타이타닉 최종.ipynb

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      "name": "titanic.ipynb",
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      "name": "python3",
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      "metadata": {
        "id": "NQObRfn0WuAO",
        "colab_type": "code",
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      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DOUOKrtUXzGR",
        "colab_type": "code",
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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": []
    },
    {
      "cell_type": "code",
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        "id": "ZwXGN71zYHi-",
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          "height": 419
        },
        "outputId": "e79beec4-910b-40d6-ecde-cff3682ab06c"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
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              "      <td>Dooley, Mr. Patrick</td>\n",
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              "      <td>Q</td>\n",
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              "<p>891 rows × 12 columns</p>\n",
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              "     PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0              1         0       3  ...   7.2500   NaN         S\n",
              "1              2         1       1  ...  71.2833   C85         C\n",
              "2              3         1       3  ...   7.9250   NaN         S\n",
              "3              4         1       1  ...  53.1000  C123         S\n",
              "4              5         0       3  ...   8.0500   NaN         S\n",
              "..           ...       ...     ...  ...      ...   ...       ...\n",
              "886          887         0       2  ...  13.0000   NaN         S\n",
              "887          888         1       1  ...  30.0000   B42         S\n",
              "888          889         0       3  ...  23.4500   NaN         S\n",
              "889          890         1       1  ...  30.0000  C148         C\n",
              "890          891         0       3  ...   7.7500   NaN         Q\n",
              "\n",
              "[891 rows x 12 columns]"
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        "outputId": "4560c19f-1548-4c22-bb24-50fc03534622"
      },
      "source": [
        "test_df"
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      "execution_count": 4,
      "outputs": [
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              "     PassengerId  Pclass  ... Cabin Embarked\n",
              "0            892       3  ...   NaN        Q\n",
              "1            893       3  ...   NaN        S\n",
              "2            894       2  ...   NaN        Q\n",
              "3            895       3  ...   NaN        S\n",
              "4            896       3  ...   NaN        S\n",
              "..           ...     ...  ...   ...      ...\n",
              "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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          "metadata": {
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        "colab_type": "code",
        "colab": {
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          "height": 359
        },
        "outputId": "62a2ea8b-dc9e-4101-c36a-3aad2f0b94a6"
      },
      "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": [
        "test_df.info()"
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      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 11 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  418 non-null    int64  \n",
            " 1   Pclass       418 non-null    int64  \n",
            " 2   Name         418 non-null    object \n",
            " 3   Sex          418 non-null    object \n",
            " 4   Age          332 non-null    float64\n",
            " 5   SibSp        418 non-null    int64  \n",
            " 6   Parch        418 non-null    int64  \n",
            " 7   Ticket       418 non-null    object \n",
            " 8   Fare         417 non-null    float64\n",
            " 9   Cabin        91 non-null     object \n",
            " 10  Embarked     418 non-null    object \n",
            "dtypes: float64(2), int64(4), object(5)\n",
            "memory usage: 36.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
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        "outputId": "53e93aea-2932-4299-fded-8fb74da10920"
      },
      "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": "-Yo488NoY9Vl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "e61b1cce-0414-40c3-faeb-d9b01130f14d"
      },
      "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>Ostby, Mr. Engelhart Cornelius</td>\n",
              "      <td>male</td>\n",
              "      <td>1601</td>\n",
              "      <td>B96 B98</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>577</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>644</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                  Name   Sex Ticket    Cabin Embarked\n",
              "count                              891   891    891      204      889\n",
              "unique                             891     2    681      147        3\n",
              "top     Ostby, Mr. Engelhart Cornelius  male   1601  B96 B98        S\n",
              "freq                                 1   577      7        4      644"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93oDF2emZjbe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C_cZyo4oaMZR",
        "colab_type": "text"
      },
      "source": [
        "#데이터 분석\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9Wz8RoIaOaf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "1106d614-2f58-44dc-fe12-e75e81e9671c"
      },
      "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": "iqLwKrSBaean",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "c8e199c8-6bf3-45ae-c7f2-e74ccbe6117d"
      },
      "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": "UfysAZdsbDUx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "bbc05b69-0112-4292-d5d1-a70068e43380"
      },
      "source": [
        "train_df[['Embarked','Survived']].groupby('Embarked').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>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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FI4HJ8NwbgpO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "1178d36c-05b3-4b2e-eb94-2f808f3b5172"
      },
      "source": [
        "train_df[['SibSp','Survived']].groupby('SibSp').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>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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9eU4ejyLb0WO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b7DsVF1tb5wS",
        "colab_type": "text"
      },
      "source": [
        "#Visualization data analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ypvAqBHFcBMs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "7c2f85fc-39d0-49f9-dded-48df5bc4241b"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 13,
      "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": "XaxxqMMIcMg_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "outputId": "58477a40-b1ff-43de-b13b-78847f2ccf28"
      },
      "source": [
        "plt.hist(train_df['Age'],bins = 30)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
            "  keep = (tmp_a >= first_edge)\n",
            "/usr/local/lib/python3.6/dist-packages/numpy/lib/histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
            "  keep &= (tmp_a <= last_edge)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([30., 14., 10., 14.,  3., 29., 39., 65., 73., 42., 65., 62., 33.,\n",
              "        47., 38., 21., 28., 21., 16., 14., 15.,  7.,  9.,  8.,  4.,  0.,\n",
              "         5.,  1.,  0.,  1.]),\n",
              " array([ 0.42      ,  3.07266667,  5.72533333,  8.378     , 11.03066667,\n",
              "        13.68333333, 16.336     , 18.98866667, 21.64133333, 24.294     ,\n",
              "        26.94666667, 29.59933333, 32.252     , 34.90466667, 37.55733333,\n",
              "        40.21      , 42.86266667, 45.51533333, 48.168     , 50.82066667,\n",
              "        53.47333333, 56.126     , 58.77866667, 61.43133333, 64.084     ,\n",
              "        66.73666667, 69.38933333, 72.042     , 74.69466667, 77.34733333,\n",
              "        80.        ]),\n",
              " <a list of 30 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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o3n+pIzseM8kbgS8Dt1XVSzNvG2W2qvp19X69XUPvw88uH0WOmZJ8EDheVftGnWUO76yqt9GbNvxIknfNvHFEr+W5wNuAz1bVVcDPmDUlMQbv//OAm4Avzb5tFNm6OfeN9P7z+11gBWdOwQ7EOBf4uC/VP5ZkNUB3eXwUIZK8hl55f7GqHhynbKdU1QngUXq/Np6f5NQCslG8pu8AbkpyiN4naF5Hb4531LlOjdyoquP05nKvZvSv5WHgcFXt7bYfoFfoo8410/uBx6rqWLc96mzvBb5fVdNV9SvgQXrvu4G/x8a5wMd9qf5uYHN3fTO9+edllSTA3cCBqvrkmGWbSHJ+d/319ObmD9Ar8g+NKltV3V5Va6pqLb331L9X1YdHnSvJiiRvOnWd3pzufkb8WlbVUeCHSS7rdm0Anhp1rllu5fT0CYw+2w+Aa5K8ofs3eupnNvj32Cj/8NDHHwM+APwPvbnTvxthjnvozWX9it6IZAu9edM9wEHg34CVI8j1Tnq/Hn4HeKL7+sCYZPtD4PEu237g77v9vw98E3iW3q+8rx3h6/pu4OFxyNU9/7e7rydPvd/H5LVcD0x1r+W/AheMQ64u2wrgx8CbZ+wbeTbgE8DT3Xv/n4HXDuM95lJ6SWrUOE+hSJLOwgKXpEZZ4JLUKAtckhplgUtSoyxwSWqUBS5Jjfp/Y0mBgVAYKmAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gABOkxXdcXdv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "67892eb3-ee32-41ca-980c-132f48b5eb08"
      },
      "source": [
        "grid = sns.FacetGrid(train_df,col = 'Survived', hue = 'Survived')\n",
        "grid.map(plt.hist, 'Age' ,bins = 30)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f082c2a09e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "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": "QQOe8LBgc3Cv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "outputId": "09386704-596a-471a-d9fa-519d8952af33"
      },
      "source": [
        "grid = sns.FacetGrid(train_df,row = 'Pclass' ,col = 'Survived')\n",
        "grid.map(plt.hist,'Age', bins = 30)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f082c23d2e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "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": "1SEpNJHzdhQX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "outputId": "8710df1d-7dbd-4568-9a5d-a7cb86ae0d69"
      },
      "source": [
        "grid = sns.FacetGrid(train_df, row = 'Embarked')\n",
        "grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex',palette='deep')\n",
        "grid.add_legend()"
      ],
      "execution_count": 17,
      "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 0x7f082c2952e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "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": "eMK4NgTqeGpX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "outputId": "c778b8c6-c7ea-44ab-d324-e124ffc5abb7"
      },
      "source": [
        "grid = sns.FacetGrid(train_df, row = 'Embarked',col='Survived')\n",
        "grid.map(sns.barplot, 'Sex','Fare')"
      ],
      "execution_count": 18,
      "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 0x7f0829790eb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x648 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ex94Px1nfXNR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "3bbd31f7-cf83-45d2-bf67-e22b30277a4b"
      },
      "source": [
        "train_df.isnull().sum()"
      ],
      "execution_count": 19,
      "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": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PuudxIUlf79P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "a3c40e27-9201-4042-cb9a-f909755219db"
      },
      "source": [
        "test_df.isnull().sum()"
      ],
      "execution_count": 20,
      "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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bo6ou_3bgA-P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "b24f897c-af29-4ef7-a457-dc1be7657240"
      },
      "source": [
        "train_df['Name']"
      ],
      "execution_count": 21,
      "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": 21
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PUFl0SBRgMlZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combine = [train_df, test_df]"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vD4ikAY1hL6v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "bb7ed2d9-7db4-49f3-8fd9-14cda99a81e8"
      },
      "source": [
        "combine"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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],      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": {
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          "execution_count": 23
        }
      ]
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      "outputs": [
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              "     PassengerId  Pclass  ... Cabin Embarked\n",
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              "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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        }
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        "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": "f5T1zA0jjw14",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
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      "execution_count": 25,
      "outputs": []
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        "colab_type": "code",
        "colab": {
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          "height": 233
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        "outputId": "b155b0c3-ca6e-49ec-97f4-c80c512c8113"
      },
      "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
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "zSB3Sx3HiVSQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "a97e144d-cb63-4cf0-f371-09c751512d12"
      },
      "source": [
        "combine[0].head"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of      PassengerId  Survived  Pclass  ... Cabin Embarked  Title\n",
              "0              1         0       3  ...   NaN        S     Mr\n",
              "1              2         1       1  ...   C85        C    Mrs\n",
              "2              3         1       3  ...   NaN        S   Miss\n",
              "3              4         1       1  ...  C123        S    Mrs\n",
              "4              5         0       3  ...   NaN        S     Mr\n",
              "..           ...       ...     ...  ...   ...      ...    ...\n",
              "886          887         0       2  ...   NaN        S    Rev\n",
              "887          888         1       1  ...   B42        S   Miss\n",
              "888          889         0       3  ...   NaN        S   Miss\n",
              "889          890         1       1  ...  C148        C     Mr\n",
              "890          891         0       3  ...   NaN        Q     Mr\n",
              "\n",
              "[891 rows x 13 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1AIsP-fIjUvc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "7c207f7b-2833-40c6-f9c0-04250747e1b5"
      },
      "source": [
        "pd.crosstab(train_df['Title'],train_df['Sex'])"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "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",
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              "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"
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          "metadata": {
            "tags": []
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          "execution_count": 28
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKN7zAASj02o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "3188bed5-174d-46e2-d745-2b7ca881a34f"
      },
      "source": [
        "train_df['Title'].unique()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Mr', 'Mrs', 'Miss', 'Master', 'Don', 'Rev', 'Dr', 'Mme', 'Ms',\n",
              "       'Major', 'Lady', 'Sir', 'Mlle', 'Col', 'Capt', 'Countess',\n",
              "       'Jonkheer'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QSep8GNWlDWE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Title'] = dataset['Title'].replace(['Capt','Lady','Countess','Col','Don','Dr','Major','Rev','Sir','Jonkheer','Mlle'],'Other')\n",
        "  dataset['Title'] = dataset['Title'].replace('Mlle','Miss')\n",
        "  dataset['Title'] = dataset['Title'].replace('Ms','Miss')\n",
        "  dataset['Title'] = dataset['Title'].replace('Mme','Mr')"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cR973SbUo4O6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "47fa777c-7c51-468c-a805-646b87ed7003"
      },
      "source": [
        "train_df['Title'].unique()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Mr', 'Mrs', 'Miss', 'Master', 'Other'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1b3Irg2LpdiG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "outputId": "3d3467eb-fd3b-4895-b240-c351d9dcba8a"
      },
      "source": [
        "train_df"
      ],
      "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>Mr</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>Mrs</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>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <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>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ... Cabin Embarked  Title\n",
              "0              1         0       3  ...   NaN        S     Mr\n",
              "1              2         1       1  ...   C85        C    Mrs\n",
              "2              3         1       3  ...   NaN        S   Miss\n",
              "3              4         1       1  ...  C123        S    Mrs\n",
              "4              5         0       3  ...   NaN        S     Mr\n",
              "..           ...       ...     ...  ...   ...      ...    ...\n",
              "886          887         0       2  ...   NaN        S  Other\n",
              "887          888         1       1  ...   B42        S   Miss\n",
              "888          889         0       3  ...   NaN        S   Miss\n",
              "889          890         1       1  ...  C148        C     Mr\n",
              "890          891         0       3  ...   NaN        Q     Mr\n",
              "\n",
              "[891 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7g-algBho_US",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "facd1d2f-46e0-4ca7-81d4-c7d6fa2da33b"
      },
      "source": [
        "train_df.describe()"
      ],
      "execution_count": 33,
      "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": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j983fXyjpklm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "fffda9cc-3fe9-42c1-c8fe-0126c756de72"
      },
      "source": [
        "train_df[['Title','Survived']].groupby('Title',as_index = False).mean()"
      ],
      "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>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>Mr</td>\n",
              "      <td>0.158301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Mrs</td>\n",
              "      <td>0.792000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Other</td>\n",
              "      <td>0.400000</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      Mr  0.158301\n",
              "3     Mrs  0.792000\n",
              "4   Other  0.400000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "faL2WArpp2Wq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "ffee6988-85ee-4abf-ab11-6066a7863356"
      },
      "source": [
        "train_df['Title']"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         Mr\n",
              "1        Mrs\n",
              "2       Miss\n",
              "3        Mrs\n",
              "4         Mr\n",
              "       ...  \n",
              "886    Other\n",
              "887     Miss\n",
              "888     Miss\n",
              "889       Mr\n",
              "890       Mr\n",
              "Name: Title, Length: 891, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GPEVQjM1rC2a",
        "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) #비어있는 값을 0으로 채운다."
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HE3mLU0ar8EV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "b76f38ff-f7a8-45c5-ad41-1538a042b838"
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 37,
      "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": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "obdW2QbBsAUa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "338ab3e7-84ef-4625-c4b2-312b52dfc78e"
      },
      "source": [
        "train_df.drop(['PassengerId','Name','Ticket','Cabin'],axis=1)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
              "0           0       3    male  22.0      1      0   7.2500        S      1\n",
              "1           1       1  female  38.0      1      0  71.2833        C      3\n",
              "2           1       3  female  26.0      0      0   7.9250        S      2\n",
              "3           1       1  female  35.0      1      0  53.1000        S      3\n",
              "4           0       3    male  35.0      0      0   8.0500        S      1\n",
              "..        ...     ...     ...   ...    ...    ...      ...      ...    ...\n",
              "886         0       2    male  27.0      0      0  13.0000        S      5\n",
              "887         1       1  female  19.0      0      0  30.0000        S      2\n",
              "888         0       3  female   NaN      1      2  23.4500        S      2\n",
              "889         1       1    male  26.0      0      0  30.0000        C      1\n",
              "890         0       3    male  32.0      0      0   7.7500        Q      1\n",
              "\n",
              "[891 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iMS7ssQwtaOy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combine = [train_df, test_df]"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1HzQlpGdt-m7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Sex'] = dataset['Sex'].map({'male':0, 'female':1})"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pdgz23PkusVL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "a6097b0e-2b95-42f5-bb1c-88a895eca485"
      },
      "source": [
        "train_df.isnull().sum()"
      ],
      "execution_count": 41,
      "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",
              "Title            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Oo6T_4jgenl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "295d9b57-c152-4c36-826e-9f5c64b3b115"
      },
      "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             86\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": "cUClgsSPgl6c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "dd66358c-3e00-4a89-fb79-de0fb9997138"
      },
      "source": [
        "train_df['Age'].isnull().sum()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "177"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7AmhneBIg6JF",
        "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": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6hP4PosfhMBz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2c908285-0f3a-47a3-a7ef-d1ce64d8b5fa"
      },
      "source": [
        "train_df['Age'].isnull().sum()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "puAB6KV2hRbr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "d1311306-5b09-47c1-e695-316826231801"
      },
      "source": [
        "test_df.isnull().sum()"
      ],
      "execution_count": 46,
      "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": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zx6eF1WNhXVc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "f2137989-7aeb-48bd-8198-e15e51f5a697"
      },
      "source": [
        "train_df['Age'].unique()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([22, 38, 26, 35, 29, 54,  2, 27, 14,  4, 58, 20, 39, 55, 31, 34, 15,\n",
              "       28,  8, 19, 40, 66, 42, 21, 18,  3,  7, 49, 65,  5, 11, 45, 17, 32,\n",
              "       16, 25,  0, 30, 33, 23, 24, 46, 59, 71, 37, 47, 70, 12,  9, 36, 51,\n",
              "       44,  1, 61, 56, 50, 62, 41, 52, 63, 43, 60, 10, 64, 13, 48, 53, 57,\n",
              "       80,  6, 74])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b2G6rpYyhimT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['AgeBand'] = pd.cut(train_df['Age'],5) #cut은 동일한 길이로 나누기"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vxh4iD2Rh9Fk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "b540b10e-d8f9-449a-d928-d1442e5bfa5d"
      },
      "source": [
        "train_df['AgeBand'].unique()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(16.0, 32.0], (32.0, 48.0], (48.0, 64.0], (-0.08, 16.0], (64.0, 80.0]]\n",
              "Categories (5, interval[float64]): [(-0.08, 16.0] < (16.0, 32.0] < (32.0, 48.0] < (48.0, 64.0] <\n",
              "                                    (64.0, 80.0]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WdBLyxJviHHM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "d8d4eaaa-08cf-4dee-e1f9-fa17a25bcaad"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\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",
              "      <th>AgeBand</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>0</td>\n",
              "      <td>22</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",
              "      <td>(16.0, 32.0]</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>1</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
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              "      <td>(32.0, 48.0]</td>\n",
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              "    <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>1</td>\n",
              "      <td>26</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",
              "      <td>(16.0, 32.0]</td>\n",
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              "    <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>1</td>\n",
              "      <td>35</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",
              "      <td>(32.0, 48.0]</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>35</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",
              "      <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>0</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
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              "      <td>S</td>\n",
              "      <td>5</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>1</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "      <td>1</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>1</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ... Embarked  Title       AgeBand\n",
              "0              1         0       3  ...        S      1  (16.0, 32.0]\n",
              "1              2         1       1  ...        C      3  (32.0, 48.0]\n",
              "2              3         1       3  ...        S      2  (16.0, 32.0]\n",
              "3              4         1       1  ...        S      3  (32.0, 48.0]\n",
              "4              5         0       3  ...        S      1  (32.0, 48.0]\n",
              "..           ...       ...     ...  ...      ...    ...           ...\n",
              "886          887         0       2  ...        S      5  (16.0, 32.0]\n",
              "887          888         1       1  ...        S      2  (16.0, 32.0]\n",
              "888          889         0       3  ...        S      2  (16.0, 32.0]\n",
              "889          890         1       1  ...        C      1  (16.0, 32.0]\n",
              "890          891         0       3  ...        Q      1  (16.0, 32.0]\n",
              "\n",
              "[891 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DIpYtWCJiO9k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "a3ef486f-7f9b-438e-b281-e463f50c3160"
      },
      "source": [
        "train_df[['AgeBand','Survived']].groupby('AgeBand').mean()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "               Survived\n",
              "AgeBand                \n",
              "(-0.08, 16.0]  0.550000\n",
              "(16.0, 32.0]   0.344762\n",
              "(32.0, 48.0]   0.403226\n",
              "(48.0, 64.0]   0.434783\n",
              "(64.0, 80.0]   0.090909"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G0p_HYoIic7s",
        "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": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WEY6xRpBizI0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "23ed496d-a521-4f33-f978-8d325c29f7d5"
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>4</td>\n",
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              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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              "      <td>3</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
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              "  </tbody>\n",
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            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ... Embarked  Title       AgeBand\n",
              "0            1         0       3  ...        S      1  (16.0, 32.0]\n",
              "1            2         1       1  ...        C      3  (32.0, 48.0]\n",
              "2            3         1       3  ...        S      2  (16.0, 32.0]\n",
              "3            4         1       1  ...        S      3  (32.0, 48.0]\n",
              "4            5         0       3  ...        S      1  (32.0, 48.0]\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E4nTli8dj-kl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "2ca31022-a0ad-4305-a748-11693e111164"
      },
      "source": [
        "train_df[train_df['Embarked'].isnull()]"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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            "text/plain": [
              "     PassengerId  Survived  Pclass  ... Embarked  Title       AgeBand\n",
              "61            62         1       1  ...      NaN      2  (32.0, 48.0]\n",
              "829          830         1       1  ...      NaN      3  (48.0, 64.0]\n",
              "\n",
              "[2 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BUdx8PKDpmvw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "5374665a-42f6-49cb-e593-d3c755b745be"
      },
      "source": [
        "train_df[train_df['Pclass']==1 & (train_df['Survived'] == 1)]['Embarked'].value_counts()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    74\n",
              "C    59\n",
              "Q     1\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X-jnD79jqK7l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['Embarked'] = train_df['Embarked'].fillna('S')"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aDflFLyiroq_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['Embarked'] = train_df['Embarked'].astype(str)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W60mGA3DsPTu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Embarked'] = dataset['Embarked'].map({'S':0, 'C':1, 'Q': 2})"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kpjxGZtysfFO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "8146f05a-e759-4f10-82d8-4aebc7d4b28c"
      },
      "source": [
        "train_df.isnull().sum()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age              0\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         0\n",
              "Title            0\n",
              "AgeBand          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vdRPBdRjstK3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "01f53219-72a8-4b78-df11-3cb7a10c4afd"
      },
      "source": [
        "test_df[test_df['Fare'].isnull()]"
      ],
      "execution_count": 60,
      "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",
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              "</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>1.0</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   1.0\n",
              "\n",
              "[1 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKHEtrHqs592",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "6c39175a-7a60-4f69-a420-87f67e2ebd55"
      },
      "source": [
        "test_df[['Pclass', 'Fare']].groupby('Pclass').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",
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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": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "frd3_DGTtxMX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_df['Fare'].fillna(12.459678, inplace=True)"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bT1VEUOKuJIJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "5ee0597c-d500-4a6a-bc69-5dd6dc9155a6"
      },
      "source": [
        "train_df['FareBand'] = pd.qcut(train_df['Fare'],4)\n",
        "train_df[['FareBand', 'Survived']].groupby('FareBand',as_index=False).mean()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AgP8qZuLvENn",
        "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 , 'Fare'] = 3"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cw1_zCRGvghu",
        "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": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9dC_Txvfv4Bn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "outputId": "a665279b-1afc-4884-8c9a-2942ba903025"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe thead th {\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>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",
              "      <th>AgeBand</th>\n",
              "      <th>FareBand</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
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              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
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              "      <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>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>(31.0, 512.329]</td>\n",
              "      <td>1</td>\n",
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              "    <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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>3.0</td>\n",
              "      <td>C123</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>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</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",
              "    <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
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              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
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              "      <td>(7.91, 14.454]</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>2.0</td>\n",
              "      <td>B42</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "      <td>(14.454, 31.0]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "      <td>(14.454, 31.0]</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>2.0</td>\n",
              "      <td>C148</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "      <td>(14.454, 31.0]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "      <td>(-0.001, 7.91]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...       AgeBand         FareBand  Family\n",
              "0              1         0       3  ...  (16.0, 32.0]   (-0.001, 7.91]       1\n",
              "1              2         1       1  ...  (32.0, 48.0]  (31.0, 512.329]       1\n",
              "2              3         1       3  ...  (16.0, 32.0]   (7.91, 14.454]       0\n",
              "3              4         1       1  ...  (32.0, 48.0]  (31.0, 512.329]       1\n",
              "4              5         0       3  ...  (32.0, 48.0]   (7.91, 14.454]       0\n",
              "..           ...       ...     ...  ...           ...              ...     ...\n",
              "886          887         0       2  ...  (16.0, 32.0]   (7.91, 14.454]       0\n",
              "887          888         1       1  ...  (16.0, 32.0]   (14.454, 31.0]       0\n",
              "888          889         0       3  ...  (16.0, 32.0]   (14.454, 31.0]       3\n",
              "889          890         1       1  ...  (16.0, 32.0]   (14.454, 31.0]       0\n",
              "890          891         0       3  ...  (16.0, 32.0]   (-0.001, 7.91]       0\n",
              "\n",
              "[891 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1kD99Pfmv5wn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df.drop(['Name','AgeBand','FareBand','SibSp','Parch','Ticket','Cabin','PassengerId'],axis=1,inplace=True)"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQZQPwsFwm01",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combine = [train_df, test_df]"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "26CwDz_QwS-3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_df.drop(['Name','SibSp','Parch','Ticket','Cabin','PassengerId'],axis=1,inplace=True)"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pF-_YmbOwhmH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "f915b2bc-f431-4c25-9434-d75d2bea090e"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 70,
      "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",
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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>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",
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              "  </thead>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Survived  Pclass  Sex  Age  Fare  Embarked  Title  Family\n",
              "0           0       3    0    1   0.0         0      1       1\n",
              "1           1       1    1    2   3.0         1      3       1\n",
              "2           1       3    1    1   1.0         0      2       0\n",
              "3           1       1    1    2   3.0         0      3       1\n",
              "4           0       3    0    2   1.0         0      1       0\n",
              "..        ...     ...  ...  ...   ...       ...    ...     ...\n",
              "886         0       2    0    1   1.0         0      5       0\n",
              "887         1       1    1    1   2.0         0      2       0\n",
              "888         0       3    1    1   2.0         0      2       3\n",
              "889         1       1    0    1   2.0         1      1       0\n",
              "890         0       3    0    1   0.0         2      1       0\n",
              "\n",
              "[891 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s_ZPggcfxXjH",
        "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": "sYrVGZ0azOvD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "34893cd1-fd15-4fd0-bce6-c67d9b58759d"
      },
      "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": "m_AHRd6DzX4Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "from sklearn.utils import shuffle"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2iVX-BF14gq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, y_train = shuffle(X_train,y_train,random_state = 23)"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "StW6A7VT2CbZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_and_test(model):\n",
        "  model.fit(X_train,y_train)\n",
        "  pred = model.predict(X_test)\n",
        "  accuracy = round(model.score(X_train,y_train) * 100,2)\n",
        "  print('Accuracy: ',accuracy,'%')\n",
        "  return pred, accuracy"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gILX_7Tq2wOo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "f1acfaad-8ac0-4b37-82ba-9720b2420ca8"
      },
      "source": [
        "log_pred, log_acc = train_and_test(LogisticRegression())\n",
        "svc_pred, svm_acc = train_and_test(SVC())\n",
        "rf_pred, rf_acc = train_and_test(RandomForestClassifier())\n",
        "nb_pred, nb_acc = train_and_test(GaussianNB())\n",
        "k_pred, k_acc = train_and_test(KNeighborsClassifier())"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  80.47 %\n",
            "Accuracy:  83.28 %\n",
            "Accuracy:  88.78 %\n",
            "Accuracy:  80.58 %\n",
            "Accuracy:  85.86 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BxQdprTk3P-g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DvUPGVZm3-bS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "92e3c3a5-979a-4cb5-bfb8-48b91c4a2bb6"
      },
      "source": [
        "y_train"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "727    1\n",
              "668    0\n",
              "210    0\n",
              "654    0\n",
              "228    0\n",
              "      ..\n",
              "31     1\n",
              "488    0\n",
              "40     0\n",
              "742    1\n",
              "595    0\n",
              "Name: Survived, Length: 891, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PV-lsSRl6X7b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}