9_9 (1).ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "9/9.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7rRtL4OWkpq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8e5963c4-d7b5-41b4-8a39-66bd19e4db11"
      },
      "source": [
        "!git clone https://github.com/OhyunHighSchoolML/dosoung.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: destination path 'dosoung' already exists and is not an empty directory.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eNo_QNRvWrn7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rg3hJNbTXw_x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = pd.read_csv('/content/dosoung/train.csv')\n",
        "test_df = pd.read_csv('/content/dosoung/test.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-RZhiSjX6Z-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "7ec17565-0912-44bb-d3bd-2e218f09892e"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>1</td>\n",
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              "      <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",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
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              "    <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
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              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
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              "      <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",
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              "      <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",
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              "      <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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0              1         0       3  ...   7.2500   NaN         S\n",
              "1              2         1       1  ...  71.2833   C85         C\n",
              "2              3         1       3  ...   7.9250   NaN         S\n",
              "3              4         1       1  ...  53.1000  C123         S\n",
              "4              5         0       3  ...   8.0500   NaN         S\n",
              "..           ...       ...     ...  ...      ...   ...       ...\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]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yYtJBASgYMCm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "2666bcad-b64b-462a-87ff-b05a46958a27"
      },
      "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"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rCCF3e0PYkLW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "outputId": "159ce158-61c6-427c-cd6c-2f496e2a128a"
      },
      "source": [
        "test_df.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 11 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  418 non-null    int64  \n",
            " 1   Pclass       418 non-null    int64  \n",
            " 2   Name         418 non-null    object \n",
            " 3   Sex          418 non-null    object \n",
            " 4   Age          332 non-null    float64\n",
            " 5   SibSp        418 non-null    int64  \n",
            " 6   Parch        418 non-null    int64  \n",
            " 7   Ticket       418 non-null    object \n",
            " 8   Fare         417 non-null    float64\n",
            " 9   Cabin        91 non-null     object \n",
            " 10  Embarked     418 non-null    object \n",
            "dtypes: float64(2), int64(4), object(5)\n",
            "memory usage: 36.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qZgRyHT6Y0Sl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "63ef89f2-db30-4cff-d4ae-510f44eb562f"
      },
      "source": [
        "train_df.describe()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<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": "7hVuu2V6ZAL1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "37bb244b-f603-4309-c554-0a4d56e65698"
      },
      "source": [
        "train_df.describe(include=['O'])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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>Bostandyeff, Mr. Guentcho</td>\n",
              "      <td>male</td>\n",
              "      <td>347082</td>\n",
              "      <td>C23 C25 C27</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     Bostandyeff, Mr. Guentcho  male  347082  C23 C25 C27        S\n",
              "freq                            1   577       7            4      644"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JM09FITSZtJf",
        "colab_type": "text"
      },
      "source": [
        "#데이터 분석"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0uUjZpwyZdDW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "bb05df12-1c07-4084-b07d-c79231c582e6"
      },
      "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": "xM9kWXOHaV7X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "ceaa0b55-25eb-43b6-a3ac-990ee359ba1f"
      },
      "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": "B-cnCi2AbBiu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "bf17dd6e-6d20-4796-ceab-d024d74c0bf2"
      },
      "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": "mDFRVmS8bgH1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "0ef8fcb8-0b9d-4a70-9a00-405feb26e16c"
      },
      "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": "markdown",
      "metadata": {
        "id": "zQopxYUBb53p",
        "colab_type": "text"
      },
      "source": [
        "#visualization data analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MMy7neQFbvPu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "75fa6956-90e9-4626-f39d-2f30cebb934c"
      },
      "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": "FB0w9qF6cFY-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "outputId": "d39e3559-b048-417b-e966-cf8d6a89aef1"
      },
      "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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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wKEyp5zzcSE-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "7453d39d-f770-48db-b3a7-b009bccc8b59"
      },
      "source": [
        "grid = sns.FacetGrid(train_df,col='Survived',hue='Survived')\n",
        "grid.map(plt.hist,'Age',bins=20)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f9b8dc28278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6HgNsA/6562b5aJKDafC6VtXDwN8DPwQeAZ4G7mT6r+usyBXzZKBGOk9aKFCzQpJDgM8Ab6+qH4/fV72PBjM+Xj/JecDWqrpzpmPpwzzg5cBHquoU4Kfs1E3R0HU9nN5EyMcAvwEcDPQ1x96oMU8GbqTzpIUC1fyUSUn2p5d0n6iqz3abH0uysNu/ENg6U/GNcybwxiSb6c0ufxa9/uvDkuz4UXYr13cLsKWq7ujWb6CXiC1e13OAH1TVtqr6BfBZetd6uq9r07lingzFSOdJCwWq6SmTkgS4Frivqj4wbtdaYFm3vIxen/uMqqrLq+qoqlpE7zreWlWXALcBS7vDWon1UeChJMd3m84G7qXB60qvy+L0JL/a/XvYEet0X9dmc8U8GY6Rz5OZ/mKt++LsDcD3gO8D753peHaK7VX0ms93A3d1jzfQ67NeD9wPfBGYP9Ox7hT37wPruuXfAr4GbAI+DRw40/F1cZ0MbOiu7b8Dh7d6XYH3Ad8B7gGuAw6cievaaq6YJ0ONcWTzxKmOJElNaqGLT5KkXVigJElNskBJkppkgZIkNckCJUlqkgVqDkhyQZJKcsJMxyK1zFyZXSxQc8PFwJe7Z0m7Z67MIhaoWa6b++xV9Kawv6jb9oIk13T3j7klyX8kWdrtOzXJfya5M8nNO6ZLkeY6c2X2sUDNfufTu1fM94AnkpwK/BGwiN49g95Cb4r7HXOl/QOwtKpOBT4G/O1MBC3NAHNllpk3+SFq3MX0JrqE3sSXF9P77/rpqnoOeDTJbd3+44GXArf0pspiP3rT4kujwFyZZSxQs1iS+fRmYn5ZkqKXRAXcuLtTgI1VdcY0hSg1wVyZnezim92WAtdV1W9W1aKqOpre3Td/BLyp619fQG9CTOjdhXMsyS+7MZK8ZCYCl6aZuTILWaBmt4vZ9RPgZ4Bfp3cfmXuBfwW+ATxdVT+nl6hXJfkWvRmnXzl94UozxlyZhZzNfI5KckhVPZPkCHpT3Z9ZvXvLSBrHXGmX30HNXeuSHAYcAPyNCSftlrnSKFtQkqQm+R2UJKlJFihJUpMsUJKkJlmgJElNskBJkpr0f0uVXO2t0k33AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z1cOIDDIc1VG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "outputId": "2e18e0f0-e3b4-4934-a95a-428e981960d9"
      },
      "source": [
        "grid = sns.FacetGrid(train_df,row='Pclass',col='Survived')\n",
        "grid.map(plt.hist,'Age',bins=20)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f9b8ade1eb8>"
            ]
          },
          "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": "YTpjKNn5dhu9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "outputId": "5edb6500-c534-49bc-cc1a-ee22757c87f5"
      },
      "source": [
        "grid = sns.FacetGrid(train_df,row='Embarked')\n",
        "grid.map(sns.pointplot,'Pclass','Survived','Sex')\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 0x7f9b8aa6c128>"
            ]
          },
          "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": "bYd_nQ6feERm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "outputId": "0665cca7-24ca-4e61-dbf9-a561cd7dabbb"
      },
      "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 0x7f9b8a8c04a8>"
            ]
          },
          "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": "g9ddGwAgfP1I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "68f3ece6-6c4c-42c8-c92c-83865f801c1c"
      },
      "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": "xaRlF9Aff67O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "4dc570c0-3e6e-488d-d6bc-4860b09a333e"
      },
      "source": [
        "train_df['Name']"
      ],
      "execution_count": 20,
      "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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NSyJq0xjgLmF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combine = [train_df,test_df]"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SkbuFYdWhI7i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "bb356886-ae13-43e4-a75d-a4219ad2d711"
      },
      "source": [
        "combine"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[     PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              " 0              1         0       3  ...   7.2500   NaN         S\n",
              " 1              2         1       1  ...  71.2833   C85         C\n",
              " 2              3         1       3  ...   7.9250   NaN         S\n",
              " 3              4         1       1  ...  53.1000  C123         S\n",
              " 4              5         0       3  ...   8.0500   NaN         S\n",
              " ..           ...       ...     ...  ...      ...   ...       ...\n",
              " 886          887         0       2  ...  13.0000   NaN         S\n",
              " 887          888         1       1  ...  30.0000   B42         S\n",
              " 888          889         0       3  ...  23.4500   NaN         S\n",
              " 889          890         1       1  ...  30.0000  C148         C\n",
              " 890          891         0       3  ...   7.7500   NaN         Q\n",
              " \n",
              " [891 rows x 12 columns],      PassengerId  Pclass  ... Cabin Embarked\n",
              " 0            892       3  ...   NaN        Q\n",
              " 1            893       3  ...   NaN        S\n",
              " 2            894       2  ...   NaN        Q\n",
              " 3            895       3  ...   NaN        S\n",
              " 4            896       3  ...   NaN        S\n",
              " ..           ...     ...  ...   ...      ...\n",
              " 413         1305       3  ...   NaN        S\n",
              " 414         1306       1  ...  C105        C\n",
              " 415         1307       3  ...   NaN        S\n",
              " 416         1308       3  ...   NaN        S\n",
              " 417         1309       3  ...   NaN        C\n",
              " \n",
              " [418 rows x 11 columns]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0QvZrOHhOeG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Title'] = dataset['Name'].str.extract(' ([A-Za-z]+)\\. ',expand=False)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WtLGxlGSiGs-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "c6a03a55-ce52-4dcb-9427-5021489b5963"
      },
      "source": [
        "train_df['Title'].unique()"
      ],
      "execution_count": 24,
      "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": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TKn05C0GiTP2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "9b50fa77-df93-47df-806d-488275783ff4"
      },
      "source": [
        "pd.crosstab(train_df['Title'],train_df['Sex'])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Sex</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Capt</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Col</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Countess</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Don</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dr</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jonkheer</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lady</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Major</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Master</th>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Miss</th>\n",
              "      <td>182</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mlle</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mme</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mr</th>\n",
              "      <td>0</td>\n",
              "      <td>517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mrs</th>\n",
              "      <td>125</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ms</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rev</th>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sir</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Sex       female  male\n",
              "Title                 \n",
              "Capt           0     1\n",
              "Col            0     2\n",
              "Countess       1     0\n",
              "Don            0     1\n",
              "Dr             1     6\n",
              "Jonkheer       0     1\n",
              "Lady           1     0\n",
              "Major          0     2\n",
              "Master         0    40\n",
              "Miss         182     0\n",
              "Mlle           2     0\n",
              "Mme            1     0\n",
              "Mr             0   517\n",
              "Mrs          125     0\n",
              "Ms             1     0\n",
              "Rev            0     6\n",
              "Sir            0     1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lg4RiOHdjEb-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Title'] = dataset['Title'].replace(['Capt','Apt','Countess','Col','Don','Dr','Major','Rev','Sir','Jonkheer','Dona','Lady'], '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": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4djbbFtVnqif",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6529e9ef-3556-4021-9f99-2c818f0af40a"
      },
      "source": [
        "train_df['Title'].unique()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Mr', 'Mrs', 'Miss', 'Master', 'Other'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4mzB0Z_Mn0Bk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "9c146f78-d916-4a54-d888-b1da76bcbe35"
      },
      "source": [
        "train_df[['Title','Survived']].groupby('Title',as_index=False).mean()"
      ],
      "execution_count": 28,
      "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>0.702703</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>0.347826</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Title  Survived\n",
              "0  Master  0.575000\n",
              "1    Miss  0.702703\n",
              "2      Mr  0.158301\n",
              "3     Mrs  0.792000\n",
              "4   Other  0.347826"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8d41gO0-p0Bn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "9466cfdd-7bb8-44a9-9d82-c95ab66138b7"
      },
      "source": [
        "train_df['Title']"
      ],
      "execution_count": 29,
      "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": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1IiA7eCbrEyF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "title_column={'Mr':1,'Miss':2,'Mrs':3,'Master':4,'Other':5}\n",
        "for dataset in combine:\n",
        "  dataset['Title'] = dataset['Title'].map(title_column)\n",
        "  dataset['Title'] = dataset['Title'].fillna(0)\n",
        "  dataset['Title'] = dataset['Title'].astype(int)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nJoWp78FrtFQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "42de9335-ca13-4a70-b6e4-54117e59fa26"
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 31,
      "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": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0gj2cY_tdtP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "combine=[train_df,test_df]"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wkqEnayAuBUH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Sex'] = dataset['Sex'].replace({'male': 0,'female': 1}) "
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4B15EIVu5iX",
        "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": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhA-hlbOhKTV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "444c5dc4-e70c-4150-e97c-bffb412d6e6f"
      },
      "source": [
        "train_df['Age'].isnull().sum()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9SvPA9dDhOsp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "fe8730b3-fdc7-4066-c8f3-95bf349950e9"
      },
      "source": [
        "test_df.isnull().sum()"
      ],
      "execution_count": 36,
      "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": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XRBTGWyshSG4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "f88a469c-9ece-4cc6-cae4-af1a89d6a37f"
      },
      "source": [
        "train_df['Age'].unique()"
      ],
      "execution_count": 37,
      "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": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1x5XS8rkhiVx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['AgeBand'] = pd.cut(train_df['Age'],5) #cut은 동일한 길이로 나누기"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p537L9W8h9Vx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "6f32a0f4-5e15-4b95-e676-54d7deca3e9b"
      },
      "source": [
        "train_df['AgeBand'].unique()"
      ],
      "execution_count": 39,
      "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": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nwWwHde6iAQY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "f6c01c28-be7a-4afd-d93b-5541d41af33d"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>890</th>\n",
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              "    </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": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TCpvwuHXiQHp",
        "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": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cGhxVXLcjgu7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "7a164e59-2b70-47be-9045-62b59890e371"
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
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              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
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              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ... Title       AgeBand  Age;\n",
              "0            1         0       3  ...     1  (16.0, 32.0]   NaN\n",
              "1            2         1       1  ...     3  (32.0, 48.0]   NaN\n",
              "2            3         1       3  ...     2  (16.0, 32.0]   NaN\n",
              "3            4         1       1  ...     3  (32.0, 48.0]   NaN\n",
              "4            5         0       3  ...     1  (32.0, 48.0]   NaN\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZeSeuTVCjiVb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "779458cc-c485-4caa-bf8d-3801d429f03c"
      },
      "source": [
        "train_df.isnull().sum()"
      ],
      "execution_count": 43,
      "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         2\n",
              "Title            0\n",
              "AgeBand          0\n",
              "Age;           791\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JHlgvAdNpE4V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "569b72a2-3f19-4d4a-f7da-6d2dbbd6e514"
      },
      "source": [
        "train_df[train_df['Embarked'].isnull()]"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
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              "      <th>Parch</th>\n",
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              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
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              "      <th>Age;</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>62</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Icard, Miss. Amelie</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
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              "      <td>80.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>829</th>\n",
              "      <td>830</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113572</td>\n",
              "      <td>80.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>(48.0, 64.0]</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ... Title       AgeBand  Age;\n",
              "61            62         1       1  ...     2  (32.0, 48.0]   NaN\n",
              "829          830         1       1  ...     3  (48.0, 64.0]   NaN\n",
              "\n",
              "[2 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2Q1IcpVpL7y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "402d1d32-1ec5-4e8b-f91f-1b3f4c37ba52"
      },
      "source": [
        "train_df[(train_df['Pclass'] == 1) &  (train_df['Survived'] == 1) ]['Embarked'].value_counts()"
      ],
      "execution_count": 45,
      "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": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U4OlwL_Tqx2F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['Embarked'] = train_df['Embarked'].fillna('S')"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NpN3nffbrcrT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['Embarked'] = train_df['Embarked'].astype(str)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L8SBPFUdrsx6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "20d03ec1-fff3-420e-ae51-d101c171ee09"
      },
      "source": [
        "train_df.dtypes"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId       int64\n",
              "Survived          int64\n",
              "Pclass            int64\n",
              "Name             object\n",
              "Sex               int64\n",
              "Age               int64\n",
              "SibSp             int64\n",
              "Parch             int64\n",
              "Ticket           object\n",
              "Fare            float64\n",
              "Cabin            object\n",
              "Embarked         object\n",
              "Title             int64\n",
              "AgeBand        category\n",
              "Age;            float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9pMjZntYsIwy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Embarked'] = dataset['Embarked'].map({'S':0,'C':1,'Q':2})"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B79BKwLQsik6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "86f6d933-fb1f-4e4e-b363-ef584342b8a1"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>NaN</td>\n",
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              "    <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>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
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              "      <td>890</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "      <td>NaN</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>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ... Title       AgeBand  Age;\n",
              "0              1         0       3  ...     1  (16.0, 32.0]   NaN\n",
              "1              2         1       1  ...     3  (32.0, 48.0]   NaN\n",
              "2              3         1       3  ...     2  (16.0, 32.0]   NaN\n",
              "3              4         1       1  ...     3  (32.0, 48.0]   NaN\n",
              "4              5         0       3  ...     1  (32.0, 48.0]   NaN\n",
              "..           ...       ...     ...  ...   ...           ...   ...\n",
              "886          887         0       2  ...     5  (16.0, 32.0]   NaN\n",
              "887          888         1       1  ...     2  (16.0, 32.0]   NaN\n",
              "888          889         0       3  ...     2  (16.0, 32.0]   NaN\n",
              "889          890         1       1  ...     1  (16.0, 32.0]   NaN\n",
              "890          891         0       3  ...     1  (16.0, 32.0]   NaN\n",
              "\n",
              "[891 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXt4nmPtsk-T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "8321f7e3-fd33-4cfd-e563-455117d308c1"
      },
      "source": [
        "test_df.isnull().sum()"
      ],
      "execution_count": 51,
      "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",
              "Age;           384\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fWZ1BSTAsxLD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "788eeaaf-c3d1-4d46-b8bc-36e31680148f"
      },
      "source": [
        "test_df[test_df['Fare'].isnull()]"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\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",
              "      <th>Age;</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",
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              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Pclass                Name  Sex  ...  Cabin  Embarked  Title Age;\n",
              "152         1044       3  Storey, Mr. Thomas    0  ...    NaN         0      1  NaN\n",
              "\n",
              "[1 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x9NUampetClK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_df[test_df['Fare'].isnull()] = test_df[test_df['Pclass'] == 3]['Fare'].mean()"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "32-EWQbntsWd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "c900c334-5ec8-49bd-e9fe-2c75d40c29a7"
      },
      "source": [
        "train_df['FareBand'] = pd.qcut(train_df['Fare'],4) #pd.qcut은 동일한 값으로 나누기\n",
        "train_df[['FareBand','Survived']].groupby('FareBand',as_index=False).mean()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>FareBand</th>\n",
              "      <th>Survived</th>\n",
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              "      <th>1</th>\n",
              "      <td>(7.91, 14.454]</td>\n",
              "      <td>0.303571</td>\n",
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              "      <th>3</th>\n",
              "      <td>(31.0, 512.329]</td>\n",
              "      <td>0.581081</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          FareBand  Survived\n",
              "0   (-0.001, 7.91]  0.197309\n",
              "1   (7.91, 14.454]  0.303571\n",
              "2   (14.454, 31.0]  0.454955\n",
              "3  (31.0, 512.329]  0.581081"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sce2ob0KuDsH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset.loc[dataset['Fare']<=7.91, 'Fare'] = 0\n",
        "  dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <=14.454),'Fare'] = 1\n",
        "  dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <=31),'Fare'] = 2\n",
        "  dataset.loc[(dataset['Fare'] > 31) & (dataset['Fare']),'Fare']= 3"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zUSPvwMlw5O7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_df['Fare'].fillna(12.459678,inplace=True)"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oLQJUnadxSN_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in combine:\n",
        "  dataset['Fare'] = dataset['Fare'].astype(int)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wBuymHCyvfYn",
        "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": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jmoMUTQRvzIr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "outputId": "5e300e6f-f020-45e8-db1d-b6b7837e02a5"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 17 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ... Age;         FareBand  Family\n",
              "0              1         0       3  ...  NaN   (-0.001, 7.91]       1\n",
              "1              2         1       1  ...  NaN  (31.0, 512.329]       1\n",
              "2              3         1       3  ...  NaN   (7.91, 14.454]       0\n",
              "3              4         1       1  ...  NaN  (31.0, 512.329]       1\n",
              "4              5         0       3  ...  NaN   (7.91, 14.454]       0\n",
              "..           ...       ...     ...  ...  ...              ...     ...\n",
              "886          887         0       2  ...  NaN   (7.91, 14.454]       0\n",
              "887          888         1       1  ...  NaN   (14.454, 31.0]       0\n",
              "888          889         0       3  ...  NaN   (14.454, 31.0]       3\n",
              "889          890         1       1  ...  NaN   (14.454, 31.0]       0\n",
              "890          891         0       3  ...  NaN   (-0.001, 7.91]       0\n",
              "\n",
              "[891 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4iYzVq3EwAHc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df.drop(['AgeBand','FareBand','SibSp','Parch','Name','Ticket','Cabin'], axis=1, inplace=True)"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3YMG5qxQz0ca",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df.drop(['Age;'], axis=1, inplace=True)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LExQHSiYzi-s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_df.drop(['SibSp','Parch','Name','Ticket','Cabin'], axis=1, inplace=True)"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1nb9C37NwTvM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "bafc9ed9-1312-4f74-fd5f-46637274f4d1"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  Sex  Age  Fare  Embarked  Title  Family\n",
              "0              1         0       3    0    1     0         0      1       1\n",
              "1              2         1       1    1    2     3         1      3       1\n",
              "2              3         1       3    1    1     1         0      2       0\n",
              "3              4         1       1    1    2     3         0      3       1\n",
              "4              5         0       3    0    2     1         0      1       0\n",
              "..           ...       ...     ...  ...  ...   ...       ...    ...     ...\n",
              "886          887         0       2    0    1     1         0      5       0\n",
              "887          888         1       1    1    1     2         0      2       0\n",
              "888          889         0       3    1    1     2         0      2       3\n",
              "889          890         1       1    0    1     2         1      1       0\n",
              "890          891         0       3    0    1     0         2      1       0\n",
              "\n",
              "[891 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "89_l1ncrwU3U",
        "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": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WbAAxUdzyvyF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "edf32a9e-5488-451f-fabf-6b355dce50d6"
      },
      "source": [
        "X_train.shape, y_train.shape, X_test.shape"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((891, 7), (891,), (418, 7))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ngpqXFpKzFmO",
        "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": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Am0km9XN0XmW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, y_train = shuffle(X_train,y_train,random_state = 23)"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z0DTvClh1uUc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_and_test(model):\n",
        "  model.fit(X_train,y_train)\n",
        "  pred = model.predict(X_test)\n",
        "  accuracy = round(model.score(X_train,y_train) * 100,2)\n",
        "  print('Accuracy: ', accuracy)\n",
        "  return pred, accuracy\n"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w4u1YG6J4w6X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df.drop(['PassengerId'],axis=1,inplace=True)"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cDhgNADt5Azn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_df.drop(['PassengerId','Age;'],axis=1,inplace=True)"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHkuDzBH5Ldm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "45d3407c-9dbf-41af-f925-bbc9574bb5ae"
      },
      "source": [
        "test_df"
      ],
      "execution_count": 71,
      "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>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "      <th>Family</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>418 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pclass  Sex  Age  Fare  Embarked  Title  Family\n",
              "0       3.0  0.0  2.0     0       2.0    1.0       0\n",
              "1       3.0  1.0  2.0     0       0.0    3.0       1\n",
              "2       2.0  0.0  3.0     1       2.0    1.0       0\n",
              "3       3.0  0.0  1.0     1       0.0    1.0       0\n",
              "4       3.0  1.0  1.0     1       0.0    3.0       2\n",
              "..      ...  ...  ...   ...       ...    ...     ...\n",
              "413     3.0  0.0  1.0     1       0.0    1.0       0\n",
              "414     1.0  1.0  2.0     3       1.0    5.0       0\n",
              "415     3.0  0.0  2.0     0       0.0    1.0       0\n",
              "416     3.0  0.0  1.0     1       0.0    1.0       0\n",
              "417     3.0  0.0  1.0     2       1.0    4.0       2\n",
              "\n",
              "[418 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZZGaX9hq2z-v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "ecd2f8e8-915f-489d-d5aa-7577237c3ab9"
      },
      "source": [
        "log_pred,log_acc = train_and_test(LogisticRegression())\n",
        "svm_pred, svm_acc = train_and_test(SVC())\n",
        "rf_pred, rf_acc = train_and_test(RandomForestClassifier())\n",
        "nb_pred, nb_acc = train_and_test(GaussianNB())\n",
        "k_pred, k_acc = train_and_test(KNeighborsClassifier())"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  79.8\n",
            "Accuracy:  83.39\n",
            "Accuracy:  89.34\n",
            "Accuracy:  80.47\n",
            "Accuracy:  85.75\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rmsb3OKB3ayd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}