완결된_웹툰의_관심_수_예측.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "reQY7Pvx8IjG"
      },
      "source": [
        "# 주제 : 웹툰이 완결 되었을때 관심 수 예측\n",
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ZLOmgqG1nkb"
      },
      "source": [
        "# 데이터 및 라이브러리 로딩"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "owI7dWuCBgKs"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np    \n",
        "import pandas as pd\n",
        "from bs4 import BeautifulSoup\n",
        "import requests"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "chhlg0o3n4H4"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yQXJaEgHjfzN"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv('naver.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Xyu0RmT3ddq"
      },
      "source": [
        "# 데이터 설명\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "> **x**\n",
        "\n",
        "\n",
        "*   title : 웹툰 제목\n",
        "*   author : 작가\n",
        "*   genre : 장르\n",
        "*   describtion : 웹툰 소개\n",
        "*   rating : 웹툰 평점\n",
        "*   date : 최근 업데이트 날짜(완결 욉툰은 완결 날짜)\n",
        "*   completed : 완결여부\n",
        "*   age : 관람 제한 나이\n",
        "*   free : 무료 여부\n",
        "*   link : 웹툰 링크\n",
        "*   previous(추가 column) : 작가의 전 작품의 개수(해당 작품이 나올 때 기준)\n",
        "*   episode(추가 column) : 웹툰의 에피소드의 개수\n",
        "*   remake(추가 column) : 원작 유무의 여부\n",
        "*   heart(추가 column) : 웹툰이 받은 하트의 개수\n",
        "\n",
        "\n",
        "> **y(target)**\n",
        "\n",
        "\n",
        "*   interest_num(추가 column) : 관심웹툰으로 등록 한 사람의 수"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V3yePkuzPaxr"
      },
      "source": [
        "**insight :**\n",
        "\n",
        "*   완결된 웹툰을 대상으로 하기 때문에 completed가 True인 데이터만을 다룸\n",
        "*   previous(추가 column) : 날짜 순 정렬 -> 작가 2명을 1명으로 만들기 -> 작가 이름이 나올때 마다 count+1 해준다\n",
        "*   episode(추가 column) : 크롤링\n",
        "*   remake(추가 column) : 개수가 적어서 직접 작성하여 merge\n",
        "*   heart(추가 column) : 크롤링\n",
        "*   interest_num(추가 column) : 크롤링\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j-R58lQnvFe_"
      },
      "source": [
        "\n",
        "\n",
        "> **완결된 웹툰만을 뽑음**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9PI8DxJcgPFo"
      },
      "outputs": [],
      "source": [
        "# 완결된 웹툰 \n",
        "data=data[data[\"completed\"]==True]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H6E-oXufvPzT"
      },
      "source": [
        "\n",
        "\n",
        "> **previous(추가 column) : 날짜 순 정렬 -> 작가 2명을 1명으로 만들기 -> 작가 이름이 나올때 마다 count+1 해준다**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dAfxCftX5d-I"
      },
      "outputs": [],
      "source": [
        "# 날짜별로 정렬\n",
        "data=data.sort_values(by=['date'])\n",
        "\n",
        "# 이름 두 명 중 한 명만 남기기('/'로 구분되어 있는 것을 나누고 첫 번째 사람만 남김 because 첫 번째 사람이 유명)\n",
        "authors=[]\n",
        "for author in data['author']:\n",
        "  author=author.replace('/',',').split(',')\n",
        "  authors.append(author[0])\n",
        "\n",
        "# split()이 남긴 빈칸 없애기\n",
        "for i in range(len(authors)):\n",
        "  authors[i]=authors[i].strip()\n",
        "\n",
        "# 'author'열을 한 명만 남긴 열로 대체\n",
        "data['author']=authors\n",
        "\n",
        "# 'previous'열을 만들어서 추가\n",
        "# 'previous'열 : 작품이 나온 시점에서 전 작품의 수의 개수를 담음\n",
        "authors_dic={}\n",
        "previous=[]\n",
        "for i in data['author']:\n",
        "  if i not in authors_dic.keys():\n",
        "    authors_dic[i]=0\n",
        "  else :\n",
        "    authors_dic[i] +=1\n",
        "  previous.append(authors_dic[i])\n",
        "data['previous']=previous"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0b-QnTf02ZGA"
      },
      "outputs": [],
      "source": [
        "# 기존 파일 내보내기(셀 실행x)\n",
        "data.to_csv(\"naver_previous.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lKfm1qukzqLG"
      },
      "source": [
        "\n",
        "> **remake(추가 column) : 전 작품이 있는 개수가 거의 없다 -> 파일을 다운 받아서 remake열을 만들어 remake작만 1을 넣고 다시 코랩으로 연다 -> null값에 0을 집어넣는다**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cn0q4aXI3wCP"
      },
      "outputs": [],
      "source": [
        "# remake열을 만들고 remake작에 1 넣은 파일 다운받기\n",
        "data=pd.read_csv('naver_remake.csv',encoding='cp949')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xaLYPaXVCNam"
      },
      "outputs": [],
      "source": [
        "# null값에 0넣기\n",
        "data = data.fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RSS5eglPC__R"
      },
      "outputs": [],
      "source": [
        "# float형을 int형으로 변환\n",
        "data = data.astype({'remake':'int'})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nU_R-W4BQzO4"
      },
      "source": [
        " **heart(추가 column) : 크롤링(동적 크롤링이라 beautifulsoup이 아니라 selenium사용해야함)**\n",
        "\n",
        "*   사이트 : https://comic.naver.com/webtoon/list?titleId=786987\n",
        "*   colab에서는 selenium이 내장되어 있지 않아서 따로 다운 받아야함\n",
        "*   데이터에 link가 있어서 모든 link 크롤링 돌면 됨\n",
        "*   중간중간에 읽을 수 없는 url이 있어서 timeout을 걸어놓고 timeout하면 for문을 넘어가는 형식으로 함\n",
        "*   딕셔너리를 사용하여 기존 csv파일에 추가할 예정임\n",
        "*   99999+가 있어서 범위를 나눠서 인코딩을 해야하한다"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6fGM0FJlkOdu",
        "outputId": "f532b929-f4c6-4a4f-f216-536dc93a4ff0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting selenium\n",
            "  Downloading selenium-4.7.2-py3-none-any.whl (6.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.3 MB 30.8 MB/s \n",
            "\u001b[?25hCollecting trio~=0.17\n",
            "  Downloading trio-0.22.0-py3-none-any.whl (384 kB)\n",
            "\u001b[K     |████████████████████████████████| 384 kB 64.7 MB/s \n",
            "\u001b[?25hCollecting urllib3[socks]~=1.26\n",
            "  Downloading urllib3-1.26.13-py2.py3-none-any.whl (140 kB)\n",
            "\u001b[K     |████████████████████████████████| 140 kB 65.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: certifi>=2021.10.8 in /usr/local/lib/python3.8/dist-packages (from selenium) (2022.9.24)\n",
            "Collecting trio-websocket~=0.9\n",
            "  Downloading trio_websocket-0.9.2-py3-none-any.whl (16 kB)\n",
            "Collecting sniffio\n",
            "  Downloading sniffio-1.3.0-py3-none-any.whl (10 kB)\n",
            "Requirement already satisfied: idna in /usr/local/lib/python3.8/dist-packages (from trio~=0.17->selenium) (2.10)\n",
            "Requirement already satisfied: sortedcontainers in /usr/local/lib/python3.8/dist-packages (from trio~=0.17->selenium) (2.4.0)\n",
            "Collecting exceptiongroup>=1.0.0rc9\n",
            "  Downloading exceptiongroup-1.0.4-py3-none-any.whl (14 kB)\n",
            "Collecting outcome\n",
            "  Downloading outcome-1.2.0-py2.py3-none-any.whl (9.7 kB)\n",
            "Collecting async-generator>=1.9\n",
            "  Downloading async_generator-1.10-py3-none-any.whl (18 kB)\n",
            "Requirement already satisfied: attrs>=19.2.0 in /usr/local/lib/python3.8/dist-packages (from trio~=0.17->selenium) (22.1.0)\n",
            "Collecting wsproto>=0.14\n",
            "  Downloading wsproto-1.2.0-py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: PySocks!=1.5.7,<2.0,>=1.5.6 in /usr/local/lib/python3.8/dist-packages (from urllib3[socks]~=1.26->selenium) (1.7.1)\n",
            "Collecting h11<1,>=0.9.0\n",
            "  Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
            "\u001b[K     |████████████████████████████████| 58 kB 5.2 MB/s \n",
            "\u001b[?25hInstalling collected packages: sniffio, outcome, h11, exceptiongroup, async-generator, wsproto, urllib3, trio, trio-websocket, selenium\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "requests 2.23.0 requires urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1, but you have urllib3 1.26.13 which is incompatible.\u001b[0m\n",
            "Successfully installed async-generator-1.10 exceptiongroup-1.0.4 h11-0.14.0 outcome-1.2.0 selenium-4.7.2 sniffio-1.3.0 trio-0.22.0 trio-websocket-0.9.2 urllib3-1.26.13 wsproto-1.2.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "urllib3"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Reading package lists... Done\n",
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            "Building dependency tree       \n",
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            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'apt autoremove' to remove it.\n",
            "The following additional packages will be installed:\n",
            "  chromium-browser chromium-browser-l10n chromium-codecs-ffmpeg-extra\n",
            "Suggested packages:\n",
            "  webaccounts-chromium-extension unity-chromium-extension\n",
            "The following NEW packages will be installed:\n",
            "  chromium-browser chromium-browser-l10n chromium-chromedriver\n",
            "  chromium-codecs-ffmpeg-extra\n",
            "0 upgraded, 4 newly installed, 0 to remove and 7 not upgraded.\n",
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            "Get:1 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 chromium-codecs-ffmpeg-extra amd64 107.0.5304.87-0ubuntu11.18.04.1 [1,158 kB]\n",
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            "Get:4 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 chromium-chromedriver amd64 107.0.5304.87-0ubuntu11.18.04.1 [5,570 kB]\n",
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            "(Reading database ... 124015 files and directories currently installed.)\n",
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            "Preparing to unpack .../chromium-chromedriver_107.0.5304.87-0ubuntu11.18.04.1_amd64.deb ...\n",
            "Unpacking chromium-chromedriver (107.0.5304.87-0ubuntu11.18.04.1) ...\n",
            "Setting up chromium-codecs-ffmpeg-extra (107.0.5304.87-0ubuntu11.18.04.1) ...\n",
            "Setting up chromium-browser (107.0.5304.87-0ubuntu11.18.04.1) ...\n",
            "update-alternatives: using /usr/bin/chromium-browser to provide /usr/bin/x-www-browser (x-www-browser) in auto mode\n",
            "update-alternatives: using /usr/bin/chromium-browser to provide /usr/bin/gnome-www-browser (gnome-www-browser) in auto mode\n",
            "Setting up chromium-chromedriver (107.0.5304.87-0ubuntu11.18.04.1) ...\n",
            "Setting up chromium-browser-l10n (107.0.5304.87-0ubuntu11.18.04.1) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Processing triggers for hicolor-icon-theme (0.17-2) ...\n",
            "Processing triggers for mime-support (3.60ubuntu1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.6) ...\n",
            "cp: missing destination file operand after '/usr/lib/chromium-browser/chromedriver/usr/bin'\n",
            "Try 'cp --help' for more information.\n"
          ]
        }
      ],
      "source": [
        "# colab에서는 selenium이 없어서 다운받아야함\n",
        "!pip install selenium \n",
        "!apt-get update\n",
        "!apt install chromium-chromedriver\n",
        "!cp /usr/lib/chromium-browser/chromedriver/usr/bin"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1lahISaIlDWW"
      },
      "outputs": [],
      "source": [
        "from selenium import webdriver # request모듈은 javascripts로 변형되기 전의 주소라서 제대로 못받아옴 \n",
        "from selenium.webdriver.common.by import By\n",
        "from selenium.webdriver.support.ui import WebDriverWait\n",
        "options = webdriver.ChromeOptions()\n",
        "options.add_argument('--headless')        # Head-less 설정\n",
        "options.add_argument('--no-sandbox')\n",
        "options.add_argument('--disable-dev-shm-usage')\n",
        "driver = webdriver.Chrome('chromedriver', options=options)\n",
        "driver.implicitly_wait(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "93yAWJjD6LW0",
        "outputId": "02f59428-5b30-4f95-ec47-78ad9e2e5f9c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "타투 113\n",
            "와탕카 352\n",
            "까뱅 51\n",
            "사랑in 68\n",
            "AA 117\n",
            "도자기 2,233\n",
            "펫다이어리 11,577\n",
            "체스아일 185\n",
            "구석구석캠페인 92\n",
            "탐구생활2 69\n",
            "식스센스 89\n",
            "트라우마 6,452\n",
            "제로 64\n",
            "태왕광개토 127\n",
            "취업의소리 99\n",
            "에피소드메이비 280\n",
            "개편 축하 릴레이 카툰 125\n",
            "향수 3,107\n",
            "와탕카2 134\n",
            "반지의제왕 35\n",
            "골방환상곡 4,848\n",
            "3단합체김창남 11,928\n",
            "N의등대-thecalling 2,501\n",
            "내일은 럭키곰스타 2,820\n",
            "N의등대-busted 3,869\n",
            "N의등대-도망자 2,714\n",
            "핑크레이디 21,052\n",
            "창위의일루젼 59\n",
            "탐구생활3 1,238\n",
            "고향의꽃 1,503\n",
            "슈퍼트리오 35\n",
            "N의등대-눈의등대 7,394\n",
            "고(故)노무현 前대통령 추모웹툰 977\n",
            "사노라면 35\n",
            "러브판타지페이퍼 3,847\n",
            "플루타크 영웅전 186\n",
            "번데기스 15\n",
            "일편단심화 1,526\n",
            "카라멜마끼아또 3,572\n",
            "번개기동대 2009 25\n",
            "로봇빠찌 30\n",
            "나는 어디에 있는 거니 433\n",
            "우월한 하루 10,803\n",
            "새끼손가락 8,152\n",
            "두근두근두근거려 9,094\n",
            "연 401\n",
            "고(故)길창덕화백 추모웹툰 78\n",
            "펫다이어리2.런 6,352\n",
            "색으로 말하다 309\n",
            "악연 4,615\n",
            "겨울동화 1,356\n",
            "판타스틱 어른백서 20\n",
            "탐구생활4-그대와 함께 하이킹 523\n",
            "몽타주 691\n",
            "17살, 그 여름날의 기적 2,045\n",
            "연옥님이 보고계셔 1,236\n",
            "스마일브러시 2,032\n",
            "구름의 노래 5,358\n",
            "낢에게와요 400\n",
            "패밀리맨 2,206\n",
            "싸우자귀신아 26,822\n",
            "심장이뛰다 1,634\n",
            "소설가J 3,683\n",
            "피터팬날다 28\n",
            "공부하기 좋은 날 12,934\n",
            "의령수 135\n",
            "증거 76\n",
            "거상 김만덕 235\n",
            "심부름센터 K 1,710\n",
            "동경소녀 512\n",
            "열아홉스물하나 18,048\n",
            "안나라수마나라 27,151\n",
            "미호이야기 10,476\n",
            "키스우드 7,426\n",
            "입시명문사립 정글고등학교 27,659\n",
            "열쇠줍는아이 1,623\n",
            "보톡스 636\n",
            "그린스마일 1,449\n",
            "힘내요 일본!-[릴레이웹툰] 225\n",
            "핑크레이디 클래식 9,454\n",
            "혜성같은 소년 614\n",
            "흐드러지다 2,156\n",
            "리턴 1,040\n",
            "징글정글 462\n",
            "견우와 직녀 17,647\n",
            "바이올린처럼. 2,309\n",
            "묵회 1,630\n",
            "은하연인전 3,734\n",
            "테제 3,920\n",
            "프로젝트 X 3,244\n",
            "스마일 브러시, 오래된 사진. 1,477\n",
            "스토커 9,559\n",
            "불량 뱀파이어 270\n",
            "멜로홀릭 3,989\n",
            "굿모닝 스페이스 4,495\n",
            "잉잉잉 2,665\n",
            "2011 미스테리 단편 20,356\n",
            "단편 할머니/나는내일죽는다 65\n",
            "어서오세요.305호에 27,794\n",
            "단군할배요 1,441\n",
            "다욤이의 다이어트 다이어리 4,085\n",
            "레드초콜릿 4,138\n",
            "남기한엘리트만들기 28,953\n",
            "[드라마원작] 지금 우리 학교는 27,541\n",
            "세개의시간 5,199\n",
            "우연일까? 4,240\n",
            "2011 루키 단편선 3,428\n",
            "수사9단 8,871\n",
            "지원 3,848\n",
            "야! 오이 3,251\n",
            "폭풍의 전학생 15,521\n",
            "만화사랑 캠페인 20\n",
            "지상 최악의 소년 6,347\n",
            "LOST 4,147\n",
            "요리대마왕 1,648\n",
            "콘스탄쯔 이야기 5,728\n",
            "봄이여 오라 4,539\n",
            "얼룩말 6,038\n",
            "나란의사 그런의사 1,215\n",
            "뜨거운 것이 좋아. 4,035\n",
            "연애세포 4,369\n",
            "그런지 1,866\n",
            "부토 3,491\n",
            "커피우유신화 6,371\n",
            "고시생툰 7,870\n",
            "멍순이 1,064\n",
            "키드갱 101\n",
            "폭풍의 전학생 - 리부트 4,814\n",
            "별의 유언 10,581\n",
            "국가의 탄생 837\n",
            "슈퍼트리오 시즌2 34\n",
            "질풍기획 시즌1 12,559\n",
            "MODERN BOYS 1,874\n",
            "자율공상축구탐구만화 260\n",
            "실질객관동화 14,174\n",
            "초록인간 2,367\n",
            "한섬세대 1,176\n",
            "리버스 3,855\n",
            "아스란영웅전 13,927\n",
            "연 시즌2 2,893\n",
            "비바 산티아고 920\n",
            "2012 지구가 멸망한다면? 9,462\n",
            "악당의 사연 4,170\n",
            "크레이지 커피 캣 2,616\n",
            "아부쟁이 6,668\n",
            "방울토마토 20,663\n",
            "옆집화랑 4,829\n",
            "진진돌이 에볼루션 3,518\n",
            "네이버 앱피소드 12,011\n",
            "오란씨100 1,628\n",
            "2012 루키 단편선 5,532\n",
            "S라인 6,178\n",
            "삼봉이발소 10,735\n",
            "한 살이라도 어릴 때 17,779\n",
            "웨스트우드 비브라토 4,257\n",
            "MLB카툰 236\n",
            "후유증 11,192\n",
            "이말년씨리즈 26,994\n",
            "스쿨홀릭 9,806\n",
            "스마트폰 게임 개발 이야기 1,721\n",
            "길에서 만나다 3,899\n",
            "고삼이 집나갔다 13,523\n",
            "꽃밭에솔 10,101\n",
            "스펙트럼 분석기 14,476\n",
            "경운기를 탄 왕자님 4,092\n",
            "달콤한 인생 25,596\n",
            "무한동력 6,452\n",
            "비흔 24,909\n",
            "일상날개짓 15,958\n",
            "그린보이 27,958\n",
            "닭통령 계양반 7,556\n",
            "오늘의 낭만부 9,588\n",
            "패션왕 44,957\n",
            "아는사람 이야기 35,133\n",
            "인형의 기사 3,834\n",
            "개판 8,573\n",
            "기타맨 5,816\n",
            "사랑을 연기하다 2,060\n",
            "우주전함 몰라몰라 886\n",
            "사랑일까? 3,172\n",
            "소년전[Limit] 5,953\n",
            "나의 목소리를 들어라 2,851\n",
            "새벽9시 1,883\n",
            "카오스어택 13,610\n",
            "삵의 발톱 1,118\n",
            "레사 시즌1 31,297\n",
            "타임인조선 25,650\n",
            "진진돌이 제로 3,160\n",
            "레인보우 로즈 1,543\n",
            "사랑의 아쿠아리움 7,788\n",
            "바람의 색 4,157\n",
            "2013 전설의고향 24,976\n",
            "위아더 능력자! 9,661\n",
            "아빠를 찾습니다 12,615\n",
            "움비처럼 4,150\n",
            "수업시간그녀 12,721\n",
            "기태류 5,706\n",
            "아이들의 권 선생님 34,456\n",
            "월남특급 2,712\n",
            "네로의 실험실 7,121\n",
            "맛집남녀 3,534\n",
            "OH, MY GOD! 11,483\n",
            "미숙한 친구는 G구인 6,303\n",
            "방과 후 전쟁활동 16,689\n",
            "아랫집 시누이 29,192\n",
            "내일은 웹툰 2,947\n",
            "ENT. 11,676\n",
            "기사도 4,479\n",
            "오렌지 마말레이드 34,858\n",
            "터치! 메리크리스마스 4,008\n",
            "키드갱 시즌2 3,550\n",
            "라이징패스트볼 10,546\n",
            "2013 루키 단편선 4,493\n",
            "소나기야 4,518\n",
            "그날의 생존자들 9,447\n",
            "좀비를 위한 나라는 없다 8,931\n",
            "한줌물망초 19,874\n",
            "악플게임 23,785\n",
            "국립자유경제 고등학교 세실고 17,713\n",
            "빵점동맹 12,649\n",
            "닥터 프로스트 시즌 1~2 31,411\n",
            "모던패밀리 20,290\n",
            "한국만화거장전 4,289\n",
            "그녀는 흡!혈귀 25,097\n",
            "새와 같이 15,227\n",
            "판타지스케치 - 더 게임 9,162\n",
            "네가 없는 세상 43,409\n",
            "코끼리를 끌어안는 방법 16,678\n",
            "우리의 이상한 여행 19,010\n",
            "실질객관영화 26,525\n",
            "죄의 파편 25,592\n",
            "와라! 편의점 56,996\n",
            "너와 너 사이 30,942\n",
            "아이들은 즐겁다 39,006\n",
            "열무가 익어간다 17,032\n",
            "기적! 우리에게 일어난 일들 15,745\n",
            "피리부는 남자 16,577\n",
            "투엔티스 61,053\n",
            "드래곤레시피 42,932\n",
            "은주의 방 51,779\n",
            "섀도우 31,138\n",
            "강시대소동 36,323\n",
            "헬로 미스터 테디 34,054\n",
            "어른스러운 철구 35,871\n",
            "레드카펫 32,150\n",
            "사랑의 외계인 16,486\n",
            "역전! 야매요리 99,999+\n",
            "킥 25,310\n",
            "시타를 위하여 70,335\n",
            "2014 네이버 웹툰, 왓 이프? 74,159\n",
            "버프소녀 오오라 47,248\n",
            "꽃가족 93,680\n",
            "소년들은 무엇을 하고 있을까 99,999+\n",
            "2014 루키 단편선 11,305\n",
            "미쳐 날뛰는 생활툰 27,993\n",
            "운빨로맨스 59,217\n",
            "아프니까 병원이다 39,541\n",
            "출격! 반보트 16,579\n",
            "SM 플레이어 89,847\n",
            "리즌 24,114\n",
            "미결 22,819\n",
            "싸귀2 : 퇴마록 74,069\n",
            "뱀파이어 43,885\n",
            "미선 임파서블 43,690\n",
            "빙의 45,943\n",
            "모두에게 완자가 69,038\n",
            "2015 우주특집 단편 30,654\n",
            "퇴마전쟁 47,819\n",
            "사이드킥 75,908\n",
            "혈액형에 관한 간단한 고찰 71,264\n",
            "파라다이스 17,899\n",
            "송곳 1~3부 89,079\n",
            "오늘 밤은 어둠이 무서워요 99,999+\n",
            "스튜디오 짭쪼롬 96,120\n",
            "격투기특성화사립고교 극지고 80,250\n",
            "알게뭐야 99,999+\n",
            "MY OH 33,498\n",
            "여탕보고서 99,999+\n",
            "영수의 봄 45,924\n",
            "붉은 실 99,999+\n",
            "올해의 벚꽃도 함께 30,002\n",
            "문아 54,690\n",
            "낢 부럽지 않은 신혼여행기 43,846\n",
            "헬퍼 99,999+\n",
            "제페토 38,839\n",
            "일사부재리 14,630\n",
            "버퍼링 24,321\n",
            "질풍기획 시즌2 60,946\n",
            "딥(DEEP) 50,554\n",
            "플로우 99,999+\n",
            "언더프린 79,921\n",
            "한국만화거장전 : 순정만화특집 15,867\n",
            "빨간책 87,510\n",
            "재앙은 미묘하게 76,346\n",
            "차차차 34,711\n",
            "불만시대 55,385\n",
            "버닝헬 36,323\n",
            "지새는 달 27,741\n",
            "노네임드(NoNameD) 55,777\n",
            "프린스의 왕자 99,999+\n",
            "웃지 않는 개그반 시즌1~2 99,999+\n",
            "조석축구만화 28,324\n",
            "언더클래스 히어로 47,272\n",
            "모태솔로수용소 99,999+\n",
            "우리 헤어졌어요 99,999+\n",
            "낚시신공 99,999+\n",
            "저승에서 만난 사람들 88,870\n",
            "신령 99,999+\n",
            "Ho! 99,999+\n",
            "일등당첨 99,999+\n",
            "두근거려요 90,225\n",
            "2015 소름 99,999+\n",
            "남과 여 99,999+\n",
            "심연의 하늘 시즌 1~3 99,999+\n",
            "크레이터 56,531\n",
            "시큼새큼 99,999+\n",
            "바람이 머무는 난 61,380\n",
            "안 돼요 마왕님! 77,392\n",
            "공주는 잠 못 이루고 71,810\n",
            "장미아파트 공경비 62,393\n",
            "아이소포스 1, 2부 48,651\n",
            "데드데이즈(DEAD DAYS) 99,999+\n",
            "라크리모사 41,869\n",
            "고고고 71,364\n",
            "리얼주주 47,550\n",
            "미래소녀 65,951\n",
            "늘 푸른 찻집 99,999+\n",
            "나는 너를 보았다 40,894\n",
            "스페이스 차이나드레스 41,980\n",
            "2015 루키 단편선 51,169\n",
            "천국의 신화 48,172\n",
            "사또(Satto) 21,610\n",
            "오민혁 단편선 59,790\n",
            "아이덴티티 63,978\n",
            "2015 사이 99,999+\n",
            "마녀사냥 99,999+\n",
            "철벽! 연애 시뮬레이션 99,999+\n",
            "어빌리티 29,445\n",
            "하이브 1~2 99,999+\n",
            "오빠 왔다 99,999+\n",
            "천적 35,465\n",
            "화이트멜로우 41,560\n",
            "후유증 2 60,712\n",
            "2호선 세입자 64,941\n",
            "손의 흔적 99,999+\n",
            "삼국전투기 27,104\n",
            "동네변호사 조들호 시즌1 64,262\n",
            "오성X한음 26,494\n",
            "샌프란시스코 화랑관 81,019\n",
            "엄마와 딸 x2 99,999+\n",
            "스페이스 킹 34,124\n",
            "마야고 77,268\n",
            "국립자유경제고등학교 세실고 2학기 67,297\n",
            "소울카르텔 99,999+\n",
            "프린스의 왕자 - 카페 드 쇼콜라 - 48,162\n",
            "2016 학교 다녀오겠습니다 70,130\n",
            "레드돌 40,901\n",
            "후레자식 99,999+\n",
            "전설의 레전드 99,999+\n",
            "우렁집사 55,904\n",
            "적생 77,687\n",
            "소곤소곤 95,300\n",
            "해피 23,908\n",
            "모던패밀리 2 65,652\n",
            "뱀피르 37,788\n",
            "놓정동화 99,999+\n",
            "아이돌 연구소 70,395\n",
            "판다독 99,999+\n",
            "중립디자인구역 70,944\n",
            "놓지마 정신줄 99,999+\n",
            "밥풀때기 49,843\n",
            "상중하 99,999+\n",
            "이말년 서유기 99,999+\n",
            "독신으로 살겠다 95,981\n",
            "천년구미호 99,999+\n",
            "2016 비명 99,999+\n",
            "첩보의 별 59,470\n",
            "3인칭 53,755\n",
            "크리퍼스큘 99,999+\n",
            "죽은 마법사의 도시 99,999+\n",
            "아일랜드 1부 95,824\n",
            "귀도 99,999+\n",
            "소녀더와일즈 99,999+\n",
            "폰령 57,158\n",
            "3P 50,829\n",
            "만렙소녀 오오라 65,297\n",
            "레코닝 85,830\n",
            "한국만화거장전 : 만화보물섬 24,624\n",
            "매지컬 고삼즈 90,371\n",
            "심연의 하늘 시즌4 59,921\n",
            "우바우 93,068\n",
            "악의는 없다 99,999+\n",
            "2016 루키 단편선 42,557\n",
            "스퍼맨 시즌1 99,999+\n",
            "하나(HANA) 95,644\n",
            "대작 63,114\n",
            "윈터우즈 99,999+\n",
            "인챈트-나람이야기 61,890\n",
            "숲 속의 미마 99,999+\n",
            "MZ 81,397\n",
            "원 뿔러스 원 99,999+\n",
            "찌질의 역사 99,999+\n",
            "투명한 동거 99,999+\n",
            "2017 사이다를 부탁해! 99,999+\n",
            "참치와 돌고래 99,999+\n",
            "언터처블 99,999+\n",
            "바로잡는 순애보 88,004\n",
            "블랙수트 81,175\n",
            "컨트롤제트 99,999+\n",
            "이기자, 그린 80,365\n",
            "오즈랜드 99,999+\n",
            "원티드 73,989\n",
            "완벽한 허니문 99,999+\n",
            "부부생활 99,999+\n",
            "여중생A 99,999+\n",
            "미라클! 용사님 93,524\n",
            "나는 귀머거리다 99,999+\n",
            "잡다한컷 99,999+\n",
            "열일곱 55,894\n",
            "녹두전 99,999+\n",
            "절벽귀 58,628\n",
            "오늘도 형제는 평화롭다 99,999+\n",
            "달수 이야기 99,999+\n",
            "낮에 뜨는 달 99,999+\n",
            "송곳 4~5부 51,826\n",
            "생활의참견 99,999+\n",
            "이런 영웅은 싫어 99,999+\n",
            "웃지 않는 개그반 시즌3 99,999+\n",
            "문유 99,999+\n",
            "군인RPG 68,682\n",
            "당신만 몰라! 99,999+\n",
            "2017 멋진 신세계 48,945\n",
            "차원이 다른 만화 99,999+\n",
            "킬러 김빵빵 43,353\n",
            "야부리맨 99,999+\n",
            "공감.jpg 99,999+\n",
            "천국의 신화 6부 40,132\n",
            "달콤한 인생_스페셜 에피소드 40,754\n",
            "데모니악 99,999+\n",
            "파도의 주인 96,459\n",
            "DEY 호러채널 99,999+\n",
            "오늘도 핸드메이드! 99,999+\n",
            "오늘부터 주군 99,999+\n",
            "동네변호사 조들호 시즌2 57,741\n",
            "[드라마원작] 내 ID는 강남미인! 99,999+\n",
            "목욕의 신 99,999+\n",
            "조선좀비실록 99,999+\n",
            "구름의 이동속도 99,999+\n",
            "슈퍼 시크릿 99,999+\n",
            "인간의 숲 99,999+\n",
            "심심한 마왕 99,999+\n",
            "아일랜드 2부 88,933\n",
            "MZ-퓨어 이블 64,740\n",
            "돌아온 럭키짱 99,999+\n",
            "마술사 99,999+\n",
            "선천적 얼간이들 99,999+\n",
            "그들에게 사면초가 99,999+\n",
            "계룡선녀전 99,999+\n",
            "[드라마원작] 한번 더 해요 99,999+\n",
            "갸오오와 사랑꾼들 99,999+\n",
            "이별만화 완성도 79,101\n",
            "탈(TAL) 99,999+\n",
            "기로 48,409\n",
            "오!주예수여 99,999+\n",
            "미스터리 호러 지하철 59,510\n",
            "우리집에 곰이 이사왔다 99,999+\n",
            "첩보의 별 시즌2 59,409\n",
            "한국만화 1990 14,635\n",
            "Penguin loves Mev 99,999+\n",
            "10월 28일 99,999+\n",
            "마스크걸 99,999+\n",
            "여중생A-우리들의 축제 48,992\n",
            "금수저 99,999+\n",
            "뱀이 앉은 자리 99,999+\n",
            "팀 피닉스 80,039\n",
            "[드라마원작] 쌉니다 천리마마트 99,999+\n",
            "심연의 하늘 시즌5 74,061\n",
            "아기낳는만화 99,999+\n",
            "내 ID는 강남미인! - 둘만의 시간 99,999+\n",
            "공대생 너무만화 99,999+\n",
            "연애학 94,523\n",
            "지금 이 순간 마법처럼 99,999+\n",
            "감염자 99,999+\n",
            "환생동물학교 99,999+\n",
            "하루 3컷 99,999+\n",
            "퍼스트 미션 99,999+\n",
            "이말년씨리즈 2018 78,781\n",
            "스피릿 핑거스 99,999+\n",
            "죽음에 관하여 99,999+\n",
            "야채호빵의 봄방학 99,999+\n",
            "진눈깨비 소년 78,658\n",
            "에피소드칵테일 99,999+\n",
            "서북의 저승사자 99,999+\n",
            "피에는 피 92,867\n",
            "1인용 기분 99,999+\n",
            "간질간질 61,351\n",
            "만 화 고 29,583\n",
            "마이너스의 손 99,999+\n",
            "연의 편지 99,999+\n",
            "일진에게 회초리 99,999+\n",
            "파우스트 인 러브 76,594\n",
            "조선왕조실톡 99,999+\n",
            "하이브 3 99,999+\n",
            "2018 루키 단편선 55,541\n",
            "불괴 26,960\n",
            "나의 짝사랑 고백법 91,584\n",
            "반투명인간 42,758\n",
            "나노리스트 99,999+\n",
            "완벽하지 않은 키스 83,290\n",
            "펀브로커 42,464\n",
            "열대어 64,570\n",
            "노블레스 99,999+\n",
            "신과함께 99,999+\n",
            "엔드리스 57,457\n",
            "2D남친 별책부록 99,999+\n",
            "환상적인 소년 89,933\n",
            "황금의 핸드메이커 46,251\n",
            "혈투 39,856\n",
            "병의 맛 99,999+\n",
            "암흑도시 59,025\n",
            "아메리카노 엑소더스 99,999+\n",
            "와장창창! 자취맨 54,923\n",
            "방백남녀 99,999+\n",
            "귀각시 99,999+\n",
            "홍차리브레 84,598\n",
            "2019 병영일기 52,649\n",
            "이상하고 아름다운 99,999+\n",
            "비둘기가 물고 온 남자 35,378\n",
            "강변살다 99,999+\n",
            "찬란하지 않아도 괜찮아 99,999+\n",
            "35cm 99,999+\n",
            "대학일기 99,999+\n",
            "에이틴 76,544\n",
            "캉타우 75,717\n",
            "화양연화 Pt.0 ＜SAVE ME＞ 99,999+\n",
            "수학 잘하는 법 43,320\n",
            "구구까까 99,999+\n",
            "사라지다 28,502\n",
            "파도를 찾아라! 99,999+\n",
            "결계녀 99,999+\n",
            "오늘도 사랑스럽개 99,999+\n",
            "꼬리 61,019\n",
            "나는 남 너는 녀 81,931\n",
            "알파 21,201\n",
            "놓지마 정신줄 시즌2 99,999+\n",
            "공복의 저녁식사 99,999+\n",
            "파리의 우리동네 35,007\n",
            "왕으로 살다 42,664\n",
            "조의 영역 99,999+\n",
            "미시령 99,999+\n",
            "고교생을 환불해 주세요 99,999+\n",
            "한국만화 또 다른 시선 16,716\n",
            "데우스 엑스 마키나 46,629\n",
            "오직 나의 주인님 99,999+\n",
            "[드라마원작] 알고있지만 99,999+\n",
            "언원티드 99,999+\n",
            "클로즈업 42,514\n",
            "하나의 하루 99,999+\n",
            "창궐 47,327\n",
            "밥 먹고 갈래요? 99,999+\n",
            "귀도호가록 99,999+\n",
            "하늘쌤은 피곤해 36,164\n",
            "허니버니 96,924\n",
            "몽연 54,067\n",
            "연애의 정령 99,999+\n",
            "12차원 소년들 44,887\n",
            "허니허니 웨딩 99,999+\n",
            "둥굴레차! 99,999+\n",
            "소유 99,999+\n",
            "타인지옥 - 연쇄살묘사건 73,448\n",
            "여기가 씨름부입니까? 41,789\n",
            "2019 귀신사용설명서 - 괴담의 재구성 50,058\n",
            "식스틴 80,230\n",
            "신암행어사 99,999+\n",
            "천리마 네버다이 15,029\n",
            "좋아하는 부분 99,999+\n",
            "호러와 로맨스 99,999+\n",
            "메이크 업 드림 - Make Up Dream 18,376\n",
            "안녕, 대학생 87,322\n",
            "성공한 덕후 99,999+\n",
            "신시의 손님 36,605\n",
            "썸머 브리즈 99,999+\n",
            "한남동 케이하우스 99,999+\n",
            "푸들과 Dog거중 87,016\n",
            "돼지만화 32,967\n",
            "숨:킬더바디 25,899\n",
            "위장불륜 (僞裝不倫) 66,248\n",
            "수능일기 99,999+\n",
            "그랜드 배틀 토너먼트 22,016\n",
            "타인은 지옥이다 99,999+\n",
            "늑대와 빨간모자 99,999+\n",
            "2019 루키 단편선 72,135\n",
            "킬러분식 62,297\n",
            "살인자o난감 63,759\n",
            "퓨리스틱 13,536\n",
            "[드라마원작] 치즈인더트랩 99,999+\n",
            "MZ-레이징 인페르노 50,567\n",
            "냥하무인 69,326\n",
            "신석기녀 99,999+\n",
            "부활남 99,999+\n",
            "덴마 99,999+\n",
            "개장수 99,999+\n",
            "백귀야행지 78,881\n",
            "부로콜리왕자 99,999+\n",
            "머니게임 99,999+\n",
            "꿀벌과 아카시아 59,868\n",
            "옆반의 인어 99,999+\n",
            "5kg을 위하여 99,999+\n",
            "라스트 서브미션 67,799\n",
            "금요일 베스트 99,999+\n",
            "헬프탑 44,187\n",
            "귀인 47,729\n",
            "내 여자친구는 상남자 99,999+\n",
            "가령의 정체불명 이야기 38,392\n",
            "Doll 체인지 99,999+\n",
            "플랫다이어리 67,582\n",
            "참새는 새!신부 99,999+\n",
            "내 어린고양이와 늙은개 87,045\n",
            "용비불패 완전판 99,999+\n",
            "소녀 연대기 88,377\n",
            "우리 오빠는 아이돌 99,999+\n",
            "마왕이 되는 중2야 99,999+\n",
            "로베스의 완전감각 45,826\n",
            "용이산다 99,999+\n",
            "아르마 69,698\n",
            "꽃미남 저승사자 99,999+\n",
            "동토의 여명 99,999+\n",
            "다함께 이겨내요 31,020\n",
            "어서와 7,414\n",
            "모퉁이 뜨개방 91,298\n",
            "열렬하게, 단하나 20,980\n",
            "또한번 엔딩 61,402\n",
            "씬커 54,568\n",
            "점핑오버 99,999+\n",
            "수평선 5,197\n",
            "스퍼맨 시즌2 5,274\n",
            "구원자 28,799\n",
            "신을 죽이는 방법 99,999+\n",
            "쎈놈 3,235\n",
            "2인용 인간 99,999+\n",
            "스퍼맨3: 현자단의 역습 85,260\n",
            "블러드레인 1,497\n",
            "블랙엔젤 53,747\n",
            "아이키우는만화 48,966\n",
            "자취로운 생활 88,872\n",
            "두근두근 마감 중 57,222\n",
            "타인의 로맨스 54,284\n",
            "대표님이 구독하셨습니다 70,722\n",
            "호랭총각 79,809\n",
            "호곡 34,767\n",
            "같은도장 99,999+\n",
            "테러맨 99,999+\n",
            "창공의 타이양사무소 1,858\n",
            "냄새를 보는 소녀 6,985\n",
            "악마에게 은총을 1,300\n",
            "견원지간 로맨스 541\n",
            "텍사스홀덤 4,604\n",
            "언럭키 맨션 15,086\n",
            "금붕어 99,961\n",
            "악취 99,999+\n",
            "루시퍼의 경호원 36,007\n",
            "고인의 명복 99,999+\n",
            "플라워(FLOWAR) 3,723\n",
            "영월동 534번지 468\n",
            "한스와 에밀리 956\n",
            "Yes my boss(예스마이보스) 6,837\n",
            "태릉좀비촌 779\n",
            "토니와 함께 1,557\n",
            "돼지우리 99,999+\n",
            "203호 저승사자 99,999+\n",
            "갓!김치 14,350\n",
            "첫사랑입니다만 99,999+\n",
            "푸른사막 아아루 94,598\n",
            "데이빗 47,787\n",
            "방정사 2,099\n",
            "환생소녀 623\n",
            "좀비소녀 1,363\n",
            "신의한수 2,014\n",
            "오빠야 누나야 10,128\n",
            "조선방랑야사 1,572\n",
            "[드라마원작] 스위트홈 99,999+\n",
            "사소한 냐냐 46,933\n",
            "열정호구 99,999+\n",
            "오라존미 73,573\n",
            "여고전설 22,107\n",
            "호러전파상 63,427\n",
            "검은인간 99,999+\n",
            "뚝배기 깨러 왔습니다 219\n",
            "102030 1,643\n",
            "살의 329\n",
            "진정친구 1,015\n",
            "우리 안의 주 223\n",
            "빙탕후루 99,999+\n",
            "밀웜 24,640\n",
            "방 안의 코끼리 62,693\n",
            "이제 곧 죽습니다 99,999+\n",
            "마음의 숙제 95,083\n",
            "속삭이는 e로맨스 47,039\n",
            "에이머 99,999+\n",
            "그래서 나는 안티팬과 결혼했다 99,999+\n",
            "레이디악숀 162\n",
            "안녕, 나는 너를 좋아해 525\n",
            "시노딕 32,535\n",
            "하르모니아 99,999+\n",
            "귀전구담 99,999+\n",
            "마음의소리 99,999+\n",
            "어쩐지 수상하더라 35,790\n",
            "머리카락을 뽑으면 89,978\n",
            "닭강정 99,999+\n",
            "김왕짱 3,186\n",
            "개미잡이 571\n",
            "킬링타임 82,732\n",
            "하루 24,345\n",
            "유령극단 60,939\n",
            "요괴대전 42,070\n",
            "체인지 4,871\n",
            "너에게 하고 싶은 말 1,871\n",
            "좋아하게 해줘 1,460\n",
            "귀신이지만 사랑하고 싶어 41,511\n",
            "낢이 사는 이야기 479\n",
            "니편내편 99,999+\n",
            "이모네 집에 갔는데 이모는 없고 2,943\n",
            "로딩 350\n",
            "특수 영능력 수사반 83,827\n",
            "파리대왕 282\n",
            "영광의 교실 1,956\n",
            "피와 살 75,305\n",
            "텃밭부 사건일지 57,403\n",
            "갓도령스 49,306\n",
            "[드라마원작] 모범택시 2,118\n",
            "세대전쟁 534\n",
            "호야 543\n",
            "어글리 피플즈 43,973\n",
            "법대로 사랑하라 19,759\n",
            "우주최강대스타 76,350\n",
            "꽃 피는 날 99,999+\n",
            "문래빗 99,999+\n",
            "POGO 공포단편선 - 혼집 35,727\n",
            "천사가 아니야 46,171\n",
            "11me 23,552\n",
            "친애하는 X 99,999+\n",
            "만찢남녀 61,034\n",
            "공포단편선X 1,818\n",
            "데빌카운터 158\n",
            "빨강 993\n",
            "[드라마원작] 지옥 26,078\n",
            "야자괴담 80,564\n",
            "열등의 조건 32,064\n",
            "2020 최애캐의 MBTI 99,999+\n",
            "닥터 하운드 99,999+\n",
            "위대한 방옥숙 75,388\n",
            "가족의 초상 222\n",
            "비스트사인 311\n",
            "패밀리 사이즈 99,999+\n",
            "오버더문 48,362\n",
            "헬로도사 33,867\n",
            "떨림 983\n",
            "갑자기 커피 13,061\n",
            "어쩔꼰대 33,392\n",
            "가타부타타 99,999+\n",
            "개미 20,276\n",
            "거미 20,302\n",
            "데드라이프 99,999+\n",
            "웅이는 배고파 88,985\n",
            "8월의 눈보라 96,439\n",
            "교환일기 99,999+\n",
            "형의 그녀 229\n",
            "가난을 등에 업은 소녀 344\n",
            "나랑 살래? 86\n",
            "오컬트 85\n",
            "그녀의 버킷리스트 66,051\n",
            "운수 오진 날 56,732\n",
            "노력의 결과 46,474\n",
            "선녀님은 휴가중 56\n",
            "미스터 해녀 32\n",
            "그녀는 무사다 60\n",
            "세상과 하늘 사이 20\n",
            "사우러스 51,966\n",
            "링크보이 99,999+\n",
            "반중력소녀 150\n",
            "헌팅 50\n",
            "물고기가 보이는 남자 154\n",
            "회춘 99,999+\n",
            "이웃집의 훈훈한 스토커군 30\n",
            "2020 호랑 공포 단편선 33,804\n",
            "현혹 93,068\n",
            "[드라마원작] 유미의 세포들 99,999+\n",
            "유비님께 투자하겠어 13\n",
            "물레 15,273\n",
            "사이드킥 2~3 99,999+\n",
            "땅 보고 걷는 아이 99,999+\n",
            "악마와 계약연애 99,999+\n",
            "화장 지워주는 남자 99,999+\n",
            "언데드 99,999+\n",
            "괴물신부 97,049\n",
            "하늘아래 우체부 41\n",
            "라라파루자 29\n",
            "레사 시즌2~3 99,999+\n",
            "무모협지 31,988\n",
            "꼬맹이를 부탁해! 50,290\n",
            "에리타 20,540\n",
            "인싸라이프 84,865\n",
            "잭슨의 관 208\n",
            "봉이 김선달 34\n",
            "일진이 사나워 99,999+\n",
            "인생존망 99,999+\n",
            "연하의 맛 378\n",
            "커넥트 58,058\n",
            "선녀야 야옹해봐! 31,207\n",
            "극야 97,318\n",
            "죽어도 로맨스 35\n",
            "갓물주 99,999+\n",
            "나의 첫번째 새벽 36,925\n",
            "열불 로맨스 99,999+\n",
            "우리 사장님은 개 76\n",
            "차가운 장례식 41\n",
            "헬 인 파라다이스 19\n",
            "나의 우주 99,999+\n",
            "스크립토리움 10,720\n",
            "옆집친구 99,999+\n",
            "사냥개들 50,686\n",
            "차라리 만나지 않았더라면 12\n",
            "이별의 품격 45\n",
            "체크포인트 99,999+\n",
            "날 가져요 99,999+\n",
            "깁스맨 25,303\n",
            "병의 기록 47,231\n",
            "무주의 맹시 99,999+\n",
            "너에게 나를 던진다 15\n",
            "우리는 여기! 187\n",
            "4층복도끝천국 17\n",
            "우리는 요정! 14,588\n",
            "비질란테 99,999+\n",
            "이것도 친구라고 99,999+\n",
            "너에게만 보이는 95,480\n",
            "아이고 아이고 21,679\n",
            "미스테리오소 80\n",
            "남다른 신부름 80\n",
            "비스타 14,197\n",
            "FM보이 44,398\n",
            "9등급 뒤집기 30,910\n",
            "후기 69,005\n",
            "신의 언어 99,999+\n",
            "지랄발광 32\n",
            "관계의 종말 81,126\n",
            "블루투스 9,862\n",
            "그날 죽은 나는 70,342\n",
            "[드라마원작] 간 떨어지는 동거 99,999+\n",
            "손아귀 29,293\n",
            "지구멸망버튼 33,842\n",
            "저세상 클라스! 62,212\n",
            "오늘도 사랑하세요 79,560\n",
            "Here U Are 99,999+\n",
            "악령주의보 7,184\n",
            "셧업앤댄스 61,111\n",
            "얌전한 사이 46,752\n",
            "공방의 마녀 32\n",
            "그놈은 흑염룡 63,948\n",
            "신상 미스터리 극장 46,806\n",
            "냐한남자 99,999+\n",
            "틴맘 63,196\n",
            "불발소년 14,735\n",
            "안녕 나의 모모로 16,798\n",
            "캡틴 비앙카 10\n",
            "리안소울의 엑소클럽 32\n",
            "삶이 우리를 속일지라도 99,999+\n",
            "하루달콤 하루쌉싸름 105\n",
            "한강예찬 32,977\n",
            "소심한 팔레트 99,999+\n",
            "정순애 식당 75,743\n",
            "피노키오 소녀 16,769\n",
            "여기 악마가 있어 21,298\n",
            "서브 콤플렉스 99,999+\n",
            "달달한 그녀 41\n",
            "장단에 맞춰줘! 46,287\n",
            "커피도둑 55,256\n",
            "아테나 컴플렉스 99,999+\n",
            "도망자 99,999+\n",
            "도플갱어의 게임 54,963\n",
            "평화선도부 525\n",
            "오일머니 35,718\n",
            "스윗솔티 74,364\n",
            "여름은 뜨겁다 25,057\n",
            "스트러글 692\n",
            "두근두근 네가 좋아서 45,259\n",
            "사장님을 잠금해제 58,900\n",
            "상연 11\n",
            "용련 13\n",
            "스테어스 19,449\n",
            "정보전사 202 17,558\n",
            "꿀잠, 재워주세요! 11\n",
            "고수 99,999+\n",
            "도롱이 37,344\n",
            "인어를 위한 수영교실 99,999+\n",
            "그 해 여름 74\n",
            "혼신 52\n",
            "필살VS로맨스 35,124\n",
            "전자오락수호대 99,999+\n",
            "민간인 통제구역 50,932\n",
            "꼬리 있는 연애 37,316\n",
            "바이러스X 12,966\n",
            "미스테리 게임 23\n",
            "유니버스 000 14\n",
            "뱀파이어의 꽃 61,818\n",
            "인간의 온도 19,540\n",
            "피플 71,121\n",
            "지금은 삽질 중 43\n",
            "알바걸과 워킹맨 21\n",
            "별종 19,567\n",
            "인터셉트 37,163\n",
            "감히 23,668\n",
            "하슬라 33,530\n",
            "줄라이 블루 7\n",
            "소년, 남자의 이름으로 8\n",
            "셈하는 사이 79,198\n",
            "죽여주는 탐정님 15,469\n",
            "흥부놀부전 10\n",
            "늑대가 되는 법 14\n",
            "당신의 과녁 82,898\n",
            "금혼령-조선혼인금지령 99,999+\n",
            "불티 6\n",
            "제7원 7\n",
            "DEY 호러채널(일반판) 27\n",
            "도를 아십니까 19,555\n",
            "스몰 18,176\n",
            "격기3반 99,999+\n",
            "당신의 향수 63,452\n",
            "살人스타그램 99,999+\n",
            "복학생 정순이 87,090\n",
            "토끼대왕 33,801\n",
            "아내를 죽였다 10\n",
            "오늘또오늘 5\n",
            "도리내리 18\n",
            "대디고라운드 3\n",
            "로맨스는 살벌하게 9\n",
            "맘마미안 99,999+\n",
            "마도 21,314\n",
            "연주동 일가족 살인사건 14\n",
            "의도적 외면 4\n",
            "말하는대로 1,109\n",
            "노선도 11,885\n",
            "각자의 디데이 99,999+\n",
            "더 트웰브 28,994\n",
            "불릿 6미리 23,592\n",
            "고래별 99,999+\n",
            "갓핑크 99,999+\n",
            "누군가의 로섬 30,609\n",
            "어른의 계절 16,393\n",
            "라일락 200% 70,214\n",
            "침범 16,550\n",
            "브랜든 552\n",
            "마물전담부대 24\n",
            "빛빛빛 22,711\n",
            "복학왕 99,999+\n",
            "난약 99,999+\n",
            "행성인간 61,039\n",
            "다이스(DICE) 99,999+\n",
            "탐의 굴 7\n",
            "바른연애 길잡이 99,999+\n",
            "동트는 로맨스 99,999+\n",
            "너와 나의 눈높이 44\n",
            "무의식의 숲 17\n",
            "착하게 살자 시즌1~2 14\n",
            "미드나잇 체이서 66,097\n",
            "언플래시레슨 214\n",
            "두번째 생일 99,999+\n",
            "두근두근 연극부 1,490\n",
            "나의 스마트보이 10\n",
            "위치헌트 (Witch Hunt) 6\n",
            "바른탕진 프로젝트 498\n",
            "구독금지 65,551\n",
            "이세계 세입자들 4\n",
            "이츠마인 99,999+\n",
            "성경의 역사 29,349\n",
            "친구의 비밀계정 960\n",
            "국왕과 성기사의 약탈혼 87\n",
            "럭키언럭키 52,484\n",
            "먹이 99,999+\n",
            "함부로 대해줘 25,616\n",
            "EVE 18\n",
            "동네몬스터 16,379\n",
            "소녀 해미 10,396\n",
            "XXX 좋아요\n",
            "블랙 위도우 428\n",
            "완벽한 가족 61,873\n",
            "환상의 용 926\n",
            "도깨비 고개 141\n",
            "구주의 시간 10,254\n",
            "하울링 10\n",
            "유미의 세포들 외전 : 프로 직장인 921\n",
            "욕망일기 6,116\n",
            "구름이 피워낸 꽃 99,999+\n",
            "산신의 주인입니다 4\n",
            "언메이크 19,315\n",
            "샹치 76\n",
            "롤랑롤랑 99,999+\n",
            "밤하늘에 구름운 44,654\n",
            "모죠의 일지 99,999+\n",
            "나라 구한 전생 5\n",
            "수상한 비밀상담부 31,666\n",
            "닥터 프로스트 시즌 3~4 96,192\n",
            "AI가 세상을 지배한다면 17,261\n",
            "약초마을 연쇄살초사건 241\n",
            "롭플롭 37\n",
            "우투리 11\n",
            "원수를 사랑하라 99,999+\n",
            "단편.zip 12,004\n",
            "웰캄투실버라이프 47,217\n",
            "엑스트라 데이즈 10\n",
            "노곤하개 99,999+\n",
            "평행도시 59,780\n",
            "야수의 날 9\n",
            "용감한 시민 19\n",
            "압락사스 27,611\n",
            "유일무이 로맨스 99,999+\n",
            "D.P 개의날 39\n",
            "인문학적 감수성 1,593\n",
            "신선비 33,038\n",
            "길티액스 153\n",
            "화이트 블러드 99,999+\n",
            "남자주인공의 여자사람친구입니다 64,896\n",
            "비밀상자 12\n",
            "크라임 퍼즐 11\n",
            "마스코마스코 9\n",
            "조선홍보대행사 조대박 16,511\n",
            "헤어진 다음날 9\n",
            "강남도깨비 72,279\n",
            "영앤리치가 아니야! 289\n",
            "트로이 목마 10\n",
            "저승사자 출입금지 24,755\n",
            "광장 44,912\n",
            "위험한 신입사원 44,327\n",
            "구원자 요즘 뭐해요 7\n",
            "닥터앤닥터 육아일기 55,066\n",
            "율리 1,541\n",
            "기억흔적 22,782\n",
            "집사레인저 40,497\n",
            "수영만화일기 882\n",
            "이터널스 136\n",
            "방과후 선녀 1,649\n",
            "2021 최애캐 안녕, 잘 지내니? 1,073\n",
            "호시탐탐 858\n",
            "파이게임 56,003\n",
            "분당구의 용사 지망생 1\n",
            "아이레 15,067\n",
            "악인 1,277\n",
            "연애는 전쟁! 667\n",
            "헬프미 1,119\n",
            "군주 1,019\n",
            "선배, 그 립스틱 바르지 마요 2,294\n",
            "리프로듀싱 2\n",
            "오피스 누나 이야기 27,352\n",
            "선의의 경쟁 99,696\n",
            "살아간다 283\n",
            "삼매경 1,529\n",
            "트리니티 원더 20\n",
            "오늘의 순정망화 99,999+\n",
            "진짜 정말 맹세코 좋아해 32,531\n",
            "후아유! 39,710\n",
            "잉여특공대 70,392\n",
            "모기전쟁 31\n",
            "찬란하지 않아도 괜찮아, 새벽 541\n",
            "썸내일 833\n",
            "걸어서 30분 99,999+\n",
            "손 잡아 볼래? 5,458\n",
            "왕세자 입학도 43,781\n",
            "바스키 2\n",
            "홀더 1\n",
            "갤러리L : 당신의 기억을 보관해드립니다 3\n",
            "여고생 임연수 8\n",
            "스파이더맨 단편선 245\n",
            "천도 14,457\n",
            "하루의 하루 619\n",
            "38의 살인 10\n",
            "안드로이드는 혈이 없어 2\n",
            "새벽 두 시의 신데렐라 83,994\n",
            "급식아빠 7,586\n",
            "내 룸메이트는 마네킹 568\n",
            "로그아웃 35,025\n",
            "지옥급식 2,651\n",
            "호수의 인어 417\n",
            "나만 보여! 4,647\n",
            "라서드 886\n",
            "내 몸을 부탁해 4\n",
            "성인초딩 21,039\n",
            "무능빌런 48\n",
            "비인간 483\n",
            "시에라 611\n",
            "이탄국의 자청비 1,483\n",
            "드로잉 레시피 18,424\n",
            "착한건 돈이된다 750\n",
            "3cm 헌터 58,466\n",
            "변방의 외노자 234\n",
            "가슴털 로망스 54,596\n",
            "빨리감기 1,455\n",
            "라나 1\n",
            "소녀행 3\n",
            "보에 4\n",
            "지원이들 215\n",
            "안식의 밤 17,220\n",
            "달콤살벌한 부부 78,083\n",
            "속보입니다 41,173\n",
            "매지컬 메디컬 8,017\n",
            "맥시멈퓨리 5\n",
            "잔인한 축제 12\n",
            "달빛세면소 3\n",
            "자판귀 99,999+\n",
            "빌드업 51,370\n",
            "겟라이프 29,735\n",
            "트리거 898\n",
            "평행우주 7\n",
            "W : 너와 나의 세계 9\n",
            "오파츠 29,817\n",
            "스캔들 23,611\n",
            "시효완성 2,553\n",
            "피와 나비 99,999+\n",
            "보살님이 캐리해! 1,519\n",
            "용돈이 계속 늘어! 8\n",
            "행운소년소녀 3\n",
            "탈영일지 236\n",
            "강림전기 개정기 724\n",
            "2022 서브병에 빠지다! 480\n",
            "인간대전 51\n",
            "후작님을 녹이는 방법 8\n",
            "빌런투킬 1,875\n",
            "살아남은 로맨스 6,260\n",
            "장난감 371\n",
            "불어오는 밤 2\n",
            "배틀트레인 좋아요\n",
            "인간졸업 9,117\n",
            "내게 필요한 NO맨스 18,488\n",
            "바로 보지 않는 371\n",
            "파이어스 2\n",
            "캐슬 99,999+\n",
            "가짜인간 321\n",
            "그 해 우리는 - 초여름이 좋아! 1,486\n",
            "열렙전사 99,999+\n",
            "이중첩자 22,476\n",
            "피차일반 12\n",
            "여주실격! 99,999+\n",
            "온새미로 56,981\n",
            "어차피 남편은! 1,683\n",
            "오늘의 컨셉 61\n",
            "나이트런 99,999+\n",
            "좀비딸 99,999+\n",
            "와이키키 뱀파이어 52,063\n",
            "채수연 5\n",
            "밤낚시 393\n",
            "묵시의 인플루언서 1,323\n",
            "루커피쳐 2,786\n",
            "꽃 피우는 남자 1,731\n",
            "감자마을 2,917\n",
            "세기말 풋사과 보습학원 11,077\n",
            "재난소년 101\n",
            "불편한 관계 1,518\n",
            "야만의 시대 321\n",
            "소극적 인간 299\n",
            "쌈빡 551\n",
            "화가 살리에르 461\n",
            "겟백 99,999+\n",
            "그 개, 만두 960\n",
            "신비 99,999+\n",
            "스퍼맨4 : 전하지 못한 이야기 801\n",
            "개밥 먹는 남자 1,442\n",
            "아는 여자애 99,999+\n",
            "아이리스 5\n",
            "사람의 조각 1,390\n",
            "숨겨진 성녀 336\n",
            "피라미드 게임 75,633\n",
            "팔이피플 2,045\n",
            "오만상과 편견 3\n",
            "고등단편.zip 349\n",
            "칼부림 50,361\n",
            "어느날 네가 떠올라! 287\n",
            "오늘부터 천생연분 1,673\n",
            "어글리후드 99,999+\n",
            "불순물 179\n",
            "푸른불꽃 320\n",
            "평범한 8반 99,999+\n",
            "오징어도 사랑이 되나요? 1,204\n",
            "버그: 스티그마 34,226\n",
            "홍대 바이브 196\n",
            "벚꽃이 흩날릴 무렵 8,268\n",
            "마지막 지수 1,901\n",
            "블러드레인2: 천외천 9\n",
            "소년의 기록 193\n",
            "화사 홍천기 2\n",
            "정년이 99,999+\n",
            "오늘 밤만 재워줘 2,774\n",
            "마루한 - 구현동화전 99,999+\n",
            "길고양이 행진 42\n",
            "이상형은 아닙니다 794\n",
            "유리와 유리와 유리 1,227\n",
            "평범한 낙원 306\n",
            "웰컴 온보드 299\n",
            "귀곡의 문 99,999+\n",
            "관종교장 566\n",
            "기기괴괴 99,999+\n"
          ]
        }
      ],
      "source": [
        "heart={}\n",
        "for url in data['link']:\n",
        "  driver.get(url) # 각 url접속\n",
        "  try:\n",
        "    wait=WebDriverWait(driver, 5)\n",
        "    name_info=wait.until(lambda x: x.find_element(By.XPATH,('//*[@id=\"content\"]/div[1]/div[2]/h2/span[1]')))\n",
        "    heart_info=wait.until(lambda x: x.find_element(By.XPATH,('//*[@id=\"content\"]/div[1]/div[2]/ul/li[5]/div/a/em')))\n",
        "    heart[name_info.text]=heart_info.text\n",
        "    print(name_info.text,heart_info.text)\n",
        "  except :\n",
        "        continue\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "oQNOoZZmTg0i",
        "outputId": "94edc3eb-e483-44eb-a8dc-6f518a2f2b85"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       title    heart\n",
              "0         타투      113\n",
              "1        와탕카      352\n",
              "2         까뱅       51\n",
              "3       사랑in       68\n",
              "4         AA      117\n",
              "...      ...      ...\n",
              "1193  평범한 낙원      306\n",
              "1194  웰컴 온보드      299\n",
              "1195   귀곡의 문  99,999+\n",
              "1196    관종교장      566\n",
              "1197    기기괴괴  99,999+\n",
              "\n",
              "[1198 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3dc7788d-daa7-46ba-b131-6a5f63dd22c2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead 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>title</th>\n",
              "      <th>heart</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>타투</td>\n",
              "      <td>113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>와탕카</td>\n",
              "      <td>352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>까뱅</td>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>사랑in</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AA</td>\n",
              "      <td>117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1193</th>\n",
              "      <td>평범한 낙원</td>\n",
              "      <td>306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1194</th>\n",
              "      <td>웰컴 온보드</td>\n",
              "      <td>299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1195</th>\n",
              "      <td>귀곡의 문</td>\n",
              "      <td>99,999+</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1196</th>\n",
              "      <td>관종교장</td>\n",
              "      <td>566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1197</th>\n",
              "      <td>기기괴괴</td>\n",
              "      <td>99,999+</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1198 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3dc7788d-daa7-46ba-b131-6a5f63dd22c2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3dc7788d-daa7-46ba-b131-6a5f63dd22c2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3dc7788d-daa7-46ba-b131-6a5f63dd22c2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# merge를 위하여 데이터 프레임으로 만들기\n",
        "title_heart=pd.DataFrame({'title':heart.keys(), 'heart':heart.values()})\n",
        "display(title_heart)\n",
        "# merge\n",
        "data=pd.merge(data,title_heart)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-SNWofZBSYRf"
      },
      "outputs": [],
      "source": [
        "# 혹시 몰라서 하트의 개수만 csv파일로 빼놓기\n",
        "title_heart.to_csv(\"only heart.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6GVUXZ44YL7j"
      },
      "outputs": [],
      "source": [
        "# 왼쪽에 쓰레기열 제거\n",
        "data=data.drop(['Unnamed: 0'],axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0LE_AAOLZnKL"
      },
      "source": [
        " **episode(추가 column) : 크롤링**\n",
        "\n",
        "*   사이트 : https://series.naver.com/comic/categoryProductList.series?categoryTypeCode=webtoon&genreCode=&orderTypeCode=new&is&isFinished=true\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nsRHtwQM3Asc"
      },
      "outputs": [],
      "source": [
        "# 총 웹툰의 개수를 크롤링함\n",
        "driver.get('https://series.naver.com/comic/categoryProductList.series?categoryTypeCode=webtoon&genreCode=&orderTypeCode=new&is&isFinished=true')\n",
        "all_cnt_path='//*[@id=\"content\"]/div/div/div[1]' # 총 개수가 쓰여져 있는 xpath\n",
        "all_cnt=driver.find_element(By.XPATH,(all_cnt_path)).text\n",
        "all_cnt=int(all_cnt[1]+all_cnt[3:6])\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XaSySDnhvCAs"
      },
      "outputs": [],
      "source": [
        "# 모든 웬툰의 페이지 url을 크롤링함\n",
        "import sys as s \n",
        "import time as t\n",
        "urls=[]\n",
        "cnt=1\n",
        "page_url='https://series.naver.com/comic/categoryProductList.series?categoryTypeCode=webtoon&genreCode=&orderTypeCode=new&is&isFinished=true'\n",
        "\n",
        "while 1:\n",
        "  driver.get(page_url)\n",
        "  \n",
        "  for i in range(1,6):\n",
        "    for j in range(1,6):\n",
        "      wait=WebDriverWait(driver, 5)\n",
        "      such_urls_path='//*[@id=\"content\"]/div/ul['+str(i)+']/li['+str(j)+']/a'\n",
        "      such_urls=wait.until(lambda x: x.find_element(By.XPATH,(such_urls_path)).get_attribute('href')) # href의 하이퍼링크를 가져오는 것이기 때문에 find한 것에 get_attribute사용\n",
        "      urls.append(such_urls)\n",
        "      print(such_urls, cnt)\n",
        "      if cnt < all_cnt :\n",
        "        cnt += 1\n",
        "      else :\n",
        "        s.exit()\n",
        "  \n",
        "  # 페이지 넘어가기\n",
        "  \n",
        "  nextpage_urls_path='//*[@id=\"content\"]/p/span[2]/a'\n",
        "  page_url=driver.find_element(By.XPATH,(nextpage_urls_path)).get_attribute('href')\n",
        "  \n",
        "  \n",
        "\n",
        "  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zr9YAOf8q-TG"
      },
      "outputs": [],
      "source": [
        "### 로그인 ###\n",
        "driver.implicitly_wait(3)\n",
        "driver.find_element(By.XPATH,('//*[@id=\"gnb_login_button\"]')).click()\n",
        "\n",
        "\n",
        "# 아이디\n",
        "driver.find_element(By.XPATH,('//*[@id=\"id\"]')).send_keys('gunwoof12')\n",
        "driver.implicitly_wait(3)\n",
        "\n",
        "# 비밀번호 입력\n",
        "driver.find_element(By.XPATH,('//*[@id=\"pw\"]')).send_keys('rjsdndi0625#')\n",
        "driver.implicitly_wait(3)\n",
        "driver.find_element(By.XPATH,('//*[@id=\"log.login\"]')).click()\n",
        "\n",
        "# cookie_dic={}\n",
        "# cookie_contents=driver.get_cookies() # 쿠키정보\n",
        "# for cookie in cookie_contents:\n",
        "#   cookie_dic[cookie['name']]=cookie['value']\n",
        "\n",
        "cookie_contents=driver.get_cookies()\n",
        "s = requests.Session()\n",
        "for cookie in cookie_contents:    \n",
        "    s.cookies.set(cookie['name'], cookie['value'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NSIh1vAqKszB"
      },
      "outputs": [],
      "source": [
        "episode={}\n",
        "for url in urls:\n",
        "  driver.get(url)\n",
        "  try:\n",
        "      wait=WebDriverWait(driver, 5)\n",
        "      name_path='//*[@id=\"content\"]/div[1]/h2' # 웹툰 이름 x_path\n",
        "      name_info=wait.until(lambda x: x.find_element(By.XPATH,(name_path))).text\n",
        "      if '[독점]' in name_info: \n",
        "        name_info=name_info[:-5]\n",
        "      episode_path='//*[@id=\"content\"]/h5/strong' # episode의 개수가 적혀있는 x_path\n",
        "      episode_info=wait.until(lambda x: x.find_element(By.XPATH,(episode_path))).text\n",
        "      episode[name_info]=episode_info\n",
        "      print(name_info,episode_info)\n",
        "  except :\n",
        "        continue  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bOSljKanZF7w"
      },
      "outputs": [],
      "source": [
        "# merge를 위하여 데이터 프레임으로 만들기\n",
        "title_episode=pd.DataFrame({'title':episode.keys(), 'episode':episode.values()})\n",
        "display(title_episode)\n",
        "# merge\n",
        "data=pd.merge(data,title_episode)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1L4jB6JwQ7JX"
      },
      "outputs": [],
      "source": [
        "# 혹시 몰라서 하트의 개수만 csv파일로 빼놓기\n",
        "title_episode.to_csv(\"only_episode.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "htRlO9KdC5fT"
      },
      "source": [
        "\n",
        "\n",
        "> **interest_num(추가 column) : web에는 없고 app으로 틀었을 때만 있다 -> 어쩔 수 없는 노가다**\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M9S4FY3Ue04e"
      },
      "outputs": [],
      "source": [
        "interested_num=pd.read_csv(\"naver_interest_num.csv\",encoding='cp949')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CBN0rzrlf_vf"
      },
      "outputs": [],
      "source": [
        "interested_num=interested_num[['title','interest_num']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BBwax77KeoWu"
      },
      "outputs": [],
      "source": [
        "data=pd.merge(data,interested_num)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tNdsOSgCIYcy"
      },
      "outputs": [],
      "source": [
        "data.to_csv(\"naver_all_add.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFlEsmUIvUV6"
      },
      "source": [
        "# 데이터 탐색"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uPDsi1aRkPTp"
      },
      "outputs": [],
      "source": [
        "data=pd.read_csv(\"naver_all_add.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dq_6aIQhmHH4"
      },
      "outputs": [],
      "source": [
        "data=data.astype({'interest_num': 'int'}) # interest_num(추가 column) int로 바꾸기\n",
        "data=data.drop(['Unnamed: 0'],axis=1) # 가장 왼쪽에 쓰래기열 지우기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o8UT32kK6Mq1",
        "outputId": "7c196508-1d3c-4fa1-f1c6-703ed5e599d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-35-35deedfb6862>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['age'][i]='전체연령가'\n"
          ]
        }
      ],
      "source": [
        "# age에 소수의 전체연령가가 0으로 되어있어 전처리\n",
        "for i in range(len(data['age'])):\n",
        "  if data['age'][i]=='0':\n",
        "    data['age'][i]='전체연령가'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hZutdcNcWwSX",
        "outputId": "0bd5f4e7-1d62-415e-88d6-ac7dab8b296b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id      title author      genre  \\\n",
              "0     25695         향수     석우   스토리, 스릴러   \n",
              "1     26310    3단합체김창남    하일권   스토리, 드라마   \n",
              "2     52993     우월한 하루  팀 겟네임   스토리, 스릴러   \n",
              "3     65410   두근두근두근거려    하일권   스토리, 드라마   \n",
              "4     73352          연    구아진  옴니버스, 스릴러   \n",
              "..      ...        ...    ...        ...   \n",
              "677  776093  이상형은 아닙니다    박윤영   스토리, 로맨스   \n",
              "678  772192     평범한 낙원    후드새   스토리, 스릴러   \n",
              "679  718020      귀곡의 문     삼촌   스토리, 드라마   \n",
              "680  774088       관종교장    좌승훈    스토리, 개그   \n",
              "681  557672       기기괴괴    오성대  옴니버스, 스릴러   \n",
              "\n",
              "                                           description  rating  \\\n",
              "0    행복했었던 나의 어린 시절. 그러나, 그에게는 잔인한 기억일지도 모른다. 향수, 그...    9.93   \n",
              "1                           삼봉이발소의 하일권이 돌아왔다.본격 SF청춘만화    9.95   \n",
              "2    교수인형의 팀 겟네임, 그들의 두번째 스릴러.평범한 한 남자의 평범하지 않은 하루....    9.94   \n",
              "3          ‘3단 합체 김창남’의 하일권 신작.건조했던 일상이 두근두근 거리기 시작한다.    9.95   \n",
              "4       구아바 작가의 옴니버스 스릴러 ‘연’오늘, 지금, 당신에게도 일어날 수 있는 일들.    9.78   \n",
              "..                                                 ...     ...   \n",
              "677  연애 경험치 만렙 29살 여자와 연애는 커녕 썸 한번 못 타본 모쏠 29년차 남자의...    9.61   \n",
              "678  학교에서 매일 괴롭힘당하는 현서운은 하굣길에 이상한 시선을 느낀다.그리고 다음 날 ...    9.89   \n",
              "679              귀신소굴로 유명한 삼도천동의 흉가,평범한(?) 세입자들의 일상 개그    9.98   \n",
              "680  전 재산을 가족 몰래 주식으로 날린 교장 정도만. 이제 은퇴까지 1년 밖에 남지 않...    9.93   \n",
              "681      <절벽귀> 오성대 작가의 신작 옴니버스 미스테리 스릴러.기묘하고 괴상한 이야기들.    9.90   \n",
              "\n",
              "                 date  completed      age   free  \\\n",
              "0    2008.11.25 12:13       True    전체연령가  False   \n",
              "1    2008.12.26 09:58       True    전체연령가   True   \n",
              "2    2009.10.13 00:04       True  15세 이용가   True   \n",
              "3    2009.12.02 00:04       True    전체연령가   True   \n",
              "4    2010.02.02 10:40       True  12세 이용가  False   \n",
              "..                ...        ...      ...    ...   \n",
              "677  2022.05.23 23:00       True  15세 이용가  False   \n",
              "678  2022.05.25 22:59       True  15세 이용가  False   \n",
              "679  2022.05.31 22:59       True  12세 이용가  False   \n",
              "680  2022.05.31 22:59       True  12세 이용가  False   \n",
              "681  2022.06.01 22:59       True  15세 이용가  False   \n",
              "\n",
              "                                                  link  previous  remake  \\\n",
              "0    https://comic.naver.com/webtoon/list?titleId=2...         0       0   \n",
              "1    https://comic.naver.com/webtoon/list?titleId=2...         0       0   \n",
              "2    https://comic.naver.com/webtoon/list?titleId=5...         0       0   \n",
              "3    https://comic.naver.com/webtoon/list?titleId=6...         1       0   \n",
              "4    https://comic.naver.com/webtoon/list?titleId=7...         0       0   \n",
              "..                                                 ...       ...     ...   \n",
              "677  https://comic.naver.com/webtoon/list?titleId=7...         1       0   \n",
              "678  https://comic.naver.com/webtoon/list?titleId=7...         0       0   \n",
              "679  https://comic.naver.com/webtoon/list?titleId=7...         1       0   \n",
              "680  https://comic.naver.com/webtoon/list?titleId=7...         0       0   \n",
              "681  https://comic.naver.com/webtoon/list?titleId=5...         3       0   \n",
              "\n",
              "       heart  episode  interest_num  \n",
              "0      3,106       50         19940  \n",
              "1     11,928       27         83625  \n",
              "2     10,803       41         78613  \n",
              "3      9,094       32         82946  \n",
              "4        401       22          8337  \n",
              "..       ...      ...           ...  \n",
              "677      793       46        126827  \n",
              "678      306       54         29553  \n",
              "679  99,999+      190        196170  \n",
              "680      565       49         50531  \n",
              "681  99,999+      401       1000820  \n",
              "\n",
              "[682 rows x 16 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>title</th>\n",
              "      <th>author</th>\n",
              "      <th>genre</th>\n",
              "      <th>description</th>\n",
              "      <th>rating</th>\n",
              "      <th>date</th>\n",
              "      <th>completed</th>\n",
              "      <th>age</th>\n",
              "      <th>free</th>\n",
              "      <th>link</th>\n",
              "      <th>previous</th>\n",
              "      <th>remake</th>\n",
              "      <th>heart</th>\n",
              "      <th>episode</th>\n",
              "      <th>interest_num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>25695</td>\n",
              "      <td>향수</td>\n",
              "      <td>석우</td>\n",
              "      <td>스토리, 스릴러</td>\n",
              "      <td>행복했었던 나의 어린 시절. 그러나, 그에게는 잔인한 기억일지도 모른다. 향수, 그...</td>\n",
              "      <td>9.93</td>\n",
              "      <td>2008.11.25 12:13</td>\n",
              "      <td>True</td>\n",
              "      <td>전체연령가</td>\n",
              "      <td>False</td>\n",
              "      <td>https://comic.naver.com/webtoon/list?titleId=2...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3,106</td>\n",
              "      <td>50</td>\n",
              "      <td>19940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>26310</td>\n",
              "      <td>3단합체김창남</td>\n",
              "      <td>하일권</td>\n",
              "      <td>스토리, 드라마</td>\n",
              "      <td>삼봉이발소의 하일권이 돌아왔다.본격 SF청춘만화</td>\n",
              "      <td>9.95</td>\n",
              "      <td>2008.12.26 09:58</td>\n",
              "      <td>True</td>\n",
              "      <td>전체연령가</td>\n",
              "      <td>True</td>\n",
              "      <td>https://comic.naver.com/webtoon/list?titleId=2...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11,928</td>\n",
              "      <td>27</td>\n",
              "      <td>83625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>52993</td>\n",
              "      <td>우월한 하루</td>\n",
              "      <td>팀 겟네임</td>\n",
              "      <td>스토리, 스릴러</td>\n",
              "      <td>교수인형의 팀 겟네임, 그들의 두번째 스릴러.평범한 한 남자의 평범하지 않은 하루....</td>\n",
              "      <td>9.94</td>\n",
              "      <td>2009.10.13 00:04</td>\n",
              "      <td>True</td>\n",
              "      <td>15세 이용가</td>\n",
              "      <td>True</td>\n",
              "      <td>https://comic.naver.com/webtoon/list?titleId=5...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10,803</td>\n",
              "      <td>41</td>\n",
              "      <td>78613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>65410</td>\n",
              "      <td>두근두근두근거려</td>\n",
              "      <td>하일권</td>\n",
              "      <td>스토리, 드라마</td>\n",
              "      <td>‘3단 합체 김창남’의 하일권 신작.건조했던 일상이 두근두근 거리기 시작한다.</td>\n",
              "      <td>9.95</td>\n",
              "      <td>2009.12.02 00:04</td>\n",
              "      <td>True</td>\n",
              "      <td>전체연령가</td>\n",
              "      <td>True</td>\n",
              "      <td>https://comic.naver.com/webtoon/list?titleId=6...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9,094</td>\n",
              "      <td>32</td>\n",
              "      <td>82946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>73352</td>\n",
              "      <td>연</td>\n",
              "      <td>구아진</td>\n",
              "      <td>옴니버스, 스릴러</td>\n",
              "      <td>구아바 작가의 옴니버스 스릴러 ‘연’오늘, 지금, 당신에게도 일어날 수 있는 일들.</td>\n",
              "      <td>9.78</td>\n",
              "      <td>2010.02.02 10:40</td>\n",
              "      <td>True</td>\n",
              "      <td>12세 이용가</td>\n",
              "      <td>False</td>\n",
              "      <td>https://comic.naver.com/webtoon/list?titleId=7...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>401</td>\n",
              "      <td>22</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>677</th>\n",
              "      <td>776093</td>\n",
              "      <td>이상형은 아닙니다</td>\n",
              "      <td>박윤영</td>\n",
              "      <td>스토리, 로맨스</td>\n",
              "      <td>연애 경험치 만렙 29살 여자와 연애는 커녕 썸 한번 못 타본 모쏠 29년차 남자의...</td>\n",
              "      <td>9.61</td>\n",
              "      <td>2022.05.23 23:00</td>\n",
              "      <td>True</td>\n",
              "      <td>15세 이용가</td>\n",
              "      <td>False</td>\n",
              "      <td>https://comic.naver.com/webtoon/list?titleId=7...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>793</td>\n",
              "      <td>46</td>\n",
              "      <td>126827</td>\n",
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              "    <tr>\n",
              "      <th>678</th>\n",
              "      <td>772192</td>\n",
              "      <td>평범한 낙원</td>\n",
              "      <td>후드새</td>\n",
              "      <td>스토리, 스릴러</td>\n",
              "      <td>학교에서 매일 괴롭힘당하는 현서운은 하굣길에 이상한 시선을 느낀다.그리고 다음 날 ...</td>\n",
              "      <td>9.89</td>\n",
              "      <td>2022.05.25 22:59</td>\n",
              "      <td>True</td>\n",
              "      <td>15세 이용가</td>\n",
              "      <td>False</td>\n",
              "      <td>https://comic.naver.com/webtoon/list?titleId=7...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>306</td>\n",
              "      <td>54</td>\n",
              "      <td>29553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>679</th>\n",
              "      <td>718020</td>\n",
              "      <td>귀곡의 문</td>\n",
              "      <td>삼촌</td>\n",
              "      <td>스토리, 드라마</td>\n",
              "      <td>귀신소굴로 유명한 삼도천동의 흉가,평범한(?) 세입자들의 일상 개그</td>\n",
              "      <td>9.98</td>\n",
              "      <td>2022.05.31 22:59</td>\n",
              "      <td>True</td>\n",
              "      <td>12세 이용가</td>\n",
              "      <td>False</td>\n",
              "      <td>https://comic.naver.com/webtoon/list?titleId=7...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>99,999+</td>\n",
              "      <td>190</td>\n",
              "      <td>196170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>680</th>\n",
              "      <td>774088</td>\n",
              "      <td>관종교장</td>\n",
              "      <td>좌승훈</td>\n",
              "      <td>스토리, 개그</td>\n",
              "      <td>전 재산을 가족 몰래 주식으로 날린 교장 정도만. 이제 은퇴까지 1년 밖에 남지 않...</td>\n",
              "      <td>9.93</td>\n",
              "      <td>2022.05.31 22:59</td>\n",
              "      <td>True</td>\n",
              "      <td>12세 이용가</td>\n",
              "      <td>False</td>\n",
              "      <td>https://comic.naver.com/webtoon/list?titleId=7...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>565</td>\n",
              "      <td>49</td>\n",
              "      <td>50531</td>\n",
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              "    <tr>\n",
              "      <th>681</th>\n",
              "      <td>557672</td>\n",
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              "      <td>오성대</td>\n",
              "      <td>옴니버스, 스릴러</td>\n",
              "      <td>&lt;절벽귀&gt; 오성대 작가의 신작 옴니버스 미스테리 스릴러.기묘하고 괴상한 이야기들.</td>\n",
              "      <td>9.90</td>\n",
              "      <td>2022.06.01 22:59</td>\n",
              "      <td>True</td>\n",
              "      <td>15세 이용가</td>\n",
              "      <td>False</td>\n",
              "      <td>https://comic.naver.com/webtoon/list?titleId=5...</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>99,999+</td>\n",
              "      <td>401</td>\n",
              "      <td>1000820</td>\n",
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              "<p>682 rows × 16 columns</p>\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ed9803a3-fdae-4225-8cb9-9ac235d31af7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ed9803a3-fdae-4225-8cb9-9ac235d31af7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fsIWUXcn--oU",
        "outputId": "c2729121-1c1f-41b6-8930-12b7fbb96c7c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 682 entries, 0 to 681\n",
            "Data columns (total 16 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   id            682 non-null    int64  \n",
            " 1   title         682 non-null    object \n",
            " 2   author        682 non-null    object \n",
            " 3   genre         682 non-null    object \n",
            " 4   description   682 non-null    object \n",
            " 5   rating        682 non-null    float64\n",
            " 6   date          682 non-null    object \n",
            " 7   completed     682 non-null    bool   \n",
            " 8   age           682 non-null    object \n",
            " 9   free          682 non-null    bool   \n",
            " 10  link          682 non-null    object \n",
            " 11  previous      682 non-null    int64  \n",
            " 12  remake        682 non-null    int64  \n",
            " 13  heart         682 non-null    object \n",
            " 14  episode       682 non-null    int64  \n",
            " 15  interest_num  682 non-null    int64  \n",
            "dtypes: bool(2), float64(1), int64(5), object(8)\n",
            "memory usage: 76.1+ KB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fn3NbboovySP"
      },
      "source": [
        "**insight :**\n",
        "\n",
        "*   str형(object) : title, author, genre, description, date, age, link\n",
        "*   int형 : id, previous(추가 column), episode(추가 column), heart(추가 column), interest_num(추가 column)\n",
        "*   float형 : rating\n",
        "*   boolean : completed, free, remake(추가 column)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dr8Blrk6yYa2"
      },
      "source": [
        "# 데이터 분석(EDA)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W_8vnxuyyhLD"
      },
      "source": [
        ">  **주관적 분석**\n",
        "\n",
        "*   id, title, date, description, completed(완료된거 한정이기 때문), link는 아무리 생각해도 관객수와 관련이 없어 보이다 -> 제거 확정!!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_-gl_REhmx6N"
      },
      "outputs": [],
      "source": [
        "data=data.drop(['id','title','date','description','completed','link','author','genre','age','remake'],axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iHFO9gzY-ItF"
      },
      "source": [
        ">  **author은 너무 많아서 나중에 고려 -> 일단 뺌**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9ph_6uqdnszr"
      },
      "outputs": [],
      "source": [
        "plt.figure(figsize=(100, 20))\n",
        "plt.xlabel('저자', fontsize=20)\n",
        "plt.ylabel('개수', fontsize=500)\n",
        "plt.hist(data['author'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nz4eibhVXQTf"
      },
      "outputs": [],
      "source": [
        "#data['author'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3mx0e9Co-dKZ"
      },
      "outputs": [],
      "source": [
        "#data=data.drop(['author'],axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u5ZG7vnstkwZ"
      },
      "source": [
        "\n",
        "\n",
        "> **genre 인코딩**\n",
        "\n",
        "\n",
        "\n",
        "*   **(스토리, 무협)(옵니버스, 무협)와 멜로, 무협 같은 세부장르 뿐만아니라 스토리와 옵니버스도 두 가지로 나눠진다 -> genre를 열 두개로 쪼갠다**\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XMxbde-anhWG"
      },
      "outputs": [],
      "source": [
        "genre1=[]\n",
        "genre2=[]\n",
        "for i in data['genre']:\n",
        "  genre1.append(i.split(',')[0])\n",
        "  genre2.append(i.split(',')[1])\n",
        "\n",
        "data['genre1']=genre1\n",
        "data['genre2']=genre2\n",
        "data=data.drop(['genre'],axis=1)\n",
        "data=data[['genre1','genre2','rating','age','free','previous','remake','heart','episode','interest_num']]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KSuNSI391Ees"
      },
      "source": [
        "*  **label incording -> sklearn의 LabelEncoder 모듈 사용**(one hot과 더불어 Frequency, Target Encoding도 고려)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1CympUmO3W4c"
      },
      "source": [
        "*  label incording\n",
        "\n",
        "\n",
        "> **label incording의 문제점 :** 숫자값을 가중치로 잘못 인식하여 값에 왜곡 \n",
        "-> 이러한 특성은 예측 성능의 저하를 일으킨다.\n",
        "\n",
        "> **label incording의 해결 :** 레이블 인코딩은 선형회귀와 같은 ML알고리즘에는 보통 적용하지 않는다. 하지만 트리계열의 ML알고리즘은 숫자의 ordinal 특성을 반영하지 않으므로 레이블 인코딩도 별 문제 없다.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "*  one hot incording\n",
        "\n",
        "> **one hot incording의 문제점 :** 다중공선성\n",
        "\n",
        "\n",
        "> **label incording의 해결 :** \n",
        "1.   상관관계 높은 열을 제거\n",
        "2.   pca(주성분 분석)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dzfYdDDyyS-Z"
      },
      "outputs": [],
      "source": [
        "# 라벨 인코딩\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "encoder = LabelEncoder()\n",
        "\n",
        "encoder.fit(data['genre1'])\n",
        "data['genre1']=encoder.transform(data['genre1'])\n",
        "encoder.fit(data['genre2'])\n",
        "data['genre2']=encoder.transform(data['genre2'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xFtIexpotaoQ"
      },
      "outputs": [],
      "source": [
        "display(data['genre1'].value_counts())\n",
        "print('\\n')\n",
        "display(data['genre2'].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 원 핫 인코딩\n",
        "from sklearn.preprocessing import OneHotEncoder \n",
        "encoder = OneHotEncoder(sparse=False)\n",
        "\n",
        "encoder.fit(data['genre1'].values.reshape(-1,1))\n",
        "one_hot1=encoder.transform(data['genre1'].values.reshape(-1,1))\n",
        "one_hot1=pd.DataFrame(one_hot1, columns=['genre1_' + col for col in list(encoder.categories_)])\n",
        "data=pd.concat([data.drop(columns=['genre1']),one_hot1],axis=1)\n",
        "\n",
        "encoder.fit(data['genre2'].values.reshape(-1,1))\n",
        "one_hot2=encoder.transform(data['genre2'].values.reshape(-1,1))\n",
        "one_hot2=pd.DataFrame(one_hot2, columns=['genre2_' + col for col in list(encoder.categories_)])\n",
        "data=pd.concat([data.drop(columns=['genre2']),one_hot2],axis=1)\n"
      ],
      "metadata": {
        "id": "BvWTeRvezIWt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VmUp-JR98roq"
      },
      "source": [
        ">  **age 인코딩**\n",
        "\n",
        "\n",
        "*   **label incording -> sklearn의 LabelEncoder 모듈 사용**\n",
        "(one hot 고려)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "id": "dcEVt0XdCNag"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0m3l6am21WGu"
      },
      "outputs": [],
      "source": [
        "data['age'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0W_QVuht8rBu"
      },
      "outputs": [],
      "source": [
        "# 라벨 인코딩\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "encoder = LabelEncoder()\n",
        "\n",
        "encoder.fit(data['age'])\n",
        "data['age']=encoder.transform(data['age'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 원 핫 인코딩\n",
        "from sklearn.preprocessing import OneHotEncoder \n",
        "encoder = OneHotEncoder(sparse=False)\n",
        "\n",
        "encoder.fit(data['age'].values.reshape(-1,1))\n",
        "one_hot1=encoder.transform(data['age'].values.reshape(-1,1))\n",
        "one_hot1=pd.DataFrame(one_hot1, columns=['age_' + col for col in list(encoder.categories_)])\n",
        "data=pd.concat([data.drop(columns=['age']),one_hot1],axis=1)"
      ],
      "metadata": {
        "id": "Ge9trNN9B3ap"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dHTlTHl-9dzy"
      },
      "source": [
        ">  **free 인코딩**\n",
        "\n",
        "*   **label incording -> sklearn의 LabelEncoder 모듈 사용** (one hot 고려)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xfQQv6EP9292",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eae3f18b-6379-425f-f0e5-bae0c66ca68b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True     496\n",
              "False    186\n",
              "Name: free, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "data['free'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_LuKQqqB98AT"
      },
      "outputs": [],
      "source": [
        "# 라벨 인코딩\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "encoder = LabelEncoder()\n",
        "\n",
        "encoder.fit(data['free'])\n",
        "data['free']=encoder.transform(data['free'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 원 핫 인코딩\n",
        "from sklearn.preprocessing import OneHotEncoder \n",
        "encoder = OneHotEncoder(sparse=False)\n",
        "\n",
        "data = data.astype({'free':'str'}) # boolean에서 string으로 바꿔야 one-hot 가능\n",
        "\n",
        "encoder.fit(data['free'].values.reshape(-1,1))\n",
        "one_hot1=encoder.transform(data['free'].values.reshape(-1,1))\n",
        "one_hot1=pd.DataFrame(one_hot1, columns=['free_' + col for col in list(encoder.categories_)])\n",
        "data=pd.concat([data.drop(columns=['free']),one_hot1],axis=1)"
      ],
      "metadata": {
        "id": "kZFDYgdFF1GS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**heart 정규화**\n",
        "\n"
      ],
      "metadata": {
        "id": "EqXJM_fi8t6J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 하트에 '좋아요'라고 있던 행 지우기\n",
        "data=data[data['heart']!='좋아요']"
      ],
      "metadata": {
        "id": "NvZBPdHZ-d8S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 중간에 행 지워서 인덱스 중간에 빔 -> 인덱스 초기화\n",
        "data.reset_index(inplace=True,drop=True)"
      ],
      "metadata": {
        "id": "dY-6L_9tApe2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 하트\n",
        "\n",
        "for i in range(len(data['heart'])):\n",
        "  data['heart'][i]=data['heart'][i].rstrip('+').split(',')\n",
        "  data['heart'][i]=\"\".join(data['heart'][i])\n",
        "\n",
        "# 하트 정수형으로 변환 \n",
        "data = data.astype({'heart':'int'})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mleWfrRX81ah",
        "outputId": "526ad099-6182-4398-fb50-88e1456bfe24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-42-52ac6bb992e1>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['heart'][i]=\"\".join(data['heart'][i])\n",
            "<ipython-input-42-52ac6bb992e1>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['heart'][i]=data['heart'][i].rstrip('+').split(',')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(data['heart'].values.reshape(-1,1))\n",
        "scaled_heart = scaler.transform(data['heart'].values.reshape(-1,1))\n",
        "scaled_heart = pd.DataFrame(scaled_heart,columns=['scaled_heart'])\n",
        "data=pd.concat([data.drop(columns=['heart']),scaled_heart],axis=1)"
      ],
      "metadata": {
        "id": "WndojjIPePf-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "id": "GIJjgQdUd6kQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**rating 정규화**"
      ],
      "metadata": {
        "id": "UZGLvkfCTpYC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(data['rating'].values.reshape(-1,1))\n",
        "scaled_rating = scaler.transform(data['rating'].values.reshape(-1,1))\n",
        "scaled_rating = pd.DataFrame(scaled_rating,columns=['scaled_rating'])\n",
        "data=pd.concat([data.drop(columns=['rating']),scaled_rating],axis=1)"
      ],
      "metadata": {
        "id": "Hi5VPdsNUWvu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**episode 정규화**"
      ],
      "metadata": {
        "id": "T1r_foFbXgwV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(data['episode'].values.reshape(-1,1))\n",
        "scaled_episode\t = scaler.transform(data['episode'].values.reshape(-1,1))\n",
        "scaled_episode\t = pd.DataFrame(scaled_episode\t,columns=['scaled_episode\t'])\n",
        "data=pd.concat([data.drop(columns=['episode']),scaled_episode],axis=1)"
      ],
      "metadata": {
        "id": "1wHyFwClXmOj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 열 정리\n",
        "data=pd.concat([data.drop(columns=['interest_num']), data['interest_num']],axis=1)"
      ],
      "metadata": {
        "id": "cpZLkM-TuCtP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A4apWB_-eCj3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "4aee5315-b0a0-4cd6-b658-05373fadb729"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      free  previous  scaled_heart  scaled_rating  \\\n",
              "free              1.000000  0.055363      0.186681       0.035050   \n",
              "previous          0.055363  1.000000      0.068218       0.044512   \n",
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              "scaled_rating     0.035050  0.044512      0.179987       1.000000   \n",
              "scaled_episode\\t  0.079988  0.048320      0.292544       0.017756   \n",
              "interest_num      0.149750  0.095509      0.536404       0.110019   \n",
              "\n",
              "                  scaled_episode\\t  interest_num  \n",
              "free                      0.079988      0.149750  \n",
              "previous                  0.048320      0.095509  \n",
              "scaled_heart              0.292544      0.536404  \n",
              "scaled_rating             0.017756      0.110019  \n",
              "scaled_episode\\t          1.000000      0.454465  \n",
              "interest_num              0.454465      1.000000  "
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              "          const element = document.querySelector('#df-55b51969-50f7-46f8-8379-8fd039cdf845');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "data.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mdQoUZ39eJzp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 960
        },
        "outputId": "b9124c3f-7294-453f-a99c-2347bb69c1b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 9 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.8/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 9 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe6e1033970>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(15,15))\n",
        "sns.heatmap(data = data.corr(), annot=True, fmt = '.2f', linewidths=.5, cmap='Blues')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 모델 만들기"
      ],
      "metadata": {
        "id": "n1hrjea4bU4W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x=data.drop(['interest_num'],axis=1)\n",
        "y=data['interest_num']\n",
        "train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "SoTyEHb0cB3B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 단일 회귀\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.linear_model import Lasso\n",
        "\n",
        "# 앙상블\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from xgboost import XGBRegressor\n",
        "from lightgbm import LGBMRegressor\n",
        "\n",
        "\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error"
      ],
      "metadata": {
        "id": "VKi-X-XZsWJh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()\n",
        "\n",
        "model.fit(train_x, train_y)\n",
        "pred = model.predict(test_x)\n",
        "MAE = np.sqrt(mean_absolute_error(pred, test_y))\n",
        "print('MAE',MAE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LGf8OtPtbUZV",
        "outputId": "1bfff423-ac3e-467e-af93-aa75a53b3c1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE 288.333599401284\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Ridge()\n",
        "\n",
        "model.fit(train_x, train_y)\n",
        "pred = model.predict(test_x)\n",
        "MAE = np.sqrt(mean_absolute_error(pred, test_y))\n",
        "print('MAE',MAE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BfbIm4XT4dqb",
        "outputId": "77c80a21-b0c6-4441-cdc6-83f525cabe7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE 290.05082850150814\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Lasso()\n",
        "\n",
        "model.fit(train_x, train_y)\n",
        "pred = model.predict(test_x)\n",
        "MAE = np.sqrt(mean_absolute_error(pred, test_y))\n",
        "print('MAE',MAE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cItUZqx44jMk",
        "outputId": "956f9c64-5650-42a0-f22a-cf3ee07257e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE 288.3364353962343\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestRegressor()\n",
        "\n",
        "model.fit(train_x, train_y)\n",
        "pred = model.predict(test_x)\n",
        "MAE = np.sqrt(mean_absolute_error(pred, test_y))\n",
        "print('MAE',MAE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pvVrySZMyoqV",
        "outputId": "90dac30b-c002-4ea5-a9b0-3d181f4dc749"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE 283.13376291203764\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = XGBRegressor()\n",
        "\n",
        "model.fit(train_x, train_y)\n",
        "pred = model.predict(test_x)\n",
        "MAE = np.sqrt(mean_absolute_error(pred, test_y))\n",
        "print('MAE',MAE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P10ol1En1Hvb",
        "outputId": "8f610c7f-404b-46f3-b532-ed4b39fbaddb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[14:50:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "MAE 287.21427991552173\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = LGBMRegressor()\n",
        "\n",
        "model.fit(train_x, train_y)\n",
        "pred = model.predict(test_x)\n",
        "MAE = np.sqrt(mean_absolute_error(pred, test_y))\n",
        "print('MAE',MAE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_uq3SSmx2j_J",
        "outputId": "edd61214-5ad9-47da-a953-3200689d0a6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE 280.5441704766332\n"
          ]
        }
      ]
    }
  ],
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    },
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