Titanic(2).ipynb

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      "name": "Titanic(2)",
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      "name": "python3",
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      "metadata": {
        "id": "cdP_e7kIXgtD",
        "colab_type": "code",
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      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pwyO_eC0YAHD",
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      "source": [
        "test_df = pd.read_csv ('/content/test.csv')\n",
        "train_df = pd.read_csv ('/content/train.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
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        "outputId": "bcdd0d11-6103-493f-af62-aec9249a7282"
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      "source": [
        "train_df"
      ],
      "execution_count": 3,
      "outputs": [
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
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              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
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              "     PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0              1         0       3  ...   7.2500   NaN         S\n",
              "1              2         1       1  ...  71.2833   C85         C\n",
              "2              3         1       3  ...   7.9250   NaN         S\n",
              "3              4         1       1  ...  53.1000  C123         S\n",
              "4              5         0       3  ...   8.0500   NaN         S\n",
              "..           ...       ...     ...  ...      ...   ...       ...\n",
              "886          887         0       2  ...  13.0000   NaN         S\n",
              "887          888         1       1  ...  30.0000   B42         S\n",
              "888          889         0       3  ...  23.4500   NaN         S\n",
              "889          890         1       1  ...  30.0000  C148         C\n",
              "890          891         0       3  ...   7.7500   NaN         Q\n",
              "\n",
              "[891 rows x 12 columns]"
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      "source": [
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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",
              "413         1305       3  ...   NaN        S\n",
              "414         1306       1  ...  C105        C\n",
              "415         1307       3  ...   NaN        S\n",
              "416         1308       3  ...   NaN        S\n",
              "417         1309       3  ...   NaN        C\n",
              "\n",
              "[418 rows x 11 columns]"
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          "height": 359
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        "outputId": "f94f7551-991a-499b-8b84-97816d72b515"
      },
      "source": [
        "train_df.info()"
      ],
      "execution_count": 5,
      "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"
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      "source": [
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      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 11 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  418 non-null    int64  \n",
            " 1   Pclass       418 non-null    int64  \n",
            " 2   Name         418 non-null    object \n",
            " 3   Sex          418 non-null    object \n",
            " 4   Age          332 non-null    float64\n",
            " 5   SibSp        418 non-null    int64  \n",
            " 6   Parch        418 non-null    int64  \n",
            " 7   Ticket       418 non-null    object \n",
            " 8   Fare         417 non-null    float64\n",
            " 9   Cabin        91 non-null     object \n",
            " 10  Embarked     418 non-null    object \n",
            "dtypes: float64(2), int64(4), object(5)\n",
            "memory usage: 36.0+ KB\n"
          ],
          "name": "stdout"
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          "height": 297
        },
        "outputId": "04e55465-c022-4ee2-fc92-c6e1e297e42a"
      },
      "source": [
        "train_df.describe()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       PassengerId    Survived      Pclass  ...       SibSp       Parch        Fare\n",
              "count   891.000000  891.000000  891.000000  ...  891.000000  891.000000  891.000000\n",
              "mean    446.000000    0.383838    2.308642  ...    0.523008    0.381594   32.204208\n",
              "std     257.353842    0.486592    0.836071  ...    1.102743    0.806057   49.693429\n",
              "min       1.000000    0.000000    1.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     223.500000    0.000000    2.000000  ...    0.000000    0.000000    7.910400\n",
              "50%     446.000000    0.000000    3.000000  ...    0.000000    0.000000   14.454200\n",
              "75%     668.500000    1.000000    3.000000  ...    1.000000    0.000000   31.000000\n",
              "max     891.000000    1.000000    3.000000  ...    8.000000    6.000000  512.329200\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUIks7iYZBWz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "b4cf4e2a-a81d-4b5e-86ef-cee5cf22fa63"
      },
      "source": [
        "train_df.describe(include=['O']) # category에 관하여 describe를 해준다."
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891</td>\n",
              "      <td>891</td>\n",
              "      <td>891</td>\n",
              "      <td>204</td>\n",
              "      <td>889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>891</td>\n",
              "      <td>2</td>\n",
              "      <td>681</td>\n",
              "      <td>147</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Sirayanian, Mr. Orsen</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     Sirayanian, Mr. Orsen  male   1601    G6        S\n",
              "freq                        1   577      7     4      644"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6IZawZUtZoKT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s-aQCeGKZrtV",
        "colab_type": "text"
      },
      "source": [
        "#데이터 분석\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sOtnCkuzZuPS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "fca49cd0-923b-4938-e8f7-0efa2b753e46"
      },
      "source": [
        "train_df[['Pclass', 'Survived']].groupby('Pclass').mean()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.629630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.472826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.242363</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Survived\n",
              "Pclass          \n",
              "1       0.629630\n",
              "2       0.472826\n",
              "3       0.242363"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7PsQL_2rayrp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "913ba683-564a-45a5-8f0f-7045932ee6e2"
      },
      "source": [
        "train_df[['Sex', 'Survived']].groupby('Sex').mean()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>0.742038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.188908</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Survived\n",
              "Sex             \n",
              "female  0.742038\n",
              "male    0.188908"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5S4RkuBrbI_j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "c3e009a2-f20e-42ec-9404-bec13a4c074c"
      },
      "source": [
        "train_df[['Age', 'Survived']].groupby('Age').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>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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VY8NHq2pbbSr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "e2f0f802-d48c-495e-fa8f-24f2e46a4b30"
      },
      "source": [
        "train_df[['Embarked', 'Survived']].groupby('Embarked').mean()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>0.553571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Q</th>\n",
              "      <td>0.389610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S</th>\n",
              "      <td>0.336957</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Survived\n",
              "Embarked          \n",
              "C         0.553571\n",
              "Q         0.389610\n",
              "S         0.336957"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2mlQ2JFbomz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "172a9fcb-a7c2-4d52-de45-62a3c513becd"
      },
      "source": [
        "train_df[['SibSp', 'Survived']].groupby('SibSp').mean()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.345395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.535885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.464286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Survived\n",
              "SibSp          \n",
              "0      0.345395\n",
              "1      0.535885\n",
              "2      0.464286\n",
              "3      0.250000\n",
              "4      0.166667\n",
              "5      0.000000\n",
              "8      0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IhduLfnIb7rO",
        "colab_type": "text"
      },
      "source": [
        "#Visualization data analysis\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uire1xg_cB66",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "cd398a9c-0772-4e0b-db2c-69331c7d9a3f"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 14,
      "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": "dTwtnWkScMoc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "outputId": "e52dfbff-1d62-48b4-db4a-4e7407a0925b"
      },
      "source": [
        "plt.hist(train_df['Age'])"
      ],
      "execution_count": 15,
      "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([ 54.,  46., 177., 169., 118.,  70.,  45.,  24.,   9.,   2.]),\n",
              " array([ 0.42 ,  8.378, 16.336, 24.294, 32.252, 40.21 , 48.168, 56.126,\n",
              "        64.084, 72.042, 80.   ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "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": "7pFhPZqcch1z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "72b40df1-3c0b-4087-866d-468b5e6ee803"
      },
      "source": [
        "grid = sns.FacetGrid(train_df, col='Survived', hue='Survived')\n",
        "grid.map(plt.hist , 'Age',bins=20)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f13b85ebc18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "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": "2ccnca5AdSGd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "outputId": "cc55c2cf-2ba7-4412-c24b-78879e708d6f"
      },
      "source": [
        "grid = sns.FacetGrid(train_df, row ='Pclass', col='Survived')\n",
        "grid.map(plt.hist,'Age', bins=20)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f13b580d358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "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": "MmHG9EVFd2pK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "outputId": "6381b75c-55ba-4698-fd96-ef633499a537"
      },
      "source": [
        "grid = sns.FacetGrid(train_df , row='Embarked')\n",
        "grid.map(sns.pointplot, 'Pclass', 'Survived','Sex')\n",
        "grid.add_legend()"
      ],
      "execution_count": 18,
      "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 0x7f13b55e7e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 286.5x648 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E3XmavfGe37p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "outputId": "1c4df001-2cbd-4b32-9533-f9daef05823c"
      },
      "source": [
        "grid = sns.FacetGrid(train_df, row ='Embarked', col='Survived')\n",
        "grid.map(sns.barplot,'Sex','Fare',)"
      ],
      "execution_count": 19,
      "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 0x7f13b536fd30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "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": "Yq-Q8I-tf21p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "640b348f-31b1-4c42-eef6-8ca6d55bb867"
      },
      "source": [
        "train_df.isnull().sum()\n"
      ],
      "execution_count": 20,
      "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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nsEWAKHhf8sr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "cb5f6595-810c-4df5-b80d-9b6599d7a24b"
      },
      "source": [
        "test_df.isnull().sum()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age             86\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             1\n",
              "Cabin          327\n",
              "Embarked         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OMx1yuWngKLz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "7b7d53b2-89c3-47ea-d31a-8e9ab3e31ffb"
      },
      "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": "gnLzYzLsgW1b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combine = [train_df,test_df]"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "or4TlunFhI1a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "ef72992f-dfc1-41c6-ec28-eb7ddba2e76a"
      },
      "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": "wRYtEKT1hQR6",
        "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": "zbwUkNqdiPz7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "eddc5eac-5292-4288-86f8-e82989cbf27a"
      },
      "source": [
        "train_df['Title']"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        Mr\n",
              "1       Mrs\n",
              "2      Miss\n",
              "3       Mrs\n",
              "4        Mr\n",
              "       ... \n",
              "886     Rev\n",
              "887    Miss\n",
              "888    Miss\n",
              "889      Mr\n",
              "890      Mr\n",
              "Name: Title, Length: 891, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NKX7mHRmi8ej",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "7d3b7a40-309c-41df-bde5-3ff17d0d55c2"
      },
      "source": [
        "pd.crosstab(train_df['Title'],train_df['Sex'])"
      ],
      "execution_count": 27,
      "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>Sex</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Capt</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Col</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Countess</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Don</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <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",
              "      <td>1</td>\n",
              "    </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": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7hzcEcUmiw8D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Title'] = dataset['Title'].replace(['Capt', 'Lady', 'Countess', 'Col', \n",
        "                                               'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer','Dona'] ,'Other')\n",
        "  dataset['Title'] = dataset['Title'].replace('Mile', 'Miss')\n",
        "  dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
        "  dataset['Title'] = dataset['Title'].replace('Mme', 'Mr')"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tlCCEM5jqFGr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4605078f-397e-4778-973b-20d4f97712f5"
      },
      "source": [
        "\n",
        "train_df['Title'].unique()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Mr', 'Mrs', 'Miss', 'Master', 'Other', 'Mlle'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xw66ROx5o0UM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "55da51e9-8f9c-42d6-ebd0-fb90717dcc64"
      },
      "source": [
        "train_df[['Title', 'Survived']].groupby('Title',as_index=False).mean()\n"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Title</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Master</td>\n",
              "      <td>0.575000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Miss</td>\n",
              "      <td>0.699454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mlle</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Mr</td>\n",
              "      <td>0.158301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Mrs</td>\n",
              "      <td>0.792000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Other</td>\n",
              "      <td>0.347826</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Title  Survived\n",
              "0  Master  0.575000\n",
              "1    Miss  0.699454\n",
              "2    Mlle  1.000000\n",
              "3      Mr  0.158301\n",
              "4     Mrs  0.792000\n",
              "5   Other  0.347826"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wui5cnJSq8Rz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "title_column={'Mr':1, 'Miss':2, 'Mrs':3, 'Master':4,'Other':5}\n",
        "for dataset in combine:\n",
        "  dataset['Title'] = dataset['Title'].map(title_column)\n",
        "  dataset['Title'] = dataset['Title'].fillna(0)\n",
        "  dataset['Title'] = dataset['Title'].astype(int)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0fcwzH_wsD4J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "outputId": "75c36715-9273-4be7-b86f-f9760af40b0d"
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ... Cabin Embarked  Title\n",
              "0            1         0       3  ...   NaN        S      1\n",
              "1            2         1       1  ...   C85        C      3\n",
              "2            3         1       3  ...   NaN        S      2\n",
              "3            4         1       1  ...  C123        S      3\n",
              "4            5         0       3  ...   NaN        S      1\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dEco8x-7pdu1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df.drop(['PassengerId','Name', 'Ticket','Cabin'],axis =1, inplace=True)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WYWXE4pgtcfB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "outputId": "3ba47aeb-0133-4173-9a69-2c7a2ef20ab6"
      },
      "source": [
        "test_df\n"
      ],
      "execution_count": 34,
      "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>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>3</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>1305</td>\n",
              "      <td>3</td>\n",
              "      <td>Spector, Mr. Woolf</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A.5. 3236</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1306</td>\n",
              "      <td>1</td>\n",
              "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
              "      <td>female</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17758</td>\n",
              "      <td>108.9000</td>\n",
              "      <td>C105</td>\n",
              "      <td>C</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>1307</td>\n",
              "      <td>3</td>\n",
              "      <td>Saether, Mr. Simon Sivertsen</td>\n",
              "      <td>male</td>\n",
              "      <td>38.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>SOTON/O.Q. 3101262</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>1308</td>\n",
              "      <td>3</td>\n",
              "      <td>Ware, Mr. Frederick</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>359309</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>1309</td>\n",
              "      <td>3</td>\n",
              "      <td>Peter, Master. Michael J</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2668</td>\n",
              "      <td>22.3583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>418 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Pclass  ... Embarked Title\n",
              "0            892       3  ...        Q     1\n",
              "1            893       3  ...        S     3\n",
              "2            894       2  ...        Q     1\n",
              "3            895       3  ...        S     1\n",
              "4            896       3  ...        S     3\n",
              "..           ...     ...  ...      ...   ...\n",
              "413         1305       3  ...        S     1\n",
              "414         1306       1  ...        C     5\n",
              "415         1307       3  ...        S     1\n",
              "416         1308       3  ...        S     1\n",
              "417         1309       3  ...        C     4\n",
              "\n",
              "[418 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4RhtqtpuAVF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combine=[train_df,test_df]"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rrvVgd8-fljM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "25290798-a782-4452-85c3-27046c9078b9"
      },
      "source": [
        "combine"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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],      PassengerId  Pclass  ... Embarked Title\n",
              " 0            892       3  ...        Q     1\n",
              " 1            893       3  ...        S     3\n",
              " 2            894       2  ...        Q     1\n",
              " 3            895       3  ...        S     1\n",
              " 4            896       3  ...        S     3\n",
              " ..           ...     ...  ...      ...   ...\n",
              " 413         1305       3  ...        S     1\n",
              " 414         1306       1  ...        C     5\n",
              " 415         1307       3  ...        S     1\n",
              " 416         1308       3  ...        S     1\n",
              " 417         1309       3  ...        C     4\n",
              " \n",
              " [418 rows x 12 columns]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ISBNcfh2uLcj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Sex_column={'male':0 , 'female':1}\n",
        "for dataset in combine:\n",
        "  dataset['Sex'] = dataset['Sex'].map(Sex_column)"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e6qYjRPngZUU",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHhkRD4Dfejm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "ae03c524-62b9-4b81-8efe-6d764b021d0c"
      },
      "source": [
        "train_df.isnull().sum()"
      ],
      "execution_count": 38,
      "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": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "inRfBP5igs4s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "911e46bb-d3ef-4215-b061-9b9c4c5c3369"
      },
      "source": [
        "train_df['Age'].isnull().sum()\n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "177"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-IJD_StUg8_V",
        "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": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S0ImIb27hPp1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f81be7b4-fc22-4f4a-e9de-6714c8d430c9"
      },
      "source": [
        "train_df['Age'].isnull().sum()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPj2vHoghZXs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "ac0d610e-bffe-4b38-e007-38c63e684a0a"
      },
      "source": [
        "test_df.isnull().sum()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age              0\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             1\n",
              "Cabin          327\n",
              "Embarked         0\n",
              "Title            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1HTrjwhhoLc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "c4f2ffb4-5fb4-4197-f6d6-522e8c8ab3bb"
      },
      "source": [
        "train_df['Age'].unique()"
      ],
      "execution_count": 43,
      "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": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sKepv3nhhr9d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['AgeBand'] = pd.cut(train_df['Age'],5) # cut은 동일한 길이로 나누기"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQafehr8h_Or",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "7bf37fa2-862f-4057-dacc-11b6ad162140"
      },
      "source": [
        "train_df['AgeBand'].unique()"
      ],
      "execution_count": 45,
      "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": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1K9SyXo3iGU7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "b084b125-83d7-430a-8b6a-eff62b0897e8"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 46,
      "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",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "      <th>AgeBand</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>3</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>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",
              "      <td>...</td>\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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <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": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VhIcmEw9iQR0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "6fc79ef4-3939-41be-85e8-4fa150239395"
      },
      "source": [
        "train_df[['AgeBand', 'Survived']].groupby('AgeBand').mean()"
      ],
      "execution_count": 47,
      "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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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AgeBand</th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>(-0.08, 16.0]</th>\n",
              "      <td>0.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(16.0, 32.0]</th>\n",
              "      <td>0.344762</td>\n",
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              "    <tr>\n",
              "      <th>(32.0, 48.0]</th>\n",
              "      <td>0.403226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(48.0, 64.0]</th>\n",
              "      <td>0.434783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(64.0, 80.0]</th>\n",
              "      <td>0.090909</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Survived\n",
              "AgeBand                \n",
              "(-0.08, 16.0]  0.550000\n",
              "(16.0, 32.0]   0.344762\n",
              "(32.0, 48.0]   0.403226\n",
              "(48.0, 64.0]   0.434783\n",
              "(64.0, 80.0]   0.090909"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r28rCrepijSE",
        "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": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K2AJqM3mj1fJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "0c2bb81a-9882-4ba7-bf0f-319c2d1acebf"
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 49,
      "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",
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              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "      <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": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UcsmSYk6oHXG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "5e61dc4b-2093-4549-bdc0-b15af88db909"
      },
      "source": [
        "train_df.isnull().sum()"
      ],
      "execution_count": 50,
      "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": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GQCybmnzpG-7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "0245d0ac-c928-4a30-ebdd-2c659f25e3e5"
      },
      "source": [
        "train_df[train_df['Embarked'].isnull()]"
      ],
      "execution_count": 51,
      "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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              "<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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              "  </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",
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              "      <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",
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              "      <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": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRgoGnlTpzLi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "6229373a-d86d-4426-bb2a-0a28795844d7"
      },
      "source": [
        "train_df[(train_df['Survived'] == 1) & (train_df['Sex'] == 1) & (train_df['Pclass'] == 1)]['Embarked'].value_counts()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    46\n",
              "C    42\n",
              "Q     1\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dN7ywblgrTVg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['Embarked'] = train_df['Embarked'].fillna('S')"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VWNvCtIYrm1q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['Embarked']= train_df['Embarked'].astype(str)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yDNqZ_zwr5br",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Embarked'] = dataset['Embarked'].map({'S':0, 'C':1, 'Q':2})"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b0YktPoQsllY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "3ef6eb9f-1ed1-46d4-fed4-f569b6248a03"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 56,
      "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>Pclass</th>\n",
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              "    </tr>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>(32.0, 48.0]</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",
              "      <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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>(16.0, 32.0]</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>(16.0, 32.0]</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>1</td>\n",
              "      <td>2</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>(16.0, 32.0]</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>(16.0, 32.0]</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>0</td>\n",
              "      <td>7.7500</td>\n",
              "      <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": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FH8Wa1hisora",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "fe8f84c8-3151-400b-9a80-f79d5d05b5c8"
      },
      "source": [
        "train_df.isnull().sum()"
      ],
      "execution_count": 57,
      "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": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1fsFjaHLswSS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "b05407b5-650e-461b-cc32-911d83b79489"
      },
      "source": [
        "test_df[test_df['Fare'].isnull()]"
      ],
      "execution_count": 58,
      "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>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>1044</td>\n",
              "      <td>3</td>\n",
              "      <td>Storey, Mr. Thomas</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3701</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Pclass                Name  Sex  ...  Fare  Cabin  Embarked Title\n",
              "152         1044       3  Storey, Mr. Thomas    0  ...   NaN    NaN         0     1\n",
              "\n",
              "[1 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ONh5rDHysumS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "4c5dddc9-20b3-4c97-82f2-e63612b84913"
      },
      "source": [
        "test_df[['Pclass', 'Fare']].groupby('Pclass').mean()"
      ],
      "execution_count": 59,
      "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",
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              "\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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <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": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KMw7J4Cat1sw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_df['Fare'].fillna(12.459678,inplace =True)"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XMzExyFvt8za",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "d39cc110-9c54-4d94-b76a-e303de9f17fe"
      },
      "source": [
        "train_df['FareBand'] = pd.qcut(train_df['Fare'],4) # pd.qcut\n",
        "train_df[['FareBand','Survived']].groupby('FareBand', as_index= False).mean()"
      ],
      "execution_count": 61,
      "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",
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              "    }\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>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": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nfor9tg9uh7K",
        "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\n",
        "  \n"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yM2VaksDveFI",
        "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": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9RyLtrVMv7bz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "4b7a9372-36f5-4216-8056-16871d0a2955"
      },
      "source": [
        "test_df.head(1)"
      ],
      "execution_count": 64,
      "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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              "\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>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",
              "      <th>Family</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Pclass              Name  Sex  ...  Cabin  Embarked  Title Family\n",
              "0          892       3  Kelly, Mr. James    0  ...    NaN         2      1      0\n",
              "\n",
              "[1 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntIR2K084PaQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "90b1ef3c-93e6-41bf-e91d-0023740ef150"
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 65,
      "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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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "      <th>AgeBand</th>\n",
              "      <th>FareBand</th>\n",
              "      <th>Family</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "      <td>(-0.001, 7.91]</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "      <td>(31.0, 512.329]</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "      <td>(7.91, 14.454]</td>\n",
              "      <td>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>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "      <td>(31.0, 512.329]</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "      <td>(7.91, 14.454]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\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",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mdrQepH13_2S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_df.drop(['Name', 'Ticket', 'Cabin', 'PassengerId','SibSp','Parch'],axis=1, inplace=True)"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PbdIPQEOwF05",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df.drop(['AgeBand', 'FareBand', 'SibSp', 'Parch'],axis=1, inplace=True)"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GI3nqd9Pwop6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combine = [train_df , test_df]"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TAXK1GMGwtG5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Fare'] = dataset['Fare'].astype(int)"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m3sk3bhHxh2h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_df[test_df['Fare'] >10] = 1"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtgj2rBixlpR",
        "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": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zBdxU3Z9y9B2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6927f041-8226-407c-c56d-1ad2640edaf1"
      },
      "source": [
        "X_train.shape , Y_train.shape, X_test.shape"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((891, 7), (891,), (418, 7))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SDpyCQmQzRgJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "7b77cd96-99c9-4dcb-f1f1-39ee25a02f40"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "      <th>Family</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>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>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</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</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</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": "CgtEJX5czwnR",
        "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\n"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Upe7Rckq0v-B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, Y_train = shuffle(X_train, Y_train,random_state =23)"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jfrJtIn81wo_",
        "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",
        "  \n",
        "  \n"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJhnaynu204J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "85920a9a-c0d4-4894-e9c9-74c0f3ed9f5a"
      },
      "source": [
        "model = LogisticRegression()\n",
        "model.fit(X_train, Y_train)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d5AyPQ2b3EoI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "4092094b-bd4e-4bfc-bce4-54af4a4c7e8c"
      },
      "source": [
        "log_pred,log_acc = train_and_test(LogisticRegression()) #수치를 따라가는  숫자예측\n",
        "svm_pred, svm_acc = train_and_test(SVC())\n",
        "rf_pred, rf_acc = train_and_test(RandomForestClassifier()) #구별\n",
        "nb_pred, nb_acc =train_and_test(GaussianNB())\n",
        "k_pred, k_acc = train_and_test(KNeighborsClassifier())"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  80.47\n",
            "Accuracy:  83.16\n",
            "Accuracy:  88.78\n",
            "Accuracy:  80.58\n",
            "Accuracy:  85.86\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}