8.0. inflearn-newyear-event-text-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8-nSjvLMBIa3"
   },
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytextbook/pytextbook/blob/main/8.0.%20inflearn-newyear-event-text-analysis.ipynb) \n",
    "\n",
    "\n",
    "## 인프런 2020년 새해 다짐 이벤트 댓글 분석\n",
    "* https://www.inflearn.com/pages/newyear-event-20200102\n",
    "* 영상 튜토리얼 : [댓글 수백 수천개 분석하기?! [1/5] 이벤트 데이터 크롤링 feat. 인프런 새해 다짐 이벤트 - YouTube](https://www.youtube.com/watch?v=OUSwQk79H8I&list=PLaTc2c6yEwmohRzCxWQqJ7Z9aqc-sQ5gC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6PS7JxMbBIa7"
   },
   "source": [
    "## 라이브러리 로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 492,
     "status": "ok",
     "timestamp": 1672572556547,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "-lwiBQ-XBIa7"
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gWzwV7MvBIa8"
   },
   "source": [
    "## 시각화를 위한 한글폰트 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2857,
     "status": "ok",
     "timestamp": 1672572559835,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "cwDJ2YFSBIa8",
    "outputId": "aa6fb6fa-66e7-4f9d-96af-aa5aaa00108e"
   },
   "outputs": [],
   "source": [
    "!pip install koreanize-matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "executionInfo": {
     "elapsed": 393,
     "status": "ok",
     "timestamp": 1672572560221,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "MOTdJ33DBIa9",
    "outputId": "9f6a9f13-595e-465d-a900-15333e8e4e56"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': '한글'}>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 433,
       "width": 539
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import koreanize_matplotlib\n",
    "\n",
    "# 그래프에 retina display 적용\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "pd.Series([1, 3, 5, -7, 9]).plot(title=\"한글\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Bvcf0KM1BIa-"
   },
   "source": [
    "## 수집된 파일 로드하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 370,
     "status": "ok",
     "timestamp": 1672572560589,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "npwNu3VnBIa-",
    "outputId": "02c69be2-90ea-493b-9467-716afb1e8916",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2449, 1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df 라는 변수에 이벤트 댓글 파일을 로드합니다.\n",
    "df = pd.read_csv(\"https://bit.ly/inflearn-event-text-csv\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 42,
     "status": "ok",
     "timestamp": 1672572560590,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "f8iB7SNsBIa-",
    "outputId": "fe97008e-6207-439a-8f62-342dcc34c9bd"
   },
   "outputs": [
    {
     "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>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020년 목표: 스프링 열심히 공부하서 직장에서 사랑받고 싶어요!!\\n관심 있는 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020년 목표: C++ 열심히 공부해서, 학교에서 꼭 A 맞기..!! \\n관심있는...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020년 목표 : 리액트 공부하기</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40대 프로그래밍 시작! : 우리를 위한 프로그래밍 : 파이썬 중급</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020년 목표 : 돌머리 비전공자가 멋진 전공자 되기!</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text\n",
       "0  2020년 목표: 스프링 열심히 공부하서 직장에서 사랑받고 싶어요!!\\n관심 있는 ...\n",
       "1  2020년 목표: C++ 열심히 공부해서, 학교에서 꼭 A 맞기..!! \\n관심있는...\n",
       "2                                2020년 목표 : 리액트 공부하기\n",
       "3              40대 프로그래밍 시작! : 우리를 위한 프로그래밍 : 파이썬 중급\n",
       "4                    2020년 목표 : 돌머리 비전공자가 멋진 전공자 되기!"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# head 로 일부보기\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 39,
     "status": "ok",
     "timestamp": 1672572560590,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "S1fxls7xBIa_",
    "outputId": "5f92b7d7-050d-4c63-9057-6e25f8973a20"
   },
   "outputs": [
    {
     "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>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2444</th>\n",
       "      <td>작년 한해도 일이 바쁘다, 야근해서 힘들다는 핑계로 ***님의 JPA 강의를 또 스...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2445</th>\n",
       "      <td>저는 졸업을 1년 남기고 있는 컴퓨터공학과 학생입니다. 졸업 프로젝트로 웹/앱 개발...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2446</th>\n",
       "      <td>에프터 이펙트를 써본 적은 있는데, 매번 기초만 배우다 말았어요. 이걸 할 줄 안다...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2447</th>\n",
       "      <td>저번에 인프런에서 페이스북 마케팅 강의를 듣고 많은 도움을 받았습니다. 마케팅 업무...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2448</th>\n",
       "      <td>인프런 0호 팀원이에요!\\n그동안 서비스 개발 때문에 js 를 많이 했었는데 앞으론...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text\n",
       "2444  작년 한해도 일이 바쁘다, 야근해서 힘들다는 핑계로 ***님의 JPA 강의를 또 스...\n",
       "2445  저는 졸업을 1년 남기고 있는 컴퓨터공학과 학생입니다. 졸업 프로젝트로 웹/앱 개발...\n",
       "2446  에프터 이펙트를 써본 적은 있는데, 매번 기초만 배우다 말았어요. 이걸 할 줄 안다...\n",
       "2447  저번에 인프런에서 페이스북 마케팅 강의를 듣고 많은 도움을 받았습니다. 마케팅 업무...\n",
       "2448  인프런 0호 팀원이에요!\\n그동안 서비스 개발 때문에 js 를 많이 했었는데 앞으론..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tail 로 일부보기\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G6rPuEF0BIa_"
   },
   "source": [
    "## 데이터 전처리"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hqBkLRA9BIa_"
   },
   "source": [
    "### 네트워크 오류 등으로 발생한 중복 입력 값을 제거\n",
    "* 빈도 수 중복을 방지하기 위해"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 36,
     "status": "ok",
     "timestamp": 1672572560590,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "uVHHio1jBIbA",
    "outputId": "2a943632-1f2b-490f-e1c4-7299f14f6859"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2449, 1)\n",
      "(2410, 1)\n"
     ]
    }
   ],
   "source": [
    "# drop_duplicates 를 통해 중복을 제거하며, 이때 마지막 글을 남김\n",
    "print(df.shape)\n",
    "df = df.drop_duplicates([\"text\"], keep=\"last\")\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AuByXFTrBIbA"
   },
   "source": [
    "### 원본은 따로 보존"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 302
    },
    "executionInfo": {
     "elapsed": 29,
     "status": "ok",
     "timestamp": 1672572560591,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "dVM3FRk-BIbA",
    "outputId": "0ea91e10-a7a6-4c4e-cf15-fea5086e84af"
   },
   "outputs": [
    {
     "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>text</th>\n",
       "      <th>origin_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020년 목표: 스프링 열심히 공부하서 직장에서 사랑받고 싶어요!!\\n관심 있는 ...</td>\n",
       "      <td>2020년 목표: 스프링 열심히 공부하서 직장에서 사랑받고 싶어요!!\\n관심 있는 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020년 목표: C++ 열심히 공부해서, 학교에서 꼭 A 맞기..!! \\n관심있는...</td>\n",
       "      <td>2020년 목표: C++ 열심히 공부해서, 학교에서 꼭 A 맞기..!! \\n관심있는...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40대 프로그래밍 시작! : 우리를 위한 프로그래밍 : 파이썬 중급</td>\n",
       "      <td>40대 프로그래밍 시작! : 우리를 위한 프로그래밍 : 파이썬 중급</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020년 목표 : 돌머리 비전공자가 멋진 전공자 되기!</td>\n",
       "      <td>2020년 목표 : 돌머리 비전공자가 멋진 전공자 되기!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020 년목표: 비전공자(경영)가 전공자(it) 되기!!!</td>\n",
       "      <td>2020 년목표: 비전공자(경영)가 전공자(it) 되기!!!</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  \\\n",
       "0  2020년 목표: 스프링 열심히 공부하서 직장에서 사랑받고 싶어요!!\\n관심 있는 ...   \n",
       "1  2020년 목표: C++ 열심히 공부해서, 학교에서 꼭 A 맞기..!! \\n관심있는...   \n",
       "3              40대 프로그래밍 시작! : 우리를 위한 프로그래밍 : 파이썬 중급   \n",
       "4                    2020년 목표 : 돌머리 비전공자가 멋진 전공자 되기!   \n",
       "5                  2020 년목표: 비전공자(경영)가 전공자(it) 되기!!!   \n",
       "\n",
       "                                         origin_text  \n",
       "0  2020년 목표: 스프링 열심히 공부하서 직장에서 사랑받고 싶어요!!\\n관심 있는 ...  \n",
       "1  2020년 목표: C++ 열심히 공부해서, 학교에서 꼭 A 맞기..!! \\n관심있는...  \n",
       "3              40대 프로그래밍 시작! : 우리를 위한 프로그래밍 : 파이썬 중급  \n",
       "4                    2020년 목표 : 돌머리 비전공자가 멋진 전공자 되기!  \n",
       "5                  2020 년목표: 비전공자(경영)가 전공자(it) 되기!!!  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 전처리 전에 원본을 보존하기 위해 origin_text 라는 컬럼에 복\n",
    "df[\"origin_text\"] = df[\"text\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "APp_0BM9BIbA"
   },
   "source": [
    "### 소문자 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "executionInfo": {
     "elapsed": 27,
     "status": "ok",
     "timestamp": 1672572560591,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "fwhhp9xJBIbB"
   },
   "outputs": [],
   "source": [
    "# \"text\" 파이썬은 대소문자를 구분하기 때문에 데이터 필터링을 위해 대문자를 모두 소문자로 변경\n",
    "df[\"text\"] = df[\"text\"].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 26,
     "status": "ok",
     "timestamp": 1672572560591,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "LUqkdzQ9BIbB",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 같은 의미의 단어를 하나로 통일 예) python => 파이썬\n",
    "# replace 는 텍스트가 완전히 일치될 때만 사용할 수 있음\n",
    "# 일부만 일치한다면 str.replace 를 사용하면 원하는 텍스트로 변경이 가능\n",
    "df[\"text\"] = df[\"text\"].str.replace(\n",
    "    \"python\", \"파이썬\").str.replace(\n",
    "    \"pandas\", \"판다스\").str.replace(\n",
    "    \"javascript\", \"자바스크립트\").str.replace(\n",
    "    \"java\", \"자바\").str.replace(\n",
    "    \"react\", \"리액트\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m45GzMj4BIbB"
   },
   "source": [
    "## 문자열 분리로 관심 강의 분리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 26,
     "status": "ok",
     "timestamp": 1672572560591,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "bm1mXxqABIbB",
    "outputId": "15221c88-1bbb-42c3-f8e9-1d92e9791f1a",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2020년 목표 스프링 열심히 공부하서 직장에서 사랑받고 싶어요!!\\n관심 있는 강...\n",
       "1    2020년 목표 c++ 열심히 공부해서, 학교에서 꼭 a 맞기..!! \\n관심있는 ...\n",
       "3                  40대 프로그래밍 시작!  우리를 위한 프로그래밍  파이썬 중급\n",
       "4                       2020년 목표  돌머리 비전공자가 멋진 전공자 되기!\n",
       "5                     2020 년목표 비전공자(경영)가 전공자(it) 되기!!!\n",
       "Name: course, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 이 이벤트에는 \"관심강의\"라는 텍스트가 있음\n",
    "# \"관심강의\"를 기준으로 텍스트를 분리하고 관심강의 뒤에 있는 텍스트를 가져옴\n",
    "# 대부분 \"관심강의\"라는 텍스트를 쓰고 뒤에 강의명을 쓰기 때문\n",
    "# 전처리한 내용은 실수를 방지하기 위해 \"course\" 라는 새로운 컬럼에 담고\n",
    "# \"관심 강의\", \"관심 강좌\" 에 대해서도 똑같이 전처리\n",
    "# \":\" 특수문자를 빈문자로 변경\n",
    "\n",
    "df[\"course\"] = df[\"text\"].apply(lambda x: x.split(\"관심강의\")[-1])\n",
    "df[\"course\"] = df[\"course\"].apply(lambda x: x.split(\"관심 강의\")[-1])\n",
    "df[\"course\"] = df[\"course\"].apply(lambda x: x.split(\"관심 강좌\")[-1])\n",
    "df[\"course\"] = df[\"course\"].str.replace(\":\", \"\")\n",
    "df[\"course\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 302
    },
    "executionInfo": {
     "elapsed": 23,
     "status": "ok",
     "timestamp": 1672572560592,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "F_fnL4s8BIbC",
    "outputId": "e0270846-05bd-45a8-f50c-fc76f74ec022"
   },
   "outputs": [
    {
     "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>text</th>\n",
       "      <th>course</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020년 목표: 스프링 열심히 공부하서 직장에서 사랑받고 싶어요!!\\n관심 있는 ...</td>\n",
       "      <td>2020년 목표 스프링 열심히 공부하서 직장에서 사랑받고 싶어요!!\\n관심 있는 강...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020년 목표: c++ 열심히 공부해서, 학교에서 꼭 a 맞기..!! \\n관심있는...</td>\n",
       "      <td>2020년 목표 c++ 열심히 공부해서, 학교에서 꼭 a 맞기..!! \\n관심있는 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40대 프로그래밍 시작! : 우리를 위한 프로그래밍 : 파이썬 중급</td>\n",
       "      <td>40대 프로그래밍 시작!  우리를 위한 프로그래밍  파이썬 중급</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020년 목표 : 돌머리 비전공자가 멋진 전공자 되기!</td>\n",
       "      <td>2020년 목표  돌머리 비전공자가 멋진 전공자 되기!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020 년목표: 비전공자(경영)가 전공자(it) 되기!!!</td>\n",
       "      <td>2020 년목표 비전공자(경영)가 전공자(it) 되기!!!</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  \\\n",
       "0  2020년 목표: 스프링 열심히 공부하서 직장에서 사랑받고 싶어요!!\\n관심 있는 ...   \n",
       "1  2020년 목표: c++ 열심히 공부해서, 학교에서 꼭 a 맞기..!! \\n관심있는...   \n",
       "3              40대 프로그래밍 시작! : 우리를 위한 프로그래밍 : 파이썬 중급   \n",
       "4                    2020년 목표 : 돌머리 비전공자가 멋진 전공자 되기!   \n",
       "5                  2020 년목표: 비전공자(경영)가 전공자(it) 되기!!!   \n",
       "\n",
       "                                              course  \n",
       "0  2020년 목표 스프링 열심히 공부하서 직장에서 사랑받고 싶어요!!\\n관심 있는 강...  \n",
       "1  2020년 목표 c++ 열심히 공부해서, 학교에서 꼭 a 맞기..!! \\n관심있는 ...  \n",
       "3                40대 프로그래밍 시작!  우리를 위한 프로그래밍  파이썬 중급  \n",
       "4                     2020년 목표  돌머리 비전공자가 멋진 전공자 되기!  \n",
       "5                   2020 년목표 비전공자(경영)가 전공자(it) 되기!!!  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"text\", \"course\" 전처리 내용 미리보기\n",
    "df[[\"text\", \"course\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YeusbDQyBIbC"
   },
   "source": [
    "## 띄어 쓰기를 제거한 텍스트에서 키워드 추출\n",
    "* TIOBE 프로그래밍 언어 순위 : [index | TIOBE - The Software Quality Company](https://www.tiobe.com/tiobe-index/?fbclid=IwAR34dJfgDHq2DK0C6X3g8IsUno2NhOiikMyxT6fw9SoyujFhy5FPvQogMoA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "executionInfo": {
     "elapsed": 22,
     "status": "ok",
     "timestamp": 1672572560592,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "XipYvRtrBIbC"
   },
   "outputs": [],
   "source": [
    "# 특정 키워드가 들어가는 댓글을 찾음\n",
    "search_keyword = ['머신러닝', '딥러닝', '파이썬', '판다스', '공공데이터',\n",
    "                  'django', '크롤링', '시각화', '데이터분석', \n",
    "                  '웹개발', '엑셀', 'c', '자바', '자바스크립트', \n",
    "                  'node', 'vue', '리액트']\n",
    "\n",
    "# for 문을 통해 해당 키워드가 있는지 여부를 True, False값으로 표시하도록 함\n",
    "# 키워드에 따라 컬럼을 새로 만듭니다.\n",
    "for keyword in search_keyword:\n",
    "    df[keyword] = df[\"course\"].str.contains(keyword)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 21,
     "status": "ok",
     "timestamp": 1672572560592,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "ZmEPMl8fBIbC",
    "outputId": "406cc036-36b0-4d32-8b87-05e9e00225b3"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>origin_text</th>\n",
       "      <th>course</th>\n",
       "      <th>머신러닝</th>\n",
       "      <th>딥러닝</th>\n",
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       "      <th>판다스</th>\n",
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       "      <th>django</th>\n",
       "      <th>크롤링</th>\n",
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       "      <th>엑셀</th>\n",
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       "      <th>node</th>\n",
       "      <th>vue</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2020년 목표: 스프링 열심히 공부하서 직장에서 사랑받고 싶어요!!\\n관심 있는 ...</td>\n",
       "      <td>2020년 목표: 스프링 열심히 공부하서 직장에서 사랑받고 싶어요!!\\n관심 있는 ...</td>\n",
       "      <td>2020년 목표 스프링 열심히 공부하서 직장에서 사랑받고 싶어요!!\\n관심 있는 강...</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020년 목표: c++ 열심히 공부해서, 학교에서 꼭 a 맞기..!! \\n관심있는...</td>\n",
       "      <td>2020년 목표: C++ 열심히 공부해서, 학교에서 꼭 A 맞기..!! \\n관심있는...</td>\n",
       "      <td>2020년 목표 c++ 열심히 공부해서, 학교에서 꼭 a 맞기..!! \\n관심있는 ...</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40대 프로그래밍 시작! : 우리를 위한 프로그래밍 : 파이썬 중급</td>\n",
       "      <td>40대 프로그래밍 시작! : 우리를 위한 프로그래밍 : 파이썬 중급</td>\n",
       "      <td>40대 프로그래밍 시작!  우리를 위한 프로그래밍  파이썬 중급</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020년 목표 : 돌머리 비전공자가 멋진 전공자 되기!</td>\n",
       "      <td>2020년 목표 : 돌머리 비전공자가 멋진 전공자 되기!</td>\n",
       "      <td>2020년 목표  돌머리 비전공자가 멋진 전공자 되기!</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020 년목표: 비전공자(경영)가 전공자(it) 되기!!!</td>\n",
       "      <td>2020 년목표: 비전공자(경영)가 전공자(it) 되기!!!</td>\n",
       "      <td>2020 년목표 비전공자(경영)가 전공자(it) 되기!!!</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                                                text  \\\n",
       "0  2020년 목표: 스프링 열심히 공부하서 직장에서 사랑받고 싶어요!!\\n관심 있는 ...   \n",
       "1  2020년 목표: c++ 열심히 공부해서, 학교에서 꼭 a 맞기..!! \\n관심있는...   \n",
       "3              40대 프로그래밍 시작! : 우리를 위한 프로그래밍 : 파이썬 중급   \n",
       "4                    2020년 목표 : 돌머리 비전공자가 멋진 전공자 되기!   \n",
       "5                  2020 년목표: 비전공자(경영)가 전공자(it) 되기!!!   \n",
       "\n",
       "                                         origin_text  \\\n",
       "0  2020년 목표: 스프링 열심히 공부하서 직장에서 사랑받고 싶어요!!\\n관심 있는 ...   \n",
       "1  2020년 목표: C++ 열심히 공부해서, 학교에서 꼭 A 맞기..!! \\n관심있는...   \n",
       "3              40대 프로그래밍 시작! : 우리를 위한 프로그래밍 : 파이썬 중급   \n",
       "4                    2020년 목표 : 돌머리 비전공자가 멋진 전공자 되기!   \n",
       "5                  2020 년목표: 비전공자(경영)가 전공자(it) 되기!!!   \n",
       "\n",
       "                                              course   머신러닝    딥러닝    파이썬  \\\n",
       "0  2020년 목표 스프링 열심히 공부하서 직장에서 사랑받고 싶어요!!\\n관심 있는 강...  False  False  False   \n",
       "1  2020년 목표 c++ 열심히 공부해서, 학교에서 꼭 a 맞기..!! \\n관심있는 ...  False  False  False   \n",
       "3                40대 프로그래밍 시작!  우리를 위한 프로그래밍  파이썬 중급  False  False   True   \n",
       "4                     2020년 목표  돌머리 비전공자가 멋진 전공자 되기!  False  False  False   \n",
       "5                   2020 년목표 비전공자(경영)가 전공자(it) 되기!!!  False  False  False   \n",
       "\n",
       "     판다스  공공데이터  django    크롤링    시각화  데이터분석    웹개발     엑셀      c     자바  \\\n",
       "0  False  False   False  False  False  False  False  False   True  False   \n",
       "1  False  False   False  False  False  False  False  False   True  False   \n",
       "3  False  False   False  False  False  False  False  False  False  False   \n",
       "4  False  False   False  False  False  False  False  False  False  False   \n",
       "5  False  False   False  False  False  False  False  False  False  False   \n",
       "\n",
       "   자바스크립트   node    vue    리액트  \n",
       "0   False  False  False  False  \n",
       "1   False  False  False  False  \n",
       "3   False  False  False  False  \n",
       "4   False  False  False  False  \n",
       "5   False  False  False  False  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 미리보기\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 21,
     "status": "ok",
     "timestamp": 1672572560593,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "sOHNchsQBIbD",
    "outputId": "a7dd5d56-8b8d-4601-8e4b-d2ab71c3d766",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(429, 20)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 파이썬|공공데이터|판다스 라는 텍스트가 들어가는 데이터가 있는지 찾음\n",
    "df_python = df[df[\"text\"].str.contains(\"파이썬|공공데이터|판다스\")].copy()\n",
    "df_python.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1672572560593,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "Rkc7ANtFBIbD",
    "outputId": "8f78b1b2-f0ee-45df-b123-77a86b789314",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c         409\n",
       "파이썬       405\n",
       "자바        341\n",
       "자바스크립트    140\n",
       "리액트       137\n",
       "머신러닝      133\n",
       "vue       132\n",
       "node      117\n",
       "크롤링        56\n",
       "딥러닝        52\n",
       "django     31\n",
       "시각화        30\n",
       "웹개발        27\n",
       "데이터분석      24\n",
       "엑셀         18\n",
       "공공데이터      12\n",
       "판다스         6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 결과를 모두 더하면 해당 키워드의 등장 빈도수를 카운트\n",
    "# search_keyword 컬럼만 가져와서 빈도수를 sum으로 합계를 구함\n",
    "df[search_keyword].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1672572560593,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "BUt8S06jBIbD",
    "outputId": "52e23b6e-9a87-4829-c5c5-0061dc691d34"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------\n",
      "#관심강의: 프로그래밍 시작하기 : 파이썬 입문, 공공데이터로 파이썬 데이터 분석 시작하기\n",
      "파이썬의 고수가 되고싶어요\n",
      "--------------------\n",
      "자바기반 웹 개발자입니다. 데이터 분석에 많이 쓰이는 파이썬이 궁금합니다.\n",
      "#관심강의: 프로그래밍 시작하기 : 파이썬 입문, 공공데이터로 파이썬 데이터 분석 시작하기\n",
      "--------------------\n",
      "올해 안에 원하는 공부 다 끝내보려고요. 내년이면 수능이라..\n",
      "\n",
      "#관심강의: 공공데이터로 파이썬 데이터 분석 시작하기\n",
      "--------------------\n",
      "it개발자입니다. 개발을 오래 안하고 관리만 했더니 다시 개발이 너무 하고싶습니다. 올해는 인프런을 통해서 개발쪽 스킬셋을 업그레이드 하고 싶습니다!\n",
      "#관심강의: 공공데이터로 파이썬 데이터 분석 시작하기\n",
      "--------------------\n",
      "파이썬 데이터분석 마스터\n",
      "#관심강의:공공데이터로 파이썬 데이터 분석 시작하기\n",
      "--------------------\n",
      "올해 졸업하는 컴퓨터공학과 학생입니다!\n",
      "백엔드 혹은 데이터 엔지니어 쪽으로 취업하고 싶습니다!\n",
      "#관심강의: 공공데이터로 파이썬 데이터 분석 시작하기\n",
      "--------------------\n",
      "텍스트 마이닝만 수강한 뒤 현재 맡은 마케팅 업무 능률이 엄청나게 올라가는 것을 확인했으며 승진과 고가에 엄청나게 득을 보았습니다 감사합니다! 앞으로도 인프런 애용하겠습니다.\n",
      "추가적으로 해 *** 강사님의 [공공데이터로 파이썬 데이터 분석 시작하기] 강의를 듣고 상권분석과 공공데이터 활용 역량을 올릴 예정입니다. 화이팅!!!\n",
      "#관심강의 : 공공데이터로 파이썬 데이터 분석 시작하기\n",
      "--------------------\n",
      "데이터분석 2년차가 되는데 작년에 파이썬 강의를 담아놓고 그대로 1년간 방치... 올해는 꼭 완강하고 실습해서 개발자에게 의존하지 않고 혼자 크롤링&분석 해내고 싶어요! \n",
      "#관심강의: 공공데이터로 파이썬 데이터분석 시작하기, database 1&2 - mysql\n",
      "--------------------\n",
      "2020년에는 데이터 분석쪽으로 이직하고 싶어요! 인프런 강의 듣고 이직에 성공하고 싶습니다!!\n",
      "#관심강의: 공공데이터로 파이썬 데이터 분석 시작하기\n",
      "--------------------\n",
      "경영학과 졸업생입니다. 새해엔 엑셀! 파이썬! 취직! 해내겠습니다. 좋은 강의, 커리큘럼이 있는 인프런 너무 감사합니다 ~^^\n",
      "#관심강의: 공공데이터로 분석하기\n",
      "--------------------\n",
      "인프런으로 파이썬 기초문법부터 입문했어요! 실무 데이터분석에 활용할 수 있는 수준으로 실력을 올리고 싶습니다:)\n",
      "#관심강의: 공공데이터로 파이썬 데이터 분석 시작하기\n",
      "--------------------\n",
      "나만의 프로젝트 하나 완성할 수 있을 정도의 지식 쌓기!\n",
      "#관심강의:공공데이터로 파이썬 데이터 분석 시작하기\n"
     ]
    }
   ],
   "source": [
    "# 공공데이터 텍스트가 들어가는 문장만 찾음\n",
    "# pandas 를 통해 볼때 문장이 길면 끝까지 보이지 않음\n",
    "# 문장의 전체를 보기 위해 for문을 통해 해당 텍스트를 순회하며 출력\n",
    "# 이 때, 데이터 사이에 ------ 줄로 구분해서 표시하도록 함\n",
    "text = df.loc[(df[\"공공데이터\"] == True), \"text\"]\n",
    "for t in text:\n",
    "    print(\"-\"*20)\n",
    "    print(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B6YqsciHBIbD"
   },
   "source": [
    "## 판다스 단어가 들어가는 텍스트만 찾기\n",
    "* 이미 str.contains 를 통해 판다스가 들어가는 텍스트에 대해 컬럼을 만들어 놨습니다. 이 값이  True 라면 판다스 강좌 입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 440,
     "status": "ok",
     "timestamp": 1672572561020,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "uxkD3wzABIbD",
    "outputId": "1be2e63e-f1f7-4823-a10c-8ce53fef2d6b",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "541     2020년에는 데이터분석 관련한 실력을 쌓고싶습니다!\\n#관심강의 : 파이썬, 판다...\n",
       "690     취미로 안드로이드 개발하는 사람입니다. 자바로 작성하다 보니, kotlin이 안드로...\n",
       "758            판다스 라입러리에 관심이 많아서 배워보려 합니다 #관심강의 : *** 판다스\n",
       "1237      2020년!! 올 해는 빅데이터 분석 전문가 되기!!\\n#관심강의 : 파이썬, 판다스\n",
       "2155    2020년 목표  파이썬 을  활용해  데이터 분석 작업을 진행하고 싶습니다.  관...\n",
       "2365    저는 백세시대에 조금 더 오랫동안 it일을 하기위해서 it전략기획에서 데이터분석가로...\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pandas 라는 텍스트가 들어가는 내용만 찾음\n",
    "\n",
    "df.loc[df[\"판다스\"] == True, \"text\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L2Hj7SKJBIbE"
   },
   "source": [
    "## 빈도수 계산을 위한 텍스트 데이터 벡터화\n",
    "* BOW 단어 가방에 단어를 토큰화 해서 담아줌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1672572561021,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "GbEGG6N1BIbE",
    "outputId": "d18bc27f-c369-4369-eacb-d3ea09516e35"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['파이썬', '데이터', '분석']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split으로 \"파이썬 데이터 분석\" 이라는 텍스트를 토큰화 \n",
    "\"파이썬 데이터 분석\".split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1672572561021,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "pjbVAqJ8BIbE",
    "outputId": "94d98c34-0693-49a4-ab3c-70dc5a0ebaca"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CountVectorizer(max_features=2000, min_df=2, ngram_range=(3, 6))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer(max_features=2000, min_df=2, ngram_range=(3, 6))</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "CountVectorizer(max_features=2000, min_df=2, ngram_range=(3, 6))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 사이킷런의 CountVectorizer 를 통해 벡터화\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "vectorizer = CountVectorizer(\n",
    "    analyzer = 'word', # 캐릭터 단위로 벡터화 할 수도 있음\n",
    "    tokenizer = None, # 토크나이저를 따로 지정해 줄 수도 있음\n",
    "    preprocessor = None, # 전처리 도구\n",
    "    stop_words = None, # 불용어 nltk등의 도구를 사용할 수도 있음\n",
    "    min_df = 2, # 토큰이 나타날 최소 문서 개수로 오타나 자주 나오지 않는 특수한 전문용어 제거에 좋음 \n",
    "    ngram_range=(3, 6), # BOW의 단위 갯수의 범위를 지정\n",
    "    max_features = 2000 # 만들 피처의 수, 단어의 수\n",
    "    )\n",
    "vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1672572561022,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "bP6znpjJBIbE",
    "outputId": "5ac69de5-fc42-4f1a-bbf4-440b96dfbe05"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2410, 2000)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df['course'] 만 벡터화\n",
    "feature_vector = vectorizer.fit_transform(df['course'])\n",
    "feature_vector.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 25,
     "status": "ok",
     "timestamp": 1672572561376,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "BLmxi-ruBIbE",
    "outputId": "23979829-afd6-4bf1-fdda-ecc74323ea26"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['12개 만들면서 배우는', '12개 만들면서 배우는 ios', '12개 만들면서 배우는 ios 아이폰',\n",
       "       '12개 만들면서 배우는 ios 아이폰 개발', '12개를 만들며 배우는', '12개를 만들며 배우는 swift4',\n",
       "       '12개를 만들며 배우는 swift4 ios11', '12개를 만들며 배우는 swift4 ios11 아이폰',\n",
       "       '2018 do it', '2018 do it 안드로이드'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vectorizer 에서 get_feature_names 를 추출\n",
    "vocab = vectorizer.get_feature_names_out()\n",
    "print(len(vocab))\n",
    "vocab[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 612
    },
    "executionInfo": {
     "elapsed": 25,
     "status": "ok",
     "timestamp": 1672572561377,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "EJsGp2tWBIbE",
    "outputId": "81197868-aaac-4713-ca99-080d240bcb14"
   },
   "outputs": [
    {
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       "    .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>12개 만들면서 배우는</th>\n",
       "      <th>12개 만들면서 배우는 ios</th>\n",
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       "      <th>2018 do it</th>\n",
       "      <th>2018 do it 안드로이드</th>\n",
       "      <th>...</th>\n",
       "      <th>활용2 api 개발과 성능 최적화</th>\n",
       "      <th>활용한 다양한 자동화</th>\n",
       "      <th>활용한 다양한 자동화 어플리케이션</th>\n",
       "      <th>활용한 다양한 자동화 어플리케이션 제작하기</th>\n",
       "      <th>활용한 데이터분석과 it보안</th>\n",
       "      <th>활용한 리액트 native</th>\n",
       "      <th>활용한 리액트 native 개발</th>\n",
       "      <th>활용한 메신져 만들기</th>\n",
       "      <th>활용한 메신져 만들기 android</th>\n",
       "      <th>활용한 파이썬 프로그래밍</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "<p>5 rows × 2000 columns</p>\n",
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       "   12개 만들면서 배우는  12개 만들면서 배우는 ios  12개 만들면서 배우는 ios 아이폰  \\\n",
       "0             0                 0                     0   \n",
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       "\n",
       "   12개 만들면서 배우는 ios 아이폰 개발  12개를 만들며 배우는  12개를 만들며 배우는 swift4  \\\n",
       "0                        0             0                    0   \n",
       "1                        0             0                    0   \n",
       "2                        0             0                    0   \n",
       "3                        0             0                    0   \n",
       "4                        0             0                    0   \n",
       "\n",
       "   12개를 만들며 배우는 swift4 ios11  12개를 만들며 배우는 swift4 ios11 아이폰  2018 do it  \\\n",
       "0                          0                              0           0   \n",
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       "3                          0                              0           0   \n",
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       "\n",
       "   2018 do it 안드로이드  ...  활용2 api 개발과 성능 최적화  활용한 다양한 자동화  활용한 다양한 자동화 어플리케이션  \\\n",
       "0                 0  ...                   0            0                   0   \n",
       "1                 0  ...                   0            0                   0   \n",
       "2                 0  ...                   0            0                   0   \n",
       "3                 0  ...                   0            0                   0   \n",
       "4                 0  ...                   0            0                   0   \n",
       "\n",
       "   활용한 다양한 자동화 어플리케이션 제작하기  활용한 데이터분석과 it보안  활용한 리액트 native  \\\n",
       "0                        0                0               0   \n",
       "1                        0                0               0   \n",
       "2                        0                0               0   \n",
       "3                        0                0               0   \n",
       "4                        0                0               0   \n",
       "\n",
       "   활용한 리액트 native 개발  활용한 메신져 만들기  활용한 메신져 만들기 android  활용한 파이썬 프로그래밍  \n",
       "0                  0            0                    0              0  \n",
       "1                  0            0                    0              0  \n",
       "2                  0            0                    0              0  \n",
       "3                  0            0                    0              0  \n",
       "4                  0            0                    0              0  \n",
       "\n",
       "[5 rows x 2000 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 각 리뷰마다 등장하는 단어에 빈도수가 표현됨 0 은 등장하지 않음을 의미\n",
    "pd.DataFrame(feature_vector[:10].toarray(), columns=vocab).head()"
   ]
  },
  {
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   "execution_count": 24,
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       "      <th>12개 만들면서 배우는</th>\n",
       "      <th>12개 만들면서 배우는 ios</th>\n",
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       "   12개 만들면서 배우는  12개 만들면서 배우는 ios  12개 만들면서 배우는 ios 아이폰  \\\n",
       "0             2                 2                     2   \n",
       "\n",
       "   12개 만들면서 배우는 ios 아이폰 개발  12개를 만들며 배우는  12개를 만들며 배우는 swift4  \\\n",
       "0                        2             2                    2   \n",
       "\n",
       "   12개를 만들며 배우는 swift4 ios11  12개를 만들며 배우는 swift4 ios11 아이폰  2018 do it  \\\n",
       "0                          2                              2           2   \n",
       "\n",
       "   2018 do it 안드로이드  ...  활용2 api 개발과 성능 최적화  활용한 다양한 자동화  활용한 다양한 자동화 어플리케이션  \\\n",
       "0                 2  ...                  16           13                  13   \n",
       "\n",
       "   활용한 다양한 자동화 어플리케이션 제작하기  활용한 데이터분석과 it보안  활용한 리액트 native  \\\n",
       "0                       12                2               8   \n",
       "\n",
       "   활용한 리액트 native 개발  활용한 메신져 만들기  활용한 메신져 만들기 android  활용한 파이썬 프로그래밍  \n",
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   "source": [
    "# 위에서 구한 단어벡터를 더하면 단어가 전체에서 등장하는 횟수를 알 수 있음\n",
    "# 벡터화 된 피처를 확인해 봄\n",
    "# Bag of words 에 몇 개의 단어가 들어있는지 확인\n",
    "dist = np.sum(feature_vector, axis=0)\n",
    "    \n",
    "df_freq = pd.DataFrame(dist, columns=vocab)\n",
    "df_freq"
   ]
  },
  {
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   "execution_count": 25,
   "metadata": {
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       "      <th>nodebird sns 만들기</th>\n",
       "      <td>60</td>\n",
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       "      <th>스프링 데이터 jpa</th>\n",
       "      <td>59</td>\n",
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       "      <td>47</td>\n",
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       "      <td>35</td>\n",
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       "      <td>33</td>\n",
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       "      <td>33</td>\n",
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       "      <td>33</td>\n",
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       "      <td>33</td>\n",
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       "    <tr>\n",
       "      <th>위한 알고리즘 입문</th>\n",
       "      <td>33</td>\n",
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       "      <td>31</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                        0\n",
       "nodebird sns 만들기       60\n",
       "스프링 데이터 jpa            59\n",
       "리액트로 nodebird sns      49\n",
       "머신러닝 완벽 가이드            49\n",
       "리액트로 nodebird sns 만들기  49\n",
       "파이썬 머신러닝 완벽            49\n",
       "프로그래밍과 자바스크립트 es6      48\n",
       "파이썬 머신러닝 완벽 가이드        48\n",
       "함수형 프로그래밍과 자바스크립트 es6  47\n",
       "2020년 공부 목표            47\n",
       "함수형 프로그래밍과 자바스크립트      47\n",
       "스프링 부트와 jpa            40\n",
       "관심있는 인프런 강의명           39\n",
       "실전 스프링 부트와             39\n",
       "실전 스프링 부트와 jpa         39\n",
       "부트 개념과 활용              36\n",
       "스프링 부트 개념과             35\n",
       "표준 jpa 프로그래밍           34\n",
       "스프링 부트 개념과 활용          34\n",
       "orm 표준 jpa             34\n",
       "orm 표준 jpa 프로그래밍       34\n",
       "자바 orm 표준              33\n",
       "위한 알고리즘 입문 with        33\n",
       "자바 orm 표준 jpa          33\n",
       "알고리즘 입문 with           33\n",
       "자바 orm 표준 jpa 프로그래밍    33\n",
       "위한 알고리즘 입문             33\n",
       "rest api 개발            32\n",
       "it 취업을 위한              31\n",
       "파이썬 실전 활용              31"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 행과 열의 축을 T로 바꿔주고 빈도수로 정렬\n",
    "df_freq.T.sort_values(by=0, ascending=False).head(30)"
   ]
  },
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       "    }\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>course</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12개 만들면서 배우는</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12개 만들면서 배우는 ios</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12개 만들면서 배우는 ios 아이폰</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12개 만들면서 배우는 ios 아이폰 개발</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12개를 만들며 배우는</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    course  freq\n",
       "0             12개 만들면서 배우는     2\n",
       "1         12개 만들면서 배우는 ios     2\n",
       "2     12개 만들면서 배우는 ios 아이폰     2\n",
       "3  12개 만들면서 배우는 ios 아이폰 개발     2\n",
       "4             12개를 만들며 배우는     2"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# [\"course\", \"freq\"] 라는 컬럼명을 주어 위에서 만든 데이터프레임을 변환\n",
    "df_freq_T = df_freq.T.reset_index()\n",
    "df_freq_T.columns = [\"course\", \"freq\"]\n",
    "df_freq_T.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "executionInfo": {
     "elapsed": 23,
     "status": "ok",
     "timestamp": 1672572561378,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "rjM2RVAKBIbF",
    "outputId": "9ee174f0-683f-4b8e-fb14-031a287eb730"
   },
   "outputs": [
    {
     "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>course</th>\n",
       "      <th>freq</th>\n",
       "      <th>course_find</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>활용한 파이썬 프로그래밍</td>\n",
       "      <td>2</td>\n",
       "      <td>활용한 파이썬 프로그래밍</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>활용한 메신져 만들기 android</td>\n",
       "      <td>2</td>\n",
       "      <td>활용한 메신져 만들기 android</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>활용한 메신져 만들기</td>\n",
       "      <td>2</td>\n",
       "      <td>활용한 메신져 만들기</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>활용한 리액트 native 개발</td>\n",
       "      <td>8</td>\n",
       "      <td>활용한 리액트 native 개발</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>활용한 리액트 native</td>\n",
       "      <td>8</td>\n",
       "      <td>활용한 리액트 native</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>활용한 데이터분석과 it보안</td>\n",
       "      <td>2</td>\n",
       "      <td>활용한 데이터분석과 it보안</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>활용한 다양한 자동화 어플리케이션</td>\n",
       "      <td>13</td>\n",
       "      <td>활용한 다양한 자동화 어플리케이션</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>활용한 다양한 자동화 어플리케이션 제작하기</td>\n",
       "      <td>12</td>\n",
       "      <td>활용한 다양한 자동화 어플리케이션</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>활용한 다양한 자동화</td>\n",
       "      <td>13</td>\n",
       "      <td>활용한 다양한 자동화</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>활용2 api 개발과 성능</td>\n",
       "      <td>16</td>\n",
       "      <td>활용2 api 개발과 성능</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       course  freq          course_find\n",
       "1999            활용한 파이썬 프로그래밍     2        활용한 파이썬 프로그래밍\n",
       "1998      활용한 메신져 만들기 android     2  활용한 메신져 만들기 android\n",
       "1997              활용한 메신져 만들기     2          활용한 메신져 만들기\n",
       "1996        활용한 리액트 native 개발     8    활용한 리액트 native 개발\n",
       "1995           활용한 리액트 native     8       활용한 리액트 native\n",
       "1994          활용한 데이터분석과 it보안     2      활용한 데이터분석과 it보안\n",
       "1992       활용한 다양한 자동화 어플리케이션    13   활용한 다양한 자동화 어플리케이션\n",
       "1993  활용한 다양한 자동화 어플리케이션 제작하기    12   활용한 다양한 자동화 어플리케이션\n",
       "1991              활용한 다양한 자동화    13          활용한 다양한 자동화\n",
       "1989           활용2 api 개발과 성능    16       활용2 api 개발과 성능"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 강의명을 토큰 3개로 중복제거하기 위해, 강좌명에서 지식공유자의 이름을 빈문자열로 변경\n",
    "# 강의명을 lambda 식을 사용해서 x.split() 으로 나누고 [:4] 앞에서 4개까지만 텍스트를 가져오고 다시 join으로 합쳐줌\n",
    "# 중복된 텍스트를 구분해서 보기 위함\n",
    "df_freq_T[\"course_find\"] = df_freq_T[\"course\"].str.replace(\"박조은\", \"\")\n",
    "df_freq_T[\"course_find\"] = df_freq_T[\"course_find\"].apply(lambda x : \" \". join(x.split()[:4]))\n",
    "df_freq_T.sort_values([\"course_find\", \"freq\"], ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 23,
     "status": "ok",
     "timestamp": 1672572561378,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "mldva_YmBIbF",
    "outputId": "e907b85e-f283-4bc6-8a3a-e33804cb44f9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 3)\n",
      "(1441, 3)\n"
     ]
    }
   ],
   "source": [
    "# 3개의 ngram과 빈도수로 역순 정렬을 하게 되면 빈도수가 높고, ngram수가 많은 순으로 정렬이 됨 \n",
    "# 여기에서 drop_duplicates로 첫 번째 강좌를 남기고 나머지 중복을 삭제\n",
    "print(df_freq_T.shape)\n",
    "df_course = df_freq_T.drop_duplicates([\"course_find\", \"freq\"], keep=\"first\")\n",
    "print(df_course.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 677
    },
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1672572561378,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "Mg-RnCUKBIbF",
    "outputId": "439a546e-da5f-4cd4-d7bd-83b11533c8b4"
   },
   "outputs": [
    {
     "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",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>course</th>\n",
       "      <th>freq</th>\n",
       "      <th>course_find</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>nodebird sns 만들기</td>\n",
       "      <td>60</td>\n",
       "      <td>nodebird sns 만들기</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1094</th>\n",
       "      <td>스프링 데이터 jpa</td>\n",
       "      <td>59</td>\n",
       "      <td>스프링 데이터 jpa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>리액트로 nodebird sns 만들기</td>\n",
       "      <td>49</td>\n",
       "      <td>리액트로 nodebird sns 만들기</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1761</th>\n",
       "      <td>파이썬 머신러닝 완벽</td>\n",
       "      <td>49</td>\n",
       "      <td>파이썬 머신러닝 완벽</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>906</th>\n",
       "      <td>머신러닝 완벽 가이드</td>\n",
       "      <td>49</td>\n",
       "      <td>머신러닝 완벽 가이드</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>리액트로 nodebird sns</td>\n",
       "      <td>49</td>\n",
       "      <td>리액트로 nodebird sns</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>프로그래밍과 자바스크립트 es6</td>\n",
       "      <td>48</td>\n",
       "      <td>프로그래밍과 자바스크립트 es6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1762</th>\n",
       "      <td>파이썬 머신러닝 완벽 가이드</td>\n",
       "      <td>48</td>\n",
       "      <td>파이썬 머신러닝 완벽 가이드</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>함수형 프로그래밍과 자바스크립트 es6</td>\n",
       "      <td>47</td>\n",
       "      <td>함수형 프로그래밍과 자바스크립트 es6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1935</th>\n",
       "      <td>함수형 프로그래밍과 자바스크립트</td>\n",
       "      <td>47</td>\n",
       "      <td>함수형 프로그래밍과 자바스크립트</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020년 공부 목표</td>\n",
       "      <td>47</td>\n",
       "      <td>2020년 공부 목표</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1108</th>\n",
       "      <td>스프링 부트와 jpa</td>\n",
       "      <td>40</td>\n",
       "      <td>스프링 부트와 jpa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>관심있는 인프런 강의명</td>\n",
       "      <td>39</td>\n",
       "      <td>관심있는 인프런 강의명</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1191</th>\n",
       "      <td>실전 스프링 부트와</td>\n",
       "      <td>39</td>\n",
       "      <td>실전 스프링 부트와</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1192</th>\n",
       "      <td>실전 스프링 부트와 jpa</td>\n",
       "      <td>39</td>\n",
       "      <td>실전 스프링 부트와 jpa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>부트 개념과 활용</td>\n",
       "      <td>36</td>\n",
       "      <td>부트 개념과 활용</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>스프링 부트 개념과</td>\n",
       "      <td>35</td>\n",
       "      <td>스프링 부트 개념과</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>orm 표준 jpa</td>\n",
       "      <td>34</td>\n",
       "      <td>orm 표준 jpa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1840</th>\n",
       "      <td>표준 jpa 프로그래밍</td>\n",
       "      <td>34</td>\n",
       "      <td>표준 jpa 프로그래밍</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>스프링 부트 개념과 활용</td>\n",
       "      <td>34</td>\n",
       "      <td>스프링 부트 개념과 활용</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     course  freq            course_find\n",
       "319        nodebird sns 만들기    60       nodebird sns 만들기\n",
       "1094            스프링 데이터 jpa    59            스프링 데이터 jpa\n",
       "885   리액트로 nodebird sns 만들기    49  리액트로 nodebird sns 만들기\n",
       "1761            파이썬 머신러닝 완벽    49            파이썬 머신러닝 완벽\n",
       "906             머신러닝 완벽 가이드    49            머신러닝 완벽 가이드\n",
       "884       리액트로 nodebird sns    49      리액트로 nodebird sns\n",
       "1891      프로그래밍과 자바스크립트 es6    48      프로그래밍과 자바스크립트 es6\n",
       "1762        파이썬 머신러닝 완벽 가이드    48        파이썬 머신러닝 완벽 가이드\n",
       "1936  함수형 프로그래밍과 자바스크립트 es6    47  함수형 프로그래밍과 자바스크립트 es6\n",
       "1935      함수형 프로그래밍과 자바스크립트    47      함수형 프로그래밍과 자바스크립트\n",
       "11              2020년 공부 목표    47            2020년 공부 목표\n",
       "1108            스프링 부트와 jpa    40            스프링 부트와 jpa\n",
       "706            관심있는 인프런 강의명    39           관심있는 인프런 강의명\n",
       "1191             실전 스프링 부트와    39             실전 스프링 부트와\n",
       "1192         실전 스프링 부트와 jpa    39         실전 스프링 부트와 jpa\n",
       "988               부트 개념과 활용    36              부트 개념과 활용\n",
       "1104             스프링 부트 개념과    35             스프링 부트 개념과\n",
       "338              orm 표준 jpa    34             orm 표준 jpa\n",
       "1840           표준 jpa 프로그래밍    34           표준 jpa 프로그래밍\n",
       "1105          스프링 부트 개념과 활용    34          스프링 부트 개념과 활용"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 빈도수로 정렬을 하고 어떤 강좌가 댓글에서 가장 많이 언급되었는지 봄\n",
    "df_course = df_course.sort_values(by=\"freq\", ascending=False)\n",
    "df_course.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1672572561378,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "2UA82ENcBIbG"
   },
   "outputs": [],
   "source": [
    "# 전처리가 다 되었다면 다른 팀 또는 담당자에게 전달하기 위해 csv 형태로 저장\n",
    "df_course.to_csv(\"event-course-name-freq.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GZk1hKdLBIbG"
   },
   "source": [
    "## TF-IDF 로 가중치를 주어 벡터화\n",
    "### TfidfTransformer()\n",
    "* norm='l2' 각 문서의 피처 벡터를 어떻게 벡터 정규화 할지 정합니다.\n",
    "    - L2 : 벡터의 각 원소의 제곱의 합이 1이 되도록 만드는 것이고 기본 값(유클리디안거리)\n",
    "    - L1 : 벡터의 각 원소의 절댓값의 합이 1이 되도록 크기를 조절(맨하탄거리)\n",
    "* smooth_idf=False\n",
    "    - 피처를 만들 때 0으로 나오는 항목에 대해 작은 값을 더해서(스무딩을 해서) 피처를 만들지 아니면 그냥 생성할지를 결정\n",
    "* sublinear_tf=False\n",
    "* use_idf=True\n",
    "    - TF-IDF를 사용해 피처를 만들 것인지 아니면 단어 빈도 자체를 사용할 것인지 여부"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1672572561378,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "jtOtCDXKBIbG",
    "outputId": "9279c47a-e869-4246-c4b6-26e86ca0eff1"
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "TfidfTransformer(smooth_idf=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TfidfTransformer 를 불러와서 가중치를 주어 벡터화\n",
    "# transformer 라는 변수로 저장하고 재사용합니다.\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "tfidftrans = TfidfTransformer(smooth_idf=False)\n",
    "tfidftrans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1672572561379,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "MyGBOTgOBIbG",
    "outputId": "8165001d-4851-4d49-9b21-980188b04baa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2410, 2000)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_tfidf = tfidftrans.fit_transform(feature_vector)\n",
    "feature_tfidf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 612
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1672572561379,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "eC3FOTP2BIbG",
    "outputId": "d1d19625-4d37-42f7-86d0-0988a47347d8"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>12개 만들면서 배우는</th>\n",
       "      <th>12개 만들면서 배우는 ios</th>\n",
       "      <th>12개 만들면서 배우는 ios 아이폰</th>\n",
       "      <th>12개 만들면서 배우는 ios 아이폰 개발</th>\n",
       "      <th>12개를 만들며 배우는</th>\n",
       "      <th>12개를 만들며 배우는 swift4</th>\n",
       "      <th>12개를 만들며 배우는 swift4 ios11</th>\n",
       "      <th>12개를 만들며 배우는 swift4 ios11 아이폰</th>\n",
       "      <th>2018 do it</th>\n",
       "      <th>2018 do it 안드로이드</th>\n",
       "      <th>...</th>\n",
       "      <th>활용2 api 개발과 성능 최적화</th>\n",
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       "      <th>활용한 다양한 자동화 어플리케이션</th>\n",
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      "text/plain": [
       "   12개 만들면서 배우는  12개 만들면서 배우는 ios  12개 만들면서 배우는 ios 아이폰  \\\n",
       "0           0.0               0.0                   0.0   \n",
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       "2           0.0               0.0                   0.0   \n",
       "3           0.0               0.0                   0.0   \n",
       "4           0.0               0.0                   0.0   \n",
       "\n",
       "   12개 만들면서 배우는 ios 아이폰 개발  12개를 만들며 배우는  12개를 만들며 배우는 swift4  \\\n",
       "0                      0.0           0.0                  0.0   \n",
       "1                      0.0           0.0                  0.0   \n",
       "2                      0.0           0.0                  0.0   \n",
       "3                      0.0           0.0                  0.0   \n",
       "4                      0.0           0.0                  0.0   \n",
       "\n",
       "   12개를 만들며 배우는 swift4 ios11  12개를 만들며 배우는 swift4 ios11 아이폰  2018 do it  \\\n",
       "0                        0.0                            0.0         0.0   \n",
       "1                        0.0                            0.0         0.0   \n",
       "2                        0.0                            0.0         0.0   \n",
       "3                        0.0                            0.0         0.0   \n",
       "4                        0.0                            0.0         0.0   \n",
       "\n",
       "   2018 do it 안드로이드  ...  활용2 api 개발과 성능 최적화  활용한 다양한 자동화  활용한 다양한 자동화 어플리케이션  \\\n",
       "0               0.0  ...                 0.0          0.0                 0.0   \n",
       "1               0.0  ...                 0.0          0.0                 0.0   \n",
       "2               0.0  ...                 0.0          0.0                 0.0   \n",
       "3               0.0  ...                 0.0          0.0                 0.0   \n",
       "4               0.0  ...                 0.0          0.0                 0.0   \n",
       "\n",
       "   활용한 다양한 자동화 어플리케이션 제작하기  활용한 데이터분석과 it보안  활용한 리액트 native  \\\n",
       "0                      0.0              0.0             0.0   \n",
       "1                      0.0              0.0             0.0   \n",
       "2                      0.0              0.0             0.0   \n",
       "3                      0.0              0.0             0.0   \n",
       "4                      0.0              0.0             0.0   \n",
       "\n",
       "   활용한 리액트 native 개발  활용한 메신져 만들기  활용한 메신져 만들기 android  활용한 파이썬 프로그래밍  \n",
       "0                0.0          0.0                  0.0            0.0  \n",
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       "3                0.0          0.0                  0.0            0.0  \n",
       "4                0.0          0.0                  0.0            0.0  \n",
       "\n",
       "[5 rows x 2000 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_freq = pd.DataFrame(feature_tfidf.toarray(), columns=vocab)\n",
    "tfidf_freq.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1672572561379,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "KAiJZeduBIbG",
    "outputId": "08de96aa-8a0d-476f-fa8e-93402e87d63f"
   },
   "outputs": [
    {
     "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>스프링 데이터 jpa</th>\n",
       "      <td>35.203215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>파이썬 실전 활용</th>\n",
       "      <td>27.690173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nodebird sns 만들기</th>\n",
       "      <td>26.817724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>머신러닝 완벽 가이드</th>\n",
       "      <td>24.470756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>파이썬 머신러닝 완벽</th>\n",
       "      <td>24.470756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>리액트로 nodebird sns</th>\n",
       "      <td>24.187518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>리액트로 nodebird sns 만들기</th>\n",
       "      <td>24.187518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>파이썬 머신러닝 완벽 가이드</th>\n",
       "      <td>23.569611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>프로그래밍과 자바스크립트 es6</th>\n",
       "      <td>20.056301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>함수형 프로그래밍과 자바스크립트 es6</th>\n",
       "      <td>19.143864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               0\n",
       "스프링 데이터 jpa            35.203215\n",
       "파이썬 실전 활용              27.690173\n",
       "nodebird sns 만들기       26.817724\n",
       "머신러닝 완벽 가이드            24.470756\n",
       "파이썬 머신러닝 완벽            24.470756\n",
       "리액트로 nodebird sns      24.187518\n",
       "리액트로 nodebird sns 만들기  24.187518\n",
       "파이썬 머신러닝 완벽 가이드        23.569611\n",
       "프로그래밍과 자바스크립트 es6      20.056301\n",
       "함수형 프로그래밍과 자바스크립트 es6  19.143864"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tfidf = pd.DataFrame(tfidf_freq.sum())\n",
    "df_tfidf_top = df_tfidf.sort_values(by=0, ascending=False)\n",
    "df_tfidf_top.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1672572561379,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "2oZkoSMSBIbH",
    "outputId": "e60f9fcd-735c-461e-f068-db28124a1c9d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "스프링 데이터 jpa\n",
      "파이썬 실전 활용\n",
      "nodebird sns 만들기\n",
      "머신러닝 완벽 가이드\n",
      "파이썬 머신러닝 완벽\n",
      "리액트로 nodebird sns\n",
      "리액트로 nodebird sns 만들기\n",
      "파이썬 머신러닝 완벽 가이드\n",
      "프로그래밍과 자바스크립트 es6\n",
      "함수형 프로그래밍과 자바스크립트 es6\n",
      "함수형 프로그래밍과 자바스크립트\n",
      "따라하며 배우는 c언어\n",
      "부트 개념과 활용\n",
      "스프링 부트 개념과\n",
      "스프링 부트 개념과 활용\n",
      "실전 스프링 데이터\n",
      "2020년 공부 목표\n",
      "실전 스프링 데이터 jpa\n",
      "파이썬 머신러닝 완벽가이드\n",
      "관심있는 인프런 강의명\n",
      "rest api 개발\n",
      "대세는 쿠버네티스 기초편\n",
      "스프링부트 개념과 활용\n",
      "orm 표준 jpa 프로그래밍\n",
      "표준 jpa 프로그래밍\n",
      "orm 표준 jpa\n",
      "js 완벽 가이드\n",
      "vue js 완벽 가이드\n",
      "vue js 완벽\n",
      "자바 orm 표준\n"
     ]
    }
   ],
   "source": [
    "# 중간에 생략되는 단어를 자세히 보고자 할 때\n",
    "for t in df_tfidf_top.index[:30]:\n",
    "    print(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KPqDEEHaBIbH"
   },
   "source": [
    "## 군집화 \n",
    "* 실루엣 분석추가 https://www.kaggle.com/fabiendaniel/customer-segmentation\n",
    "### KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 53352,
     "status": "ok",
     "timestamp": 1672572614716,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "_IaVwNcPBIbH",
    "outputId": "932e24c9-c63b-4542-ed63-1dd61422d8e5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:21<00:00,  2.80it/s]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from tqdm import trange\n",
    "inertia = []\n",
    "\n",
    "start = 10\n",
    "end = 70\n",
    "\n",
    "# 적절한 클러스터의 갯수를 알기 위해 inertia 값을 구함\n",
    "# trange 를 통해 시작과 끝 값을 지정해 주면 진행 정도를 알 수 있습니다.\n",
    "# 학습을 할 때는 feature_tfidf 값을 사용합니다.\n",
    "# cls.inertia_ 값을 inertia 리스트에 저장합니다.\n",
    "for i in trange(start, end):\n",
    "    kmeans = KMeans(n_clusters=i, random_state=42)\n",
    "    kmeans.fit(feature_tfidf)\n",
    "    inertia.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1672572614717,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "DewJGA5HBIbH",
    "outputId": "7b60c57d-d006-4dd4-f476-38377a925311"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'KMeans 클러스터 수 비교')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 433,
       "width": 559
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 위에서 구한 값을 시각화\n",
    "# x축에는 클러스터의 수를 y축에는 inertia 값을 넣어 그림\n",
    "\n",
    "plt.plot(range(start, end), inertia)\n",
    "plt.title(\"KMeans 클러스터 수 비교\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ikSiyg7NBIbH"
   },
   "source": [
    "* 적정한 클러스터 갯수를 넣어 군집화 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "executionInfo": {
     "elapsed": 748,
     "status": "ok",
     "timestamp": 1672572615452,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "vIk4Z25KBIbH"
   },
   "outputs": [],
   "source": [
    "# n_clusters 에 적절한 값을 넣어줌\n",
    "# fit.predict 를 하고 결과를 cluster 라는 새로운 컬럼에 담음\n",
    "n_clusters = 50\n",
    "kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n",
    "kmeans.fit(feature_tfidf)\n",
    "prediction = kmeans.predict(feature_tfidf)\n",
    "df[\"cluster\"] = prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1672572638138,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "j8-SJZ71BIbI",
    "outputId": "9ba683f4-dea6-419f-e63a-5f220e494d40"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     1704\n",
       "6       44\n",
       "2       41\n",
       "17      36\n",
       "3       32\n",
       "16      28\n",
       "9       27\n",
       "12      26\n",
       "4       24\n",
       "22      23\n",
       "Name: cluster, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df[\"cluster\"] 의 빈도수를 value_counts로 세어봅니다.\n",
    "df[\"cluster\"].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GybUGQHMBIbI"
   },
   "source": [
    "### MiniBatchKMeans\n",
    "* [Comparison of the K-Means and MiniBatchKMeans clustering algorithms — scikit-learn documentation](https://scikit-learn.org/stable/auto_examples/cluster/plot_mini_batch_kmeans.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 15275,
     "status": "ok",
     "timestamp": 1672572653397,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "nfEAI1TXBIbJ",
    "outputId": "8690205e-7276-49e2-e25a-076ff327e567"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 60/60 [00:11<00:00,  5.28it/s]\n"
     ]
    }
   ],
   "source": [
    "# batch_size 를 쓸 수 있는 MiniBatchKMeans 로 군집화\n",
    "from sklearn.cluster import MiniBatchKMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "b_inertia = []\n",
    "silhouettes = []\n",
    "\n",
    "# 적절한 클러스터의 갯수를 알기 위해 inertia 값을 구함\n",
    "# trange 를 통해 시작과 끝 값을 지정해 주면 진행 상태를 확인 할 수 있음\n",
    "# b_inertia 리스트에 cls.inertia_ 값을 넣어줍니다.\n",
    "for i in trange(start, end):\n",
    "    mkmeans = MiniBatchKMeans(n_clusters=i, random_state=42)\n",
    "    mkmeans.fit(feature_tfidf)\n",
    "    b_inertia.append(mkmeans.inertia_)\n",
    "    silhouettes.append(silhouette_score(feature_tfidf, mkmeans.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1672572653398,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "TNK758SgBIbJ",
    "outputId": "f9b10e66-1bf0-417a-bbf5-1e476f0a3111"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'MiniBatchKMeans 클러스터 수 비교')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 433,
       "width": 559
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 위에서 구한 값을 시각화 합니다.\n",
    "# x축에는 클러스터의 수를 y축에는 b_inertia 값을 넣어 시각화\n",
    "plt.plot(range(start, end), b_inertia)\n",
    "plt.title(\"MiniBatchKMeans 클러스터 수 비교\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 280
    },
    "executionInfo": {
     "elapsed": 960,
     "status": "ok",
     "timestamp": 1672572654347,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "RvrzMGb4BIbJ",
    "outputId": "6476a091-f549-4163-a849-7832d86db73b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 371,
       "width": 1221
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 4))\n",
    "plt.title('Silhouette Score')\n",
    "plt.plot(range(start, end), silhouettes)\n",
    "plt.xticks(range(start, end))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# yellowbrick 은 머신러닝 시각화 도구로 별도의 설치가 필요\n",
    "# !pip install yellowbrick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x550 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 506,
       "width": 749
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Distortion Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='distortion score'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# yellowbrick.cluster 에서 KElbowVisualizer 불러오기\n",
    "from yellowbrick.cluster import KElbowVisualizer\n",
    "\n",
    "KElbowM = KElbowVisualizer(kmeans, k=(start, end))\n",
    "KElbowM.fit(feature_tfidf.toarray())\n",
    "KElbowM.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1672572654347,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "uB_Vx-LmBIbJ"
   },
   "outputs": [],
   "source": [
    "# MiniBatchKMeans 를 통해 학습\n",
    "# 결과를 bcluster 라는 변수에 저장\n",
    "mkmeans = MiniBatchKMeans(n_clusters=n_clusters, random_state=42)\n",
    "mkmeans.fit(feature_tfidf)\n",
    "prediction = mkmeans.predict(feature_tfidf)\n",
    "df[\"bcluster\"] = prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1672572654347,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "7lO5HiacBIbJ",
    "outputId": "89e44a77-1ec6-43a8-cfda-909035f1f55c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25    1108\n",
       "0      243\n",
       "3      147\n",
       "9       76\n",
       "7       64\n",
       "38      48\n",
       "1       44\n",
       "47      39\n",
       "27      37\n",
       "22      36\n",
       "Name: bcluster, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bcluster의 빈도수\n",
    "df[\"bcluster\"].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1672572654348,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "b132rhKjBIbJ",
    "outputId": "d9821067-6963-4f3c-8e77-5776b62253cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "데이터 분석가 가 되고 싶은 현업 종사자 입니다. 2020년 목표는 파이썬을 통한 콜드/핫 데이터의 전처리 능력 습득, 데이터 유형과 목적에 맞는 머신러닝 알고리즘을 적용한 모델링 구축 입니다. \\n관심있는 강의는 ***의 파이썬 실전 활용 입니다.    1\n",
       "Name: course, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 어떤 강좌명이 있는지 특정 클러스터의 값을 봄  \n",
    "df.loc[df[\"bcluster\"] == 21, \"course\"].value_counts().head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81
    },
    "executionInfo": {
     "elapsed": 640,
     "status": "ok",
     "timestamp": 1672572654982,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "89Hz-4EuBIbK",
    "outputId": "c19abb10-cfb8-4054-9f2d-cc33d5411984"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bcluster</th>\n",
       "      <th>cluster</th>\n",
       "      <th>course</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>데이터 분석가 가 되고 싶은 현업 종사자 입니다. 2020년 목표는 파이썬을 통한 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     bcluster  cluster                                             course\n",
       "567        21        9  데이터 분석가 가 되고 싶은 현업 종사자 입니다. 2020년 목표는 파이썬을 통한 ..."
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"bcluster\"] == 21, [\"bcluster\", \"cluster\", \"course\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 581
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1672572654983,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "dqIgLTTkBIbK",
    "outputId": "cb515ae9-18db-4129-fc4d-ee7b24d24ed1"
   },
   "outputs": [
    {
     "data": {
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       "      <th>900</th>\n",
       "      <td>24</td>\n",
       "      <td>18</td>\n",
       "      <td>C언어 공부 할래요~\\n#관심강의 : ***의 따라하며 배우는 C언어</td>\n",
       "      <td>***의 따라하며 배우는 c언어</td>\n",
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       "      <td>올해에는 저만의 서비스를 꼭 만들어보고 싶습니다~!\\n\\n강의명  ***의 따라하며...</td>\n",
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       "    <tr>\n",
       "      <th>1326</th>\n",
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       "      <td>올해부터 새롭게 C언어 공부를 시작한 비전공자 학생입니다. 컴퓨터 언어가 무엇인지 ...</td>\n",
       "      <td>올해부터 새롭게 c언어 공부를 시작한 비전공자 학생입니다. 컴퓨터 언어가 무엇인지 ...</td>\n",
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       "    <tr>\n",
       "      <th>1398</th>\n",
       "      <td>24</td>\n",
       "      <td>18</td>\n",
       "      <td>소프트웨어 전공 학생입니다 \\n개강하기전에  꼭 C언어 강의를 수료해서 이번 학기 ...</td>\n",
       "      <td>***의 따라하며 배우는  c언어</td>\n",
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       "      <th>1428</th>\n",
       "      <td>24</td>\n",
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       "      <td>***의 따라하며 배우는 c언어</td>\n",
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       "    <tr>\n",
       "      <th>1489</th>\n",
       "      <td>24</td>\n",
       "      <td>18</td>\n",
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       "      <td>***의 따라하며 배우는 c언어</td>\n",
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       "      <th>1511</th>\n",
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       "      <td>프로그래밍에 흥미를 가지게 되어 인프런을 이용하게 된 학생입니다.\\n*** 강사님이...</td>\n",
       "      <td>***의 따라하며 배우는 c언어</td>\n",
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       "      <td>2020년 공부 목표 :\\nc언어, c++ ,알고리즘과 자료구조의 탄탄한 기본을 다...</td>\n",
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      ],
      "text/plain": [
       "      bcluster  cluster                                        origin_text  \\\n",
       "900         24       18             C언어 공부 할래요~\\n#관심강의 : ***의 따라하며 배우는 C언어   \n",
       "955         24       18  올해는 꼭 리버싱 마스터가되어 대회참가하겠습니다 화이팅\\n'***의 따라하며 배우는...   \n",
       "1068        24       18  올해에는 저만의 서비스를 꼭 만들어보고 싶습니다~!\\n\\n강의명 : ***의 따라하...   \n",
       "1326        24       18  올해부터 새롭게 C언어 공부를 시작한 비전공자 학생입니다. 컴퓨터 언어가 무엇인지 ...   \n",
       "1398        24       18  소프트웨어 전공 학생입니다 \\n개강하기전에  꼭 C언어 강의를 수료해서 이번 학기 ...   \n",
       "1428        24       18  고등학교를 졸업한 예비 컴퓨터 공학도입니다. 그동안 입시를 준비하며 ps공부를 병행...   \n",
       "1489        24       18  요즘 들어 동아리 활동하는 데 C언어를 많이 활용하는 것 같아서 몇 달 전부터 C언...   \n",
       "1511        24       18  프로그래밍에 흥미를 가지게 되어 인프런을 이용하게 된 학생입니다.\\n*** 강사님이...   \n",
       "2238        24       18  경영학과 졸업생인데 개발자로 진로를 변경하기로 했습니다. 올해 안에 꼭 백엔드 개발...   \n",
       "2426        24       18  2020년 공부 목표 :\\nc언어, c++ ,알고리즘과 자료구조의 탄탄한 기본을 다...   \n",
       "\n",
       "                                                 course  \n",
       "900                                   ***의 따라하며 배우는 c언어  \n",
       "955   올해는 꼭 리버싱 마스터가되어 대회참가하겠습니다 화이팅\\n'***의 따라하며 배우는...  \n",
       "1068  올해에는 저만의 서비스를 꼭 만들어보고 싶습니다~!\\n\\n강의명  ***의 따라하며...  \n",
       "1326  올해부터 새롭게 c언어 공부를 시작한 비전공자 학생입니다. 컴퓨터 언어가 무엇인지 ...  \n",
       "1398                                 ***의 따라하며 배우는  c언어  \n",
       "1428                                  ***의 따라하며 배우는 c언어  \n",
       "1489                                  ***의 따라하며 배우는 c언어  \n",
       "1511                                  ***의 따라하며 배우는 c언어  \n",
       "2238    ***의 따라하며 배우는 c언어,  ***의 스프링 강의 전부, ***님의 jp...  \n",
       "2426                                  ***의 따라하며 배우는 c언어  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"bcluster\"] == 24, [\"bcluster\", \"cluster\", \"origin_text\", \"course\"]].tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tQe-VJW1BIbK"
   },
   "source": [
    "### 클러스터 예측 평가하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1672572654983,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "JDsb58coBIbK"
   },
   "outputs": [],
   "source": [
    "# n_clusters 위에서 정의한 클러스터 수를 사용\n",
    "feature_array = feature_vector.toarray()\n",
    "# 예측한 클러스터의 유니크 값\n",
    "labels = np.unique(prediction)\n",
    "df_cluster_score = []\n",
    "df_cluster = []\n",
    "for label in labels:\n",
    "    id_temp = np.where(prediction==label) # 예측한 값이 클러스터 번호와 매치 되는 것을 가져옴\n",
    "    x_means = np.mean(feature_array[id_temp], axis = 0) # 클러스터의 평균 값을 구함\n",
    "    sorted_means = np.argsort(x_means)[::-1][:n_clusters] # 값을 역순으로 정렬해서 클러스터 수 만큼 가져옴\n",
    "    features = vectorizer.get_feature_names_out()\n",
    "    best_features = [(features[i], x_means[i]) for i in sorted_means] \n",
    "    # 클러스터별 전체 스코어\n",
    "    df_score = pd.DataFrame(best_features, columns = ['features', 'score'])\n",
    "    df_cluster_score.append(df_score)\n",
    "    # 클러스터 대표 키워드\n",
    "    df_cluster.append(best_features[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1672572654983,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "4cZVGEdBBIbK",
    "outputId": "dbbd1455-38c5-4199-e715-f7258b47f3a1"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>score</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>프로그래밍과 자바스크립트 es6</td>\n",
       "      <td>1.102564</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
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       "      <th>36</th>\n",
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       "      <td>1.000000</td>\n",
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       "      <th>39</th>\n",
       "      <td>파이썬을 이용한 it 해킹과</td>\n",
       "      <td>1.000000</td>\n",
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       "      <th>21</th>\n",
       "      <td>파이썬 실전 활용</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>파이썬 실전 활용</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>코틀린 기반 안드로이드</td>\n",
       "      <td>0.315789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>코드를 조작하는 다양한 방법</td>\n",
       "      <td>1.000000</td>\n",
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       "      <th>12</th>\n",
       "      <td>취업을 위해 제대로 배워보는</td>\n",
       "      <td>0.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>취업을 위한 알고리즘</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>제주코딩베이스캠프 code festival</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>정말 쉽게 풀어보는</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>자바스크립트 언어 기본 자바스크립트</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>자바 프로그래밍 입문</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>자바 애플리케이션을 테스트하는 다양한 방법</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>자바 orm 표준</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>예민한 ux 디자인</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>실전 스프링 부트와 jpa</td>\n",
       "      <td>1.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>실전 스프링 데이터</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>시스템 프로그래밍 이론과</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>스프링 프레임워크 핵심 기술</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>스프링 프레임워크 핵심</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>스프링 부트와 jpa 활용1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>스프링 데이터 jpa</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>부트 개념과 활용</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>부트 개념과 활용</td>\n",
       "      <td>0.078125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>벡터 미적분학 시리즈</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>모두를 위한 딥러닝</td>\n",
       "      <td>0.184211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>머신러닝 완벽 가이드</td>\n",
       "      <td>0.977273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>리액트로 nodebird sns 만들기</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>리버싱 분석 초급과정</td>\n",
       "      <td>0.217391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>리눅스 시스템 프로그래밍</td>\n",
       "      <td>0.002708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>따라하며 배우는 따라하며</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>따라하며 배우는 c언어</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>데이터 분석과 시각화</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>기반 rest api</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>근본적인 핵심 이해하기</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>그로스해킹 데이터와 실험을</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>게임 만들기 연습</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>개발자가 되고 싶습니다</td>\n",
       "      <td>1.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>vuejs vuex vue</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>vue js 완벽 가이드</td>\n",
       "      <td>0.102041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>tdd 개발 방법론을 활용한 리액트 native</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>opencv 파이썬 딥러닝 영상처리 프로젝트</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>nodebird sns 만들기</td>\n",
       "      <td>1.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>node js로 크롤링하기</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>node js 교과서 기본부터 프로젝트</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>js django 연동 프로그래밍</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>flutter 입문 안드로이드 ios 개발을 번에</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020년 공부 목표</td>\n",
       "      <td>0.106996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       features     score\n",
       "47            프로그래밍과 자바스크립트 es6  1.102564\n",
       "18            프로그래밍 시작하기 파이썬 입문  1.000000\n",
       "36                 표준 jpa 프로그래밍  1.000000\n",
       "39              파이썬을 이용한 it 해킹과  1.000000\n",
       "21                    파이썬 실전 활용  1.000000\n",
       "32                    파이썬 실전 활용  1.000000\n",
       "28                 코틀린 기반 안드로이드  0.315789\n",
       "45              코드를 조작하는 다양한 방법  1.000000\n",
       "12              취업을 위해 제대로 배워보는  0.117647\n",
       "19                  취업을 위한 알고리즘  1.000000\n",
       "38      제주코딩베이스캠프 code festival  0.083333\n",
       "22                   정말 쉽게 풀어보는  0.166667\n",
       "46          자바스크립트 언어 기본 자바스크립트  1.000000\n",
       "42                  자바 프로그래밍 입문  1.000000\n",
       "5       자바 애플리케이션을 테스트하는 다양한 방법  1.000000\n",
       "13                    자바 orm 표준  1.000000\n",
       "49                   예민한 ux 디자인  1.000000\n",
       "48               실전 스프링 부트와 jpa  1.133333\n",
       "37                   실전 스프링 데이터  1.000000\n",
       "44                시스템 프로그래밍 이론과  1.000000\n",
       "31              스프링 프레임워크 핵심 기술  0.300000\n",
       "26                 스프링 프레임워크 핵심  1.000000\n",
       "14              스프링 부트와 jpa 활용1  1.000000\n",
       "2                   스프링 데이터 jpa  1.000000\n",
       "4                     부트 개념과 활용  1.000000\n",
       "7                     부트 개념과 활용  0.078125\n",
       "29                  벡터 미적분학 시리즈  1.000000\n",
       "9                    모두를 위한 딥러닝  0.184211\n",
       "1                   머신러닝 완벽 가이드  0.977273\n",
       "27        리액트로 nodebird sns 만들기  1.000000\n",
       "30                  리버싱 분석 초급과정  0.217391\n",
       "25                리눅스 시스템 프로그래밍  0.002708\n",
       "23                따라하며 배우는 따라하며  1.000000\n",
       "24                 따라하며 배우는 c언어  1.000000\n",
       "16                  데이터 분석과 시각화  0.111111\n",
       "17                  기반 rest api  1.000000\n",
       "6                  근본적인 핵심 이해하기  1.000000\n",
       "15               그로스해킹 데이터와 실험을  1.000000\n",
       "10                    게임 만들기 연습  1.000000\n",
       "40                 개발자가 되고 싶습니다  1.142857\n",
       "34               vuejs vuex vue  1.000000\n",
       "3                 vue js 완벽 가이드  0.102041\n",
       "35   tdd 개발 방법론을 활용한 리액트 native  1.000000\n",
       "33     opencv 파이썬 딥러닝 영상처리 프로젝트  1.000000\n",
       "8              nodebird sns 만들기  1.166667\n",
       "11               node js로 크롤링하기  1.000000\n",
       "41        node js 교과서 기본부터 프로젝트  1.000000\n",
       "20           js django 연동 프로그래밍  1.000000\n",
       "43  flutter 입문 안드로이드 ios 개발을 번에  1.000000\n",
       "0                   2020년 공부 목표  0.106996"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 개별 클러스터에서 점수가 가장 높은 단어를 추출 아래 점수가 클수록 예측 정확도가 높음\n",
    "# MiniBatchKMeans 로 예측한 값 기준\n",
    "pd.DataFrame(df_cluster, columns = ['features', 'score']).sort_values(by=[\"features\", \"score\"], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1672572654984,
     "user": {
      "displayName": "송영숙",
      "userId": "01138230862948206591"
     },
     "user_tz": -540
    },
    "id": "NIYGbZeRBIbK",
    "outputId": "83b64483-8034-41f3-fdaf-51907f96d3ba"
   },
   "outputs": [
    {
     "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>bcluster</th>\n",
       "      <th>cluster</th>\n",
       "      <th>origin_text</th>\n",
       "      <th>course</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>번 아웃에 빠졌어요. 새로운거 해보려구요.\\n#관심강의 : 유니티 기초 강좌 - 기...</td>\n",
       "      <td>유니티 기초 강좌 - 기획부터 사용까지</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>R 데이터분석의 전문가가 되고싶어용\\n#관심강의 : R로 하는 텍스트마이닝 (Top...</td>\n",
       "      <td>r로 하는 텍스트마이닝 (top keyword부터 감성분석까지)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>웹 프론트엔드 개발자가 되기 위해 공부하고 있습니다.\\n#관심강의 : Redux v...</td>\n",
       "      <td>redux vs mobx (둘 다 배우자!)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>컴공 대학교 졸업후 진로에 대해 생각해 볼 틈도 없이 바로 취업을 했습니다. 모바일...</td>\n",
       "      <td>컴공 대학교 졸업후 진로에 대해 생각해 볼 틈도 없이 바로 취업을 했습니다. 모바일...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>일반회사원으로 일하고 있는데요. \\n자기개발 및 커리어 전환을 위해서 개발자관련 강...</td>\n",
       "      <td>r로 하는 텍스트마이닝 (top keyword부터 감성분석까지), 쉽고 빠르게 끝...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>코틀린으로 역량을 강화시켜서 기가 막히고 코가 막히는 앱을 만들고 싶습니다. ......</td>\n",
       "      <td>코틀린 기반 안드로이드 앱개발 고급 3단계</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>2020 올해의 목표는 제가 만든 웹서비스를 출시해보는 것입니다. 웹 서비스를 만드...</td>\n",
       "      <td>2020 올해의 목표는 제가 만든 웹서비스를 출시해보는 것입니다. 웹 서비스를 만드...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1314</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>공부열심히 해서 올해는 꼭 개인 안드로이드 앱을 개발 &amp; 배포하겠습니다 !!\\n/ ...</td>\n",
       "      <td>공부열심히 해서 올해는 꼭 개인 안드로이드 앱을 개발 &amp; 배포하겠습니다 !!\\n/ ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>임베디드 개발자인데 모바일로 업무환경을 바꾸려고 합니다!  성공적인 전업이 되었으면...</td>\n",
       "      <td>코틀린 기반 안드로이드 앱개발 기본 1단계</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>프론트 개발자였다가, 최근에 앱 개발자가 되었습니다. 인프런과 함께 안드로이드를 마...</td>\n",
       "      <td>코틀린 기반 안드로이드 앱 1~3, flutter 입문~중급</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>안녕하세요~ 인프런덕에 많은 도움 받고있습니다. 2020년에도 건승하시길 바랍니다!...</td>\n",
       "      <td>안녕하세요~ 인프런덕에 많은 도움 받고있습니다. 2020년에도 건승하시길 바랍니다!...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>운영 업무에서 개발업무로 넘어가며 개발역량에 대해 필요성을 느끼고 있습니다.\\n**...</td>\n",
       "      <td>운영 업무에서 개발업무로 넘어가며 개발역량에 대해 필요성을 느끼고 있습니다.\\n**...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1521</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>악성코드 분석에 관심이 있습니다. 악성코드 열심히 공부해서 관련 업무로 취업하고 싶...</td>\n",
       "      <td>악성코드 분석 중급과정 (유형별분석)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1782</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>이번해에는 모바일 모의해킹쪽 배우고 싶습니다\\n관심있는 강의 : 안드로이드 모바일 ...</td>\n",
       "      <td>이번해에는 모바일 모의해킹쪽 배우고 싶습니다\\n관심있는 강의  안드로이드 모바일 앱...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1793</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>실무에서 웹 개발을 하고 있는 개발자이지만, 항상 부족한 점을 느끼고 있습니다.\\n...</td>\n",
       "      <td>실무에서 웹 개발을 하고 있는 개발자이지만, 항상 부족한 점을 느끼고 있습니다.\\n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1850</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>올해는 자바에 대해서 더 깊이 공부하고 싶고, 주변분들과 함께 나누고 싶습니다.\\n...</td>\n",
       "      <td>올해는 자바에 대해서 더 깊이 공부하고 싶고, 주변분들과 함께 나누고 싶습니다.\\n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>리액트를 이용해서 게시판을 만드는 수준까지 실력 올리기\\n리액트 네이티브를 이용해서...</td>\n",
       "      <td>노마드코더의 리액트 네이티브 강의 전부, redux vs mobx, 리액트로 no...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2215</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>2020년 목표 UNITY초보단계 강의 수강 끝\\n#수강중인 강의 명 : 유니티기초...</td>\n",
       "      <td>2020년 목표 unity초보단계 강의 수강 끝\\n#수강중인 강의 명  유니티기초 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2236</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>그동안 깊이 없이 그저 다양한 언어를 배우기만 했었습니다.  앱 개발 과제를 받아서...</td>\n",
       "      <td>코틀린 기반 안드로이드 앱개발 심화 2단계</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      bcluster  cluster                                        origin_text  \\\n",
       "111         28        1  번 아웃에 빠졌어요. 새로운거 해보려구요.\\n#관심강의 : 유니티 기초 강좌 - 기...   \n",
       "122         28        1  R 데이터분석의 전문가가 되고싶어용\\n#관심강의 : R로 하는 텍스트마이닝 (Top...   \n",
       "459         28        1  웹 프론트엔드 개발자가 되기 위해 공부하고 있습니다.\\n#관심강의 : Redux v...   \n",
       "620         28        1  컴공 대학교 졸업후 진로에 대해 생각해 볼 틈도 없이 바로 취업을 했습니다. 모바일...   \n",
       "992         28        1  일반회사원으로 일하고 있는데요. \\n자기개발 및 커리어 전환을 위해서 개발자관련 강...   \n",
       "996         28        1  코틀린으로 역량을 강화시켜서 기가 막히고 코가 막히는 앱을 만들고 싶습니다. ......   \n",
       "1028        28        1  2020 올해의 목표는 제가 만든 웹서비스를 출시해보는 것입니다. 웹 서비스를 만드...   \n",
       "1314        28        1  공부열심히 해서 올해는 꼭 개인 안드로이드 앱을 개발 & 배포하겠습니다 !!\\n/ ...   \n",
       "1319        28        1  임베디드 개발자인데 모바일로 업무환경을 바꾸려고 합니다!  성공적인 전업이 되었으면...   \n",
       "1385        28        1  프론트 개발자였다가, 최근에 앱 개발자가 되었습니다. 인프런과 함께 안드로이드를 마...   \n",
       "1440        28        1  안녕하세요~ 인프런덕에 많은 도움 받고있습니다. 2020년에도 건승하시길 바랍니다!...   \n",
       "1442        28        1  운영 업무에서 개발업무로 넘어가며 개발역량에 대해 필요성을 느끼고 있습니다.\\n**...   \n",
       "1521        28        1  악성코드 분석에 관심이 있습니다. 악성코드 열심히 공부해서 관련 업무로 취업하고 싶...   \n",
       "1782        28        1  이번해에는 모바일 모의해킹쪽 배우고 싶습니다\\n관심있는 강의 : 안드로이드 모바일 ...   \n",
       "1793        28        1  실무에서 웹 개발을 하고 있는 개발자이지만, 항상 부족한 점을 느끼고 있습니다.\\n...   \n",
       "1850        28        1  올해는 자바에 대해서 더 깊이 공부하고 싶고, 주변분들과 함께 나누고 싶습니다.\\n...   \n",
       "2002        28        1  리액트를 이용해서 게시판을 만드는 수준까지 실력 올리기\\n리액트 네이티브를 이용해서...   \n",
       "2215        28        1  2020년 목표 UNITY초보단계 강의 수강 끝\\n#수강중인 강의 명 : 유니티기초...   \n",
       "2236        28        1  그동안 깊이 없이 그저 다양한 언어를 배우기만 했었습니다.  앱 개발 과제를 받아서...   \n",
       "\n",
       "                                                 course  \n",
       "111                               유니티 기초 강좌 - 기획부터 사용까지  \n",
       "122                 r로 하는 텍스트마이닝 (top keyword부터 감성분석까지)  \n",
       "459                            redux vs mobx (둘 다 배우자!)  \n",
       "620   컴공 대학교 졸업후 진로에 대해 생각해 볼 틈도 없이 바로 취업을 했습니다. 모바일...  \n",
       "992    r로 하는 텍스트마이닝 (top keyword부터 감성분석까지), 쉽고 빠르게 끝...  \n",
       "996                             코틀린 기반 안드로이드 앱개발 고급 3단계  \n",
       "1028  2020 올해의 목표는 제가 만든 웹서비스를 출시해보는 것입니다. 웹 서비스를 만드...  \n",
       "1314  공부열심히 해서 올해는 꼭 개인 안드로이드 앱을 개발 & 배포하겠습니다 !!\\n/ ...  \n",
       "1319                            코틀린 기반 안드로이드 앱개발 기본 1단계  \n",
       "1385                  코틀린 기반 안드로이드 앱 1~3, flutter 입문~중급  \n",
       "1440  안녕하세요~ 인프런덕에 많은 도움 받고있습니다. 2020년에도 건승하시길 바랍니다!...  \n",
       "1442  운영 업무에서 개발업무로 넘어가며 개발역량에 대해 필요성을 느끼고 있습니다.\\n**...  \n",
       "1521                               악성코드 분석 중급과정 (유형별분석)  \n",
       "1782  이번해에는 모바일 모의해킹쪽 배우고 싶습니다\\n관심있는 강의  안드로이드 모바일 앱...  \n",
       "1793  실무에서 웹 개발을 하고 있는 개발자이지만, 항상 부족한 점을 느끼고 있습니다.\\n...  \n",
       "1850  올해는 자바에 대해서 더 깊이 공부하고 싶고, 주변분들과 함께 나누고 싶습니다.\\n...  \n",
       "2002   노마드코더의 리액트 네이티브 강의 전부, redux vs mobx, 리액트로 no...  \n",
       "2215  2020년 목표 unity초보단계 강의 수강 끝\\n#수강중인 강의 명  유니티기초 ...  \n",
       "2236                            코틀린 기반 안드로이드 앱개발 심화 2단계  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# score 정확도가 1이 나온 클러스터를 찾아봄 - 같은 강좌끼리 묶였는지 확인 함\n",
    "df.loc[df[\"bcluster\"] == 28, [\"bcluster\", \"cluster\", \"origin_text\", \"course\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 506,
       "width": 694
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Silhouette Plot of MiniBatchKMeans Clustering for 2410 Samples in 50 Centers'}, xlabel='silhouette coefficient values', ylabel='cluster label'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from yellowbrick.cluster import SilhouetteVisualizer\n",
    "visualizer = SilhouetteVisualizer(mkmeans, colors='yellowbrick')\n",
    "\n",
    "visualizer.fit(feature_tfidf.toarray())\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "256px"
   },
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}