Decision_Tree,_One_hot_Encoding_(Titanic).ipynb

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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Heoquixote/Data_Scientist_Course/blob/main/Decision_Tree%2C_One_hot_Encoding_(Titanic).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EbJJHCUhyUpw",
        "outputId": "b2df17dc-d173-4082-890f-0477f94b2f8b"
      },
      "id": "EbJJHCUhyUpw",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3a56b883",
      "metadata": {
        "id": "3a56b883"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4e402695",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4e402695",
        "outputId": "05a564af-ef05-44c6-f9ad-db5d71191ffd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(891, 12) (418, 11)\n"
          ]
        }
      ],
      "source": [
        "train = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/data/titanic/train.csv\")\n",
        "test = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/data/titanic/test.csv\")\n",
        "print(train.shape, test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train"
      ],
      "metadata": {
        "colab": {
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          "height": 537
        },
        "id": "IifiGu0x1dbD",
        "outputId": "6813fe4a-9017-40ca-e97a-70de53bd520b"
      },
      "id": "IifiGu0x1dbD",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     PassengerId  Survived  Pclass  \\\n",
              "0              1         0       3   \n",
              "1              2         1       1   \n",
              "2              3         1       3   \n",
              "3              4         1       1   \n",
              "4              5         0       3   \n",
              "..           ...       ...     ...   \n",
              "886          887         0       2   \n",
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              "888          889         0       3   \n",
              "889          890         1       1   \n",
              "890          891         0       3   \n",
              "\n",
              "                                                  Name     Sex   Age  SibSp  \\\n",
              "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                             Allen, Mr. William Henry    male  35.0      0   \n",
              "..                                                 ...     ...   ...    ...   \n",
              "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
              "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
              "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
              "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
              "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
              "\n",
              "     Parch            Ticket     Fare Cabin Embarked  \n",
              "0        0         A/5 21171   7.2500   NaN        S  \n",
              "1        0          PC 17599  71.2833   C85        C  \n",
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              "..     ...               ...      ...   ...      ...  \n",
              "886      0            211536  13.0000   NaN        S  \n",
              "887      0            112053  30.0000   B42        S  \n",
              "888      2        W./C. 6607  23.4500   NaN        S  \n",
              "889      0            111369  30.0000  C148        C  \n",
              "890      0            370376   7.7500   NaN        Q  \n",
              "\n",
              "[891 rows x 12 columns]"
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          "metadata": {},
          "execution_count": 4
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    },
    {
      "cell_type": "markdown",
      "id": "46114566",
      "metadata": {
        "id": "46114566"
      },
      "source": [
        "### EDA\n",
        "\n",
        "1. 데이터 크기 확인\n",
        "\n",
        "\n",
        "2. 결측치 체크\n",
        "\n",
        "\n",
        "3. dtype이 object인 column 확인\n",
        "\n",
        "\n",
        "4. target distribution --> target value에 대한 분포 확인!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2ab0b2cf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ab0b2cf",
        "outputId": "f8860df3-238c-4c74-d442-0325c3987491"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ],
      "source": [
        "train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4e6083c350>"
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          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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BH2pjzgXOq6qfBe583N9QGpGhoEWvqh4EXgCsB6aAi5O84TE221RV/9PalwCvbu3TgUvnGH8x8JrWPqN9xqHAi4GPJ7kO+DumZy0ALwE+1toX7tEXkh6HJeMuQFoIquoh4Grg6iQ3AGuBHTz8P06H7LTJ92Ztuy3JPUmew/Qf/t+a4yM2AX+W5EimA+hK4MnA/VV13K7K2suvI+01Zwpa9JI8K8nqWV3HAbcDtzH9Bxzg1x9jNxcDbwcOq6rrd17ZZiNfZfqw0Keq6qGq+g7w30lOa3UkyXPbJl9iekYB8Lo9/1bS3jEUJDgU2Jjk5iTXM32+4B3AO4Fzk0wCDz3GPi5l+o/4JbsZczHw+vY+43XAuiRfB27i4Z9CfRNwdpu1+Et4mjdekipJ6pwpSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSer+H80GyUuZYvvSAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.countplot(data=train, x=\"Survived\", palette=\"Set2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fdc11612",
      "metadata": {
        "id": "fdc11612"
      },
      "source": [
        "### pivot_table을 통한 EDA\n",
        "\n",
        "- 특정 기준으로 데이터를 reconstruct하고 싶을 때. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1d264008",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1d264008",
        "outputId": "34c3f41f-292f-4351-dfd8-83edfcfe60c3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male      577\n",
              "female    314\n",
              "Name: Sex, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "train.Sex.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9163169a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "9163169a",
        "outputId": "6c35338d-5633-4cbc-8754-b0e519a05730"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   mean      sum    count\n",
              "               Survived Survived Survived\n",
              "Sex    Pclass                            \n",
              "female 1       0.968085       91       94\n",
              "       2       0.921053       70       76\n",
              "       3       0.500000       72      144\n",
              "male   1       0.368852       45      122\n",
              "       2       0.157407       17      108\n",
              "       3       0.135447       47      347"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-143c440c-4454-480d-a666-0f07e9e3e3be\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>sum</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <th>Pclass</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
              "      <th>1</th>\n",
              "      <td>0.968085</td>\n",
              "      <td>91</td>\n",
              "      <td>94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.921053</td>\n",
              "      <td>70</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>72</td>\n",
              "      <td>144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
              "      <th>1</th>\n",
              "      <td>0.368852</td>\n",
              "      <td>45</td>\n",
              "      <td>122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.157407</td>\n",
              "      <td>17</td>\n",
              "      <td>108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.135447</td>\n",
              "      <td>47</td>\n",
              "      <td>347</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-143c440c-4454-480d-a666-0f07e9e3e3be')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-143c440c-4454-480d-a666-0f07e9e3e3be button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-143c440c-4454-480d-a666-0f07e9e3e3be');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "pd.pivot_table(data=train, index=[\"Sex\" ,\"Pclass\"],\n",
        "               values=[\"Survived\"],\n",
        "               aggfunc=[\"mean\", 'sum', 'count'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. 데이터 전처리"
      ],
      "metadata": {
        "id": "2P18Q948073u"
      },
      "id": "2P18Q948073u"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0b44f8b7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "0b44f8b7",
        "outputId": "770166b7-b03a-4418-c6c6-08cc7861d35d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
              "0           0       3    male  22.0      1      0   7.2500        S\n",
              "1           1       1  female  38.0      1      0  71.2833        C\n",
              "2           1       3  female  26.0      0      0   7.9250        S\n",
              "3           1       1  female  35.0      1      0  53.1000        S\n",
              "4           0       3    male  35.0      0      0   8.0500        S\n",
              "..        ...     ...     ...   ...    ...    ...      ...      ...\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# 0. 분석에 사용하지 않을 column 제거\n",
        "\n",
        "## PassengerId, Name, Ticket, Cabin drop!!\n",
        "train = train.drop(columns=[\"PassengerId\", \"Name\", \"Ticket\", \"Cabin\"])\n",
        "train"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. 결측치 처리\n",
        "\n",
        "## Age, Embarked\n",
        "train.Age = train.Age.fillna(train.Age.mean())\n",
        "train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "F20Crn1q2pGM",
        "outputId": "2ddf817c-a71c-4f6b-b636-f1df16ac8ed1"
      },
      "id": "F20Crn1q2pGM",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Survived  Pclass     Sex        Age  SibSp  Parch     Fare Embarked\n",
              "0           0       3    male  22.000000      1      0   7.2500        S\n",
              "1           1       1  female  38.000000      1      0  71.2833        C\n",
              "2           1       3  female  26.000000      0      0   7.9250        S\n",
              "3           1       1  female  35.000000      1      0  53.1000        S\n",
              "4           0       3    male  35.000000      0      0   8.0500        S\n",
              "..        ...     ...     ...        ...    ...    ...      ...      ...\n",
              "886         0       2    male  27.000000      0      0  13.0000        S\n",
              "887         1       1  female  19.000000      0      0  30.0000        S\n",
              "888         0       3  female  29.699118      1      2  23.4500        S\n",
              "889         1       1    male  26.000000      0      0  30.0000        C\n",
              "890         0       3    male  32.000000      0      0   7.7500        Q\n",
              "\n",
              "[891 rows x 8 columns]"
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. dtype이 object이 column 처리\n",
        "\n",
        "## Embarked\n",
        "\n",
        "## Pclass가 1이고, Sex가 female인 사람들이 가장 많이 탄 항구의 이름으로 결측치를 채워주세요!\n",
        "train.loc[train.isnull().any(axis=1), \"Embarked\"] = train.loc[(train.Pclass == 1) & (train.Sex == 'female'), \"Embarked\"].mode()[0]"
      ],
      "metadata": {
        "id": "0jT_Butm3QlE"
      },
      "id": "0jT_Butm3QlE",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "A6P-Od2P_lWV",
        "outputId": "bbd61233-8af6-4ed0-d94a-03ecfc9bbf8b"
      },
      "id": "A6P-Od2P_lWV",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Survived  Pclass     Sex        Age  SibSp  Parch     Fare Embarked\n",
              "0           0       3    male  22.000000      1      0   7.2500        S\n",
              "1           1       1  female  38.000000      1      0  71.2833        C\n",
              "2           1       3  female  26.000000      0      0   7.9250        S\n",
              "3           1       1  female  35.000000      1      0  53.1000        S\n",
              "4           0       3    male  35.000000      0      0   8.0500        S\n",
              "..        ...     ...     ...        ...    ...    ...      ...      ...\n",
              "886         0       2    male  27.000000      0      0  13.0000        S\n",
              "887         1       1  female  19.000000      0      0  30.0000        S\n",
              "888         0       3  female  29.699118      1      2  23.4500        S\n",
              "889         1       1    male  26.000000      0      0  30.0000        C\n",
              "890         0       3    male  32.000000      0      0   7.7500        Q\n",
              "\n",
              "[891 rows x 8 columns]"
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## categorical feature : Sex, Embarked\n",
        "\n",
        "# 1. Ordinal Encoding\n",
        "# from sklearn.preprocessing import OrdinalEncoder\n",
        "\n",
        "# encoder = OrdinalEncoder()\n",
        "# encoder2 = OrdinalEncoder()\n",
        "\n",
        "# train.Sex = encoder.fit_transform(train[\"Sex\"].values.reshape(-1, 1))\n",
        "# train.Embarked = encoder2.fit_transform(train[\"Embarked\"].values.reshape(-1, 1))\n",
        "# train\n",
        "\n",
        "# 2. One-hot Encoding\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "encoder = OneHotEncoder()\n",
        "encoder2 = OneHotEncoder()\n",
        "\n",
        "temp = encoder.fit_transform(train.Embarked.values.reshape(-1, 1)).toarray()\n",
        "cols = [\"embarked_\"+col for col in encoder.categories_[0]]\n",
        "embarked_OHE = pd.DataFrame(data=temp, columns=cols)\n",
        "display(embarked_OHE)\n",
        "\n",
        "temp2 = encoder2.fit_transform(train.Sex.values.reshape(-1, 1)).toarray()\n",
        "cols2 = [\"sex_\"+col for col in encoder2.categories_[0]]\n",
        "sex_OHE = pd.DataFrame(data=temp2, columns=cols2)\n",
        "display(sex_OHE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "jbI7HQnJ6GsJ",
        "outputId": "94529d0e-1a5f-4512-daa2-37f46c1050a3"
      },
      "id": "jbI7HQnJ6GsJ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     embarked_C  embarked_Q  embarked_S\n",
              "0           0.0         0.0         1.0\n",
              "1           1.0         0.0         0.0\n",
              "2           0.0         0.0         1.0\n",
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              "..          ...         ...         ...\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0b2d68a4-9066-41d7-b5df-475c308e7cf9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     sex_female  sex_male\n",
              "0           0.0       1.0\n",
              "1           1.0       0.0\n",
              "2           1.0       0.0\n",
              "3           1.0       0.0\n",
              "4           0.0       1.0\n",
              "..          ...       ...\n",
              "886         0.0       1.0\n",
              "887         1.0       0.0\n",
              "888         1.0       0.0\n",
              "889         0.0       1.0\n",
              "890         0.0       1.0\n",
              "\n",
              "[891 rows x 2 columns]"
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sex_female</th>\n",
              "      <th>sex_male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
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              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <th>887</th>\n",
              "      <td>1.0</td>\n",
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              "      <th>888</th>\n",
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              "      <th>890</th>\n",
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              "      <td>1.0</td>\n",
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              "  </tbody>\n",
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              "<p>891 rows × 2 columns</p>\n",
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              "      </button>\n",
              "      \n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-238138d9-6c6f-4934-a677-c3a8bf03e9d0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Training"
      ],
      "metadata": {
        "id": "SS9hsQ_5CwD8"
      },
      "id": "SS9hsQ_5CwD8"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = pd.concat([train, embarked_OHE, sex_OHE], axis=1).drop(columns=[\"Survived\", \"Sex\", \"Embarked\"])\n",
        "y = train.Survived\n",
        "# X_train = X\n",
        "# y_train = y\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "print(X_train.shape, X_val.shape, y_train.shape, y_val.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AgcgW3OT_eB-",
        "outputId": "b6a333d8-90ea-4e1a-a7c2-4c962b7ccaae"
      },
      "id": "AgcgW3OT_eB-",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(712, 10) (179, 10) (712,) (179,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.linear_model import SGDClassifier # Linear Classfier\n",
        "# from sklearn.svm import SVC # Support Vector machine Classifer\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "#clf = SGDClassifier()\n",
        "#clf = SVC()\n",
        "#clf = DecisionTreeClassifier()\n",
        "clf = RandomForestClassifier(n_estimators=500,\n",
        "                             max_features=\"auto\",\n",
        "                             min_samples_leaf=5,\n",
        "                             min_samples_split=10,\n",
        "                             random_state=0xC0FFEE)\n",
        "clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9cpCNQkkDPs-",
        "outputId": "f29f5fb5-c8f2-49d5-f42b-fff87133679b"
      },
      "id": "9cpCNQkkDPs-",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(min_samples_leaf=5, min_samples_split=10,\n",
              "                       n_estimators=500, random_state=12648430)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. Evaluation\n",
        "\n",
        "\n",
        "- validation 데이터로 overfitting 같은 학습이 잘못되는 케이스를 확인."
      ],
      "metadata": {
        "id": "mfZOvF--Eoah"
      },
      "id": "mfZOvF--Eoah"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "pred = clf.predict(X_train)\n",
        "pred2 = clf.predict(X_val)\n",
        "\n",
        "print(\"Train accuracy : %.4f\" % accuracy_score(y_train, pred))\n",
        "print(\"Validation accuracy : %.4f\" % accuracy_score(y_val, pred2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "36hCf_UrEB01",
        "outputId": "080568b3-9d61-4b1b-e752-d604acb894c4"
      },
      "id": "36hCf_UrEB01",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train accuracy : 0.8581\n",
            "Validation accuracy : 0.8045\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5. Prediction\n",
        "\n",
        "\n",
        "- test 데이터 **train 데이터에 적용한 전처리 방식과 같은** 방법을 적용해주어야 합니다."
      ],
      "metadata": {
        "id": "dv8uLPEKG53p"
      },
      "id": "dv8uLPEKG53p"
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. column 제거\n",
        "# 2. 결측치 처리 (Age, Embarked)\n",
        "# 3. Ordinal Encoding\n",
        "test = test.drop(columns=[\"PassengerId\", \"Name\", \"Ticket\", \"Cabin\"])\n",
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "0VzwjWR9FEqS",
        "outputId": "52f21e6e-1a5f-413e-95e3-ff11cb07481c"
      },
      "id": "0VzwjWR9FEqS",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pclass     Sex   Age  SibSp  Parch      Fare Embarked\n",
              "0         3    male  34.5      0      0    7.8292        Q\n",
              "1         3  female  47.0      1      0    7.0000        S\n",
              "2         2    male  62.0      0      0    9.6875        Q\n",
              "3         3    male  27.0      0      0    8.6625        S\n",
              "4         3  female  22.0      1      1   12.2875        S\n",
              "..      ...     ...   ...    ...    ...       ...      ...\n",
              "413       3    male   NaN      0      0    8.0500        S\n",
              "414       1  female  39.0      0      0  108.9000        C\n",
              "415       3    male  38.5      0      0    7.2500        S\n",
              "416       3    male   NaN      0      0    8.0500        S\n",
              "417       3    male   NaN      1      1   22.3583        C\n",
              "\n",
              "[418 rows x 7 columns]"
            ],
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              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1cc51274-8885-4ef7-976a-f100b07dca0b')\"\n",
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              "\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-1cc51274-8885-4ef7-976a-f100b07dca0b button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1cc51274-8885-4ef7-976a-f100b07dca0b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2.\n",
        "test.Age = test.Age.fillna(train.Age.mean())\n",
        "test.Fare = test.Fare.fillna(train.Fare.mean())"
      ],
      "metadata": {
        "id": "3vMIbxmfHJaf"
      },
      "id": "3vMIbxmfHJaf",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# One-hot Encoding\n",
        "temp = encoder.transform(test.Embarked.values.reshape(-1, 1)).toarray()\n",
        "cols = [\"embarked_\"+col for col in encoder.categories_[0]]\n",
        "embarked_OHE = pd.DataFrame(data=temp, columns=cols)\n",
        "display(embarked_OHE)\n",
        "\n",
        "temp2 = encoder2.transform(test.Sex.values.reshape(-1, 1)).toarray()\n",
        "cols2 = [\"sex_\"+col for col in encoder2.categories_[0]]\n",
        "sex_OHE = pd.DataFrame(data=temp2, columns=cols2)\n",
        "display(sex_OHE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "ap2yHmIMH2oe",
        "outputId": "1290e475-bbca-4399-842f-a07431a54c68"
      },
      "id": "ap2yHmIMH2oe",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     embarked_C  embarked_Q  embarked_S\n",
              "0           0.0         1.0         0.0\n",
              "1           0.0         0.0         1.0\n",
              "2           0.0         1.0         0.0\n",
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              "4           0.0         0.0         1.0\n",
              "..          ...         ...         ...\n",
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              "416         0.0         0.0         1.0\n",
              "417         1.0         0.0         0.0\n",
              "\n",
              "[418 rows x 3 columns]"
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              "<p>418 rows × 3 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
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            "text/plain": [
              "     sex_female  sex_male\n",
              "0           0.0       1.0\n",
              "1           1.0       0.0\n",
              "2           0.0       1.0\n",
              "3           0.0       1.0\n",
              "4           1.0       0.0\n",
              "..          ...       ...\n",
              "413         0.0       1.0\n",
              "414         1.0       0.0\n",
              "415         0.0       1.0\n",
              "416         0.0       1.0\n",
              "417         0.0       1.0\n",
              "\n",
              "[418 rows x 2 columns]"
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              "      <th>sex_female</th>\n",
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              "      <td>1.0</td>\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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    {
      "cell_type": "code",
      "source": [
        "X_test = pd.concat([test, embarked_OHE, sex_OHE], axis=1).drop(columns=[\"Sex\", \"Embarked\"])\n",
        "result = clf.predict(X_test)\n",
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sE5UPmwzJ9FT",
        "outputId": "6fffe1c2-aeaa-4662-ce68-6edb1339400f"
      },
      "id": "sE5UPmwzJ9FT",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0,\n",
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          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/data/titanic/gender_submission.csv\")\n",
        "submission"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
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        "id": "IZ_71d4wKtMa",
        "outputId": "c33aac24-2cd6-47e7-a427-e26e7eb0ab55"
      },
      "id": "IZ_71d4wKtMa",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived\n",
              "0            892         0\n",
              "1            893         1\n",
              "2            894         0\n",
              "3            895         0\n",
              "4            896         1\n",
              "..           ...       ...\n",
              "413         1305         0\n",
              "414         1306         1\n",
              "415         1307         0\n",
              "416         1308         0\n",
              "417         1309         0\n",
              "\n",
              "[418 rows x 2 columns]"
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              "<p>418 rows × 2 columns</p>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission[\"Survived\"] = result\n",
        "submission.to_csv(\"submission.csv\", index=False)"
      ],
      "metadata": {
        "id": "6K5RzuDZLC7X"
      },
      "id": "6K5RzuDZLC7X",
      "execution_count": null,
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