[PMPG_CH02_Titanic].ipynb

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  "metadata": {
    "colab": {
      "name": "[PMPG_CH02_Titanic].ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BYZlXN93g1j4"
      },
      "source": [
        "#***CH_01 ~ CH_02 배운 내용 복습 (Titanic 직접 해보기)***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "J4cpq5-OezgS",
        "outputId": "4697251d-7273-47a2-8ef8-f57b57899103"
      },
      "source": [
        "import pandas as pd\r\n",
        "titanic_df = pd.read_csv('train.csv')\r\n",
        "titanic_df.head(3)"
      ],
      "execution_count": null,
      "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",
              "    </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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\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",
              "\n",
              "[3 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74wJA-n5XBbb",
        "outputId": "0d1ae87c-aec2-4d74-c6f0-a09e0d962a11"
      },
      "source": [
        "titanic_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mq7CeJpFhXWk",
        "outputId": "2b17acee-1fe4-4c8b-882c-a074992a38a8"
      },
      "source": [
        "titanic_df.info()"
      ],
      "execution_count": null,
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_L8J-c7Xhpe8",
        "outputId": "e5bc4dbc-d106-4631-8491-8124e3afcebf"
      },
      "source": [
        "titanic_df.isna().sum()"
      ],
      "execution_count": null,
      "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": 161
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LzrSapohnQ-P"
      },
      "source": [
        "**NAN(결측치)값 대체하기**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2CXwQxeAhwsr"
      },
      "source": [
        "titanic_df['Age'].fillna(titanic_df['Age'].mean(),inplace = True)\r\n",
        "titanic_df['Cabin'].fillna('N',inplace = True)\r\n",
        "titanic_df['Embarked'].fillna('N',inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iu0m9OVBndq-"
      },
      "source": [
        "**NAME(이름) 전처리**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "54gK68arUSFw",
        "outputId": "3bbd807e-38a3-402b-c994-bafb5cf6324f"
      },
      "source": [
        "titanic_df['Name']"
      ],
      "execution_count": null,
      "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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJSeAO_InhgY"
      },
      "source": [
        "titanic_df['pre_Name'] = titanic_df['Name'].str.extract(' ([A-Za-z]+)\\.') # 정규 표현식 ([A-Za-z]+)\\. 사용"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3K0dn99mn-Rv",
        "outputId": "dd145fc0-8ca3-44f5-c8b1-6c46efecdcc4"
      },
      "source": [
        "titanic_df['pre_Name']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        Mr\n",
              "1       Mrs\n",
              "2      Miss\n",
              "3       Mrs\n",
              "4        Mr\n",
              "       ... \n",
              "886     Rev\n",
              "887    Miss\n",
              "888    Miss\n",
              "889      Mr\n",
              "890      Mr\n",
              "Name: pre_Name, Length: 891, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "awb8orL-nJe2"
      },
      "source": [
        "**AGE(나이) 전처리**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0WwBRP8inZ0o",
        "outputId": "83c5ff50-5674-415a-cfa0-dba55c242d4c"
      },
      "source": [
        "titanic_df['Age'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.699118    177\n",
              "24.000000     30\n",
              "22.000000     27\n",
              "18.000000     26\n",
              "28.000000     25\n",
              "            ... \n",
              "55.500000      1\n",
              "53.000000      1\n",
              "20.500000      1\n",
              "23.500000      1\n",
              "0.420000       1\n",
              "Name: Age, Length: 89, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqw6SZoIiwJZ"
      },
      "source": [
        "def cat_age(age):\r\n",
        "  cat = \"\"\r\n",
        "  if age <= -1: cat = 'unKnown' #np.nan\r\n",
        "  if age <= 5: cat = \"Baby\"\r\n",
        "  elif age <= 12: cat = \"Child\"\r\n",
        "  elif age <= 18: cat = \"Teenager\"\r\n",
        "  elif age <= 25: cat = \"Student\"\r\n",
        "  elif age <= 35: cat = \"YoungAdult\"\r\n",
        "  elif age <= 60: cat = \"Adult\"\r\n",
        "  else: cat = \"Elderly\"\r\n",
        "\r\n",
        "  return cat\r\n",
        "\r\n",
        "titanic_df['Cat_age'] = titanic_df['Age'].apply(lambda x: cat_age(x))\r\n",
        "# titanic_df['Cat_age'].fillna(titanic_df['Cat_age'].mode(),inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SKSPCSdqkaWP",
        "outputId": "78b5f375-c050-4cff-ee20-2867621cf34c"
      },
      "source": [
        "titanic_df['Cat_age']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         Student\n",
              "1           Adult\n",
              "2      YoungAdult\n",
              "3      YoungAdult\n",
              "4      YoungAdult\n",
              "          ...    \n",
              "886    YoungAdult\n",
              "887       Student\n",
              "888    YoungAdult\n",
              "889    YoungAdult\n",
              "890    YoungAdult\n",
              "Name: Cat_age, Length: 891, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ZXHKIrFoKz0"
      },
      "source": [
        "**SibSp, Parch 전처리** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8EfKtXCGkbNM"
      },
      "source": [
        "titanic_df['Family'] = titanic_df['SibSp']+titanic_df['Parch']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uisqsrSNrkft"
      },
      "source": [
        "**Fare 전처리**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HoB1C0PAZzZ5",
        "outputId": "14450a92-9773-430e-bee8-84236390ee4c"
      },
      "source": [
        "titanic_df['Fare']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       7.2500\n",
              "1      71.2833\n",
              "2       7.9250\n",
              "3      53.1000\n",
              "4       8.0500\n",
              "        ...   \n",
              "886    13.0000\n",
              "887    30.0000\n",
              "888    23.4500\n",
              "889    30.0000\n",
              "890     7.7500\n",
              "Name: Fare, Length: 891, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gCDQF7QQrpkP",
        "outputId": "90167faa-e9de-40e0-cbd6-d72aa25407d4"
      },
      "source": [
        "titanic_df['pre_Fare'] = pd.cut(titanic_df['Fare'],5)\r\n",
        "titanic_df['pre_Fare']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      (-0.512, 102.466]\n",
              "1      (-0.512, 102.466]\n",
              "2      (-0.512, 102.466]\n",
              "3      (-0.512, 102.466]\n",
              "4      (-0.512, 102.466]\n",
              "             ...        \n",
              "886    (-0.512, 102.466]\n",
              "887    (-0.512, 102.466]\n",
              "888    (-0.512, 102.466]\n",
              "889    (-0.512, 102.466]\n",
              "890    (-0.512, 102.466]\n",
              "Name: pre_Fare, Length: 891, dtype: category\n",
              "Categories (5, interval[float64]): [(-0.512, 102.466] < (102.466, 204.932] < (204.932, 307.398] <\n",
              "                                    (307.398, 409.863] < (409.863, 512.329]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zoW6U0nhtvIN"
      },
      "source": [
        "**Cabin 전처리**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MXpd23aprrOk",
        "outputId": "e7156a2d-7fe9-41a5-e12d-961ea508e150"
      },
      "source": [
        "titanic_df['pre_Cabin'] = titanic_df['Cabin'].str[:1]\r\n",
        "titanic_df['pre_Cabin']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      N\n",
              "1      C\n",
              "2      N\n",
              "3      C\n",
              "4      N\n",
              "      ..\n",
              "886    N\n",
              "887    B\n",
              "888    N\n",
              "889    C\n",
              "890    N\n",
              "Name: pre_Cabin, Length: 891, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ijZ8LlsZv5Ed"
      },
      "source": [
        "**Embarked 전처리**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zFUHjGqnbmmE",
        "outputId": "67a0c5ba-1535-4f5a-ca9b-fb6d8d3197ad"
      },
      "source": [
        "titanic_df['Embarked'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    644\n",
              "C    168\n",
              "Q     77\n",
              "N      2\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8u79EpC7ug6w"
      },
      "source": [
        "titanic_df['pre_Embarked'] = titanic_df['Embarked'].str.replace('N','S')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQLpomDIuyPO",
        "outputId": "fd4261bd-fa66-4e1a-e875-89b550ab79aa"
      },
      "source": [
        "titanic_df['pre_Embarked'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    646\n",
              "C    168\n",
              "Q     77\n",
              "Name: pre_Embarked, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 938
        },
        "id": "4P8gEe_zvw7F",
        "outputId": "c66f37dd-d640-42ff-a1da-76bee5768c05"
      },
      "source": [
        "titanic_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>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>pre_Name</th>\n",
              "      <th>Cat_age</th>\n",
              "      <th>Family</th>\n",
              "      <th>pre_Fare</th>\n",
              "      <th>pre_Cabin</th>\n",
              "      <th>pre_Embarked</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>22.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>N</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "      <td>Student</td>\n",
              "      <td>1</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>N</td>\n",
              "      <td>S</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>38.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>Adult</td>\n",
              "      <td>1</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>C</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>26.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>N</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "      <td>YoungAdult</td>\n",
              "      <td>0</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>N</td>\n",
              "      <td>S</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>35.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>YoungAdult</td>\n",
              "      <td>1</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>C</td>\n",
              "      <td>S</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>35.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>N</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "      <td>YoungAdult</td>\n",
              "      <td>0</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>N</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>N</td>\n",
              "      <td>S</td>\n",
              "      <td>Rev</td>\n",
              "      <td>YoungAdult</td>\n",
              "      <td>0</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>N</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "      <td>Student</td>\n",
              "      <td>0</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>B</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>N</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "      <td>YoungAdult</td>\n",
              "      <td>3</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>N</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "      <td>Mr</td>\n",
              "      <td>YoungAdult</td>\n",
              "      <td>0</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>C</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>N</td>\n",
              "      <td>Q</td>\n",
              "      <td>Mr</td>\n",
              "      <td>YoungAdult</td>\n",
              "      <td>0</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>N</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 18 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...           pre_Fare pre_Cabin  pre_Embarked\n",
              "0              1         0       3  ...  (-0.512, 102.466]         N             S\n",
              "1              2         1       1  ...  (-0.512, 102.466]         C             C\n",
              "2              3         1       3  ...  (-0.512, 102.466]         N             S\n",
              "3              4         1       1  ...  (-0.512, 102.466]         C             S\n",
              "4              5         0       3  ...  (-0.512, 102.466]         N             S\n",
              "..           ...       ...     ...  ...                ...       ...           ...\n",
              "886          887         0       2  ...  (-0.512, 102.466]         N             S\n",
              "887          888         1       1  ...  (-0.512, 102.466]         B             S\n",
              "888          889         0       3  ...  (-0.512, 102.466]         N             S\n",
              "889          890         1       1  ...  (-0.512, 102.466]         C             C\n",
              "890          891         0       3  ...  (-0.512, 102.466]         N             Q\n",
              "\n",
              "[891 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kDpe05a1v-YH"
      },
      "source": [
        "drop_list = ['PassengerId','Name','Age','Ticket','Fare','Cabin','Embarked']\r\n",
        "titanic_df.drop(drop_list,axis = 1, inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "B0q1w8TMwbyl",
        "outputId": "6c5dc2f6-f1f2-49c2-d172-5e5d05713cb0"
      },
      "source": [
        "titanic_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>pre_Name</th>\n",
              "      <th>Cat_age</th>\n",
              "      <th>Family</th>\n",
              "      <th>pre_Fare</th>\n",
              "      <th>pre_Cabin</th>\n",
              "      <th>pre_Embarked</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>Student</td>\n",
              "      <td>1</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>N</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>Adult</td>\n",
              "      <td>1</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>C</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Miss</td>\n",
              "      <td>YoungAdult</td>\n",
              "      <td>0</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>N</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>YoungAdult</td>\n",
              "      <td>1</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>C</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Mr</td>\n",
              "      <td>YoungAdult</td>\n",
              "      <td>0</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>N</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Rev</td>\n",
              "      <td>YoungAdult</td>\n",
              "      <td>0</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>N</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Miss</td>\n",
              "      <td>Student</td>\n",
              "      <td>0</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>B</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Miss</td>\n",
              "      <td>YoungAdult</td>\n",
              "      <td>3</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>N</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Mr</td>\n",
              "      <td>YoungAdult</td>\n",
              "      <td>0</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>C</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Mr</td>\n",
              "      <td>YoungAdult</td>\n",
              "      <td>0</td>\n",
              "      <td>(-0.512, 102.466]</td>\n",
              "      <td>N</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Survived  Pclass     Sex  ...           pre_Fare  pre_Cabin pre_Embarked\n",
              "0           0       3    male  ...  (-0.512, 102.466]          N            S\n",
              "1           1       1  female  ...  (-0.512, 102.466]          C            C\n",
              "2           1       3  female  ...  (-0.512, 102.466]          N            S\n",
              "3           1       1  female  ...  (-0.512, 102.466]          C            S\n",
              "4           0       3    male  ...  (-0.512, 102.466]          N            S\n",
              "..        ...     ...     ...  ...                ...        ...          ...\n",
              "886         0       2    male  ...  (-0.512, 102.466]          N            S\n",
              "887         1       1  female  ...  (-0.512, 102.466]          B            S\n",
              "888         0       3  female  ...  (-0.512, 102.466]          N            S\n",
              "889         1       1    male  ...  (-0.512, 102.466]          C            C\n",
              "890         0       3    male  ...  (-0.512, 102.466]          N            Q\n",
              "\n",
              "[891 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "axq_jccixGwG",
        "outputId": "444b383f-8a9b-49b9-b12c-1d6ac5f97817"
      },
      "source": [
        "titanic_df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Survived', 'Pclass', 'Sex', 'SibSp', 'Parch', 'pre_Name', 'Cat_age',\n",
              "       'Family', 'pre_Fare', 'pre_Cabin', 'pre_Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r-rD2Yt9zW20"
      },
      "source": [
        "**레이블 인코딩**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDe5ruGrwezb"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\r\n",
        "\r\n",
        "encoder = LabelEncoder()\r\n",
        "for col in titanic_df.columns:\r\n",
        "  if titanic_df[col].dtypes != 'int64':\r\n",
        "    encoder.fit(titanic_df[col])\r\n",
        "    titanic_df[col] = encoder.transform(titanic_df[col])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "muLy4x58y6nA",
        "outputId": "64c3f9e0-6904-47b2-a5e4-f6c556f54d61"
      },
      "source": [
        "titanic_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>pre_Name</th>\n",
              "      <th>Cat_age</th>\n",
              "      <th>Family</th>\n",
              "      <th>pre_Fare</th>\n",
              "      <th>pre_Cabin</th>\n",
              "      <th>pre_Embarked</th>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Survived  Pclass  Sex  SibSp  ...  Family  pre_Fare  pre_Cabin  pre_Embarked\n",
              "0           0       3    1      1  ...       1         0          7             2\n",
              "1           1       1    0      1  ...       1         0          2             0\n",
              "2           1       3    0      0  ...       0         0          7             2\n",
              "3           1       1    0      1  ...       1         0          2             2\n",
              "4           0       3    1      0  ...       0         0          7             2\n",
              "..        ...     ...  ...    ...  ...     ...       ...        ...           ...\n",
              "886         0       2    1      0  ...       0         0          7             2\n",
              "887         1       1    0      0  ...       0         0          1             2\n",
              "888         0       3    0      1  ...       3         0          7             2\n",
              "889         1       1    1      0  ...       0         0          2             0\n",
              "890         0       3    1      0  ...       0         0          7             1\n",
              "\n",
              "[891 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gVYTWdRB0w0A"
      },
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2KYAcxjzTr1"
      },
      "source": [
        "X_titanic_df = titanic_df.drop('Survived',axis = 1)\r\n",
        "y_titanic_df = titanic_df['Survived']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bsTm0yztzkl4",
        "outputId": "655f2f17-032d-49d7-e1a1-acfcfe960e51"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\r\n",
        "from sklearn.linear_model import LogisticRegression\r\n",
        "from sklearn.ensemble import RandomForestClassifier\r\n",
        "from sklearn.metrics import accuracy_score\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "\r\n",
        "X_train,X_test,y_train,y_test = train_test_split(X_titanic_df,y_titanic_df,test_size = 0.2, random_state = 11)\r\n",
        "\r\n",
        "dt_clf = DecisionTreeClassifier(random_state = 11)\r\n",
        "rt_clf = RandomForestClassifier(random_state = 11)\r\n",
        "lr_clf = LogisticRegression()\r\n",
        "\r\n",
        "dt_clf.fit(X_train,y_train)\r\n",
        "dt_pred = dt_clf.predict(X_test)\r\n",
        "dt_acc = accuracy_score(y_test,dt_pred)\r\n",
        "print('DecisionTreeClassifier 정확도 :',dt_acc)\r\n",
        "\r\n",
        "rt_clf.fit(X_train,y_train)\r\n",
        "rt_pred = rt_clf.predict(X_test)\r\n",
        "rt_acc = accuracy_score(y_test,rt_pred)\r\n",
        "print('RandomForestClassion 정확도 :',rt_acc)\r\n",
        "\r\n",
        "lr_clf.fit(X_train,y_train)\r\n",
        "pred = lr_clf.predict(X_test)\r\n",
        "print('LogisticRegression 정확도 :{0:4f}'.format(accuracy_score(y_test,pred)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DecisionTreeClassifier 정확도 : 0.8212290502793296\n",
            "RandomForestClassion 정확도 : 0.8715083798882681\n",
            "LogisticRegression 정확도 :0.854749\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pi0hvdNz14OB",
        "outputId": "8bd46626-2b49-4962-b929-68d09009b8b9"
      },
      "source": [
        "import numpy as np\r\n",
        "from sklearn.model_selection import KFold\r\n",
        "\r\n",
        "def exec_kfold(clf,folds=5):\r\n",
        "  kfold = KFold(n_splits = folds)\r\n",
        "  scores = []\r\n",
        "  for iter_count, (train_index,test_index) in enumerate(kfold.split(X_titanic_df)):\r\n",
        "    X_train,X_test = X_titanic_df.values[train_index],X_titanic_df.values[test_index]\r\n",
        "    y_train,y_test = y_titanic_df.values[train_index],y_titanic_df.values[test_index]\r\n",
        "    clf.fit(X_train,y_train)\r\n",
        "    predictions = clf.predict(X_test)\r\n",
        "    accuracy = accuracy_score(y_test, predictions)\r\n",
        "    scores.append(accuracy)\r\n",
        "    print('교차 검증 {0} 정확도:{1:4f}'.format(iter_count,accuracy))\r\n",
        "  mean_score = np.mean(accuracy)\r\n",
        "  print('평균 검증 정확도:{0:4f}'.format(mean_score))\r\n",
        "\r\n",
        "exec_kfold(dt_clf,folds=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "교차 검증 0 정확도:0.765363\n",
            "교차 검증 1 정확도:0.752809\n",
            "교차 검증 2 정확도:0.792135\n",
            "교차 검증 3 정확도:0.769663\n",
            "교차 검증 4 정확도:0.814607\n",
            "평균 검증 정확도:0.814607\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t6rcw9RZ16qk",
        "outputId": "5aa51466-0977-44ed-f90e-dcf655f25853"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\r\n",
        "\r\n",
        "scores = cross_val_score(dt_clf, X_titanic_df, y_titanic_df, cv=5)\r\n",
        "for iter_count, accuracy in enumerate(scores):\r\n",
        "  print('교차 검증 {0} 정확도: {1:4f}'.format(iter_count,accuracy))\r\n",
        "\r\n",
        "print('평균 검증 정확도',np.mean(scores))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "교차 검증 0 정확도: 0.754190\n",
            "교차 검증 1 정확도: 0.764045\n",
            "교차 검증 2 정확도: 0.780899\n",
            "교차 검증 3 정확도: 0.775281\n",
            "교차 검증 4 정확도: 0.825843\n",
            "평균 검증 정확도 0.780051471972883\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j9n05uPP2BFl",
        "outputId": "f55482e4-7b4e-41cc-b4fb-b23c1dd689e3"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\r\n",
        "\r\n",
        "parameters = {'max_depth':[2,3,5,10],'min_samples_split':[2,3,5],'min_samples_leaf':[1,5,8]}\r\n",
        "\r\n",
        "grid_dclf = GridSearchCV(dt_clf,param_grid=parameters, scoring='accuracy',cv=5)\r\n",
        "grid_dclf.fit(X_train,y_train)\r\n",
        "\r\n",
        "print('GridSearchCV 최적 하이퍼 파라미터:',grid_dclf.best_params_)\r\n",
        "print('GridSearchCV 최고 정확도 {0:4f}:'.format(grid_dclf.best_score_))\r\n",
        "best_dclf = grid_dclf.best_estimator_\r\n",
        "\r\n",
        "dprediction = best_dclf.predict(X_test)\r\n",
        "accuracy = accuracy_score(y_test,dprediction)\r\n",
        "print('테스트 세트에서 최고 정확도 {0:4f}'.format(accuracy))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GridSearchCV 최적 하이퍼 파라미터: {'max_depth': 5, 'min_samples_leaf': 1, 'min_samples_split': 3}\n",
            "GridSearchCV 최고 정확도 0.803378:\n",
            "테스트 세트에서 최고 정확도 0.877095\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M2wkKHv52Dn9"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}