AI_Class/009/k_Means.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Means 군집"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 군집(clustering)\n",
    "군집 분석은 데이터셋의 관측값이 갖고 있는 여러 속성을 분석하여 서로 비슷한 특징을 같는 관측값끼리 같은 집단으로 묶는 알고리즘\n",
    "\n",
    "다른 집단 간에는 서로 완전하게 구분되는 특징을 갖기 때문에 어느 집단에도 속하지 못하는 관측값이 존재할 수 있음\n",
    "\n",
    "이런 특성을 이용해 특정 데이터(이상값, 중복값)를 찾는데 활용가능"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "군집 분석은 비지도학습!\n",
    "\n",
    "관측값을 몇 개의 집단으로 나눈다는 점에서 지도학습의 분류 알고리즘과 비슷하지만\n",
    "\n",
    "군집 분석은 정답이 없는 상태에서 데이터 자체의 유사성만을 기준으로 판단한다는 점에서 정답을 알고 있는 상태에서 학습 과정을 거치는 분류 알고리즘과는 차이가 있음"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### k-Means\n",
    "k-Means 알고리즘은 데이터 간의 유사성을 측정하는 기준으로 각 클러스터의 중심까지의 거리를 이용\n",
    "\n",
    "벡터 공간에 위치한 어떤 데이터에 대하여 k개의 클러스터가 주어졌을 때 클러스터의 중심까지 거리가 가장 가까운 클러스터로 해당 데이터를 할당\n",
    "\n",
    "다른 클러스터 간에는 서로 완전하게 구분하기 위하여 일정한 거리 이상 떨어져야 함\n",
    "\n",
    "한편 몇 개의 클러스터로 데이터를 구분할 것인지를 결정하는 k 값에 따라 모형의 성능이 달라짐\n",
    "\n",
    "일반적으로 k가 클수록 모형의 정확도는 개선되지만, k 값이 너무 커지면 선택지가 너무 많아지므로 분석의 효과가 사라짐"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import lib & data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 도매 고객 데이터 세트\n",
    "각 고객의 연간 구매금액을 상품 카테고리별로 구분하여 정리한 데이터\n",
    "\n",
    "첫 2개 열은 상품 구매금액이 아니라 고객의 일반 정보를 담고있음\n",
    "\n",
    "'Channel' 열은 호텔 / 레스토랑 또는 소매점 등 판매채널 값이고, 'Region' 열은 고객 소재지를 나타냄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "uci_path = 'https://archive.ics.uci.edu/ml/machine-learning-databases/\\\n",
    "00292/Wholesale%20customers%20data.csv'\n",
    "df = pd.read_csv(uci_path, header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
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       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
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       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
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       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
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       "<p>440 rows × 8 columns</p>\n",
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       "     Channel  Region  Fresh   Milk  Grocery  Frozen  Detergents_Paper  \\\n",
       "0          2       3  12669   9656     7561     214              2674   \n",
       "1          2       3   7057   9810     9568    1762              3293   \n",
       "2          2       3   6353   8808     7684    2405              3516   \n",
       "3          1       3  13265   1196     4221    6404               507   \n",
       "4          2       3  22615   5410     7198    3915              1777   \n",
       "..       ...     ...    ...    ...      ...     ...               ...   \n",
       "435        1       3  29703  12051    16027   13135               182   \n",
       "436        1       3  39228   1431      764    4510                93   \n",
       "437        2       3  14531  15488    30243     437             14841   \n",
       "438        1       3  10290   1981     2232    1038               168   \n",
       "439        1       3   2787   1698     2510      65               477   \n",
       "\n",
       "     Delicassen  \n",
       "0          1338  \n",
       "1          1776  \n",
       "2          7844  \n",
       "3          1788  \n",
       "4          5185  \n",
       "..          ...  \n",
       "435        2204  \n",
       "436        2346  \n",
       "437        1867  \n",
       "438        2125  \n",
       "439          52  \n",
       "\n",
       "[440 rows x 8 columns]"
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   "source": [
    "df"
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  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 440 entries, 0 to 439\n",
      "Data columns (total 8 columns):\n",
      " #   Column            Non-Null Count  Dtype\n",
      "---  ------            --------------  -----\n",
      " 0   Channel           440 non-null    int64\n",
      " 1   Region            440 non-null    int64\n",
      " 2   Fresh             440 non-null    int64\n",
      " 3   Milk              440 non-null    int64\n",
      " 4   Grocery           440 non-null    int64\n",
      " 5   Frozen            440 non-null    int64\n",
      " 6   Detergents_Paper  440 non-null    int64\n",
      " 7   Delicassen        440 non-null    int64\n",
      "dtypes: int64(8)\n",
      "memory usage: 27.6 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Channel             0\n",
       "Region              0\n",
       "Fresh               0\n",
       "Milk                0\n",
       "Grocery             0\n",
       "Frozen              0\n",
       "Detergents_Paper    0\n",
       "Delicassen          0\n",
       "dtype: int64"
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     },
     "execution_count": 57,
     "metadata": {},
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   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
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   "outputs": [
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       "      <td>440.000000</td>\n",
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       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>1.322727</td>\n",
       "      <td>2.543182</td>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
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       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
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       "      <td>4767.854448</td>\n",
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       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
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       "      <td>2.000000</td>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
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       "      <th>50%</th>\n",
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       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
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       "      <td>3.000000</td>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
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       "      <th>max</th>\n",
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       "      <td>3.000000</td>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
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      "text/plain": [
       "          Channel      Region          Fresh          Milk       Grocery  \\\n",
       "count  440.000000  440.000000     440.000000    440.000000    440.000000   \n",
       "mean     1.322727    2.543182   12000.297727   5796.265909   7951.277273   \n",
       "std      0.468052    0.774272   12647.328865   7380.377175   9503.162829   \n",
       "min      1.000000    1.000000       3.000000     55.000000      3.000000   \n",
       "25%      1.000000    2.000000    3127.750000   1533.000000   2153.000000   \n",
       "50%      1.000000    3.000000    8504.000000   3627.000000   4755.500000   \n",
       "75%      2.000000    3.000000   16933.750000   7190.250000  10655.750000   \n",
       "max      2.000000    3.000000  112151.000000  73498.000000  92780.000000   \n",
       "\n",
       "             Frozen  Detergents_Paper    Delicassen  \n",
       "count    440.000000        440.000000    440.000000  \n",
       "mean    3071.931818       2881.493182   1524.870455  \n",
       "std     4854.673333       4767.854448   2820.105937  \n",
       "min       25.000000          3.000000      3.000000  \n",
       "25%      742.250000        256.750000    408.250000  \n",
       "50%     1526.000000        816.500000    965.500000  \n",
       "75%     3554.250000       3922.000000   1820.250000  \n",
       "max    60869.000000      40827.000000  47943.000000  "
      ]
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     "execution_count": 58,
     "metadata": {},
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   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0        2       3  12669  9656     7561     214              2674        1338\n",
       "1        2       3   7057  9810     9568    1762              3293        1776\n",
       "2        2       3   6353  8808     7684    2405              3516        7844\n",
       "3        1       3  13265  1196     4221    6404               507        1788\n",
       "4        2       3  22615  5410     7198    3915              1777        5185"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df #독립변수\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = preprocessing.StandardScaler().fit(X).transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "km = KMeans(init='k-means++', n_clusters=8, n_init=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=8, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.fit(X) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 6, 6, 0, 6, 6, 6, 6, 0, 6, 6, 6, 6, 6, 6, 0, 6, 0, 6, 0, 6, 0,\n",
       "       4, 1, 6, 6, 0, 0, 1, 4, 0, 0, 0, 4, 0, 6, 4, 6, 6, 4, 4, 0, 6, 1,\n",
       "       6, 1, 1, 3, 6, 1, 0, 0, 4, 6, 0, 0, 1, 6, 0, 0, 6, 3, 6, 6, 0, 1,\n",
       "       0, 6, 0, 0, 0, 4, 0, 6, 6, 0, 0, 1, 0, 0, 0, 6, 6, 0, 6, 3, 3, 4,\n",
       "       0, 4, 0, 0, 1, 7, 6, 0, 6, 0, 0, 0, 6, 6, 6, 4, 0, 0, 6, 6, 6, 6,\n",
       "       0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 4, 4, 0, 6, 0, 4, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 0, 0, 1, 0, 0, 0, 4, 0, 0, 0, 0,\n",
       "       0, 1, 6, 0, 6, 6, 6, 0, 0, 1, 6, 6, 6, 0, 0, 0, 6, 1, 0, 6, 0, 6,\n",
       "       4, 0, 0, 0, 0, 4, 0, 5, 0, 0, 0, 0, 6, 6, 0, 0, 0, 6, 0, 0, 4, 2,\n",
       "       2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 1, 2, 2, 6, 2, 1, 2, 1, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2,\n",
       "       2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 4, 4, 2, 2, 2, 2,\n",
       "       1, 2, 1, 2, 1, 2, 2, 2, 2, 4, 0, 0, 4, 0, 0, 6, 0, 6, 4, 4, 4, 4,\n",
       "       0, 0, 0, 4, 0, 0, 0, 6, 2, 6, 2, 6, 6, 2, 6, 1, 6, 6, 1, 6, 1, 2,\n",
       "       2, 1, 2, 4, 1, 2, 2, 6, 2, 2, 2, 1, 2, 2, 2, 2, 2, 7, 2, 2, 2, 2,\n",
       "       2, 1, 2, 3, 6, 6, 2, 2, 2, 2, 6, 6, 0, 1, 0, 0, 6, 6, 0, 1, 0, 1,\n",
       "       0, 6, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 4, 0, 0, 6,\n",
       "       0, 0, 6, 4, 0, 6, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0,\n",
       "       6, 0, 0, 0, 0, 4, 0, 0, 0, 0, 4, 6, 6, 0, 0, 0, 0, 0, 0, 6, 6, 0,\n",
       "       6, 0, 0, 6, 0, 6, 6, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 4, 4, 1, 0, 0],\n",
       "      dtype=int32)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "모델을 훈련(fit)시키면 아래처럼 \"labels_\" 이 만들어짐\n",
    "\n",
    "이를 우리의 데이터프레임에 추가시켜 시각화하면 됨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = km.labels_   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "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>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "      <td>6</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>29703</td>\n",
       "      <td>12051</td>\n",
       "      <td>16027</td>\n",
       "      <td>13135</td>\n",
       "      <td>182</td>\n",
       "      <td>2204</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>39228</td>\n",
       "      <td>1431</td>\n",
       "      <td>764</td>\n",
       "      <td>4510</td>\n",
       "      <td>93</td>\n",
       "      <td>2346</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>14531</td>\n",
       "      <td>15488</td>\n",
       "      <td>30243</td>\n",
       "      <td>437</td>\n",
       "      <td>14841</td>\n",
       "      <td>1867</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10290</td>\n",
       "      <td>1981</td>\n",
       "      <td>2232</td>\n",
       "      <td>1038</td>\n",
       "      <td>168</td>\n",
       "      <td>2125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2787</td>\n",
       "      <td>1698</td>\n",
       "      <td>2510</td>\n",
       "      <td>65</td>\n",
       "      <td>477</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Channel  Region  Fresh   Milk  Grocery  Frozen  Detergents_Paper  \\\n",
       "0          2       3  12669   9656     7561     214              2674   \n",
       "1          2       3   7057   9810     9568    1762              3293   \n",
       "2          2       3   6353   8808     7684    2405              3516   \n",
       "3          1       3  13265   1196     4221    6404               507   \n",
       "4          2       3  22615   5410     7198    3915              1777   \n",
       "..       ...     ...    ...    ...      ...     ...               ...   \n",
       "435        1       3  29703  12051    16027   13135               182   \n",
       "436        1       3  39228   1431      764    4510                93   \n",
       "437        2       3  14531  15488    30243     437             14841   \n",
       "438        1       3  10290   1981     2232    1038               168   \n",
       "439        1       3   2787   1698     2510      65               477   \n",
       "\n",
       "     Delicassen  Cluster  \n",
       "0          1338        6  \n",
       "1          1776        6  \n",
       "2          7844        6  \n",
       "3          1788        0  \n",
       "4          5185        6  \n",
       "..          ...      ...  \n",
       "435        2204        4  \n",
       "436        2346        4  \n",
       "437        1867        1  \n",
       "438        2125        0  \n",
       "439          52        0  \n",
       "\n",
       "[440 rows x 9 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cluster'] = label\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 결과물 시각화\n",
    "지도 학습에서의 시각화는 모델을 돌리기전에 이상치 등을 탐지하기 위해서라면\n",
    "\n",
    "비지도 학습에서의 시각화는 군집화가 제대로 되었는지, 혹은 결과의 해석을 위해 시각화를 한다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter', x='Grocery', y='Frozen', c='Cluster', cmap='Set1', \n",
    "        colorbar=True, figsize=(10, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter', x='Milk', y='Delicassen', c='Cluster', cmap='Set1', \n",
    "        colorbar=True, figsize=(10, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = (df['Cluster'] == 0) | (df['Cluster'] == 4)\n",
    "ndf = df[~mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndf.plot(kind='scatter', x='Grocery', y='Frozen', c='Cluster', cmap='Set1', \n",
    "        colorbar=False, figsize=(10, 10))\n",
    "\n",
    "\n",
    "ndf.plot(kind='scatter', x='Milk', y='Delicassen', c='Cluster', cmap='Set1', \n",
    "        colorbar=True, figsize=(10, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}