kagglessu2.ipynb

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  "metadata": {
    "colab": {
      "name": "kagglessu2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyN3L+BIEOOLqN9bpjp3qppJ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KSY1526/MyProject/blob/master/%EC%BA%90%EA%B8%80%EC%8A%A4%ED%84%B0%EB%94%94/kagglessu2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "inkSSpQba4gy"
      },
      "source": [
        "# 심장병 데이터 분석 with Kaggle"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3PNXPNabAcqU"
      },
      "source": [
        "# 데이터 불러오기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6JKWWLiAizU",
        "outputId": "6145eb60-22c6-4dcf-9929-d930fb8cafef"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t9YlcPd5AlIS"
      },
      "source": [
        "import pandas as pd\n",
        "data = pd.read_csv(\"/content/drive/MyDrive/heart.csv\")"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oWhyTRZKCwUS"
      },
      "source": [
        "# Verson 1. 심플한 로지스틱 회귀 모형"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t041CfgwDLkV"
      },
      "source": [
        "# 데이터 이해"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "IcWueau7BT6b",
        "outputId": "367f14c5-779e-4518-9cbb-429d1f9ac8da"
      },
      "source": [
        "df = data.copy()\n",
        "df.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trtbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalachh</th>\n",
              "      <th>exng</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slp</th>\n",
              "      <th>caa</th>\n",
              "      <th>thall</th>\n",
              "      <th>output</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  sex  cp  trtbps  chol  fbs  ...  exng  oldpeak  slp  caa  thall  output\n",
              "0   63    1   3     145   233    1  ...     0      2.3    0    0      1       1\n",
              "1   37    1   2     130   250    0  ...     0      3.5    0    0      2       1\n",
              "2   41    0   1     130   204    0  ...     0      1.4    2    0      2       1\n",
              "3   56    1   1     120   236    0  ...     0      0.8    2    0      2       1\n",
              "4   57    0   0     120   354    0  ...     1      0.6    2    0      2       1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lZ9DLx0mDSRY"
      },
      "source": [
        "디폴트 값은 5입니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C7B_yCrgDPEN",
        "outputId": "b1955bd8-81d6-4d04-82ac-6f874f71ec93"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'sex', 'cp', 'trtbps', 'chol', 'fbs', 'restecg', 'thalachh',\n",
              "       'exng', 'oldpeak', 'slp', 'caa', 'thall', 'output'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gt5ViY0MDXT5",
        "outputId": "bb50d4de-6b5c-4a58-bba8-22d74c675659"
      },
      "source": [
        "df.columns.values.tolist()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['age',\n",
              " 'sex',\n",
              " 'cp',\n",
              " 'trtbps',\n",
              " 'chol',\n",
              " 'fbs',\n",
              " 'restecg',\n",
              " 'thalachh',\n",
              " 'exng',\n",
              " 'oldpeak',\n",
              " 'slp',\n",
              " 'caa',\n",
              " 'thall',\n",
              " 'output']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KzRSccLODqU3"
      },
      "source": [
        "컬럼은 이런 방식으로 확인할 수 있습니다.\n",
        "\n",
        "밑에 DataFrame.columns.values.tolist() 함수는 컬럼 추출 중 가장 런타임이 빠르다고 합니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GMqSUy2pDn4s",
        "outputId": "09b88e7e-06a6-4a91-ab1e-b64b5e000049"
      },
      "source": [
        "print('Shape is',df.shape)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape is (303, 14)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U_00KuKDD4jS"
      },
      "source": [
        "303개 데이터, 14개 특성값이 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A4T6c7yOD306",
        "outputId": "ce96f213-d9f6-4bd8-e705-691169f3ff4b"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "cp          0\n",
              "trtbps      0\n",
              "chol        0\n",
              "fbs         0\n",
              "restecg     0\n",
              "thalachh    0\n",
              "exng        0\n",
              "oldpeak     0\n",
              "slp         0\n",
              "caa         0\n",
              "thall       0\n",
              "output      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MXxUTjODEALs",
        "outputId": "4f5ecead-8f0e-4693-be4d-12e705518081"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    int64  \n",
            " 1   sex       303 non-null    int64  \n",
            " 2   cp        303 non-null    int64  \n",
            " 3   trtbps    303 non-null    int64  \n",
            " 4   chol      303 non-null    int64  \n",
            " 5   fbs       303 non-null    int64  \n",
            " 6   restecg   303 non-null    int64  \n",
            " 7   thalachh  303 non-null    int64  \n",
            " 8   exng      303 non-null    int64  \n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slp       303 non-null    int64  \n",
            " 11  caa       303 non-null    int64  \n",
            " 12  thall     303 non-null    int64  \n",
            " 13  output    303 non-null    int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 33.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w2ecRiyVEEI0"
      },
      "source": [
        "글쓴이는 윗방식으로 null값 유무를 체크했습니다.\n",
        "\n",
        "그러나 df.info() 방식이 여러가지 정보를 같이 줘 더 효율적입니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "j4slltzcECJ2",
        "outputId": "2e1e701d-f7e5-4075-bfd2-f818d17f08a1"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "        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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trtbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalachh</th>\n",
              "      <th>exng</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slp</th>\n",
              "      <th>caa</th>\n",
              "      <th>thall</th>\n",
              "      <th>output</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age         sex          cp  ...         caa       thall      output\n",
              "count  303.000000  303.000000  303.000000  ...  303.000000  303.000000  303.000000\n",
              "mean    54.366337    0.683168    0.966997  ...    0.729373    2.313531    0.544554\n",
              "std      9.082101    0.466011    1.032052  ...    1.022606    0.612277    0.498835\n",
              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     47.500000    0.000000    0.000000  ...    0.000000    2.000000    0.000000\n",
              "50%     55.000000    1.000000    1.000000  ...    0.000000    2.000000    1.000000\n",
              "75%     61.000000    1.000000    2.000000  ...    1.000000    3.000000    1.000000\n",
              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000    1.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V0MItRiZEbed"
      },
      "source": [
        "데이터를 보면 어느정도 스케일링이 필요하다는 것을 알 수 있습니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QJA_iMD_ElNC"
      },
      "source": [
        "# 특성 스케일링"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "adM4LwVPES4P"
      },
      "source": [
        "df['age'] = df['age']/max(df['age'])\n",
        "df['cp'] = df['cp']/max(df['cp'])\n",
        "df['trtbps'] = df['trtbps']/max(df['trtbps'])\n",
        "df['chol'] = df['chol']/max(df['chol'])\n",
        "df['thalachh'] = df['thalachh']/max(df['thalachh'])"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "RUgwgeoWIL-6",
        "outputId": "c3c14367-1572-4fee-c5dc-07baa487726b"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 17,
      "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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trtbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalachh</th>\n",
              "      <th>exng</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slp</th>\n",
              "      <th>caa</th>\n",
              "      <th>thall</th>\n",
              "      <th>output</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.706056</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.322332</td>\n",
              "      <td>0.658119</td>\n",
              "      <td>0.436638</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>0.740826</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.117949</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>0.344017</td>\n",
              "      <td>0.087691</td>\n",
              "      <td>0.091898</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>0.113392</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.376623</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.470000</td>\n",
              "      <td>0.223404</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.351485</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.616883</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.374113</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.660891</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.714286</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.650000</td>\n",
              "      <td>0.425532</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.757426</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.792208</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>0.486702</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.821782</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age         sex          cp  ...         caa       thall      output\n",
              "count  303.000000  303.000000  303.000000  ...  303.000000  303.000000  303.000000\n",
              "mean     0.706056    0.683168    0.322332  ...    0.729373    2.313531    0.544554\n",
              "std      0.117949    0.466011    0.344017  ...    1.022606    0.612277    0.498835\n",
              "min      0.376623    0.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "25%      0.616883    0.000000    0.000000  ...    0.000000    2.000000    0.000000\n",
              "50%      0.714286    1.000000    0.333333  ...    0.000000    2.000000    1.000000\n",
              "75%      0.792208    1.000000    0.666667  ...    1.000000    3.000000    1.000000\n",
              "max      1.000000    1.000000    1.000000  ...    4.000000    3.000000    1.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Llq7tiSUISR4"
      },
      "source": [
        "이전과 달리 특성 스케일이 확실히 비슷해졌습니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BrT5So5PIYVn"
      },
      "source": [
        "# 데이터 모델링"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6yWwWg8PINfJ"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "#splitting data into training data and testing data\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    df.drop(['output'], axis=1),\n",
        "    df.output,\n",
        "    test_size= 0.2,  # 20% test data & 80% train data\n",
        "    random_state=0,\n",
        "    stratify=df.output\n",
        ")"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJJp1IcPIgRP"
      },
      "source": [
        "stratify 속성 => y값의 공평한 분배를 위해 사용하는 속성입니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Phb15d8JIfkL",
        "outputId": "78159cc1-e1a0-44e0-cb44-7e4a88adc36a"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "clf = LogisticRegression()\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "Y_pred = clf.predict(X_test)\n",
        "acc=accuracy_score(y_test, Y_pred)\n",
        "print('Accuracy is',round(acc,2)*100,'%')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy is 89.0 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1P9EpgQpIwHS"
      },
      "source": [
        "로지스틱 회귀 모형을 별다른 튜닝 없이 사용했습니다.\n",
        "\n",
        "정확도 측면에서만 보면 캐글에 있는 다른 코드와 별반 다르지 않습니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X2eDiOqSJC8O"
      },
      "source": [
        "# Verson 2. 심플한 딥러닝 모형"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mM-GVBVlJ3WO"
      },
      "source": [
        "# 데이터 이해2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MCpxy95dIsjY",
        "outputId": "64d00d1e-5dc3-49d3-e6ab-0b933d721d72"
      },
      "source": [
        "df = data.copy()\n",
        "df.output.value_counts()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    165\n",
              "0    138\n",
              "Name: output, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jg2p1jLrJa9Q"
      },
      "source": [
        "이전 모델에서 생략(?)된 부분인거 같은데 1과 0 값의 비율이 조금 차이가 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r-CBartCJZWG",
        "outputId": "9ae47c30-dd7b-4a67-ab3a-58b61cefd3fa"
      },
      "source": [
        "df.corr().abs()['output'].sort_values(ascending = False)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "output      1.000000\n",
              "exng        0.436757\n",
              "cp          0.433798\n",
              "oldpeak     0.430696\n",
              "thalachh    0.421741\n",
              "caa         0.391724\n",
              "slp         0.345877\n",
              "thall       0.344029\n",
              "sex         0.280937\n",
              "age         0.225439\n",
              "trtbps      0.144931\n",
              "restecg     0.137230\n",
              "chol        0.085239\n",
              "fbs         0.028046\n",
              "Name: output, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fpM4vPTJ6ad"
      },
      "source": [
        "Y값과의 상관계수가 어느정도 되는지 확인해보았습니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UVIj0wsdJ_Uu"
      },
      "source": [
        "# 데이터 모델링2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hszEtq2WJ5Ck",
        "outputId": "51454299-8330-43d3-c9f4-af7fdf54cfe8"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "X = df.drop('output', axis = 1)\n",
        "y = df['output']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n",
        "X_train.shape"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(242, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GeFi-VF5Nwo8"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "obXHL5qtNzQi"
      },
      "source": [
        "여기서는 StandardScaler를 사용해 스케일링을 했습니다.\n",
        "\n",
        "평균 0, 분산 1로 조정합니다. 이 스케일링은 이상치가 있을때 잘 작용하지 않을 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fgWi4cGEKO71",
        "outputId": "8cc94de3-2c8f-410b-ad4a-fd182e651896"
      },
      "source": [
        "from tensorflow import keras\n",
        "\n",
        "model = keras.Sequential(\n",
        "    [\n",
        "        keras.layers.Dense(\n",
        "            256, activation=\"relu\", input_shape=[13]\n",
        "        ),\n",
        "        keras.layers.Dense(515, activation=\"relu\"),\n",
        "        keras.layers.Dropout(0.3),\n",
        "        keras.layers.Dense(50, activation=\"relu\"),\n",
        "        keras.layers.Dropout(0.3),\n",
        "        keras.layers.Dense(1, activation=\"sigmoid\"),\n",
        "    ]\n",
        ")\n",
        "model.summary()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_20 (Dense)             (None, 256)               3584      \n",
            "_________________________________________________________________\n",
            "dense_21 (Dense)             (None, 515)               132355    \n",
            "_________________________________________________________________\n",
            "dropout_10 (Dropout)         (None, 515)               0         \n",
            "_________________________________________________________________\n",
            "dense_22 (Dense)             (None, 50)                25800     \n",
            "_________________________________________________________________\n",
            "dropout_11 (Dropout)         (None, 50)                0         \n",
            "_________________________________________________________________\n",
            "dense_23 (Dense)             (None, 1)                 51        \n",
            "=================================================================\n",
            "Total params: 161,790\n",
            "Trainable params: 161,790\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ybMxqRtmKZNf"
      },
      "source": [
        "활성화 함수로 제일 많이 사용하는 relu와 sigmoid함수를 사용했습니다.\n",
        "\n",
        "relu함수 : 입력이 양수일 경우 그대로 반환, 음수일경우 0으로 만듭니다.\n",
        "\n",
        "sigmoid함수 : 1 / (1 + e^z) 함수. 값을 0에서 1 사이로 변환합니다.\n",
        "\n",
        "첫번째 구간에 아웃풋 값을 256개 주었는데, 변수값이 13개임으로 모수가 14개입니다.\n",
        "\n",
        "그래서 256*14 = 3584개 파라미터가 나오게 된 것입니다.\n",
        "\n",
        "중간에 있는 드롭아웃은 일정 비율만큼 뉴런을 랜덤하게 꺼서 과대적합을 막는 역할을 합니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1IC-aIIaMnc1",
        "outputId": "e1c86407-bd93-4006-8a4f-4d3c468c63ec"
      },
      "source": [
        "model.compile(optimizer = 'Adam', loss = 'binary_crossentropy', metrics = ['binary_accuracy'])\n",
        "\n",
        "early_stopping = keras.callbacks.EarlyStopping( patience = 20, min_delta = 0.001,\n",
        "                                               restore_best_weights =True )\n",
        "history = model.fit(\n",
        "    X_train, y_train,\n",
        "    validation_data=(X_test, y_test),\n",
        "    batch_size=15,\n",
        "    epochs=50,\n",
        "    callbacks = [early_stopping],\n",
        "    verbose=1, \n",
        ")"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "17/17 [==============================] - 1s 15ms/step - loss: 0.5500 - binary_accuracy: 0.7190 - val_loss: 0.3814 - val_binary_accuracy: 0.8852\n",
            "Epoch 2/50\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.3823 - binary_accuracy: 0.8347 - val_loss: 0.3797 - val_binary_accuracy: 0.8852\n",
            "Epoch 3/50\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.3354 - binary_accuracy: 0.8719 - val_loss: 0.4391 - val_binary_accuracy: 0.8197\n",
            "Epoch 4/50\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.3017 - binary_accuracy: 0.8802 - val_loss: 0.4147 - val_binary_accuracy: 0.8689\n",
            "Epoch 5/50\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.2589 - binary_accuracy: 0.9091 - val_loss: 0.4388 - val_binary_accuracy: 0.8689\n",
            "Epoch 6/50\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.2579 - binary_accuracy: 0.9256 - val_loss: 0.4795 - val_binary_accuracy: 0.8525\n",
            "Epoch 7/50\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 0.2019 - binary_accuracy: 0.9256 - val_loss: 0.4895 - val_binary_accuracy: 0.8689\n",
            "Epoch 8/50\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.1889 - binary_accuracy: 0.9298 - val_loss: 0.5359 - val_binary_accuracy: 0.8361\n",
            "Epoch 9/50\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.1887 - binary_accuracy: 0.9215 - val_loss: 0.5324 - val_binary_accuracy: 0.8525\n",
            "Epoch 10/50\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.1578 - binary_accuracy: 0.9545 - val_loss: 0.5441 - val_binary_accuracy: 0.8689\n",
            "Epoch 11/50\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.1686 - binary_accuracy: 0.9215 - val_loss: 0.6338 - val_binary_accuracy: 0.8689\n",
            "Epoch 12/50\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.1448 - binary_accuracy: 0.9504 - val_loss: 0.6872 - val_binary_accuracy: 0.8197\n",
            "Epoch 13/50\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.1065 - binary_accuracy: 0.9628 - val_loss: 0.7682 - val_binary_accuracy: 0.8197\n",
            "Epoch 14/50\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 0.0879 - binary_accuracy: 0.9835 - val_loss: 0.8583 - val_binary_accuracy: 0.8197\n",
            "Epoch 15/50\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0877 - binary_accuracy: 0.9711 - val_loss: 0.9300 - val_binary_accuracy: 0.8361\n",
            "Epoch 16/50\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0688 - binary_accuracy: 0.9835 - val_loss: 0.9281 - val_binary_accuracy: 0.8361\n",
            "Epoch 17/50\n",
            "17/17 [==============================] - 0s 8ms/step - loss: 0.0615 - binary_accuracy: 0.9835 - val_loss: 0.9688 - val_binary_accuracy: 0.8361\n",
            "Epoch 18/50\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0496 - binary_accuracy: 0.9835 - val_loss: 1.0818 - val_binary_accuracy: 0.8197\n",
            "Epoch 19/50\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0915 - binary_accuracy: 0.9628 - val_loss: 1.3326 - val_binary_accuracy: 0.8525\n",
            "Epoch 20/50\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0953 - binary_accuracy: 0.9669 - val_loss: 1.1602 - val_binary_accuracy: 0.8525\n",
            "Epoch 21/50\n",
            "17/17 [==============================] - 0s 7ms/step - loss: 0.0366 - binary_accuracy: 0.9959 - val_loss: 1.1617 - val_binary_accuracy: 0.8525\n",
            "Epoch 22/50\n",
            "17/17 [==============================] - 0s 6ms/step - loss: 0.0407 - binary_accuracy: 0.9876 - val_loss: 1.2300 - val_binary_accuracy: 0.8361\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wCUhXU7XNPAF",
        "outputId": "30646700-bef6-4f08-e900-260d807230ae"
      },
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 7ms/step - loss: 0.3797 - binary_accuracy: 0.8852\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3796648383140564, 0.8852459192276001]"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TrsMhTB7NaM6",
        "outputId": "b46cd08b-9ca2-43f3-f685-22b372b5f469"
      },
      "source": [
        "predictions =(model.predict(X_test)>0.5).astype(\"int32\")\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "accuracy_score(y_test, predictions)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8852459016393442"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wUo-aSKCPPMI"
      },
      "source": [
        "아까 결과와 비슷한 수치를 보입니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lHybZLubOWWb",
        "outputId": "84e8d3dd-4f46-41c7-c45e-38688fe4e01f"
      },
      "source": [
        "print(classification_report(y_test, predictions))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00        29\n",
            "           1       0.52      1.00      0.69        32\n",
            "\n",
            "    accuracy                           0.52        61\n",
            "   macro avg       0.26      0.50      0.34        61\n",
            "weighted avg       0.28      0.52      0.36        61\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XmsU2JqcOimi"
      },
      "source": [
        "classification_report 함수가 상당히 유용한 걸 알 수있습니다.\n",
        "\n",
        "한번에 정밀도, 재현율, f1-score 값 까지 보여줍니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "986B12wOPUQU"
      },
      "source": [
        "# 느낀점"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pIvT_iEwPYBh"
      },
      "source": [
        "분류에 기본적인 로지스틱 회귀모형과 단순한 딥러닝 코드를 따라해봤습니다.\n",
        "\n",
        "특히 딥러닝 부분에 경우 정말 기본적인 것밖에 몰라 코드 해석에 시간이 많이 걸렸네요.\n",
        "\n",
        "여러가지로 코드를 만져가며 느낀점은 이번 데이터에 경우 스케일링이 많이 중요한 것 같습니다.\n",
        "\n",
        "스케일링 종류에 따라서 정확도 값이 크게 변하는 것을 관찰했습니다.\n",
        "\n",
        "특히 트리기반 부스팅 모델이 아니라 더 그런 것 같습니다.\n",
        "\n",
        "너무 복잡한 모델을 급하게 이해하기 보다, 이해할 수 있는 모델을 관찰하며 데이터 분석은 어떤 과정으로 하는가를 살펴봤습니다."
      ]
    }
  ]
}