cCVNWZSV5M7814c5Ijq+qTSTZ2bZ9L8qaq+sKE55zWnuETquqLSZ6XZN8n/qPoR1X9f8AlwCXpXRg9DHiMp57i3WPc8P+qqsfHjV8KvIXeaYFNVfUPkzzN5cDJwC/S21OE3t7gH1fVX4yfMclb+6l7mnuG9wMHjBtf3rVp+uwni7efLCxVNTIP4NFJ2g4Fvgk8txvfp/v7XuDt3fAOeqcNdgOuBdZ17b88bj030TsUfx6Qru1PgLfOsuaDx63vRfT+080k890ArJikfTWwWzf8i8AD3d+XAV+h1/kOAH4AHDvZdqJ3+uU+ep3rlHHt9wH7jtuOX+m25bKu7TeBjcBe3fj+3XZ8EXAbsCe9vcS7n9jWs9hOS4F7gYOA3YHNwKHz/Z5biA/7yeLtJ+NqWgHcMd/vtakeo3YEtZOqujPJB4C/TfI4vcPs0yfM9nv03kDbu7/P6to/2F3cDb1PEW0G3gWcluQnwIPAH82yxH8DvL5b34+A11T3DuvTbwIXJIlW6uoAAA3GSURBVPlf3fg7qurBJA8B3wbuArbQO789qap6vLvgezqwZhfz3JnkWcD9VfVA1/Y3SX4F+Gq3B/4o8DtV9fVu73gzsI3ef1qzUr1TLG+hd1ppCXBJVd052/Wqx36yOPoJQJJP0Tu63TfJVuAPquriQax70DK9f0O1KskNwOlVdd88lyI1y36ysIzyx8wlSQ0zoBaPdUBLXwaUWrQO+8mC4Sk+SVKTPIKSJDVpTgNq9erVRe8WJD58jMJjRuwnPkbwMak5DagdO/r+zpw0suwnUo+n+CRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0a+buZT9eKsz/7tNPvO3e6P+QpSZqMR1CSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCb1HVBJliS5Jck13fhBSTYmuSfJhiS7D69MSdKomc4R1FnAlnHj5wHnV9XBwMPAGYMsTJI02voKqCTLgROAi7rxAMcBn+5mWQ+cNIwCJUmjqd8jqA8D7wR+2o0/F3ikqh7rxrcC+0+2YJK1STYl2bR9+/ZZFSstVvYTaWdTBlSSVwHbqurmmTxBVV1YVauqatXY2NhMViEtevYTaWf93Cz2GODVSV4J7AE8G7gA2DvJ0u4oajlw//DKlCSNmimPoKrqnKpaXlUrgNcCX6iq1wHXAyd3s60BrhpalZKkkTObn9t4F3BpkvcDtwAXD6ak+TXVz2lIkubGtAKqqm4AbuiG7wWOGnxJkiR5JwlJUqMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpNmcy8+TWKqe/ndd+4Jc1SJJC1sHkFJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmuTPbcyxp/s5Dn+KQ5J+xiMoSVKTDChJUpMMKElSk6YMqCR7JLkxyeYkdyZ5X9d+UJKNSe5JsiHJ7sMvV5I0Kvo5gvon4LiqOhw4Alid5GjgPOD8qjoYeBg4Y3hlSpJGzZQBVT2PdqO7dY8CjgM+3bWvB04aSoWSpJHU1zWoJEuS3ApsA64FvgU8UlWPdbNsBfYfTomSpFHUV0BV1eNVdQSwHDgKeEG/T5BkbZJNSTZt3759hmVKi5v9RNrZtD7FV1WPANcDLwH2TvLEF32XA/fvYpkLq2pVVa0aGxubVbHSYmU/kXbWz6f4xpLs3Q3vCfwGsIVeUJ3czbYGuGpYRUqSRk8/tzpaBqxPsoReoF1WVdckuQu4NMn7gVuAi4dYpyRpxEwZUFV1G/DCSdrvpXc9SpKkgfNOEpKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCZNGVBJDkhyfZK7ktyZ5KyufZ8k1ya5u/v7nOGXK0kaFf0cQT0GvK2qVgJHA29OshI4G7iuqg4BruvGJUkaiCkDqqoeqKqvd8P/AGwB9gdOBNZ3s60HThpWkZKk0bN0OjMnWQG8ENgI7FdVD3STHgT228Uya4G1AAceeOBM6xwJK87+7NNOv+/cE+aoEs01+4m0s74/JJFkL+AK4K1V9YPx06qqgJpsuaq6sKpWVdWqsbGxWRUrLVb2E2lnfQVUkt3ohdMnqurKrvmhJMu66cuAbcMpUZI0ivr5FF+Ai4EtVfWhcZOuBtZ0w2uAqwZfniRpVPVzDeoY4DTg9iS3dm2/C5wLXJbkDOA7wCnDKVH98hqWpMVkyoCqqi8D2cXklw+2HEmSeryThCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUnTuhef5tdU33OSpMXEIyhJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTmvzJ96f7afP7zj1hDiuRJM0Xj6AkSU0yoCRJTTKgJElNmvIaVJJLgFcB26rqsK5tH2ADsAK4Dzilqh4eXpn9e7rrV+A1LElaKPo5gloHrJ7QdjZwXVUdAlzXjUuSNDBTBlRVfRH4/oTmE4H13fB64KQB1yVJGnEz/Zj5flX1QDf8ILDfrmZMshZYC3DggQfO8Ok0CFOd/pyKp0eHZ7H1k5l+VWRXy/neG02z/pBEVRVQTzP9wqpaVVWrxsbGZvt00qJkP5F2NtOAeijJMoDu77bBlSRJ0swD6mpgTTe8BrhqMOVIktTTz8fMPwUcC+ybZCvwB8C5wGVJzgC+A5wyzCIlPdWo3Q5s1F6veqYMqKo6dReTXj7gWiRJepJ3kpAkNcmAkiQ1qcmf25A0c4P+LtFsvz8nzZRHUJKkJhlQkqQmGVCSpCZ5DUrSnPKalvrlEZQkqUkGlCSpSQaUJKlJXoOStKB5n77FyyMoSVKTDChJUpMMKElSkxbcNajZfofC72DM3FTbzvP9kgbJIyhJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMW3K2OpJnwNk2j+bMUM7m12WLdFguRR1CSpCYZUJKkJhlQkqQmeQ1KAzOfP2XidYPZ8Wdofmam22Iu34O7qnGx9YNZHUElWZ3kG0nuSXL2oIqSJGnGAZVkCfBR4HhgJXBqkpWDKkySNNpmcwR1FHBPVd1bVT8GLgVOHExZkqRRl6qa2YLJycDqqnpDN34a8OKqesuE+dYCa7vR5wPf2MUq9wV2zKiYuWetw7GQaoWp691RVav7WdE0+kk/z9sSax2OxVbrpH1l6B+SqKoLgQunmi/JpqpaNex6BsFah2Mh1QqDrbfffjLo5x02ax2OUal1Nqf47gcOGDe+vGuTJGnWZhNQNwGHJDkoye7Aa4GrB1OWJGnUzfgUX1U9luQtwOeBJcAlVXXnLGrp6/RGI6x1OBZSrTB/9S6k7WStwzEStc74QxKSJA2TtzqSJDXJgJIkNamJgGr5lklJDkhyfZK7ktyZ5KyufZ8k1ya5u/v7nPmu9QlJliS5Jck13fhBSTZ223dD96GWeZdk7ySfTvL3SbYkeUmr2zXJ/9X9+9+R5FNJ9piP7dpqX7GfDM8o95N5D6gFcMukx4C3VdVK4GjgzV19ZwPXVdUhwHXdeCvOAraMGz8POL+qDgYeBs6Yl6p2dgHw11X1AuBwejU3t12T7A/8B2BVVR1G70NBr2WOt2vjfcV+Mjyj20+qal4fwEuAz48bPwc4Z77repp6rwJ+g943/Zd1bcuAb8x3bV0ty+m9YY8DrgFC71vcSyfb3vNY5z8Dvk33QZ1x7c1tV2B/4LvAPvQ++XoN8Iq53q4Lqa/YTwZW50j3k3k/guJnL+oJW7u25iRZAbwQ2AjsV1UPdJMeBPabp7Im+jDwTuCn3fhzgUeq6rFuvJXtexCwHfjL7jTLRUmeSYPbtaruB/4E+B/AA8D/BG5m7rfrgugr9pOBGul+0kJALQhJ9gKuAN5aVT8YP616uwbz/nn9JK8CtlXVzfNdSx+WAi8C/ryqXgj8kAmnKRrars+hdyPkg4BfAp4J9HWPvVFjPxm4ke4nLQRU87dMSrIbvU73iaq6smt+KMmybvoyYNt81TfOMcCrk9xH7+7yx9E7f713kie+lN3K9t0KbK2qjd34p+l1xBa3678Gvl1V26vqJ8CV9Lb1XG/XpvuK/WQoRrqftBBQTd8yKUmAi4EtVfWhcZOuBtZ0w2vonXOfV1V1TlUtr6oV9LbjF6rqdcD1wMndbK3U+iDw3STP75peDtxFg9uV3imLo5M8o3s/PFHrXG/XZvuK/WQ4Rr6fzPeFte7C2SuBbwLfAt493/VMqO1l9A6fbwNu7R6vpHfO+jrgbuD/BfaZ71on1H0scE03/DzgRuAe4HLg5+e7vq6uI4BN3bb9K+A5rW5X4H3A3wN3AB8Hfn4+tmurfcV+MtQaR7afeKsjSVKTWjjFJ0nSTgwoSVKTDChJUpMMKElSkwwoSVKTDKhFIMlJSSrJC+a7Fqll9pWFxYBaHE4Fvtz9lbRr9pUFxIBa4Lp7n72M3i3sX9u1/VyS/9r9fsy1ST6X5ORu2pFJ/jbJzUk+/8TtUqTFzr6y8BhQC9+J9H4r5pvA95IcCfwfwAp6vxl0Gr1b3D9xr7Q/A06uqiOBS4APzEfR0jywrywwS6eeRY07ld6NLqF348tT6f27Xl5VPwUeTHJ9N/35wGHAtb1bZbGE3m3xpVFgX1lgDKgFLMk+9O7E/C+TFL1OVMBndrUIcGdVvWSOSpSaYF9ZmDzFt7CdDHy8qv55Va2oqgPo/frm94F/051f34/eDTGh9yucY0mePI2R5ND5KFyaY/aVBciAWthOZec9wCuAX6T3OzJ3Af8P8HXgf1bVj+l11POSbKZ3x+mXzl250ryxryxA3s18kUqyV1U9muS59G51f0z1fltG0jj2lXZ5DWrxuibJ3sDuwB/a4aRdsq80yiMoSVKTvAYlSWqSASVJapIBJUlqkgElSWqSASVJatL/D+TmcFB4kUiXAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x648 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tEYnNl1KdgMV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "outputId": "1c835fa6-e336-45df-d80b-184bfb6103ab"
      },
      "source": [
        "grid = sns.FacetGrid(train_df, row='Embarked')\n",
        "grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex')\n",
        "grid.add_legend()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/axisgrid.py:723: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
            "  warnings.warn(warning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/axisgrid.py:728: UserWarning: Using the pointplot function without specifying `hue_order` is likely to produce an incorrect plot.\n",
            "  warnings.warn(warning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f4718199ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 286.5x648 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NRmL_2wjeSKD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "outputId": "e3aaec34-5942-4eef-f1c1-4439b9b7adff"
      },
      "source": [
        "grid = sns.FacetGrid(train_df, row='Embarked', col='Survived')\n",
        "grid.map(plt.hist, 'Age', bins=20)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f4717f946a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JSJKaNN/LzCU1zkui526QfaY9xzMoSVKTDChJUpMMKElSk/wMSkOzkLvQe2mupKk8g5IkNcmAkiQ1yS4+STPysmstJs+gJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTfIyc+0RC71c2TtNSOPHMyhJUpMMKElSk5ZcF9+ou4qkcWJ72POG8UWS4/JllJ5BSZKaZEBJkppkQEmSmrTkPoOSpuNl6FpO/Gyw4xmUJKlJBpQkqUkL6uJLsh74ALAC+GhVnTKUqqQhswtQGo1Rtq15n0ElWQH8KfAq4BDgxCSHzLsSSZImWUgX3+HAbVV1e1X9M3AOcOxwypIkjbuFBNQBwHcnDd/Rj5MkacFGfpl5ko3Axn7wB0lumWHWJwP3jrqeIbHW0Vi0WnPqvBabrd5Lqmr9QNsfvJ0Mst2WWOtozFrrPN/TQ5dTB9qv07aVVNX8Npq8EHhnVb2yH34HQFW9d57r21JV6+ZVzB5mraOxlGqFxat3Ke0nax2Ncal1IV18VwMHJzkwyd7A64CLFrA+SZJ+bN5dfFW1I8mbgUvpLjM/q6puHFplkqSxtqDPoKrqs8Bnh1TLGUNaz55graOxlGqFxat3Ke0nax2Nsah13p9BSZI0St7qSJLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1KRlEVBJHk5y3aTHyXNY9sgkFy9w+1ckmdcXciU5O8nxC9l+v55nJPlskluTXJPkvCSrpplv6wzLvyDJlf3+uznJOxda06R1/82Q1jOsfbWh30+3JtkwjNqWAtuJ7WSO67kkyQML/b0vxMi/8n0P+VFVHboYG06yYjG2O6WGfYDPAG+tqv+vH3ckMAHcM+BqNgEnVNXX+tf0zDlsP3R3xn9kuulV9R8GXdeoJdkf+ENgHVDAV5NcVFX3L25le4TtxHYyF6cBPwX86mIVsCzOoGaSZGuS9/ZHO1uSPC/JpUm+meTXJs36+CSfSXJLkg8neVS//If65W5M8q4p6z01yTXAL00a/6j+6OXdSVYkOS3J1UmuT/Kr/TxJ8sF+W58HnjKEl/rLwFd2NjqAqrqiqm6YwzqeAtzVL/twVd3U1/vOJG+b9BpvSLK2f9yS5P8FbgD+c5LTJs33xiQf7J//oP95TpKjJ81zdpLj9/C+eiVwWVXd14fSZcD6Iax3ybKd2E6mU1WbgQeHsa75Wi5nUPsmuW7S8Hur6tz++Xeq6tAkpwNnA0cA+9C9WT7cz3M4cAjwbeAS4BeA84E/qKr7+iOlzUmeU1XX98t8r6qeB9A34pXAx4Ebquo9STYC36+q5yd5NPDlJJ8DDqM76joEWAXcBJw19QUl+V3g9dO81i9W1W9OGfds4Kuz76bdOh24JckVdPtgU1X90yzLHAxsqKq/TTIBfAX43X7aa4H3TJn/XOAE4DNJ9gZeCvw6cBJ7bl8dAHx30vAd/bhxYDuxnQy6r5qwXAJqd10XF/U/vw48tqoeBB5M8lCS/fppV1XV7QBJPgG8iK7hndA3oJXAaro3wM6Gt7Nh7/QR4Lyq2vlmewXwnPykL/gJdG/UFwOfqKqHgb9P8oXpiq6q0+hOsfeIqvqjJB+nq/uXgROBI2dZ7NtV9bf98tuT3J7kBcCtwLOAL0+Z/6+AD/SNaz1dw/hRkiW1r5Yw28kC2U72rOUSULvzUP/zkUnPdw7vfP1Tv1a4khwIvA14flXdn+RsuiPKnX44ZZm/AV6S5E/6I6oAv1FVl06eKcmrByl6jkc7NwL/cZD17k5VfRP4UJI/B7YneRKwg3/dFby7fXAO3ZHfN4ALasrXNVfVP/VHnq+kO3I8p5+0J/fVnfzrPyhrgCsG2c4yZzsZ0Ji0kzZU1ZJ/AD+YYfxW4Mn98zcCH5w6je6P1Y+AA+neYJcCvwg8F/haP24V3Yeob5y63n74CroP3d9KdyS6EtgI/A9gr36eZwCPoesWuRRYQXe0eT9w/AJf/77AbcDRk8a9GHj2dPtkhnUcTfcBLsC/A+7ta/zfgHP68c8DHgbW9o8bpqzjicA3gcuBw6f7/fTbuYCum23vftye3Ff7A9/qa31i/3z/xX4P205sJy21k0l1HAlcvFjv2eVyBjW1b/2Sqhr4ElrgauCDwEF0b5oLquqRJNfSHeV8l11Pw3dRVe9L8gTgY3RHKmuBa5IE2A4cR/emO4qun/g7dP3RC1Ld6f8xwPuTvB/4F7oulrfMYTVvAE5P8o90R4Ovr6qHk3wK+N+T3AhcCfzdbuq4P8nNwCFVddUMs32Obv9cWFX/3I/7KHtuX92X5I/pfucAf1RV9y10vUuE7cR2MrAkf03XBfnYJHcAJ9WUs7dR23kkoDGRZGtVrV3sOqSW2U7asKwvM5ckLV0G1Ph5/2IXIC0BtpMGDNTFl+62Hw/SffC3o6rWpfuP/HPp+kS30v139Tj8N74kaQ+YyxnUS6rq0KraeS+tk4HNVXUwsLkf3q3169cX3aWqPnyMw2NebCc+xvAxrYV08R1Ld18q+p/HzbbAvffeu4DNSePBdiJ1Bg2oAj6X5Kv9f4wDrKqqu/rnd9P9D8QukmxMd5+uLdu3b19gudLyZDuRdjVoQL2ouvtpvQp4U5IXT55Y3QdZ056mVdUZVbWuqtZNTEwsrFppmbKdSLsaKKCq6s7+5za6fwo7HLgnyWqA/ue2URUpSRo/swZUksckedzO53Q3SbyB7lYlG/rZNgAXjqpISdL4GeRWR6uAC7o7a7AS+MuquiTJ1cB5SU6iu/3+CaMrU5I0bmYNqOpur//cacZ/j+57SiRJGjrvJCFJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJatLAAZVkRZJrk1zcDx+Y5MoktyU5N8neoytTkjRu5nIG9Rbg5knDpwKnV9VBwP3AScMsTJI03gYKqCRrgKOBj/bDAY4Czu9n2QQcN4oCJUnjadAzqPcDbwce6YefBDxQVTv64TuAA6ZbMMnGJFuSbNm+ffuCipWWK9uJtKtZAyrJMcC2qvrqfDZQVWdU1bqqWjcxMTGfVUjLnu1E2tXKAeY5AnhNklcD+wCPBz4A7JdkZX8WtQa4c3RlSpLGzaxnUFX1jqpaU1VrgdcBX6iq1wOXA8f3s20ALhxZlZKksbOQ/4P6PeCtSW6j+0zqzOGUJEnSYF18P1ZVVwBX9M9vBw4ffkmSJHknCUlSowwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSk2YNqCT7JLkqydeS3JjkXf34A5NcmeS2JOcm2Xv05UqSxsUgZ1APAUdV1XOBQ4H1SV4AnAqcXlUHAfcDJ42uTEnSuJk1oKrzg35wr/5RwFHA+f34TcBxI6lQkjSWBvoMKsmKJNcB24DLgG8CD1TVjn6WO4ADRlOiJGkcDRRQVfVwVR0KrAEOB5416AaSbEyyJcmW7du3z7NMaXmznUi7mtNVfFX1AHA58EJgvyQr+0lrgDtnWOaMqlpXVesmJiYWVKy0XNlOpF0NchXfRJL9+uf7Ai8HbqYLquP72TYAF46qSEnS+Fk5+yysBjYlWUEXaOdV1cVJbgLOSfJu4FrgzBHWKUkaM7MGVFVdDxw2zfjb6T6PkiRp6LyThCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJswZUkqcluTzJTUluTPKWfvz+SS5Lcmv/84mjL1eSNC4GOYPaAfxOVR0CvAB4U5JDgJOBzVV1MLC5H5YkaShmDaiququqrumfPwjcDBwAHAts6mfbBBw3qiIlSeNnTp9BJVkLHAZcCayqqrv6SXcDq2ZYZmOSLUm2bN++fQGlSsuX7UTa1cABleSxwKeA36qqf5g8raoKqOmWq6ozqmpdVa2bmJhYULHScmU7kXY1UEAl2YsunD5eVZ/uR9+TZHU/fTWwbTQlSpLG0SBX8QU4E7i5qt43adJFwIb++QbgwuGXJ0kaVysHmOcI4A3A15Nc14/7feAU4LwkJwHfBk4YTYmSpHE0a0BV1ZeAzDD5pcMtR5KkjneSkCQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1aeVsMyQ5CzgG2FZVz+7H7Q+cC6wFtgInVNX9oytT0lK39uTPTDt+6ylH7+FKtFQMcgZ1NrB+yriTgc1VdTCwuR+WJGloZg2oqvoicN+U0ccCm/rnm4DjhlyXJGnMzfczqFVVdVf//G5g1UwzJtmYZEuSLdu3b5/n5qTlzXYi7WrBF0lUVQG1m+lnVNW6qlo3MTGx0M1Jy5LtRNrVfAPqniSrAfqf24ZXkiRJ8w+oi4AN/fMNwIXDKUeSpM6sAZXkE8BXgGcmuSPJScApwMuT3Aq8rB+WJGloZv0/qKo6cYZJLx1yLZIk/Zh3kpAkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1adar+DQa3tlZknbPMyhJUpMMKElSk+ziW6CZuurA7jqNzmJ3EQ9z+7trQzOxbY0Hz6AkSU0yoCRJTTKgJElN8jOoEZpP37rUkqX2HvYz4eXFMyhJUpMMKElSk+zim6SF7oH5dqnYfaEWtNwluCcuZ2/hb8hy4hmUJKlJBpQkqUl28Y2x+XRHzLcLY7HvfKDlpeWuxJnMpw2Me7vxDEqS1CQDSpLUJANKktSkZfsZ1FLsox6FcdsP436Z77i//mHbE+1n3NroXHgGJUlqkgElSWrSsu3iG7bWT8OHXd+4vd5x5r5UqxZ0BpVkfZJbktyW5ORhFSVJ0rwDKskK4E+BVwGHACcmOWRYhUmSxttCzqAOB26rqtur6p+Bc4Bjh1OWJGncparmt2ByPLC+qn6lH34D8O+r6s1T5tsIbOwHnwncMsMqnwzcO69i9jxrHY2lVCvMXu+9VbV+kBXNoZ0Mst2WWOtoLLdap20rI79IoqrOAM6Ybb4kW6pq3ajrGQZrHY2lVCsMt95B28mwtztq1joa41LrQrr47gSeNml4TT9OkqQFW0hAXQ0cnOTAJHsDrwMuGk5ZkqRxN+8uvqrakeTNwKXACuCsqrpxAbUM1L3RCGsdjaVUKyxevUtpP1nraIxFrfO+SEKSpFHyVkeSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmLfmASvJwkusmPU6ew7JHJrl4gdu/Ism8vowrydn9NxMvSJKfTfKFJLck+WaSdyWZ9nebZOsM449Jcm2SryW5KcmvLrSufr1PTXL+kNY17309ZT3vSHJbv79eOYzalgLbim1ljut4UpLLk/wgyQeHUddcjfwbdfeAH1XVoYux4SQrFmO7U2rYl+57uH69qj6X5KeATwFvAU4fcB170d0S//CquiPJo4G1c6hhZVXtmG5aVf09sOA/LMOS5BC67y77WeCpwOeTPKOqHl7cyvYI24ptZS7+CfjPwLP7xx635M+gZpJka5L39keKW5I8L8ml/VHTr02a9fFJPtMfUX1459FUkg/1y92Y5F1T1ntqkmuAX5o0/lH9Ud67k6xIclqSq5Ncv/MIK50P9tv6PPCUIbzUXwa+XFWfA6iqfwTeDPzuHNbxOLqDlVPL7jIAABIESURBVO/163ioqm7pa/5XR65JftD/PDLJXye5CLgpySlJ3jRpvncmeVuStUlu6Mf9bZKfnTTPFUnWJXlMkrOSXNUfmR7bT983yTlJbk5yAbDvfHbQFMcC5/Sv8VvAbcDhQ1jvkmVbsa1Mp6p+WFVfoguqRbEczqD2TXLdpOH3VtW5/fPvVNWhSU4HzgaOAPYBbgA+3M9zOHAI8G3gEuAXgPOBP6iq+/ojv81JnlNV1/fLfK+qngfQN+CVwMeBG6rqPUk2At+vquf3R1hfTvI54DDgmf32VgE3AWdNfUFJfhd4/TSv9YtV9ZtTxv0s8NXJI6rqm/0bdr+qemCmHTdp/vv6xvPtJJuBi4FPVNUjsyz6PODZVfWtJIcB7wf+tJ92AvBKui+z3OncfvwfJlkNrK6qLUn+C/CFqvo/kuwHXNX/UfpV4B+r6t8leQ5wzXRF9L/fl0wz6ZyqOmXKuAOAv500fEc/bhzYVmwrc2kri245BNTuui12fgX914HHVtWDwINJHup/uQBXVdXtAEk+AbyIrtGd0DeelcBquoays9HtbNQ7fQQ4r6re0w+/AnjOpKOpJwAHAy+mezM/DPx9ki9MV3RVnQacNsBrH5qq+pUkPwe8DHgb8HLgjbMsdlV/FkJVXZvkKUmeCkwA91fVd5OsnTT/ecDngD+ka3w7+9tfAbwmydv64X2Ap9Ptr//ar//6JNczjar67Tm81HFmWxkC28qesxwCance6n8+Mun5zuGdr33qVwpXkgPp3njPr6r7k5xN90bY6YdTlvkb4CVJ/qSq/gkI8BtVdenkmZK8epCi53hUeBPdm3Py8j9Dd+Q66xHhZFX1deDrST4GfIuu0e2g7wruu3T2nrTI1P3wSbo+9J9m1z9MVNWdSb7XH+G9FtjZfRTgF3d2lUx6HQPVPcejwjuBp00aXtOPG3e2lTkYk7ay6JbtZ1BzcHiSA/s31GuBLwGPp3tDfT/JKuBVs6zjTOCzwHlJVgKXAr+e7gNVkjwjyWOALwKvTdfvvprp3yhU1WlVdeg0j6kNDrrukhcleVm/rX3pjqT+cNAdkOSxSY6cNOpQum4cgK3Az/fPXwPstZtVnUt3AcLxdA1wpnneDjxhUjfQpcBvpG9lfRcIdPvrl/txzwaeM90Kq+q3Z9hf0zW4i4DXJXl0/8f1YOCq3bwm/YRtZbzayqJbDmdQU/vVL6mqgS+fBa4GPggcBFwOXFBVjyS5FvgG8F3gy7OtpKrel+QJwMfojujWAtf0b6TtwHHABcBRdEdy3wG+Moc6Z9ruj5K8BvhvSf6M7vOUd1fVx+ewmgBvT/IR4Ed0f3De2E/7c+DCJF+j+9xh6pHg5FpuTPI44M6qumuG2c4HPgD88aRxf0zXJ399/8fvW8AxwIeA/57kZuBmpnx+MB99jefR/Q52AG+q8biCD2wrtpU5Snep/eOBvZMcB7yiqm4axroH2n7V1LN2LWX9m+h9wEuq6tvTTN9aVWv3eGFSY2wr7TOgxoyNThqMbWXx+RnU+Hn/YhcgLRG2lUU20BlU3w/5IPAwsKOq1iXZn+5DvLV0Hw6eUFX3j6xSSdJYmcsZ1Ev6qz123t/pZGBzVR0MbO6HJUkaioV08R0LbOqfb6K78ma31q9fX3T/S+HDxzg85sV24mMMH9Ma9DLzAj6XpICPVNUZwKpJl0feTXc7kl30/2G+EeDpT3/6gJuTxovtRNrVoGdQL+rvp/Uq4E1J/tV/Y1f3Qda0KVhVZ1TVuqpaNzExsbBqpWXKdiLtaqCAqqo7+5/b6P6B7nDgnv4/vOl/bhtVkZKk8TNrQKW7vfvjdj6nu1nhDXS3jNnQz7YBuHBURUqSxs8gn0GtAi7ob/20EvjLqrokydV099M6ie5eVCeMrkxJ0riZNaD62+s/d5rx3wNeOoqiJEnyThKSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYNHFBJViS5NsnF/fCBSa5McluSc5PsPboyJUnjZi5nUG8Bbp40fCpwelUdBNwPnDTMwiRJ422ggEqyBjga+Gg/HOAo4Px+lk3AcaMoUJI0ngY9g3o/8HbgkX74ScADVbWjH74DOGC6BZNsTLIlyZbt27cvqFhpubKdSLuaNaCSHANsq6qvzmcDVXVGVa2rqnUTExPzWYW07NlOpF2tHGCeI4DXJHk1sA/weOADwH5JVvZnUWuAO0dXpiRp3Mx6BlVV76iqNVW1Fngd8IWqej1wOXB8P9sG4MKRVSlJGjsL+T+o3wPemuQ2us+kzhxOSZIkDdbF92NVdQVwRf/8duDw4ZckSZJ3kpAkNcqAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNWnWgEqyT5KrknwtyY1J3tWPPzDJlUluS3Jukr1HX64kaVwMcgb1EHBUVT0XOBRYn+QFwKnA6VV1EHA/cNLoypQkjZtZA6o6P+gH9+ofBRwFnN+P3wQcN5IKJUljaaDPoJKsSHIdsA24DPgm8EBV7ehnuQM4YDQlSpLG0UABVVUPV9WhwBrgcOBZg24gycYkW5Js2b59+zzLlJY324m0qzldxVdVDwCXAy8E9kuysp+0BrhzhmXOqKp1VbVuYmJiQcVKy5XtRNrVIFfxTSTZr3++L/By4Ga6oDq+n20DcOGoipQkjZ+Vs8/CamBTkhV0gXZeVV2c5CbgnCTvBq4FzhxhnZKkMTNrQFXV9cBh04y/ne7zKEmShs47SUiSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaNGtAJXlaksuT3JTkxiRv6cfvn+SyJLf2P584+nIlSeNikDOoHcDvVNUhwAuANyU5BDgZ2FxVBwOb+2FJkoZi1oCqqruq6pr++YPAzcABwLHApn62TcBxoypSkjR+5vQZVJK1wGHAlcCqqrqrn3Q3sGqGZTYm2ZJky/bt2xdQqrR82U6kXQ0cUEkeC3wK+K2q+ofJ06qqgJpuuao6o6rWVdW6iYmJBRUrLVe2E2lXAwVUkr3owunjVfXpfvQ9SVb301cD20ZToiRpHA1yFV+AM4Gbq+p9kyZdBGzon28ALhx+eZKkcbVygHmOAN4AfD3Jdf243wdOAc5LchLwbeCE0ZQoSRpHswZUVX0JyAyTXzrcciRJ6ngnCUlSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSk2YNqCRnJdmW5IZJ4/ZPclmSW/ufTxxtmZKkcTPIGdTZwPop404GNlfVwcDmfliSpKGZNaCq6ovAfVNGHwts6p9vAo4bcl2SpDE338+gVlXVXf3zu4FVM82YZGOSLUm2bN++fZ6bk5Y324m0qwVfJFFVBdRupp9RVeuqat3ExMRCNyctS7YTaVfzDah7kqwG6H9uG15JkiTNP6AuAjb0zzcAFw6nHEmSOoNcZv4J4CvAM5PckeQk4BTg5UluBV7WD0uSNDQrZ5uhqk6cYdJLh1yLJEk/5p0kJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTZr1+6C0Z6w9+TMzTtt6ytF7sBJJaoNnUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCY1fZn57i69ns18L81ejG2Okpeva1R2vrd8H2lUPIOSJDXJgJIkNanpLr6lZjG60xbSJTnbsotR8+62udy6X5eq2X4PM3X9LaRLcOo2h7lutWtBZ1BJ1ie5JcltSU4eVlGSJM07oJKsAP4UeBVwCHBikkOGVZgkabwt5AzqcOC2qrq9qv4ZOAc4djhlSZLGXapqfgsmxwPrq+pX+uE3AP++qt48Zb6NwMZ+8JnALTOs8snAvfMqZs+z1tFYSrXC7PXeW1XrB1nRHNrJINttibWOxnKrddq2MvKLJKrqDOCM2eZLsqWq1o26nmGw1tFYSrXCcOsdtJ0Me7ujZq2jMS61LqSL707gaZOG1/TjJElasIUE1NXAwUkOTLI38DrgouGUJUkad/Pu4quqHUneDFwKrADOqqobF1DLQN0bjbDW0VhKtcLi1buU9pO1jsZY1DrviyQkSRolb3UkSWqSASVJalITAdXyLZOSPC3J5UluSnJjkrf04/dPclmSW/ufT1zsWndKsiLJtUku7ocPTHJlv3/P7S9qWXRJ9ktyfpJvJLk5yQtb3a9Jfrv//d+Q5BNJ9lmM/dpqW7GdjM44t5NFD6glcMukHcDvVNUhwAuAN/X1nQxsrqqDgc39cCveAtw8afhU4PSqOgi4HzhpUara1QeAS6rqWcBz6Wpubr8mOQD4TWBdVT2b7qKg17GH92vjbcV2Mjrj206qalEfwAuBSycNvwN4x2LXtZt6LwReTvef/qv7cauBWxa7tr6WNXRv2KOAi4HQ/Rf3yun29yLW+QTgW/QX6kwa39x+BQ4AvgvsT3fl68XAK/f0fl1KbcV2MrQ6x7qdLPoZFD95UTvd0Y9rTpK1wGHAlcCqqrqrn3Q3sGqRyprq/cDbgUf64ScBD1TVjn64lf17ILAd+O99N8tHkzyGBvdrVd0J/D/Ad4C7gO8DX2XP79cl0VZsJ0M11u2khYBaEpI8FvgU8FtV9Q+Tp1V3aLDo1+snOQbYVlVfXexaBrASeB7woao6DPghU7opGtqvT6S7EfKBwFOBxwAD3WNv3NhOhm6s20kLAdX8LZOS7EXX6D5eVZ/uR9+TZHU/fTWwbbHqm+QI4DVJttLdXf4ouv7r/ZLs/KfsVvbvHcAdVXVlP3w+XUNscb++DPhWVW2vqn8BPk23r/f0fm26rdhORmKs20kLAdX0LZOSBDgTuLmq3jdp0kXAhv75Bro+90VVVe+oqjVVtZZuP36hql4PXA4c38/WSq13A99N8sx+1EuBm2hwv9J1WbwgyU/174edte7p/dpsW7GdjMbYt5PF/mCt/+Ds1cDfAd8E/mCx65lS24voTp+vB67rH6+m67PeDNwKfB7Yf7FrnVL3kcDF/fOfAa4CbgM+CTx6sevr6zoU2NLv2/8BPLHV/Qq8C/gGcAPwMeDRi7FfW20rtpOR1ji27cRbHUmSmtRCF58kSbswoCRJTTKgJElNMqAkSU0yoCRJTTKgloEkxyWpJM9a7FqkltlWlhYDank4EfhS/1PSzGwrS4gBtcT19z57Ed0t7F/Xj3tUkj/rvz/msiSfTXJ8P+3nk/zPJF9NcunO26VIy51tZekxoJa+Y+m+K+bvgO8l+XngF4C1dN8Z9Aa6W9zvvFfafwOOr6qfB84C3rMYRUuLwLayxKycfRY17kS6G11Cd+PLE+l+r5+sqkeAu5Nc3k9/JvBs4LLuVlmsoLstvjQObCtLjAG1hCXZn+5OzD+XpOgaUQEXzLQIcGNVvXAPlSg1wbayNNnFt7QdD3ysqv5NVa2tqqfRffvmfcAv9v3rq+huiAndt3BOJPlxN0aSn12MwqU9zLayBBlQS9uJ7HoE+Cngp+m+R+Ym4C+Aa4DvV9U/0zXUU5N8je6O0/9hz5UrLRrbyhLk3cyXqSSPraofJHkS3a3uj6juu2UkTWJbaZefQS1fFyfZD9gb+GMbnDQj20qjPIOSJDXJz6AkSU0yoCRJTTKgJElNMqAkSU0yoCRJTfr/AQCVx15hxtlDAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x648 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J10UIDfTfGWT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "outputId": "d9de4803-3386-446b-e128-9d19f6be5f82"
      },
      "source": [
        "grid = sns.FacetGrid(train_df, row='Embarked', col='Survived')\n",
        "grid.map(sns.barplot, 'Sex', 'Fare')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/axisgrid.py:723: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
            "  warnings.warn(warning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f4717ccd780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x648 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0wlLFmX_fYOc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "036e1189-29dd-499c-c10f-3a40e8822d14"
      },
      "source": [
        "train_df.isnull().sum()"
      ],
      "execution_count": 22,
      "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": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YeodiaY7f6LM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "be8ca5d6-dc6b-4b6e-9cb6-3ca77fe844b5"
      },
      "source": [
        "test_df.isnull().sum()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age             86\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             1\n",
              "Cabin          327\n",
              "Embarked         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m-tApurYgAOc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "267aae17-f7c1-4754-b81e-83fe9c588030"
      },
      "source": [
        "train_df['Name']"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                                Braund, Mr. Owen Harris\n",
              "1      Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
              "2                                 Heikkinen, Miss. Laina\n",
              "3           Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
              "4                               Allen, Mr. William Henry\n",
              "                             ...                        \n",
              "886                                Montvila, Rev. Juozas\n",
              "887                         Graham, Miss. Margaret Edith\n",
              "888             Johnston, Miss. Catherine Helen \"Carrie\"\n",
              "889                                Behr, Mr. Karl Howell\n",
              "890                                  Dooley, Mr. Patrick\n",
              "Name: Name, Length: 891, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r46JYOINgSq9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combine =  [train_df, test_df]"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mhhMcchphKIM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "ca6c73ef-f9eb-4dd5-d249-08c6dcca0ec7"
      },
      "source": [
        "combine"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              " ..           ...       ...     ...  ...      ...   ...       ...\n",
              " 886          887         0       2  ...  13.0000   NaN         S\n",
              " 887          888         1       1  ...  30.0000   B42         S\n",
              " 888          889         0       3  ...  23.4500   NaN         S\n",
              " 889          890         1       1  ...  30.0000  C148         C\n",
              " 890          891         0       3  ...   7.7500   NaN         Q\n",
              " \n",
              " [891 rows x 12 columns],      PassengerId  Pclass  ... Cabin Embarked\n",
              " 0            892       3  ...   NaN        Q\n",
              " 1            893       3  ...   NaN        S\n",
              " 2            894       2  ...   NaN        Q\n",
              " 3            895       3  ...   NaN        S\n",
              " 4            896       3  ...   NaN        S\n",
              " ..           ...     ...  ...   ...      ...\n",
              " 413         1305       3  ...   NaN        S\n",
              " 414         1306       1  ...  C105        C\n",
              " 415         1307       3  ...   NaN        S\n",
              " 416         1308       3  ...   NaN        S\n",
              " 417         1309       3  ...   NaN        C\n",
              " \n",
              " [418 rows x 11 columns]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45DkJTbqhLNk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Title'] = dataset['Name'].str.extract((' ([A-Za-z]+)\\. '))"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "orypJ0q9iAlM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "781b3552-95c8-4504-dcac-0e4e12daf104"
      },
      "source": [
        "train_df['Title'].unique()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Mr', 'Mrs', 'Miss', 'Master', 'Don', 'Rev', 'Dr', 'Mme', 'Ms',\n",
              "       'Major', 'Lady', 'Sir', 'Mlle', 'Col', 'Capt', 'Countess',\n",
              "       'Jonkheer'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sD1pYXOmiWOE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "7d4091b7-62b8-4879-802c-c12621d15f4f"
      },
      "source": [
        "pd.crosstab(train_df['Sex'], train_df['Title'])"
      ],
      "execution_count": 29,
      "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>Title</th>\n",
              "      <th>Capt</th>\n",
              "      <th>Col</th>\n",
              "      <th>Countess</th>\n",
              "      <th>Don</th>\n",
              "      <th>Dr</th>\n",
              "      <th>Jonkheer</th>\n",
              "      <th>Lady</th>\n",
              "      <th>Major</th>\n",
              "      <th>Master</th>\n",
              "      <th>Miss</th>\n",
              "      <th>Mlle</th>\n",
              "      <th>Mme</th>\n",
              "      <th>Mr</th>\n",
              "      <th>Mrs</th>\n",
              "      <th>Ms</th>\n",
              "      <th>Rev</th>\n",
              "      <th>Sir</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>182</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>125</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>517</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Title   Capt  Col  Countess  Don  Dr  Jonkheer  ...  Mme   Mr  Mrs  Ms  Rev  Sir\n",
              "Sex                                             ...                             \n",
              "female     0    0         1    0   1         0  ...    1    0  125   1    0    0\n",
              "male       1    2         0    1   6         1  ...    0  517    0   0    6    1\n",
              "\n",
              "[2 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uWk8YM0HjBt0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Title'] = dataset['Title'].replace(['Capt', 'Lady', 'Countess', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Other')\n",
        "  dataset['Title'] = dataset['Title'].replace('Mile', 'Miss')\n",
        "  dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
        "  dataset['Title'] = dataset['Title'].replace('Mme', 'Miss')"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GcDBsxKvmwMH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "85c7f39b-ee22-4682-bdd2-9596ee26b4d6"
      },
      "source": [
        "train_df['Title'].unique()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Mr', 'Mrs', 'Miss', 'Master', 'Other', 'Mlle'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LJ3FXXewo4S3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "77e42c7b-5f31-4798-9de3-2f9fbbd41b3a"
      },
      "source": [
        "train_df[['Title', 'Survived']].groupby('Title', as_index=False).mean()"
      ],
      "execution_count": 32,
      "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>Title</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Master</td>\n",
              "      <td>0.575000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Miss</td>\n",
              "      <td>0.701087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mlle</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Mr</td>\n",
              "      <td>0.156673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Mrs</td>\n",
              "      <td>0.792000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Other</td>\n",
              "      <td>0.347826</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Title  Survived\n",
              "0  Master  0.575000\n",
              "1    Miss  0.701087\n",
              "2    Mlle  1.000000\n",
              "3      Mr  0.156673\n",
              "4     Mrs  0.792000\n",
              "5   Other  0.347826"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OnIKGo_nqSz9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "990119fa-9c08-4c0a-acad-349fedcaaa01"
      },
      "source": [
        "train_df['Title']"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         Mr\n",
              "1        Mrs\n",
              "2       Miss\n",
              "3        Mrs\n",
              "4         Mr\n",
              "       ...  \n",
              "886    Other\n",
              "887     Miss\n",
              "888     Miss\n",
              "889       Mr\n",
              "890       Mr\n",
              "Name: Title, Length: 891, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K78Vk4pBrBN9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "title_column={'Mr': 1, 'Miss': 2, 'Mrs': 3, 'Master': 4, 'Other': 5}\n",
        "for dataset in combine:\n",
        "  dataset['Title'] = dataset['Title'].map(title_column)\n",
        "  dataset['Title'] = dataset['Title'].fillna(0)\n",
        "  dataset['Title'] = dataset['Title'].astype(int)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bwIxBFI9r_5k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "outputId": "baa6c28b-2c86-4457-f3c1-addb84442e18"
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 35,
      "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",
              "      <th>Title</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>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>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",
              "      <td>3</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>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>2</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",
              "      <td>3</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>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ... Cabin Embarked  Title\n",
              "0            1         0       3  ...   NaN        S      1\n",
              "1            2         1       1  ...   C85        C      3\n",
              "2            3         1       3  ...   NaN        S      2\n",
              "3            4         1       1  ...  C123        S      3\n",
              "4            5         0       3  ...   NaN        S      1\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ltvEO5fksCSN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1, inplace=True)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vBIHk2uStjwA",
        "colab_type": "code",
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        "outputId": "6142def2-a922-4bcf-f4ee-f0e7ede36dcf"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<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",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
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              "      <td>22.0</td>\n",
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              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
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              "      <td>23.4500</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C</td>\n",
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              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
              "0           0       3    male  22.0      1      0   7.2500        S      1\n",
              "1           1       1  female  38.0      1      0  71.2833        C      3\n",
              "2           1       3  female  26.0      0      0   7.9250        S      2\n",
              "3           1       1  female  35.0      1      0  53.1000        S      3\n",
              "4           0       3    male  35.0      0      0   8.0500        S      1\n",
              "..        ...     ...     ...   ...    ...    ...      ...      ...    ...\n",
              "886         0       2    male  27.0      0      0  13.0000        S      5\n",
              "887         1       1  female  19.0      0      0  30.0000        S      2\n",
              "888         0       3  female   NaN      1      2  23.4500        S      2\n",
              "889         1       1    male  26.0      0      0  30.0000        C      1\n",
              "890         0       3    male  32.0      0      0   7.7500        Q      1\n",
              "\n",
              "[891 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LrbZzpdZtmTs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1, inplace=True)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wB4v4meetoVM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "2c243fee-54aa-4b50-f34d-7ccd4c20b66c"
      },
      "source": [
        "test_df"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>7.8292</td>\n",
              "      <td>Q</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
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              "      <td>2</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>Q</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>108.9000</td>\n",
              "      <td>C</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>38.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>22.3583</td>\n",
              "      <td>C</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>418 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pclass     Sex   Age  SibSp  Parch      Fare Embarked  Title\n",
              "0         3    male  34.5      0      0    7.8292        Q      1\n",
              "1         3  female  47.0      1      0    7.0000        S      3\n",
              "2         2    male  62.0      0      0    9.6875        Q      1\n",
              "3         3    male  27.0      0      0    8.6625        S      1\n",
              "4         3  female  22.0      1      1   12.2875        S      3\n",
              "..      ...     ...   ...    ...    ...       ...      ...    ...\n",
              "413       3    male   NaN      0      0    8.0500        S      1\n",
              "414       1  female  39.0      0      0  108.9000        C      5\n",
              "415       3    male  38.5      0      0    7.2500        S      1\n",
              "416       3    male   NaN      0      0    8.0500        S      1\n",
              "417       3    male   NaN      1      1   22.3583        C      4\n",
              "\n",
              "[418 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eKFHt5Bdtprs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combine = [train_df, test_df]"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R6CnT_3ot9nk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Sex'] = dataset['Sex'].replace({'male': 0, 'female': 1})"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gkph-65nuQuk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "e34b09fb-fe7c-4185-a13f-8e2e6cf7b5a5"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
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              "      <th>Pclass</th>\n",
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              "      <th>2</th>\n",
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              "      <th>4</th>\n",
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              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>887</th>\n",
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              "      <th>889</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
              "0           0       3    0  22.0      1      0   7.2500        S      1\n",
              "1           1       1    1  38.0      1      0  71.2833        C      3\n",
              "2           1       3    1  26.0      0      0   7.9250        S      2\n",
              "3           1       1    1  35.0      1      0  53.1000        S      3\n",
              "4           0       3    0  35.0      0      0   8.0500        S      1\n",
              "..        ...     ...  ...   ...    ...    ...      ...      ...    ...\n",
              "886         0       2    0  27.0      0      0  13.0000        S      5\n",
              "887         1       1    1  19.0      0      0  30.0000        S      2\n",
              "888         0       3    1   NaN      1      2  23.4500        S      2\n",
              "889         1       1    0  26.0      0      0  30.0000        C      1\n",
              "890         0       3    0  32.0      0      0   7.7500        Q      1\n",
              "\n",
              "[891 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I2XSOO5ZeDtL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "dc5f2b86-77c3-4b2e-d95d-b0dbb002cfac"
      },
      "source": [
        "train_df.isnull().sum()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived      0\n",
              "Pclass        0\n",
              "Sex           0\n",
              "Age         177\n",
              "SibSp         0\n",
              "Parch         0\n",
              "Fare          0\n",
              "Embarked      2\n",
              "Title         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YjiUUj50gblf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        },
        "outputId": "9bbf7639-0f47-488a-d2ea-fc2e9a40e2a2"
      },
      "source": [
        "test_df.isnull().sum()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass       0\n",
              "Sex          0\n",
              "Age         86\n",
              "SibSp        0\n",
              "Parch        0\n",
              "Fare         1\n",
              "Embarked     0\n",
              "Title        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHu9ssBIgp9O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Age'].fillna(dataset['Age'].mean(), inplace=True)\n",
        "  dataset['Age'] = dataset['Age'].astype(int)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EY0Q0BRLhMph",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "df38059b-7b50-4852-ef7d-7914971aa85c"
      },
      "source": [
        "train_df['Age'].isnull().sum()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yt5FnUoShR92",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "c4e27f8e-68e0-4975-94a5-7d34570b1aaf"
      },
      "source": [
        "train_df['Age'].unique()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([22, 38, 26, 35, 29, 54,  2, 27, 14,  4, 58, 20, 39, 55, 31, 34, 15,\n",
              "       28,  8, 19, 40, 66, 42, 21, 18,  3,  7, 49, 65,  5, 11, 45, 17, 32,\n",
              "       16, 25,  0, 30, 33, 23, 24, 46, 59, 71, 37, 47, 70, 12,  9, 36, 51,\n",
              "       44,  1, 61, 56, 50, 62, 41, 52, 63, 43, 60, 10, 64, 13, 48, 53, 57,\n",
              "       80,  6, 74])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "29L9nbz4hjbO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['AgeBand'] = pd.cut(train_df['Age'], 5) # cut은 동일한 길이로 나누기"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gXX9vZSfhze_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "ba0c1d62-43d7-4a37-fd26-efbac0e04e1d"
      },
      "source": [
        "train_df['AgeBand'].unique()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(16.0, 32.0], (32.0, 48.0], (48.0, 64.0], (-0.08, 16.0], (64.0, 80.0]]\n",
              "Categories (5, interval[float64]): [(-0.08, 16.0] < (16.0, 32.0] < (32.0, 48.0] < (48.0, 64.0] <\n",
              "                                    (64.0, 80.0]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zg2noZGniAh-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "3744f2e1-ba90-4a5c-df79-ed16558ecd86"
      },
      "source": [
        "train_df[['AgeBand', 'Survived']].groupby('AgeBand').mean()"
      ],
      "execution_count": 50,
      "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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AgeBand</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>(-0.08, 16.0]</th>\n",
              "      <td>0.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(16.0, 32.0]</th>\n",
              "      <td>0.344762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(32.0, 48.0]</th>\n",
              "      <td>0.403226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(48.0, 64.0]</th>\n",
              "      <td>0.434783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(64.0, 80.0]</th>\n",
              "      <td>0.090909</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Survived\n",
              "AgeBand                \n",
              "(-0.08, 16.0]  0.550000\n",
              "(16.0, 32.0]   0.344762\n",
              "(32.0, 48.0]   0.403226\n",
              "(48.0, 64.0]   0.434783\n",
              "(64.0, 80.0]   0.090909"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlnIPjGAiP4v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "    dataset.loc[dataset['Age'] <= 16 , 'Age'] = 0\n",
        "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <=32),'Age'] = 1\n",
        "    dataset.loc[(dataset['Age']>32) & (dataset['Age'] <=48) , 'Age'] =2 \n",
        "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <=64),'Age'] =3\n",
        "    dataset.loc[(dataset['Age']>64),'Age'] = 4"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UL5FcX-Di16Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "92c5872d-cada-4737-f0e4-1dc35570bb59"
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 52,
      "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",
              "      <th>Title</th>\n",
              "      <th>AgeBand</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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>3</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex  Age  ...     Fare  Embarked  Title       AgeBand\n",
              "0         0       3    0    1  ...   7.2500         S      1  (16.0, 32.0]\n",
              "1         1       1    1    2  ...  71.2833         C      3  (32.0, 48.0]\n",
              "2         1       3    1    1  ...   7.9250         S      2  (16.0, 32.0]\n",
              "3         1       1    1    2  ...  53.1000         S      3  (32.0, 48.0]\n",
              "4         0       3    0    2  ...   8.0500         S      1  (32.0, 48.0]\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I--a__BnlJsh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "e625d6db-cbf0-466c-a0f1-e62e1d9a1717"
      },
      "source": [
        "train_df[train_df['Embarked'].isnull()]"
      ],
      "execution_count": 63,
      "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",
              "      <th>Title</th>\n",
              "      <th>AgeBand</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>829</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>(48.0, 64.0]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Survived  Pclass  Sex  Age  ...  Fare  Embarked  Title       AgeBand\n",
              "61          1       1    1    2  ...  80.0       NaN      2  (32.0, 48.0]\n",
              "829         1       1    1    3  ...  80.0       NaN      3  (48.0, 64.0]\n",
              "\n",
              "[2 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-z-slR4pOLr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "2621a99c-01a3-43d7-bc4c-3f1642fe52e2"
      },
      "source": [
        "train_df[(train_df['Pclass'] == 1) & (train_df['Survived'] == 1)]['Embarked'].value_counts()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    74\n",
              "C    59\n",
              "Q     1\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7O43G-2BqZ5c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['Embarked'] = train_df['Embarked'].fillna('S')"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6I8WBynFreks",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['Embarked'] = train_df['Embarked'].astype(str)"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rqD52nR1rpqr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "bd77e04b-83ea-447a-abe2-303aa609f9ce"
      },
      "source": [
        "train_df.dtypes"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived       int64\n",
              "Pclass         int64\n",
              "Sex            int64\n",
              "Age            int64\n",
              "SibSp          int64\n",
              "Parch          int64\n",
              "Fare         float64\n",
              "Embarked      object\n",
              "Title          int64\n",
              "AgeBand     category\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIFgYclvr6Ay",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Embarked'] = dataset['Embarked'].map({'S': 0, 'C': 1, 'Q': 2})"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RO3BkChxscOZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "898d2b08-ef4f-42e3-c1bb-a441a2d1cac2"
      },
      "source": [
        "train_df.isnull().sum()"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived    0\n",
              "Pclass      0\n",
              "Sex         0\n",
              "Age         0\n",
              "SibSp       0\n",
              "Parch       0\n",
              "Fare        0\n",
              "Embarked    0\n",
              "Title       0\n",
              "AgeBand     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wDQQJsuVsdTK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "0645140f-1323-45f4-8325-dcc024c5bc87"
      },
      "source": [
        "test_df[test_df['Fare'].isnull()]"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
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            ],
            "text/plain": [
              "     Pclass  Sex  Age  SibSp  Parch  Fare  Embarked  Title\n",
              "152       3    0    3      0      0   NaN         0      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q5bKlIXXsw8K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_df[test_df['Fare'].isnull()] = test_df[test_df['Pclass'] == 3]['Fare'].mean()"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X0pw9LEwtXRB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)  #pd.qcut은 동일한 값으로 나누기"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UgWFqTNPtkAt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "76b1a4ba-cd32-4856-8d0c-6b91c2c3b9c7"
      },
      "source": [
        "train_df[['FareBand', 'Survived']].groupby('FareBand', as_index=False).mean()"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>FareBand</th>\n",
              "      <th>Survived</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>(-0.001, 7.91]</td>\n",
              "      <td>0.197309</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(7.91, 14.454]</td>\n",
              "      <td>0.303571</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(14.454, 31.0]</td>\n",
              "      <td>0.454955</td>\n",
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              "      <th>3</th>\n",
              "      <td>(31.0, 512.329]</td>\n",
              "      <td>0.581081</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          FareBand  Survived\n",
              "0   (-0.001, 7.91]  0.197309\n",
              "1   (7.91, 14.454]  0.303571\n",
              "2   (14.454, 31.0]  0.454955\n",
              "3  (31.0, 512.329]  0.581081"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LjM-duozuTCN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "    dataset.loc[dataset['Fare'] <= 7.91 , 'Fare'] = 0\n",
        "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454),'Fare'] = 1\n",
        "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31) , 'Fare'] =2 \n",
        "    dataset.loc[(dataset['Fare'] > 31) & (dataset['Fare']),'Fare'] = 3"
      ],
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z1W7eTOTvgkc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Family'] = dataset['SibSp']+dataset['Parch']\n",
        "  dataset['Family'] = dataset['Family'].astype(int)"
      ],
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S6DtsOBRv0zU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "b96f82da-a7e4-45c0-c5be-7621e3f7d584"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  </tbody>\n",
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              "<p>891 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Survived  Pclass  Sex  Age  ...  Title       AgeBand         FareBand  Family\n",
              "0           0       3    0    1  ...      1  (16.0, 32.0]   (-0.001, 7.91]       1\n",
              "1           1       1    1    2  ...      3  (32.0, 48.0]  (31.0, 512.329]       1\n",
              "2           1       3    1    1  ...      2  (16.0, 32.0]   (7.91, 14.454]       0\n",
              "3           1       1    1    2  ...      3  (32.0, 48.0]  (31.0, 512.329]       1\n",
              "4           0       3    0    2  ...      1  (32.0, 48.0]   (7.91, 14.454]       0\n",
              "..        ...     ...  ...  ...  ...    ...           ...              ...     ...\n",
              "886         0       2    0    1  ...      5  (16.0, 32.0]   (7.91, 14.454]       0\n",
              "887         1       1    1    1  ...      2  (16.0, 32.0]   (14.454, 31.0]       0\n",
              "888         0       3    1    1  ...      2  (16.0, 32.0]   (14.454, 31.0]       3\n",
              "889         1       1    0    1  ...      1  (16.0, 32.0]   (14.454, 31.0]       0\n",
              "890         0       3    0    1  ...      1  (16.0, 32.0]   (-0.001, 7.91]       0\n",
              "\n",
              "[891 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZN6HCJQv5Jc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df.drop(['AgeBand', 'FareBand', 'SibSp', 'Parch'], axis=1, inplace=True)"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3RwyemYwSbk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "a8437592-0c76-48df-aeef-01d3db1cffdd"
      },
      "source": [
        "test_df"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>418 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pclass  Sex  Age  SibSp  Parch  Fare  Embarked  Title  Family\n",
              "0       3.0  0.0  2.0    0.0    0.0   0.0       2.0    1.0       0\n",
              "1       3.0  1.0  2.0    1.0    0.0   0.0       0.0    3.0       1\n",
              "2       2.0  0.0  3.0    0.0    0.0   1.0       2.0    1.0       0\n",
              "3       3.0  0.0  1.0    0.0    0.0   1.0       0.0    1.0       0\n",
              "4       3.0  1.0  1.0    1.0    1.0   1.0       0.0    3.0       2\n",
              "..      ...  ...  ...    ...    ...   ...       ...    ...     ...\n",
              "413     3.0  0.0  1.0    0.0    0.0   1.0       0.0    1.0       0\n",
              "414     1.0  1.0  2.0    0.0    0.0   3.0       1.0    5.0       0\n",
              "415     3.0  0.0  2.0    0.0    0.0   0.0       0.0    1.0       0\n",
              "416     3.0  0.0  1.0    0.0    0.0   1.0       0.0    1.0       0\n",
              "417     3.0  0.0  1.0    1.0    1.0   2.0       1.0    4.0       2\n",
              "\n",
              "[418 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENW_NpaNwTf7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_df.drop(['SibSp', 'Parch'], axis=1, inplace=True)"
      ],
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "koTA55KAwaSM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Fare'] = dataset['Fare'].astype(int)"
      ],
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WMwgJ4cXw9-c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "8ca6c5bd-1779-49cc-d72a-7379cf6178d2"
      },
      "source": [
        "test_df"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    <tr>\n",
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              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>3.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
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              "      <th>417</th>\n",
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              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>418 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pclass  Sex  Age  Fare  Embarked  Title  Family\n",
              "0       3.0  0.0  2.0     0       2.0    1.0       0\n",
              "1       3.0  1.0  2.0     0       0.0    3.0       1\n",
              "2       2.0  0.0  3.0     1       2.0    1.0       0\n",
              "3       3.0  0.0  1.0     1       0.0    1.0       0\n",
              "4       3.0  1.0  1.0     1       0.0    3.0       2\n",
              "..      ...  ...  ...   ...       ...    ...     ...\n",
              "413     3.0  0.0  1.0     1       0.0    1.0       0\n",
              "414     1.0  1.0  2.0     3       1.0    5.0       0\n",
              "415     3.0  0.0  2.0     0       0.0    1.0       0\n",
              "416     3.0  0.0  1.0     1       0.0    1.0       0\n",
              "417     3.0  0.0  1.0     2       1.0    4.0       2\n",
              "\n",
              "[418 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FkjOXaktw_dU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "5f7c2bcb-88c5-42a5-cd18-364126887b75"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "      <td>2</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Survived  Pclass  Sex  Age  Fare  Embarked  Title  Family\n",
              "0           0       3    0    1     0         0      1       1\n",
              "1           1       1    1    2     3         1      3       1\n",
              "2           1       3    1    1     1         0      2       0\n",
              "3           1       1    1    2     3         0      3       1\n",
              "4           0       3    0    2     1         0      1       0\n",
              "..        ...     ...  ...  ...   ...       ...    ...     ...\n",
              "886         0       2    0    1     1         0      5       0\n",
              "887         1       1    1    1     2         0      2       0\n",
              "888         0       3    1    1     2         0      2       3\n",
              "889         1       1    0    1     2         1      1       0\n",
              "890         0       3    0    1     0         2      1       0\n",
              "\n",
              "[891 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lAYArcgdxRtF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = train_df.drop('Survived', axis=1)\n",
        "Y_train = train_df['Survived']"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uRkmQlcOyyTX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test = test_df"
      ],
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tU72HcEoy87V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "02fb6166-9658-4a8e-8973-1699f5f570fc"
      },
      "source": [
        "X_train.shape, Y_train.shape, X_test.shape"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((891, 7), (891,), (418, 7))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ooEZ-mU0zFN-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "from sklearn.utils import shuffle"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "reS44MZO0NI9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, Y_train = shuffle(X_train, Y_train, random_state=23)"
      ],
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ui86sRH1YD_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_and_test(model):\n",
        "  model.fit(X_train, Y_train)\n",
        "  pred = model.predict(X_test)\n",
        "  accuracy = round(model.score(X_train, Y_train) * 100, 2)\n",
        "  print('Accuracy: ', accuracy, '%')\n",
        "  return pred, accuracy"
      ],
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aCl12WdM10uv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "0c694c3b-6a8c-414f-d592-f308b3f5a715"
      },
      "source": [
        "log_pred, log_acc = train_and_test(LogisticRegression())\n",
        "svm_pred, svm_acc = train_and_test(SVC())\n",
        "rf_pred, rf_acc = train_and_test(RandomForestClassifier())\n",
        "nb_pred, nb_acc = train_and_test(GaussianNB())\n",
        "k_pred, k_acc = train_and_test(KNeighborsClassifier())"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  80.47 %\n",
            "Accuracy:  83.16 %\n",
            "Accuracy:  88.78 %\n",
            "Accuracy:  80.58 %\n",
            "Accuracy:  85.07 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mhiJFiAj2-Kw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}