0204-clustering-shopping-output.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bf4278a1",
   "metadata": {},
   "source": [
    "🌱 인프런 📚 모두의 한국어 텍스트 분석과 자연어처리 with 파이썬 🐍 https://inf.run/FX4TP\n",
    "\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/corazzon/python-text-analysis/blob/main/0204-clustering-shopping-output.ipynb)\n",
    "\n",
    "\n",
    "## 라이브러리 로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "22aab1e4",
   "metadata": {
    "executionInfo": {
     "elapsed": 499,
     "status": "ok",
     "timestamp": 1695352149111,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "22aab1e4"
   },
   "outputs": [],
   "source": [
    "# pandas, numpy\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3b055aad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 시각화 관련 matplotlib, seaborn, koreanize_matplotlib\n",
    "# !pip install koreanize-matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import koreanize_matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d71e321",
   "metadata": {
    "id": "3d71e321"
   },
   "source": [
    "## 텍스트 데이터 로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "15baea1a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 927
    },
    "executionInfo": {
     "elapsed": 790,
     "status": "ok",
     "timestamp": 1695352188367,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "15baea1a",
    "outputId": "3312e973-8790-41f9-bcb7-70511aa24ef7",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5996, 4)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/shop-review.csv\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "09f0e44c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>content</th>\n",
       "      <th>product</th>\n",
       "      <th>mallName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...</td>\n",
       "      <td>에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...</td>\n",
       "      <td>에어팟프로2세대</td>\n",
       "      <td>애플 공식 브랜드스토어</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&amp;lt;새로운 것과 좋았던 것의 균형감&amp;gt;1. 노이즈 캔슬링에어팟 1세대 대비 ...</td>\n",
       "      <td>&amp;lt;새로운 것과 좋았던 것의 균형감&amp;gt;&lt;br&gt;&lt;br&gt;&lt;br&gt;1. 노이즈 캔슬...</td>\n",
       "      <td>에어팟프로2세대</td>\n",
       "      <td>애플 공식 브랜드스토어</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>번개 같은 빠름으로? 사전예약 후 지난 10월 21일 수령해서 지금까지 한달 넘게 ...</td>\n",
       "      <td>번개 같은 빠름으로? 사전예약 후 지난 10월 21일 수령해서 지금까지 한달 넘게 ...</td>\n",
       "      <td>에어팟프로2세대</td>\n",
       "      <td>애플 공식 브랜드스토어</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>먼저 빠른배송 감사합니다. 21일 12시에 받고 현재 2시간동안 귀에서 빼지않고있습...</td>\n",
       "      <td>먼저 &lt;em&gt;빠른배송 감사합니다&lt;/em&gt;. 21일 12시에 받고 현재 2시간동안 귀...</td>\n",
       "      <td>에어팟프로2세대</td>\n",
       "      <td>애플 공식 브랜드스토어</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>에어팟 프로 2세대 구매&amp;amp; 사용 후기 1. 가격&amp;amp;배송    우선 애플...</td>\n",
       "      <td>에어팟 프로 2세대 구매&amp;amp; 사용 후기&lt;br&gt; 1. 가격&amp;amp;배송&lt;br&gt; ...</td>\n",
       "      <td>에어팟프로2세대</td>\n",
       "      <td>애플 공식 브랜드스토어</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  \\\n",
       "0  에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...   \n",
       "1  &lt;새로운 것과 좋았던 것의 균형감&gt;1. 노이즈 캔슬링에어팟 1세대 대비 ...   \n",
       "2  번개 같은 빠름으로? 사전예약 후 지난 10월 21일 수령해서 지금까지 한달 넘게 ...   \n",
       "3  먼저 빠른배송 감사합니다. 21일 12시에 받고 현재 2시간동안 귀에서 빼지않고있습...   \n",
       "4  에어팟 프로 2세대 구매&amp; 사용 후기 1. 가격&amp;배송    우선 애플...   \n",
       "\n",
       "                                             content   product      mallName  \n",
       "0  에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...  에어팟프로2세대  애플 공식 브랜드스토어  \n",
       "1  &lt;새로운 것과 좋았던 것의 균형감&gt;<br><br><br>1. 노이즈 캔슬...  에어팟프로2세대  애플 공식 브랜드스토어  \n",
       "2  번개 같은 빠름으로? 사전예약 후 지난 10월 21일 수령해서 지금까지 한달 넘게 ...  에어팟프로2세대  애플 공식 브랜드스토어  \n",
       "3  먼저 <em>빠른배송 감사합니다</em>. 21일 12시에 받고 현재 2시간동안 귀...  에어팟프로2세대  애플 공식 브랜드스토어  \n",
       "4  에어팟 프로 2세대 구매&amp; 사용 후기<br> 1. 가격&amp;배송<br> ...  에어팟프로2세대  애플 공식 브랜드스토어  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cf65c2e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['에어팟프로2세대', '선크림', '물티슈'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# product unique\n",
    "df[\"product\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "30d770be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product\n",
       "물티슈         2000\n",
       "선크림         1999\n",
       "에어팟프로2세대    1997\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value_counts\n",
    "df[\"product\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "73faa185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='count', ylabel='product'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# countplot\n",
    "sns.countplot(data=df, y=\"product\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91449aef",
   "metadata": {},
   "source": [
    "## 전처리\n",
    "* HTML 태그와 엔티티 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4cbc5a2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>content</th>\n",
       "      <th>product</th>\n",
       "      <th>mallName</th>\n",
       "      <th>title_re</th>\n",
       "      <th>content_re</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...</td>\n",
       "      <td>에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...</td>\n",
       "      <td>에어팟프로2세대</td>\n",
       "      <td>애플 공식 브랜드스토어</td>\n",
       "      <td>에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...</td>\n",
       "      <td>에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&amp;lt;새로운 것과 좋았던 것의 균형감&amp;gt;1. 노이즈 캔슬링에어팟 1세대 대비 ...</td>\n",
       "      <td>&amp;lt;새로운 것과 좋았던 것의 균형감&amp;gt;&lt;br&gt;&lt;br&gt;&lt;br&gt;1. 노이즈 캔슬...</td>\n",
       "      <td>에어팟프로2세대</td>\n",
       "      <td>애플 공식 브랜드스토어</td>\n",
       "      <td>새로운 것과 좋았던 것의 균형감 1. 노이즈 캔슬링에어팟 1세대 대비 좋아진 것을...</td>\n",
       "      <td>새로운 것과 좋았던 것의 균형감    1. 노이즈 캔슬링  에어팟 1세대 대비 좋...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>번개 같은 빠름으로? 사전예약 후 지난 10월 21일 수령해서 지금까지 한달 넘게 ...</td>\n",
       "      <td>번개 같은 빠름으로? 사전예약 후 지난 10월 21일 수령해서 지금까지 한달 넘게 ...</td>\n",
       "      <td>에어팟프로2세대</td>\n",
       "      <td>애플 공식 브랜드스토어</td>\n",
       "      <td>번개 같은 빠름으로? 사전예약 후 지난 10월 21일 수령해서 지금까지 한달 넘게 ...</td>\n",
       "      <td>번개 같은 빠름으로? 사전예약 후 지난 10월 21일 수령해서 지금까지 한달 넘게 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>먼저 빠른배송 감사합니다. 21일 12시에 받고 현재 2시간동안 귀에서 빼지않고있습...</td>\n",
       "      <td>먼저 &lt;em&gt;빠른배송 감사합니다&lt;/em&gt;. 21일 12시에 받고 현재 2시간동안 귀...</td>\n",
       "      <td>에어팟프로2세대</td>\n",
       "      <td>애플 공식 브랜드스토어</td>\n",
       "      <td>먼저 빠른배송 감사합니다. 21일 12시에 받고 현재 2시간동안 귀에서 빼지않고있습...</td>\n",
       "      <td>먼저  빠른배송 감사합니다 . 21일 12시에 받고 현재 2시간동안 귀에서 빼지않고...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>에어팟 프로 2세대 구매&amp;amp; 사용 후기 1. 가격&amp;amp;배송    우선 애플...</td>\n",
       "      <td>에어팟 프로 2세대 구매&amp;amp; 사용 후기&lt;br&gt; 1. 가격&amp;amp;배송&lt;br&gt; ...</td>\n",
       "      <td>에어팟프로2세대</td>\n",
       "      <td>애플 공식 브랜드스토어</td>\n",
       "      <td>에어팟 프로 2세대 구매  사용 후기 1. 가격 배송    우선 애플 공식 인증 스...</td>\n",
       "      <td>에어팟 프로 2세대 구매  사용 후기  1. 가격 배송     우선  애플 공식 인...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5991</th>\n",
       "      <td>늘 쓰는 상품이에요</td>\n",
       "      <td>늘 쓰는 상품이에요</td>\n",
       "      <td>물티슈</td>\n",
       "      <td>미엘물티슈</td>\n",
       "      <td>늘 쓰는 상품이에요</td>\n",
       "      <td>늘 쓰는 상품이에요</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5992</th>\n",
       "      <td>또 주문했어요 근데 이번에는 좀 바꼈네요?? 처음본 로고가 있네요 ㅎㅎ</td>\n",
       "      <td>또 주문했어요 근데 이번에는 좀 바꼈네요?? 처음본 로고가 있네요 ㅎㅎ</td>\n",
       "      <td>물티슈</td>\n",
       "      <td>미엘물티슈</td>\n",
       "      <td>또 주문했어요 근데 이번에는 좀 바꼈네요?? 처음본 로고가 있네요 ㅎㅎ</td>\n",
       "      <td>또 주문했어요 근데 이번에는 좀 바꼈네요?? 처음본 로고가 있네요 ㅎㅎ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5993</th>\n",
       "      <td>오랜만에 미엘 물티슈~가격대비 물디슈평량 용량 대비만족입니다!</td>\n",
       "      <td>오랜만에 미엘 물티슈~&lt;br&gt;가격대비 물디슈평량 용량 대비&lt;br&gt;만족입니다!</td>\n",
       "      <td>물티슈</td>\n",
       "      <td>미엘물티슈</td>\n",
       "      <td>오랜만에 미엘 물티슈~가격대비 물디슈평량 용량 대비만족입니다!</td>\n",
       "      <td>오랜만에 미엘 물티슈~ 가격대비 물디슈평량 용량 대비 만족입니다!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5994</th>\n",
       "      <td>쓰기 편하고좋아요</td>\n",
       "      <td>쓰기 편하고&lt;br&gt;좋아요</td>\n",
       "      <td>물티슈</td>\n",
       "      <td>미엘물티슈</td>\n",
       "      <td>쓰기 편하고좋아요</td>\n",
       "      <td>쓰기 편하고 좋아요</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5995</th>\n",
       "      <td>두번째 구매하고 쓰고있어요 수분도 많고 두께도 두꺼웠어 좋아요 .계속했어 쓰고싶어요.</td>\n",
       "      <td>두번째 구매하고 쓰고있어요 수분도 많고 두께도 두꺼웠어 좋아요 .계속했어 쓰고싶어요.</td>\n",
       "      <td>물티슈</td>\n",
       "      <td>미엘물티슈</td>\n",
       "      <td>두번째 구매하고 쓰고있어요 수분도 많고 두께도 두꺼웠어 좋아요 .계속했어 쓰고싶어요.</td>\n",
       "      <td>두번째 구매하고 쓰고있어요 수분도 많고 두께도 두꺼웠어 좋아요 .계속했어 쓰고싶어요.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5996 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  title  \\\n",
       "0     에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...   \n",
       "1     &lt;새로운 것과 좋았던 것의 균형감&gt;1. 노이즈 캔슬링에어팟 1세대 대비 ...   \n",
       "2     번개 같은 빠름으로? 사전예약 후 지난 10월 21일 수령해서 지금까지 한달 넘게 ...   \n",
       "3     먼저 빠른배송 감사합니다. 21일 12시에 받고 현재 2시간동안 귀에서 빼지않고있습...   \n",
       "4     에어팟 프로 2세대 구매&amp; 사용 후기 1. 가격&amp;배송    우선 애플...   \n",
       "...                                                 ...   \n",
       "5991                                         늘 쓰는 상품이에요   \n",
       "5992            또 주문했어요 근데 이번에는 좀 바꼈네요?? 처음본 로고가 있네요 ㅎㅎ   \n",
       "5993                 오랜만에 미엘 물티슈~가격대비 물디슈평량 용량 대비만족입니다!   \n",
       "5994                                          쓰기 편하고좋아요   \n",
       "5995    두번째 구매하고 쓰고있어요 수분도 많고 두께도 두꺼웠어 좋아요 .계속했어 쓰고싶어요.   \n",
       "\n",
       "                                                content   product  \\\n",
       "0     에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...  에어팟프로2세대   \n",
       "1     &lt;새로운 것과 좋았던 것의 균형감&gt;<br><br><br>1. 노이즈 캔슬...  에어팟프로2세대   \n",
       "2     번개 같은 빠름으로? 사전예약 후 지난 10월 21일 수령해서 지금까지 한달 넘게 ...  에어팟프로2세대   \n",
       "3     먼저 <em>빠른배송 감사합니다</em>. 21일 12시에 받고 현재 2시간동안 귀...  에어팟프로2세대   \n",
       "4     에어팟 프로 2세대 구매&amp; 사용 후기<br> 1. 가격&amp;배송<br> ...  에어팟프로2세대   \n",
       "...                                                 ...       ...   \n",
       "5991                                         늘 쓰는 상품이에요       물티슈   \n",
       "5992            또 주문했어요 근데 이번에는 좀 바꼈네요?? 처음본 로고가 있네요 ㅎㅎ       물티슈   \n",
       "5993         오랜만에 미엘 물티슈~<br>가격대비 물디슈평량 용량 대비<br>만족입니다!       물티슈   \n",
       "5994                                      쓰기 편하고<br>좋아요       물티슈   \n",
       "5995    두번째 구매하고 쓰고있어요 수분도 많고 두께도 두꺼웠어 좋아요 .계속했어 쓰고싶어요.       물티슈   \n",
       "\n",
       "          mallName                                           title_re  \\\n",
       "0     애플 공식 브랜드스토어  에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...   \n",
       "1     애플 공식 브랜드스토어   새로운 것과 좋았던 것의 균형감 1. 노이즈 캔슬링에어팟 1세대 대비 좋아진 것을...   \n",
       "2     애플 공식 브랜드스토어  번개 같은 빠름으로? 사전예약 후 지난 10월 21일 수령해서 지금까지 한달 넘게 ...   \n",
       "3     애플 공식 브랜드스토어  먼저 빠른배송 감사합니다. 21일 12시에 받고 현재 2시간동안 귀에서 빼지않고있습...   \n",
       "4     애플 공식 브랜드스토어  에어팟 프로 2세대 구매  사용 후기 1. 가격 배송    우선 애플 공식 인증 스...   \n",
       "...            ...                                                ...   \n",
       "5991         미엘물티슈                                         늘 쓰는 상품이에요   \n",
       "5992         미엘물티슈            또 주문했어요 근데 이번에는 좀 바꼈네요?? 처음본 로고가 있네요 ㅎㅎ   \n",
       "5993         미엘물티슈                 오랜만에 미엘 물티슈~가격대비 물디슈평량 용량 대비만족입니다!   \n",
       "5994         미엘물티슈                                          쓰기 편하고좋아요   \n",
       "5995         미엘물티슈    두번째 구매하고 쓰고있어요 수분도 많고 두께도 두꺼웠어 좋아요 .계속했어 쓰고싶어요.   \n",
       "\n",
       "                                             content_re  \n",
       "0     에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...  \n",
       "1      새로운 것과 좋았던 것의 균형감    1. 노이즈 캔슬링  에어팟 1세대 대비 좋...  \n",
       "2     번개 같은 빠름으로? 사전예약 후 지난 10월 21일 수령해서 지금까지 한달 넘게 ...  \n",
       "3     먼저  빠른배송 감사합니다 . 21일 12시에 받고 현재 2시간동안 귀에서 빼지않고...  \n",
       "4     에어팟 프로 2세대 구매  사용 후기  1. 가격 배송     우선  애플 공식 인...  \n",
       "...                                                 ...  \n",
       "5991                                         늘 쓰는 상품이에요  \n",
       "5992            또 주문했어요 근데 이번에는 좀 바꼈네요?? 처음본 로고가 있네요 ㅎㅎ  \n",
       "5993               오랜만에 미엘 물티슈~ 가격대비 물디슈평량 용량 대비 만족입니다!  \n",
       "5994                                         쓰기 편하고 좋아요  \n",
       "5995    두번째 구매하고 쓰고있어요 수분도 많고 두께도 두꺼웠어 좋아요 .계속했어 쓰고싶어요.  \n",
       "\n",
       "[5996 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# <.*?>|&[^;]+;\n",
    "df[\"title_re\"] = df[\"title\"].str.replace(\"<.*?>|&[^;]+;\", \" \", regex=True)\n",
    "df[\"content_re\"] = df[\"content\"].str.replace(\"<.*?>|&[^;]+;\", \" \", regex=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d581269a",
   "metadata": {
    "id": "d581269a"
   },
   "source": [
    "## TF-IDF\n",
    "* https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7e149324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>content</th>\n",
       "      <th>product</th>\n",
       "      <th>mallName</th>\n",
       "      <th>title_re</th>\n",
       "      <th>content_re</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...</td>\n",
       "      <td>에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...</td>\n",
       "      <td>에어팟프로2세대</td>\n",
       "      <td>애플 공식 브랜드스토어</td>\n",
       "      <td>에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...</td>\n",
       "      <td>에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&amp;lt;새로운 것과 좋았던 것의 균형감&amp;gt;1. 노이즈 캔슬링에어팟 1세대 대비 ...</td>\n",
       "      <td>&amp;lt;새로운 것과 좋았던 것의 균형감&amp;gt;&lt;br&gt;&lt;br&gt;&lt;br&gt;1. 노이즈 캔슬...</td>\n",
       "      <td>에어팟프로2세대</td>\n",
       "      <td>애플 공식 브랜드스토어</td>\n",
       "      <td>새로운 것과 좋았던 것의 균형감 1. 노이즈 캔슬링에어팟 1세대 대비 좋아진 것을...</td>\n",
       "      <td>새로운 것과 좋았던 것의 균형감    1. 노이즈 캔슬링  에어팟 1세대 대비 좋...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  \\\n",
       "0  에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...   \n",
       "1  &lt;새로운 것과 좋았던 것의 균형감&gt;1. 노이즈 캔슬링에어팟 1세대 대비 ...   \n",
       "\n",
       "                                             content   product      mallName  \\\n",
       "0  에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...  에어팟프로2세대  애플 공식 브랜드스토어   \n",
       "1  &lt;새로운 것과 좋았던 것의 균형감&gt;<br><br><br>1. 노이즈 캔슬...  에어팟프로2세대  애플 공식 브랜드스토어   \n",
       "\n",
       "                                            title_re  \\\n",
       "0  에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...   \n",
       "1   새로운 것과 좋았던 것의 균형감 1. 노이즈 캔슬링에어팟 1세대 대비 좋아진 것을...   \n",
       "\n",
       "                                          content_re  \n",
       "0  에어팟프로1세대를 계속 사용 했으나 배터리 빠른 소모로 인해 2세대로 교체 했습니다...  \n",
       "1   새로운 것과 좋았던 것의 균형감    1. 노이즈 캔슬링  에어팟 1세대 대비 좋...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "513cd25f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "executionInfo": {
     "elapsed": 439,
     "status": "ok",
     "timestamp": 1695359628065,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "513cd25f",
    "outputId": "2e70c67a-179e-46e5-d4f3-890c4ca6f659"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TfidfVectorizer(max_features=2000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(max_features=2000)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "TfidfVectorizer(max_features=2000)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidfvect = TfidfVectorizer(max_features=2000)\n",
    "tfidfvect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "8be6019b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1695359628527,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "8be6019b",
    "outputId": "abd13324-2eb8-43b3-98f3-d768fe9b54c3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<5996x2000 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 72089 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtm_tfidf = tfidfvect.fit_transform(df[\"title_re\"] + \" \" + df[\"content_re\"]+ \" \" + df[\"product\"])\n",
    "dtm_tfidf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "c4159275",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 773,
     "status": "ok",
     "timestamp": 1695359649205,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "c4159275",
    "outputId": "4a00662f-81d2-4318-c024-e3415568bcd8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>10</th>\n",
       "      <th>100</th>\n",
       "      <th>100매</th>\n",
       "      <th>100매라</th>\n",
       "      <th>100매에</th>\n",
       "      <th>10월</th>\n",
       "      <th>10팩</th>\n",
       "      <th>11번가</th>\n",
       "      <th>11번가에서</th>\n",
       "      <th>1년</th>\n",
       "      <th>...</th>\n",
       "      <th>회사에서도</th>\n",
       "      <th>효과</th>\n",
       "      <th>효과가</th>\n",
       "      <th>후기</th>\n",
       "      <th>후기가</th>\n",
       "      <th>후기를</th>\n",
       "      <th>후기보고</th>\n",
       "      <th>훌륭합니다</th>\n",
       "      <th>훨씬</th>\n",
       "      <th>흡수도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.031364</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029016</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 2000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         10  100  100매  100매라  100매에  10월  10팩  11번가  11번가에서        1년  ...  \\\n",
       "0  0.079696  0.0   0.0    0.0    0.0  0.0  0.0   0.0     0.0  0.000000  ...   \n",
       "1  0.000000  0.0   0.0    0.0    0.0  0.0  0.0   0.0     0.0  0.031364  ...   \n",
       "\n",
       "   회사에서도   효과       효과가   후기  후기가  후기를  후기보고  훌륭합니다   훨씬  흡수도  \n",
       "0    0.0  0.0  0.000000  0.0  0.0  0.0   0.0    0.0  0.0  0.0  \n",
       "1    0.0  0.0  0.029016  0.0  0.0  0.0   0.0    0.0  0.0  0.0  \n",
       "\n",
       "[2 rows x 2000 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dtm_tfidf = pd.DataFrame(dtm_tfidf.toarray(), \n",
    "                            columns=tfidfvect.get_feature_names_out())\n",
    "df_dtm_tfidf.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "a60c60c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_dtm_tfidf.sum().nlargest(30).plot(kind=\"bar\", figsize=(10, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48e10f0a",
   "metadata": {
    "id": "48e10f0a"
   },
   "source": [
    "## 군집화\n",
    "\n",
    "## KMeans\n",
    "\n",
    "* [2.3. Clustering — scikit-learn documentation](https://scikit-learn.org/stable/modules/clustering.html#k-means)\n",
    "*\n",
    "https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "7e6ed957",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 354,
     "status": "ok",
     "timestamp": 1695359495845,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "7e6ed957",
    "outputId": "0028f13c-d1eb-4f38-9089-04ca48a11897"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.07969557, 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.04885186, 0.04494495, 0.        , ..., 0.        , 0.03685102,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_dtm_tfidf.values\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "abf7f5bd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 642
    },
    "executionInfo": {
     "elapsed": 30548,
     "status": "error",
     "timestamp": 1695359526847,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "abf7f5bd",
    "outputId": "df96e47d-8164-4f8f-9902-e7700d19faad"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 18/18 [00:29<00:00,  1.62s/it]\n"
     ]
    }
   ],
   "source": [
    "# cluster.KMeans, metrics.silhouette_score 불러오기\n",
    "# rfm_norm 값을 학습하고\n",
    "# elbow 값을 보기위해 inertia_ 값을 리스트로 만듭니다.\n",
    "# silhouette_score(X, kmeans.labels_) 값도 리스트로 만듭니다.\n",
    "\n",
    "\n",
    "# KMeans(\n",
    "#     n_clusters=8,\n",
    "#     *,\n",
    "#     init='k-means++',\n",
    "#     n_init=10,\n",
    "#     max_iter=300,\n",
    "#     tol=0.0001,\n",
    "#     verbose=0,\n",
    "#     random_state=None,\n",
    "#     copy_x=True,\n",
    "#     algorithm='lloyd',\n",
    "# )\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from tqdm import trange\n",
    "\n",
    "inertia = []\n",
    "silhouettes = []\n",
    "\n",
    "for n in trange(2, 20):\n",
    "    kmeans = KMeans(n_clusters=n, n_init=\"auto\", random_state=42)\n",
    "    kmeans.fit(X)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "    silhouettes.append(silhouette_score(X, kmeans.labels_))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a69ca946",
   "metadata": {
    "id": "a69ca946"
   },
   "source": [
    "## Elbow Method\n",
    "\n",
    "* Number of clusters\n",
    "* Within-Cluster Sum of Square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "943c045a",
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "aborted",
     "timestamp": 1695359526848,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "943c045a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,4))\n",
    "\n",
    "plt.plot(range(2, 20), inertia)\n",
    "plt.title(\"The Elbow Method\")\n",
    "plt.xlabel(\"number of cluster\")\n",
    "plt.ylabel(\"Within Sum of Square\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9d28992",
   "metadata": {
    "id": "e9d28992"
   },
   "source": [
    "## Silhouette Score\n",
    "\n",
    "\n",
    "군집화 평가 방법으로 실루엣 분석(Shilhouette Analysis)이 있습니다. 실루엣 분석은 각 군집 간의 거리가 얼마나 효율적으로 분리돼 있는지를 나타냅니다. 효율적으로 잘 분리됐다는 것은 다른 군집과의 거리는 떨어져 있고 동일 군집끼리의 데이터는 서로 가깝게 잘 뭉쳐 있다는 의미입니다. 군집화가 잘 될수록 개별 군집은 비슷한 정도의 여유공간을 가지고 떨어져 있을 것입니다.\n",
    "\n",
    "* 좋은 군집화가 되기 위한 기준 조건\n",
    "    * 전체 실루엣 계수의 평균값, 즉 사이킷런의 silhouette_score() 값은 -1~1 사이의 값을 가지며, 1에 가까울수록 좋습니다.\n",
    "    * 하지만 전체 실루엣 계수의 평균값과 더불어 개별 군집의 평균값의 편차가 크지 않아야 합니다. 즉, 개별 군집의 실루엣 계수 평균값이 전체 실루엣 계수의 평균값에서 크게 벗어나지 않는 것이 중요합니다. 만약 전체 실루엣 계수의 평균값은 높지만, 특정 군집의 실루엣 계수 평균값만 유난히 높고 다른 군집들의 실루엣 계수 평균값은 낮으면 좋은 군집화 조건이 아닙니다.\n",
    "\n",
    "* https://scikit-learn.org/stable/modules/clustering.html#silhouette-coefficient\n",
    "\n",
    "정답 레이블을 모르는 경우 모델 자체를 사용하여 평가를 수행해야 합니다. 실루엣 계수( sklearn.metrics.silhouette_score)는 이러한 평가의 한 예이며 실루엣 계수 점수가 높을수록 클러스터가 더 잘 정의된 모델과 관련됩니다. 실루엣 계수는 각 샘플에 대해 정의되며 두 가지 점수로 구성됩니다.\n",
    "\n",
    "* a : 샘플과 동일한 클래스의 다른 모든 점 사이의 평균 거리.\n",
    "* b : 표본과 다음으로 가장 가까운 군집 의 다른 모든 점 사이의 평균 거리\n",
    "* 단일 샘플에 대한 실루엣 계수 s 는 다음과 같이 주어집니다.\n",
    "    * `s = (b-a)/max(a,b)`\n",
    "    \n",
    "\n",
    "* 사이킷런의 실루엣 분석 메서드\n",
    "\n",
    "* https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html\n",
    "\n",
    "* `silhouette_samples(X, labels, metric='euclidean', **kwds)` : X feature 데이터 세트와 각 feature데이터 세트가 속한 군집 레이블 값인 labels데이터를 입력해주면 각 데이터 포인트의 실루엣 계수를 계산해 반환합니다.\n",
    "* `silhouette_score(X, labels, metric='euclidean', sample_size=None, **kwds)`: 인자로 X feature데이터 세트와 각 feature 데이터 세트가 속한 레이블 값인 labels데이터를 입력해주면 전체 데이터의 실루엣 계수 값을 평균해 반환합니다. 즉 np.mean(silhouette_samples())입니다. 일반적으로 이 값이 높을수록 군집화가 어느 정도 잘 됐다고 판단할 수 있습니다. 하지만 무조건 이 값이 높다고 해서 군집화가 잘 됐다고 판단할 수는 없습니다.\n",
    "\n",
    "* https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html#sklearn.metrics.silhouette_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "4b5addef",
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "aborted",
     "timestamp": 1695359526848,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "4b5addef"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 실루엣 점수가 높은 클러스터 수는 데이터가 잘 그룹화되어 있음을 나타냅니다.\n",
    "plt.figure(figsize=(15, 4))\n",
    "plt.plot(range(2, 20), silhouettes)\n",
    "plt.title(\"Shilhouette Score\")\n",
    "plt.xlabel(\"number of cluster\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b05607c6",
   "metadata": {
    "id": "b05607c6"
   },
   "source": [
    "## 클러스터의 수를 정해서 군집화 하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "3e94c16b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "executionInfo": {
     "elapsed": 2269,
     "status": "ok",
     "timestamp": 1695359689234,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "3e94c16b",
    "outputId": "2592398f-5e20-4da6-b1c4-ca6d8778fc59"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-15 {color: black;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-15 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-15 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=4, n_init=&#x27;auto&#x27;, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=4, n_init=&#x27;auto&#x27;, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=4, n_init='auto', random_state=42)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_clusters = 4\n",
    "kmeans = KMeans(n_clusters=n_clusters, n_init=\"auto\", random_state=42)\n",
    "kmeans.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "8BdTMNHH5pHt",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 473,
     "status": "ok",
     "timestamp": 1695359704287,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "8BdTMNHH5pHt",
    "outputId": "b26cfb6f-9d04-451c-b6a1-6e3d75037347"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(kmeans.labels_ == kmeans.predict(X)).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "18c3e366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "2    1934\n",
       "3    1852\n",
       "0    1654\n",
       "1     556\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"cluster\"] = kmeans.labels_\n",
    "df[\"cluster\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "b579488d",
   "metadata": {
    "executionInfo": {
     "elapsed": 8200,
     "status": "ok",
     "timestamp": 1695359712485,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "b579488d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0075788223096755645"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# silhouette_score 구하기\n",
    "silhouette_avg = silhouette_score(X, kmeans.labels_)\n",
    "silhouette_avg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78be88a4",
   "metadata": {},
   "source": [
    "### 실루엣 분석 시각화 \n",
    "* https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "5d65c7b3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 473
    },
    "executionInfo": {
     "elapsed": 8910,
     "status": "ok",
     "timestamp": 1695359723514,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "5d65c7b3",
    "outputId": "dc81221b-8e4d-4202-dbe2-f95271f210c8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html\n",
    "from sklearn.metrics import silhouette_samples\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "# Create a subplot with 1 row and 2 columns\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "fig.set_size_inches(18, 7)\n",
    "\n",
    "# The 1st subplot is the silhouette plot\n",
    "# The silhouette coefficient can range from -1, 1 but in this example all\n",
    "# lie within [-0.1, 1]\n",
    "ax1.set_xlim([-0.1, 1])\n",
    "# The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
    "# plots of individual clusters, to demarcate them clearly.\n",
    "ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n",
    "\n",
    "cluster_labels = kmeans.labels_\n",
    "sample_silhouette_values = silhouette_samples(X, cluster_labels)\n",
    "\n",
    "y_lower = 10\n",
    "for i in range(n_clusters):\n",
    "    # Aggregate the silhouette scores for samples belonging to\n",
    "    # cluster i, and sort them\n",
    "    ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n",
    "\n",
    "    ith_cluster_silhouette_values.sort()\n",
    "\n",
    "    size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "    y_upper = y_lower + size_cluster_i\n",
    "\n",
    "    color = cm.nipy_spectral(float(i) / n_clusters)\n",
    "    ax1.fill_betweenx(\n",
    "        np.arange(y_lower, y_upper),\n",
    "        0,\n",
    "        ith_cluster_silhouette_values,\n",
    "        facecolor=color,\n",
    "        edgecolor=color,\n",
    "        alpha=0.7,\n",
    "    )\n",
    "\n",
    "    # Label the silhouette plots with their cluster numbers at the middle\n",
    "    ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "\n",
    "    # Compute the new y_lower for next plot\n",
    "    y_lower = y_upper + 10  # 10 for the 0 samples\n",
    "\n",
    "ax1.set_title(\"The silhouette plot for the various clusters.\")\n",
    "ax1.set_xlabel(\"The silhouette coefficient values\")\n",
    "ax1.set_ylabel(\"Cluster label\")\n",
    "\n",
    "# The vertical line for average silhouette score of all the values\n",
    "ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
    "\n",
    "ax1.set_yticks([])  # Clear the yaxis labels / ticks\n",
    "ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "\n",
    "# 2nd Plot showing the actual clusters formed\n",
    "colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)\n",
    "ax2.scatter(\n",
    "    X[:, 0], X[:, 1], marker=\".\", s=30, lw=0, alpha=0.7, c=colors, edgecolor=\"k\"\n",
    ")\n",
    "\n",
    "# Labeling the clusters\n",
    "centers = kmeans.cluster_centers_\n",
    "# Draw white circles at cluster centers\n",
    "ax2.scatter(\n",
    "    centers[:, 0],\n",
    "    centers[:, 1],\n",
    "    marker=\"o\",\n",
    "    c=\"white\",\n",
    "    alpha=1,\n",
    "    s=200,\n",
    "    edgecolor=\"k\",\n",
    ")\n",
    "\n",
    "for i, c in enumerate(centers):\n",
    "    ax2.scatter(c[0], c[1], marker=\"$%d$\" % i, alpha=1, s=50, edgecolor=\"k\")\n",
    "\n",
    "ax2.set_title(\"The visualization of the clustered data.\")\n",
    "ax2.set_xlabel(\"Feature space for the 1st feature\")\n",
    "ax2.set_ylabel(\"Feature space for the 2nd feature\")\n",
    "\n",
    "plt.suptitle(\n",
    "    \"Silhouette analysis for KMeans clustering on sample data with n_clusters = %d\"\n",
    "    % n_clusters,\n",
    "    fontsize=14,\n",
    "    fontweight=\"bold\",\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "026ac426",
   "metadata": {},
   "source": [
    "### 실제 제품과 군집 비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "4fc7d37a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1695359732174,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "4fc7d37a",
    "outputId": "e52712c9-a09e-476c-9d85-29441244d9eb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_3175b_row0_col0, #T_3175b_row2_col0, #T_3175b_row2_col3 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3175b_row0_col1 {\n",
       "  background-color: #f7f0f7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3175b_row0_col2 {\n",
       "  background-color: #faf2f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3175b_row0_col3 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3175b_row1_col0 {\n",
       "  background-color: #045382;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_3175b_row1_col1 {\n",
       "  background-color: #e7e3f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3175b_row1_col2 {\n",
       "  background-color: #fbf3f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3175b_row1_col3 {\n",
       "  background-color: #fef6fa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3175b_row2_col1 {\n",
       "  background-color: #f1ebf4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_3175b_row2_col2 {\n",
       "  background-color: #02395a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_3175b\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >cluster</th>\n",
       "      <th id=\"T_3175b_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
       "      <th id=\"T_3175b_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
       "      <th id=\"T_3175b_level0_col2\" class=\"col_heading level0 col2\" >2</th>\n",
       "      <th id=\"T_3175b_level0_col3\" class=\"col_heading level0 col3\" >3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >product</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_3175b_level0_row0\" class=\"row_heading level0 row0\" >물티슈</th>\n",
       "      <td id=\"T_3175b_row0_col0\" class=\"data row0 col0\" >8</td>\n",
       "      <td id=\"T_3175b_row0_col1\" class=\"data row0 col1\" >99</td>\n",
       "      <td id=\"T_3175b_row0_col2\" class=\"data row0 col2\" >68</td>\n",
       "      <td id=\"T_3175b_row0_col3\" class=\"data row0 col3\" >1825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3175b_level0_row1\" class=\"row_heading level0 row1\" >선크림</th>\n",
       "      <td id=\"T_3175b_row1_col0\" class=\"data row1 col0\" >1643</td>\n",
       "      <td id=\"T_3175b_row1_col1\" class=\"data row1 col1\" >280</td>\n",
       "      <td id=\"T_3175b_row1_col2\" class=\"data row1 col2\" >55</td>\n",
       "      <td id=\"T_3175b_row1_col3\" class=\"data row1 col3\" >21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3175b_level0_row2\" class=\"row_heading level0 row2\" >에어팟프로2세대</th>\n",
       "      <td id=\"T_3175b_row2_col0\" class=\"data row2 col0\" >3</td>\n",
       "      <td id=\"T_3175b_row2_col1\" class=\"data row2 col1\" >177</td>\n",
       "      <td id=\"T_3175b_row2_col2\" class=\"data row2 col2\" >1811</td>\n",
       "      <td id=\"T_3175b_row2_col3\" class=\"data row2 col3\" >6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x13c118110>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# crosstab product, kmeans\n",
    "pd.crosstab(df[\"product\"], df[\"cluster\"]).style.background_gradient(axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "b5fac138",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 465
    },
    "executionInfo": {
     "elapsed": 674,
     "status": "ok",
     "timestamp": 1695359760146,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "b5fac138",
    "outputId": "c967e63d-1286-4979-b75d-10a63575fc8c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='cluster', ylabel='count'>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# countplot\n",
    "plt.figure(figsize=(6, 3))\n",
    "sns.countplot(data=df, x=\"cluster\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "887cd437",
   "metadata": {},
   "source": [
    "## 각 군집별 키워드 TF-IDF 값 합계 순위"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "PDdEula9OM9_",
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1695359787689,
     "user": {
      "displayName": "Joeun Park",
      "userId": "17163383815352082166"
     },
     "user_tz": -540
    },
    "id": "PDdEula9OM9_"
   },
   "outputs": [],
   "source": [
    "def kmeans_tfidf(no):\n",
    "    df_cluster = df_dtm_tfidf.loc[df[df[\"cluster\"] == no].index]\n",
    "    df_cluster.sum().nlargest(20).plot(kind=\"bar\", figsize=(6, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "L-VzK8fBPnbo",
   "metadata": {
    "id": "L-VzK8fBPnbo"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 군집별 TF-IDF 키워드 시각화\n",
    "\n",
    "kmeans_tfidf(no=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "07ba83cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans_tfidf(no=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "add9b886",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans_tfidf(no=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "1d464e88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans_tfidf(no=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7c40627",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": [
    {
     "file_id": "1wcVKbIPzx14nlpHSTJWtcol8Bh73T41B",
     "timestamp": 1695352044574
    }
   ],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": false,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": false,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}