241011_연습문제_텍스트분석.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ggoddll99/24-2ESAA/blob/main/241011_%EC%97%B0%EC%8A%B5%EB%AC%B8%EC%A0%9C_%ED%85%8D%EC%8A%A4%ED%8A%B8%EB%B6%84%EC%84%9D.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1kpwaWyzo1Yr"
      },
      "source": [
        "# **문서 유사도 분석**\n",
        "* 각 단어를 축으로 하는 특성 공간(feature space)에서 문서들을 하나의 위치로 보고 특성 공간 상에서 거리를 이용해 두 문서의 유사성(similarity)을 측정하는 방식\n",
        "    * 코사인 유사도(cosine similarity) : 두 벡터 간의 각도의 코사인 값을 이용하여 유사도를 계산, 크기가 아닌 방향성에 중점, 벡터의 길이(문서의 길이)에 영향을 받지 않음"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt-get install -y fonts-nanum\n",
        "!sudo fc-cache -fv\n",
        "!rm ~/.cache/matplotlib -rf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a8eoSq_SAyoE",
        "outputId": "ecc72e7d-699f-472a-9cb9-3751f8fcc956"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "The following NEW packages will be installed:\n",
            "  fonts-nanum\n",
            "0 upgraded, 1 newly installed, 0 to remove and 49 not upgraded.\n",
            "Need to get 10.3 MB of archives.\n",
            "After this operation, 34.1 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy/universe amd64 fonts-nanum all 20200506-1 [10.3 MB]\n",
            "Fetched 10.3 MB in 3s (3,549 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 1.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package fonts-nanum.\n",
            "(Reading database ... 123621 files and directories currently installed.)\n",
            "Preparing to unpack .../fonts-nanum_20200506-1_all.deb ...\n",
            "Unpacking fonts-nanum (20200506-1) ...\n",
            "Setting up fonts-nanum (20200506-1) ...\n",
            "Processing triggers for fontconfig (2.13.1-4.2ubuntu5) ...\n",
            "/usr/share/fonts: caching, new cache contents: 0 fonts, 1 dirs\n",
            "/usr/share/fonts/truetype: caching, new cache contents: 0 fonts, 3 dirs\n",
            "/usr/share/fonts/truetype/humor-sans: caching, new cache contents: 1 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype/liberation: caching, new cache contents: 16 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype/nanum: caching, new cache contents: 12 fonts, 0 dirs\n",
            "/usr/local/share/fonts: caching, new cache contents: 0 fonts, 0 dirs\n",
            "/root/.local/share/fonts: skipping, no such directory\n",
            "/root/.fonts: skipping, no such directory\n",
            "/usr/share/fonts/truetype: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/humor-sans: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/liberation: skipping, looped directory detected\n",
            "/usr/share/fonts/truetype/nanum: skipping, looped directory detected\n",
            "/var/cache/fontconfig: cleaning cache directory\n",
            "/root/.cache/fontconfig: not cleaning non-existent cache directory\n",
            "/root/.fontconfig: not cleaning non-existent cache directory\n",
            "fc-cache: succeeded\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt # 맷플롯립 import 하기\n",
        "plt.rcParams['font.family'] = 'NanumBarunGothic' # 나눔바른고딕 적용하기\n",
        "plt.rcParams['axes.unicode_minus'] = False\n",
        "\n",
        "# 플롯 예시\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot([1, 2, 3, 4], [10, 20, 25, 30])\n",
        "plt.title('테스트 플롯')\n",
        "plt.xlabel('X 축')\n",
        "plt.ylabel('Y 축')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "M4BR-G23A7Cj",
        "outputId": "4a94d369-8c9d-4352-e76a-19f999101062"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j9OJaKLoHt8v",
        "outputId": "174e06ca-316f-42d7-ea18-e13f89c16a64"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: koreanize-matplotlib in /usr/local/lib/python3.10/dist-packages (0.1.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from koreanize-matplotlib) (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->koreanize-matplotlib) (1.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->koreanize-matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->koreanize-matplotlib) (4.54.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->koreanize-matplotlib) (1.4.7)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib->koreanize-matplotlib) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->koreanize-matplotlib) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->koreanize-matplotlib) (10.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->koreanize-matplotlib) (3.1.4)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->koreanize-matplotlib) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->koreanize-matplotlib) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install koreanize-matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "7jEmmKL0o1Ys"
      },
      "outputs": [],
      "source": [
        "# 필요한 라이브러리를 import하세요.\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3MzjjX3ho1Ys"
      },
      "source": [
        "## **1. 데이터 로드**\n",
        "\n",
        "### 서울특별시 다산콜센터(☎120)의 주요 민원\n",
        "* 서울특별시 다산콜센터(☎120)의 주요 민원(자주 묻는 질문)에 대한 답변정보\n",
        "* https://opengov.seoul.go.kr/civilappeal/list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s11JARRabZjF",
        "outputId": "83e4175c-54ab-4511-e605-38f0a055b76b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2645, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df = pd.read_csv(\"https://bit.ly/seoul-120-text-csv\")\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "WJ7xaO9SHt8w",
        "outputId": "fa2b5c3f-c871-40c5-90ae-3895e28466cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     번호  분류                           제목  \\\n",
              "0  2645  복지                  아빠 육아휴직 장려금   \n",
              "1  2644  경제            [서울산업진흥원] 서울메이드란?   \n",
              "2  2643  환경                    (강북구) 정비중   \n",
              "3  2642  복지  광진맘택시 운영(임산부,영아 양육가정 전용 택시)   \n",
              "4  2641  복지               마포 뇌병변장애인 비전센터   \n",
              "\n",
              "                                                  내용      내용번호  \n",
              "0  아빠 육아휴직 장려금   업무개요  남성근로자의 육아휴직을 장려하고 양육에 따른 경...  23522464  \n",
              "1  서울산업진흥원 서울메이드란 서울의 감성을 담은 다양하고 새로운 경험을 제공하기 위해...  23194045  \n",
              "2  강북구 정비중  업무개요  투명 폐트병을 교환보상하므로 수거율을 높이고 폐기물을 감...  23032485  \n",
              "3  광진맘택시 운영임산부영아 양육가정 전용 택시  업무개요 교통약자인 임산부와 영아가정...  22904492  \n",
              "4  마포 뇌병변장애인 비전센터 마포뇌병변장애인 비전센터 운영   구분 내용  목적  학...  22477798  "
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              "      <th>0</th>\n",
              "      <td>2645</td>\n",
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              "      <td>아빠 육아휴직 장려금</td>\n",
              "      <td>아빠 육아휴직 장려금   업무개요  남성근로자의 육아휴직을 장려하고 양육에 따른 경...</td>\n",
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              "      <td>서울산업진흥원 서울메이드란 서울의 감성을 담은 다양하고 새로운 경험을 제공하기 위해...</td>\n",
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              "      <td>22904492</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2641</td>\n",
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            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "otWESxHlbZjG",
        "outputId": "4dcb43ad-e7a7-4625-8294-2a1c40cc16d0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# 중복데이터가 있는지 확인하세요.\n",
        "df.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "YXAtNDKco1Yv",
        "outputId": "d8d42791-b0d3-4a78-f31a-f8e499649f63",
        "scrolled": false
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "번호      0\n",
              "분류      0\n",
              "제목      0\n",
              "내용      0\n",
              "내용번호    0\n",
              "dtype: int64"
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "      <td>0</td>\n",
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              "      <th>분류</th>\n",
              "      <td>0</td>\n",
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              "      <th>제목</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>내용</th>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
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          "metadata": {},
          "execution_count": 7
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      ],
      "source": [
        "# 결측치를 확인하세요.\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lMII7R0go1Yx"
      },
      "source": [
        "## **2. 벡터화**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* '제목' 열과 '내용' 열을 합친 '제목내용' 열을 만드세요."
      ],
      "metadata": {
        "id": "v5I8aV59KbDZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "oxcuJF0akErO"
      },
      "outputs": [],
      "source": [
        "df['제목내용']=df['제목']+df['내용']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "Kt3hsZa9Ktws",
        "outputId": "cb94dfda-6439-47d4-b71f-74681c029827"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     번호  분류                           제목  \\\n",
              "0  2645  복지                  아빠 육아휴직 장려금   \n",
              "1  2644  경제            [서울산업진흥원] 서울메이드란?   \n",
              "2  2643  환경                    (강북구) 정비중   \n",
              "3  2642  복지  광진맘택시 운영(임산부,영아 양육가정 전용 택시)   \n",
              "4  2641  복지               마포 뇌병변장애인 비전센터   \n",
              "\n",
              "                                                  내용      내용번호  \\\n",
              "0  아빠 육아휴직 장려금   업무개요  남성근로자의 육아휴직을 장려하고 양육에 따른 경...  23522464   \n",
              "1  서울산업진흥원 서울메이드란 서울의 감성을 담은 다양하고 새로운 경험을 제공하기 위해...  23194045   \n",
              "2  강북구 정비중  업무개요  투명 폐트병을 교환보상하므로 수거율을 높이고 폐기물을 감...  23032485   \n",
              "3  광진맘택시 운영임산부영아 양육가정 전용 택시  업무개요 교통약자인 임산부와 영아가정...  22904492   \n",
              "4  마포 뇌병변장애인 비전센터 마포뇌병변장애인 비전센터 운영   구분 내용  목적  학...  22477798   \n",
              "\n",
              "                                                제목내용  \n",
              "0  아빠 육아휴직 장려금아빠 육아휴직 장려금   업무개요  남성근로자의 육아휴직을 장려...  \n",
              "1  [서울산업진흥원] 서울메이드란?서울산업진흥원 서울메이드란 서울의 감성을 담은 다양하...  \n",
              "2  (강북구) 정비중강북구 정비중  업무개요  투명 폐트병을 교환보상하므로 수거율을 높...  \n",
              "3  광진맘택시 운영(임산부,영아 양육가정 전용 택시)광진맘택시 운영임산부영아 양육가정 ...  \n",
              "4  마포 뇌병변장애인 비전센터마포 뇌병변장애인 비전센터 마포뇌병변장애인 비전센터 운영 ...  "
            ],
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              "      <th></th>\n",
              "      <th>번호</th>\n",
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              "      <td>아빠 육아휴직 장려금   업무개요  남성근로자의 육아휴직을 장려하고 양육에 따른 경...</td>\n",
              "      <td>23522464</td>\n",
              "      <td>아빠 육아휴직 장려금아빠 육아휴직 장려금   업무개요  남성근로자의 육아휴직을 장려...</td>\n",
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              "      <td>경제</td>\n",
              "      <td>[서울산업진흥원] 서울메이드란?</td>\n",
              "      <td>서울산업진흥원 서울메이드란 서울의 감성을 담은 다양하고 새로운 경험을 제공하기 위해...</td>\n",
              "      <td>23194045</td>\n",
              "      <td>[서울산업진흥원] 서울메이드란?서울산업진흥원 서울메이드란 서울의 감성을 담은 다양하...</td>\n",
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              "      <th>2</th>\n",
              "      <td>2643</td>\n",
              "      <td>환경</td>\n",
              "      <td>(강북구) 정비중</td>\n",
              "      <td>강북구 정비중  업무개요  투명 폐트병을 교환보상하므로 수거율을 높이고 폐기물을 감...</td>\n",
              "      <td>23032485</td>\n",
              "      <td>(강북구) 정비중강북구 정비중  업무개요  투명 폐트병을 교환보상하므로 수거율을 높...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2642</td>\n",
              "      <td>복지</td>\n",
              "      <td>광진맘택시 운영(임산부,영아 양육가정 전용 택시)</td>\n",
              "      <td>광진맘택시 운영임산부영아 양육가정 전용 택시  업무개요 교통약자인 임산부와 영아가정...</td>\n",
              "      <td>22904492</td>\n",
              "      <td>광진맘택시 운영(임산부,영아 양육가정 전용 택시)광진맘택시 운영임산부영아 양육가정 ...</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
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              "      <td>마포 뇌병변장애인 비전센터 마포뇌병변장애인 비전센터 운영   구분 내용  목적  학...</td>\n",
              "      <td>22477798</td>\n",
              "      <td>마포 뇌병변장애인 비전센터마포 뇌병변장애인 비전센터 마포뇌병변장애인 비전센터 운영 ...</td>\n",
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              "  <style>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-3f7ac9bb-aae1-4513-bae9-7a29d0e01b17 button.colab-df-convert');\n",
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              "        const element = document.querySelector('#df-3f7ac9bb-aae1-4513-bae9-7a29d0e01b17');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-91e712b5-ac1b-413e-8160-d0213a4cbef3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
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\"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2644,\n        \"samples\": [\n          \"\\uc0c1\\ubd098\\uc7ac\\uc815\\ube44\\ucd09\\uc9c4\\uad6c\\uc5ed\\uc0c1\\ubd09\\uc7ac\\uc815\\ube44\\ucd09\\uc9c4\\uad6c\\uc5ed \\uc0c1\\ubd09\\uc7ac\\uc815\\ube44\\ucd09\\uc9c4\\uad6c\\uc5ed\\uc5d0\\uc11c \\uc2e0\\ucd95\\ud558\\ub294 \\uc8fc\\ud0dd\\uc740 \\uba87\\uc138\\ub300 \\uc785\\ub2c8\\uae4c \\uacf5\\ub3d9\\uc8fc\\ud0dd \\uc138\\ub300\\ub97c \\uc2e0\\ucd95\\ud569\\ub2c8\\ub2e4  \\uc0c1\\ubd09\\uc7ac\\uc815\\ube44\\ucd09\\uc9c4\\uad6c\\uc5ed\\uc740 \\uc784\\ub300\\uc8fc\\ud0dd\\uc774 \\uc788\\uc2b5\\ub2c8\\uae4c \\uc784\\ub300\\uc8fc\\ud0dd\\uc740 \\uc138\\ub300\\uc785\\ub2c8\\ub2e4  \\uc0c1\\ubd09\\uc7ac\\uc815\\ube44\\ucd09\\uc9c4\\uad6c\\uc5ed\\uc740 \\uba87\\uc138\\ub300\\ub97c \\ubd84\\uc591\\ud569\\ub2c8\\uae4c \\ubd84\\uc591\\uc8fc\\ud0dd\\uc740 \\uc138\\ub300\\uc785\\ub2c8\\ub2e4  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\\ub3d9\\ud638\\ud68c \\ubaa8\\uc784\\uc5d0 \\uacf5\\uc6a9\\ucc28\\ub7c9\\uc744 \\uc9c0\\uc6d0\\ud560 \\uc218 \\uc788\\ub294\\uc9c0\\uc694                   \\ub3cb\\uc6c0                \\ub3cb\\uc6c0                \\ub3cb\\uc6c0                      \\ub3cb\\uc6c0                \\ub3cb\\uc6c0                \\ub3cb\\uc6c0        \\ub3cb\\uc6c0     \\ub3cb\\uc6c0     \\ub3cb\\uc6c0     \\ub3cb\\uc6c0                \\uad6c\\uc815\\uc5d0 \\ub300\\ud55c \\uae4a\\uc740 \\uad00\\uc2ec\\uc5d0 \\uac10\\uc0ac\\ub4dc\\ub9bd\\ub2c8\\ub2e4  \\uacf5\\ubb34\\uc6d0\\uc758 \\ub3d9\\ud638\\ud68c \\ud65c\\uc131\\ud654 \\ub4f1 \\uc815\\ub2f9\\ud55c \\uc0ac\\uc720\\uac00 \\uc778\\uc815\\ub418\\uc5b4 \\uc18c\\uc18d \\uae30\\uad00\\uc7a5\\uc774 \\uc77c\\uc2dc\\uc801\\uc73c\\ub85c \\uc2b9\\uc778\\uc744 \\ud558\\uba74 \\uc774\\uc6a9\\uc774 \\uac00\\ub2a5\\ud569\\ub2c8\\ub2e4 \",\n          \"\\uc8fc\\ubbfc\\uc81c\\uc548\\uc2dc \\ub3d9\\uc758\\uc728\\uc740?\\uc8fc\\ubbfc\\uc81c\\uc548\\uc2dc \\ub3d9\\uc758\\uc728\\uc740  \\uc81c\\uc548\\uad6c\\uc5ed\\ub0b4 \\ud1a0\\uc9c0 \\uba74\\uc801\\uc758 \\uc774\\uc0c1 \\uc785\\ub2c8\\ub2e4 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* TfidfVectorizer를 이용하여 '제목내용' 열을 피처 벡터화하세요.\n",
        "  * 'max_features=2000'으로 설정하세요.\n",
        "  * 변환된 문서 용어 matrix는 `dtm`이라는 변수명으로 저장해주세요."
      ],
      "metadata": {
        "id": "zsqycybvKeOa"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "tEUNepLOo1Y1"
      },
      "outputs": [],
      "source": [
        "# '제목내용' 열 피처 벡터화 변환\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "tfidfvect=TfidfVectorizer(max_features=2000)\n",
        "tfidfvect.fit(df['제목내용'])\n",
        "dtm=tfidfvect.transform(df['제목내용'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ft2MvWD57MjT"
      },
      "source": [
        "### 참고: fit, transform, fit_transfrom의 차이점\n",
        "- fit(): 원시 문서에 있는 모든 토큰의 어휘 사전을 배웁니다.\n",
        "- transform(): 문서를 문서 용어 매트릭스로 변환합니다. transform 이후엔 매트릭스로 변환되어 숫자형태로 변경됩니다.\n",
        "- fit_transform(): 어휘 사전을 배우고 문서 용어 매트릭스를 반환합니다. fit 다음에 변환이 오는 것과 동일하지만 더 효율적으로 구현됩니다.\n",
        "\n",
        "* API Document: https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html#sklearn.feature_extraction.text.CountVectorizer.fit_transform"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "UKEXeuKQo1Y1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "4acc659f-4e7a-4976-cc4a-f867d9e3e028"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   amp  apos   가격   가구  가구의  가급적  가까운  가꾸기        가능  가능하나  ...  회의실  횡단보도  \\\n",
              "0  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.000000   0.0  ...  0.0   0.0   \n",
              "1  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.000000   0.0  ...  0.0   0.0   \n",
              "2  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.056218   0.0  ...  0.0   0.0   \n",
              "3  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.113171   0.0  ...  0.0   0.0   \n",
              "4  0.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.000000   0.0  ...  0.0   0.0   \n",
              "\n",
              "   효율적으로   후에   후원   훈련   휴관  휴관일   휴무  희망하는  \n",
              "0    0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0  \n",
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              "2    0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0  \n",
              "3    0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0  \n",
              "4    0.0  0.0  0.0  0.0  0.0  0.0  0.0   0.0  \n",
              "\n",
              "[5 rows x 2000 columns]"
            ],
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              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>amp</th>\n",
              "      <th>apos</th>\n",
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              "      <th>가구</th>\n",
              "      <th>가구의</th>\n",
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              "      <th>효율적으로</th>\n",
              "      <th>후에</th>\n",
              "      <th>후원</th>\n",
              "      <th>훈련</th>\n",
              "      <th>휴관</th>\n",
              "      <th>휴관일</th>\n",
              "      <th>휴무</th>\n",
              "      <th>희망하는</th>\n",
              "    </tr>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.056218</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.113171</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 2000 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9ee2fa0d-5d15-4739-9eb9-4d82c373d090')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9ee2fa0d-5d15-4739-9eb9-4d82c373d090 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9ee2fa0d-5d15-4739-9eb9-4d82c373d090');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f9c00777-e8df-429a-a033-a2c19d9143b7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f9c00777-e8df-429a-a033-a2c19d9143b7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f9c00777-e8df-429a-a033-a2c19d9143b7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_dtm_tfidf"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# 문장에서 노출되는 feature(특징이 될만한 단어) 수를 합한 변수 Document Term Matrix(이하 dtm)를 생성합니다.\n",
        "# 아래 코드를 실행해주세요.\n",
        "df_dtm_tfidf = pd.DataFrame(dtm.toarray(), columns=tfidfvect.get_feature_names_out())\n",
        "df_dtm_tfidf.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "l61luP9mo1Y2",
        "outputId": "9d9b16b1-9006-42d0-8a85-2e7025e7f635"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 각 row에서 전체 단어가방에 있는 어휘에서 등장하는 단어에 대한 가중치를 적용한 vector를 확인합니다.\n",
        "# 아래 셀을 실행해주세요.\n",
        "df_dtm_tfidf.sum().nlargest(20).plot(kind=\"bar\", figsize=(10, 4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5SfyYdvZo1Y3"
      },
      "source": [
        "## 코사인 유사도\n",
        "\n",
        "\n",
        "<img src=\"https://i.imgur.com/RdPPH33.png\">\n",
        "* 이미지 출처 : https://pyimagesearch.com/\n",
        "\n",
        "<img src=\"https://storage.googleapis.com/lds-media/images/cosine-similarity-vectors.original.jpg\">\n",
        "* 이미지 출처 : https://www.learndatasci.com/glossary/cosine-similarity/\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "- 내적 공간 내에서 두 벡터 사이의 코사인 각도를 구하는 방법( –1∼1 사이 값을 가지며, 1에 가까울수록 유사도가 높음)\n",
        "- 벡터의 크기가 아닌 방향의 유사도를 판단하는 목적으로 사용되며, 두 벡터의 방향이 완전히 같을 경우 1, 90°의 각을 이룰 경우 0, 180°로 완전히 반대 방향인 경우 -1의 값을 갖음 >> 코사인 유사도에서는 각 데이터 간의 각도가 작을 수록 데이터가 비슷\n",
        "- 장점: 어떤 개수의 차원에도 적용할 수 있지만 흔히 다차원의 양수 공간에서의 유사도 측정에서 자주 이용\n",
        "- 단점: 상호 상관관계를 가지는 특성(키, 몸무게 등)를 갖는 원소들간의 유사도를 계산할때에 성능이 낮음\n",
        "\n",
        "\n",
        "* 코사인 유사도는 원점(모든 단어의 빈도가 0인 경우)에서 보았을 때 두 문서의 각도에 바탕을 둔 거리 측정 방식\n",
        "* 내적공간의 두 벡터간 각도의 코사인값을 이용하여 측정된 벡터간의 유사한 정도를 의미한다. 각도가 0°일 때의 코사인값은 1이며, 다른 모든 각도의 코사인값은 1보다 작다. 따라서 이 값은 벡터의 크기가 아닌 방향의 유사도를 판단하는 목적으로 사용되며, 두 벡터의 방향이 완전히 같을 경우 1, 90°의 각을 이룰 경우 0, 180°로 완전히 반대 방향인 경우 -1의 값을 갖는다. 이 때 벡터의 크기는 값에 아무런 영향을 미치지 않는다. 코사인 유사도는 특히 결과값이 [0,1]의 범위로 떨어지는 양수 공간에서 사용된다.\n",
        "* 코사인 유사도는 어떤 개수의 차원에도 적용이 가능하여 흔히 다차원의 양수 공간에서의 유사도 측정에 자주 이용된다. 예를 들어 정보 검색 및 텍스트 마이닝 분야에서, 단어 하나 하나는 각각의 차원을 구성하고 문서는 각 단어가 문서에 나타나는 회수로 표현되는 벡터값을 가진다. 이러한 다차원 공간에서 코사인 유사도는 두 문서의 유사를 측정하는 매우 유용한 방법이다.\n",
        "* 코사인 유사도는 데이터 마이닝 분야에서 클러스터들간의 응집도를 측정하는 방법으로도 사용된다.\n",
        "\n",
        "* 출처: https://ko.wikipedia.org/wiki/%EC%BD%94%EC%82%AC%EC%9D%B8_%EC%9C%A0%EC%82%AC%EB%8F%84\n",
        "* API Document: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_similarity.html\n",
        "\n",
        "\n",
        "\n",
        "$$ \\text{{Cosine Similarity}}(A, B) = \\frac{{A \\cdot B}}{{\\|A\\|\\|B\\|}} = \\frac{{\\sum^n_{i=1}A_iB_i}}{{\\sqrt{\\sum^n_{i=1}A_i^2}\\sqrt{\\sum^n_{i=1}B_i^2}}} $$\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XiRyNAtSbZjK"
      },
      "source": [
        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_similarity.html#sklearn-metrics-pairwise-cosine-similarity"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* `sklearn.metrics.pairwise`에서 `cosine_similarity`를 불러온 후 코사인 유사도를 `ndarray` 형태로 반환하세요.\n",
        "  * 생성된 코사인 유사도 행렬을 `cosine_matrix`라는 변수명으로 저장해주세요."
      ],
      "metadata": {
        "id": "Dtm88bimk4GW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AC98n4w5o1Y3",
        "outputId": "b5b0d4b1-5e8f-4c85-91b9-97ed5612d17e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 0.        , 0.03938162, ..., 0.01994409, 0.        ,\n",
              "        0.        ],\n",
              "       [0.        , 1.        , 0.        , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.03938162, 0.        , 1.        , ..., 0.02733107, 0.        ,\n",
              "        0.        ],\n",
              "       ...,\n",
              "       [0.01994409, 0.        , 0.02733107, ..., 1.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 1.        ,\n",
              "        0.32093317],\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 0.32093317,\n",
              "        1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "cosine_matrix=cosine_similarity(df_dtm_tfidf, df_dtm_tfidf)\n",
        "cosine_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "c2av-wxhbZjK",
        "outputId": "03f002c7-f973-44b5-a06c-e327b7cc35bc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 코사인 유사도를 시각화합니다.\n",
        "# 아래 셀을 실행해주세요.\n",
        "import seaborn as sns\n",
        "sns.heatmap(cosine_matrix[:100, :100], cmap=\"gray_r\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Fl13qB5bZjK"
      },
      "source": [
        "## 유사도가 높은 문서"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 첫 번째 행에 대한 코사인 유사도를 'similarity_0'이라는 파생변수로 생성하고 유사도가 높은 순으로 정렬합니다.\n",
        "  * '번호', '제목', 'similarity_0' 열이 모두 나오게 출력하세요.\n",
        "  * 제안 : `nlargest(n, columns)` 메소드를 써도 좋습니다."
      ],
      "metadata": {
        "id": "kbj6YhRvl9hG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "XiIUgZHCHt8y",
        "outputId": "4fff476c-ca1c-4e2f-c51d-513e27053abf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        번호                                         제목  similarity_0\n",
              "0     2645                                아빠 육아휴직 장려금      1.000000\n",
              "1772   873        도시계획시설부지 재결신청 이후 진행단계는 어떤 과정을 거칩니까?      0.213203\n",
              "1390  1255                 서울시 연수시설의 각 홈페이지 주소를 알려주세요      0.186110\n",
              "1073  1572                      부기등기 이후 제한물권 설정의 효력은?      0.182378\n",
              "155   2490  [농업기술센터] 후계농업경영인 선정 및 청년창업형 후계농업경영인 신청 안내      0.179294\n",
              "...    ...                                        ...           ...\n",
              "1252  1393                         기능직 공무원은 현재 몇명인가요?      0.000000\n",
              "1250  1395                           콘크리트 타설시 점검사항은 ?      0.000000\n",
              "1249  1396                      주거환경개선지구 내 건축허가 가능 여부      0.000000\n",
              "1245  1400                              착공 및 공종보고내용은?      0.000000\n",
              "2644     1                      폐기대상 문서의 폐기는 어떻게 하나요?      0.000000\n",
              "\n",
              "[2645 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e5069ae0-fa93-4db7-bf1b-98541d097b44\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead 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>번호</th>\n",
              "      <th>제목</th>\n",
              "      <th>similarity_0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2645</td>\n",
              "      <td>아빠 육아휴직 장려금</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1772</th>\n",
              "      <td>873</td>\n",
              "      <td>도시계획시설부지 재결신청 이후 진행단계는 어떤 과정을 거칩니까?</td>\n",
              "      <td>0.213203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1390</th>\n",
              "      <td>1255</td>\n",
              "      <td>서울시 연수시설의 각 홈페이지 주소를 알려주세요</td>\n",
              "      <td>0.186110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1073</th>\n",
              "      <td>1572</td>\n",
              "      <td>부기등기 이후 제한물권 설정의 효력은?</td>\n",
              "      <td>0.182378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>2490</td>\n",
              "      <td>[농업기술센터] 후계농업경영인 선정 및 청년창업형 후계농업경영인 신청 안내</td>\n",
              "      <td>0.179294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1252</th>\n",
              "      <td>1393</td>\n",
              "      <td>기능직 공무원은 현재 몇명인가요?</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1250</th>\n",
              "      <td>1395</td>\n",
              "      <td>콘크리트 타설시 점검사항은 ?</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1249</th>\n",
              "      <td>1396</td>\n",
              "      <td>주거환경개선지구 내 건축허가 가능 여부</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1245</th>\n",
              "      <td>1400</td>\n",
              "      <td>착공 및 공종보고내용은?</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2644</th>\n",
              "      <td>1</td>\n",
              "      <td>폐기대상 문서의 폐기는 어떻게 하나요?</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2645 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e5069ae0-fa93-4db7-bf1b-98541d097b44')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e5069ae0-fa93-4db7-bf1b-98541d097b44 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e5069ae0-fa93-4db7-bf1b-98541d097b44');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-934e93a4-a090-467d-98fd-32488d6fbc1e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_199a3bab-07c2-4ad0-b7a2-60a73e127bdd\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('result')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_199a3bab-07c2-4ad0-b7a2-60a73e127bdd button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('result');\n",
              "      }\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "result",
              "summary": "{\n  \"name\": \"result\",\n  \"rows\": 2645,\n  \"fields\": [\n    {\n      \"column\": \"\\ubc88\\ud638\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 763,\n        \"min\": 1,\n        \"max\": 2645,\n        \"num_unique_values\": 2645,\n        \"samples\": [\n          484,\n          1038,\n          1004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc81c\\ubaa9\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2638,\n        \"samples\": [\n          \"\\uac00\\ucd95\\uc804\\uc5fc\\ubcd1\\ube44\\ubc1c\\uc0dd\\ud655\\uc778\\uc11c \\uc99d\\uba85\\uc740 \\uc5b4\\ub5bb\\uac8c \\ubc1b\\uc744 \\uc218 \\uc788\\ub098\\uc694?\",\n          \"\\uacf5\\uc0ac\\uc190\\ud574\\ubcf4\\ud5d8\\uc774\\ub780?\",\n          \"\\uace0\\uac1d\\uc11c\\ube44\\uc2a4 \\ud3c9\\uac00\\ub780 \\ubb34\\uc5c7\\uc778\\uac00\\uc694?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"similarity_0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.034484872514441565,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1301,\n        \"samples\": [\n          0.036990131971261844,\n          0.024099427609728746,\n          0.049942903582518315\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# 첫 번째 행과의 코사인 유사도 계산\n",
        "cos_similarities = cosine_similarity(df_dtm_tfidf[0:1],df_dtm_tfidf).flatten()\n",
        "\n",
        "# 'similarity_0' 파생변수로 추가\n",
        "df['similarity_0'] = cos_similarities\n",
        "\n",
        "result = df.nlargest(len(df), 'similarity_0')[['번호', '제목', 'similarity_0']]\n",
        "result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QO-owdewbZjK"
      },
      "source": [
        "## 유사도가 낮은 문서"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 위와 같은 방법으로 유사도가 낮은 순으로 정렬합니다.\n",
        "  * 제안 : `nsmallest(n, columns)` 메소드를 써도 좋습니다."
      ],
      "metadata": {
        "id": "PVzOuaBWmsYu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "RXCQLJKGo1Y3",
        "outputId": "fa6640b4-52cb-4941-cfb0-a57a9444f195"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        번호                                         제목  similarity_0\n",
              "1322  1323                    구청장의 업무 추진비는 어떻게 책정됩니까?      0.000000\n",
              "1422  1223                            서대문구 규칙은 몇개인가요?      0.000000\n",
              "1418  1227                      감리원의 자격은 어디에 나와 았나요 ?      0.000000\n",
              "1417  1228                                원가계산의 비목이란?      0.000000\n",
              "1416  1229                                     제조원가란?      0.000000\n",
              "...    ...                                        ...           ...\n",
              "155   2490  [농업기술센터] 후계농업경영인 선정 및 청년창업형 후계농업경영인 신청 안내      0.179294\n",
              "1073  1572                      부기등기 이후 제한물권 설정의 효력은?      0.182378\n",
              "1390  1255                 서울시 연수시설의 각 홈페이지 주소를 알려주세요      0.186110\n",
              "1772   873        도시계획시설부지 재결신청 이후 진행단계는 어떤 과정을 거칩니까?      0.213203\n",
              "0     2645                                아빠 육아휴직 장려금      1.000000\n",
              "\n",
              "[2645 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e1df4c66-85a3-4dbf-ab2b-cbe33406f5a2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>번호</th>\n",
              "      <th>제목</th>\n",
              "      <th>similarity_0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1322</th>\n",
              "      <td>1323</td>\n",
              "      <td>구청장의 업무 추진비는 어떻게 책정됩니까?</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1422</th>\n",
              "      <td>1223</td>\n",
              "      <td>서대문구 규칙은 몇개인가요?</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1418</th>\n",
              "      <td>1227</td>\n",
              "      <td>감리원의 자격은 어디에 나와 았나요 ?</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1417</th>\n",
              "      <td>1228</td>\n",
              "      <td>원가계산의 비목이란?</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1416</th>\n",
              "      <td>1229</td>\n",
              "      <td>제조원가란?</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>2490</td>\n",
              "      <td>[농업기술센터] 후계농업경영인 선정 및 청년창업형 후계농업경영인 신청 안내</td>\n",
              "      <td>0.179294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1073</th>\n",
              "      <td>1572</td>\n",
              "      <td>부기등기 이후 제한물권 설정의 효력은?</td>\n",
              "      <td>0.182378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1390</th>\n",
              "      <td>1255</td>\n",
              "      <td>서울시 연수시설의 각 홈페이지 주소를 알려주세요</td>\n",
              "      <td>0.186110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1772</th>\n",
              "      <td>873</td>\n",
              "      <td>도시계획시설부지 재결신청 이후 진행단계는 어떤 과정을 거칩니까?</td>\n",
              "      <td>0.213203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2645</td>\n",
              "      <td>아빠 육아휴직 장려금</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2645 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "        document.querySelector('#df-e1df4c66-85a3-4dbf-ab2b-cbe33406f5a2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e1df4c66-85a3-4dbf-ab2b-cbe33406f5a2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0b090f15-a0a2-4f1a-a799-a3fde450b514 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_40e70490-514d-4b5f-84f7-3729a6e5cf28\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        border: none;\n",
              "        border-radius: 50%;\n",
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              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('result')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_40e70490-514d-4b5f-84f7-3729a6e5cf28 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('result');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "result",
              "summary": "{\n  \"name\": \"result\",\n  \"rows\": 2645,\n  \"fields\": [\n    {\n      \"column\": \"\\ubc88\\ud638\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 763,\n        \"min\": 1,\n        \"max\": 2645,\n        \"num_unique_values\": 2645,\n        \"samples\": [\n          765,\n          1596,\n          1967\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"\\uc81c\\ubaa9\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2638,\n        \"samples\": [\n          \"\\uad6d\\ubbfc\\uc8fc\\ud0dd\",\n          \"\\uc7a5\\uc9c0\\ucc9c \\ud48d\\ub825 \\uac00\\ub85c\\ub4f1 \\uc124\\uce58\\ub294?\",\n          \"[\\uc778\\uc7ac\\uac1c\\ubc1c\\uc6d0]\\uc2dc\\ubbfc\\uac15\\uc5f0\\uc790 \\ubaa8\\uc9d1\\uacf5\\uace0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"similarity_0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.034484872514442017,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1301,\n        \"samples\": [\n          0.01989071492479301,\n          0.031195456568314246,\n          0.013929360084667698\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "result = df.nsmallest(len(df), 'similarity_0')[['번호', '제목', 'similarity_0']]\n",
        "result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lui3k4b6Ht8y"
      },
      "source": [
        "### 유사도가 높은 문서 찾는 함수 만들기\n",
        "* chatGPT, 구글링을 이용하여 함수를 만들어보세요.\n",
        "  * 함수명은 `find_similarity`로 설정해주세요."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "wu4qQpTLHt8y"
      },
      "outputs": [],
      "source": [
        "def find_similarity(n):\n",
        "  tfidfvect=TfidfVectorizer(max_features=2000)\n",
        "  tfidfvect.fit(df['제목내용'])\n",
        "  dtm=tfidfvect.transform(df['제목내용'])\n",
        "  df_dtm_tfidf = pd.DataFrame(dtm.toarray(), columns=tfidfvect.get_feature_names_out())\n",
        "  cos_similarities = cosine_similarity(df_dtm_tfidf[n:n+1],df_dtm_tfidf).flatten()\n",
        "  similarity_column = f'similarity_{n}'\n",
        "  df[similarity_column] = cos_similarities\n",
        "  result = df.nlargest(len(df), similarity_column)[['번호', '제목', similarity_column]]\n",
        "  return result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "WvgfJ7vmo1Y3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "dfd39e04-63c8-4117-fb9b-725a8726fe29"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        번호                              제목  similarity_75\n",
              "75    2570                친환경급식 안심식재료 지킴이단       1.000000\n",
              "85    2560            친환경급식 안심식재료 모니터링 체험단       0.634592\n",
              "70    2575  서울시 친환경 먹거리교육 학부모강사 양성과정 수강생모집       0.273706\n",
              "2179   466                    서울시 함께꿈 학부모단       0.200108\n",
              "310   2335        소비자식품위생감시원 및 농,수산물 명예감시원       0.196450\n",
              "...    ...                             ...            ...\n",
              "1266  1379              비계조립 및 해체시 유희사항은 ?       0.000000\n",
              "1265  1380                 사다리 사용시 점검사항은 ?       0.000000\n",
              "1263  1382         낙하물 방지망(철망) 설치시 유의사항은 ?       0.000000\n",
              "1260  1385      각 가정집에서 TV로 EBN방송을 볼수 있나요?       0.000000\n",
              "2644     1           폐기대상 문서의 폐기는 어떻게 하나요?       0.000000\n",
              "\n",
              "[2645 rows x 3 columns]"
            ],
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              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>번호</th>\n",
              "      <th>제목</th>\n",
              "      <th>similarity_75</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>2570</td>\n",
              "      <td>친환경급식 안심식재료 지킴이단</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>2560</td>\n",
              "      <td>친환경급식 안심식재료 모니터링 체험단</td>\n",
              "      <td>0.634592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>2575</td>\n",
              "      <td>서울시 친환경 먹거리교육 학부모강사 양성과정 수강생모집</td>\n",
              "      <td>0.273706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2179</th>\n",
              "      <td>466</td>\n",
              "      <td>서울시 함께꿈 학부모단</td>\n",
              "      <td>0.200108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>310</th>\n",
              "      <td>2335</td>\n",
              "      <td>소비자식품위생감시원 및 농,수산물 명예감시원</td>\n",
              "      <td>0.196450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1266</th>\n",
              "      <td>1379</td>\n",
              "      <td>비계조립 및 해체시 유희사항은 ?</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1265</th>\n",
              "      <td>1380</td>\n",
              "      <td>사다리 사용시 점검사항은 ?</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1263</th>\n",
              "      <td>1382</td>\n",
              "      <td>낙하물 방지망(철망) 설치시 유의사항은 ?</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1260</th>\n",
              "      <td>1385</td>\n",
              "      <td>각 가정집에서 TV로 EBN방송을 볼수 있나요?</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2644</th>\n",
              "      <td>1</td>\n",
              "      <td>폐기대상 문서의 폐기는 어떻게 하나요?</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2645 rows × 3 columns</p>\n",
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            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "# 아래 셀을 실행시켜 점검해보세요.\n",
        "find_similarity(75)"
      ]
    }
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