titanic2.ipynb

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      "name": "titanic.ipynb",
      "provenance": []
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      "name": "python3",
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          "height": 73
        },
        "outputId": "34a09bfa-ae31-4004-85b5-674e8b446dce"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "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": "eqd9J9xJXv8f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_df = pd.read_csv(\"/content/test.csv\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rlZfHviCX2wl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = pd.read_csv(\"/content/train.csv\")"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c98EMn6ZYh57",
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        "colab": {
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          "height": 419
        },
        "outputId": "f3c98bf8-83ea-47b5-f97d-97fda5a3121e"
      },
      "source": [
        "test_df"
      ],
      "execution_count": 4,
      "outputs": [
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              "     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",
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              "416         1308       3  ...   NaN        S\n",
              "417         1309       3  ...   NaN        C\n",
              "\n",
              "[418 rows x 11 columns]"
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      "source": [
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      "outputs": [
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            "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]"
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          },
          "metadata": {
            "tags": []
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          "execution_count": 5
        }
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    },
    {
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      "metadata": {
        "id": "78GDlWdXYMJX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "24d730cb-9e5a-4ff6-8fe8-2258577eefb2"
      },
      "source": [
        "train_df.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vy3IKpYWYlhj",
        "colab_type": "code",
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        "outputId": "3ee5faf9-8889-421d-80fb-1bbf496e99aa"
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      "source": [
        "test_df.info()"
      ],
      "execution_count": 7,
      "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": "e5MhI3TLYzcr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "68bd183c-898d-4efa-bb9a-8bc37fe50ee9"
      },
      "source": [
        "train_df.describe()"
      ],
      "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>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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z439dbJGY8_U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "d9d1cbc1-c3f1-48eb-99b6-0d1db16f4fb8"
      },
      "source": [
        "train_df.describe(include=[ \"O\" ]) #category에 관하여 describe를 해준다."
      ],
      "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>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>Windelov, Mr. Einar</td>\n",
              "      <td>male</td>\n",
              "      <td>1601</td>\n",
              "      <td>G6</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     Windelov, Mr. Einar  male   1601    G6        S\n",
              "freq                      1   577      7     4      644"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_xcqhHFFZejc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "avzaJrdDZvu_",
        "colab_type": "text"
      },
      "source": [
        "데이터 분석"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ofloqVpxZxJM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "cb435f96-1fdc-479f-df2c-1719acaaddcd"
      },
      "source": [
        "train_df[['Pclass','Survived']].groupby('Pclass').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>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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S4__BEtEab72",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "e4611339-990a-41e3-b0d3-e03d2fd1c7cf"
      },
      "source": [
        "train_df[['Sex','Survived']].groupby('Sex').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>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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gY-ihYbNbBPM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "6f7982e4-eb96-482f-8203-76f44b9b131a"
      },
      "source": [
        "train_df[['Age','Survived']].groupby('Age').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>Age</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.42</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.67</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.75</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.83</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.92</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70.00</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70.50</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71.00</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74.00</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80.00</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>88 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Survived\n",
              "Age            \n",
              "0.42        1.0\n",
              "0.67        1.0\n",
              "0.75        1.0\n",
              "0.83        1.0\n",
              "0.92        1.0\n",
              "...         ...\n",
              "70.00       0.0\n",
              "70.50       0.0\n",
              "71.00       0.0\n",
              "74.00       0.0\n",
              "80.00       1.0\n",
              "\n",
              "[88 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NnWe25dKbMg0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "73389c3f-6aa2-42d5-ea4f-ffbc2b408b24"
      },
      "source": [
        "train_df[['Embarked','Survived']].groupby('Embarked').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>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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4JqbSgWWbfC0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "b4c011e3-fdde-4606-cf66-b11caef3c471"
      },
      "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": "code",
      "metadata": {
        "id": "tYAZMo7nbqv8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "ee753cdd-764b-4c79-e275-83fa476edfde"
      },
      "source": [
        "train_df[['SibSp','Survived']].groupby('SibSp').mean()"
      ],
      "execution_count": 15,
      "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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PdZke2hFbxcc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4TdOm5VPb55n",
        "colab_type": "text"
      },
      "source": [
        "#visualization data analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KpR5CBOUb_6M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "outputId": "d245b9d0-8d87-4dc8-e2ab-cb5257bda994"
      },
      "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": "rMnSFn6JcYAU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "7abaef69-7533-422f-d0ab-35b5fe935596"
      },
      "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 0x7f55ac80b160>"
            ]
          },
          "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": "_9Lp9dFic2FP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "outputId": "319af848-d3c4-4f2f-b8bf-cb0a56afd709"
      },
      "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 0x7f55a9a2b588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "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": "rqnDLiUPdhll",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "outputId": "5914df7c-fb94-458f-b941-d4c51fc28f99"
      },
      "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 0x7f55a9790c88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lNarDiO5tAdhzIJ+x79sk0lVRUKEhIh1E5CMRWeI+7yQify/rNaqaB9wMpAPLgTRVXSoiD4pIb3ezdGCbiCwD5gN3qqqN7hLFbvxTW1ok1Abg3e82sPCX7T5XZMJNgrlnQEQ+Be4EXlDV09xlS1T1ZI/rO0xycrJmZGRU9tuaELz/4yZGvL4IgJNbNOA/I88htnCex8gV8QVGimD7NOqo6rfFltk1w6ZEqSc34+x2RwGwZMMu0jLWl/MKE02CDY2tItIWUAAR6Q9s8qwqE9VEhPsuPamodfFE+k9k77VJpKuKYENjJPACcIKIbMC5cvNGz6oyUa/DMfX58x+dSaS37znAuA9/9rkiEy7BhsYvqno+0BQ4QVXPUdVfPKzLVAF/Pb8DjevWBGDa17/w02abRLoqCDY01orIZOBMYLeH9ZgqpGHtGtyV4kwinV+gNlhPFRFsaJwAfIhzmLJWRJ4TkXO8K8tUFQOTW9IpqSEAX63ZxvtLNvtckamoYC/u2quqaaraFzgNaAB86mllpkqIiZFD7kt5ZPZy9h2w+1KiWdDjaYjIn0Tkn8BCoBbOZeXGlKvLsY3o29m5V3HDzn1M+nS1zxWZigj2itB1OGdM/gf8QVUHquo7XhZmqpbRqSdQ151EetKnq1m/fa/PFZkjFWxLo5OqXq6qb6qqDc1kQnZ0g1rc0tO5FzE3r4BHZtsk0tEqrqyVInKXqj4OPCIih3V7q+otnlVmqpxrz27D2wvWs2brHuYu3cznK7dyTvsmfpdlQlReS6Pwz0EGTl9G8YcxQasZF8O9lx46ifRBm0Q66pTZ0lDV/7rf/qiqiyqhHlPFdT/+aM4/8Wg+XL6FlVt289pXvzDsnDalbn/Ny9+QuWMfSY1qM23YGZVYqSlNsH0aT4nIchF5SEQq/c5WU7X8/ZKO1Ix1/us988HPbN2dW+q2mTv2sXbrHjJ37Kus8kw5gr1OowfQA8gCXhCRH8sbTyNaDRkyhJ49ezJkyBC/S6myWjepyw3dnNZFTm4eT8y1SaSjSdDXaajqZlUdj3Oj2vfAvZ5V5aPMzEzWrVtHZmam36VUaTd1b0ezBrUASFu4nh/W7/S5IhOsYK/TOFFE7heRH4EJwJc4Y34ac0Tqxsdx98W/TyJ936ylFBTYfSnRINiWxhRgB5Ciqt1V9Xmbo8RUVO9TmnN660YAfL9+J+9+V3zcaROJyg0Nd6a0tar6rKra1OAmbEScSaQLRwJ87P0V5Oy3wXoiXbmhoar5QEt3RHFjwuqk5g258oxWAGzdncuEj20S6UgX9HgawBcico+I3Fb48LIwU33cfsHxNKxdA4Apn69l1RYbsiWSBRsaq4H33O3rBzyMqbBGdWtyx4XOJNJ5BcqD7y2zwXoiWJlXhBZS1Qe8LsRUb4O7tuL1b35lxeYcPvs5iw+Xb+GCjsf4XZYpQbCnXOeLyMfFH0G8LlVEfhKRVSIyuozt+omIikhyKMWbqiMuNoYHev8+WM9D7y1j/0EbrCcSBdXSAO4I+L4W0I9y5j1xz7pMBC7AmbN1gYjMUtVlxbarD9wKfBNs0aZqOuO4o7j0lOb894eN/Lp9Ly9/vtbvkkwJgr2MfGHA4wtVvQ13DtYydAVWqeoaVT0AvAX0KWG7h4CxgM0WbLj7ohOoXcMZrOe5j1eRZ3fBRpxgD08aBzyaiEgq0LCcl7UAAqfWynSXBe63M9BSVWeX8/7DRSRDRDKysrKCKdlEqeYJtRnZw5lEet/BfDbsdG5U27hzH2kL1pNvV436LtizJwtxxtTIwLmE/DZgWEXeWERigKeB28vbVlUnq2qyqiY3bdq0Im9rosC1Z7Uuam0UZkRuXgF3vbOYka8vstaHz8oMDRE5XUSaqWobVT0OeABY4T6WlfVaYAPQMuB5krusUH3gZOATdwzSM4FZ1hlqZv+4mX2ldILOXbrZLjf3WXktjReAAwAi0g14FHgVyAYml/PaBUB7EWnjXk16BTCrcKWqZqtqE1Vtraqtga+B3qrqy5TweXl5TJ8+nU2bnClqN23axPTp08nPtx78yvZ2ORNGpy2wCaX9VF5oxKrqdvf7QcBkVX1HVe8B2pX1QlXNA24G0nGGDUxT1aUi8qCI9K5o4eGUl5fHLbfcwujRo8nNdQaEyc3NZfTo0YwaNYq8vDJPFJkw27Sz7AF3Npaz3nir3NAQkcLTsj2BwGszyj1dq6pzVLWDqrZV1UfcZfeq6qwStu3uVytjxowZpKenl7guPT2dmTNnVnJF1VtiQu0y1zcvZ73xVnmh8SbwqYj8B9iHM+8JItIO5xClSkhLS6vQehNeg5Jblrl+4OllrzfeKjM03NbB7cBU4Bz9/YaAGGCUt6VVnsJ+jNJs2GAdb5WpX5ckUk9qVuK61JOa0a+zjf/kp2Bujf9aVWcETpKkqj9XpdHJExMTy1y/e/duVq+2qQQrS2yM8NyVp/F4/07Exzn/RePjYni8fycmXtWZ2MIBOIwvgh4jtCobOLDsaWl3795Nr169mDBhAgcOHKikqqq3uNgYBia3LOq/aJ5Qm4HJLS0wIoCFBtC3b19SUlJKXNegQQMADhw4wDPPPEPv3r1ZuNDmiTLVl4UGEBsby/jx4xk7dizx8fEAxMfHM3bsWL755hvGjBlD7drOX7yVK1cyaNAg7rvvPnJycvws2xhfWGi44uLi6N+/f1H/RmJiIv3796dmzZpcd911zJ07l27dugGgqvzrX/8iNTWVefPm+Vm2MZXOQiNISUlJTJkyhXHjxtG4cWMANm/ezIgRI7jpppv47bfffK7QmMphoRECEaF3797MmzePfv36FS1PT08nJSWFN954g4ICu5nKVG0WGkegUaNGPP7440ybNo1WrZyRtHNycrjnnnsYPHiwnZ41VZqFRgWcddZZvP/++9x4443Exjq3cmdkZNCrVy/Gjx9fdB+LMVWJhUYF1apVizvvvJP//Oc/dOrUCXBOzz777LP07t2bjAxfbqcxxjMWGmFy4okn8u9//5u///3v1KlTB4BVq1YxaNAg7rnnHjs9a6oMC40wio2N5dprr2Xu3Ll07969aPkbb7xBSkpKqXfSGhNNLDQ80KJFC1566SWeeeYZjjrqKAB+++03brrpJkaMGGGnZ01Us9DwiIhw6aWXMm/ePAYMGFC0fN68eVx44YV2ejZISY1q06ZJXZIa2RgakUKibfq75ORk9bJzsWfPnqxbt47WrVvz0UcfhW2/X331FWPGjOGXX34pWtalSxceeeQR2rdvH7b3MUfM7oQLkrU0Kskf//hH5syZw4gRI4iLcwY9W7hwIZdeeinPPvusnZ41UcNCo5ikpCRat25NUlL4B3qpVasWd9xxxyGnZw8ePMj48ePt9KyJGnZ44pP8/HymTZvGU089xd69e4uWX3nlldx1113Ur1/fx+qqJTs8CZK1NHwSGxvL0KFDSU9Pp0ePHkXL33jjDS688MIKn54dMmQIPXv2ZMiQIRUt1ZhDWGj4rHnz5rz44os8++yzRadnt2zZwk033cSNN97I5s2bj2i/mZmZrFu3jszMzHCWa4y3oSEiqSLyk4isEpHRJay/TUSWichiEflIRI71sp5IJSL06tWLefPmHTL04AcffEBKSgr/+te/7PSsiRiehYaIxAITgYuAjsBgEelYbLPvgGRV7QT8G3jcq3qiQUJCAo8++iivv/46rVu3BpzxSe+77z4GDRrEypUr/S3QGLxtaXQFVqnqGlU9ALwF9AncQFXnq2phL+DXOPO9Vntnnnkms2fPPuT07KJFi7j00kt55pln7PSs8ZWXodECCJx0M9NdVpphwPse1hNVCk/Pzpo1i1NPPRVwTs9OmDCBXr16sWDBAp8rNNVVRHSEisjVQDLwRCnrh4tIhohkZGVlVW5xPjv++ONJS0vj3nvvpW7dugCsWbOGK664gjFjxrBr1y6fKzTVjZehsQEInD8vyV12CBE5HxiDM2N8ie1uVZ2sqsmqmty0aVNPio1ksbGxDBkyhLlz53LeeecVLX/rrbdISUlh7ty5RNv1NiZ6eRkaC4D2ItJGRGoCVwCHTPwsIqcBL+AExhYPa6kSmjdvzuTJkxk/fjxNmjQBnNOzI0eO5MYbbyx3ekljwsGz0FDVPOBmIB1YDqSp6lIReVBEerubPQHUA6aLyPcicths8uZQIsIll1xy2OnZDz/8kNTUVF599VXefvvtogDZtGkT06dPJz8/36+SK8QuUos8dhl5lCuczGnt2rVlbpeSksL48eOLzsZEC6/uOi6BXUYepIjoCDVH7owzzmD27NmMHDmSmJjS/znT09OZOHEimzZtslO2pkKspVGF9OrVi+XLlwe1bb169TjqqKNo3LjxIV9LWtaoUSNq1qzpcfUls5ZG5Imutqop086dO4Pedvfu3ezevfuQQYHK0qBBg8PCpHHjxoeETeGyRo0aUaNGjSP9GADk5eUxY8aMw/pm+vbtWzRdhPGHhUYVkpiYWOYZlGbNmtGtWze2b9/Otm3bir7u3r273H3v2rWLXbt2sW7duqBqadiwYakBUzxoEhISDulrycvL45ZbbjnkTt/c3FxGjx7N/Pnzo7Jvpiqxn3wVMnDgQBYtWlTq+r/97W/079//sOW5ubls37696BEYKCV9DSZksrOzyc7OZs2aNeVuKyIkJCQUhcnevXtZsmRJidump6czc+bMEj+HqRzWp1GF5OfnM2rUqBLH4khJSWHChAlhadrn5uaWGyyBXwMHGQqHLl26kJaWFtZ9Yn0aQbPQqGLy8vKYOXMm9957L7m5ucTHx/Pggw9y+eWX+9YXsH//frZv387WrVuDCpp9+/aVub/ExEQ+//zzcJdpoREkOzypYuLi4ujfvz/PP/8869atIzEx0femfK1atWjevDnNmzcPavu+ffvyww8/lLo+2P0Yb9h1GibiDB48uMz1gVfCmspnoWEiTt++fUlJSSlxXUpKCpdffnklV2QCWWiYiBMbG8v48eMZO3Ys8fHxAMTHxzN27NiwdeaaI2ehYSJSYd9MYmIiQFHfjAWG/yw0jDEhsbMnJqIVznTnxYx35shYaFRRVeWX7dVXX/W7BFOMhUYVZb9sxivWp2GMCYmFhjEmJBYaxpiQWGgYY0JioWGMCYmFhjEmJBYaxpiQeBoaIpIqIj+JyCoRGV3C+ngRedtd/42ItPayHmNMxXkWGiISC0wELgI6AoNFpGOxzYYBO1S1HTAOGOtVPcaY8PCypdEVWKWqa1T1APAW0KfYNn2AwksX/w30FBEbds2YCOZlaLQA1gc8z3SXlbiNO/drNnBU8R2JyHARyRCRjKysLI/KNcYEIyo6QlV1sqomq2py06ZN/S7HmGrNy9DYALQMeJ7kLitxGxGJAxoC2zysyRhTQV6GxgKgvYi0EZGawBXArGLbzAKGuN/3Bz7WaJtTwZhqxrNb41U1T0RuBtKBWGCKqi4VkQeBDFWdBbwMTBORVcB2nGAxxkQwmyzJGIedtQtSVHSEGmMih4WGMSYkUXd4IiJZwC8ev00TYKvH71EZ7HMEb6uqpnr8HlVC1IVGZRCRDFVN9ruOirLPYbxghyfGmJBYaBhjQmKhUbLJfhcQJvY5TNhZn4YxJiTW0jDGhMRCwxgTEgsNY0xILDSMMSGx0DDGhMRCwxgTEgsNY0xILDSMMSGx0DDGhMRCI8xEJF9Evg94HDazXBmv7S4i71Xw/T8RkSO6I1REpopI/4q8v7ufDiIyR0RWisgiEUkTkWMqul8TGTwbI7Qa26eqp/rxxu6sdr4SkVrAbOA2Vf2vu6w70BT4zcfSTJhYS6OSiMg6EXnUbX1kiEhnEUkXkdUicmPApg1EZLY7B+4kEYlxX/+8+7qlIvJAsf2OFZFFwICA5TFuy+FhEYkVkSdEZIGILBaRv7jbiIg8577Xh8DRYfioVwJfFQYGgKp+oqpLwrBvEwGspRF+tUXk+4Dnj6rq2+73v6rqqSIyDpgKnA3UApYAk9xtuuLMffsLMBfoizNl5RhV3e62Jj4SkU6quth9zTZV7QzgBlAc8DqwRFUfEZHhQLaqni4i8cAXIjIPOA043n2/Y4BlwJTiH0hE7gSuKuGzfqaqtxRbdjKwsBDjTCYAACAASURBVPwfk4lWFhrhV9bhSeG8Lz8C9VQ1B8gRkVwRSXDXfauqawBE5E3gHJzQGOj+8scBiTi/6IWhURhKhV4A0lT1Eff5hUCngP6KhkB7oBvwpqrmAxtF5OOSilbVJ4Angvjsphqw0Khcue7XgoDvC58X/lsUH6tARaQNcAdwuqruEJGpOC2UQnuKveZLoIeIPKWq+3GG5x+lqumBG4nIxcEUHWJLYynwp2D2a6KT9WlEnq7urHQxwCDgc6ABTjBku2chLipnHy8Dc4A0d7rLdGCEiNSAorMbdYHPgEFun0ci0KOknanqE6p6agmP4oEB8AZwlohcUrhARLqJyMmh/BBM5LKWRvgV79OYq6pBn3bFmc7yOaAdMB+YoaoFIvIdsAJYD3xR3k5U9WkRaQhMw2kltAYWiYgAWcBlwAzgPJy+jF+Br0Kos7T33ScivYBnROQZ4CDOYdStFd23iQw2cpcxJiR2eGKMCYmFhjEmJBYaxpiQWGgYY0JioWGMCUnUhUZqaqriXABlD3uE82GCFHWhsXVrVZgE3ZjoFXWhYYzxl4WGMSYknoWGiEwRkS0iUuI4Cu5YDuNFZJU7xkNnr2qpTvLy8pg+fToDBgzgnHPOYcCAAUyfPp38/Hy/SzNVhJf3nkzFuYfitVLWX4Rze3Z74AzgeferOUJ5eXnccsstpKf/fjPrpk2bWLRoEfPnz2f8+PHExdntRqZiPPsfpKqfiUjrMjbpA7ymzs0vX4tIgogkquomr2qqCgoKCsjJyWHHjh3s3Lmz6OvOnTv58ssv+fjjEofEID09nZkzZ9K/f4WHADXVnJ9/dlrg3LFZKNNd5kto5OXlMWPGDB555BH27t1LnTp1GDNmDH379iU21puhN3Nzcw/75Q8MgZKWZWdnU1BQcETvl5aWZqFhKiwq2qruiFXDAVq1ahX2/ZfUrM/JyWH06NFBNesLCgrYtWtXub/wxdft27cv7J+lLBs3bqzU9zNVk5+hsQFoGfA8yV12GFWdDEwGSE5ODvuFODNmzDgkMAKlp6czevRojjvuOE/++gejXr16NGrUiISEBBISEoq+L77sscceY8WKFaXuJzEx0bMaTfXhZ2jMAm4WkbdwOkCz/erPSEtLK3P9jBkzwvI+NWrUOOwXv6QQaNSoEQ0bNiz6WqNGjaD2v3nzZkaPLn28n6ZNm4blc5jqzbPQcAfF7Q40EZFM4D6gBoCqTsIZju5iYBWwF7jWq1rKs2lT6FlVv379Q37xgwmDunXr4gyc5Y2+ffsyf/78MltNaWlpDBw40LMaTNUXdSN3JScna0ZGRlj3OWDAABYtWlTq+rZt2/KPf/yjKAxC+etf2fLy8pg5cyZpaWls3LiR5s2b065dO95+2xmwPCYmhkmTJtGzZ0+fK4043qV5FRMVHaFeGzhwYJmhMXz4cJKTj2imw0oXFxdH//79DztL0rp1a8aOHUtBQQGjRo1i2rRpdOnSxacqTTSzy8hxmvUpKSklrktJSeHyyy+v5IrC74YbbmDYsGGAc6r3hhtuYOXKlT5XZaKRhQYQGxvL+PHjGTt2LPHx8QDEx8czduxYJkyY4Nl1GpVJRBg9ejR9+vQBIDs7m6FDh9ppWBMyCw1XYbO+8LRkYmIi/fv3rxKBUSgmJobHHnuMc889F3DOtlx33XXs3LnT58pMNLHQqGZq1qzJc889x8knO3MXrVy5kuHDh7N//36fKzPRwkKjGqpXrx4vv/wyxx57LAALFy7k1ltvJS8vz+fKTDSw0KimmjRpwtSpU2nSpAkAH374Iffccw/RdgreVD4LjWKSkpJo3bo1SUlJfpfiuVatWjFlyhTq1asHOFfGjhs3zueqTKSzi7sMX375JcOGDePAgQMAPPDAA1x99dU+V1Xp7OKuIFlLw3DWWWfx5JNPFl3ifv/99/P+++/7XJWJVBYaBoBLLrmEe++9FwBV5bbbbuOrryo8ibypgiw0TJE///nPjBgxAoADBw5w4403snz5cp+rMpHGQsMc4vbbb2fAgAEA7N69m2uvvZb169eX8ypTnVhomEOICA8//DDnnXceAFlZWQwdOpRt27b5XJmJFBYa5jBxcXGMHz+e0047DYB169Zx/fXXs2fPHp8rM5HAQsOUqHbt2rz00ku0a9cOgMWLFzNy5Mii07Km+rLQMKVKSEjglVdeoVmzZgD873//4+677/Z0PFQT+Sw0TJmaN2/OK6+8QoMGDQCYOXMmY8eO9bkq4ycLDVOuDh068OKLLxaNNfLSSy/x0ksv+VyV8YuFhglKcnIy48ePJybG+S/z6KOPMnPmTJ+rMn6w0DBBO//883n44YeLnv/f//0fn332mY8VGT9YaJiQDBo0iL/97W+AM/L5yJEjWbx4sWfvN2TIEHr27MmQIUM8ew8TGgsNE7KRI0dyzTXXALB3716GDRvG2rVrPXmvzMxM1q1bR2Zmpif7N6HzNDREJFVEfhKRVSJy2NRfItJKROaLyHcislhELvayHhMeIsI999zDRRddBMD27dsZOnQoW7Zs8bkyUxk8Cw0RiQUmAhcBHYHBItKx2GZ/B9JU9TTgCuCfXtVjwis2NpannnqKM844A3BaBNdddx05OTk+V2a85mVLoyuwSlXXqOoB4C2gT7FtFGjgft8QsPH0o0h8fDwvvPACJ554IgDLly/nL3/5C7m5uT5XZrzkZWi0AAJvj8x0lwW6H7janet1DjCqpB2JyHARyRCRjKysLC9qNUeofv36TJkypWh4xG+++Ybbb7+d/Px8nyszXvG7I3QwMFVVk3Amg54mIofVpKqTVTVZVZNt5vPIc/TRRzN16lQaN24MwPvvv89DDz1kgxRXUV6GxgagZcDzJHdZoGFAGoCqfgXUApp4WJPxSJs2bXjppZeoU6cOANOmTeOf/7QuqqrIy9BYALQXkTYiUhOno3NWsW1+BXoCiMiJOKFhxx9R6pRTTmHixInExTnzij/99NOkpaX5XJUJN89CQ1XzgJuBdGA5zlmSpSLyoIj0dje7HbhBRH4A3gSGqrVpo1q3bt0OuaFtzJgxfPjhhz5WZMItzsudq+ocnA7OwGX3Bny/DDjbyxpM5bvsssvIysriscceo6CggFtuuYXXXnuN5ORkv0szYeB3R6ipom644QaGDRsGQG5uLsOHD+fnn3/2uSoTDhYaxjOjR4+mTx/n0pzs7GyuvfZaNm60S3GinYWG8UxMTAyPPfYY5557LgCbN2/muuuuY+fOnT5XZirCQsN4qmbNmkycOJE//OEPAKxcuZIbbriBffv2+VyZOVIWGsZzdevW5aWXXqJ169YALFq0iFtvvZW8vDx/CzNHxELDVIomTZowdepUCq/o/eijj7jnnnvsqtEoZKFhKk3Lli2ZMmUK9erVAyAtLY1x48b5XJUJlYWGqVQdO3Zk0qRJ1KxZE4CJEyfy2muv+VyVCYWFhql0f/zjH3n66acREQAefPBB5syZU86rTKSw0DC+uOiii7jvvvsAUFVuv/12vvrqK5+rMsGw0DC+ueaaa7jpppsAOHDgADfeeCPLli3zuSpTHgsN46vbbruNgQMHArB7926uu+461q9fX86rjJ8sNIyvRISHHnqI888/H4CsrCyGDh3Ktm3bfK7MlMZCw/guLi6OZ555hs6dOwOwbt06rr/+evbs2eNzZaYkFhomItSuXZsXX3yR9u3bA7B48WL69evHpk2bANi0aRPTp0+3sUcjgIWGiRgJCQlMmTKFZs2aAc59KoUjm+fm5jJ69GhGjRpll5/7zELDRJTmzZszePDgUtenp6fbxNM+s9AwEefTTz8tc72NO+ovCw0TcQr7MUpjA/n4y0LDRJzExMQy1zdv3rySKjElsdAwEafwYq8jXW+8ZaFhIk7fvn1JSUkpcV1KSgqXX355JVdkAllomIgTGxvL+PHjGTt2LPHx8YAz2fTYsWOZMGECsbGxPldYvXkaGiKSKiI/icgqERldyjYDRWSZiCwVkTe8rMdEj7i4OPr371/Uv5GYmEj//v0tMCJAmZMliUgOUOp4bKraoIzXxgITgQtwZoxfICKz3AmSCrdpD9wNnK2qO0Tk6BDrN8ZUsjJDQ1XrA4jIQ8AmYBogwFVA2V3c0BVYpapr3H28BfQBAu99vgGYqKo73PfbcgSfwRhTiYI9POmtqv9U1RxV3aWqz+MEQFlaAIH3OGe6ywJ1ADqIyBci8rWIpJa0IxEZLiIZIpKRlWXzQxvjp2BDY4+IXCUisSISIyJXAeG4BTEOaA90BwYDL4pIQvGNVHWyqiaranLhaNbGGH8EGxpXAgOB39zHAHdZWTYALQOeJ7nLAmUCs1T1oKquBX7GCRFjTIQKatZ4VV1H+YcjxS0A2otIG5ywuILDg2YmTgvjFRFpgnO4sibE9zHGVKKgWhoi0kFEPhKRJe7zTiLy97Jeo6p5wM1AOrAcSFPVpSLyoIj0djdLB7aJyDJgPnCnqtqQTcZEsKBaGsCLwJ3ACwCquti9puLhsl6kqnOAOcWW3RvwvQK3uQ9jTBQItk+jjqp+W2yZjYRiTDUUbGhsFZG2uBd6iUh/nOs2jDHVTLCHJyOBycAJIrIBWItzgVeVc83L35C5Yx9JjWozbdgZfpdjTMQJNjR+UdXzRaQuEKOqOV4W5afMHftYu9VGwTamNMEenqwVkcnAmcBuD+sxxkS4YEPjBOBDnMOUtSLynIic411ZxphIFVRoqOpeVU1T1b7AaUADoOzRX40xVVKwfRqIyJ+AQUAqkIFzWbkx1U7btm3jgD8Dw3BulVgPvAy8unr1al9mcxKR7sAdqtrL6/cKKjREZB3wHZCGc9Wm9RSaaskNjLeBvgGLWwJnAZe0bdt20OrVq6v0NUzBtjQ6qeouTysxYWWnjj3zZw4NjEB9gWuAV45kxyLSGpgLfI0TQgvcfT0AHM3vlzk8C9QC9gHXqupPxfZTF5gAnAzUAO5X1f8cSU0lKW/krrtU9XHgERE5bAQvVb0lXIWY8LJTx54ZFsT6IwoNVzucu8ivwwmNK4FzgN7A/8MJrXNVNU9Ezgf+AfQrto8xwMeqep071MS3IvJhuI4QymtpLHe/ZoTjzYypAlqWs75VBfe/VlV/BBCRpcBHqqoi8iPQGmgIvOoOlak4LYniLgR6i8gd7vNabl3LS9g2ZOUN9/df99sfVXVRON7QmCi3nrKD49cK7j834PuCgOcFOL+vDwHzVfVy93DmkxL2IUC/4oct4RLsdRpPichyEXlIRE72ohBjosTLFVxfUQ35fTCroaVskw6MEhEBEJHTwllAsNdp9AB6AFnACyLyY3njaRhTRb0KvFvKuneB1zx+/8eBR0XkO0o/UngI57BlsXuI81A4Cwj6Og1V3QyMF5H5wF3AvZQznoYxVc3q1avz27ZtOwjnLMkwnL6CX3FaGK9V5DoNd4S8kwOeDy1lXYeAl/3dXf8J7qGKqu4D/nKkdZQn2Os0TsS5sKsfsA3nPPXtXhVlTCRzr8N4hYqdJYlawbY0pgBvASmqutHDeowxEa7c0HBnSlurqs9WQj3GmAhXbkeoquYDLUWkZiXUY4yJcMEenqwFvhCRWQRMkqSqT3tSlTEmYgUbGqvdRwxQ37ty/JOXX8C7izawcec+ADbu3EfagvX065JEbIz4XF31lZSUdMhX479gJ0t6wOtC/JSXX8DNb3zH3KWbi5bl5hVw1zuL+XjFFp678jTiYoO9Ds6E06uvvup3CYdpPXp2qbfGr3vskiM+5SoitwAjgEWqGvYxeEXkfmC3qj5Zkf0EO1nSfBH5uPgjiNelishPIrJKREaXsV0/EVERSQ6l+HB5d9GGQwIj0Nylm3n3u+KzSZrqyg2Mt3FC4ix+vy3+ZSDNXX+kbgIu8CIwwinYD3hHwPe1cK7XKHPMAPesy0TgApw5WxeIyCxVXVZsu/rArcA3wRYdbm9nrC9zfdqC9QxMLu8+JVNNeHJrvIhMAo4D3heRt4C2FLu1XUSGApcBdXHmPH4SqOm+Zy5wsapuF5EbgOHuulXANaq6t9j7tcX5/WwK7AVuUNUVwdQa7GXkCwMeX6jqbTgzvZelK7BKVdeo6gGc6zxKmg/2IWAssD+YWrywye3HKM3GctabaiWYW+NDpqo3Ahtxbteoi3Nre1f3+RPuGBngBElf4HTgEWCvqp4GfIUTaADvqurpqnoKzp2tJdU0GRilql1wGgX/DLbWYK8IbRzwNAZIxrlxpiwtcI71CmUCh4wGIyKdgZaqOltE7izj/YfjJCetWlX0zuPDJSbUZmN26ZklAgfzC6hh/RrG+1vjofRb28G5wzUHyBGRbKDoTnSgk/v9ySLyMJAA1MO5ga2IiNTDOaSa7t7TBhAfbHHBHp4sxJ1dDeewZB1HmKiFRCQGeJrS79QroqqTcZKR5OTkwwYDqqhByS1Z+MuOUtdv2LmfyyZ+weP9O3FS8/Ky0lRxXt8aD6Xc2i4iZ1D+rfMAU4HLVPUH95Cme7H9xwA7VfXUIymuzD+dInK6iDRT1TaqehzOsGMr3Meysl6Lc/tu4A83id9v6QXn1O3JwCfuGKRnArP86Azt1yWJ1JOalbiu8Gzr0o276PPcFzyZ/hP7D/oydqyJDJVxa3xFb22vD2wSkRqUMBOiO3TnWhEZ4O5fROSUYHdeXnv7BeCAu+NuwKM4twZn4/7lL8MCoL2ItHGvJr0CmBVQeLaqNlHV1qraGmdcxN6qWumjhMXGCM9deRqP9+9EfJzzI4mPi+Hx/p344LY/cUYb5+gsr0B5bv4qLhn/vzJbJqZKq4xb4yt6a/s9OCcWvsD5A1+Sq4BhIvIDsJSS+xtLJKqlt/ZF5Ae3MwURmQhkqer97vPvy2veiMjFwDNALDBFVR8RkQeBDFWdVWzbT3CGYC8zNJKTkzUjw7tc6fHkJ6zduoc2Teoy/47uABQUKG8u+JVH56xgd26eWy8MPas1d6YcT52aFTnL5o2SPocpU9BX8LmnVUu8Nb4i12lEi/L+t8eKSJyq5gE9cTsjg3wtqjoHmFNs2b2lbNu9vP35JSZGuOqMY+lx/NGMmfEj83/KQhVe+WIdHy7/jcf6duLsdk38LtNUknWPXVKtb40v7/DkTeBTEfkPznDp/wMQkXY4hyjVSvOE2kwZejrjBp1CQh1nPNf12/dx1UvfMPqdxWTvO+hzhcZ4r8zQUNVHcAbbmQqco78fy8QAo7wtLTKJCJeflsQHf/sTl/whsWj5WwvWc+G4T/lg2W8+Vlf1XPPyN/R48hOuedm3a/9MMcHcGv+1qs4InDNBVX+u7qOTN60fz8SrOjPp6i40re+c4v5tVy43vJbBqDe/Y9vu3HL2YIJROH9L5g67wC5S2NVKFZR6cjM+/NufGNDl97sw//vDRi4Y9xn/+X4DZXU0GxONLDTCoGGdGjwx4BReu64rLRJqA7B9zwFufet7bngtg81lXG1qTLSx0Aijbh2aMu9v3Rjyx2OLln24fAsXPP0pb377q7U6TJVgoRFmdePjeKDPyUy/8Y8c18S5xygnN4+73/2Rq176hl+37S1nD8ZENgsNj5zeujFzbj2XEd3bFo389eXqbaQ88xkvf76W/AJrdZjoZKHhoVo1Yvm/1BOYedPZnJjYAIB9B/N56L1lDJj0Jau25PhcoTGhs9CoBH9Iasism8/m9gs6UNO9vX7Rrzu5+NnPee7jlRzML/C5QmOCZ6FRSWrExjCqZ3tm33IOp7ZMAOBAfgFPzvuZ3s99wZIN1e4CWxOlLDQqWftj6vPOiLP4+yUnUquG8+NfvmkXfSZ+wdi5K+y2exPxLDR8EBsjXH/uccz76584q+1RAOQXKM9/spqLx/+PjHXbfa7QmNJZaPio1VF1eP36M3i07x+oH+/cNLwmaw8DXviK+2ctZU9umWM3G+MLCw2fiQiDu7Zi3m3d6HnC0QCowtQv13HhuM/438osnys05lAWGhEisWFtXhqSzLNXnErjus60uRt27uOal7/lzuk/kL3Xbrs3kcFCI4KICH1ObcEHf+vGpac0L1o+fWEm54/7lPRSJnQypjJZaBST1Kg2bZrUJalRbd9qOKpePBMGn8aLf07maPe2+6ycXP4ybSEj31jE1mpw231efgFpC9YfNreuXUnrv8gb3NJn04adUf5GleSCjsfQtU1j/jF7edEscLMXb+LLVVu579KT6HNqcwLmragybG7dyGY/+QjXsHYNxvbvxOvXn0HLxk7rZ8feg/z17e8Z9mpGlZj9TVXZtf8gq7bk8PnKrYyZscTm1o1g1tKIEme3a0L6X7vxRPpPTP1yHarw8YotXDjuM+6++AQGn96KmJjIa3Xk5uWzZVcuW3L2szk7l9927S96bN61n992Ocv2Hgj+ojabW9dfFhpRpE7NOO679CR6dUrkrn8vZnXWHnbn5jFmxhL++8NGHuvbidZN6pa/ozAoKFC27z3A5uz9RYGwedd+thQLg+17DoT9vatC6yqaWWhEoS7HNmb2Lecy4eOVTPp0DfkFytdrtpP67Gf89fwOJNSucVgHYr8uSUW36Jdnd26eEwbFAqCwdVDYcjiYX7FOyZqxMRzTMJ5j6tfimIa1OKZ+LZo1jOfNb9ezduueUl/XPMG/TmpTzmRJFd65SCrwLM5kSS+p6mPF1t8GXI8zP2wWcJ2q/lLWPr2eLCnaLNmQzV3/XsyyTbvK3C71pGY8PfAUduw7WGYg/Ja9nz0hHCqURASOqhtPs4bxNGtQi6Mb1KJZg1oc0yCeYxrU4hj3eUKdGiV25KYtWM9d7ywudf+P9+/kxeFJ5B3bRSjPQkNEYoGfgQtwZoxfAAxW1WUB2/QAvlHVvSIyAuiuqoPK2q+FxuEO5hcw+bM1jPvgZ/I8PiVZPz7OaRUUC4DA503rx1OjAmc38guUka8vKrEzNPWkZky8qnPQraYQWGgEycvDk67AKlVdAyAib+HMF1kUGqo6P2D7r4GrPaynyqoRG8PIHu2Y8+Mmlm4su8VR+j6Eo+vXolnDWm7rIN4Ng8KHEwp1470/oi2cW/fd7zZwz8wl5OYVEB8Xw0OXnUy/zsEfZhlvePk/oAWwPuB5JlDWRRDDgPdLWiEiw3GnhGzVqlW46qtydpTT6RgfF0PfzkkcU0IgNK5bM6Ku+YiLjWFgckue/2Q1a7fuoXlCbTtjEiEioiNURK4GkoE/lbReVSfjzlKfnJxslwSWIjGhNhvLmC7hDy0a8mjfP1RiRaYq8vLirg1A4J+GJHfZIUTkfGAM0FtVq/710R4aVM5f4oGn219qU3FehsYCoL2ItBGRmsAVwKzADUTkNOAFnMDY4mEt1UK/LkmkntSsxHWpJzWjX+ekEtcZEwrPQkNV84CbgXRgOZCmqktF5EER6e1u9gRQD5guIt+LyKxSdmeCUNiB+Hj/TsTHOf+08XExPN6/k1dnHEw15GmfhqrOAeYUW3ZvwPfne/n+1ZF1IBqv2Q1rxpiQWGgYY0JioWGMCYmFhjEmJBYaxpiQWGgYY0JioWGMCYmFhjEmJBFxw5oxpSmcSsLPKSXMoSw0TESLpCkljMMOT4wxIbHQMMaExELDGBMSCw1jTEgsNIwxIbHQMMaExELDGBMSCw1jTEgsNIwxIbHQMMaExELDGBMSCw1jTEg8DQ0RSRWRn0RklYiMLmF9vIi87a7/RkRae1mPMabiPAsNEYkFJgIXAR2BwSLSsdhmw4AdqtoOGAeM9aoeY0x4eNnS6AqsUtU1qnoAeAvoU2ybPsCr7vf/BnpKJE1dbow5jJeh0QJYH/A8011W4jbuNI7ZwFEe1mSMqaCo6AgVkeEikiEiGVlZWX6XY0y15mVobAACJxFNcpeVuI2IxAENgW3Fd6Sqk1U1WVWTmzZt6lG5VUtSo9q0aVLXhskzYeflcH8LgPYi0gYnHK4Ariy2zSxgCPAV0B/4WFXVw5qqDRsmz3jFs9BQ1TwRuRlIB2KBKaq6VEQeBDJUdRbwMjBNRFYB23GCxRgTwSTa/rAnJydrRkaG32WYqsfO2gUpKjpCjTGRw0LDGBMSCw1jTEiirk9DRLKAXzx+mybAVo/fozLY5wjeVlVN9fg9qoSoC43KICIZqprsdx0VZZ/DeMEOT4wxIbHQMMaExEKjZJP9LiBM7HOYsLM+DWNMSKylYYwJiYWGMSYkFhrGmJBYaBhjQmKhYYwJiYWGMSYkFhrGmJBYaBhjQmKhYYwJiYVGmIlIvoh8H/A4bDrKMl7bXUTeq+D7fyIiR3RHqIhMFZH+FXl/dz8nicjH7pScq0XkARGx/2tVhJejkVdX+1T1VD/e2J0K01ciUhtnlPkRqjpPROoA7wC34ky9aaKcpX8lEZF1IvKo2/rIEJHOIpLu/iW+MWDTBiIy2/0rPanwL7SIPO++bqmIPFBsv2NFZBEwIGB5jNtyeFhEYkXkCRFZICKLReQv7jYiIs+57/UhcHQYPuqVwBeqOg9AVfcCNwN3hmHfJgJYSyP8aovI9wHPH1XVt93vf1XVU0VkHDAVOBuoBSwBJrnbdMWZMPsXYC7QF2ee2zGqut1tTXwkIp1UdbH7mm2q2hnADaA44HVgiao+IiLDgWxVPV1E4oEvRGQecBpwvPt+xwDLgCnFP5CI3AlcVcJn/UxVbym27CRgYeACVV0tIrVFJEFVd5b2gzPRwUIj/Mo6PJnlfv0RqKeqOUCOiOSKSIK77ltVXQMgIm8C5+CExkD3lz8OSMT5RS8MjcJQKvQCkKaqj7jPLwQ6BfRXNATaA92AN1U1H9goIh+XVLSqPgE8EcRnN9WAhUblynW/FgR8X/i88N+i+FgF6s5SdwdwuqruEJGpOC2UQnuKveZLoIeIPKWq+3Hm9BilqumBG4nIxcEUHWJLYxlOGAW+/jic1pC1MqoA69OIPF1FpI3blzEI+BxogBMM2SJyDHBROft4GZgDY2w4NgAAGQJJREFUpLlz5KYDI0SkBoCIdBCRusBnwCC3zyMR6FHSzlT1CVU9tYRH8cAA57DoHBE5332v2sB44L7QfgwmUllLI/yK92nMVdWgT7vizIH7HNAOmA/MUNUCEfkOWAGsB74obyeq+rSINASm4bQSWgOLRESALOAyYAZwHk7r4FecOXUrRFX3iUhvYIKI/BNoATysqq9XdN8mMtjIXcZTInIZ8DTQQ1W9nnrCVAILDWNMSKxPwxgTEgsNY0xILDSMMSGJutBITU1VnGsZ7GGPcD5MkKIuNLZurQrzGRsTvaIuNIwx/rLQMMaExK4IdeXlF/Duog28nbGeTTv3kZhQm0HJLenXJYnYGPG7vKBVlc9hIpdnoSEiU4BewBZVPbmE9QI8C1wM7AWGquoir+opS15+ATe/8R1zl24uWvb/27v76KrqO9/j728SwpMgIijBgFhEKQjDwwm0cmdaa3vFdpYW0IgyVryM1Kq1XeN0ytzbcVla71g712lVtFJrFaeVhvIwdOqVKYrVgiQ5BEQI4mUQIRAlIE/Kkwnf+8fZpIeQk5wD2ech+bzWOivn7P0753z3Ivmw92/v32/vOnCUNe/t45W3d/P4LaMpyM/+nbL2sh2S3cLc03iW2BiKeQnWX0tsePYQYDzwZPAz7RZV7TzlDy3eSxvf52/nRRnar2eaq0rd27UHefWdumbXvbTxfRat3UlpZECaq5L2JrTQcPfXzGxQC02uB+Z57Dr21WbWy8yK3L02rJoS+U10R4vrX91cx6ubm/9jzCVllTsUGnLWMrmvehGxEZsn1QTLTmNmM4Op7qJ1dW3/x1u7/0ibf2Y22tVBtlPClRMdoe4+F5gLEIlE2vxCnKJeXdl14GjC9cOLevKvUzMyV3BKvj1/HdW1BxOuP697YRqrkfYqk6GxE4jfVy4OlqXdTZEBrHlvX8L1t00YxGUX9khjRWdm+pWD+IeF6xOu31r3Mau37uUznzo/jVVJe5PJw5OlwNeCGbE/Q2zi27T3ZwBMGVvMxOH9ml03cXg/powpTnNFZ6al7QA48kkDt/6inCVrM5LN0k6ENp9GMCnu54E+wAfEpnvrBODuPwtOuT4OTCR2yvV2d4+29rmRSMSj0Vabpay+4QSL1u6krHIHu/YfoX+vrpSWDGDKmNy6vqG57Zg89iLWbNvHwqo/h8V9X7qMe75wKbF/BiE2j6okIecm4QkrNNo7d+exV7bwyB/eaVxWGinmwUkj6KRrN0ChkTT9tnQQZsa9Vw/hkdK/oFN+7O+jLFrD7b+s5ODRTzJcneQShUYHM3lMMc/9j3H06BLrA//Tlj3c+OQb7NTpWEmSQqMDunJwHxZ940ou6tUVgM0fHGLSnJVs2Hkgw5VJLlBodFBDLuzB4ruvZGTxuQDsPnSM0qfeYMXbuzNcmWQ7hUYHdkGPLsyf+Rm++OkLATh8vIEZz1Xyb6t1pwFJTKHRwXUrLOCpW8cy/cpBAJxw+N6SDfzzi5s4cSK3zqxJeig0hPw844HrhvNPfz2Mk5dtPPXaVr75wlqOftKQ2eIk6yg0pNGM/3YJT04bS5dOsV+L379Vy7Sny/nw4+MZrkyyiUJDTjHxin68cMdnOD8Y3LbmvX1MfmIl7+5pemN66agUGnKa0QPPY/FdE/hU3+4AbNt7mMlPrCS67cMMVybZQKEhzRp4fjcWfeNKxl/SG4B9hz/hlqfL+d2buzJcmWSaQkMS6tWtkHkzxvHVUf0BOF5/gm++sJYnX/0vcm3MkrSdnJiERzKnc0E+/3rTKAb07sZjr2wB4Ecvvc2OfYeZfd3w0CcqvvUX5dTsO0LxeV15fkZGppCVJrSnIa0yM+7775fz8JSRFATTBPy6fDsznovy0bH6UL+7Zt8R3t3zMTX7NDYmWyg0JGmlJQP45e0l9Ogc20H94zt1lP7sDd5vYapEaX8UGpKSvxzSlwXf+CxF53YBoLr2IJOeWMmmFuYmlfZFoSEpG9qvJ0vunsCwoti9YGoPHOXGn73BawnuuSLti0JDzsiFPbtQdudnueryvgB8dKye25+t5DeV2zNcmYRNoSFn7JzOBfz8axGmjR8IQMMJ57sL3+Jflm3WKdl2TKEhZ6UgP48ffvUK/vHaoY3LHl+xhW//Zh3H6jXYrT1SaMhZMzO+/rnBzLllDIUFsV+pf1+3i1ufrmD/YQ12a28UGtJmvjKyiBfuGM953ToBULHtQyY/uYrtew9nuDJpS6GGhplNNLPNZrbFzGY1s36gma0ws7Vmtt7MvhxmPRK+sRf3ZvFdExh0fjcgdle3SU+spGp74jvYSW4JLTTMLB+YA1wLDANuNrNhTZp9Dyhz99HAVOCJsOqR9BnUpzuL7ppA5OLzANj78XFunrualzZk5AZ60sbC3NMYB2xx963ufhyYD1zfpI0DPYPn5wIaQtlO9O5eyL/97Xi+MrIIgGP1J/jGr6p4+vWtOrOS48IMjYuAHXGva4Jl8R4A/sbMaoAXgW+GWI+kWZdO+Tw2dTR3fm4wAO7ww99v4vu/q6ZB84/mrEx3hN4MPOvuxcCXgefN7LSazGymmUXNLFpXp6sOc0lenjHr2qE8OOmKxnviPrtqG19/Psrh4+EOdpNwhBkaO4EBca+Lg2XxZgBlAO7+BtCF2A2jT+Huc9094u6Rvn37hlSuhGna+It5+rYI3QvzAVi+aTc3PbWa3Yc02C3XhBkalcAQM7vEzAqJdXQubdJmO3A1gJl9mlhoaFeinbrq8gsou/OzXNizMwBv7TzApDmreOeDQxmuTFIRWmi4ez1wD7AM2ETsLMlGM5ttZtcFze4D7jCzN4EXgOmuXrJ2bXj/c1l81wSG9usBwM79R5jy5CpWbdmT4cokWaH2abj7i+5+mbsPdvcHg2X3u/vS4Hm1u09w979w91Hu/p9h1iPZoX+vriy487P85ZDYkeiho/Xc9ssKFq6pyXBlkoxMd4RKB9WjSyeemV7CTZFYt9cnDc59C97kJ8vf0SnZLKfQkIzplJ/HQ1NG8J1rLm9c9pPl/4/7FrzJ8foTGaxMWqKJhSWjzIy7r7qU4vO68p0F6znecIJFVTvZtf8I1w4vYtf+2Nygu/YfoaxyB1PGFjeeupXMsFzbFYxEIh6NRjNdhoSgfOteZj6/hgNHPknYZuLwfjx+y+gwZkFXEiVJhyeSNcZ/6nwW3XUlvbsVJmzz0sb3WbS26eU+kk4KDckqg/ueQ3Hvri22Kavc0eJ6CZdCQ7LOnkPHWlx/sp9DMkOhIVmnqFfLexr9W1kv4VJoSNY5ee1GIqUlLa+XcCk0JOtMGVvMxOH9ml03cXg/powpTnNFEk+hIVknP894/JbRPHzDSDoHExV3Lsjj4RtGMmfaGF2nkWEKDclKBfl5lEYGNPZf9O/VldLIAAVGFlBoiEhKFBoikhKFhoikRKEhIilRaIhIShQaIpIShYaIpEShISIpUWiISEoUGiKSEoWGiKQk1NAws4lmttnMtpjZrARtSs2s2sw2mtmvw6xHRM5ei7ORm9khIOHMw+7es4X35gNzgC8Ru2N8pZktdffquDZDgH8EJrj7PjO7IMX6RSTNWgwNd+8BYGY/AGqB54nN2jwNKGrls8cBW9x9a/AZ84Hrgeq4NncAc9x9X/B9u89gG0QkjZI9PLnO3Z9w90PuftDdnyQWAC25CIifAbYmWBbvMuAyM1tpZqvNbGJzH2RmM80sambRujrdH1okk5INjY/NbJqZ5ZtZnplNAz5ug+8vAIYAnwduBn5uZr2aNnL3ue4ecfdI37592+BrReRMJRsatwClwAfB48ZgWUt2AvGTORYHy+LVAEvd/RN3fxd4h1iIiEiWSuq2jO6+jdYPR5qqBIaY2SXEwmIqpwfNEmJ7GL80sz7EDle2pvg9IpJGSe1pmNllZvaymW0IXo80s++19B53rwfuAZYBm4Ayd99oZrPN7Lqg2TJgr5lVAyuA77j73jPdGBEJX7I3gP458B3gKQB3Xx9cU/HDlt7k7i8CLzZZdn/ccwf+LniISA5Itk+jm7tXNFlW39bFiEj2SzY09pjZYIILvczsBmLXbYhIB5Ps4cndwFxgqJntBN4ldoGXiHQwyYbGe+7+RTPrDuS5+6EwixKR7JVsaLxrZi8BvwFeCbEekVMUn9f1lJ+SeRY7gdFKI7NuwF8Tu9ZiDPAfwHx3/1O45Z0uEol4NBpN99dK+6dbtyUpqY5Qdz/s7mXuPhkYDfQE/hhqZSKSlZKeT8PMPmdmTwBrgC7ELisXkQ4mqT4NM9sGrAXKiF212RaD1UQkByXbETrS3Q+GWomI5ITWZu76B3d/GHjQzE7rMXX3e0OrTESyUmt7GpuCnzpdISJA69P9/S54+pa7V6WhHhHJcsmePfk/ZrbJzH5gZleEWpGIZLVkr9O4CrgKqAOeMrO3WptPQ0Tap6Sv03D39939UeBOYB1wfytvEZF2KNmZuz5tZg+Y2VvAY8AqYnN+ikgHk+x1Gs8A84Fr3H1XiPWISJZrNTSCO6W96+4/TUM9IpLlWj08cfcGYICZFaahHhHJcknPpwGsNLOlxN0kyd0fCaUqEclayYbGfwWPPKBHeOWISLZL9mZJ3w+7EBHJDckOjV9BMBN5PHf/Qivvmwj8FMgHnnb3hxK0mwL8Fihxd41zEcliyR6e/H3c8y7AFFq570lw1mUO8CVi92ytNLOl7l7dpF0P4FtAebJFi0jmJHt4sqbJopVm1vTmSU2NA7a4+1YAM5tP7H6w1U3a/QD4EbE7uIlIlkv2itDecY8+wWHHua287SJgR9zrmmBZ/OeOAQa4++9b+f6ZZhY1s2hdXV0yJYtISJI9PFnDn/s06oFtwIyz+WIzywMeAaa31tbd5xK7WRORSKT16dNFJDStzdxVAuxw90uC17cR68/YxumHGU3tBAbEvS4Olp3UA7gCeNXMAPoBS83sOnWGimSv1g5PngKOA5jZXwH/DDwHHCD4n78FlcAQM7skuJp0KrD05Ep3P+Dufdx9kLsPAlYDCgyRLNfa4Um+u38YPL8JmOvuC4GFZraupTe6e72Z3QMsI3bK9Rl332hms4Gouy9t6f0ikp1aDQ0zK3D3euBqYGYK78XdXwRebLKs2Xk43P3zrX2eiGRea3/4LwB/NLM9wBHgdQAzu5TYIYqIdDCtTSz8oJm9DBQB/+l/vvFrHvDNsIsTkeyTzCHG6maWvRNOOSKS7ZKeI1REBBQaIpIihYaIpEShISIpUWiISEoUGiKSEoWGiKREoSEiKVFoiEhKFBoikhKFhoikRKEhIilRaIhIShQaIpIShYaIpEShISIpUWiISEoUGiKSEoWGiKREoSEiKQk1NMxsopltNrMtZjarmfV/Z2bVZrbezF42s4vDrEdEzl5ooWFm+cAc4FpgGHCzmQ1r0mwtEHH3kcBvgYfDqkdE2kaYexrjgC3uvtXdjwPzgevjG7j7Cnc/HLxcTewm0SKSxcIMjYuAHXGva4JlicwA/m9zK8xspplFzSxaV1fXhiWKSKqyoiPUzP4GiAA/bm69u89194i7R/r27Zve4kTkFK3eYe0s7AQGxL0uDpadwsy+CPwv4HPufizEekSkDYS5p1EJDDGzS8ysEJgKLI1vYGajgaeA69x9d4i1iEgbCS003L0euAdYBmwCytx9o5nNNrPrgmY/Bs4BFpjZOjNbmuDjRCRL2J9vBJ8bIpGIR6PRTJch7Y9luoBcEWafhshZu+2226ipqaG4uJjnnnsu0+UICg3JcjU1NWzbti3TZUicrDjlKiK5Q6EhIilRaIhIShQaIpIShYaIpEShISIpUWiISEoUGiKSEoWGZKX6+noWLFhAbW0tALW1tSxYsICGhoYMVyYaeyJZp76+nnvvvZdly5adtu6aa67h0UcfpaCgzS9m1tiTJGlPQ7LOokWLmg0MgGXLlrFkyZI0VyTxNPZEMq6hoYHNmzdTXl5OZWUlL7/8covty8rKuOGGG9JUnTSl0JC0q6+vp7q6mvLycioqKohGoxw8eDDp9+/atSvE6qQ1Cg0J3fHjx1m/fj2VlZWUl5dTVVXFxx9/nLB9586dOXYs8cyP/fv3D6NMSZJCQ9rc0aNHWbduHRUVFVRUVLB27VqOHj2asP3gwYMZN25c4+P1119n1qzT7q3VqLS0NIyyJUkKDTlrhw8fpqqqqvFwY/369Rw/fjxh+6FDh1JSUsL48eMpKSmhT58+p6yfPHkyK1asSHj2ZNKkSW2+DZI8nXKVlB06dIhoNNq4J7Fhwwbq6+ubbZuXl8ewYcMaA6KkpIRevXq1+h319fUsWbKE+++/n2PHjtG5c2dmz57NpEmTyM/Pb+tNAp1yTZpCQ1q1b98+KisrG/skNm3axIkTJ5ptW1BQwIgRIxoPNcaOHUuPHj3O+Luvvvpqtm3bxqBBg1o9q3KWFBpJ0uGJnGbPnj2NexEVFRVs3rw5YdvCwkJGjRrVeLgxevRounXrlsZqJd0UGkJtbW3jXkRFRQVbt25N2LZLly6MGTOmcU9i1KhRdO7cOY3VSqYpNAL19fUsXryYsrIyamtrKSoqorS0lMmTJ4d1DB2K1rbD3ampqWm8kKqiooLt27cn/Lzu3bszduzYxj6JESNGUFhYmMYtkmwTamiY2UTgp0A+8LS7P9RkfWdgHjAW2Avc5O7bwqypOc2NdaitraWqqooVK1aENdahzbW0HfPmzWPw4MFEo9HGQWDN6dmzJyUlJY17EsOGDcuJbZf0Ce23wczygTnAl4jdMb7SzJa6e3VcsxnAPne/1MymAj8CbgqrpkQWL17c4liHu+++m2HDhqW5qtRt3LgxYWdhdXU11dXVpy3v3bs348aNa+yTuPzyy8nL05AkSSzM/0LGAVvcfSuAmc0Hrgfif3OvBx4Inv8WeNzMzNN8SqesrKzF9cuXL2f58uVpqiZcF1xwAePGjWs83Lj00ksx04kDSV6YoXERsCPudQ0wPlEbd683swPA+cCe+EZmNhOYCTBw4MA2L7Sl3fX2pG/fvqxatUohIWclJw5W3X0uMBdi12m09ecXFRW1GBxDhw7loYceSrg+W8yaNYu333474fqBAwfmXGAUFxef8lMyL8zQ2AkMiHtdHCxrrk2NmRUA5xLrEE2r0tJSqqqqEq6//fbbGTFiRBorOjPTp09vd2M2dP/W7BNmj1clMMTMLjGzQmAqsLRJm6XAbcHzG4BX0t2fAbGxDtdcc02z63JprEN72Q7JbqFeRm5mXwZ+QuyU6zPu/qCZzQai7r7UzLoAzwOjgQ+BqSc7ThMJ6zLyk2MdysrK2LVrF/3796e0tDTMsQ6haC/bkQG5ddyWQRp7IhKj0EiSTsiLSEoUGiKSkpw7PDGzOuC9kL+mD02uFclR2o7k7XH3iSF/R7uQc6GRDmYWdfdIpus4W9oOCYMOT0QkJQoNEUmJQqN5czNdQBvRdkibU5+GiKREexoikhKFhoikRKERx8yeMbPdZrYh07WcDTMbYGYrzKzazDaa2bcyXdOZMLMuZlZhZm8G2/H9TNck6tM4hZn9FfARMM/dr8h0PWfKzIqAInevMrMewBrgq02mWsx6Fpv8o7u7f2RmnYA/Ad9y99UZLq1D055GHHd/jdho25zm7rXuXhU8PwRsIjZLWk7xmI+Cl52Ch/6XyzCFRjtnZoOITT1QntlKzoyZ5ZvZOmA38Ad3z8ntaE8UGu2YmZ0DLAS+7e4HM13PmXD3BncfRWzmt3FmlrOHje2FQqOdCvoAFgK/cvdFma7nbLn7fmAFoEFlGabQaIeCDsRfAJvc/ZFM13OmzKyvmfUKnncldg+dxDMnS1ooNOKY2QvAG8DlZlZjZjMyXdMZmgDcCnzBzNYFjy9nuqgzUASsMLP1xOac/YO7/0eGa+rwdMpVRFKiPQ0RSYlCQ0RSotAQkZQoNEQkJQoNEUmJQiMHmFlDcNp0g5ktMLNuLbR9wMz+Pp31Scei0MgNR9x9VDDy9jhwZ6YLko5LoZF7XgcuBTCzr5nZ+mC+ieebNjSzO8ysMli/8OQeipndGOy1vGlmrwXLhgdzV6wLPnNIWrdKcoYu7soBZvaRu59jZgXExpO8BLwGLAaudPc9Ztbb3T80sweAj9z9X8zsfHffG3zGD4EP3P0xM3sLmOjuO82sl7vvN7PHgNXu/iszKwTy3f1IRjZYspr2NHJD12B4eBTYTmxcyReABe6+B8Ddm5sH5Aozez0IiWnA8GD5SuBZM7sDOHkr+TeA/2lm3wUuVmBIIgWZLkCSciQYHt4oNiatVc8Sm7HrTTObDnwewN3vNLPxwFeANWY21t1/bWblwbIXzezr7v5KG26DtBPa08hdrwA3mtn5AGbWu5k2PYDaYJj8tJMLzWywu5e7+/1AHTDAzD4FbHX3R4F/B0aGvgWSk7SnkaPcfaOZPQj80cwagLXA9CbN/onYjF11wc8ewfIfBx2dBrwMvAl8F7jVzD4B3gf+d+gbITlJHaEikhIdnohIShQaIpIShYaIpEShISIpUWiISEoUGiKSEoWGiKTk/wOl+dhmJaRz5AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 286.5x648 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4PN9Oxy_eSdE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "outputId": "9f5e4806-1779-4c92-d50f-7c27b5896177"
      },
      "source": [
        "grid = sns.FacetGrid(train_df,row='Embarked',col='Survived')\n",
        "grid.map(sns.barplot,'Sex','Fare')"
      ],
      "execution_count": 20,
      "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 0x7f55a95235f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "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": "80JwRIRhfTsr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "c0b7fc6b-043c-47b8-9d68-8a2010dfbef4"
      },
      "source": [
        "train_df.isnull().sum()"
      ],
      "execution_count": 21,
      "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": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1e43DCPef7Ek",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "ca9797bc-3cb0-4180-ead8-dfdbcb5fa5b9"
      },
      "source": [
        "train_df['Name']"
      ],
      "execution_count": 22,
      "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": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_dMgohXpgKyV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combine = [train_df,test_df]"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FcKLC15qhJAF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "bc7c775f-3520-4e74-d47f-c84e518c271c"
      },
      "source": [
        "combine"
      ],
      "execution_count": 24,
      "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": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X_H9imSThKGV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Title'] = dataset['Name'].str.extract(('([A-Za-z]+)\\.'))"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LapwdCogjRFN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "outputId": "b21ec9d5-1376-4e9e-f22c-a2dd68782781"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
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              "      <td>Braund, Mr. Owen Harris</td>\n",
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              "      <td>22.0</td>\n",
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              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
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              "      <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>Mrs</td>\n",
              "    </tr>\n",
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              "      <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",
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              "      <td>Miss</td>\n",
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              "      <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",
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              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
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              "      <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>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Rev</td>\n",
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              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ... Cabin Embarked  Title\n",
              "0              1         0       3  ...   NaN        S     Mr\n",
              "1              2         1       1  ...   C85        C    Mrs\n",
              "2              3         1       3  ...   NaN        S   Miss\n",
              "3              4         1       1  ...  C123        S    Mrs\n",
              "4              5         0       3  ...   NaN        S     Mr\n",
              "..           ...       ...     ...  ...   ...      ...    ...\n",
              "886          887         0       2  ...   NaN        S    Rev\n",
              "887          888         1       1  ...   B42        S   Miss\n",
              "888          889         0       3  ...   NaN        S   Miss\n",
              "889          890         1       1  ...  C148        C     Mr\n",
              "890          891         0       3  ...   NaN        Q     Mr\n",
              "\n",
              "[891 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A53zwXc5iG4d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "680299a1-946d-4767-e638-d3c27c386d43"
      },
      "source": [
        "train_df['Title'].unique()"
      ],
      "execution_count": 27,
      "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": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EQmSO21CiPuV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "56cc52a9-7040-4c66-f4e9-53d983642883"
      },
      "source": [
        "pd.crosstab(train_df['Title'],train_df['Sex'])"
      ],
      "execution_count": 28,
      "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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              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Sex</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
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              "      <th>Title</th>\n",
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              "  <tbody>\n",
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              "      <th>Capt</th>\n",
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              "      <th>Col</th>\n",
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              "      <th>Don</th>\n",
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              "    <tr>\n",
              "      <th>Dr</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jonkheer</th>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lady</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Major</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Master</th>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Miss</th>\n",
              "      <td>182</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mlle</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mme</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mr</th>\n",
              "      <td>0</td>\n",
              "      <td>517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mrs</th>\n",
              "      <td>125</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ms</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rev</th>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sir</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Sex       female  male\n",
              "Title                 \n",
              "Capt           0     1\n",
              "Col            0     2\n",
              "Countess       1     0\n",
              "Don            0     1\n",
              "Dr             1     6\n",
              "Jonkheer       0     1\n",
              "Lady           1     0\n",
              "Major          0     2\n",
              "Master         0    40\n",
              "Miss         182     0\n",
              "Mlle           2     0\n",
              "Mme            1     0\n",
              "Mr             0   517\n",
              "Mrs          125     0\n",
              "Ms             1     0\n",
              "Rev            0     6\n",
              "Sir            0     1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t6bLqqGZjFaM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Title'] = dataset['Title'].replace(['Capt','Lady','Countess','Don','Dr','Jonkheer','Major','Mlle','Mme'\n",
        "                    ,'Rev','Sir','Col'],'Other')\n",
        "  dataset['Title'] = dataset['Title'].replace('Mlle','Miss')\n",
        "  dataset['Title'] = dataset['Title'].replace('Ms','Miss')\n",
        "  dataset['Title'] = dataset['Title'].replace('Mme','Mrs')"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "busYsexcnrDF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "644cbb6f-2657-4451-d4b7-88a402375e25"
      },
      "source": [
        "train_df['Title'].unique()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Mr', 'Mrs', 'Miss', 'Master', 'Other'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4rlqwaBPoyke",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "outputId": "274c7fcc-d1f2-47ff-8840-a44c5348808f"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 31,
      "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>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",
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              "      <th>0</th>\n",
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              "      <td>Braund, Mr. Owen Harris</td>\n",
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              "      <td>A/5 21171</td>\n",
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              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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              "      <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>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <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>Miss</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>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
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              "      <td>Allen, Mr. William Henry</td>\n",
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              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
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              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
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              "      <td>Montvila, Rev. Juozas</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ... Cabin Embarked  Title\n",
              "0              1         0       3  ...   NaN        S     Mr\n",
              "1              2         1       1  ...   C85        C    Mrs\n",
              "2              3         1       3  ...   NaN        S   Miss\n",
              "3              4         1       1  ...  C123        S    Mrs\n",
              "4              5         0       3  ...   NaN        S     Mr\n",
              "..           ...       ...     ...  ...   ...      ...    ...\n",
              "886          887         0       2  ...   NaN        S  Other\n",
              "887          888         1       1  ...   B42        S   Miss\n",
              "888          889         0       3  ...   NaN        S   Miss\n",
              "889          890         1       1  ...  C148        C     Mr\n",
              "890          891         0       3  ...   NaN        Q     Mr\n",
              "\n",
              "[891 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xx3Jkircoz51",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "9f6f15ae-2ef3-46d3-da91-4781eabc1cb5"
      },
      "source": [
        "train_df[['Title','Survived']].groupby('Title',as_index=False).mean()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</div>"
            ],
            "text/plain": [
              "    Title  Survived\n",
              "0  Master  0.575000\n",
              "1    Miss  0.699454\n",
              "2      Mr  0.156673\n",
              "3     Mrs  0.792000\n",
              "4   Other  0.423077"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9mR0nKAHrA42",
        "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)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1PpaROIfrtfi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "a8259f8a-f80b-4b05-97c5-ab754bd52c23"
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
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              "      <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",
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              "  </tbody>\n",
              "</table>\n",
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            ],
            "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": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JGe0-lfhsE7P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df.drop(['PassengerId','Name','Ticket','Cabin'],axis=1,inplace=True)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "afi9LLeatYGY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_df.drop(['PassengerId','Name','Ticket','Cabin'],axis=1,inplace=True)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DZhji5nftkzf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "29935df3-cc78-4725-a0c9-9fdee308ea48"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <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",
              "      <td>2</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <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",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <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": "XnByBHx1tqDW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combine = [train_df,test_df]"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kl6XExf6t8FW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Sex'] = dataset['Sex'].map({'male':0,'female':1})\n"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7snFiLHluo24",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "6c8e8dbb-7422-4c92-ba10-8a098dd97d09"
      },
      "source": [
        "train_df.isnull().sum()"
      ],
      "execution_count": 40,
      "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": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QIizaW-Yu0_x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        },
        "outputId": "134ce016-9b74-4015-d4fe-6af0ec58430f"
      },
      "source": [
        "test_df.isnull().sum()"
      ],
      "execution_count": 41,
      "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": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78y5i9SUgmCo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a99fb5fa-f3a9-4f9c-e162-c9f2caeaafb7"
      },
      "source": [
        "train_df['Age'].isnull().sum()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "177"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UDUKNJ6gg3on",
        "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": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MNxFmX_qhNg-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9804874a-1698-4554-c792-c70b60a73bf2"
      },
      "source": [
        "train_df['Age'].isnull().sum()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dyyoeyHChSHG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        },
        "outputId": "a15cb63e-2dc7-41c3-b18f-9797d4bd31d7"
      },
      "source": [
        "test_df.isnull().sum()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass      0\n",
              "Sex         0\n",
              "Age         0\n",
              "SibSp       0\n",
              "Parch       0\n",
              "Fare        1\n",
              "Embarked    0\n",
              "Title       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dt8hCysHhXIP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "02d940ff-c191-4855-d9a6-8b4fc23ab07d"
      },
      "source": [
        "train_df['Age'].unique()"
      ],
      "execution_count": 46,
      "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": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3VT2TufYhjTe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['AgeBand'] = pd.cut(train_df['Age'],5) #cut은 동일한 길이로 나누기"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yp2WeXLeh8JO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "16fef90b-cae8-4744-a04d-5a6852349522"
      },
      "source": [
        "train_df['AgeBand'].unique()"
      ],
      "execution_count": 48,
      "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": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DQrRhBV_iInO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "b4d3cf19-064a-4fb3-eed0-8447c2c706a3"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <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",
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              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>35</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>35</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",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <th>886</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>5</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
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              "      <th>887</th>\n",
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              "      <td>0</td>\n",
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              "      <td>2</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
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              "      <th>888</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "      <td>23.4500</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
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              "      <th>889</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C</td>\n",
              "      <td>1</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
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              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Survived  Pclass  Sex  Age  ...     Fare  Embarked  Title       AgeBand\n",
              "0           0       3    0   22  ...   7.2500         S      1  (16.0, 32.0]\n",
              "1           1       1    1   38  ...  71.2833         C      3  (32.0, 48.0]\n",
              "2           1       3    1   26  ...   7.9250         S      2  (16.0, 32.0]\n",
              "3           1       1    1   35  ...  53.1000         S      3  (32.0, 48.0]\n",
              "4           0       3    0   35  ...   8.0500         S      1  (32.0, 48.0]\n",
              "..        ...     ...  ...  ...  ...      ...       ...    ...           ...\n",
              "886         0       2    0   27  ...  13.0000         S      5  (16.0, 32.0]\n",
              "887         1       1    1   19  ...  30.0000         S      2  (16.0, 32.0]\n",
              "888         0       3    1   29  ...  23.4500         S      2  (16.0, 32.0]\n",
              "889         1       1    0   26  ...  30.0000         C      1  (16.0, 32.0]\n",
              "890         0       3    0   32  ...   7.7500         Q      1  (16.0, 32.0]\n",
              "\n",
              "[891 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9gJ6MqYNiOPW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "d1538433-1e14-4b7d-82ea-f120f203ce9f"
      },
      "source": [
        "train_df[['AgeBand','Survived']].groupby('AgeBand').mean()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
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              "      <th>AgeBand</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>(-0.08, 16.0]</th>\n",
              "      <td>0.550000</td>\n",
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              "      <th>(16.0, 32.0]</th>\n",
              "      <td>0.344762</td>\n",
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            "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": "-gOf7GSvidlG",
        "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": "PhkLmlhzjg-u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "622a3e5c-e6b0-4033-ba7a-0814e9b8fce4"
      },
      "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",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
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              "      <th>Title</th>\n",
              "      <th>AgeBand</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <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",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <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",
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              "      <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": "uUIucFC7kH6X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "02af861d-1b25-483a-9aa8-f36947294c55"
      },
      "source": [
        "train_df.isnull().sum()"
      ],
      "execution_count": 53,
      "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    2\n",
              "Title       0\n",
              "AgeBand     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6lBNNdBdpEcO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "15e223b8-1aba-449d-878a-75543ec3be45"
      },
      "source": [
        "train_df[train_df['Embarked'].isnull()]"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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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",
              "      <th>Parch</th>\n",
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              "    </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": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQSxWa-gpLSN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "eb26c3d3-9577-462a-b019-bf1a27b55068"
      },
      "source": [
        "train_df[(train_df['Survived'] ==1) & (train_df['Pclass'] ==1)]['Embarked'].value_counts()"
      ],
      "execution_count": 55,
      "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": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pW1PBAXqqeiD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df[\"Embarked\"] = train_df['Embarked'].fillna('S')"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PQdARgoFrgq2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['Embarked'] = train_df['Embarked'].astype(str)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ykw8v92GrnGE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "c418b167-134b-48af-eddd-188219178042"
      },
      "source": [
        "train_df.dtypes"
      ],
      "execution_count": 58,
      "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": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4xO1TdavsJAt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "    dataset['Embarked'] = dataset['Embarked'].map({'S':0,'C':1,'Q':2})"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6A_GqgPGsdhc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "b4d769a9-07b2-44ae-8c0e-c28bf7053980"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
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              "      <td>1</td>\n",
              "      <td>3</td>\n",
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              "    </tr>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "    </tr>\n",
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              "      <td>(32.0, 48.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>13.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
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              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
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              "      <td>2</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
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              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>2</td>\n",
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              "      <td>1</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Survived  Pclass  Sex  Age  ...     Fare  Embarked  Title       AgeBand\n",
              "0           0       3    0    1  ...   7.2500         0      1  (16.0, 32.0]\n",
              "1           1       1    1    2  ...  71.2833         1      3  (32.0, 48.0]\n",
              "2           1       3    1    1  ...   7.9250         0      2  (16.0, 32.0]\n",
              "3           1       1    1    2  ...  53.1000         0      3  (32.0, 48.0]\n",
              "4           0       3    0    2  ...   8.0500         0      1  (32.0, 48.0]\n",
              "..        ...     ...  ...  ...  ...      ...       ...    ...           ...\n",
              "886         0       2    0    1  ...  13.0000         0      5  (16.0, 32.0]\n",
              "887         1       1    1    1  ...  30.0000         0      2  (16.0, 32.0]\n",
              "888         0       3    1    1  ...  23.4500         0      2  (16.0, 32.0]\n",
              "889         1       1    0    1  ...  30.0000         1      1  (16.0, 32.0]\n",
              "890         0       3    0    1  ...   7.7500         2      1  (16.0, 32.0]\n",
              "\n",
              "[891 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6C7u9i8lsfnc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "e339d516-07fb-472c-bda2-f656d8eb4a41"
      },
      "source": [
        "train_df.isnull().sum()"
      ],
      "execution_count": 61,
      "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": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JT0xZjUmssqM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "c83f1068-8ce0-4090-b4b0-3f2c42b37058"
      },
      "source": [
        "test_df[test_df['Fare'].isnull()]"
      ],
      "execution_count": 62,
      "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",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <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",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>3</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pclass  Sex  Age  SibSp  Parch  Fare  Embarked  Title\n",
              "152       3    0    3      0      0   NaN         0    1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGSbDLois6KE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "3aeab3bf-dc17-446f-f830-1991b9c60426"
      },
      "source": [
        "test_df[['Pclass','Fare']].groupby('Pclass').mean()"
      ],
      "execution_count": 63,
      "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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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fare</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>94.280297</td>\n",
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              "      <th>2</th>\n",
              "      <td>22.202104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12.459678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Fare\n",
              "Pclass           \n",
              "1       94.280297\n",
              "2       22.202104\n",
              "3       12.459678"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QZiZaDwjtxOm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_df['Fare'].fillna(1, inplace=True)"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8OsAGROft4iD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "9d08c35d-e43e-485e-eb74-bc96adb45b26"
      },
      "source": [
        "train_df['FareBand'] = pd.qcut(train_df['Fare'],4) #pd.qcut은 동일한 값으로 나누기\n",
        "train_df[['FareBand','Survived']].groupby('FareBand',as_index=False).mean()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>FareBand</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(-0.001, 7.91]</td>\n",
              "      <td>0.197309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(7.91, 14.454]</td>\n",
              "      <td>0.303571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(14.454, 31.0]</td>\n",
              "      <td>0.454955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iy3wDDLGuEgz",
        "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": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LicqCSWHvCZM",
        "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": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LyJ9SBLnvyJd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "373e4e20-1a28-4db5-8ebf-647b17f975e2"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Survived</th>\n",
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              "      <td>0</td>\n",
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              "      <td>(14.454, 31.0]</td>\n",
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              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<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": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cy3_Sis-v6EL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df.drop(['SibSp','FareBand','AgeBand','Parch'], axis=1 , inplace=True)"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yUdPi38-wSrk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "11bd91df-fa0b-490e-fce9-91de61fd53db"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
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              "      <th>Pclass</th>\n",
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              "      <th>4</th>\n",
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              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</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>2.0</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>0.0</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         0      1       1\n",
              "1           1       1    1    2   3.0         1      3       1\n",
              "2           1       3    1    1   1.0         0      2       0\n",
              "3           1       1    1    2   3.0         0      3       1\n",
              "4           0       3    0    2   1.0         0      1       0\n",
              "..        ...     ...  ...  ...   ...       ...    ...     ...\n",
              "886         0       2    0    1   1.0         0      5       0\n",
              "887         1       1    1    1   2.0         0      2       0\n",
              "888         0       3    1    1   2.0         0      2       3\n",
              "889         1       1    0    1   2.0         1      1       0\n",
              "890         0       3    0    1   0.0         2      1       0\n",
              "\n",
              "[891 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AXZT3NrnwcyU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "e30de4b5-5264-4fad-b3b7-3e221e1c5f13"
      },
      "source": [
        "test_df"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>3</th>\n",
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              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</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    2      0      0   0.0         2    1.0       0\n",
              "1         3    1    2      1      0   0.0         0    3.0       1\n",
              "2         2    0    3      0      0   1.0         2    1.0       0\n",
              "3         3    0    1      0      0   1.0         0    1.0       0\n",
              "4         3    1    1      1      1   1.0         0    3.0       2\n",
              "..      ...  ...  ...    ...    ...   ...       ...    ...     ...\n",
              "413       3    0    1      0      0   1.0         0    1.0       0\n",
              "414       1    1    2      0      0   3.0         1    0.0       0\n",
              "415       3    0    2      0      0   0.0         0    1.0       0\n",
              "416       3    0    1      0      0   1.0         0    1.0       0\n",
              "417       3    0    1      1      1   2.0         1    4.0       2\n",
              "\n",
              "[418 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvtD2LRYxODC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dz8GGuIDweeD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "    dataset['Fare'] = dataset['Fare'].astype(int)"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ghrg48RCxJSj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "9e6bccb9-2db2-4954-ff20-5959eab85f37"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 73,
      "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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              "<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",
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              "      <th>4</th>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <th>886</th>\n",
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              "      <td>0</td>\n",
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              "      <th>887</th>\n",
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <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": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MTxJRIXrxTEL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_df.drop(['SibSp','Parch'],axis=1, inplace=True)"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wtu2yv5CxTrT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "outputId": "67b67e95-70a9-4b4d-dac1-5681bfd282da"
      },
      "source": [
        "test_df.isnull().sum()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass      0\n",
              "Sex         0\n",
              "Age         0\n",
              "Fare        0\n",
              "Embarked    0\n",
              "Title       0\n",
              "Family      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hYlAfYlIxp6N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = train_df.drop('Survived',axis=1)\n",
        "Y_train = train_df['Survived']\n",
        "X_test = test_df"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tWfojtMsy9zb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "66f844e9-3092-4e96-afed-fc8a7e83639d"
      },
      "source": [
        "X_train.shape , Y_train.shape, X_test.shape"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((891, 7), (891,), (418, 7))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cC_HQJb_zRTD",
        "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": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RJzpB_Ji0vtW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, Y_train = shuffle(X_train,Y_train,random_state =23)"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L_fUzOdp1WdK",
        "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\n"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cyfr7kKf2z_U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "1759a6d1-c480-4cf7-c1d3-57257fb9715b"
      },
      "source": [
        "log_pred, log_acc = train_and_test(LogisticRegression())\n",
        "svc_pred, svc_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": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  80.47 %\n",
            "Accuracy:  83.28 %\n",
            "Accuracy:  88.78 %\n",
            "Accuracy:  80.58 %\n",
            "Accuracy:  85.97 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "26wuiQ913izi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 81,
      "outputs": []
    }
  ]
}