Wholesale Cutomers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5dc47667",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "# Load Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.cluster import KMeans\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5dd1d8d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Wholesale customers data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e642361e",
   "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>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",
       "    </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",
       "    </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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b73f641f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel :\n",
      " [2 1]\n",
      "1    298\n",
      "2    142\n",
      "Name: Channel, dtype: int64\n",
      "\n",
      " **************************************** \n",
      "\n",
      "Region :\n",
      " [3 1 2]\n",
      "3    316\n",
      "1     77\n",
      "2     47\n",
      "Name: Region, dtype: int64\n",
      "\n",
      " **************************************** \n",
      "\n",
      "Fresh :\n",
      " [ 12669   7057   6353  13265  22615   9413  12126   7579   5963   6006\n",
      "   3366  13146  31714  21217  24653  10253   1020   5876  18601   7780\n",
      "  17546   5567  31276  26373  22647  16165   9898  14276   4113  43088\n",
      "  18815   2612  21632  29729   1502    688  29955  15168   4591  56159\n",
      "  24025  19176  10850    630   9670   5181   3103  44466  11519   4967\n",
      "   6269   3347  40721    491  27329   5264   4098   5417  13779   6137\n",
      "   8590  35942   7823   9396   4760     85      9  19913   2446   8352\n",
      "  16705  18291   4420  19899   8190  20398    717  12205  10766   1640\n",
      "   7005    219  10362  20874  11867  16117  22925  43265   7864  24904\n",
      "  11405  12754   9198  11314   5626      3     23    403    503   9658\n",
      "  11594   1420   2932  56082  14100  15587   1454   8797   1531   1406\n",
      "  11818  12579  19046  14438  18044  11134  11173   6990  20049   8258\n",
      "  17160   4020  12212  11170  36050  76237  19219  21465    140  42312\n",
      "   7149   2101  14903   9434   7388   6300   4625   3087  13537   5387\n",
      "  17623  30379  37036  10405  18827  22039   7769   9203   5924  31812\n",
      "  16225   1289  18840   3463    622   1989   3830  17773   2861    355\n",
      "   1725  12434  15177   5531   5224  15615   4822   2926   5809   5414\n",
      "    260    200    955    514    286   2343  45640  12759  11002   3157\n",
      "  12356 112151    694  36847    327   8170   3009   2438   8040    834\n",
      "  16936  13624   5509    180   7107  17023  30624   2427  11686   3067\n",
      "   4484  25203    583   1956   1107   6373   2541   1537   5550  18567\n",
      "  12119   7291   3317   2362   2806   2532     18   4155  14755   5396\n",
      "   5041   2790   7274  12680  20782   4042   1869   8656  11072   2344\n",
      "  25962    964  15603   1838   8635  18692   7363  47493  22096  24929\n",
      "  18226  11210   6202   3062   8885  13569  15671   3191   6134   6623\n",
      "  29526  10379  31614  11092   8475  56083  53205   9193   7858  23257\n",
      "   2153   1073   5909    572  20893  11908  15218   4720   2083  36817\n",
      "    894    680  27901   9061  11693  17360  12238  49063  25767  68951\n",
      "  40254  15354  16260  42786   2708   6022   2838   3996  21273   7588\n",
      "  19087   8090   6758    444  16448   5283   2886   2599    161    243\n",
      "   6468  17327   6987    918   7034  29635   2137   9784  10617   1479\n",
      "   7127   1182  11800   9759   1774   9155  15881  13360  25977  32717\n",
      "   4414    542  16933   5113   9790  11223  22321   8565  16823  27082\n",
      "  13970   9351   2617    381   2320    255   1689   3043   1198   2771\n",
      "  27380   3428   5981   3521   1210    608    117  14039    190  22686\n",
      "     37    759    796  19746   4734   2121   4627   2615   4692   9561\n",
      "   3477  22335   6211  39679  20105   3884  15076   6338   5841   3136\n",
      "  38793   3225   4048  28257  17770  34454   1821  10683  11635   1206\n",
      "  20918   9785   9385   3352   2647    518  23632  12377   9602   4515\n",
      "  11535  11442   9612   4446  27167  26539  25606  18073   6884  25066\n",
      "   7362   8257   8708   6633   2126     97   4983   5969   7842   4389\n",
      "   5065    660   8861   4456  17063  26400  17565  16980  11243  13134\n",
      "  31012   3047   8607   3097   8533  21117   1982  16731  29703  39228\n",
      "  14531  10290   2787]\n",
      "9670     2\n",
      "3        2\n",
      "18044    2\n",
      "8040     2\n",
      "514      2\n",
      "        ..\n",
      "18827    1\n",
      "10405    1\n",
      "37036    1\n",
      "30379    1\n",
      "2787     1\n",
      "Name: Fresh, Length: 433, dtype: int64\n",
      "\n",
      " **************************************** \n",
      "\n",
      "Milk :\n",
      " [ 9656  9810  8808  1196  5410  8259  3199  4956  3648 11093  5403  1124\n",
      " 12319  6208  9465  1114  8816  6157  6327  2495  4519   871  1917 36423\n",
      "  9776  4230   961   803 20484  2100  3610  4339  1318  4786  1979  5491\n",
      "  4362 10556 15729   555  4332  3065  7555 11095  7027 22044 14069 54259\n",
      "  6152 21412  1095  4051  3916 10473  1449  3683 29892  9933  1970  5360\n",
      "  3045 38369  6245 11601  1227 20959  1534  6759  7260  2820  2037  1266\n",
      "  5139  5332  6343  1137  3587 12697  1175  3259   829  9540  9232  1563\n",
      "  3327 46197 73498  5025   542  3836   596  2762 27472  3090 12220  2920\n",
      "  2616   254   112  2182  7779 10810  6459  3504  2132  1014  6337 10646\n",
      "  8397 16729  1648 11114  2770  2295  1080   793  2521  3880  1891  2344\n",
      "  1200  3234   201 10769  1642  3473  1840  7243  8847   926  2428   589\n",
      "  2032  1042  1882  1289  8579  8080  4257  4979  4280 13252  7152  1596\n",
      "  3677  8384  1936  3373   584  1433  1825  3328  1371  9250    55 10690\n",
      "  5291  1366  6570  7704  3651   540  2024 15726  7603 12653  6721  3195\n",
      "   735   717  8675 25862  5479  7677  1208  7845  6958  7330  7075  4888\n",
      "  6036 29627  8533 43950   918  6448   521  8002  7639 11577  6250   295\n",
      "  1461  3485  1012  7209  7097  2154  2280 13240 14399 11487   685   891\n",
      " 11711   780  4737  3748 12729  1895 28326  6602  6551 10765 16599  1475\n",
      "  7504   367   899  7503  1115  2527   659  3243  5921  2204   577  2746\n",
      "  5989 10678  1780  4984  2703  6380   820  3838   475  2567  3575  1801\n",
      "  3576  7775  6154   346  5279  3795  1993 23133  1860  7961 17972   489\n",
      "  5008  1931  4563  4959  4885  1110  1372  9679 23527  9763  1222  8053\n",
      "   258  1032  5007  8323  1703  1610  3749  2317  6200  2884  7108  3965\n",
      "  3613  4411   640  2247  2102   594   286  2160  3354  3086 11103  2013\n",
      "  1897  1304  4560   879  6243 13316  5302  3688  7460 12939 12867  2374\n",
      "  1020 20655  1492  2335  3737   925  1795 14982  1375  3088  2713 25071\n",
      "  3696   713   944 16784  2209  1486  1786 14881  3216  4980   928  6817\n",
      "  1511  1347   333  1188  4025  5763  5758  6964  1172  2602  6939  7184\n",
      "  2380 14641  1099 10044  1106  6264  7393   727   134  1275 18664  5878\n",
      "  2872   607  1601   997   873  6128  2217   894   337  3944  1887  3801\n",
      "  6257  2256  1450  8630  3154  3294  5164  4591  7435  1364 21858   922\n",
      "  3620  1916   848  1530  1181  2761  4180  6730   865  1316 11991  1666\n",
      "   906  2801  4753 11006  4613  1046  5010 12844  3634  2096  3289  3605\n",
      "  4859  1990  6046 10940  5499  8494  3783  5266  4847  1377  3686  2408\n",
      "  9347 16687  5970  1750  5506  1162  3218  3922 12051  1431 15488  1981\n",
      "  1698]\n",
      "3045     2\n",
      "1610     2\n",
      "5139     2\n",
      "2428     2\n",
      "3587     2\n",
      "        ..\n",
      "1596     1\n",
      "7152     1\n",
      "13252    1\n",
      "4280     1\n",
      "1698     1\n",
      "Name: Milk, Length: 421, dtype: int64\n",
      "\n",
      " **************************************** \n",
      "\n",
      "Grocery :\n",
      " [ 7561  9568  7684  4221  7198  5126  6975  9426  6192 18881 12974  4523\n",
      " 11757 14982 12091  3821 12121  2933 10099  9464  4602  2010  4469 22019\n",
      " 13792  7595  2861  3045 25957  2609 11107  3133  2886  7326  2262 11091\n",
      "  5428 12477 16709   902  4757  5956 14961 23998 10471 21531 21955 55571\n",
      " 10868 28921  1980  6996  5876 11532  1947  5005 26866 10487  1648  8040\n",
      "  7854 59598  6544 15775  3250 45828  7417 13462  3993  1293  3202 21042\n",
      "  2661  8713  9794     3  6532 28540  2067  3655  3009 14403 11009  1783\n",
      "  4814 92780 32114  8117  4042  5330  1638  2530 32034  2062 11323  6252\n",
      "  8118   610   778  1909 12144 16267  7677  8906  3445  3970 10704 14886\n",
      "  6981 28986  1694 17569  2469  1733  2000  2988  3355  5380  2362  2147\n",
      "  3412  1498   245  8814  2961  7102  1658 10685  3823  1510   699   314\n",
      "  2479  1235  2174  2591  7030  8282  5034  3343  7305  5189  8253  1096\n",
      "  1988 34792  2177  2707   542  1651  1765  2022  3135  2368   137 19460\n",
      " 14855  2474  9618 14682 12822   283  3810 26870  8584 19858  9170  3268\n",
      "   803  2155 13430 19816  6536 19805  5241 11874  4533  4945  2500  8887\n",
      " 18148 10518 20170  4710  1139   854  9819 11687 11522  1981  1381  2251\n",
      " 20292  2974  5230  4897 10391  6824  2112 23127 24708  9490  2216  5226\n",
      " 23596   950  6089  5838 16767  1393 39694  6861 11364 15538 36486  2046\n",
      " 15205  1390  1382 10646  2856  5265  1499  4157  9212  1563   572  2501\n",
      "  5615  3828  3838  3316  3833  2824  3047   593   585  3779  7041  2475\n",
      "  2914  5119 10817 13916  1777   489  2406  2070  1799 33586  4740 16966\n",
      "  4748  1495  5249  1883  2124  7336  2157  1094  1677  6684 15445 13699\n",
      " 22182  2576 19847  1138   975  6869  1493  1841   223  6964   683  2543\n",
      "  9694  2431  6235  4252  2013 12609  3600  1242  2828  1296   471  2642\n",
      "  3261  4329 12469  6550  5234  3643  6986  9965  2060  6360 20399  9785\n",
      " 13829 24773  8852 21570  2842  3007 13567  2405  8280 19172  7647 11924\n",
      "  2201  6114  3558 17645  2280  5167  3315 11593  2464 13626  1431  1664\n",
      "  3389  4583  5109 26839  1447 67298  2743 10790  1330  2611  7021  5332\n",
      "  9670 11238  5923 26316  1763  8335 15541 12311  2028 20521  1997 22294\n",
      "  1533 21203  2548  2012   218 22272  1660  2109  2006   864  2453  4438\n",
      "  1524  8025   534  4955  1939  1641  7398  1668  1162 13586  2648  1902\n",
      "  2146  1617  8469  3450 15400  1614  2857  1573  1172  1422  1328  2313\n",
      "  3842  3204  1263  9345  1428   582   935  1238  2128  5091  4604  3444\n",
      "  1167  5026 18683  6407  6100  4563  3281 12400  6633  3417  8552 10908\n",
      " 11055 18622  2223 13227  9053  4172  4657 12232  2593 14316  5429  4910\n",
      "  3580 16483  5160  4754  7994 16027   764 30243  2232  2510]\n",
      "1664    2\n",
      "2062    2\n",
      "683     2\n",
      "3600    2\n",
      "6536    2\n",
      "       ..\n",
      "7305    1\n",
      "3343    1\n",
      "5034    1\n",
      "8282    1\n",
      "2510    1\n",
      "Name: Grocery, Length: 430, dtype: int64\n",
      "\n",
      " **************************************** \n",
      "\n",
      "Frozen :\n",
      " [  214  1762  2405  6404  3915   666   480  1669   425  1159  4400  1420\n",
      "   287  3095   294   397   134   839  2205   669  1066  3383  9408  5154\n",
      "  2915   201  3151   485  1158  1200  1148  2088   266  6130   833  1729\n",
      "  1920    33 10002  9510  2033   188   787   541  1740  1668  7782   584\n",
      "  1798  3860   239   532   744  2436  1057  2616    38   596   129    96\n",
      "  3254  4154  2896  3724    36   175  1256  5870   779 10643  5373  8872\n",
      "  8132  1285  4407  7530   869  2096   868   430   283   737  2320  1178\n",
      "  1026   987  6312  9735  3443  3347  8693  3232 35009   206   440   145\n",
      "   774   895  5639  3252  1593  2561 18028  1336   910   133  2471   247\n",
      "   673  2276   805  8853  3220  2555  2715  1517  1647  5343  3896  2417\n",
      "  2395  1991  2194  4787 16538  8195   880   142  1718  6316   346   576\n",
      "   436   720  1170  4575   661   155   825  2279   321  2995  8425   118\n",
      "    42   926  1286  4052   800   853   531  3001    75   233   317  3378\n",
      "   930   398   824  1092  2665  2367  2540  4425   993   405  1393  2399\n",
      "  1116   651   333   937  2515    52  7368  1752  1152  4477   402 16745\n",
      "   443 36534    74  2181  3470  6269  2758   275  7332   890   547   959\n",
      "   806  7888 18711  1127  3527   520  3941  3549  5065   469  1383   955\n",
      "   878  2946  1859   864  1801  4736  1291  1329   913  1374   179  2532\n",
      "  2306  1765    91  7496  5612   784   660  1759  2286   950  6845  8321\n",
      "  1439   638  4260  1218  2312  4634  1112  5243 11422  2216  3752   561\n",
      "  1183   230  1777  2077   559  6340  1730  6746  7683   432  4686  3242\n",
      "   453  5004  6422  3012   327  6818   982  4324    61 10155  2221  3975\n",
      "  1069  2516  5500  1120   529  4802   862  4479 16919  5845  1293   977\n",
      "  1093  5970 10303  8692  1042  1619  8366   848  1388   502  2507  3838\n",
      "   902   909   417  3045  1455   934   264  1809   364   492   617   799\n",
      "  1840  1149   416  1465 12569  3046  1274  4447  1483   662  2679   978\n",
      "  2121  1128   514  2714  3703   915  2369 60869  3498   414  7849  5127\n",
      "  3570  1234  2208   131 11559  1365   650  8170 15601  9584   388   767\n",
      "   349  1456  2234  2693  2809  1341  2005  1796  1741   830   228  6386\n",
      "   245  3157   137  6114   340  2601  1206   560   191  1103  1173  1457\n",
      "  2046  1089  1364  8164   876  1504  1492   597  5641  1034   282   130\n",
      "  3881  9927  4006  3635  2583  1945  1960  1677  3019  5502   907   659\n",
      "  8620  1398  2921  2644  6838  5390  1601  3576 13223   220   127  2069\n",
      "  9806  2854  1646  2349  1389  1535    98 17866  5679  1691   633    25\n",
      "  1031  1059   874 15348  3141 15082  2198    47   575 13486   269  1541\n",
      "   688 13135  4510   437  1038    65]\n",
      "2540    2\n",
      "425     2\n",
      "1285    2\n",
      "4324    2\n",
      "1619    2\n",
      "       ..\n",
      "2995    1\n",
      "321     1\n",
      "2279    1\n",
      "825     1\n",
      "65      1\n",
      "Name: Frozen, Length: 426, dtype: int64\n",
      "\n",
      " **************************************** \n",
      "\n",
      "Detergents_Paper :\n",
      " [ 2674  3293  3516   507  1777  1795  3140  3321  1716  7425  5977   549\n",
      "  3881  6707  5058   964  4508   370  2767  2518  2259   375  2381  4337\n",
      "  4482  4003   242   100  8604  1107  2134   820   918   361   483  4239\n",
      "   862  6506  6956   212  1145  2575  6899  9529  4618  7353  6792 24171\n",
      "  5121 13583   609  1538  2587  5611   204  2024 17740  7572   227  3084\n",
      "  4095 26701  4074  7677  1247 24231  3468  5141   788   656   116  4173\n",
      "  1321   764  1901     3   529 12034   301  1202   610  7818  3537   550\n",
      "  3837 40827 20070  1579   165   454    69   627 18906    71  5038   223\n",
      "  3874    54    56   215  8035  6766  4573  1480  1491   139  6830  8969\n",
      "  2505   836   169  6457   585   118   276   310   319   411   266   174\n",
      "   264    25  1976   500   778   349  2386  1062   410   395    70   955\n",
      "   256    47   199  2447   721   249   637   960    51    20   399   516\n",
      " 12591    73  1082   283   113   170   255   352   302     7 11577  6694\n",
      "   811  4004  8077  4424   232 13726  3674  7108  4973  1680    79  7015\n",
      "  8773  2840  9836   153  4196  1532   120   273  1382  4948  6907   239\n",
      "   334    58   949  3459  6839  4027    43   187  5618   355   330   763\n",
      "  4314   592   402  9959 14235   284   954     5  9265   288  5316  3381\n",
      " 12420   244 19410   240  3961  5957  5828 13308   130  4797    86  4167\n",
      "   761  2568   263  4762   694  1566   409   325  1216   415    28    72\n",
      "   828   343   412   586  1682  3143  8933   430    44   562   234 18594\n",
      "   205   363  1547   111   392  3593   730   967   780    49   429  2894\n",
      "  5980   830  4882   737  6374   333   197   147    93   210   759    96\n",
      "   603   621   274  3620   167  2328  1041   314   751   436  1226   386\n",
      "   445    32   965   825  5952  2208   710  3712  4538   290  2662  8752\n",
      "  6236 10069 11783  3909  7558   351   257  6846   299   371 17120   183\n",
      "   857  3891    83   821   706 12408   275   228  1470  1679   140  1272\n",
      "   387    88   492   182  9606   178 38102   332  4111   146   442    15\n",
      "   573  7271  5162  4595 15469   217  3843  6600  4621  1184 12218   173\n",
      " 12638    90  8682  1333   184     9  6747   536   468   159   179  1335\n",
      "   514  4515   222   252   101    41   523   716   397  1916   311   476\n",
      "  4666    68   813   600   246  1711   282   192   353   231   200    95\n",
      "   122   385   149   841  3378    64    74   469    92    10   632   914\n",
      "   593  1092  7883  2730  2123  1860   235  2970   912  1135  3540  6728\n",
      "  3485  6740  1580  6818  3415   948  1803  3213   108  5079   439   850\n",
      "    84   241  1377  1328   356  2371 14841   168   477]\n",
      "118    2\n",
      "955    2\n",
      "256    2\n",
      "69     2\n",
      "918    2\n",
      "      ..\n",
      "516    1\n",
      "399    1\n",
      "51     1\n",
      "960    1\n",
      "477    1\n",
      "Name: Detergents_Paper, Length: 417, dtype: int64\n",
      "\n",
      " **************************************** \n",
      "\n",
      "Delicassen :\n",
      " [ 1338  1776  7844  1788  5185  1451   545  2566   750  2098  1744   497\n",
      "  2931   602  2168   412  1080  4478  3181   501  2124   569  4334 16523\n",
      "  5778    57   833   518  5206   823  2963   985   405  1083   395   436\n",
      "  4626   714   433  2916  5864  2802    46    72    65  4985  1452  6465\n",
      "  1476  1163  2162   301  1278   224  1333  1130  1340  1282  1603   225\n",
      "  2017   964  1295  1145  1423    27   834  3095   144  1365 14472   181\n",
      "   648  1780   975   894  1009   167  1653   529   156  2342   772   120\n",
      "  2944   903 14351  3178   360  1117  5130  2698   244   709   217    63\n",
      "   132   323  3029  1838  1386  2498   548  1378  1831  1438  1236     3\n",
      "  1647  1519  2708  1561  1266   610   222  1160   933   635  1136   255\n",
      "   860   143  1621   918   483  2749  1819   911   310   328   396   537\n",
      "   326  1542    36  3271   929  2616  1450   318   201  4430   520   526\n",
      "   434  1440  1067  1774   184  1627     8  2153  3182   418  1682   303\n",
      "  2157  2233   446   238  2379  3637   693   429  6250   707   716  1442\n",
      "  1697   230  2631  2165  2794  8550 47943    11   247   727   404  1856\n",
      "    64    84   409   666  1142  1755  2876  1468   697   347   731  1681\n",
      "  6854    18  1328   710   285   806   797  2100  2870  1775  1215   791\n",
      "  2388   674  1158  6372   130   749   239   375  1360   659   786  1553\n",
      "   689   203   980  2137   490     7  2563   295   216  2253  2564  1047\n",
      "   578  2398  1970  2784   572   291  5121  1693  1391  3265   615   373\n",
      "   987  3321   818   287   655   411  1265  3636  3628   698   204    56\n",
      "  1550  1040  1824  1153   379  2503   139  1409  1721  1104  2079  1404\n",
      "  1384  2406   128  1027   258    22  1522   686  1060   741  1854   254\n",
      "   898   531  1037   259  2005   172   555    59  2410   211  1543   925\n",
      "   656   117   142   297  1233  3508  1059  1637    51  1625  1113   229\n",
      "   573  1092  5609   522  1534   739  1043  1102  2602  3486  2139   778\n",
      "   868   550  1942  1371  2158    37  1115  1022   665   445   995  3137\n",
      "   195  1111  2341   127   110  4100   776   503   712   314   468  3105\n",
      "   447   342   558   296  2235   790  4829  3113    70  1426  1242  1114\n",
      "   179   270   532  2893   361  5120  1068   967   961   406   684  1000\n",
      "  1827   654   819   452   290  2213   743  1014  1902   340   288   715\n",
      "   378   960   553   344  5137  1892  4365    62  2435  1874   993  1063\n",
      "  1521  1393  1784  1218   668   249  1886  1894   317  2501  2080  1498\n",
      "  1449   838  2204  2346  1867  2125    52]\n",
      "3       4\n",
      "834     4\n",
      "548     3\n",
      "610     3\n",
      "395     3\n",
      "       ..\n",
      "318     1\n",
      "1450    1\n",
      "2616    1\n",
      "929     1\n",
      "52      1\n",
      "Name: Delicassen, Length: 403, dtype: int64\n",
      "\n",
      " **************************************** \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# let's check unique values\n",
    "columns=df.columns\n",
    "for col in columns:\n",
    "    print(col,':\\n',df[col].unique())\n",
    "    print(df[col].value_counts())\n",
    "    print('\\n',20*'**','\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "841aa074",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Channel'}>,\n",
       "        <AxesSubplot:title={'center':'Region'}>,\n",
       "        <AxesSubplot:title={'center':'Fresh'}>],\n",
       "       [<AxesSubplot:title={'center':'Milk'}>,\n",
       "        <AxesSubplot:title={'center':'Grocery'}>,\n",
       "        <AxesSubplot:title={'center':'Frozen'}>],\n",
       "       [<AxesSubplot:title={'center':'Detergents_Paper'}>,\n",
       "        <AxesSubplot:title={'center':'Delicassen'}>, <AxesSubplot:>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x1440 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use(\"seaborn\")\n",
    "df.hist(figsize=(25,20), bins=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "86d5777d",
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.322727</td>\n",
       "      <td>2.543182</td>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.468052</td>\n",
       "      <td>0.774272</td>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8504.000000</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5e03e096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Channel             int64\n",
       "Region              int64\n",
       "Fresh               int64\n",
       "Milk                int64\n",
       "Grocery             int64\n",
       "Frozen              int64\n",
       "Detergents_Paper    int64\n",
       "Delicassen          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "581611f4",
   "metadata": {},
   "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"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90f42d83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Channel'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "de6d6c12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    298\n",
       "2    142\n",
       "Name: Channel, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Channel'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "16c2f0cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Channel', ylabel='count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfIAAAFXCAYAAABZQMyNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAW7UlEQVR4nO3df2xVd/3H8dftvdwO2ltaB5tLWPklTQNNBVNhE4fgwMvI2MRAI5cUXQkZDQtrMxsKjB+mCONbLWy4DkSTb1KctQPGmm+iUxqwClhnEyDtUl0QmTBcStbJvZd5W+B8/zC7jq/QH/v23Mv78nz81Xvuuee+L0nzvJ/D7T0ex3EcAQAAk9KSPQAAAPj0CDkAAIYRcgAADCPkAAAYRsgBADCMkAMAYJgv2QN8Gl1d4WSPAABAwoweHbjtfazIAQAwjJADAGAYIQcAwDBCDgCAYYQcAADDCDkAAIYRcgAADCPkAAAY5toXwly/fl3PP/+8zp07J6/Xq+3bt8txHFVVVcnj8WjSpEnavHmz0tLS1NjYqIaGBvl8PpWVlWnOnDlujQUAQEpxLeRHjx6VJDU0NKi1tTUe8vLycs2YMUObNm1Sc3Ozpk6dqvr6eh08eFCxWEyhUEgzZ86U3+93azQAAFKGayGfO3euZs+eLUl67733NGrUKB07dkzTp0+XJM2aNUvHjx9XWlqapk2bJr/fL7/fr9zcXHV2dqqwsNCt0QAASBmufte6z+fT2rVr9etf/1ovvfSSjh49Ko/HI0nKyMhQOBxWJBJRIPDv75DNyMhQJBLp87g5OSPk83ndHB0AABNcv2jKjh079J3vfEfFxcWKxWLx7dFoVFlZWcrMzFQ0Gr1p+yfDfivd3VddmxcAgDtNXxdNcS3khw8f1vvvv6+nn35aw4cPl8fjUUFBgVpbWzVjxgy1tLTooYceUmFhoXbt2qVYLKaenh6dPXtWeXl5bo3Vp2drmpLyvMBQe7HyiWSPACBBXAv51772Na1bt07Lli3TtWvXtH79ek2cOFEbN25UbW2tJkyYoGAwKK/Xq5KSEoVCITmOo4qKCqWnp7s1FgAAKcXjOI6T7CEGy63rkbMiR6pgRQ6kFq5HDgBAiiLkAAAYRsgBADCMkAMAYBghBwDAMEIOAIBhhBwAAMMIOQAAhhFyAAAMI+QAABhGyAEAMIyQAwBgGCEHAMAwQg4AgGGEHAAAwwg5AACGEXIAAAwj5AAAGEbIAQAwjJADAGAYIQcAwDBCDgCAYYQcAADDCDkAAIYRcgAADCPkAAAYRsgBADCMkAMAYBghBwDAMEIOAIBhhBwAAMMIOQAAhhFyAAAMI+QAABhGyAEAMIyQAwBgGCEHAMAwQg4AgGGEHAAAwwg5AACG+dw4aG9vr9avX6+LFy+qp6dHZWVl+uxnP6tVq1Zp3LhxkqSlS5dqwYIFamxsVENDg3w+n8rKyjRnzhw3RgIAICW5EvKmpiZlZ2erpqZG3d3dWrRokVavXq2nnnpKpaWl8f26urpUX1+vgwcPKhaLKRQKaebMmfL7/W6MBQBAynEl5PPnz1cwGIzf9nq9am9v17lz59Tc3KyxY8dq/fr1OnPmjKZNmya/3y+/36/c3Fx1dnaqsLDQjbEAAEg5roQ8IyNDkhSJRLRmzRqVl5erp6dHS5YsUUFBgV555RW9/PLLys/PVyAQuOlxkUik3+Pn5IyQz+d1Y3QgJYweHeh/JwApwZWQS9KlS5e0evVqhUIhLVy4UFeuXFFWVpYkad68eaqurlZRUZGi0Wj8MdFo9Kaw305391W3xgZSQldXONkjABhCfb05d+VT65cvX1ZpaakqKyu1ePFiSdKKFSt05swZSdLJkyc1ZcoUFRYWqq2tTbFYTOFwWGfPnlVeXp4bIwEAkJJcWZHv2bNHV65cUV1dnerq6iRJVVVV2rZtm4YNG6ZRo0apurpamZmZKikpUSgUkuM4qqioUHp6uhsjAQCQkjyO4zjJHmKw3Dpt+GxNkyvHBRLtxconkj0CgCGU8FPrAAAgMQg5AACGEXIAAAwj5AAAGEbIAQAwjJADAGAYIQcAwDBCDgCAYYQcAADDCDkAAIYRcgAADCPkAAAYRsgBADCMkAMAYBghBwDAMEIOAIBhhBwAAMMIOQAAhhFyAAAMI+QAABhGyAEAMIyQAwBgGCEHAMAwQg4AgGGEHAAAwwg5AACGEXIAAAwj5AAAGEbIAQAwjJADAGAYIQcAwDBCDgCAYYQcAADDCDkAAIYRcgAADCPkAAAYRsgBADCMkAMAYBghBwDAMEIOAIBhPjcO2tvbq/Xr1+vixYvq6elRWVmZPve5z6mqqkoej0eTJk3S5s2blZaWpsbGRjU0NMjn86msrExz5sxxYyQAAFKSKyFvampSdna2ampq1N3drUWLFik/P1/l5eWaMWOGNm3apObmZk2dOlX19fU6ePCgYrGYQqGQZs6cKb/f78ZYAACkHFdCPn/+fAWDwfhtr9erjo4OTZ8+XZI0a9YsHT9+XGlpaZo2bZr8fr/8fr9yc3PV2dmpwsJCN8YCACDluBLyjIwMSVIkEtGaNWtUXl6uHTt2yOPxxO8Ph8OKRCIKBAI3PS4SifR7/JycEfL5vG6MDqSE0aMD/e8EICW4EnJJunTpklavXq1QKKSFCxeqpqYmfl80GlVWVpYyMzMVjUZv2v7JsN9Od/dVV2YGUkVXVzjZIwAYQn29OXflU+uXL19WaWmpKisrtXjxYknS5MmT1draKklqaWlRUVGRCgsL1dbWplgspnA4rLNnzyovL8+NkQAASEmurMj37NmjK1euqK6uTnV1dZKkDRs2aOvWraqtrdWECRMUDAbl9XpVUlKiUCgkx3FUUVGh9PR0N0YCACAleRzHcZI9xGC5ddrw2ZomV44LJNqLlU8kewQAQyjhp9YBAEBiEHIAAAwj5AAAGEbIAQAwjJADAGAYIQcAwDBCDgCAYYQcAADDCDkAAIYRcgAADCPkAAAYRsgBADCMkAMAYBghBwDAMEIOAIBhhBwAAMMIOQAAhhFyAAAMI+QAABhGyAEAMIyQAwBgGCEHAMAwQg4AgGGEHAAAwwg5AACGDSjk1dXV/7Ft7dq1Qz4MAAAYHF9fd27YsEF/+9vf1N7ernfeeSe+/dq1awqHw64PBwAA+tZnyMvKynTx4kV973vf0zPPPBPf7vV6NXHiRNeHAwAAfesz5GPGjNGYMWPU1NSkSCSicDgsx3EkSVevXlV2dnYiZgQAALfRZ8g/tnfvXu3du/emcHs8HjU3N7s1FwAAGIABhfy1117TkSNH9JnPfMbteQAAwCAM6FPrDzzwgEaOHOn2LAAAYJAGtCIfN26cQqGQZsyYIb/fH9/+yQ/AAQCAxBtQyO+//37df//9bs8CAAAGaUAhZ+UNAMCdaUAhz8/Pl8fjuWnbfffdp9/85jeuDAUAAAZmQCHv7OyM/9zb26sjR47o1KlTbs0EAAAGaNAXTRk2bJgee+wx/f73v3djHgAAMAgDWpEfPnw4/rPjOHrnnXfk8w3ooQAAwEUDqnFra+tNt3NycrRr165+H3f69Gl9//vfV319vTo6OrRq1SqNGzdOkrR06VItWLBAjY2NamhokM/nU1lZmebMmTPoFwEAwN1qQCHfvn27ent7de7cOV2/fl2TJk3qd0W+b98+NTU1afjw4ZKkt99+W0899ZRKS0vj+3R1dam+vl4HDx5ULBZTKBTSzJkzb/pbdQAAcHsD+j/y9vZ2BYNBVVVVad26dZo9e7ZOnz7d52Nyc3O1e/fum45x7NgxLVu2TOvXr1ckEtGZM2c0bdo0+f1+BQIB5ebm3vTBOgAA0LcBrci3bt2qnTt36vOf/7wk6dSpU6qurtaBAwdu+5hgMKgLFy7EbxcWFmrJkiUqKCjQK6+8opdffln5+fkKBALxfTIyMhSJRPqdJydnhHw+70BGB+5Ko0cH+t8JQEoYUMivXr0aj7gkTZ06VbFYbFBPNG/ePGVlZcV/rq6uVlFRkaLRaHyfaDR6U9hvp7v76qCeG7jbdHWFkz0CgCHU15vzAZ1aHzlypI4cORK/feTIkUFfi3zFihU6c+aMJOnkyZOaMmWKCgsL1dbWplgspnA4rLNnzyovL29QxwUA4G42oBV5dXW1nn76aW3YsCG+raGhYVBPtGXLFlVXV2vYsGEaNWqUqqurlZmZqZKSEoVCITmOo4qKCqWnpw/uFQAAcBcbUMhbWlo0fPhwvf7663r33XdVUVGhP/zhDxo/fnyfjxszZowaGxslSVOmTLll/IuLi1VcXPwpRgcAAAM6td7Y2Kif/exnGjFihPLz83Xo0CHt37/f7dkAAEA/BhTy3t5eDRs2LH77kz8DAIDkGdCp9blz5+pb3/qWHnvsMXk8Hr355pt69NFH3Z4NAAD0Y0Ahr6ys1C9/+Uu99dZb8vl8Wr58uebOnev2bADuIpX/83yyRwD+32oe35rw5xzwlU/mz5+v+fPnuzkLAAAYpEFfxhQAANw5CDkAAIYRcgAADCPkAAAYRsgBADCMkAMAYBghBwDAMEIOAIBhhBwAAMMIOQAAhhFyAAAMI+QAABhGyAEAMIyQAwBgGCEHAMAwQg4AgGGEHAAAwwg5AACGEXIAAAwj5AAAGEbIAQAwjJADAGAYIQcAwDBCDgCAYYQcAADDCDkAAIYRcgAADCPkAAAYRsgBADCMkAMAYBghBwDAMEIOAIBhhBwAAMMIOQAAhhFyAAAMczXkp0+fVklJiSTp/PnzWrp0qUKhkDZv3qwbN25IkhobG/WNb3xDxcXFOnr0qJvjAACQclwL+b59+/T8888rFotJkrZv367y8nK9+uqrchxHzc3N6urqUn19vRoaGvSTn/xEtbW16unpcWskAABSjmshz83N1e7du+O3Ozo6NH36dEnSrFmzdOLECZ05c0bTpk2T3+9XIBBQbm6uOjs73RoJAICU43PrwMFgUBcuXIjfdhxHHo9HkpSRkaFwOKxIJKJAIBDfJyMjQ5FIpN9j5+SMkM/nHfqhgRQxenSg/50ADLlk/O65FvL/Ky3t34v/aDSqrKwsZWZmKhqN3rT9k2G/ne7uq67MCKSKrq5wskcA7kpu/e719QYhYZ9anzx5slpbWyVJLS0tKioqUmFhodra2hSLxRQOh3X27Fnl5eUlaiQAAMxL2Ip87dq12rhxo2prazVhwgQFg0F5vV6VlJQoFArJcRxVVFQoPT09USMBAGCeqyEfM2aMGhsbJUnjx4/X/v37/2Of4uJiFRcXuzkGAAApiy+EAQDAMEIOAIBhhBwAAMMIOQAAhhFyAAAMI+QAABhGyAEAMIyQAwBgGCEHAMAwQg4AgGGEHAAAwwg5AACGEXIAAAwj5AAAGEbIAQAwjJADAGAYIQcAwDBCDgCAYYQcAADDCDkAAIYRcgAADCPkAAAYRsgBADCMkAMAYBghBwDAMEIOAIBhhBwAAMMIOQAAhhFyAAAMI+QAABhGyAEAMIyQAwBgGCEHAMAwQg4AgGGEHAAAwwg5AACGEXIAAAwj5AAAGEbIAQAwjJADAGCYL9FP+PWvf12BQECSNGbMGK1atUpVVVXyeDyaNGmSNm/erLQ03l8AADAQCQ15LBaTJNXX18e3rVq1SuXl5ZoxY4Y2bdqk5uZmzZs3L5FjAQBgVkKXvp2dnfroo49UWlqq5cuX69SpU+ro6ND06dMlSbNmzdKJEycSORIAAKYldEV+zz33aMWKFVqyZIn++te/auXKlXIcRx6PR5KUkZGhcDjc73FyckbI5/O6PS5g1ujRgWSPANyVkvG7l9CQjx8/XmPHjpXH49H48eOVnZ2tjo6O+P3RaFRZWVn9Hqe7+6qbYwLmdXX1/4YYwNBz63evrzcICT21fuDAAb3wwguSpPfff1+RSEQzZ85Ua2urJKmlpUVFRUWJHAkAANMSuiJfvHix1q1bp6VLl8rj8Wjbtm3KycnRxo0bVVtbqwkTJigYDCZyJAAATEtoyP1+v37wgx/8x/b9+/cncgwAAFIGf7ANAIBhhBwAAMMIOQAAhhFyAAAMI+QAABhGyAEAMIyQAwBgGCEHAMAwQg4AgGGEHAAAwwg5AACGEXIAAAwj5AAAGEbIAQAwjJADAGAYIQcAwDBCDgCAYYQcAADDCDkAAIYRcgAADCPkAAAYRsgBADCMkAMAYBghBwDAMEIOAIBhhBwAAMMIOQAAhhFyAAAMI+QAABhGyAEAMIyQAwBgGCEHAMAwQg4AgGGEHAAAwwg5AACGEXIAAAwj5AAAGEbIAQAwjJADAGAYIQcAwDBfsgeQpBs3bmjLli3605/+JL/fr61bt2rs2LHJHgsAgDveHbEiP3LkiHp6evTzn/9czz33nF544YVkjwQAgAl3RMjb2tr0yCOPSJKmTp2q9vb2JE8EAIANd8Sp9UgkoszMzPhtr9era9euyee79XijRwdcmePV/1rmynEB9O+/n3ox2SMAJt0RK/LMzExFo9H47Rs3btw24gAA4N/uiJB/4QtfUEtLiyTp1KlTysvLS/JEAADY4HEcx0n2EB9/av3Pf/6zHMfRtm3bNHHixGSPBQDAHe+OCDkAAPh07ohT6wAA4NMh5AAAGEbIkVCnT59WSUlJsscA7hq9vb2qrKxUKBTS4sWL1dzcnOyRMMT4Gy8kzL59+9TU1KThw4cnexTgrtHU1KTs7GzV1NSou7tbixYt0qOPPprssTCEWJEjYXJzc7V79+5kjwHcVebPn69nn302ftvr9SZxGriBkCNhgsEgX/QDJFhGRoYyMzMViUS0Zs0alZeXJ3skDDFCDgAp7tKlS1q+fLmefPJJLVy4MNnjYIixPAKAFHb58mWVlpZq06ZNevjhh5M9DlzAihwAUtiePXt05coV1dXVqaSkRCUlJfrnP/+Z7LEwhPhmNwAADGNFDgCAYYQcAADDCDkAAIYRcgAADCPkAAAYRsiBFBeJRPTd735Xjz/+uJ588kmVlJSoo6NDra2tSbuATVVVlQ4dOpSU5wZSDSEHUtiNGze0cuVKjRw5UocPH9Ybb7yh1atXa+XKlfrwww+TPR6AIcA3uwEprLW1VZcuXdKaNWuUlvav9+0PPfSQtm/frmg0qg8++EArV67Uu+++q/Hjx+ull16S3+/Xzp07dfLkSf3jH//Qfffdp507d2rUqFH68pe/rGAwqLa2Nnm9Xu3atUsPPvigvvrVr+qJJ57Q7373O3300UfasWOHCgoKdP78eW3ZskUffvih7rnnHm3cuFGTJ09O8r8KkFpYkQMp7O2331Z+fn484h/7yle+onvvvVfvvfeeNm3apF/84he6fPmyTpw4ofPnz+svf/mLGhoa9Oabb+qBBx5QU1OTJKmrq0sPP/ywDh8+rC9+8Yv66U9/Gj9mdna2Dhw4oG9+85vau3evJGnt2rWqrKzU66+/rurqalVUVCTuxQN3CVbkQApLS0tTenr6be/Pz8/Xgw8+KEmaOHGiuru7NXv2bK1du1avvfaazp07p1OnTik3Nzf+mEceeUSSNGnSJP3xj3+85fZf/epXikajam9v17p16+L7XL16Vd3d3UP6GoG7HSEHUlhBQYFeffVVOY4jj8cT315bW6svfelLN11W1uPxyHEctbe367nnntO3v/1tBYNBpaWl6ZPf5PzxG4OP97/Vdulf/z/v9/v1xhtvxPf5+9//ruzsbFdeK3C34tQ6kMKKiop077336oc//KGuX78uSfrtb3+rQ4cO6YMPPrjlY9566y1Nnz5dS5cu1bhx43Ts2LH4YwcjEAho3Lhx8ZAfP35cy5Yt+/QvBsAtsSIHUpjH41FdXZ22b9+uxx9/XD6fTzk5OfrRj36kcDh8y8csWLBAzzzzTPy61QUFBbpw4cKnev6amhpt2bJFP/7xjzVs2DDt3LnzpjMDAP7/uPoZAACGcWodAADDCDkAAIYRcgAADCPkAAAYRsgBADCMkAMAYBghBwDAMEIOAIBh/wt0SFY189GZrAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Channel\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e7814bcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Channel'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Channel'].value_counts().plot(kind='pie',autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cd04b95b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Region'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fdc302ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    316\n",
       "1     77\n",
       "2     47\n",
       "Name: Region, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Region'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "291b26b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Region', ylabel='count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Region\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9a6313db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Region'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Region'].value_counts().plot(kind='pie',autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e65dc511",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c06c185b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Channel', 'Region', 'Fresh', 'Milk', 'Grocery', 'Frozen',\n",
       "       'Detergents_Paper', 'Delicassen'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bf3fbdcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(440, 8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3b52384e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e3d14a8f10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Channel'],df['Region'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bfe3542",
   "metadata": {},
   "source": [
    "### Using Elbow Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2234a275",
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_of_sqr_distance = []\n",
    "k_range = range(1,15)\n",
    "for k in k_range:\n",
    "    km_model2 = KMeans(n_clusters=k)\n",
    "    km_model2.fit(df)\n",
    "    sum_of_sqr_distance.append(km_model2.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "978b7c02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for Elbow Method\n",
    "plt.plot(k_range,sum_of_sqr_distance)\n",
    "plt.xlabel('K- Clusters')\n",
    "plt.ylabel('Sum_SD')\n",
    "plt.title(\"Elbow Plot for Optimal K\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8a90adc",
   "metadata": {},
   "source": [
    "### Using Silhouette Method for finding Optimal K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c4363bf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import silhouette_samples,silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2593bcf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2, silhouette score is 0.5115333882980367)\n",
      "For n_clusters = 3, silhouette score is 0.4783511411368133)\n",
      "For n_clusters = 4, silhouette score is 0.41435163220688853)\n",
      "For n_clusters = 5, silhouette score is 0.3878346672087667)\n",
      "For n_clusters = 6, silhouette score is 0.37825536196370885)\n"
     ]
    }
   ],
   "source": [
    "range_n_clusters = [2, 3, 4, 5, 6]\n",
    "for n_clusters in range_n_clusters:\n",
    "    km_models3 = KMeans(n_clusters=n_clusters)\n",
    "    preds = km_models3.fit_predict(df)\n",
    "    centers = km_models3.cluster_centers_\n",
    "\n",
    "    score = silhouette_score(df, preds)\n",
    "    print(\"For n_clusters = {}, silhouette score is {})\".format(n_clusters, score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2e6c20aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "km3 = KMeans(n_clusters=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cdfc9063",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit Predict\n",
    "y_pred = km3.fit_predict(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "82dba85b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6a1cd66e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.34133333e+00, 2.52800000e+00, 7.94411200e+03, 5.15181867e+03,\n",
       "        7.53612800e+03, 2.48413067e+03, 2.87255733e+03, 1.21426133e+03],\n",
       "       [1.21538462e+00, 2.63076923e+00, 3.54013692e+04, 9.51423077e+03,\n",
       "        1.03463692e+04, 6.46309231e+03, 2.93304615e+03, 3.31684615e+03]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Centroid or Cluster Centers\n",
    "km3.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "45c9099a",
   "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>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",
       "    </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",
       "    </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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "184f4703",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['clusters'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "06037c45",
   "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>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>clusters</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>0</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>0</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>0</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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "   Delicassen  clusters  \n",
       "0        1338         0  \n",
       "1        1776         0  \n",
       "2        7844         0  \n",
       "3        1788         0  \n",
       "4        5185         1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc3eb49a",
   "metadata": {},
   "source": [
    "### Plotting the Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bd4e92f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['clusters'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0345c50e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = df[df['clusters'] == 0]\n",
    "df01 = df[df['clusters'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e64354b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cluster')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter Plot  of Clusters\n",
    "plt.scatter(df0['Channel'],df0['Region'],color='blue')\n",
    "plt.scatter(df01['Channel'],df01['Region'],color='green')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Cluster')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8c47ca62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.34133333e+00, 2.52800000e+00, 7.94411200e+03, 5.15181867e+03,\n",
       "        7.53612800e+03, 2.48413067e+03, 2.87255733e+03, 1.21426133e+03],\n",
       "       [1.21538462e+00, 2.63076923e+00, 3.54013692e+04, 9.51423077e+03,\n",
       "        1.03463692e+04, 6.46309231e+03, 2.93304615e+03, 3.31684615e+03]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km3.cluster_centers_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0588247a",
   "metadata": {},
   "source": [
    "### Scatter Plot with Centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5079c6b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e3d1db7d00>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEGCAYAAACUzrmNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAYwklEQVR4nO3de7RcZZnn8e9DckxCwi0XMpgEgnQEEkgInDBcejSRWRDFJvSgI2JzkxFHGdC2h2mUNYPBnrXsGftiGsHFAg2sQekotEZbGhBCo4jCARFI0pAICgcDHKKQeAmQ5Jk/ahOKk5PzVsKpU+fy/axVq3a9+927nl3Jqt/Z7961d2QmkiT1ZrdWFyBJGvgMC0lSkWEhSSoyLCRJRYaFJKloZKsLaJaJEyfm9OnTW12GJA0aEydO5NZbb701Mxd2nzdkw2L69Ol0dHS0ugxJGlQiYmJP7Q5DSZKKDAtJUpFhIUkqGrLHLCQNL6+++iqdnZ1s2rSp1aUMCqNHj2bq1Km0tbU11N+wkDQkdHZ2ssceezB9+nQiotXlDGiZyfr16+ns7OTAAw9saBmHobSdTS9t4kuzvsSml/wLTYPHpk2bmDBhgkHRgIhgwoQJO7UXZlhoO2v+eQ0vrHqBNd9b0+pSpJ1iUDRuZz8rh6G0zU1n3MRjyx9jy8tbAPjWWd/iOx/5DgefcjCnfe20FlcnqZXcs9A2Cy5fwF7778VubbX/Fru17cbeB+zNgs8taHFl0vDw4osvcuWVV+70ch0dHVx00UU9zps+fTovvPDCmy3NsNDrxv/ReBZcvoCtr26lbWwbW1/dyvzF8xl/0PhWlyYNC72FxZYtW3a4XHt7O0uWLGlWWYBhoW5WLltJ29g25i+eT9vYNlZ+Y2WrS5Kapq9P5rj++uuZPXs2c+bM4cwzz6Srq4vTTjuNefPmMW/ePO655x4APvvZz/LhD3+Y+fPn87a3vW3bF/0ll1zCz3/+c4444gguvvhi7rrrLhYsWMAZZ5zB4YcfzqZNmzj33HM5/PDDmTt3LitWrADgrrvu4r3vfS8A69ev58QTT2Tu3Ll89KMfpc/uhpqZQ/Jx1FFHpXZe532dufHZjZmZufHZjfnM/c+0uCKpMatWrdrpZR6+4eH8LJ/Nh7/28Jt+/0cffTTf/va3Z1dXV2Zmrl+/Pj/4wQ/mD37wg8zM/OUvf5mHHHJIZmZedtlleeyxx+amTZuyq6srx48fn6+88ko++eSTOWvWrG3rXLFiRe6+++75xBNPZGbmF77whTznnHMyM3P16tU5bdq0/MMf/pArVqzIk08+OTMzL7zwwly8eHFmZn73u99NYFtN3fX0mQEd2cN3qge49QZT5k3ZNj1u8jjGTR7Xwmqk5mjGyRx33nkn73vf+5g4sXYdvvHjx/P973+fVatWbeuzYcMGNm7cCMDJJ5/MqFGjGDVqFPvuuy/PPfdcj+s9+uijt/0W4oc//CEXXnghAIcccggHHHAAjz/++Bv633333dx8883b3mOfffbZpe3pzrCQNOwsuHwBzz70LC/+4kW2bt7aJydzZOZ2p6Nu3bqVe++9lzFjxmzXf9SoUdumR4wYwebNm3tc79ixY9/wHo1oxinEHrOQNOw042SOE044gWXLlrF+/XoAfv3rX3PiiSdyxRVXbOvz0EMP9bqOPfbYY9ueR0/e8Y53cMMNNwDw+OOP89RTT3HwwQfvsM8tt9zCb37zm13ZnO0YFpKGpb4+mWPWrFlceumlvPOd72TOnDl86lOfYsmSJXR0dDB79mxmzpzJl7/85V7XMWHCBI4//ngOO+wwLr744u3mf/zjH2fLli0cfvjhfOADH2Dp0qVv2EMBuOyyy7j77rs58sgjue2229h///3f1Ha9JhrdrRls2tvb05sfScPH6tWrOfTQQxvu/8z9z7DX/nsxbvI4fvvcb9nw9Abe2v7WJlY48PT0mUXEA5nZ3r2vxywkDUuezLFzHIaSJBUZFpKGjKE6rN4MO/tZGRaShoTRo0ezfv16A6MBWd3PYvTo0Q0v4zELSUPC1KlT6ezspKurq9WlDAqv3SmvUYaFpCGhra2t4bu+aec5DCVJKjIsJElFhoUkqciwkCQVGRaSpCLDQpJUZFhIkooMC0lSkWEhSSoyLCRJRYaFJKnIsJAkFRkWkqQiw0KSVNTUsIiIP4+IlRHxaER8PSJGR8T4iLg9ItZUz/vU9f90RKyNiMci4qS69qMi4pFq3pKIiGbWLUl6o6aFRURMAS4C2jPzMGAEcDpwCXBHZs4A7qheExEzq/mzgIXAlRExolrdVcD5wIzqsbBZdUuSttfsYaiRwJiIGAnsDvwKWARcV82/Dji1ml4E3JiZL2fmk8Ba4OiI2A/YMzPvzdr9Eq+vW0aS1A+aFhaZ+QzwBeApYB3wUmbeBkzOzHVVn3XAvtUiU4Cn61bRWbVNqaa7t28nIs6PiI6I6PDWipLUd5o5DLUPtb2FA4G3AmMj4s96W6SHtuylffvGzKszsz0z2ydNmrSzJUuSdqCZw1D/EXgyM7sy81XgZuA44LlqaInq+fmqfycwrW75qdSGrTqr6e7tkqR+0syweAo4JiJ2r85eOgFYDSwHzq76nA18u5peDpweEaMi4kBqB7Lvq4aqNkbEMdV6zqpbRpLUD0Y2a8WZ+ZOI+CbwILAZ+ClwNTAOWBYR51ELlPdX/VdGxDJgVdX/gszcUq3uY8BSYAxwS/WQJPWTqJ1gNPS0t7dnR0dHq8uQpEElIh7IzPbu7f6CW5JUZFhIkooMC0lSkWEhSSoyLCRJRYaFJKnIsJAkFRkWkqQiw0KSVGRYSJKKDAtJUpFhIUkqMiwkSUWGhSSpyLCQJBUZFpKkIsNCklRkWEiSigwLSVKRYSFJKjIsJElFhoUkqciwkCQVGRaSpCLDQpJUZFhIkooMC0lSkWEhSSoyLCRJRYaFJKnIsJAkFRkWkqSipoZFROwdEd+MiH+LiNURcWxEjI+I2yNiTfW8T13/T0fE2oh4LCJOqms/KiIeqeYtiYhoZt2SpDdq9p7FF4F/ycxDgDnAauAS4I7MnAHcUb0mImYCpwOzgIXAlRExolrPVcD5wIzqsbDJdUuS6jQtLCJiT+AdwLUAmflKZr4ILAKuq7pdB5xaTS8CbszMlzPzSWAtcHRE7AfsmZn3ZmYC19ctI0nqB83cs3gb0AV8NSJ+GhHXRMRYYHJmrgOonvet+k8Bnq5bvrNqm1JNd2/fTkScHxEdEdHR1dXVt1sjScNYM8NiJHAkcFVmzgV+RzXktAM9HYfIXtq3b8y8OjPbM7N90qRJO1uvJGkHmhkWnUBnZv6kev1NauHxXDW0RPX8fF3/aXXLTwV+VbVP7aFdktRPmhYWmfks8HREHFw1nQCsApYDZ1dtZwPfrqaXA6dHxKiIOJDagez7qqGqjRFxTHUW1Fl1y0iS+sHIJq//QuCGiHgL8ARwLrWAWhYR5wFPAe8HyMyVEbGMWqBsBi7IzC3Vej4GLAXGALdUD0lSP4naCUZDT3t7e3Z0dLS6DEkaVCLigcxs797uL7glSUWGhSSpyLCQJBUZFpKkIsNCklRUDIuIGBERf94fxUiSBqZiWFS/dVjUD7VIkgaoRn+Ud09EXAH8I7VrPAGQmQ82pSpJ0oDSaFgcVz1fXteWwLv6thxJ0kDUUFhk5oJmFyI1YtNLm7j2uGs570fnMXqv0a0uRxo2GjobKiImR8S1EXFL9XpmdW0nqV+t+ec1vLDqBdZ8b02rS5GGlYauDVWFxFeBSzNzTkSMBH6amYc3u8Bd5bWhhpabzriJx5Y/xpaXt7B181Z2G7kbI0aN4OBTDua0r53W6vKkIePNXhtqYmYuA7YCZOZmYEvvi0h9Z8HlC9hr/73Yra32X3a3tt3Y+4C9WfA5R0il/tBoWPwuIiZQ3aEuIo4BXmpaVVI34/9oPAsuX8DWV7fSNraNra9uZf7i+Yw/aHyrS5OGhUbD4lPUbk50UETcA1wPXNS0qqQerFy2kraxbcxfPJ+2sW2s/MbKVpckDRuNHrMYRW3Y6WBq98R+DNgtM19ubnm7zmMWQ88z9z/DXvvvxbjJ4/jtc79lw9MbeGv7W1tdljSk7OiYRaO/s7g3M48Etv0pFxEPUrunttQvpsybsm163ORxjJs8roXVSMNLr2EREf8OmAKMiYi51PYqAPYEdm9ybZKkAaK0Z3EScA4wFfgbXg+LjcBnmleWJGkg6TUsMvM64LqIOC0zb+qnmiRJA0yjZ0NNjYg9o+aaiHgwIk5samWSpAGj0bD4cGZuAE4E9gXOBT7ftKokSQNKo2Hx2rGK9wBfzcyf1bVJkoa4RsPigYi4jVpY3BoRe1Bd+kOSNPQ1+juL84AjgCcy8/fVpT/ObVpVkqQBpdGw+OPqeXaEo0+SNNw0GhYX102PBo4GHsA75UnSsNDonfL+pP51REwD/k9TKpIkDTiNHuDurhM4rC8LkSQNXA3tWUTEP1Ddy4JawBwB/KxJNUmSBphGj1nUX+t7M/D1zLynCfVIkgagRo9ZXNfsQiRJA1fpEuWP8Prw03Yyc3afVyRJGnBKexb/CZgMPN2t/QDgV428QUSMoDaM9UxmvjcixgP/CEwHfgH858z8TdX309R+ALgFuCgzb63ajwKWAmOA7wGfyEZu8SdJ6hOls6H+DtiQmb+sfwC/r+Y14hPA6rrXlwB3ZOYM4I7qNRExEzgdmAUsBK6sggbgKuB8YEb1WNjge0uS+kApLKZn5sPdGzOzg9qeQa8iYipwMnBNXfMi4LVjINcBp9a135iZL2fmk8Ba4OiI2A/YMzPvrfYmrq9bRpLUD0phMbqXeWMaWP/fA/+DN150cHJmrgOonvet2qfwxuGuzqptSjXdvX07EXF+RHREREdXV1cD5UmSGlEKi/sj4iPdGyPiPGqX+9ihiHgv8Hxm9tqvfpEe2rKX9u0bM6/OzPbMbJ80aVKDbytJKikd4P4k8E8R8SFeD4d24C3AnxaWPR44JSLeQ20PZc+I+H/AcxGxX2auq4aYnq/6dwLT6pafSu0gemc13b1dktRPet2zyMznMvM4YDG1M5d+ASzOzGMz89nCsp/OzKmZOZ3ages7M/PPgOXA2VW3s4FvV9PLgdMjYlREHEjtQPZ91VDVxog4JmqXvD2rbhlJUj9o9Ed5K4AVffSenweWVUNZTwHvr95jZUQsA1ZR+5X4BZm5pVrmY7x+6uwt1UOS1E9iqP5cob29PTs6OsodJUnbRMQDmdnevX1XrzorSRpGDAtJUpFhIUkqMiwkSUWGhSSpyLCQJBUZFpKkIsNCklRkWEiSigwLSVKRYSFJKjIsJElFhoUkqciwkCQVGRaSpCLDQpJUZFhIkooMC0lSkWEhSSoyLCRJRYaFJKnIsJAkFRkWkqQiw0KSVGRYSJKKDAtJUpFhIUkqMiwkSUWGhSSpyLCQJBUZFpKkIsNCklTUtLCIiGkRsSIiVkfEyoj4RNU+PiJuj4g11fM+dct8OiLWRsRjEXFSXftREfFINW9JRESz6pYkba+Zexabgb/IzEOBY4ALImImcAlwR2bOAO6oXlPNOx2YBSwEroyIEdW6rgLOB2ZUj4VNrFuS1E3TwiIz12Xmg9X0RmA1MAVYBFxXdbsOOLWaXgTcmJkvZ+aTwFrg6IjYD9gzM+/NzASur1tGktQP+uWYRURMB+YCPwEmZ+Y6qAUKsG/VbQrwdN1inVXblGq6e3tP73N+RHREREdXV1efboMkDWdND4uIGAfcBHwyMzf01rWHtuylffvGzKszsz0z2ydNmrTzxUqSetTUsIiINmpBcUNm3lw1P1cNLVE9P1+1dwLT6hafCvyqap/aQ7skqZ8082yoAK4FVmfm39bNWg6cXU2fDXy7rv30iBgVEQdSO5B9XzVUtTEijqnWeVbdMpKkfjCyies+HjgTeCQiHqraPgN8HlgWEecBTwHvB8jMlRGxDFhF7UyqCzJzS7Xcx4ClwBjgluohSeonUTvBaOhpb2/Pjo6OVpchSYNKRDyQme3d2/0FtySpyLCQJBUZFpKkIsNCklRkWEiSigwLSVKRYSFJKjIsJElFhoUkqciwkCQVGRaSpCLDQpJUZFhIkooMC0lSkWEhSSoyLCRJRYaFJKnIsJAkFRkWkqQiw0KSVGRYSJKKDAtJUpFhIUkqMiwkSUWGhSSpyLCQJBUZFpKkIsNCklRkWEiSigwLSVKRYSFJKjIsJElFhoUkqWhkqwtoVEQsBL4IjACuyczP9/l7XBYQdQ0JuTj7+m0kqc81+/trUOxZRMQI4EvAu4GZwAcjYmafvsdrH3S3R1wWvS8oSS3WH99fgyIsgKOBtZn5RGa+AtwILOrTd3jtAy61SdJA0w/fX4MlLKYAT9e97qza3iAizo+Ijojo6Orq6rfiJGmoGyxh0VM+bjcYl5lXZ2Z7ZrZPmjSpH8qSpOFhsIRFJzCt7vVU4Fd9+g7J9vHTU5skDTT98P01WMLifmBGRBwYEW8BTgeW9+Ub5OJ8/cOte3g2lKSBrj++vwbFqbOZuTki/htwK7VTZ7+SmSv7/H0MBkmDVLO/vwZFWABk5veA77W6DkkajgbLMJQkqYUMC0lSkWEhSSoyLCRJRZE5NM8Aiogu4Je7uPhE4IU+LGcwcJuHh+G2zcNte+HNbfMLAJm5sPuMIRsWb0ZEdGRme6vr6E9u8/Aw3LZ5uG0vNG+bHYaSJBUZFpKkIsOiZ1e3uoAWcJuHh+G2zcNte6FJ2+wxC0lSkXsWkqQiw0KSVDRswyIivhIRz0fEozuYHxGxJCLWRsTDEXFkf9fY1xrY5g9V2/pwRPwoIub0d419rbTNdf3mRcSWiHhff9XWLI1sc0TMj4iHImJlRPxrf9bXDA38394rIr4TET+rtvnc/q6xL0XEtIhYERGrq+35RA99+vQ7bNiGBbAU2O6HJ3XeDcyoHucDV/VDTc22lN63+UngnZk5G/gcQ+Pg4FJ632YiYgTw19QugT8ULKWXbY6IvYErgVMycxbw/v4pq6mW0vu/8wXAqsycA8wH/qa6N85gtRn4i8w8FDgGuCAiZnbr06ffYcM2LDLzbuDXvXRZBFyfNT8G9o6I/fqnuuYobXNm/igzf1O9/DG1OxIOag38OwNcCNwEPN/8ipqvgW0+A7g5M5+q+g/67W5gmxPYIyICGFf13dwftTVDZq7LzAer6Y3AamBKt259+h02bMOiAVOAp+ted7L9P8ZQdh5wS6uLaLaImAL8KfDlVtfSj94O7BMRd0XEAxFxVqsL6gdXAIdSux3zI8AnMnNra0vqGxExHZgL/KTbrD79Dhs0Nz9qgeihbVicZxwRC6iFxR+3upZ+8PfAX2bmltofncPCSOAo4ARgDHBvRPw4Mx9vbVlNdRLwEPAu4CDg9oj4QWZuaGlVb1JEjKO2V/zJHralT7/DDIsd6wSm1b2eSu2vkiEtImYD1wDvzsz1ra6nH7QDN1ZBMRF4T0RszsxvtbSq5uoEXsjM3wG/i4i7gTnAUA6Lc4HPZ+2HZWsj4kngEOC+1pa16yKijVpQ3JCZN/fQpU+/wxyG2rHlwFnVGQXHAC9l5rpWF9VMEbE/cDNw5hD/K3ObzDwwM6dn5nTgm8DHh3hQAHwb+A8RMTIidgf+PbUx76HsKWp7UkTEZOBg4ImWVvQmVMdergVWZ+bf7qBbn36HDds9i4j4OrWzIiZGRCdwGdAGkJlfpna/7/cAa4HfU/vLZFBrYJv/FzABuLL6S3vzYL9iZwPbPOSUtjkzV0fEvwAPA1uBazKz11OLB7oG/p0/ByyNiEeoDc/8ZWYO5kuXHw+cCTwSEQ9VbZ8B9ofmfId5uQ9JUpHDUJKkIsNCklRkWEiSigwLSVKRYSFJKjIspILqarQP1T2m78I6Tu3hQm/SoDFsf2ch7YQ/ZOYRb3IdpwLfBVY1ukBEjMzMQXuxOw0t7llIuyAijoqIf60uxHfra1fzjIiPRMT91X0TboqI3SPiOOAU4P9WeyYHVRfxa6+WmRgRv6imz4mIb0TEd4DbImJsda+G+yPipxGxqOo3KyLuq9b3cETMaM0noeHCsJDKxtQNQf1TdU2efwDel5lHAV8B/nfV9+bMnFfdN2E1cF5m/ojapRcuzswjMvPnhfc7Fjg7M98FXArcmZnzgAXUAmcs8F+BL1Z7PO3UrgMkNY3DUFLZG4ahIuIw4DBqVy4FGAG8ds2dwyLir4C9qd03YVduqHR7Zr52b4YTgVMi4r9Xr0dTu6TDvcClETGVWkCt2YX3kRpmWEg7L4CVmXlsD/OWAqdm5s8i4hxq1yvqyWZe37Mf3W3e77q912mZ+Vi3Pqsj4ifAycCtEfFfMvPOxjdB2jkOQ0k77zFgUkQcC7VLRUfErGreHsC6aqjqQ3XLbKzmveYX1O4pAdDbfb9vBS6srjJKRMytnt8GPJGZS6gNcc1+U1skFRgW0k7KzFeofcH/dUT8jNpNdY6rZv9Pancsux34t7rFbgQurg5SHwR8AfhYRPyI2n00duRz1K6e+nBEPFq9BvgA8Gh1xdFDgOv7YNOkHfKqs5KkIvcsJElFhoUkqciwkCQVGRaSpCLDQpJUZFhIkooMC0lS0f8HL7nT7iNXmbMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df0['Channel'],df0['Region'],color='black')\n",
    "plt.scatter(df01['Channel'],df01['Region'],color='green')\n",
    "plt.scatter(km3.cluster_centers_\n",
    "           [:,0],km3.\n",
    "            cluster_centers_[:,3],\n",
    "           color='purple',marker='*',\n",
    "           label='centroid')\n",
    "\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Cluster')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1997366a",
   "metadata": {},
   "source": [
    "### Using Silhouette Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "68168fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.cm as cm\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "def silhouette_plot(X, y, n_clusters, ax=None):\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "        # Compute the silhouette scores for each sample\n",
    "        silhouette_avg = silhouette_score(X, y)\n",
    "        sample_silhouette_values = silhouette_samples(X, y)\n",
    "        y_lower = padding = 2\n",
    "        for i in range(n_clusters):\n",
    "    # Aggregate the silhouette scores for samples belonging to\n",
    "            ith_cluster_silhouette_values = sample_silhouette_values[y == i]\n",
    "            ith_cluster_silhouette_values.sort()\n",
    "            size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "            y_upper = y_lower + size_cluster_i\n",
    "            color = cm.nipy_spectral(float(i) / n_clusters)\n",
    "            ax.fill_betweenx(np.arange(y_lower, y_upper),\n",
    "                            0,ith_cluster_silhouette_values,\n",
    "                            facecolor=color,\n",
    "                            edgecolor=color,\n",
    "                            alpha=0.7)\n",
    "            # Label the silhouette plots with their cluster numbers at the middle\n",
    "            ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i + 1))\n",
    "            # Compute the new y_lower for next plot\n",
    "            y_lower = y_upper + padding\n",
    "        ax.set_xlabel(\"The silhouette coefficient values\")\n",
    "        ax.set_ylabel(\"Cluster label\")\n",
    "        # The vertical line for average silhoutte score of all the values\n",
    "        ax.axvline(x=silhouette_avg, c='r', alpha=0.8, lw=0.8, ls='-')\n",
    "        ax.annotate('Average',\n",
    "                    xytext=(silhouette_avg, y_lower * 1.025),\n",
    "                    xy=(0, 0),\n",
    "                    ha='center',\n",
    "                    alpha=0.8,\n",
    "                    c='r')\n",
    "        ax.set_yticks([]) # Clear the yaxis labels / ticks\n",
    "        ax.set_xticks([0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "        ax.set_ylim(0, y_upper + 1)\n",
    "        ax.set_xlim(-0.075, 1.0)\n",
    "        return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3e7f4b45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='The silhouette coefficient values', ylabel='Cluster label'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEUCAYAAADZS9ZwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAlN0lEQVR4nO3deXxddZ3/8dfn5mZp07Rp2obuTSmVpmgJLaVQKLQEQZYB2a2FClSB+kDBiiLqIOroqCOOM8K4DDO4/Py5zIwzMMyMy09FfbCIICAg6rggFNrSNE2bNDd3/fz+OCcllDa9TXLvucv7+XjcR+567vsk6bsn33vO95i7IyIi5S0WdQARERk9lbmISAVQmYuIVACVuYhIBVCZi4hUAJW5iEgFUJlL5TNbg9kjmLVFHUWkUFTmUg3OAB4HTh/1ksz0b0ZKUjzqACIFZTYeOBq4BvhbzJ4CzsX9feHjy4DLcH8XZseHz6sDNgMfxr0fs/8E7gGOB76FWSNwPlALPA/cgvsAZrOBvyLYSHoAWIf7qvB91gOnhcv+Me5fLMr6S9XQVoZUutXAg7g/B+wKL6/DbFz4+OnADzBrBjYAb8d9HfBrYN2Q5SRx34D794Ef4b4e97XAs8B54XNuBL6B+3pg+95XBv9JzAHeArwZaMdsaQHWVaqYtsyl0p0B/N/w+vcJto4fAFZh9kPgJODvgGXA4cA/YQbBVveTQ5bzgyHXF2D2dqAJGAc8FN6/BHh3eP27wA3h9ePDy9fD2+MJyv2Xo147kZDKXCqX2SRgOUH5OlADOPBh4GJgN/B0OJRiwM9xf/8BlpYYcv1W4Ebcf4fZXxD8RzBsEuAu3L8z8pURGZ6GWaSSdQL34n4O7n+B+1nAC0AGWEQw7j24xf0kcDRmcwAwa8Bs7gGW2wh0YRYHzhxy/5Phe0LwF8GgB4HzwvF7MGvFrGW0KycyVKRlbmbNZvavZvYbM3vGzE6IMo9UnDcA9+1z348IivZnwMrwK7jvJNji/hhm3wS+DLQdYLmfDx//B4Ix80G3Aesw+yowFegLl/0QwbDLXZh9C/gkwVCLyJixKKfANbM+oAfoArJAp7v3RBZIZDTMGgg+KHXMTgfegPumqGNJdYhszNzMphH8uVoDTAI+oyKXMtcOvDccf+8FPhJxHqkikW2Zm1kH8AjB/ryTgSRwibvfF0kgEZEyFuWY+UyCrfLBDA3A5dHFEREpX1GW+UbgJSBFMNySBRZHmEdEpGwVZJhl6tSp3tbWdsDHe3p62L17N4lEgqlTp7Jjxw727NlDQ0MD7e3tY55HZMw89xzMPdAeiyKj8+ijj3a5+7SRvLYgH4C2tbXxyCOPHPDxm2++ma985Ss0NjayefNmstkssViMCy64gK997WuFiCQyNi6/HPQ7KgViZn8e6WsjGWb567/+a+6++26mTp3K3LlzaWxsZPbs2Vx66aVRxBERKXuRjZnX19dTW1vL4DDPVVddxTnnnBNVHBGRshbZfuZLlizhsccei+rtRUQqStlNtPXww7v40Id+T329ppWpRLt3ZwD44hcXs3BhY8RpRMpH2ZX5li1Jnn9+gOnT66KOImPAHQYGcuzZkyWddnI559JLpzN//riDv1hE9iq7MgcYNy7GuHE1UceQQ5TLOT09GZLJHPG4AUYmk2PGjHpOPXUKy5dPpL29kSOOGB8eES8i+SrLMpfy09uboa8vx9KlTXR2tjBzZgPTp9cxc2Y9jY36NRQZLf0rkoLK5ZyXXkpTXx/jjjsWsWJFc9SRRCqSylzGVCqVo7s7Q02N4e5ks86ZZ07luuvmcthh9VHHE6lYKnMZM+7O9u1prr56NsuWTWTGjHpaW+uoq9OeRyKFpjKXURvcG6WvL8sRR4zn2mvnRB1JpOpEVubPP/8869evZ+vWrcRiMa6++mquv/76qOLIIXB30mmnvz9LIpGjtjbGySdPZsmSCaxc2Rx1PJGqFFmZx+NxbrvtNpYuXUpvby/Lli3j9a9/PYsXaxbcUhKMe0Mm4+zcmaamxshmnUmTajnqqCZWrmzm5JMn09am/cJFohRZmc+YMYMZM2YA0NTURHt7Oy+88ILK/CDcnVwOslknkwk+YMxmg/vdg71H3AkvwXMHb5uBmYVfX3kB2+d9gmXlck59fQ2TJtVwxBGNXHbZDNasmazdCUVKTEn8i3z22Wd57LHHWLFiRdRRIrd7d4ZEIkc268TjMWpqhhZrUOINDTEmTKhh4sQ4TU1xJkyooaEhRl1djLo6o7Y2Rn19cBm8b/B6ba0Rjw9eYsTjts99waWpKVj2xIk1xOP6AFOk1EVe5n19fVx44YV89rOfZeLEiVHHKbqhR0XW1Bh1dTHe9KbpzJpVz9SpdbS01DJpUlDY48cHl1hMR0eKyCtFWubpdJoLL7yQdevWccEFF0QZpShSqWCvj0QiKO7B8edjj53EqlXNHH74OI48spHm5tqoo4pImYmszN2dDRs20N7ezqZNm6KKUTDuTjKZI5HIMTAQlHdDQ4wlS5o4+ugm5s5tYMaMembOrKe1VQfTiMjoRFbm999/P1/72td43eteR0dHBwAf//jHOeuss6KKNGKDu+qlUjlSqaDEczmYPr2eo45qoqOjiZNPnsz8+eM0gZSIFERkZX7SSSdRiJNJj6VsNijoTCYo63Q62IMkHjdisWCvkFwu2JukuTnOzJnB1vacOQ28/vUtLF7cFPUqiEiViPwD0FKUyznd3Wn27Mkxd24DLS21tLbWcdhhdUyfXs/UqbW0tASXyZNraW6Oa48PEYlU1Zd5IpFl164s8bjhHmyNm8GSJRO47rq5dHRU3x42IlJ+KrbMs1lnYCAXHlTz8sE12WxwUE08HgyTxOMxNm2ax2te08iUKcHWdlNTjca2RaSslH2ZZzJOX19279h2bW0McMyMWbPqwwNrapg0KU5zc7DP9sSJcRobg/uWLp2osxaJSNkr6zLPZp0tW5IsXz6JI44YT1tbA4cdVk9b2zjmzGmgpkZb1yJSHUq2zN2d3bsz7NyZobs7zc6daXbsSHPXXS8MOfkBnHjiZP7u747UB5AiUtUiL3P3YJhky5Ykf/hDP3ffvZ0//SnBzp1p3KGm5uVdADMZZ/v2FKtWTeYv/3IBhx8+Tic+EBEhojJ3d557boA779zMT36yk/7+4GztuZxTV2eMH1/DtGm1+52DJBaDlSubWbSoMYLkIiKlqeBlnkzm2L49xUsvpdi6NclDD+3iwQd72LUrgxm0tAQz82nvERGRkStImff1ZfiXf9nKV7/6Ilu2JKmpCY6YzGadWAyamuIcdlhdId5aRKQqFaTMN29O8qlPPUtjY4zp0+u01S0iUmAFKfN43Jg+XVveIiLFol1BREQqQEG2zHO54IPPQkgmveRnWxQRKbaClLkZBTu1WXNznLlzdSZ4EZGhrBBbuccee6w/8sgjY75cgGeeeYY777yTWEwjRFJ8b/rv/+abZXgClZF46aWXeOGFF7j22mu56KKLoo5TFczsUXc/diSvjfwI0EP1u9/9jnvuuYfW1taoo0gV6ty+nQceeCDqGAXX1dXFhAkTWLRoEUcddVTUcSQPZVfmAM3NzbS0tEQdQ6pQfV1dxf/uuTt79uzhnnvuYdasWVHHkTxprEJEXiGdTtPS0qIiLzMqcxF5hYGBAQ4//PCoY8ghUpmLyCskEglmzJgRdQw5RCpzEdkrkUjg7px77rlRR5FDpDIXkb36+/s59dRTWb58edRR5BCpzEVkr2QyycKFC6OOISOgMhcRAHK5HGbGqaeeGnUUGQGVuYgA0N3dzYoVK7QnS5lSmYsIEGyZr1u3LuoYMkIqcxEBwMx0oFAZU5mLCADZbLbipyqoZCpzEdn74eeECROijiIjpDIXEXp7e2lvb9f5esuYylxE2LNnj3ZJLHMqcxEhHo8ze/bsqGPIKKjMRYRYLMa0adOijiGjoDIXEdydSZMmRR1DRkFlLlLlUqkU9fX12se8zKnMRarcwMAACxcuJB4vy7NISkhlLlLlEokECxYsiDqGjJLKXKTK5XI5Fi9eHHUMGSWVuUiVMzNqa2ujjiGjpDIXqXKxWIzGxsaoY8goqcxFqlwsFmPixIlRx5BRUpmLVLlcLsf06dOjjiGjpDIXqXLZbFYHDFUAlblIFUun0zQ0NDBu3Lioo8goqcxFqtjAwAALFizQ1LcVQGUuUsX6+vpYvnx51DFkDKjMRapYPB5XmVcIlblIlZsyZUrUEWQMqMxFqpS7k8lktFtihVCZi1SpdDrNxIkTdRLnCqEyF6lSO3fu5JRTTok6howRlblIlXJ3li1bFnUMGSMqc5EqVVNTQ0tLS9QxZIyozEWqlJlpvLyCqMxFqpS709DQEHUMGSMqc5EqlEqliMfjzJgxI+ooMkZU5iJVKJlMcsQRR9DU1BR1FBkjBzwdt5n1Aj54M/zq4XV3d81mL1KmstmsZkqsMAcsc3fXf9kiFSqTyegw/gqT1zCLmZ1kZleG16ea2fzCxhKRQkqlUsyfr3/GleSgZW5mHwJuAm4O76oD/k8hQ4lIYcXjcdra2qKOIWMony3z84FzgT0A7v4ioCEYkTLm7hx55JFRx5AxlE+Zp9zdCT8MNbPGwkYSkULKZDLE43FmzZoVdRQZQ/mU+bfN7ItAs5m9Dfh/wD8WNpaIFEomk2Hy5Mk6VVyFOeDeLIPc/dNm9npgN/Aa4BZ3/0HBk4lIQaTTaR0sVIEOWuahJ4FxBEMtTxYujogUWn9/P4sXL446hoyxfPZmeSvwMHABcBHwkJldVehgIlIY2WyWRYsWRR1Dxlg+W+bvAY5x9x0AZjYFeAD450IGE5HCiMViHHfccVHHkDGWzwegm4HeIbd7gecLE0dECmlgYIDm5maNmVeg4eZm2RRefQH4uZndTTBmfh7BsIuIlJl0Ok1bW5v2ZKlAww2zDB4Y9IfwMujuwsURkULKZrOMHz8+6hhSAMNNtPXhYgYRkcLL5XKaLbFCHfQDUDObBrwXOArYe1oSdz+1gLlEpABSqRTLly+POoYUQD4fgH4d+A0wH/gw8CzwiwJmEpECSKfTxONxzjzzzKijSAHkU+ZT3P2fgLS7/8TdrwKOL3AuERljfX19LFmyhEmTJkUdRQogn/3M0+HXLWZ2NvAiMLtwkUSkEFKpFGvXro06hhRIPmX+V2Y2CXg38DlgIvCugqYSkTHn7hx99NFRx5ACyWeirXvDq7uANYWNIyKFkMlkMDMNsVSw4Q4a+hwvn9D5Vdz9nQVJJCJjrru7m9NOO41YLK8zRUoZGm7L/JGipRCRgsrlcnR2dkYdQwpouIOGvlLMICJSGIlEgqamJk488cSoo0gB6W8ukQo3MDBAR0cHDQ0NB3+ylC2VuUiFGzxNnFS2YcvczGrMTLshipSxVCqlKW+rwLBl7u5ZgilvRaQM5XI5ampqWL16ddRRpMDyOWjofjO7HfgWsGfwTnf/ZcFSiciY6O/vZ+7cuRxxxBFRR5ECy6fMV4ZfPzLkPgc0a6JIiRsYGKC9vT3qGFIE+RwBqqM+RcpUKpXSVnmVOOjeLGZ2mJn9k5n9T3h7sZltKHw0ERmtWCxGa2tr1DGkCPLZNfHLwPeAmeHt3wE3FCiPiIyheDzO/Pnzo44hRZBPmU91928DOQB3zwDZgqYSkVFzd9LpNFOmTIk6ihRBPmW+x8ymEE66ZWbHE8ygKCIlrLu7m/b2dqZNmxZ1FCmCfPZm2QTcAywws/uBacDFBU0lIqOye/duYrEY119/PWYWdRwpgnzK/GngFOBIwIDfomkAREpaX18f1157Lccee2zUUaRI8inlB9094+5Pu/tT7p4GHix0MBEZnZaWlqgjSBENd3KK6cAsYJyZHUOwVQ7BaePGFyGbiIxQPB6nubk56hhSRMMNs5wBXEFw8ubbeLnMe4H3FzaWiIxUNpsll8vxmte8JuooUkQHOznFV8zsQnf/t7F+46uuuop7772X1tZWnnrqqbFevEjV6u/vZ8GCBcyZMyfqKFJE+YyZzzaziRa408x+aWanj/aNr7jiCr773e+OdjEiso/+/n6WLl0adQwpsnzK/Cp33w2cDrQCVwKfGO0bn3zyyfqARqRAVq1aFXUEKbJ8ynxwrPws4C53f2LIfSJSQrZv3860adN47WtfG3UUKbJ8yvxRM/s+QZl/z8yaCA/tF5HSkcvlSCaT3HXXXTQ1NUUdR4osn4OGNgAdwB/dvT88tP/KgqYSkUOSyWTYsmULnZ2dOny/SuVT5ieFX5fosGCR0tTV1cVFF13EBz/4waijSETyKfP3DLneABwHPMoozzS0du1a7rvvPrq6upg9ezYf/vCH2bBB06SLHKpcLoe7c84550QdRSKUz5mG/mLobTObA3xqtG/8jW98Y7SLEBGgp6eHpUuXcvTRR0cdRSI0kgmzNgP6qFykBLg7iUSC8847T7MjVrmDbpmb2ecI5zInKP8O4IkCZhKRPCWTSSZNmsRZZ50VdRSJWD5j5o8MuZ4BvuHu9xcoj4jkKZfL0d3dzdvf/nZtlUteY+ZfKUYQETk027dvp7Ozk7e85S1RR5ESMNwUuE/y8vDKKx4C3N2XFCyViAzL3cnlclx55ZXE4/n8gS2VbrjfAu3nJFKCcrkc27ZtY/ny5bS3t0cdR0rEcGVeCxy27/i4ma0CXixoKhE5oN7eXtra2rjtttuijiIlZLhdEz9LcCKKfSXCx0QkAolEghUrVjB+vE74JS8brszb3P1X+97p7o8AbQVLJCIH5B58jNXZ2RlxEik1w5V5wzCPjRvrICJycNu2beP444/X0Z7yKsOV+S/M7G373mlmGwjmZhGRIspkMpgZn/rUp6ipqYk6jpSY4T4AvQH4dzNbx8vlfSxQB5xf4FwiMkQqlaKrq4tLLrmEceP0h7G82nAndN4GrDSzNbw8F8t/ufuPipJMRPbq7u7m3HPP5b3vfW/UUaRE5XME6I+BHxchi4jsh7uTzWZZsWKFDtuXA9KhYyIlbPfu3SQSCdrb2zn99NOjjiMlTGUuUsL6+/u55ZZbOPvss4nFRjJjtVQL/XaIlKi+vj6mT5/OmWeeqSKXg9KWuUiJSSQS9PT04O5s3LhRE2lJXvRbIlJCstksO3bsYP369axZs4aOjo6oI0mZUJmLlJCdO3eyevVq3vWud0UdRcqMBuJESsSuXbvIZDKceeaZUUeRMqQtc5ESkM1mSSQSfOlLX+KYY46JOo6UIZW5SMTS6TQ7duxgwYIFKnIZMZW5SEQGzxjU0NDAKaecwjXXXBN1JCljKnORCCSTSbq6ujjppJO49dZbaWlpiTqSlDmVuUiRZbNZurq6+MAHPsAFF1yg+VZkTKjMRYrI3dm2bRuXXXYZF154YdRxpIKozEWKZGBggG3btrFq1SpuuOGGqONIhVGZixRYT08PqVSKxsZGrrrqKtatW6e5VmTMqcxFCmxgYICbbrqJCy+8UOPjUjDaPBApoGQyybhx4zjrrLNU5FJQKnORAnF3urq6WLduHePHj486jlQ4lblIAbg7L774IieccAJvfetbo44jVUBlLjLGstksW7ZsYfXq1dx+++3U1NREHUmqgD4AFRkjmUyGHTt24O6cccYZfOADH9A4uRSNylxkDCSTSbZu3co555zDNddcw7x586KOJFVGZS4yBrq7u3nPe97D5ZdfHnUUqVIaMxcZpWw2C8C6desiTiLVTGUuMgrZbHbv8IqO6pQoaZhFZATcnZ6eHhKJBJdccgk33nhj1JGkyqnMRUZg27ZtLFy4kGuuuYaTTz5Ze61I5FTmIocgnU6zdetWmpqa+NjHPsb8+fOjjiQCqMxF8uLubN++HTPjM5/5DCtXrtTBQFJSVOYiB5FMJtmxYwfLly/n5HnzqF+1KupIIq+ij99FhjF4VOemTZu44447qK+vjzqSyH6pzEUOwN3ZunUrnZ2dvPnNb9awipQ0lbnIAWzZsoWFCxeyYcMG7a0iJU9j5iL7sXv3bhoaGvjqV79KQ0ND1HFEDkplLrKPXC7Hnj17uOOOO1TkUjZU5iIEBd7V1YWZkc1mOf/88znuuOOijiWSN5W5VL3BXQ9XrVrF2rVrWbRoEZMmTYo6lsghUZlLVUun02zevJl3vvOdXHPNNfqgU8qW9maRqtbd3c0b3/hG1q1bpyKXsqYyl6rV09PDhAkTePe7301TU1PUcURGRcMsUpVSqRSJRILbbruNKVOmRB1HZNRU5lJVMpkMvb297N69m40bN2qPFakYKnOpCnv27KG3t5fa2lqWLFnCeeedR2dnZ9SxRMaMylwqmrszMDBAd3c3n/70p1mzZo3mWJGKpDKXipTNZvfOPz5lyhQ2btzIaaedFnUskYJRmUvFyeVybN26lYsuuoi1a9fS1tam3Q6l4qnMpeJs27aNzs5Obr75ZpW4VA2VuZS1XC7HwMAAyWSSZDKJuzNr1iw++tGPqsilqqjMpSy5O11dXeRyOWbNmsW8efNYsGABCxcuZPXq1ZrtUKqOylzKTjKZpLu7myVLlvCJT3yC1tbWqCOJRE5lLmWjp6eHVCpFLBZj06ZNrF27VkMpIiGVuZQ8d6enpwd35/bbb6ejo4Pa2tqoY4mUFE20JSUtnU6zZcsWZs+ezec//3mWL1+uIhfZD22ZS8lKpVJs27aNDRs2cN1112lIRWQYKnMpSYNzqbzjHe9g/fr1KnKRg1CZS0lxd3p7e9mzZw+33XYbp5xyStSRRMqCxswlcu5OMplk165d/PnPf6alpUVFLnKItGUuRdPX18euXbuIx+N7Zy7M5XJks1kmT55Me3s7r3vd67j66qupq6uLOK1IeVGZS8G5O9u3b6empoYbbriB1tZWmpub914mT55MfX191DFFyprKXAomlUqxY8cOzIyFCxdy4403snTp0qhjiVQklbmMiUwmw8DAAKlUau+EVw0NDaxfv55zzz2Xtra2qCOKVDSVuYxaKpWiq6uLRYsWMXv2bObOncvMmTM58cQTNW+KSJGozGVUEokE3d3dvO1tb2Pjxo1RxxGpWipzGbHBLfL3ve99XHrppVHHEalqKnM5ZJlMhh07dpDNZrnuuuu49NJLdYSmSMRU5pK3XC7H9u3bcXfOPvtszjjjDI4//ngVuUgJUJnLq2SzWdLpNKlUau+ltraWXC7HmjVruPzyy3nta18bdUwRGUJlXoUymQy9vb0MDAzg7tTW1mJmuDuZTIaamhqmTp3KnDlzmDVrFkceeSTz5s1j3rx5zJ07N+r4IrIfKvMqkkwm2blzJ/F4nGOOOYYlS5bQ2trK5MmTmTx5Ms3NzbS0tNDU1KShE5EyozKvcO7OwMAA/f39JJNJNm3axMUXX6y5T0QqjMq8grj73rHuZDJJMpkEoLW1lRNPPJHOzk7e8IY3RJxSRApBZV7ment76e/vJxaLkclkmDBhArNmzWL27NkcfvjhrFixgo6OjqhjikiBqczLgLuTy+X2fs3lciQSCfr7+2lqauKWW25hwYIFzJ49m6ampqjjikgEyrLME4kEiUSiaO83tEwPdH3fr+5OLBYjFovt/TDRzF5xfejyh14fesnlcpgZtbW11NbWEo/HaWhooKOjg2XLlnHqqacyb968on0vRKQ0lV2Zz5gxgzlz5hR1/utYLEZdXd0rLvX19Xu/Dl7q6upoaGigoaGBurq6vQU8WML7u36w20NP5CAiciA2dKtwzBZqth3485gv+GVTga4CLr9Uab2rS7WuN1Tvuh/p7iMaKy3Ilrm7TyvEcgeZ2SPufmwh36MUab2rS7WuN1TvupvZIyN9rU7oLCJSAVTmIiIVoFzL/EtRB4iI1ru6VOt6Q/Wu+4jXuyAfgIqISHGV65a5iIgMoTIXEakAJVvmZvYGM/utmf3ezN63n8fNzP4+fPxXZrY0ipxjLY/1Xheu76/M7AEzOzqKnIVwsHUf8rzlZpY1s4uKma9Q8llvM1ttZo+b2dNm9pNiZyyEPH7XJ5nZf5rZE+F6XxlFzrFmZv9sZi+Z2VMHeHxk3bbv4eOlcAFqgD8AhwN1wBPA4n2ecxbwP4ABxwM/jzp3kdZ7JTA5vH5mJax3vus+5Hk/Av4buCjq3EX6mTcDvwbmhrdbo85dpPV+P/DJ8Po0oBuoizr7GKz7ycBS4KkDPD6ibivVLfPjgN+7+x/dPQV8Ezhvn+ecB3zVAw8BzWY2o9hBx9hB19vdH3D3neHNh4DZRc5YKPn8zAHeAfwb8FIxwxVQPuv9ZuA77v4cgLtXwrrns94ONFkwkdEEgjLPFDfm2HP3nxKsy4GMqNtKtcxnAc8Pub05vO9Qn1NuDnWdNhD8D14JDrruZjYLOB/4QhFzFVo+P/PXAJPN7D4ze9TM1hctXeHks963A+3Ai8CTwPXunitOvEiNqNtKdaKt/Z2zbN99KPN5TrnJe53MbA1BmZ9U0ETFk8+6fxa4yd2zFXRau3zWOw4sAzqBccCDZvaQu/+u0OEKKJ/1PgN4HDgVWAD8wMx+5u67C5wtaiPqtlIt883AnCG3ZxP873yozyk3ea2TmS0B7gTOdPcdRcpWaPms+7HAN8MinwqcZWYZd/+PoiQsjHx/17vcfQ+wx8x+ChwNlHOZ57PeVwKf8GAg+fdm9idgEfBwcSJGZkTdVqrDLL8AFprZfDOrA94E3LPPc+4B1oef/B4P7HL3LcUOOsYOut5mNhf4DnB5mW+Z7eug6+7u8929zd3bgH8F3l7mRQ75/a7fDawys7iZjQdWAM8UOedYy2e9nyP4awQzOww4EvhjUVNGY0TdVpJb5u6eMbPrgO8RfOr9z+7+tJldGz7+BYK9Gc4Cfg/0E/wvXtbyXO9bgCnAP4RbqBmvgNnl8lz3ipPPerv7M2b2XeBXQA640933u1tbucjz5/1R4Mtm9iTB0MNN7l720+Ka2TeA1cBUM9sMfAiohdF1mw7nFxGpAKU6zCIiIodAZS4iUgFU5iIiFUBlLiJSAVTmIiIVQGVeRsxsSjhz3uNmttXMXgiv95jZr8f4vWaa2b+G11eb2b3h9VvN7MaxfK/9vPcbzWzxkNtXmNnMQr7nMFkuNrNnzOzH4e1vhDPZvcvMPmJmpw3z2mPN7O9H8d7vH+lr97OsZ81s6lgtT0pPSe5nLvsXHu3ZAUGpAn3u/mkzawPuHeP3ehGIaorZNxKsz+B/UFcATxHNEb4bCA5O+rGZTQdWuvu8fF7o7o8AIz7bOsGsgR8fxeulimjLvHLUmNk/hvM+f9/MxgGY2QIz+244QdPPzGzRvi80s1OGbPE/ZmZNZtZ2oPmWgcXhpE9/NLN3DlnOJjN7KrzcEN73iuWY2Y3hf0T7zWZmK4Fzgb8J89xEcBj/18Pb48xsmZn9JHzd92w/M8qZ2WFm9u8WzIX9RLjc/WYM77/MzB4O3+OLZlZjZrcQzH3zBTP7G+D7QGv4nFVm9mUL51S3YI71B8L3ejj8Hg79i6bRgnmsfxF+j88L77/CzL4Tfh/+18w+Fd7/CWBc+F5f32fdNg4+b8gyPhde/4/w+/K0mV29n+/LIf08wvsvDr9fT1gwlYCUoqjn9tVlxHMi3wrcGF5vI5gatCO8/W3gsvD6D4GF4fUVwI/2s6z/BE4Mr08g+IutjXC+ZYKj1e4d8r4PAPUE86PsIDh6bRnBzHaN4TKeBo4Zupzw9TcCtw6XDfgyQ+YqB+4Djg2v14bvPy28fSnB0YP7rtO3gBvC6zXApGEytoffg9rw+f8ArN/Pe++7Ll8m+OuljuAw8+Xh/RPD7+HQ79vHh/xMmgnmVWkk+Kvjj2G+BuDPwJzweX0H+NlPI5g+dvD2/wAnhddbwq/jCP6amRLefjb8eY3k5/EkMGswe9S/+7rs/6JhlsrxJ3d/PLz+KNBmZhMITmbxL/byLIP1+3nt/cBnwi3A77j7Zht+VsL/cvckkDSzl4DDCLZg/92DyaAws+8Aq3j1fBuEj+ebbV9HAq8lmEEPgqLe37wVpwLrAdw9C+wyswNlzBEU/S/CZY7j0OZLPxLY4u6/CN9vd7j8oc85HTjXXv68oQGYG17/obvvCl/za2Aer5wC9RXcfXv4V9HxwP+G739/+PA7zez88PocYCHBf7jDOsjP436Cw+q/TTAvkJQglXnlSA65niUopBjQ4+4dw73Q3T9hZv9FMB/EQxZ8qDdwCO8VZ//TdkLwF8PQ4byG8Gte2fbDgKfd/YRDfN3gaw90/1fc/eYRLHPw9QebF8OAC939t6+402wF+/9+Hsy3gEuA3xD8B+Vmtho4DTjB3fvN7D5e/n4POuSfh7tfG+Y8G3jczDq8cmbrrBgaM69g4Rbin8zsYth7bsFXnTPUzBa4+5Pu/kmCD+xeNa6eh58CbzSz8WbWSHASiZ8B2wjGmaeYWT1wTh7ZeoGmIcseevu3wDQzOyF8Xa2ZHbWfPD8ENobPqTGzicNk/CFwkZm1hs9vMbO8PuQM/QaYaWbLw9c3mdm+hfw94B0Wbvaa2TF5LDdtZrUHeOw7BB8UryUodgiGanaGRb6I4JRj+zrkn0f4+/Fzd78F6OKV07NKiVCZV751wAYze4JgjHh/p2K7YfADLiDBCM5e5O6/JBhDfhj4OcHMfo+5exr4SHjfvQTFd7Bs3wTeE35QuCBc7hfM7HGCYZWLgE+Gr3ucYHhgX9cDayyYce9R4KhhMv4a+CDwfTP7FfADIO9TEHpw2rNLgc+FmX7Aq7eIP0ow3v+r8APIj+ax6C+Fz//6vg94cOrAXwPz3H1wfu/vAvFwHT5KcFrBfV83kp/H35jZk2HunxKcr1NKjGZNFBGpANoyFxGpACpzEZEKoDIXEakAKnMRkQqgMhcRqQAqcxGRCqAyFxGpAP8fHhiCemrbKm8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Silhoutte Plot\n",
    "silhouette_plot(df,y_pred,6,ax=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "02622f09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='The silhouette coefficient values', ylabel='Cluster label'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Silhoutte Plot\n",
    "silhouette_plot(df,y_pred,4,ax=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "281ca362",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='The silhouette coefficient values', ylabel='Cluster label'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Silhoutte Plot\n",
    "silhouette_plot(df,y_pred,3,ax=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dda118e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='The silhouette coefficient values', ylabel='Cluster label'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Silhoutte Plot\n",
    "silhouette_plot(df,y_pred,2,ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00e12c3a",
   "metadata": {},
   "source": [
    "### Density-Based Spatial Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b0387990",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Wholesale customers data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c3e6cbf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(440, 8)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ec9353ee",
   "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>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",
       "    </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",
       "    </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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6c237385",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale our data\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c17b6493",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = StandardScaler().fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "280635a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.44865163,  0.59066829,  0.05293319, ..., -0.58936716,\n",
       "        -0.04356873, -0.06633906],\n",
       "       [ 1.44865163,  0.59066829, -0.39130197, ..., -0.27013618,\n",
       "         0.08640684,  0.08915105],\n",
       "       [ 1.44865163,  0.59066829, -0.44702926, ..., -0.13753572,\n",
       "         0.13323164,  2.24329255],\n",
       "       ...,\n",
       "       [ 1.44865163,  0.59066829,  0.20032554, ..., -0.54337975,\n",
       "         2.51121768,  0.12145607],\n",
       "       [-0.69029709,  0.59066829, -0.13538389, ..., -0.41944059,\n",
       "        -0.56977032,  0.21304614],\n",
       "       [-0.69029709,  0.59066829, -0.72930698, ..., -0.62009417,\n",
       "        -0.50488752, -0.52286938]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b4817013",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Init Model\n",
    "from sklearn.cluster import DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "64ced54d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dbscan = DBSCAN(eps=0.3,min_samples=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "80abc54a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit Model\n",
    "y_cluster_dbs = dbscan.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "649255b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "        0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1,  0, -1, -1, -1, -1, -1, -1, -1, -1,  0, -1,  0, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1,  0, -1, -1, -1, -1, -1, -1,  0,  0, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  0,  0, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  0, -1,  0,  0,  0,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  0,  0, -1, -1, -1, -1, -1,\n",
       "       -1,  0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  0,  0,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  0, -1, -1,\n",
       "       -1, -1, -1, -1, -1,  0, -1,  0, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1,  0,  0, -1, -1, -1, -1,  0, -1, -1, -1, -1, -1,  0, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1,  0, -1, -1, -1,  0, -1,  0, -1,  0, -1, -1,  0, -1,\n",
       "       -1, -1, -1, -1,  0,  0, -1,  0, -1,  0,  0, -1,  0, -1, -1, -1, -1,\n",
       "        0,  0, -1, -1,  0, -1, -1, -1, -1, -1, -1, -1, -1, -1,  0,  0, -1,\n",
       "       -1, -1, -1,  0,  0, -1, -1, -1,  0, -1, -1, -1, -1, -1,  0, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  0],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_cluster_dbs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "458cb7f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{-1, 0}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Number of Unique Clusters\n",
    "set(y_cluster_dbs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b23e5e3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e3d2f9fd90>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Non Spherical Data\n",
    "plt.scatter(X[:,0],X[:,1],c=y_cluster_dbs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d9cb79f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of cluster\n",
    "labels = dbscan.labels_\n",
    "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "18380734",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_clusters_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cddbcbb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify Noise\n",
    "n_noise = list(dbscan.labels_).count(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "68872cd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "394"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_noise"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45000e6e",
   "metadata": {},
   "source": [
    "### Clustering using Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5d84c2d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Dataset\n",
    "df = pd.read_csv(\"Wholesale customers data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "68086d40",
   "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>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",
       "    </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",
       "    </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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "03f7a4c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalization\n",
    "from sklearn.preprocessing import normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5e5a1d82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale/Normal\n",
    "df_scaled = normalize(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c5801af2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scaled = pd.DataFrame(df_scaled,columns=df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4125eb0d",
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000112</td>\n",
       "      <td>0.000168</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>0.539874</td>\n",
       "      <td>0.422741</td>\n",
       "      <td>0.011965</td>\n",
       "      <td>0.149505</td>\n",
       "      <td>0.074809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>0.442198</td>\n",
       "      <td>0.614704</td>\n",
       "      <td>0.599540</td>\n",
       "      <td>0.110409</td>\n",
       "      <td>0.206342</td>\n",
       "      <td>0.111286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.396552</td>\n",
       "      <td>0.549792</td>\n",
       "      <td>0.479632</td>\n",
       "      <td>0.150119</td>\n",
       "      <td>0.219467</td>\n",
       "      <td>0.489619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>0.856837</td>\n",
       "      <td>0.077254</td>\n",
       "      <td>0.272650</td>\n",
       "      <td>0.413659</td>\n",
       "      <td>0.032749</td>\n",
       "      <td>0.115494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.895416</td>\n",
       "      <td>0.214203</td>\n",
       "      <td>0.284997</td>\n",
       "      <td>0.155010</td>\n",
       "      <td>0.070358</td>\n",
       "      <td>0.205294</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>0.776890</td>\n",
       "      <td>0.315197</td>\n",
       "      <td>0.419191</td>\n",
       "      <td>0.343549</td>\n",
       "      <td>0.004760</td>\n",
       "      <td>0.057646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.990872</td>\n",
       "      <td>0.036146</td>\n",
       "      <td>0.019298</td>\n",
       "      <td>0.113919</td>\n",
       "      <td>0.002349</td>\n",
       "      <td>0.059258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.364462</td>\n",
       "      <td>0.388465</td>\n",
       "      <td>0.758545</td>\n",
       "      <td>0.010961</td>\n",
       "      <td>0.372237</td>\n",
       "      <td>0.046827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.000273</td>\n",
       "      <td>0.937737</td>\n",
       "      <td>0.180530</td>\n",
       "      <td>0.203404</td>\n",
       "      <td>0.094594</td>\n",
       "      <td>0.015310</td>\n",
       "      <td>0.193653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>0.000241</td>\n",
       "      <td>0.000724</td>\n",
       "      <td>0.672296</td>\n",
       "      <td>0.409601</td>\n",
       "      <td>0.605476</td>\n",
       "      <td>0.015680</td>\n",
       "      <td>0.115065</td>\n",
       "      <td>0.012544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Channel    Region     Fresh      Milk   Grocery    Frozen  \\\n",
       "0    0.000112  0.000168  0.708333  0.539874  0.422741  0.011965   \n",
       "1    0.000125  0.000188  0.442198  0.614704  0.599540  0.110409   \n",
       "2    0.000125  0.000187  0.396552  0.549792  0.479632  0.150119   \n",
       "3    0.000065  0.000194  0.856837  0.077254  0.272650  0.413659   \n",
       "4    0.000079  0.000119  0.895416  0.214203  0.284997  0.155010   \n",
       "..        ...       ...       ...       ...       ...       ...   \n",
       "435  0.000026  0.000078  0.776890  0.315197  0.419191  0.343549   \n",
       "436  0.000025  0.000076  0.990872  0.036146  0.019298  0.113919   \n",
       "437  0.000050  0.000075  0.364462  0.388465  0.758545  0.010961   \n",
       "438  0.000091  0.000273  0.937737  0.180530  0.203404  0.094594   \n",
       "439  0.000241  0.000724  0.672296  0.409601  0.605476  0.015680   \n",
       "\n",
       "     Detergents_Paper  Delicassen  \n",
       "0            0.149505    0.074809  \n",
       "1            0.206342    0.111286  \n",
       "2            0.219467    0.489619  \n",
       "3            0.032749    0.115494  \n",
       "4            0.070358    0.205294  \n",
       "..                ...         ...  \n",
       "435          0.004760    0.057646  \n",
       "436          0.002349    0.059258  \n",
       "437          0.372237    0.046827  \n",
       "438          0.015310    0.193653  \n",
       "439          0.115065    0.012544  \n",
       "\n",
       "[440 rows x 8 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "df65e143",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Detecting Number of Clusters sing Dendogram\n",
    "import scipy.cluster.hierarchy as shc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "66441b19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlIAAAGrCAYAAAALqnk/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABI90lEQVR4nO3de5xdVX338e9KZnJPCJMLCeESgpBwiYqNVVDUVq0UrJdWrNfS1Nan7WNrq30s1ueptsVWbYtVa4uoRLyBEkWpBBVEQJDbEIHhkgQIJCEXMsmQyySTZJJZzx+/35695+ScmTNrzsw5k3zer1demTlnn73XXmvttX57rXX2hBijAAAAMHhj6p0AAACA0YpACgAAIBGBFAAAQCICKQAAgEQEUgAAAIkIpAAAABIRSAEYshDCH4YQ7qx3OgBgpBFIAUegEMIzIYSuEMLuEMKOEMIvQwh/GkLgmi8RQoghhD0hhE7/t6NG+/1aCOGyktfm+/Ga/PdnQggHQggzS7Z70LebX2lfABoDjSpw5PqdGONUSSdL+pSkv5X01fomaXCygGMEvCjGOMX/TR/sh4eYzqclvbOwr8WSJg5hfwBGEIEUcISLMe6MMd4g6fclXRJCOFuSQgjjQwj/FkJYH0J4LoRwRQhhor/3mhDCsyGED4cQtoYQNocQlmb7DCHMCCHcEELYFUK4T9KpxWOGEM4LIdwfQtjp/59XeO+UEMIdPlp2SwjhiyGEb/p72YjN+0II6yXd6q9fF0LY4vu7I4RwVmF/Xwsh/FcI4SYfUborhDAnhPAfIYTnQwirQgjnDDbfQgjHhBC+HkJoDyGsCyH832xEz6cy7wohfDaE0CHpE4Pdf8E3JP1B4fdLJH19CPsDMIIIpICjRIzxPknPSjrfX/q0pNMlvVjSCyTNk/T3hY/MkXSMv/4+SV8MIRzr731R0j5JcyX9kf+TJIUQWiTdKOnzkmZIulzSjSGEGb7JtyXd5+99QtJ7yyT31ZLOkPQG//0mSadJmi1ppaRvlWz/dkn/V9JMSfsl3e3bzZS03NMwWF+Qnf8CT88fSFpaeP9lktZ6mj6ZsP/MPZKmhRDOCCGMlQW83xzC/gCMIAIp4OiySVJLCCFI+hNJfx1j7Igx7pb0z5LeUdi2W9I/xhi7Y4wrJHVKWuid/e9J+vsY454Y4yOSri587iJJT8QYvxFjPBhjvEbSKkm/E0I4SdJL/bMHYox3SrqhTDo/4fvukqQY41Uxxt0xxv2y4OtFIYRjCttfH2N8IMa4T9L1kvbFGL8eYzwk6TuSBhqRWulryXaEED5fCGg+6sd9RtK/q2/QtynG+AU/x64B9j+QbFTq9bK82jjE/QEYISO1/gBAY5gnqUPSLEmTJD1gMZUkKUgaW9h2e4zxYOH3vZKm+GebJG0ovLeu8PPxJb9n78/z9zpijHsL722QdGLJ9r379qDmk5Iu9mP3+FszJe30n58rfLarzO9T1L+XxBifLBzzOEnjSs4jO4fD0ljBQUnNJa81y9LfU/L6NyTdIekUMa0HjCqMSAFHiRDCS2WBwJ2StskCjLNijNP93zExxoECDklqlwUJxeDnpMLPm2QL3FXy/kZJm2UjYpMK75UGUZIUCz+/S9KbJb1ONtU2PzulKtKaaptsRK54Htk5ZKL6t155WjOnSNoQY+wTSMUY18kWnV8o6fsJ6QVQJwRSwBEuhDAthPBGSddK+maMsc078i9L+mwIYbZvNy+E8Ib+9iVJPl32fUmfCCFMCiGcKVsgnVkh6fQQwrtCCE0hhN+XdKakH3nA0OqfHRdCOFfS7wxwyKmydU/bZaNo/zyI00/i5/hdSZ8MIUwNIZws6UMa3Nql70m6KITwWyGEsSGE42XruK6tsP37JP1mjHFPhffHhhAmFP6NG0RaAAwTAingyPU/IYTdsimoj8kWXBcXS/+tpCcl3RNC2CXpFkkLq9z3B2TTZVskfU3SsuyNGON2SW+U9GFZ8PMRSW+MMW7zTd4t6Vx/7zLZGqb9/Rzr67JptY2SHpMtzh4JfyFpj2xB+Z2yRfJXVfvhGOOjssca/ItsOvVuSfdK+ocK2z8VY2ztZ5eXykYRs3+3VpsWAMMnxDjQ6DQADJ8QwnckrYoxfrzeaQGAwWJECsCICiG8NIRwaghhTAjhAtn6px/UOVkAkIRv7QEYaXNka6xmyJ5r9Wcxxl/VN0kAkIapPQAAgERM7QEAACQikAIAAEg0omukZs6cGefPnz+ShwQAAEjywAMPbIsxzupvmxENpObPn6/W1v4ekwIAANAYQgilf+7qMEztAQAAJCKQAgAASEQgBQAAkIhACgAAIBGBFAAAQCICKQAAgEQEUgAAAIkIpAAAABIRSAEAACQikAIAAEhEIAUAAJCIQAoAACARgRQAAEAiAikAAIBEBFIAAACJmuqdANTet+9drx8+uLHeyQAwRG9+8Ty962Un1TsZAPox4IhUCOGqEMLWEMIjhdf+NYSwKoTwcAjh+hDC9GFNJQblhw9u1GObd9U7GQCG4LHNu7ghAkaBakakvibpPyV9vfDazZI+GmM8GEL4tKSPSvrb2icPqc6cO03f+V/n1jsZABL9/pfurncSAFRhwBGpGOMdkjpKXvtpjPGg/3qPpBOGIW0AAAANrRaLzf9I0k012A8AAMCoMqRAKoTwMUkHJX2rn23eH0JoDSG0tre3D+VwAAAADSU5kAohXCLpjZLeHWOMlbaLMV4ZY1wSY1wya9as1MMBAAA0nKTHH4QQLpAtLn91jHFvbZMEAAAwOlTz+INrJN0taWEI4dkQwvtk3+KbKunmEMKDIYQrhjmdAAAADWfAEakY4zvLvPzVYUgLAADAqMKfiAEAAEhEIAUAAJCIQAoAACARgRQAAEAiAikAAIBEBFIAAACJCKQAAAASEUgBAAAkIpACAABIRCAFAACQiEAKAAAgEYEUAABAIgIpAACARARSAAAAiQikAAAAEhFIAQAAJCKQAgAASEQgBQAAkIhACgAAIBGBFAAAQCICKQAAgEQEUgAAAIkIpAAAABIRSAEAACQikAIAAEhEIAUAAJCIQAoAACARgRQAAEAiAikAAIBEBFIAAACJCKQAAAASEUgBAAAkIpACAABIRCAFAACQiEAKAAAgEYEUAABAIgIpAACARARSAAAAiQikAAAAEhFIAQAAJCKQAgAASEQgBQAAkIhACgAAIBGBFAAAQCICKQAAgEQEUgAAAIkIpAAAABIRSAEAACQikAIAAEhEIAUAAJCIQAoAACDRgIFUCOGqEMLWEMIjhddaQgg3hxCe8P+PHd5kAgAANJ5qRqS+JumCktculfSzGONpkn7mvwMAABxVBgykYox3SOooefnNkq72n6+W9JbaJgsAAKDxpa6ROi7GuFmS/P/ZtUsSAADA6DDsi81DCO8PIbSGEFrb29uH+3AAAAAjJjWQei6EMFeS/P+tlTaMMV4ZY1wSY1wya9asxMMBAAA0ntRA6gZJl/jPl0j6YW2SAwAAMHpU8/iDayTdLWlhCOHZEML7JH1K0utDCE9Ier3/DgAAcFRpGmiDGOM7K7z12hqnBQAAYFThyeYAAACJCKQAAAASEUgBAAAkIpACAABIRCAFAACQiEAKAAAgEYEUAABAIgIpAACARARSAAAAiQikAAAAEhFIAQAAJCKQAgAASEQgBQAAkIhACgAAIBGBFAAAQCICKQAAgEQEUgAAAIkIpAAAABIRSAEAACQikAIAAEhEIAUAAJCIQAoAACARgRQAAEAiAikAAIBEBFIAAACJCKQAAAASEUgBAAAkIpACAABIRCAFAACQiEAKAAAgEYEUAABAIgIpAACARARSAAAAiQikAAAAEhFIAQAAJCKQAgAASEQgBQAAkIhACgAAIBGBFAAAQCICKQAAgEQEUgAAAIkIpAAAABIRSAEAACQikAIAAEhEIAUAAJCIQAoAACARgRQAAEAiAikAAIBEBFIAAACJCKQAAAASEUgBAAAkGlIgFUL46xDCoyGER0II14QQJtQqYQAAAI0uOZAKIcyT9JeSlsQYz5Y0VtI7apUwAACARjfUqb0mSRNDCE2SJknaNPQkAQAAjA7JgVSMcaOkf5O0XtJmSTtjjD8t3S6E8P4QQmsIobW9vT09pQAAAA1mKFN7x0p6s6RTJB0vaXII4T2l28UYr4wxLokxLpk1a1Z6SgEAABrMUKb2Xifp6Rhje4yxW9L3JZ1Xm2QBAAA0vqEEUuslvTyEMCmEECS9VtLjtUkWAABA4xvKGql7JS2XtFJSm+/ryhqlCwAAoOE1DeXDMcaPS/p4jdICAAAwqvBkcwAAgEQEUgAAAIkIpAAAABIRSAEAACQikAIAAEhEIAUAAJCIQAoAACARgRQAAEAiAikAAIBEBFIAAACJCKQAAAASEUgBAAAkIpACAABIRCAFAACQiEAKAAAgEYEUAABAIgIpAACARARSAAAAiQikAAAAEhFIAQAAJCKQAgAASEQgBQAAkIhACgAAIBGBFAAAQCICKQAAgEQEUgAAAIkIpAAAABIRSAEAACQikAIAAEhEIAUAAJCIQAoAACARgRQAAEAiAikAAIBEBFIAAACJCKQAAAASEUgBAAAkIpACAABIRCAFAACQiEAKAAAgEYEUAABAIgIpAACARARSAAAAiQikAAAAEhFIAQAAJCKQAgAASEQgBQAAkIhACgAAIBGBFAAAQCICKQAAgEQEUgAAAImGFEiFEKaHEJaHEFaFEB4PIZxbq4QBAAA0uqYhfv5zkn4cY3xbCGGcpEk1SBMAAMCokBxIhRCmSXqVpD+UpBjjAUkHapMsAACAxjeUqb0FktolLQsh/CqE8JUQwuQapQsAAKDhDSWQapL0Ekn/HWM8R9IeSZeWbhRCeH8IoTWE0Nre3j6EwwEAADSWoQRSz0p6NsZ4r/++XBZY9RFjvDLGuCTGuGTWrFlDOBwAAEBjSQ6kYoxbJG0IISz0l14r6bGapAoAAGAUGOq39v5C0rf8G3trJS0depIAAABGhyEFUjHGByUtqU1SAAAARheebA4AAJCIQAoAACARgRQAAEAiAikAAIBEBFIAAACJCKQAAAASEUgBAAAkIpACAABIRCAFAACQiEAKAAAgEYEUAABAIgIpAACARARSAAAAiQikAAAAEhFIAQAAJCKQAgAASEQgBQAAkIhACgAAIBGBFAAAQCICKQAAgEQEUgAAAIkIpAAAABIRSAEAACRqqncCAKCc69ZcpxVrV9Q7GXWzuuPVkqSlP76yzimpnwsXXKiLT7+43skA+kUgBaAhrVi7Qqs7Vmthy8J6J6Uuzjnn9nonoa5Wd6yWJAIpNDwCKQANa2HLQi27YFm9k4E6WPrjpfVOAlAV1kgBAAAkIpACAABIRCAFAACQiEAKAAAgEYEUAABAIgIpAACARARSAAAAiQikAAAAEhFIAQAAJCKQAgAASEQgBQAAkIhACgAAIBGBFAAAQCICKQAAgEQEUgAAAIkIpAAAABIRSAEAACQikAIAAEhEIAUAAJCIQAoAACARgRQAAEAiAikAAIBEBFIAAACJCKQAAAASEUgBAAAkahrqDkIIYyW1StoYY3zj0JM0AlqXSW3L652K4bPlzfb/ssvqm47htvht0pKl9U4FAOAoNuRAStIHJT0uaVoN9jUy2pZLW9qkOYvrnZJh8Z2TfljvJAy/LW32P4EUAKCOhhRIhRBOkHSRpE9K+lBNUjRS5iyWlt5Y71Qg1bKL6p0CAACGvEbqPyR9RFLP0JMCAAAwuiQHUiGEN0raGmN8YIDt3h9CaA0htLa3t6ceDgAAoOEMZUTqFZLeFEJ4RtK1kn4zhPDN0o1ijFfGGJfEGJfMmjVrCIcDAABoLMmBVIzxozHGE2KM8yW9Q9KtMcb31CxlAAAADY7nSAEAACSqxeMPFGO8TdJttdgXAADAaMGIFAAAQCICKQAAgEQEUgAAAIkIpAAAABIRSAEAACQikAIAAEhEIAUAAJCIQAoAACARgRQAAEAiAikAAIBEBFIAAACJCKQAAAASEUgBAAAkIpACAABIRCAFAACQiEAKAAAgEYEUAABAIgIpAACARARSAAAAiQikAAAAEhFIAQAAJGqqdwIAACPrujXXacXaFfVORr9WdaySJC398dI6p2RgFy64UBeffnG9k4E6YUQKAI4yK9au0OqO1fVORr8WtSzSopZF9U7GgFZ3rG74oBTDixEpADgKLWxZqGUXLKt3Mka90TBihuHFiBQAAEAiAikAAIBEBFIAAACJWCMFYFBG6htfI/WtLb5xBWAoGJECMCgj9Y2vkfjWFt+4AjBUjEghXesyqW15fY695WH7f9lF9Tn+4rdJS47eb+scKd/44htXAIaKESmka1subWmrz7HnvND+1cOWtvoFkACAhsKIFIZmzmJp6Y31TsXIqtcoGACg4TAiBQAAkIhACgAAIBGBFAAAQCICKQAAgEQEUgAAAIkIpAAAABIRSAEAACQikAIAAEhEIAUAAJCIQAoAACARgRQAAEAiAikAAIBEBFIAAACJCKQAAAASEUgBAAAkIpACAABIRCAFAACQiEAKAAAgEYEUAABAouRAKoRwYgjh5yGEx0MIj4YQPljLhAEAADS6piF89qCkD8cYV4YQpkp6IIRwc4zxsRqlDQAAoKElj0jFGDfHGFf6z7slPS5pXq0SBgAA0OhqskYqhDBf0jmS7q3F/gAAAEaDIQdSIYQpkr4n6a9ijLvKvP/+EEJrCKG1vb19qIcDAABoGENZI6UQQrMsiPpWjPH75baJMV4p6UpJWrJkSRzK8YCKWpdJbctH5lhbHrb/l100/Mda/DZpydLhPw4AIMlQvrUXJH1V0uMxxstrlyQgQdtyaUvbyBxrzgvt33Db0jZywSEAIMlQRqReIem9ktpCCA/6a38XY1wx5FQBKeYslpbeWO9U1M5IjHgBAIYkOZCKMd4pKdQwLQAAAKMKTzYHAABIRCAFAACQiEAKAAAg0ZAefzCshvPr7MP99XW+sg4AwFGhcUekhvPr7MP59XW+sg4AwFGjcUekpNH5dXa+sg4AwFGjsQMp4EgzmCnrwUxBM50MAHXRuFN7wJFoMFPW1U5BM50MAHXDiBQw0mo9Zc10Mo5w1625TivWNuYfzVjVsUqStPTHjTcifOGCC3Xx6RfXOxlHPEakAAANbcXaFVrdsbreyShrUcsiLWpZVO9kHGZ1x+qGDT6PNIxIAQAa3sKWhVp2wbJ6J2PUaMQRsiMVgRTKq2ZRNIuhMcJqPcUzXNMyTKkAR4+jO5Aajod+DtfDPkc6EMkWRc9ZXHmbap/FlS2uJpDCEGVTPAtbFtZkf8MxJZNNQRFIAUeHozuQqiZYGKzheNBnvQKRWi2KZjH0qNPfyE81ozjDOSLT6FM8TKkAR5ejO5CSRsdDPwlEMML6G/kZaBSHERkARxMCKYwOA03DVjulylqtqqWO/DAiA+BowuMPMDoM9CDLah5eyYMrAQA1xogURo+hTsMyRQoAqDFGpAAAABIRSAEAACQikAIAAEjEGimMXoN9oOpgH5bKN/wwSgz2ie+pT3Tnie3A4eofSFXqDCt1enRuyAz2gaqDeVgqT2PHKDLYJ76nPNGd54MB5dU/kKrUGZbr9OjcGttgg2Jp6IHxcD1QlW/4YZQZ7ie+83ywoav134rsz3D9HclKjubRyvoHUlL1nSGdW2MbTFAsERgDOKrU+m9F9mc4/o5kJUf7aGVjBFI4cgxmhIjAGKPQQKMK9f5bhGhsjf63IlMc7aOVfGsPAAYhG1WoZFHLon5HA1Z3rB6x6R0Aw48RKaBRVfutxMF8G5Eva9TEUEYVjva7d+BIQyAFNKpqv5VY7bcRWZM24spNA1aa+mO6DxidCKSARlbLbyWyJm3ElVtcXG7a72hfrAuMZgRSADCMqpkGPFqn+6p9HMBgv8rP6B5GEoEUAKCPaqckhxqwVPs4gMF8lZ/RPYy00RFIZYtui4tqj4RFs0fbYuLS8y13Xo2cfgyb4RiZYFQiXTVTkrUKWGr9OICjdXQP9TM6AqneRbe+qPZIWTR7tC0mLj3fOS+Udm/OA6r9u+zntuUjF1ClPI1dIuCrsVqPTDAqMXQDBTgELI1nJJ+cXjTST1EvaoQbptERSEl9F90eSYtmj7bFxKXnu+wiac+2vsHkSAaEg30au9T4AesoVcuRCTp5HI1G8snpRSP5FPWiRrlhGj2BVLWqnS6TBjdlJjEKMVzKBVf1PP5ARkPAWgfZ3XDx7rQR7haBo8mR+OT0ShrlhunIC6SqnS6Tqp8ykxprFKIefxy4mrQcaWvYMqztqkp2N5zdnTbK3SIADKcjL5CSajtdlqnFKEQ1HbI0cKfcSH8cuJiWI20NW6bc2q6ias633BcmpIYKwIrrK1JHlYp3w8N9t9jfepCB1myMtpGyof59v9F2vjg6DXaNV6M8FqM+gVSlUQypoTqWmhuoQ5aqD0Jq/ceBBwry+iuXek/NjYTSc+yTX9Hyq7+8Kv3ChDRyAWeV33otrq8YDaNK/a0HGehv3UmDO6daBJlDMdDal1qfbzmlnVxpJzZSecEfjT5yDXaNV6M8FqM+gVS5UQyp+jv7rENoXTb6gq6BAqB6BSH9BXkN1uE3hJQ6XK+AcxDfes1GlLLOKipqVceqEe8sq5WyHiRlpKwRgszUtS+1Ghks7eSKndhI5sVQgkqpsW8OMHxrvIZzhLx+U3vlAopqOpa25ZJC/nOtO9lGWn800ioFeY3U4TfSaGZqHS4NGEfihmCQ33otXe8k0QGVNvCNstA1U2mkpr8RmsEGxpU6uWrzYqBRrWrTxR+NRiMZnWukqllInmqk1x8Nx0LmoUzT1dtAHf5QRjMbRTFg3NKWl1VpAJ+6hq5GGj1wqMZw/NHgWn07sdbThZVGaiqN0NQjMO5vVKuW6UpdP1erEdeUoLaWx8fIGtlAau826xTKTd1UmrIrfX0k1Hr9UX8Gs5C52qmvRpimG06pI0GNJDuHLN3lAvihrKGrs+vWXNfbaVy35rrDOodqRiZq0akMxx8NrtW3E4djunAwIzWVOvOBym6oRuJBnynr52oZWA42qB3o+LV+8j8BW22NcCD1fOWpm0pTdqWvZ3Zvlva058HVaP4Kfn+BWzFAGMwT3us9TVeqGBAXy6a0HEciHcMxWpcyXZflyeRZ0tS55cvssKnmkoXtmTrX9dLOd8XaFQp+3a5Yu+KwRnugkYladmrD8UeDa/XtxEYc9Ruo7IrKjc5JjdFRD3b6r9Z5X8vj1/LJ/0f7FP1wGPmpvf6mbipN2ZV7fepcqeOpQgA1gl/Br+c6nZF8wnvK4u9KAVMWEM9Z3LdsSstxuNM4XKN15abrBtpP23L7szj9Pc+slt/0TFRNZ1na+UoasNHvr6NphIBipJUbCapXoFLtt6ZGci3d0f7A11otwj4ar63hNjrXSJWT+o2olBGKI2GdTlF/wU9/I2DlpmMrBUzS4dNZ1aapv9GqlL/DWGnkZ8vD+WjPQFPO/e13oPPLRuEmzyr/frlAXSpfF4vpHswomFT1qG2xs2zvateqjlXq7O7s7dCyjmwk/yxFMeioV4da2rEXp8FSAqByI0HV5v1g0yzV7vEFIzWqVssHvg7H2rmhGI76zDqtkVO/QGqwa5+Knc/Uuf3vdzAPPkwdocg6zUrPEyo9Xi0f21Drb31VE/xIhwcIlaZjBxMwDZSm3p/7UYtRukp5UItviZaWfTYK119aioH67s3SujvL/0Hnwaavv7LuR9ZZLv3xUnV0dejkaSdr3a51urz18gE/OxyyoGNhy8LkDnWoi72LHfvqjtV9psFKO/32rnat27Wu9zjFYxYDsHLBaGneLzluyWHHq1a5dVnSwEFJIwSuUu2mVGu9dm6oz9iqRX0ut89y04EzJs7Q9q7tfepiJgvSywVgoynAGulAuX6BVLmOMguWpMODg2Ln018glfLgw6GsJ6r2yd4pHXKlYDNlGmmg4GvOYuugtzxs5VCtWn2DcvcWac/WPG3l9t1f/ahmim+g0ZisHnz+JfkIT7l0lNtPf6oJCkvXipUGh9kfdi5XtybPtM9WCuJLDTHQzRrmjq4OSflUXntXu7Z3bdeMiTM0a+Ks3t+lw0drynXKgx0tKQYYKapd7F2a3vauds2aOGvANJR2+qX5Ve06pHL7LB5vsKNf5aaIyqU/K7/imrf+OvqhTL3V6huMg0lDf1Nlg32OWmnQUgxWSoOTgdJTyxG9SmXd0dXRJ8DKynpK8xRJOizI6i/AktKDkVo9DqPUcHzJpD8jG0j1dPffQe1p12EdTtZhVZoGyTrg4vuHjRb1M1JUC5WOVy5YyVTT8ffXARcDn2qmdbLga/IsC0hv/nt7vfR4ldbs9DeiVm7BeHH77/1J+Z+L2+/Zaue6f6elrdzIY2n9KE17FlxWGsGpdjRmoHVb5fYzUD5lwc7uzeVvBAY6Zn/lneVLpfPKyifL+8mzyqYza0wv/cWlWtWxSjMmziifFld6p7u9a7s6uzt7G6ztXdvLBgtZp9wysUWtz7Wqvau9zzRWFtgMZSqrv2m30nMYaFqqNL3jx47vDaQGozS/ajUVWs06pf4CjNK1WZlZE2dp3a51fda8XbjgQq3qWKVVHav6bFuajvaudrU+19qnQ+6vPMqVffHzpYHtirUr1LatTd093X3OZaDpv2oDrZS1X+UC59IybpSF3uXqfbn0ShZkZQY7ipVJCUAH2n/qs8aKwVutHjg8soHUmGbrqH/5n32Dn+J6kTmLreHvHRkJ+Tbl7NlaufOv1LlKh69/KV0XUynI6W9KcqBgpTiisnuzjTJMnmWvlaYtkwVfxRGbLMjIOvQs+Cj9bKlsX3u25emtNqhsWy4dOiAd3JcfK5MFAcVyLQaBa35c/udywaFkeZHtt9I5lFMcaak0glMuIJEGnmYuN2JU3E9pmWTnVwx2BlpYLh0+MlcaLFYqg8kz/YcyQXxWPmt+nKehzL6yjvP2Dbf3BkSVOtlqtUxs6W0Yy01hrdu1rk9QUq4z6m8qq9LoVtZAz5g4o88U5EDfQCvdl3R459E8prni5/p7XEBxhC4LUosjdv2lp/h+9nox0C3mW/Z+Mc/7CzDKfVGgT7r3tmv7PhtpHGjb0mnI4nYDlUd/I3jlgq7mMc062HPwsOCkv+m/wayzqvSU/4HWwmX1prRMqhnhGu5HT/Snv4etVgqyqlGLAHSw++zPQDdsK9auGHRAVZ+pvdLgp3S9SHEar3e67GENqFyQU9q5SnkHl3V6k2f2DWTKBWAbW+31YnCX/V6828+MP6bvsaS+Iyq7Nkpjx/nGQTr5leWn6bL9HzogjR1f2E/h/5ZT89GMagKjYrA6mMcOjB1naag0alRartkozKED0rwl9tqWhwcenelPMT+WXVR5H6VTlcW6sXtzHkT88gv++QGm3sqNGBWDpj3teTqKU22DfazDnq2VgyUprzP7dxXS7sfPAvMsiM9G4yrJyjM7l7mzD9tkxdoV6u7p1v5D+3X1o1dLUu/0XfazpIqjV6WjUpJ6g4BiAFUpiCk3ClLaqVaacio3BVluerEYbJTuS8qn4LJzzWSBUXtXe7/TdNmxunu6NW7MuD77Cgra3b1bl7deflielu63eLziyF+pSo8vKAYGWX5mU5ULWxb2diilZbN9X9+Rxiw4XrtzrQ4cOtB7jqWy7bKp0Erlcd2a6w4bXcrSm6WpOEpa3FeWJ8VtiukpLeP2rnZFxT5BcpYPlUboBrMWrlhvip/POuqTp53cO+29bte63tHY0rLL6oSUvvavmtHHlD9GPpg0SJVHfSqlpdop17ZtbTr32+dKsmm7rEyr/QJIlo7iDZuUFqQNKZAKIVwg6XOSxkr6SozxU0PZ35AVO7ZsdKTY4ZYLILIRjl0brXO69ZPSof322uK32T73bJMULaAaPy0fRcpGIvbv6jvSko2wletMs+Ntedg+J1ngsfhtFqytv7vvuptsJGP8tL6fLQZlxdG6ar+V1d9UUmmas30WA6L+Ro0yxbSXe33A0ZnCCF5x9FLBgoCsPHpfV9/pq2yqUrKgo5hfY8dZsFIa/BQDs+K0caXXisFi8fyKwVClaelKigFOcZQvS0Np2jPFEcfs5mCgYLV43mUCKclGYPYf2t/bua7bta73vUpBRlFxVKpSEFA6fVbsNIvB3GX3XNYbVGTHnTFxhi5ccKHatrWVnXJqmdiiLXu2aOXWlb2dSn/BRravlVtX6sSpJ/Z22qUjU9kInqQ+d7alHfqKtSvU2d2pKc1TDttX9nvrc61l87SYT8XjZSoFIcUgpthhXfqLS3tHHFsm9J2qLO6/v6nLbAp3iqb0pvXqR67Wlr1betORBThZWRfXz5WONqxYu0IHew4qKBwWiJQbJS3uqxio377h9sOC9qzeZEFJtr+rH7m6N0DM8iHLo9KbhyxwKzetWeyUs99L6242EtfZ3antXdt1yVmXaMXaFb2jsVkZNo9p7pM/rc+16upHru4zjXnut8/tPe+sbpSOnhSPub1r+2HnVlrv+3vsRvEcq5n+Kg0cpzRP6bNmsviFi3I3aQMFecXAdVXHKnUd7NLEpol96k3pyGc2wtRfQFzuJmMwgWZyIBVCGCvpi5JeL+lZSfeHEG6IMT6Wus+ayDqGXc9aZ1TscKfOLT8NNXVu3jkd2q/D1uqUTjlVCmJKA53BdKZty6VD3VLsqa4DLD2eZJ38zX9vwcPkWbaf9Xf7exW+9ViclpIsn4rTr9lnywVEw22gfC5XHsXpKyk/H6n/0c1ywV25aePS1yoFi8VgqNK0dH9KA+6Bgk6pb+CZGagOVRvUVlApyCgqdnblAq6s48n2tWXPFnV2d6q7p7u3Q8+mcSRrKKOiJPV2rivWrtD+Q/utUfZOUsrXXBQ76uJUVXb8YvCVdew9sUfbu7ZXvR6q2OkXO/CB1pqVKuZp6QL+UllaY4x9AtBiEFPssG5Zd4vGjRmnqc1Te0eTSqcqU9NazOOsfDKlQWBWD7Kgs2lMkxbPXFxxVKy/fRXTUDoKlp1bZ3dn31G/fVYns3wYP3a8Ors7ewOn7OZBMa/bpSOEkg4LuLLfs7qbjYBJ0tTmqYeNPmb7yupudn69+bRve2+A2hN7tO/gPm3YvUE9sac3MFm5dWVvsCCpN+hqUYuCQp9v2GbXWTHoz4JUSb0BZxZ0tExskSTtPbhXbdvatP/Qfq3qWKWrH726T70sN6W+9+BedXZ3au/Bvb3nVCmfy90wffq+T6t5THNvwHjc5ON6y+K4ycepbVuboqI+tORDuvrRq/u0I9mN1rix43rXYUo2OpbtJ7vJyPIrCwKLaajWmKq3PNyvS3oyxrg2xnhA0rWS3jyE/dVG1jGMHXf4c576bOcdYqVpoZNfae9X6gTnLB74G2vFaZhqOtNxk6XxU+3nwU55Sfk5SdJBH1WLhyxImzr38HRkQUa5tGX7qjbtw6WafB7MdiNppNOU1f16l1kZC1sWll33MKV5Su+deibr/Eo7+IlNE3XO7HMqrp/IvnGUjTQU91v8bPZ+NgqTdbKlxxqKYgfe32jdQLKAqL9gbmLTRE1untzv8Yp5Wqkshmqg8slk59TZ3dnbiWfpy8ojNc/K5VelsijmQ/H4ffZXUpcWtizsnaoq97nSuls8n4UtC7XkuCVlzy+ru+U0j2lWT+yRJE1omqCe2KMlxy3pPWZP7FHrc61asXZFb1CW3QQsbFnYu13xuFm6ikFUdi1mI0pRsXe7ntij/Yf2a0rzFC1qWaQNuzf07q/1udbe66f4uZ7YozFhTG/6tndtr5jPpWWVHS9LW+tzrb1pzdKdneeKtSsOqzfZ/rObheK1nu0nC96K+ylNQ38L6ItCjHHgrcp9MIS3SbogxvjH/vt7Jb0sxviBku3eL+n9/utCSX3HbgEAABrTyTHGfoelh7JGKpR57bCoLMZ4paQrh3AcAACAhjSUqb1nJZ1Y+P0ESZuGlhwAAIDRYyiB1P2STgshnBJCGCfpHZJuqE2yAAAAGl/y1F6M8WAI4QOSfiJ7/MFVMcZHa5YyAACABpe82BwAAOBoN5SpPQAAgKMagRQAAEAiAikAAIBEI/pHi0MI8yTt8V8PSRonqSPGGEMI0yTtlnSMpEMxxt3+mRnZNiX7miJJMcbOkten++s7ittm24UQ/lj2VHZJulXSd4r7ztJReryS/U+RtDPGuNu3zwLSsZXSWprOCvsecLvs/CQdK+n54nmW2XaGpG7l+TpFUmf2mUp5WzhOsRxKf+/Np+J7xWP6e9nvY4qfLznWNEk9xXMvnOfBGGNnoX5MLez7REk7yu2zGqXnX+Y8VPJ+sR4Vf35DjPEn2et+rn3qUAjhnbJHhPy3pHNjjDcPIp3TZdfLByV1ysoxSPqCpM9KWiHp+zHGQ5XOz/Nqt6QeSb8r6cWy+voC/+yXKxy3V8k1dYoK9a/asuhvn2XSvr9Y9mWuq+myfHmX7Bl22b7/28twevEYWX6oUIeKr5dpB6SSNikr98J5TJaVRdYenCLpeVk+7y6pW4ox7ijdR7m2qnBuY8tsM01Sc5Y/Jef2277ZyzxNl5deu5WUlE2fY5e2TYNo04rtxIn+c/Fcesu55DUpr7tTZO1db/0KIVwiqUvSKyTdFWP8bmnaSq7RPnWo5L0++VNSVn3qjKTJvu/StvWw/K1QvqXpKFf3svSfImlbSbp6+54y+Vu83gdq27PzmydpT+FcpUL77dv36bcL/V5pG1dad7NnTY5RXp96+6AydWp6YTv5/x2evxXbihSFcs3KszeeKFzDB6up49IILTYPIfyLpHMknSoLnqbIOoR1kk6WNYBbJB2QNEnSLEmbZX92ZomkF/r7+2QX1FRZ4UyTtN3/bfb9tcgqwnQ/XrOkeb7NBP9/uaS/krRB0mOSPiHp7yWd4unpkLRN0mxJB2UVIni6m2WNdpOkJ/w8TpG0w7ftkDTDf57l53uMZ8VOT+Nk39cBT+8YT/9CWUXa7tueJOk5SXtlBXuqv3ey50ePpPV+rDl+nGZ/vVlWEZ/3403yc7jb82+ypAX+89OSNkpaLOlR3/84z8d7PB9eImm/pzX6v+xC2evbT/f8eNJfH+ufnehp2u7bbfQymerp2unbPS7p55LeICvbrkIeBEnH+3kfVF6Pgp/j1/3cHpX0JklzJb3d35sv6WeSfsPTul7Szf7+8Z6+LK8mehnt8/IY68fYK6t7HZKul3SxrBGdIatXq7zsTvM0zvX8elhSmyxwaZX0cj/GGD//KZJ2+T6e9vw7y/OvU/astlZ/bafnSZOsvLd7XrRJOk9Wn/Z52YwrlGH2t1YmyhrEKZLulV1bT0la5PtYKekPlde/BySdK6sn2/y40fNnnp/fTN93l6wur/bXmmX1/xk/v+M8fe2+r9OVl+uTfp5Z/W3ybRd6WibJ6sP9svq4TVZHeiSd4efU4vk+wc97u/Ln3O2TdKek82XX6WZPzyFZefbI6t84WRl3Fv7f7elp8fTt9PTukF33832bLn9tkv971PO5yc8pq2PPyq6zXZLWyL71/ApZXThfVs+e8vdfJOkXkl4qK8cDnqZ5frxJvr/5srrbKWmrrA7+QtJr/DNNnvaHPE9mK28/viHpjX5++ySN9zLK9jnV/zX5saZ5Go/zNG2TXfc9Xj5TZW3FXlkdXyeru9sk/UpW555T3i6u9jL8nqf7gKy+nezn2CFrA7J63e35ukHWhm2SdJmfw3z/7PGet3P8vLL63Omfn+tpXicr54myevNzL9Nj/N8jfuyxsjp2rJflHk9L9vtGT9MqWV/V5eWw0T+ftV2bPN2v8Z/bfF9n+35O9ryfJCv/rbKyvsvz9FjP962eh/u8PFtk9WiG/+v0NJ+u/NmOYyRd5/s5Sfl1Ii+brH87Tnlf1uTbHfT821HIu41+/A5Zv3Ssn/cG3+8CP5dmz/OJ/i+7Tsb7Z5ok3eefmeD5v9+3ydr4Q/7eGlm9fqu/v1tWX04t/D7fj3fQ/z9BVs4T/Pi7lV/be2TXwhh/P/r5yNN9SNLXYoyf0QBGamrvNlmiN8sKba3sJFtkCX5Udlc9UVbwd8guxN+TFdzT/pkTZBfFk7LKtF7Sb8ku7pdKep2sE10i6xzG+b4PSDpTVjnvkXWgByT9l6zxukVW6eZ7+g7KOoMpso4g+s/Xygp5t6xineHpHy9rQKbJOp61soLZJCuYZ2Qd+VdkFfV4WSN1QHYxPSULyvbIgoHvKb9gr/f3gqfrLs+7dX7cV8kq45myi2et/7/Ztx8raxRu8vwdL6usZ8oq+q/8OBNkQeUE2YX2hKzCvcLPc7fvb7WnZZXnwQZZIzpWVpm7PC9f4PuZ4Z/drbyzPMPT0izpQd/n837sC2SNxxjlF1SUXbRdkv7H8/pYST/wfNgv6Y8kfc3z8h8kfVh2wW/1fH6xH2Or7AL53378Dj+Pe72MOj3Nc/z9a7z8Nss645tkdW2TH+daSV/y14/xz98oa4Qf8vT/qac5y5cpfk4rlY9e/Fx2UZ/p7+30Y2TlPlYWUO2V1Z97JN0uuzZ2eH7e6HmRBRV3+HFbZJ1cl+z62aS8kbzKfz9eNprRJKuT02UNfpOsAdsj60g3ezke62l72svqVtm1s8PTu93L5lZPQ6eX1zbZddwhC7QWyQKGs2QdyTTP/2meN897Gh/wc7pI9ienmmUd6UQ/t0f9GNd4mmfKRv8e8fQd72k6SfkNwVhZx5cFy/tl9Xq87+MXno79nvdbJH3G86BbVvc7JH3Tt5kquxnIAsKZsg7lgKx9e1AWHD7j27VI+gtZezXD93uv/z9Ndj0e63m2wc/xdJltsnrT7Wm7S1Zv9vv/Z3i+7fPy2OTl1unlmLVFL/Pzn6A8qB8v68y3yq6JzbK29vuyevqYH7PVz61V1pa82D97t5fDJM//3V5Ocz3N4/yYO2VtUZfsJuccWT041s/3flm9a5Ld+OzztO+T9AHf/z7ZqOy7C/t6wrc7JOmLXkYH/Pyf9rw9KKtTk2QBxgOSfk3WKf/A93GSn88xngcPy+r3Rs/jJ33ftygP6h+V9GU/3xmengOy8p7v+9ssu75e4uc8xz9/syyo2Ov7nebncJ6s/v7Ey+Ehz78Fsv6hQ1avTpPVqQOy+rfSy/BBf/31XkZjJP2Tl0OnrB3JAumDsut2jZ/LZklflV0Lc/18Dsrq8CTP8z/3Mm737aOfw14vvx5/bbXn2cN+rLsl/djz6QUyWd+23z+7X9aur/X9/a5vs0NWZ19c8vsEP16brCy3evkFL7fNnu71Xh7PeH7u8XzqkF2r6yU9W00QJY3g4w9CCKdLukJ2N/aY8oj8UVniT5FF2afJOoCtsg7rNNkFtU5WUSXLqN+QZeJrJC2TNQKrZQ3tk5L+WfZHlE+TVcR23+Y6SZ+T9ClZBXpCVgnGyhrml8oK5dWyAp4tC7aWy0atlsoq4A2+fZOn6wTZhfeMrNLskl3EF8guiHv9GL8tqzS/J+k/ZRX0GUnvkfS3/tm/9NdmyzrvP/X97fPX3+T50y5rqKbLGsQuWaf0M1lQ+Yys8+iUNRLZXcnNki6RVajv+OtNskbsEU/zZM/3LMB5q6wjWehpafL0/K6sEfoX2ZTpsbIK/ITny0FPz+97uUySlff/+L5+Kul3ZJ3qMpkXyBqOaX7sR3xfM2UXxYl+/q+V9HlZJ/IG2YVynvK79U7ZSNXBGGNbCOF/yxqvLC92yoK2k7w8n5N1FnNkAeqrZIHSbV5e2YjOw8pH+k5WHkA+JOlDsgbsRZ7GHbLGM2sgn/PPzpBdrK+WNVAzZR3bS2R18DZZI/uE8hHD7Z7fz3nZ3ie7Fl4nazx7ZA3vz2TXxmzljVqP58fTklbGGB8JIbzZy/FTki6XNb7vkTX6K7wMxnl6mmWd95N+7Ev8OLfI6s0LJX1X1sEt8Dw9RhbwBdn1831ZwLtVVmf3S/ql589bJX1U1oD3yDrnBz3fN8jq3L/65/5J0m9K+qGsbjwt6+B/z9OcdWyny+rVl/wczvP3spGQDkl/5vv/rufZdM/nP5Bde8/5ua6UXZ8/kgXpr5bV87Nl19M9srZoiew6uU820r3c9/NbsrrwoH/mGtkoykRZG5gFmGf7eb1H0tWyOvgmWVuxwcvrUlnHEj092SjnLFkZnyOrm++QTf+OkbW593n6npd1yi+OMf5PCOHv/LMXe75f7+XwMlkd3Sa7QcyCipmya/SDfg7dsiDybFk93O3ludLz+WRZWx9l18/nZdf1O2V1Y6uszT/gx3mlLBD6qpfhb3nZrPXjnCoLeGf7MX7qn7tN1v5no/pLZW3Da2T1eJ+XzXw/v3+T3ShO9OOPl11/Tco73vN8vxM9/3/dy/FVnn55Od0lq8/bZH3bNM+Pyf7eXFmbtFBWzp/2/Ntf2HaTrB27UVanlkj6R9n1NNP3Md/LdIYf/xRZwPxG2TX6DVld/pGs3v+Z7NrJApnnlQd862V1ernnz8tl9f1e5QMD/yLrB37keXCz8pHeCz1PsxmFnZ6WC2TtVtauzfX8PVHWB2/2NJ0uC1pPlbV9J8jK9QrZ9XSL7Fr4F3/v3bIbj9mezidl1/XzfpxnZPVmgqwuPyarN3d6Po33/b7Ot7tKVu/vkPVP2V+SfpHnTWeM8euqwkhN7X1G1tAukFXyacqnaJ6UZcIh2UU2XXmGb1JeUKf6+9mo1LO+v8dkgc52WVSdDXseq3xtzkJZR/la2d1LkFWaJuXDgO2ygs32P01WqMfJ7prv9u1PkTXS02WZHWWNSrvy4c17lF8Y2Z3uXN/mkO/zedlFO17WEWyXVb4X+Xl2Kg/O2gvHXKX8rnSV5908z6/bZYHjrZ4HX5VVwmOUT4fs83Pe6nlzqm/bIutY9vn+2n3fd/rPZ8kCwRO8zE6WddBj/Zy2ej6MVT7U/YxshOVeP+eFXpbNsgv1KVkHuMjTMd3TED0NT/l+WmRByvmyxmya8sbuW7K6dZry9UPtsmAgWxc00fc7SdJpMcYXhhB+qnzk5DxP/1Q/zmLlQ98dyqeGfybrQLOG40kvv59Ieq/y4fEDns6zZQ3GQVkHv1k2DfFiWSO0R9bI3ifrAH5L1ni8XFaH5soa8fO8DA/KAuF3+PnP8zKfLGlMjPGNIYTrvNxOltWr5/zzLbL62u77/LKskV4j6xxmeRon+7l+V9ZhXOR5mzWir1E+BbRZ1ui/SfkUQvC865Z1bl+QNWpjlY9cdvu2XbI6sc1/X+95cq6/t9mPO83/3+fnvc3z6EdeFms9r7Mp2DGe5un+8xg/5mblawW3+jFnyzrUrTHGr4UQ7vKyO0E2Yv0RP5+rPQ2nyq6FbLr0RE/XSs/D52X1J8puksbLAnMpD+5nyNqEW2QB4QxZnTkkq3NfltWFDt//qbJrIZuyHKd8KrXJ83ST7PrbIAssxstuJLNlEqfKbiDf45950veTXcfP+/4meFqekNXdSZ6P98naz+wGbobnx5nK17F0+efX+bmcLWujsuBluqdLfh7dnr4d6jvyOln5FF6351k27bJKFvxsUT6LkbUfU2XtzF4vn2x6apsfd7W/f4oskJguq29TlbfT07w8j/e8/IyX0RzlSw22+T7WyOrhTj/WNN9Pu+ff/X5+C/zz4zw/s1mCrC3+umxgoMXfW+7H7PbjTJeV70Tlyyc2+TG3eX5k04nbZO1Gt6dnjKxf2uLl0lU4xxcpn27dJQuiX+XbPKN8+cZ02bV5uuwazq7ZQ15GX5Vds/f6+b1S+XrFQ573Y2V1rEn5lOJk/5fdJEr5sp/jPV+PU750Z4znv2T9/pmezhleVmfI6unxsutmsvLRsJ/L+omTZH35Ls/3v/HyafLyOsv31+rpfSbG+HcawEhN7e2QVdYsozfIGhHJRmh6PC2vkV0UQVZpdsoatGNlF+h62YWUDRnukF1cT8oamhv8c8/KOsRdss7kCllmPiVrEM+XFfRbZRn3YuVB1BZZ5d+vvhf9Gk/X47IM3yvrWA/KItp7lA/NT5HdgS2SNW5nyBqsZlkhnSC7kG7zfLhF1hlc4Nu2yRqhacqH4bf6+WV5da3s4ox+/DtlHdrHZJ3fn/p5v8z/vUJ20XRL+rb/v0dWWTZ6fu323/fLpisekXWyr5V1TM2eBzfLOrHnPY2PKg/mrpeVcZfsgvk7WUd+qp/nLNld+UOyyvseWcXv9Ly+y/PsCVkFl+fHCbLy/Iyf/xf8vE/3dP1QdnE3+7HPkl1ox/g5vMRfmxRCWKN8vcT5so6nR9aBLJGVe4+sQWz1479WNpI1QzZltEHSJz29l8su7GbPs21ejv8ku+PbKgtit3j5nK18bdh7ZcHVSbKRrigLjrLAfoms/o+RNRonyYKjVyuviz+XtCSE8BHPs0WyqcsFsnJ/0Pf3Js/HsbIRl7P8PL8oq893+rb3ydZKfdDLaLysbpyjfD3DDs/DS2TByELZtXe88g7zE37eh2QN92bZHeizsuvmYT/u3Z7Hs/zfjbLr4TFZ4/+Q7Do+x9OxTRborZUFLa/1z2TTohNl9fQR//1xWSfa7uX2K8/7rZ6G90v6eAhhm+fZbFl9/6SsTvxENtL4114m2Q3Ycs+HT8ockF3nG3y/J/j57PR9bJHVxxtk18rFvp9VygOzb8tGpFuUt5VfktXp1Z6vL/A83eRl8Q/K1y59WtYurJaNDj4kCzh+KOvsNsqmU57xtI6R1ckuz691smC9R9bRjvVzfrvn94l+zltkAftNslGCLtm18HPl9XWF8vVw2ajqLzxv/klWB+6VjURv8Lx5WlbGq/y1Nj/Xu2Xt1ZmyevTvspHBMzx/p8mWRFzk5ZBNQ27012+U1d02WZ/xWs+7yX7Oxyi/cX+gkL+Xy0Z7emRtxS7la9zO9rQ+6OeZTbut97z7P7Jr7jc8r4714+/2z2/23/9A1gbNlNWXl8uuo0WyfmeXn99lsra53c/heFnbu1P5Uo5sVuEGT8vn/byXy+rFK2X1Z5Gf6xm+v73KpxhXyOrGKbIR4zWyNnyq+l6zj8v6xj/zcnm7bJRsnqdpo+97rfIRxGZZ3dzi+fRLz6tDfvyn/f9b/Zx2ym50TpBdy7/0/WV9y0meN+Nk1+h5nu7/ktWXW/z34z2tN8uuq7Nl15r82J1erp2ym6IzZHW6qhipqZqNauA2WUZ0yzrIcbJEj5Nl3GzZRbNJduHeLKu8TbKCGCfLwBfKTm6T8kXQzbILeI/yAp4kqygblA+Br5b07zHGzSGEe3z/P5JVmHW+7QTf1+/KKlO7rLK1Kr+YzpEV7l7ZEOEMP95kWQM9SXZh/ZryhapPyIYgZ3g6VskKaKKscr5A+cjMNuV3dwdlAc3L/By2yYKCXbKLbqLvPxutulZW0e+XNbjflV2kzypfYHu8p22q7AI54GnZ5e/v8fz+A1nFa5NdkItkDegySW/xvBkrC/LW+n6zu5HnZZ3g8hjj7SGEc2UXxxxZRc2m6ibJgqGFXqbHyBqbVZ6G3TIHlC/wvsDz+fVeRi/z9LfK6tTdkv6X59t65d8Akad1lx/jGVmQcbes013g+fU+WZ08Sfnamk5ZwPduWXD6Xs+byV5GH/L9tcjKerpsXn+S72ePH+tWWdD+oOx6mCIr912yi/cNkn4eY/yKJIUQrpJNez4h6+xvijF+K4TwUVmg9HFZZ3m3rENa5/uJslGrZ2QNbrPn4VWy8lwfY/xKCGFqjPHf/FhLZR3sX8nK+RpZXZ+nfD1iu6whz0bmfubncUB2nUz2c7nZt58nG1n5pqw+nujnuFA29b7X3z9d1uj+h6z+fkLWmZ/n5/YSL4v/kF2fd8nq/0FZ53KZb/OEHzvI6ugu2TVzWuH/Z5Tf2f5Sdt3v9LQ9XjiXZt/uKT/HX/r5TfD9zpKNshyQBZO3e548IetUnvC8n+Dn9nLPgw4/9hbPzyhrx66VjVK+xPPlEeULrrNR1g1+3Odkdegdsmvlb2Q3k1HScTHGP5cLIZzv+f0T2dSn/PPz/Dy6/Zjf93z9iKfnNbLr/njfrkdWd6Z6mRzjn10pa5NaZdflNbLOe5Ln3V/J2tmg/AajS1YXN8o6wY/LRpaf8/f3y9qrbJTu2UK5jJGN2L1Odu08ILsmPyhrCxbL6sAMP/YVyke/tsg6z49JutLLY5Oso13p+fw5P9+zZCMW3X6uk2TB3wHla62i7Nofp/zLFLNl5bvM93mcp2mq7ycbQezx4/xc0k9jjF8KIYyX9XGrfPseP4/7lQc59/s+Z8vqwAxZfdmgPMi8R/mI9wxZ3XhcVvf+1PPwHuVfvthT+H+xrD+6XvkXMw7IRrmOlV13Xcqv2Sc9v+cpX6v5Ii/X8bLr7ZCX6fPKvzDwE9no1yJZO79GVn+Wy+r9HbKp2TX+mU5ZP7PPy2mfH+txWf2438/1CeWjgzskPRZjPBRCuN/L+kLZ9f4l2TKDTt/PfuXrGx/2NDXL2rgBjVQg9ReyBD4nu+gelF2MfyE7gStknd86WaG9R3ay82UZtkn5OqX3yCrRA7IGosn/zfDXT5E1CNnd1gdkjcOpkn4zhPAr2QV3iuyu+zRZpVkqa7yWyir6AVlndb3sjmKarKJmQ+rrZBV7i+zCGivrPH7dt5ugvMBfKKs8e2WB1HmyQsqmdMZ5/mR3odmd1aOyjnSXn8uZfqwmWUMySXbBnuf7X6P821I3yzqt8bKOfabnV4/n60xZIzlVVhFn+fFOk1X8nYVtXi5rBDfKKmK23mGOn++r/PhzZI3HTFlj8rkQwiZZQ71L1lhO9Xw5X3aRvbZQrntlQd4C31+7rM6c6fvIOqStsk7/Ytkoyq/JLojxsumq/6O84QpeNjdJ+hPlDUSnrGH5tqwe3OH72ya7c7/fj/usrOz/W9b4ZHn+Hk97V+G9WbKL+GRZgPAFmfmSZscYLw0h3OxlN97TcK6sAVvgeTRPuUv9HE7113/hrzf7vu/xPJqgfGozK6seWeP3Sy+fb8qusdmSpvjXfy8JIez0PFoiWy+33/PwC7LG8ik/3q9kgcJ25YHN7yufMjvgeTWvkKeXy669qZ7udt9mriwIaFb+DZ+fyerab3ra36p88axkjd3JsvI+Xlb/innQLesA3qJ8anaT7Hrp8vRP8PQ/5WmeKwuS5Mec6P+elNWp//b9bZWN6LxTNrLxG7JO4TV+Pj2+3Rg/xn4vh4dl9WWC7/d2WR3slNWVFlk96JJNw81UPhV8k+yxDs/KOrCzPZ8W+Pm+U3knf72sbL+tvvVHnqY1spH/mZ6Gt8o63mzK5ZWFtEyVXbt3eV69TPk1nU2Bnel1+SeS2v3nWz2/PqK8/Vnkn93g5/hWWRvxrKyj+oEsuNsqu9GY4dtl7d5u5XVnlufl9Z5/OzyfsmO+zff7C9n1/4DshudUWWd6aQjh47K6PV3Wbk+W1Y81sjrT7O9Hz/PP+rGbZAHPX8varuyGO5tS2+ZpmeKvb5bVjbfI2piZyhdPZwFAu6xOvkNSSwjhGC+HA57n82V1+yXKv1H6r8q/OZvNkrzC8ylbMpPVmztlbfPlvp93ep7M9vT+hqxu7FM+S7JOVgeOU14/N/i+lsj6rqnKpwqzNuEE5e3zIs+T9bI29R2yOvqA8m+/ys9xp/Iv6PzU0/L/PH+Xyq7/Pf7zFC+vLX6uO2R1epafxwL50g3fz72elgtDCGNkAwOfVz7wkB1H/v8PZDfK+2UDKRs9HzbJ6nK/RiqQusJHJs6XJfraGOMPQwjrZCM9P5dViotlQdZlsru8H8kazSmyxZZLZNMQZ8cY/8k/v1D5sOg2SQtijMtCCC+U9Bp/LsQnZHcTWTrulBXQNFmj+gNZBXydbNRgZ4zx8yGEG2TDlVfIGq3Zsoq0z9O6VlZJ3yLLeMmCo5tkF/S1skXVT0v6Yz+fLCK/Xflw9SHZgt93yyrhLlnFu0z5eqt3+X6/KBuNeUI2bPwD2SLda2SN1vWSFsUY/zGE8CpZhbncjxlklfZNntY7ZHcPtyn/1szHCmk9w9P2S1mDcJqkaZ6/58sagxtlDVM2PJyNznxNkjy/3yvrVObLGpGPyRq4vbJRht5y9f9PlnWon5U1AHtkwdtLZaNGH5PVmU/JGr4rPM/WxhhXeXlfJbvj+HNJ3THGvwkhfEPS22OMn/L074wxrg8hPOTp/5Rsvv822ZDw8bJ6+S6vRx+Q9Ode97K68WDhvffFGN/rde/XYozLJMmDyW95nl8h68BPkzXMl/jvn/LzP0254nXTHmNc768/VDjm62OMnw4h3CRrlG6PMX6gpHzul3VWa7zsfxBj/F4IYWshjdnnnyrk4Z/I7v7nKp8yu0nWkL5UVgf/XNZx3S0bau/O8rRw7V3l+8m2aZFNQU2XXYNtsuvpEc/7blm9v0R5YDFNFij8o293sJgHsuuuRTZ1f7XsOl4tC64eknXM0/38/9aP8f9kAd+XlV8Xk2XTIr8ua6S/KnvWzKdCCGd7nn5ads3/h6x+flfWjklW78/zvF7j5TlbFhzcI2uoH5IF0NfI6nJ2M3mh5+mrZe3S47I6cr6sk7tJ+ZcIsrz/50LZvtjzpuhBb2uztuBGWcdwTCENb5d0kn9D6TMhhD+UlXHWIY2XBdUfkpXl3/m+b5fVW0m6r1Depe3PHb7NLV5WJ0p6Msb44RDCM57v2fT2blm7+hZZ5/g1WdvzJlnb81Gv2ztkX5rIjnmKbAT8hyGEjZJO9euieO09Ium2wjXVLBulPqD8pvIET+8CWRv8Ilm9elBWH94s6VZvA5fK6ua3lLfdpymvW++TBXp7ZIFGdp1/wI/3MVm/dFKM8TMhhNWyvmKqbITti37Mt8vK/mlZff2srI5slF3bL5WN8JypvD/4x0KZFNumv5TdBBT7pDNk9f5u+U2f59dJhTzdJQvaPidrFzZ6fpa2CV+Rtd0/UP7YiDNkN8tnyWYRPixpjbejl8mCxLuUfzvzD2Vtd2nb+Cbf38qSOp31b1mbsdD3XawHW73MsjxuUX7tL5EF4c1eLt9T3te8XVXgjxYDAAAkqmohFQAAAA5HIAUAAJCIQAoAACARgRQAAEAiAikAAIBE/x9ZC4nCDFzS/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "plt.title(\"Dendogram For UML\")\n",
    "dend = shc.dendrogram(shc.linkage(df_scaled,method='ward'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5e3b6ee4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1e3d35d4790>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "plt.title(\"Dendogram For UML\")\n",
    "dend = shc.dendrogram(shc.linkage(df_scaled,method='ward'))\n",
    "plt.axhline(y=2,color='r',linestyle='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "23cecf9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model building\n",
    "from sklearn.cluster import AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2d6be1ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "hcluster = AgglomerativeClustering(n_clusters=2,affinity='euclidean',linkage='ward')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "97892d9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_clust_hcl = hcluster.fit_predict(df_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "dd6f656a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1,\n",
       "       1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,\n",
       "       0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1,\n",
       "       1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "       1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_clust_hcl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5541023e",
   "metadata": {},
   "outputs": [],
   "source": [
    " df_scaled['clusters'] = y_clust_hcl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "239033de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e3d3129340>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmYAAAGbCAYAAABuyFzQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAA5j0lEQVR4nO3deZhcVZ3/8fe3eu8sZCGBQMAECUsQkPyaxYXFjU0hLKJBFIbBiaiog8sQdMYVFdERBRFEQUEQBhQlCIqIKAKCNLIGiAQEEhKyEMjWe9f5/VFF6HR3uovQy+30+/U89VTVPefcOic3mI/n3ntupJSQJEnS4MsNdgckSZJUYDCTJEnKCIOZJElSRhjMJEmSMsJgJkmSlBHlg92BvrDlllumKVOmDHY3JEmSenXfffetSClN6K5sswhmU6ZMob6+frC7IUmS1KuIeGZjZZ7KlCRJygiDmSRJUkaUFMwi4tCImB8RCyJiTjflERHnFcsfiogZvbWNiOMiYl5E5COirtP+9oiIvxXLH46I6tcySEmSpKGg12AWEWXABcBhwHTg+IiY3qnaYcC04ms2cGEJbR8BjgFu7/R75cAVwKkppd2Ag4DWTRibJEnSkFLKjNk+wIKU0lMppRbgamBmpzozgctTwd3AmIiY1FPblNJjKaX53fzewcBDKaUHi/VeSCm1b9LoJEmShpBSgtm2wMIO3xcVt5VSp5S2ne0EpIi4OSL+ERH/1V2liJgdEfURUb98+fIShiFJkpRtpQSz6GZbKrFOKW07KwfeCpxQfD86It7RZScpXZxSqksp1U2Y0O1SIJIkSUNKKcFsEbBdh++TgcUl1imlbXe/95eU0oqUUgNwEzCjlzaSJElDXinB7F5gWkRMjYhKYBYwt1OducCJxbsz9wNWpZSWlNi2s5uBPSKitngjwIHAo69iTJIkSUNSryv/p5TaIuI0CoGpDLg0pTQvIk4tll9EYVbrcGAB0ACc3FNbgIg4GjgfmADcGBEPpJQOSSm9GBHfpRDqEnBTSunGPh21JElSBkVKvV3ylX11dXXJRzJJkqShICLuSynVdVfmyv+SJEkZYTCTJEnD2uqVa/jeqT/imC1P5r1bncKFn/4ZjWsbB6UvvV5jJkmStLlqbWnlk2/6AkufXkZba2E9+xsuvJlH7nicH9zzTSK6W/mr/zhjJkmShq27fnMvK5e8uD6UAbQ2t7Hw8ed44LZHBrw/BjNJkjRsPfGPp2hc29Rle2tLK08+8PSA98dgJkmShq1tdpxE9YiqLtsrqiqYtMNWA94fg5kkSRq23jbrzVTWVG5wLVmuLMeosSPZ990D/+Ahg5kkSRq2akbW8P07v870N+1EWXkZZeVl7HnQbnzvjrMorxj4eyS9K1OSJA1rk6dN4nt3nEXjuiZyuaCqpuupzYFiMJMkSQJqRlQPdhc8lSlJkpQVBjNJkqSMMJhJkiRlhMFMkiQpIwxmkiRJGWEwkyRJygiDmSRJUkYYzCRJkjLCYCZJkpQRBjNJkqSMMJhJkiRlhMFMkiQpIwxmkiRJGWEwkyRJygiDmSRJUkYYzCRJkjLCYCZJkpQRBjNJkqSMMJhJkiRlhMFMkiQpIwxmkiRJGWEwkyRJygiDmSRJUkYYzCRJkjLCYCZJkpQRBjNJkqSMKCmYRcShETE/IhZExJxuyiMiziuWPxQRM3prGxHHRcS8iMhHRF03+9w+ItZGxGc3dXCSJElDSa/BLCLKgAuAw4DpwPERMb1TtcOAacXXbODCEto+AhwD3L6Rnz4X+N2rGYwkSdJQVl5CnX2ABSmlpwAi4mpgJvBohzozgctTSgm4OyLGRMQkYMrG2qaUHitu6/KDEXEU8BSwbtOGJUmSNPSUcipzW2Bhh++LittKqVNK2w1ExAjgDOArvdSbHRH1EVG/fPnyHgcgSZI0FJQSzLpOaUEqsU4pbTv7CnBuSmltT5VSShenlOpSSnUTJkzoZZeSJEnZV8qpzEXAdh2+TwYWl1insoS2ne0LvDcizgHGAPmIaEop/aCEvkqSJA1ZpQSze4FpETEVeA6YBXygU525wGnFa8j2BVallJZExPIS2m4gpbT/y58j4svAWkOZJEkaDnoNZimltog4DbgZKAMuTSnNi4hTi+UXATcBhwMLgAbg5J7aAkTE0cD5wATgxoh4IKV0SF8PUJIkaaiIwo2UQ1tdXV2qr68f7G5IkiT1KiLuSyl1WcMVXPlfkiQpMwxmkiRJGWEwkyRJygiDmSRJUkYYzCRJkjLCYCZJkpQRBjNJkqSMMJhJkiRlhMFMkiQpIwxmkiRJGWEwkyRJygiDmSRJUkYYzCRJkjLCYCZJkpQRBjNJkqSMMJhJkiRlhMFMkiQpIwxmkiRJGWEwkyRJygiDmSRJUkYYzCRJkjLCYCZJkpQRBjNJkqSMMJhJkiRlhMFMkiQpIwxmkiRJGWEwkyRJygiDmSRJUkYYzCRJkjLCYCZJkpQRBjNJkqSMMJhJkiRlhMFMkiQpIwxmkiRJGWEwkyRJygiDmSRJUkaUFMwi4tCImB8RCyJiTjflERHnFcsfiogZvbWNiOMiYl5E5COirsP2d0XEfRHxcPH97a91kJIkSUNBr8EsIsqAC4DDgOnA8RExvVO1w4Bpxdds4MIS2j4CHAPc3mlfK4AjUkq7AycBP3/1w5IkSRp6ykuosw+wIKX0FEBEXA3MBB7tUGcmcHlKKQF3R8SYiJgETNlY25TSY8VtG/xYSun+Dl/nAdURUZVSat6E8UmSJA0ZpZzK3BZY2OH7ouK2UuqU0rYnxwL3dxfKImJ2RNRHRP3y5ctfxS4lSZKyqZRgFt1sSyXWKaVt9z8asRvwLeAj3ZWnlC5OKdWllOomTJhQyi4lSZIyrZRTmYuA7Tp8nwwsLrFOZQltu4iIycCvgRNTSk+W0EdJkqQhr5QZs3uBaRExNSIqgVnA3E515gInFu/O3A9YlVJaUmLbDUTEGOBG4MyU0p2vbjiSJElDV6/BLKXUBpwG3Aw8BlyTUpoXEadGxKnFajcBTwELgB8DH+upLUBEHB0Ri4A3ATdGxM3FfZ0G7Aj8T0Q8UHxN7JvhSpIkZVcUbqQc2urq6lJ9ff1gd0OSJKlXEXFfSqmuuzJX/pckScoIg5kkSVJGGMwkSZIywmAmSZKUEQYzSZKkjDCYSZIkZYTBTJIkKSMMZpIkSRlhMJMkScoIg5kkSVJGGMwkSZIywmAmSZKUEQYzSZKkjDCYSZIkZYTBTJIkKSMMZpIkSRlhMJMkScoIg5kkSVJGGMwkSZIywmAmSZKUEQYzSZKkjDCYSZIkZYTBTJIkKSMMZpIkSRlhMJMkScoIg5kkSVJGGMwkSZIywmAmSZKUEQYzSZKkjDCYSZIkZYTBTJIkKSMMZpIkSRlhMJMkScoIg5kkSVJGGMwkSZIyoqRgFhGHRsT8iFgQEXO6KY+IOK9Y/lBEzOitbUQcFxHzIiIfEXWd9ndmsf78iDjktQxQkiRpqOg1mEVEGXABcBgwHTg+IqZ3qnYYMK34mg1cWELbR4BjgNs7/d50YBawG3Ao8MPifiRJkjZrpcyY7QMsSCk9lVJqAa4GZnaqMxO4PBXcDYyJiEk9tU0pPZZSmt/N780Erk4pNaeU/gUsKO5HkiRps1ZKMNsWWNjh+6LitlLqlNJ2U36PiJgdEfURUb98+fJedilJkpR9pQSz6GZbKrFOKW035fdIKV2cUqpLKdVNmDChl11KkiRlX3kJdRYB23X4PhlYXGKdyhLabsrvSZIkbXZKmTG7F5gWEVMjopLChflzO9WZC5xYvDtzP2BVSmlJiW07mwvMioiqiJhK4YaCv7+KMUmSJA1Jvc6YpZTaIuI04GagDLg0pTQvIk4tll8E3AQcTuFC/Qbg5J7aAkTE0cD5wATgxoh4IKV0SHHf1wCPAm3Ax1NK7X06akmSpAyKlHq75Cv76urqUn19/WB3Q5IkqVcRcV9Kqa67Mlf+lyRJygiDmSRJUkYYzCRJkjLCYCZJkpQRBjNJkqSMMJhJkiRlhMFMkiQpIwxmkiRJGWEwkyRJygiDmSRJUkYYzCRpiGtrbWP1C2vI5/OD3RVJr1GvDzGXJGVTe3s7l37+F1x/wc20t7UzcswIZn/7Q7zrQwcOdtckbSJnzCRpiPrxGVdw/QW/p7mhmbaWNl5atorvf/Ri7rnpH4PdNUmbyGAmSUNQS1MLv73wDzQ3tGywvbmhhcu/fM0g9UrSa2Uwk6QhaPULazZatvSZ5QPYE0l9yWAmSUPQ2K3GUFFV0W3ZjntNHeDeSOorBjNJGoLKyss4+axZVNVWbbC9qraSk886fpB6Jem18q5MSRqijvzYoWyx5Wh+/tVrWfHcSnacMZUPf/MEdq57/WB3TdImMphJ0hB24PvezIHve/Ngd0NSH/FUpiRJUkYYzCRJkjLCYCZJkpQRBjNJkqSMMJhJkiRlhMFMkiQpIwxmkiRJGWEwkyRJygiDmSRJUkYYzCRJkjLCYCZJkpQRBjNJkqSMMJhJkiRlhMFMkiQpIwxmkiRJGWEwkyRJygiDmSRJUkaUFMwi4tCImB8RCyJiTjflERHnFcsfiogZvbWNiHERcUtEPFF8H1vcXhERl0XEwxHxWESc2RcDlSRJyrpeg1lElAEXAIcB04HjI2J6p2qHAdOKr9nAhSW0nQPcmlKaBtxa/A5wHFCVUtod+H/ARyJiyqYOUJIkaagoZcZsH2BBSumplFILcDUws1OdmcDlqeBuYExETOql7UzgsuLny4Cjip8TMCIiyoEaoAVYvUmjkyRJGkJKCWbbAgs7fF9U3FZKnZ7abpVSWgJQfJ9Y3P5LYB2wBHgW+E5KaWXnTkXE7Iioj4j65cuXlzAMSZKkbCslmEU321KJdUpp29k+QDuwDTAV+ExE7NBlJyldnFKqSynVTZgwoZddSpIkZV8pwWwRsF2H75OBxSXW6ant0uLpTorvy4rbPwD8PqXUmlJaBtwJ1JXQT0mSpCGtlGB2LzAtIqZGRCUwC5jbqc5c4MTi3Zn7AauKpyd7ajsXOKn4+STg+uLnZ4G3F/c1AtgPeHwTxydJJWlubGb+vQt4/ullvVeWpH5S3luFlFJbRJwG3AyUAZemlOZFxKnF8ouAm4DDgQVAA3ByT22Luz4buCYiTqEQxo4rbr8A+CnwCIVToT9NKT3UF4OVpO7ccNHNXPy5n5Mry9HW2s60GVP58nWfY8yELQa7a5KGmUipt0u+sq+uri7V19cPdjckDUH3/+lh/ufIb9Hc0Lx+W1lFGTvvvSPfv+OsQeyZpM1VRNyXUur2Mi1X/pc0rP3q3N9uEMoA2lvbWXD/v1jy1NJB6pWk4cpgJmlYW/Fcl9V4ACivKOPFZasGuDeShjuDmaRhbZ/D9qKiqqLL9va2PFN3334QeiRpODOYSRrWjj39PYwaN5KKqlfuhaqqreLD3/wANSOqB7FnkoajXu/KlKTN2RZbjuZHD3ybX537W+656R+MnzSW9376CP7fu/Yc7K5JGoa8K1OSJGkAeVemJEnSEGAwkyRJygiDmSRJUkYYzCRJkjLCYCZJkpQRBjNJkqSMMJhJkiRlhMFMkiQpIwxmkiRJGWEwkyRJygiDmSRJUkYYzCRJkjLCYCZJkpQRBjNJkqSMMJhJkiRlhMFMkiQpIwxmkiRJGWEwkyRJygiDmSRJUkYYzCRJkjLCYCZJkpQRBjNJkqSMMJhJkiRlhMFMkiQpIwxmkiRJGWEwkyRJygiDmSRJUkYYzCRJkjLCYCZJkpQRBjNJkqSMKCmYRcShETE/IhZExJxuyiMiziuWPxQRM3prGxHjIuKWiHii+D62Q9keEfG3iJgXEQ9HRPVrHagkSVLW9RrMIqIMuAA4DJgOHB8R0ztVOwyYVnzNBi4soe0c4NaU0jTg1uJ3IqIcuAI4NaW0G3AQ0LrpQ5QkSRoaSpkx2wdYkFJ6KqXUAlwNzOxUZyZweSq4GxgTEZN6aTsTuKz4+TLgqOLng4GHUkoPAqSUXkgptW/a8CRJkoaOUoLZtsDCDt8XFbeVUqentlullJYAFN8nFrfvBKSIuDki/hER/9VdpyJidkTUR0T98uXLSxiGJElStpUSzKKbbanEOqW07awceCtwQvH96Ih4R5edpHRxSqkupVQ3YcKEXnYpSZKUfaUEs0XAdh2+TwYWl1inp7ZLi6c7Kb4v67Cvv6SUVqSUGoCbgBlIkiRt5koJZvcC0yJiakRUArOAuZ3qzAVOLN6duR+wqnh6sqe2c4GTip9PAq4vfr4Z2CMiaos3AhwIPLqJ45MkSRoyynurkFJqi4jTKASmMuDSlNK8iDi1WH4RhVmtw4EFQANwck9ti7s+G7gmIk4BngWOK7Z5MSK+SyHUJeCmlNKNfTVgSZKkrIqUervkK/vq6upSfX39YHdDkiSpVxFxX0qprrsyV/6XJEnKCIOZJElSRhjMJEmSMsJgJkmSlBEGM0mSpIwwmEmSJGWEwUySJCkjDGaSJEkZYTCTJEnKCIOZJElSRhjMJEmSMsJgJkmSlBEGM2mISCmR8utIqX2wuyJJ6icGM2kIyDfeQlp+EGlZHWnZDPKrv0VKbYPdLUlSHysf7A5I6llquRdWfQZoKm5ohIYrSamR2OLLg9k1SVIfc8ZMyri09gesD2XrNUHjr0j5tYPRJUlSPzGYSVnX9nT326MM8isGtCuSpP5lMNOQlfJrya/5Pvnlh5JfcTSp4VeklB/sbvW9it2A6KYgQdnWA90bSVI/8hozDUkpNZFeOBbaFwPNhW2rvwqt9cQW3xzczvWxGPlJUvOdQGOHrTUw4iNEVA9WtyRJ/cAZMw1NjTdC+/O8HMqKG6Hxt6S2ZwerV/0iKnYhxv8cKvaBqIWy7WD0fxMjPjrYXZMk9TFnzDQkpZbOM0hFUQ6t90P59gPep/4UFXsQ468Y7G5IkvqZwUxDU9m2FP76drOWV27iQPfmVcu3PgkNP4X2lVB1AFE7k4iawe6WJGmQGcw0JEXN+0jrLmfDYJaD2AIq9x2sbpUkv+pL0HjVKxtabiOt+wlseR2RGz14HZMkDTqvMdOQFOXbEWN/CLktC9ddUQXluxDjriAiu3+t842/3TCUAdAO+UWFcCZJGtacMdOQFVVvgQl3QPtTEDVE2baD3aXerf3+Rgry0Pg7GPXpAe2OJClbDGYa0iJyUL7jYHdjo9atbuCu39zL2pfWMeOdu7Pd+JUbrxxVA9cxSVImGcykfvLIHY/x+Xd/AxK0tbaTywXn3TyeKTuu6b5B7YkD20FJUuZk92IcaQhra23ji0edQ+OaJhrXNtHa3EpzYwvf+/QI2tsruzao2Ieofe/Ad1SSlCkGM2VOyjeQ2p4i5dcNdlc22bw759Pe1t5l+2P15Vz8jfdA1TsgxkLZVBj9LXLjs33TgiRpYHgqU5mRUp605n+h4ecQOUh5Uu0JxKjPDbnQ0l0oe9niZ7YgN/bsAeyNJGmoGFr/2mmzltZdAg1XAE2QGgrvDb8grfvRYHftVXvDW3chpdRle/WIKt75wQMGoUeSpKHAYKbsWHcJXR+z1AjrLh2M3rwmldWVnHnFp6iqqaSiqpyIQijb6x27c8Bx+w129yRJGeWpTGVHWrXR7SklImJg+/MavemIOn46/zxuvfJ21qxcy96H7sWeB+025MYhSRo4BjNlR/lO0PZYN9unDdkwM2HyeGadcfRgd0OSNER4KlOZEaO/AFQDHUNYNTHqvwepR5IkDSyDmTIjKvchxl8JlQdCbhJUHkCM+zlR9abB7pokSQOipGAWEYdGxPyIWBARc7opj4g4r1j+UETM6K1tRIyLiFsi4oni+9hO+9w+ItZGxGdfywA1tETF7uTGXUxu4l/IjfsJUbnnYHdJkqQB02swi4gy4ALgMGA6cHxETO9U7TBgWvE1G7iwhLZzgFtTStOAW4vfOzoX+N0mjEmSJGlIKmXGbB9gQUrpqZRSC3A1MLNTnZnA5angbmBMREzqpe1M4LLi58uAo17eWUQcBTwFzNukUUmSJA1BpQSzbYGFHb4vKm4rpU5PbbdKKS0BKL5PBIiIEcAZwFd66lREzI6I+oioX758eQnD2Dyl9hXk15xPfuVs8mu+R2pfNthdkiRJm6iUYNbdOgWdlzTfWJ1S2nb2FeDclNLaniqllC5OKdWllOomTJjQyy43T6ntKdKKQ2Ddj6Dlz7DuJ6QVh5Ja/znYXZMkSZuglHXMFgHbdfg+GVhcYp3KHtoujYhJKaUlxdOeL0/17Au8NyLOAcYA+YhoSin9oIS+Ditp9VchreWVrNsCqYW0+iuFuxslSdKQUsqM2b3AtIiYGhGVwCxgbqc6c4ETi3dn7gesKp6e7KntXOCk4ueTgOsBUkr7p5SmpJSmAN8DvmEo24iWv9PtBGRrfbfPaZQkSdnW64xZSqktIk4DbgbKgEtTSvMi4tRi+UXATcDhwAKgATi5p7bFXZ8NXBMRpwDPAsf16ciGg6iC1NZNQdWQXSlfkqThLDaHmZW6urpUX18/2N0YcPnVX4eGq4HmDluroOYYclv0eO+EJEkaJBFxX0qprrsyV/4fwmLUZ6ByH6AaYmThvXIGMbrLGsCSJGkI8CHmQ1hENTHuElLbAmh7Esp2ICqmDXa3JEnSJjKYbQaifEco33GwuyFJkl4jT2VKkiRlhMFMkiQpIwxmkiRJGWEwkyRJyggv/teQllKClrtITbdCbhRRczRRPmWwuyVJ0iYxmGnISilPeukT0HInpAagnLTup6TRXyZXe8xgd0+SpFfNU5kaupr/BM0vhzKANqAJVn+JlF8zmD2TJGmTGMw0ZKWmGyk8mrWTqICWvw14fyRJeq0MZhq6ohLYyMPao3JAuyJJUl8wmGnIipr3QlR3X1j55oHtjCRJfcBgpiErKveG2n8DqoAaiBEQtcTYiwhnzCRJQ5B3ZSqTmhqa+c15v+RPV91LZU0N7/nIwRx80oHkchv+f4ncqNNJNcdByx0QI6HqbURuxCD1WpKk18ZgpsxpbXiE09/0RZ59ImhpKgSxZ+Y9y/23PsyZV3yyS/0onwzlswa6m5Ik9TlPZSpTUn4Vd/7ioyx6ivWhDKBpXQt3/Poennl04SD2TpKk/mUwU7Y03sD9f62maV1Zl6KgnUfueHwQOiVJ0sAwmClTUvsitty6kYqqfJeysnIYu/WYge+UJEkDxGCmQZNSO6l9BSm1rN8WlXtx8KwmysrSBnUjEpU1Vexz2F4D3U1JkgaMwUyDIr/uatKy/UjL30Zaujf51d8mpXaoejsTttuar/xsEWMntFI9op2qmjzbvj7Hd277OuUV3q8iSdp8+a+cBlxq+j2s+SbQ+MrGhitI5MiN/gyMu5o3HnIRv3jwBp6ZX0XF6IOZvOcnyOWqBq3PkiQNhEgp9V4r4+rq6lJ9ff1gd0O9yOfzpJSIF4+Etie6VogaYmI9ERUD3zlJkgZIRNyXUqrrrsxTmep3q1eu4evHn8u7a07g8OoPcMYxwXP/6mZl/tQGad3Ad1CSpIwwmKlfpZT47Nu+zB3X3UNbaxv59jwP3lXDp94zjbWrOv31y42GGD04HZUkKQMMZuoXKSVS+1Ie+eu9PP+vZbS1tr9Sli8sHvvHX47t0KIaRn6OCP9KSpKGL/8VVJ9LLX8nrXgHafk72fX1J/HFnzzGFuPaNqjT3Jjj6X++DmIMlE8nxpxLrvaYwemwJEkZYTBTn0ptz5JW/ge0LwKayZW1s/t+q/nG1U8Cr9xoUj2iimlv+ndyW/2d3Ja/IarfMWh9liQpK1wuow+teXEtf/rFHaxYvJLd37ordYfsSS43PLLv0/MWcsvlf2HGm25iz31a6DjsikrYdmoL0/Zo5ImHasmV5agZWcM7Tth/8DosSVIGGcz6yON/f4Iz3vU12tvaaW5s4fqRv2Pq7tvz7Vu/RGV1N3cgbkZuuOhmfvSZy2lrbWO33Z4kl2vvUidXVs520+CZJyrZ9/C9+Oi5J1M7qmYQeitJUnYZzPpASomz3n8uDWteWTC1cW0TTz7wNL/5we9432dnbrRtS3MrFZXlRMRAdLVPpdbHeenZC7jw9CW0Nhf6/9DfRvDGt66hunbD9fGqqoM5V11ElG83GF2VJGlIGB7n2frZc08s4aXlq7tsb25s4ZbL/tJtm7vm3suHdvg47xlxAkeNOYnLvnwN7e1dZ5qyKKU22l76KvkVM6nkFk4+YzG1owp9//1V41m3uoy21o4taqD6MEOZJEm9MJj1gVxZjo4Xtnct29CDf57HN47/Hs8/vYyUTzSsaeTa78zlkjOv7HYfqe1p8i+eRn7p3uSXv4v8uqsYrCc2LJq/gAevfzstK68kIlEzIs8RJ7/AeTc+QUVVnnWry/j4ITtx89XjWbdmBJRNgVGfIbb45qD0V5KkocRg1gcm7bAVEyaP77K9qraKQ095e5ftl335/2hubNlgW3NDM3MvuJmmhuYNtqf2xaQXjoXmP0JaBe3PwNqzSWu+1W1fUsvfya/8CPkVR5Ff87+k/MrXMLIO+015nqmfwzkf+gQ777l0g1OVlVWJLbdu5cAjXwLgxeUV/OgrO7D4pWvJTfgDuREnElHWJ/2QJGlzZjDrAxHBF3/5WUaNG0nNqGrKK8qoHlHFHgdM54hTD96g7rKFK5h35/zu95MLXlz60gbb0tpLIDUB+Q4bG6HhSlJ+w7r5hmtIKz8MLbdB26Ow7qekFe8hta94TeNbveIx7vr5mynP38Dxn1xGd5N1NSPzzNh/DdW1eSqrK/jg/7yXnffe8TX9riRJw40X//eRKbvW8osn/4M7b1jJyufz7PaWndl1v526XNT/uXd8hXx7vvudRDB+m3Ebbmu9D2jtpm4ltD0FlTMASKkZ1nwTaOpQqQXyq0jrfkKMnvOqx5TP57n/prPYdfqV1B2UqKiE8Vu3UtHNM8bb22CH6S187Nu7sfeRn2LLbbvOIEqSpJ6VNGMWEYdGxPyIWBARXf6Fj4LziuUPRcSM3tpGxLiIuCUinii+jy1uf1dE3BcRDxffu54LzJCU2sivOpO0/CAqmz7B2955Jsee8kd23W9ql1B29433sXjB893uJyI4fs5RVFZ1Sj3lU+j2MKUWKNvmle9tT26kh63QfHvJ41m/+/ZlPHLT23jDHldQVVMIZQCVxfd852wZMHXGwRx66pcMZZIkbaJeg1kULg66ADgMmA4cHxHTO1U7DJhWfM0GLiyh7Rzg1pTSNODW4neAFcARKaXdgZOAn2/y6AZAWvcjaLwRaIG0BmiG5j+T1pzTpe7Nl8ylrKz7i/ZHbFHL8Wd2fSRRjJgNdF4HrQqq3kyUbf3KptxYSG10q2zLksYChaU/8mu+T37Z/kyfsYSKSui8ksfL31uagubG4IWl1ZSN/zG5Med4LZkkSa9BKTNm+wALUkpPpZRagKuBzgtzzQQuTwV3A2MiYlIvbWcClxU/XwYcBZBSuj+ltLi4fR5QHRFVmza8AbDu52x4+pDC94ZrNrhzMrU9y39+41eMndjNaUmg7pA9u13LLCqmE2PPh9wkCgGtEqoPJbY4d8N6ZZOgYg+6np2uIUb8e8nDSS+8D9ZdQESip4cWrFtVxuy378TVF+7PyB3uJld9YMm/IUmSulfKNWbbAgs7fF8E7FtCnW17abtVSmkJQEppSURM7Oa3jwXuTyk1dy6IiNkUZufYfvvtSxhGP0lrN1LQDLQBhVOTzz98GltObGHOBc/y3x+cSntb0NqSo6IqT2tLjg+ffcJGfyKqDoQJf4b0IkQtEdXd1xt7PunFj0PrPIjywgzaqE8RVQeVNJR889+g7cFe67W2wMrl5Zz352MZs/2/lbRvSZLUu1KCWXdL0nc+H7exOqW07f5HI3YDvgUc3F15Suli4GKAurq6wVnUC6ByL2i5p+v28p2JKISyfz38DJMnPE5ZGey+3zp+/Jf5/O7KcSx5tpLd913HyjX/yVavm0hKeWi5C1ofg/LtoeptRBROY0YExLiuv9NB5MYR468itT0L+RegfCciN6LHNvm2FfDih6B9Y9eovSIlaG0JliycyJidr2LMVpN7bSNJkkpXSjBbBHRcsn0ysLjEOpU9tF0aEZOKs2WTgGUvV4qIycCvgRNTSr0nhkEUo75AWjmrcDE+bUAZUEmM/vL6On/8+e188KNBWXkhP07ctpWT/mspAK2t5VRtN4v2ttXcftmJ3HxlCymfeNf71nLg0WdRPvGawmnKV9On8u2B3mcR77/lOkZXfJltpzZT2cvJ4vZ2WLawgqUr/o0Z7/ncq+qPJEkqTSnB7F5gWkRMBZ4DZgEf6FRnLnBaRFxN4VTlqmLgWt5D27kULu4/u/h+PUBEjAFuBM5MKd35GsY2IKJiFxg/l7TuEmh9BCp2IkZ8mCh/PVBYcuL5Bbcx+20709KUY+93rOakzz3P+K3baGkOli3bl+23g3M+8GHuuilHU0NhhuvR+hpuv2EtX7riDMrGX96nfW5pamHOwWey/Nkn+PjXK5iyc+dr5DaUEjQ1T2bCnlewbc02PdaVJEmbLkp5tE9EHA58j8J00KUppa9HxKkAKaWLonDV+g+AQ4EG4OSUUv3G2ha3jweuoTC18yxwXEppZUT8N3Am8ESHLhycUlrGRtTV1aX6+vpXM+4B892TjuNPv2ynubFwt2JZeZ5RY9o576Z/suqFSrbc/QZe+NctfObgG2lu3PBq++rads664ln2mPlXnn38Ba746rU8/vcFbLPj1pzwhWPZ44DON8f27rcXXcqFp99IS3MQOaiqzrP9Ts2cc+2T1IzYyPpq1e8nN+Zrr/q3JElSVxFxX0qprtuywXrmYl/KajBbuuB6Tt79clqbNwxclVV5dnpjI2857lSOOW1HrjlrNj87eyva2zrdBhmJE05fzv4f/BH/ecDXaWloJp8vHK+q2krOuOwT7H/sfiX1JZ/Pc/qbT+TRv788O/bK5X8VVXnec+ILnPqVDmeoc9Og5t1Q+35yZa5LJklSX+kpmPlIpn705N0/5HU7N7HvO1ez1eRXno3Z0pwjclUceepbya84ilFj2qio7BqQKysTo8dP5JLPX0fT2qb1oQyguaGFCz51aUkPM3/sr9fxgclHF0NZ0PmejNbmHH+6biwA+fbixnEXkRv1MUOZJEkDyGDWT5obXmT3fZ/hu79ewH+d/ww/vv1xzrzwacrKE2VliSm7lNOyZH8iYP93v9RlEVeAyCUOOvGLPHbPP7v9jVUr1rBm5caW6yjMkl3+pYs4/R1X8sLzFXR/k2xBe1vhAv+VyythzI/JlW+30bqSJKl/GMw2UWr7F/kXP0p+6Qzyyw4kv/YSUmpfX77g9hOpHZmnqiYxcos8VdWJ/d61muM/tZTyyjxHfriZmtpWImDkFnm+edVTjB7bRu3IdmpHtjNyiza+/H9vZ9y2uzN24hbd9iFXlqN6ZPdrmj3zyJ1ccebb2aLmZ7z50NWU9XibR6JhbY5LvnUwW+5xv4vFSpI0SLzGbBOk9udJK95dXFz25T+/Gqg5gtwWZ9HSsJCyVe/odhZs1coynv3naN6w74tdytvb4PF/1JLPBzvvvSPV214LwB8u+zPnffwnNDe8ss5uVU0lB5/8Nj75gw9vsI98Ps8Vnz+e985+gFwOKqsLoWvRk1V89pgdu9xgAImqWvjitV9gn8P2em1/MJIkqVdeY9bH0rpLITWw4Vq5jdD4G1L7CvJrLtpo29Fj2nnDvi92W1ZWDtP3buAN+66jsmwe+aV7kV/1Fd75wb05/syjqR5RRc2oGiqrKzho1lv46HdP2qD9gn/cw4dedxSHH/8w1bWJyupC/2pH5tl+pyaOPHlFx1EAie2mBdc+/3NDmSRJGVDKOmbqrOmPQHs3BZWse+EBVj19PZNe17U0JVj8TCXbTGnpdjZtfT0gaCs8UqnxWmh7jA98/ire++n38PzTyxk/aSwjx2y4ov/dv/4eXz3+r2wzNU91N8teVNck3nb0i1z7w4kUZsnyzD7nKI782ImvZuSSJKkfOWP2KqV8A+SXbKS0kU/u/yNWLt143m1Yu/FElhK0NENugyothUc0tT1MVU0Vr9t18gah7F+PPMOFHzuMr594O60tOdpac+Ry3Z+ebm0JIHHAzFZ+9vh3DGWSJGWMwezVan2Il16o5Rffm8jX/uN1XH3+BFa9UFg89oUVI9lq8ipqRuZfWXaiqL0d/vlQNVd+d2va27ruNiVY81KOqu6u5Y+A1gVdNn/t6H/n1L0+zQ67rlw/A/fcU5UsX1xJvtOkWeO64Mafb8mXf306//Pr69ly8g6bMHhJktSfPJX5Kj0zv5H/POB1tDYHLc05/n7raH554US+f+MTvLA0x//8+BmqawszVikVH/zdDC8uL2f7HVuYc8GzRA7yecgVY3FKhfXDfnv5eI75jxXr22+gfOr6j2teeI6LP/Uhbr9+LOO2aifl6XC5W/CVf5/Ct3+1gMrqwtIcBMy7dws+fO61jN2q5wehS5KkwWMwe5XO+8RtNKzJkVJhiqqlKUdrS3DJ17dlzgVPU1n1SqiKgLY2ePrxKl7/hmbKK17ZT1srtLVA5ODx+2v59ie2Z/VLZRxx0koqq9rJlRXrtZVRXr0DVLwRgOce/g6tq37Gp85pYfaXFnPDz8bzxrespr1t8vp9L1xQzQfrprP321ez5aRWxmy9Gyd+44J+/7ORJEmvjacyX4V8Ps/Cxx9m9pcWc+mdj3Hejf/koKNeJOVh33e92O3q/bkcTNmlhbWryzY4vVheUXgCwHum7M5nj57G0kVVNK4t55T9d+bBu0aSb4eW5mD5sn2IcZfRtG4l35p1CGNH/Jjtd2whl4MRo/IcdcoK1rxUwcmfX0xldZ5cWeFuS4DFT1dz8Ee+bSiTJGmIcMbs1Uir+OHN89lifNv62a///PZC3rDvGt713q6r969emeOSb0ziT78aR0owakw7HzvrOfZ/zyoAakflKa8ozJ69bNULFcx5/+uJSEx83UQufez7PHDzuXztg3/j9O+8sH4JjJdV1yYm79jMg3eN4H+ve4Lbrh/L2pfKePPRh/Pm4z5D9HT7pyRJyhSD2aux+izGTmxbf20YQM2IxKTtm2ltCapqNgxNXzllCvMfGLH+IeYrl+X49qe2Z8yWT7H7futYurCCttbuJy3HTRrHV68/nfdPOo61L5UDZdzws/HsML2JSa9r2aBuW2vwh/8bz0/O2oYRo9v49m3fZ4fdp/TlyCVJ0gDwVOar0XzTBqEMoLkJRo7J89vLx/Ozb23FQ38bQUpw759G8sg9I9eHsvX1G3P84vsTSQku+tKkbn9mu523YY8DxvKRPc8shrLCg8cfuGMUpx02jZdWbJinyysSyxdXsPP/S1y95JeGMkmShihnzEqUUivdLSp7+9wxnH/mduTz0Noc/PrHE9htn7X84/ZRbOyh4Yv/VcXqF3Pcf8fWwCuPWYpcsPXUCTSve47brl6/tUMfguaGHHN/Op4TP7cUgKaG4J4/jub8u2YzedfD+mawkiRpUDhjVrKuoay1Jfjh/0ymuTFXnBkrLKEx/4ERpHz3oSxyiZ32bODUt89g1pyjqKqppHZ0LdUjqhi39RiWPLmUZYvg5Vmyrr+Zo/7Po2hrLSzB8ej90zjww3cYyiRJ2gwYzEpWxUsrJxbWDCt67L7aLrVOPmMxjWsLIa2rRC6XeHp+FSuXtfLnq+/kmud/wuev+gQ7TF9FRfnLTxTo+YL9+ffXMmvP3Xj08dOpO/a35HJVmzwqSZKUHZ7KLNE151xK3b6r2WJsYUHY9Tc7drje/4TTn2fkmHyPz8EcO7GVZ/9ZCHTPPLqIK866hmu/MxdSFb0Fspd/8IiTX+SjF/6eisqK3qtLkqQhwxmzErQ0tXDIkd9hys5NRBRCWUowvW4d5RWFZDZuq1Y++JmlTN21af22DSVqR7WzYvErs1vHfmQp7/3Q2dz83MNcUf8Y1bXdPRj9lfaQ2P+ocj55ya2GMkmSNkMGsxKsW/IDRo1p32AmLKKwSOyXLn2a2pFtfOe6J4iAXWY08Po3NFJR1fFhlYnakXmaGgp/3FU17Vz4x8f5jy8+z5gtC2FswjatHP3h5Rt5AHkiyhLn3/0Fvnjd1d2US5KkzYHBrAQja27e4PuaVTku/uokPjBjV87++Pace8MTbDOldf1s2jd+8RRHnLSCUWPbqBnRzkFHvcSEbZrJtxf+uE/96nNM2aW5yynP4z+1jNfv3lj8lta/Djzu9fyh9Vfsss9e/T5WSZI0eCKl7mZohpa6urpUX1/fb/vPP/8GUmohAlqa4ROH7cRzT1XR2pJjpzeu47wbF2z0urKUoP62EXzrtCkc8W8rmHHAGqbXNVC2kav72tvh8O32pKK6narqxPfv+i7b7zK1+8qSJGnIiYj7Ukp13ZU5Y1aSwkr7KcG1P5zA0oWVtLbkgMR2OzZvtFVKsHxxOdf8cCJ7vHktD989kiceqqWleeN/7MsXVwCJfd9Zwa9X/tpQJknSMOJdmb1IbQuAVy74b2ooo3FdGQDT9mxgzPhWnn+2gq23b+0ya/b8wnI+9e5prF5ZQWFiMpj/QA2/u3I85//uCapr8xvUb2+DS7+xNV+45hMc9N4DB2B0kiQpSwxmvWmdR2FiMc+vfzyOSa9robq2nb32X8OcC56lvb2Qxpobg1UvlrHVtm0AnPH+7XnwjjHrA9nLWprKWLoQfveLcRx1yor1gS8luOLcbfnCdX/0weOSJA1TnsrsRWp7HsiTz8OUnZs54MiXGL91C3N++CzVtYkRo/KMGJWnujYxZlw7Ly6HM4+fysN/26JLKHtZc1MZd/1uNPl2mP9gNb+/agx/vf1sTv7f2wxlkiQNY86Y9SClPKz7AQDXXTyB+/48gm9c9TT/dsbz3V7snxI89LfRzPv7SNrbesq8iTFbtvHPh6p58pFRvPvTf6as3HXJJEka7gxmPUjN9/DyQ8avPm8Ca14qZ+nCCsrK6XYR2bLyREpBS1PPs14VlXkOOPIlbvv9CZx23hn90XVJkjQEeSqzJ823APDC82W8budmIPjwgbuw8IlKWpu7hq98PvjngzWktLFgVliXrO7t69j/3+4xlEmSpA04Y9aT1nmkBI0NZZxx/rOUVSQW/6uKy7+9FWMntnPAEauoGVG4/qy5MccffzmGaXs0Ul3bTlNDWaedFWbYdj9oF75601kDPxZJkpR5BrMeVfD3W0eyzzvWAoUlM8Zv1cbXfv4vZu74Bm6/YQzvOPZFUoI//nIc//jLSPZ910vstf8a7v/rKJoacuTKEmVlMHJMK+f89VymTHv9II9JkiRllcGsJzVHMHKLh7tc6P+X67cAgvrbRlN/2+gNyu65ZSwHznyRj37tOR7/Ry1NDTkam6bwtRsvGbh+S5KkIclg1oOffr2FWad0Xdn/5v8b32O7v1w/lr9cPwYi+EPbNS6BIUmSSuLF/z24/oJbWLmsa3bdYdfGbmq/rHCB//htKrml/VpDmSRJKpnBrAetTS384nsTaVy3Ybj64Gee30iLxKixbXzj95/n6kW/6P8OSpKkzUpJwSwiDo2I+RGxICLmdFMeEXFesfyhiJjRW9uIGBcRt0TEE8X3sR3KzizWnx8Rh7zWQW6q7XbZlj9eO44rvrs1DWtzNK7L0dwY/Om6cd3WL6ss57oXfsPeB8/otlySJKknkVLXhVI3qBBRBvwTeBewCLgXOD6l9GiHOocDnwAOB/YFvp9S2renthFxDrAypXR2MbCNTSmdERHTgauAfYBtgD8CO6WU2jfWx7q6ulRfX79pfwI9mH/vE5y27+eBwqKw4ya28dILZTQ3vrwUxst/dsEXr/sM+x+1X5/3QZIkbV4i4r6UUl13ZaXMmO0DLEgpPZVSagGuBmZ2qjMTuDwV3A2MiYhJvbSdCVxW/HwZcFSH7VenlJpTSv8CFhT3M+B23nsaY7beAiLR2hIsXVS5PpRF5KmoTOz77hnckr/WUCZJkl6zUoLZtsDCDt8XFbeVUqentlullJYAFN8nvorfIyJmR0R9RNQvX768hGFsmo+fezJVNRW88a1r2WpyM1uMb2XbHZp42zHrOP+eszjrhs/3229LkqThpZTlMrq7rbDz+c+N1Sml7ab8Himli4GLoXAqs5d9brKD3v8WUoJLPn8lzU1L2POtlRw461gOeP/7+usnJUnSMFVKMFsEbNfh+2RgcYl1KntouzQiJqWUlhRPey57Fb83oN426y28bdZbSCm5/IUkSeo3pZzKvBeYFhFTI6ISmAXM7VRnLnBi8e7M/YBVxdOTPbWdC5xU/HwScH2H7bMioioipgLTgL9v4vj6lKFMkiT1p15nzFJKbRFxGnAzUAZcmlKaFxGnFssvAm6icEfmAqABOLmntsVdnw1cExGnAM8CxxXbzIuIa4BHgTbg4z3dkSlJkrS56HW5jKGgv5bLkCRJ6muvdbkMSZIkDQCDmSRJUkYYzCRJkjLCYCZJkpQRBjNJkqSMMJhJkiRlhMFMkiQpIwxmkiRJGWEwkyRJygiDmSRJUkZsFo9kiojlwDP9+BNbAiv6cf/qex6zocdjNjR53IYej9nge11KaUJ3BZtFMOtvEVG/sWdaKZs8ZkOPx2xo8rgNPR6zbPNUpiRJUkYYzCRJkjLCYFaaiwe7A3rVPGZDj8dsaPK4DT0eswzzGjNJkqSMcMZMkiQpIwxmkiRJGTHsgllEHBoR8yNiQUTM6aY8IuK8YvlDETGjt7YRMS4ibomIJ4rvYwdqPMNBPx2z4yJiXkTkI8LbxvtBPx23b0fE48X6v46IMQM0nGGhn47Z14p1H4iIP0TENgM1nuGgP45Zh/LPRkSKiC37exzqIKU0bF5AGfAksANQCTwITO9U53Dgd0AA+wH39NYWOAeYU/w8B/jWYI91c3n14zHbFdgZ+DNQN9jj3Nxe/XjcDgbKi5+/5X9rQ+KYje7Q/pPARYM91s3l1V/HrFi+HXAzhcXbtxzssQ6n13CbMdsHWJBSeiql1AJcDczsVGcmcHkquBsYExGTemk7E7is+Pky4Kh+Hsdw0i/HLKX0WEpp/sANY9jpr+P2h5RSW7H93cDkgRjMMNFfx2x1h/YjAO846zv99W8awLnAf+HxGnDDLZhtCyzs8H1RcVspdXpqu1VKaQlA8X1iH/Z5uOuvY6b+NRDH7d8pzASob/TbMYuIr0fEQuAE4It92Ofhrl+OWUQcCTyXUnqwrzus3g23YBbdbOv8/wY2VqeUtup7HrOhqV+PW0R8AWgDrtyk3qk7/XbMUkpfSCltR+F4nbbJPVRnfX7MIqIW+AIG6EEz3ILZIgrnzV82GVhcYp2e2i4tTg1TfF/Wh30e7vrrmKl/9dtxi4iTgPcAJ6SUDNp9ZyD+W/sFcOxr7qle1h/H7PXAVODBiHi6uP0fEbF1n/ZcGzXcgtm9wLSImBoRlcAsYG6nOnOBE4t3suwHrCqenuyp7VzgpOLnk4Dr+3sgw0h/HTP1r345bhFxKHAGcGRKqWGgBjNM9Ncxm9ah/ZHA4/09kGGkz49ZSunhlNLElNKUlNIUCgFuRkrp+QEb1TBXPtgdGEgppbaIOI3CnSZlwKUppXkRcWqx/CLgJgp3sSwAGoCTe2pb3PXZwDURcQrwLHDcAA5rs9ZfxywijgbOByYAN0bEAymlQwZ2dJuvfvxv7QdAFXBLRADcnVI6deBGtvnqz/99jIidgTyFO/w8Xn2kH4+ZBpGPZJIkScqI4XYqU5IkKbMMZpIkSRlhMJMkScoIg5kkSVJGGMwkSZIywmAmSZKUEQYzSZKkjPj/xyGbZptw8yUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "plt.figure(figsize=(10,7))\n",
    "plt.scatter(df_scaled['Region'],df_scaled['Channel'],c=df_scaled['clusters'])"
   ]
  }
 ],
 "metadata": {
  "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}