Mall customers hierarchical clustering/mall customers hierarchical clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# mall customers dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>CustomerID</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0           1    Male   19                  15                      39\n",
       "1           2    Male   21                  15                      81\n",
       "2           3  Female   20                  16                       6\n",
       "3           4  Female   23                  16                      77\n",
       "4           5  Female   31                  17                      40"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset=pd.read_csv(\"Mall_customers.csv\")\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Annual Income (k$)  Spending Score (1-100)\n",
       "0                  15                      39\n",
       "1                  15                      81\n",
       "2                  16                       6\n",
       "3                  16                      77\n",
       "4                  17                      40"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=dataset.iloc[:,3:]\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using the dendrogram to find the optimal number of clusters\n",
    "import scipy.cluster.hierarchy as sch\n",
    "dendrogram=sch.dendrogram(sch.linkage(x,method=\"ward\"))\n",
    "plt.title(\"Dendrogram\")\n",
    "plt.xlabel(\"customers\")\n",
    "plt.ylabel(\"Euclidean distances\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "hc=AgglomerativeClustering(n_clusters=5,affinity=\"euclidean\",linkage=\"ward\")\n",
    "y_hc=hc.fit_predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4Tg6ViJrO5mP9P1ayPFFFFYdpnxlvYWkwKk+tgxRNQdQYi3hOFvnm8McPG8/ePfYO3d/iFxaXPDcfVVRxmH4zHeCWBqOyxMGfYwoiBpycZ38tssYaxck1hz/7zNk8cNYDOa2LHpI/YUE5c/Ol5mSqVL/ZvhNLg1E54uLPMQVRQ9IlQX+X5/ipWMRzkshnXQzedzA96gpntSllbj6qqOJS+41KYXVH4uLP6fYKwifFRVTXzRcgl2ZJnvNqIa/hRy7r4sWtLxat4VDK3Hwhn0ixjLBR9GtpMCpHXPw53VpBpEfwM3HV1zR4nRnsj3KGr5SSoLWU1yidKOfmo4oqLqXfqBRWdyQu/pxuG0ld65KiYUuC1lpeo3R8y5LmKxGaNFq3tHLLX25h9srZuyOpM8u0GsWJ+m/GIqmLUMoIvpKEDZCrtbxG6XS3ufligXhGceLyN1MTBSEil4rIKhF5RkTuFZFeInKoiPxVRNaKyH0isleUMpSa4qJSPoCwAXKWkiO52Nx8PKl1jEEh4vI3U3UFISIHAV8BRqrqUKAeOBu4CbhFVQcBrwEXRClHKSkuKukDCBsgZyk5kovNzcePOMQYFCIufzO1mmLqAewtIj1wU+evAKNxS/4BZgFnRClA2BF8pdNyhw2Qs5QcyaV1SysLnl+AIB1WpjTu1WgpqmtAXGIMihGHtObeCkJEeot4FtktgKq+DHwP+BtOMWwFlgGvq2p6LeAG4KA8ckwWkaUisnTjxo0lyxF2BF9pH0C6JGhDDjlyBchZSo5kkjlSfavtrd37U3UpdukuTh90ulkOVSYuMQY+1Nqfk1dBiEidiJwrIg+JyKvAGuCVwHcwXUQGlXJBEXkf8FngUOBAoDe5Y8FyLq9S1btVdaSqjtxvv/1KEQHwG8G3Aa/jrIIofABhSoJaSo7qUam56aSMVLsbcYkxSAKFLIg/4AawVwP7q+rBqvp+4CPAX4AbRWRCCdf8OLBOVTeqahvwIHAC8N5gygmgH/D3Evr2ptAIPpP7cf6FqHwAA3GFgLYC7cHrDDovVQ1rcRilUcm56SSNVLsTcYkxSAKFFMTHVfW/VHWlqu5K71TVLar6gKp+DrivhGv+DTheRBpERIBTgGdxCildpWQS8OsS+g5FegQ/rkCbtH/BN1qkoVyhChDG4jDCU+kRv41U44nljPInr4JQ1TZxfEhEzhSRfwveS2absBdU1b/iBrtPAk8HMtwNfB24TERagH2AH4XtuxQGAn0oPn3jy4AK9ZMPX4vDCE+lR/w2Uo0ncYkxSAJ5I6lF5JPAD4G1wMvB7n7A4cBUVV1cFQkLUKma1L5RzT40VrCvcmjFOdbn4Ka9GnGO7ssxZZIP3+hVQfj9xN8z+tDRBds1fqexg2M6b7u9Gtl2deHr5qrz/OkjPo0gzH9+vtV+DkHrllaa72xme1v+vAQNqQZWXriyy36PvpHUhRTEamCMqq7P2n8osFBVj6yEoOVQKQVRh/8Ukk9f7UVbRcsi3FxdGx0d66lgm4dNR+WiblodGuIvYdrJ07jmpGvyHh/6w6Gs2riqaD9D3z+Up6c8nff4orWLGDt3LG3tbUWnrFJ1KVL1KeZ9fp4tnS1Avu+0u3x/lUi10QO33DSbl6ncjEwsqORMY61nLSsdr9GdCDvnfO0fr2XJulw5d90o9blNz3n1s/719XmPFfKL5MJWR/lRToxBnCOwK00hBfFj4AkR+Xqw3PVcEfk68Feq5B+oFj4xBj7EIQ7BcjaVjs/cdDaX/PaSTvvSK6F2auEU32kKTXX4+EVyYaujilNKjEHcI7ArTSEn9Q3AeECAUbilqAKMD451GXxiDHzIjENI52xqxH1p6a2R4rmbfPI95WtzD5azqVR88t9k88yrz3QYQWaO+H0pZLn4rITKha2OqjzdMa6lYJkrVX0WeFZE+rqP+lp1xKou6RiDT+PnPxA6+iwy5/YHsscH8G6O/t7CLdmaRW5fQC7/QTrfU/ocCrTxfZTYupnOpPPfhH3Ap0eQs1bM4mP9PxZqxF8v9QVXy5SzwslWR1WWMKvcZpw2o0pSRUuhSOpDROQXQRT1X4HHReTVYN+AaglYLcYAiymiMQMylUMjHeMQMn0A+ZRNO7l9AT7+gzOBzxVo40utfSVxJT03HZb0CPKhlodCjfjbtZ0zjzwz7/Fy1uLbOv7K0h3jWgr5IO4DfgkcoKqDgiyrBwC/An5RDeGqzWhgPsWjq9OkgF3A6exZOurjA0iT7QvwOfdd4B3P/vMRB19JnBnYdyBD9htSlWvVSz0Prn4w7/FS/CJg6/ijoDvGtRRSEPuq6n2qunsgrKrtqvoLXCBblyQ7WlkKtM21KsgnZ1Pm+ZljDZ9zlfKX5FrOpuLcNua2qlynXdsLjjhL8YuA1ZeIgu4YgV1IQSwTkR8G0dMHBtuHROSHwFPVErAWZEYrX4hfUr+0JRB27PBmnveVwHI2lc7oQ0cz7eRpVblWoRFnoboAubD6EtHRHSOwCymIibhUGNOAh3FT9NcBz9CNZijCZnENO3ZozPO+XNK+EcvZVDrXnHQNs/9tNlLQjiyfYiPOfGv2JwybwPhh42tWK6C7EZcqb9Ukr09WVXcAdwRbtyVsFtcJ+K8myvYF+JybflQVmmZK4bIdzgg2o3T+96X/pUddj5KWmvrgO+JMr9nvKqtjkkjmKrdCEdhdyXIrqaKciOTPL9DFCFvJLUxMRbYvwOfcnkCvkP0apVNqHAJADym+Jq4SI864RfbGTZ5KEocqb9Ukby6mgieJ/E1VD4lAnlBUKhdTIaZSfFSfwk3fpMd2heIgwBXh7ol/HET6GulYCzzadK0/09oRNj9TmlRdisnHTOb0QadHmvMnbjmFopCnra2NDRs28M475a7f63706tWLfv36kUp1HHpWIlnfG/nOAfZWVZ+QgUiphoJoxRUMKhQ21YCb2880LFtxjutZdJymasRN/1xKfkdx+tzZ7MnEel7WOT5tjPLxzfCaTWY20NYtrdzyl1uYvXL27qyr5zWfx6XHX1rWdERcspKmM83es+KeotlrS5Fn3bp19OnTh3322YeMagNGEVSVzZs3s23bNg499NAOxyqhIP4GHKuq/8xx7CVVPbhEuStGNRQEWHbU7szUh6Yy88mZ3tNM1Ry5+8iWtmSi8l2EyTRbqjyrV69m8ODBphxKQFVZs2YNRx7ZMfl2JbK53gP0z3Ps5/4iJh+r5NZ98Y1DEKTqc9G1juwNm2m2HHlMOZRGud9boWR931LVx/Mc+3pZV00gVsmte1IoDiEdc7Dw3IXsunaXVzbQSlLryN5SM81GGmnc2gpTp0JTE9TVudepU91+IzShVjGJyHURyZFIfLKuGsknritXah3ZW+oKr8gijRctguZmmDkTtm0DVfc6c6bbv6iyqbivu+46vve974U+7/XXX+eHP/xh2defMWMGhx9+OCLCpk2byu4vF2GXuX4mEikSyCKc83omLpOqsiejanNw3Og6lFI7IGpqHdlbiiUQmTytrTB2LGzfDm1ZSqutze0fOzYWlkQpCkJV2bVrV4d9J554Ir///e/p3z+fJ6B8wioImwgkmVXbzPLuetQ6srcUSyAyeW6+ubNiyKatDW4prYjSPffcQ3NzM8OHD+e88zoruJNPPpn0gplNmzYxYMAAAFatWsVxxx3HiBEjaG5uZu3atVx11VW0trYyYsQIrrzySgCmT5/OscceS3NzM9deey0A69ev58gjj2Tq1KkcffTRvPTSSx2uedRRR+2+TlSEVRDHRCJFwkha1bYqW95GlfDxj0QZ2Rsm02zk8syZ46cgZod3kK9atYrrr7+eJUuWsGLFCm699Vbvc++8804uueQSli9fztKlS+nXrx833ngjAwcOZPny5UyfPp3Fixezdu1aHn/8cZYvX86yZct47LHHAHjuueeYOHEiTz31VKSWQj5CKQhV3QXdK5I6F2HzM9WSBFneRgnU0j/iu8Krca/G6OV503O6y7ddBkuWLGHs2LHsu+++APTt29f73FGjRvGd73yHm266iRdffJG99967U5vFixezePFijjrqKI4++mjWrFnD2rVrAejfvz/HH398aJkrRanBbl8Avl1JQZJE2PxMtSSM5T3D0vwkklrlafLNTVQVJ35jozOLfdqFRFWLLhft0aPHbh9BZsT3ueeey4c+9CEeeughTj31VGbOnMlhhx3Wqf+rr76aL33pSx32r1+/nt69e4eWt5IUqij3Rp5tG3BgFWWMHWHzM9WSCC1vo8bEIedRbFZ4TZgAqSLWTCoFOfwHxTjllFO4//772bx5MwBbtmzp1GbAgAEsW7YMgHnz5u3e/8ILL3DYYYfxla98hc985jOsXLmSPn36sC1DmZ166qn8+Mc/5s3Aunn55Zd59dVXQ8sZBYWmmF4HBqlqU9bWB3ilSvLFkgkUT6oXl6ptEVreRg1ZtHYRzXc2M/PJmWzbsQ1Fd9fGbr6zmUVrq+dYisUKr8sv91MQl4Z3kA8ZMoRvfvObnHTSSQwfPpzLLrusU5srrriCO+64gxNOOKHDktP77ruPoUOHMmLECNasWcPEiRPZZ599OPHEExk6dChXXnkln/zkJzn33HMZNWoUw4YNY+zYsR0USD5uu+02+vXrx4YNG2hubuYLX/hC6HsrRqFUG/8NzM8VLCciN8UhWK5aqTayKTU/Uy1oavKzvJuaYOvW6OUxyicuOZiqxerVqzulisjJokXOodbW1tFsTqXcNm8ejOl+eQ9yfX9lp9qwSOr8DMTlX8pVuzps1baog+0itLyNGuETwdzW3sYtf4nLOroqMWYMrFwJkyd3XM89ebLb3w2VQ7kUsiAGqOr6vCc6r81BqrohItmKUisLIk25GVWrkQSwtRWGDIF3383fpmdPWLUKBiZ/sNkt8M0w29Szia1XJd8s9LYgjJxEYkEA00XkARGZKCJDROT9InKIiIwWkf8C/h/QrX+1cvIzxS3Y7sUXXeBcYyOI7NkaG2sbUGcBfp2pdQ4mo/tQaIrp88B/Ah8Abgf+L/Br3BLX54DRqvq7agjZFalWsN3NN0NWhH4ndu6EU0+Fu++Gt7LS+b/1lttfi4A6C/DLTa1zMBndh4KBcqr6rKp+U1VPVtUPqOpRqnquqs5R1ZLLO4nIe0VknoisEZHVIjJKRPqKyO9EZG3w+r5S+08C1Qq281nm2t7ulER7rvJ3wfHsgLr0yL6hoaPFIQJDh8KSJeXJHTbAr5Cl0dWskFrnYDK6DyXVpK4AtwK/VdXBwHBgNXAV8IiqDgIeCT53WaoVbFfJ5avpgLr0yP6uu+Dttzu3W7UKTjkFvl1GKGWYAL9ClsaQIW7rSlZIrXMwxZmuNhioOapa1Q23UGcdgYM8Y/9zwAHB+wOA54r1dcwxx2hS6aN+X1hTudfpo+oei5XZGhtVGxr82z/ySGly9+4djTzZW0ODakuLu2ZLi+qUKe47E3GvU6bsOR4nFj6/UBuub9DUt1PKdezeUt9OacP1Dbrw+YW1FrFiPPvss17tFi50v2cq1fE3TqXc/oUV/kquvfZanT59eujzXnvtNb399tvLvv65556rRxxxhA4ZMkTPP/983bFjR852ub4/YKl6PIJqYUEcBmwEfiIiT4nITBHpDfyLqr4CELy+P9fJIjJZRJaKyNKNGzdWT+oKU61gO59lrmF4883iI/tMLrkk/DUWLersC6mUPNn4WCFxtDRiE8EcE5KUc6xS6b7Hjx/PmjVrePrpp3n77beZOXNmJcXcc+FCGy7F9wTgmuDzIcBxPtonT38jgZ3Ah4LPtwL/Bbye1e61Yn0l2YJoUdUGLXyDDUG7sq7TUt4IuxJbIdmyR+znnqu69961lbeYpWFUFx8LYsqUzpZD9pZKqV50UWkyzJo1S4cNG6bNzc06YcIEVe1oQZx00kn6xBNPqKrqxo0btX///qqq+swzz+ixxx6rw4cP12HDhunzzz+v48aN0169eunw4cP1iiuuUFXV7373uzpy5EgdNmyYXnPNNaqqum7dOh08eLBOmTJFR4wYoevXr88r3/e//339xje+kfNYORaEzwP9DtwqptXB5/cBT/h0nqe//YH1GZ8/AjxEN5tiUlVdqE7Nvo7xAAAgAElEQVQJpLTjjaWC/ZWyiAuZ3vX1bvN5SBb7BwyjIPLJJOLfb6nylLKV83AxysNHQfhOpTaVMGf7zDPP6BFHHKEbN25UVdXNmzerqp+C+PKXv6xz5sxRVdV3331Xt2/fruvWrdMhQ4bs7v/hhx/WL37xi7pr1y5tb2/X008/XR999FFdt26dioj++c9/Lijfjh079KijjtLHHnss5/Gop5g+pKoXAe8EFsdrwF5lWCz/AF4SkQ8Eu04BngXmA5OCfZNwS2q7NGNw6Tgm0zGSenKwv1KTBIUCTBcvdoFyPtTXQ48S8v9mOwoLTQe48YEfqRRUK9mlJTSMN1HmHIt7uu+pU6fy0Y9+lI985CMh7soPn3/3NhGpBxRARPYDiqysL8rFwM9EZC/gBeB83PPxfhG5APgb8Pkyr5EI0sF2USdqHjjQpfPOldJ73jz3wH733dxLXdOKQTX/UthCpOfyZ81y11qwoDy/QabcCxa4OI1S5AqLJTSMLxFm+0Y1vum+p02bxsaNG7nrrrvC3JI3PhbEbcAvgfeLyPXAn4DvlHNRVV2uqiNVtVlVz1DV11R1s6qeoqqDgtfOOXWNipC9FHDcODjjDDj77M7/QI2NcM45rt2774Yb4WeS6Si8557KKIhx42DDhuooByjt4WJUhyhzjsU13ffMmTN5+OGHuffee6mri2a9UdFeVfVnwNeAG3Bpvs9Q1bmRSGNETr7VOnPnwi9/Cfff33HWdts26NMHduyozPXb2vxXKBVj2zb4zW/82hYZABbFEhrGmwizfcc23feFF17IP//5T0aNGsWIESP4djmBR3nIm6wPQETqgJWqOrTiV64AtU7WlzRaW51y2F4gT3mvXvC5z8H8+W5KpbHRBcPt3Fk9OaNAxPkrSp0mamhwfhxLaFh9fJP1Wbbv3ESVrA91NahXiMgh5YloxAGf6OR33oGf/7yjdZF05QDuXorlpMpFKuWUw7x5phzijmX7rjw+TuoDgFUi8jiwe3JAVT8TmVRGJPjkZYLS/Qxxp5DllIumJjetdOmlphySQqHFGEZ4fBTEtMilMKpCHFbhpFKVcVCXStoXUUgJ1tfDhRfaQ8YwfJzUjwJrgD7BtjrYZySMOKzCqWTaj1JIO98L0bNnac5Mw+hqFFUQInIW8DguLuEs4K8iMjZqwYzKU+m8TGHp0cPN5dcaEedXyP4uzN9gGB3xWTz7TeBYVZ2kqhOB43CFhIyE4bMUMCrq6+Hhh+PhKOzd25yZXZXWLa1MfWgqTTc0UTetjqYbmpj60FRat8QgS18C8VEQdaqaGbWx2fM8I2YMHOhGx7lGz1GRHpUvWACjR1fnmsUYMGCPM3PrVhdot3Wr+2yWQ3JZtHYRzXc2M/PJmWzbsQ1F2bZjGzOfnEnznc0sWlvZlLzXXXcd3/ve90KfV0o211xccMEFDB8+nObmZsaOHbs70K6S+DzofysiD4vIv4vIv+MS68Us+bHhS/ZSwKg566zyRuWZI/xKKbVnnrEiMl2N1i2tjJ07lu1t22nb1XEVRNuuNra3bWfs3LGxsCQqle77lltuYcWKFaxcuZJDDjmEGRGsqvBxUl8J3AU046q/3a2qX6u4JEbVyBw9lxthXIj6enjve8sblWeO8CsZjxHXOg9Gadz855tpay+8PK6tvY1b/lJalfd77rmH5uZmhg8fznk5QupPPvlk0kG7mzZtYsCAAQCsWrWK4447jhEjRtDc3MzatWu56qqraG1tZcSIEVx55ZUATJ8+nWOPPZbm5mauvfZawOViOvLII5k6dSpHH300L730UodrNgUjPFXl7bffLpovqhR8nNSHAgtV9TJVvRRnUQyouCRGTYhyZVN7O9x+e+XKPjY0VEYuiF8RGaM85qyc08lyyKZtVxuzV4ZPybtq1Squv/56lixZwooVK7j11lu9z73zzju55JJLWL58OUuXLqVfv37ceOONDBw4kOXLlzN9+nQWL17M2rVrefzxx1m+fDnLli3jscceA+C5555j4sSJPPXUU/Tv379T/+effz77778/a9as4eKLLw59b8XwmWKaS8fsre3BPqMLUI2VTZWqzBYMyipKuqKckWze3OE3/+7bLpM4p/v+yU9+wt///neOPPJI7rvvvpB3VhwfBdFDVXenagvel1wPwogX1VrZlDli97UEsq2bdeuikcvqPCSfxr38TGHfdpmUm+57/vz57L333px66qksWbIkZ/9XX301y5cvZ/ny5bS0tHDBBRcAFE33DVBfX8+4ceN44IEHwtyWFz4KYqOI7E6rISKfBTYVaG8kiGqvbGprg8MOc47nQtTVwaRJHfeFTZXhSxwizI3ymNA8gVRd4T/gVF2K85rDp+SNY7pvVaWlpWX3+wULFjB48ODQ91YMn1QbF+KK+8zA1ad+CZhYcUmMqtHa6hL3zZmzJ2PrGWc4h/WCBfDGG9Fdu60NXniheOK8XbvgzDOjkyOTSvo2jNpw+ajLmbViVkE/RKo+xaXHhw+Rz0z3XV9fz1FHHcVPf/rTDm2uuOIKzjrrLGbPns3ojPXc9913H3PmzCGVSrH//vtzzTXX0Ldv393pvseMGcP06dNZvXo1o0aNAqCxsZE5c+ZQX1+fVyZVZdKkSbzxxhuoKsOHD+eOO+4IfW/FKJjuu0NDkcagvUfdpupg6b7D45MSecEC5zOIMmdSXV1hJVFXB1OmdMyHFNWKq6FD4emno+nbKB/vdN9rFzF27lja2ts6KIpUXYpUfYp5n5/HmEHdLwoyknTfIvJpEcl0m18G/ElE5gcrm4yEUagWdKaPYOzY6KebfCyIWbM6Vr+LivXro+vbqB5jBo1h5YUrmXzMZJp6NlEndTT1bGLyMZNZeeHKbqkcyqXQFNP1wPEAIvIpYAJwDnAUcCdwauTSGRXFpx5EWxs8+CB8/esQLMeuKGGyub75plv5lG3tVJqofBtG9RnYdyAzTpvBjNMsFW8lKKQgVFXT/zpnAj9S1WXAMhGZGr1oRqXxqQfR1gZ33RVdkaCw6b6r8fCOQ5Zbw4gjhdaSiIg0BmVHTwEeyTjWK1qxjCjwXa0ThXLIzJQaJ6zWtGHkp5CC+AGwHFiKqwGxFEBEjgJeqYJsRoWp1Ug5zplSSy1kbxjdgbwKQlV/DJwEXACclnHoH8D5EctlREC160HU18NFF8U7U6rVfuhatAJTgSbcw60p+GzZVEqjYLiSqr6sqk+p6q6Mfa+o6t+iF82oNNWuB5GvMltc4g7q62HcuD3ZYi3Da7JZhMsoOhPYBmjwOjPYX+m8jLVO953m4osvpjGi6QGr69CNqFbUdLHKbIdGsEg6lXIP/GIR2pm0t7s8UaqVyxdl1IZWYCywHcheA9EW7B9LPCyJSqX7Bli6dCmvv/56pUTrhCmIbsaYMW6VUp8+0fSfVjxvv+1G57lG5VHEHezc6R74xeIrCmEZXpPLzXRWDNm0AaXmZYxjuu/29nauvPJKvvvd75Z4Vx6oasEN6JtjSxU7rxrbMccco0Y4pk1TdWPmymyplGpDg+u3ocF9znV84cI9Moj49S2Su8+ot1RK9aKLavcbGR159tlni7bpo34PjaYSrv/MM8/oEUccoRs3blRV1c2bN6uq6rXXXqvTp09XVdWTTjpJn3jiCVVV3bhxo/bv319VVb/85S/rnDlzVFX13Xff1e3bt+u6det0yJAhu/t/+OGH9Ytf/KLu2rVL29vb9fTTT9dHH31U161bpyKif/7zn3PK9YMf/EC///3vq6pq796988qf6/sDlqrHV+ZjQTwJbASeB9YG79eJyJMickwUSsuIhiVLyg9+O+IItxoqs47zggVw003FI7TTo3JfH0R27ehqYRlek4dvvsVS8jLGMd333//+d+bOnRtJDYhMvEqOAqep6r6qug8wBrgftzigcp4WI3K+8pXyzp82DZ57zs3XZ9ZxnjfPLwAvXXfBt65DZu3oX/zC+RiqhWV4TRa+LtpSXLkaw3TfTz31FC0tLRx++OEMGDCA7du3c/jhh5dwd4XxURAjVfXh9AdVXQx8VFX/AvSsuERxoIuulVu1qrzzb7op99y8b4R2elT+wgt+10u3S+eQam/3l7VcSl0Ukpk7ylZHVY8JQLF1FymglJjIOKb7Pv300/nHP/7B+vXrWb9+PQ0NDbvTf1cSHwWxRUS+LiL9g+1rwGsiUk/HSnOhEJF6EXlKRH4TfD5URP4qImtF5D4RqU5Romxl0AAMBv6H6qyVSxD5qq/5jrbT7d5+2699Os2GTw6pSlJfX1p09aJFbhXUzJldeHVUTDXg5fgpiFJiIjPTfQ8fPpzLLrusU5srrriCO+64gxNOOIFNm/aUy7nvvvsYOnQoI0aMYM2aNUycOJF99tlnd7rvK6+8kk9+8pOce+65jBo1imHDhjF27NgOCqSmFHNSAPsC/wd4ChdZPQPYD1dV7nAfR0eefi8Dfg78Jvh8P3B28P5OYEqxPsp2Ui9U1QZVDeNyb1DVlvIuWysq4cBtyuHl69Mn3Llhrhem/0pujzwS7rttaXEO9UJ9NjS4doll4UL/lQgVxMdJrZr/3zkV7I9GuvgTqZNaVTep6sWqepSqjlDVL6vqRlXdoaol2TQi0g84HTcuR9wE32ggbZvNAs4opW9vCi2cLkQ5a+VqzJAh5feRy1rwidAuJ+dRtf0B9fUuo20YfDPlJrb+tW+u+BpaEmOAlcBkOs4OTw72xyzLSyIoqiBE5AgRuVtEFovIkvRW5nV/AHyNPVNU+wCvq2o6TdwG4KAyr1EYn4XTuWgDErrC5bbbyu8j19y8T4R2Zs4jjzK7Ha5V7RxS7e3hVzGF9cMkjoRowIG4KY6tQHvwOiPYb4THxwcxFze99C3gyoytJILaEq+qSx2+e3eOpprn/MkislRElm7cuLFUMWAOpSkIKG2tXAwYPdqtRCqVfFZAoQjtXFHVEycWX5FUX7+nJnW1c0hBeKslrB8mcXR5DWjkpNgcFLDMZ67KdwNuwFkI63GJ/7YDPwM2AT2CNqOAh4v1VZYPQsq4i1KibWLEI4+oDh3acRp50CDVXr3Km0NvaXEBZk1NqnV17vWiizqfE3a+3qd9sU3EyeMbdJfL11KIsH6YxOEb3VhXV/FL+/ogjNxEHSi3QESmisgBItI3vZWhkK5W1X6qOgA4G1iiquOBP+C8AgCTgF+Xeg0vSp22KHWtXK3JWK01+uPw9IugU0Bb3H/288+7eXdfKyAX6ZiFrVs7xklknxPW4ki3DxsHke5r4UKXgmPrVvjCF6Lxl/hYOSLwmc+E6zc2+M7zWfWlrkUxDQKsy7G94KN9PPo+mT2rmA4DHgdacNNaPYudX5YFMUXDrV5Kb0lcxRRieYevFVAJwl7rkUdUe/TwtxYqYb2EuRcfK2fvvSNb7BMtU6YUN78iylESyoJoUfe/3UfdLEGf4HPS/mcrSDkWRNkP+VpuZSmIFnUPR9+r5VorV+iPMS5/qI+oag/tMkqvEistw/TR0uKejX36OMXTp4/7nEuBLFxYfJouU4kV6it21HAdr7eCqPI618xcTGF47bXX9Pbbby/7+pMmTdIBAwbo8OHDdfjw4frUU0/lbBeJggBGB69n5tp8Oo96iywOooeq1gfH6tT5HC7Sjg/RQn+MPYOt1guyF6q7Dx/ll6DkdJWwcnz6KEUZjR/vP10fVrHVnDjHQfgM+Co8ECpVQWQn6/Mhncgvk0mTJuncuXOLnhuVD+Kk4PXTObZPlT+5FQPyLZz+EvAc8Ba518oVSz7/brDVMjF9Wkaf9BQJW7rr6+sopw+fZf+f/rRLgJjJ/PnuqelLTEII/BgzpmP2xMyMjbWuJxtxvu84pvuuCj5aJK5bzdJ9l+q/qOaIPayMlV98kmh8ptzB+URKSWVepen7LoGXBRFhvu+4pvueNGmSHnHEETps2DD96le/qu+8807OdpFYECJyWaGtijosfpQTQwHVGbGHldEWn3TAZ9k/uEJFmaP/UhfxWAhBmUSY7zuO6b4BbrjhBtasWcMTTzzBli1buOmmm8LfXBEKTTH1CbaRwBRcZPNBwIXABysuSZKoRLBT1AFTYfpP6tLdCAkT0JYZQFxOUF9ig+jiQIT5vlXjl+4b4IADDkBE6NmzJ+effz6PP/54+JsrQl4FoarTVHUaLlnf0ap6uapeDhwD9Ku4JEmiEqPtdB9RpRYPI2OpaS67MGEsgczRv0/akUpcsyrENHNrTiLM9x3HdN8Ar7zyCuAUzK9+9SuGDh0a/uaK4BModwiwI+PzDmBAxSVJEj5/jIVI/6EuwqUQn0nlU4v7ytgDlyLRktV0IKwlkB79FwoCLEQ5yQwjIWm5yyPM9x3XdN/jx49n2LBhDBs2jE2bNvGtb30r/M0Vo5iTAvgmsAK4DrgWl/L7Gz4Ojqi3mjmpw8ZQZG8N6uITivVRH7SLSsZy+u/ihE3vkZ1CI3MZrY/jOlapwGOWuzyucRBJIep039cD/wG8BrwOnK+q36m8qkoQA3Gj7gY6j1pSuDp7PfMcawjOnUdxJ3I7cCqlWRLFZGwAFuCSrBudCJPeI9foP3MZ7a5dLt1HOWlMqkpCMrd2wvJ9Vx4fLQLUAwfippsOAQ7xOS/qrWYWRJoWdctVm7RzQF2hY6r+y/LSFkepg7VichgF8Unv4TuYrmYak7KIWeZBS9ZXHuVYEOLa5kdELsZNLf0TN6YVp1e0OUK95cXIkSM1HZySOOogd0LzHKRwo6AZ0Ylj5GfRIreUta2t48A6lXLbvHm1jRGrOHV1ftF+dXVVKRS+evVqBg8eXHQlkdEZVWXNmjUceeSRHfaLyDJVHVnsfB8n9SXAB1R1iKo2q+qwOCiH2OK7KinMipWERTonnezFO+PGwRlnwFlnxS+AOBJilrm1V69ebN68mWKDWaMjqsrmzZvp1atXyX308GjzEi7ZhFGMRbj0Fm3s8S+kVyXNwvkE0g+TCcF+32A2WyNfFXJZC9u2wdy5XdRayMWECW61UiE/RBWXXfXr148NGzZQVoGwbkqvXr3o16/0qASfKaYfAR8AHsJlGAJAVb9f8lUrRKymmFpxS1O3F2jTgHOWDfRsn0kT5anpVly+mjk4ZdOIU1KXY0tcA1pb3QrO7QV+k4YGZzXExqEcBfZFFKa11Tny58xx65sbG51SvfzyxHwflZxi+hvwO2Av9kRX9ylPvC5I2GRh6VVGPkVwyo10jjLeoguR1MU7FSdsRafuRNLiQ8rFx5MdWBm9fdtWa6v5KqZMSk0WFnW9hhqkQU4qMVu8U5wwxSpK7T8Ry66qRMziQ8qBSsVBiMgoEXkWWB18Hi4iP4xWbSWQUpOFjQbmUzheoZxI54jTIHclfHMhxSJnUjVGspXIq96V6IYmps8U0w9w4VqbAVR1BfDRKIVKJOUkC4siwCe9muoO/BSErZKK2+KdjmQurRKB004rXKwiEQUmEoZPit8ulpbXR0GgqtmVKqJf/Jw0yk0WNhAX57CV3EWKwpDpc/AlDqPiGuOTf6kmOZOyrQUfuthINhYkysSsDD4K4iUROQFQEdlLRK4gmG4yMogwWVgoClW7K0TcMonWAJ9MrKkUXFrNzLeFStsVoouNZGNBrE3MaPBREBcCF+FqQbwMjAg+G5n45D6qRtZUH59DNlYPAojp4h2fee98dKGRbCyIrYkZHT7J+jap6nhV/RdV3U9VJ6jq5moIlzjikCyslGp3Vg9iN7Eru+xb2i4X1RrJJqluRDnE0sSMFp9AucOAW4HjcSvo/wxcqqovRC9eYWIVKBcXwuZ4StExwtuIF755kbJJpZxWmxFxAq/ulqiqi9xvJQPlfg7cDxyAy+g6F7i3PPGMyAgzaLQ0yPGnVCugGiPZQv6R9Gqq005z99BVLIrYmZjR4qMgRFVnq+rOYJuD/xjVqDa+q6kuovRVUkb1CFvarprOEl//yFtvda1I424UH+KjIP4gIleJyAAR6S8iXwMeEpG+ItI3agGNkMRlNZWxh3Lm6H2LXItUfyQbxj9i8RmJxMcHsa7AYVXVwyorkj/mg8hDrqyyYD6HWlCJOeu4znuX4h+plm/EKIivD6KogogzpiAK0IpLnzGbPdlbz8NZDl3PEo4nlcyK2trqAt9mz96TQfS885yfoVZTG01N/oF72edttQoCtaRsJ7WIHCsi+2d8nigivxaR22xqKQFUMjLbKI1K5u6J47x3WP9IGovPSAyFfBB3ATsAROSjwI3APbhHzd3Ri2YYCSeK3D1xijnw9Y9k04Uijbs6hRREvapuCd6PA+5W1QdU9T+Bw6MXzTASTqVz98StFkGh0PN8dLFI465OQQUhIumSpKcASzKO+ZQqzYmIHCwifxCR1SKySkQuCfb3FZHficja4PV9pV7DMGJBQ0Pl2vnEHNRihVBmXICPZdDFIo27OoUUxL3AoyLya+Bt4P8CiMjhlFf8cidwuaoeiYvOvkhEPghcBTyiqoOAR4LPhpFcBgyoXLs41yJI+0e2bYOFC2OWzMooh7wKQlWvx62q/ynwYd2z3KkOuLjUC6rqK6r6ZPB+Gy4z7EHAZ4FZQbNZwBmlXsMwYsH69ZVrl5RaBN0s0jhyauxzqukyVxEZADwGDAX+pqrvzTj2mqp2mmYSkcm4JBEccsghx7z44ovVEdYwwuIbJ1BX51YmVasvIxlEGP9SyVxMkSAijcADwFdV9Q3f81T1blUdqaoj99tvv+gEjDvpinGZWWOnBvuNeFDJ+gFJqkUQp5VWSSUmPqeaKAgRSeGUw89U9cFg9z9F5IDg+AHAq7WQLRFkVozbhsuMtS343BwcN2rPhAlQX1+4TX2936qepNQiiNtKq6QSE59T1RWEiAjwI2C1qn4/49B8YFLwfhLw62rLlggKVYxrC/aPxSyJODB2bPHpnvZ2OPPM4n0loRZBTEa93sTZ0omJz6kWFsSJuKQPo0VkebCdhgvE+4SIrAU+EXyOH7We2vGpGNeGS7Nh1JZ589yDpxB1dfDgg4XbQOGYgx49nCWiCoMGVf5B5/sgjcmo14u4WzpxqX+tqondjjnmGK0qC1W1QVVTWZKkgv0LqyBDH/X7dpqqIItRmN69Vd2jp/DW2OjfZ0uL6kUXqTY1qdbVqe69t2qPHm7L7DOVUm1oUF1Y5h/lwoWun1SqeP99+vjdb1ON/zhbWpzshWRsaHDtakXE3yWwVD2eJDVzUieOJcBnqPzUTliLxHfAYOluas9bb/m1CzMKzMzJ9PzzLs33zp1uy6TQlI6vRRB2yiguo95iJMHSiYvPyUeLxHWrmgWxUFXrPSRKqepFIfsNY5Es9JDBLIj44DMCTG+lMGVK55F99pZKOYsjTRiLIGz/SbEgkiBnxFYOnhZEzR/y5WxVURAt6h7WlX4w+/TbELQLK0dYRWVEQ9QKwncKK/2gC/vQCfsgLUVh1QIRv/uqq6utnGGUeUh8FYRNMRXDxymcia/1HNbZHEYOqxgXD3r39mtXSuzCokXhp7DCTq2EnTJKwkorSE5MSRyi0n20SFy3qlgQvk7h9ObrbwzrbA4jRzWc5UZhWlpUhwwpPkqtrw8/ovaxBDK3tBM8rEVQylRMhKPeipEUSydCMAuiQoT1pw2ocL9vZr0WQ7ByorUmvYTyueeKt+3ZM/yI2scSyCSdDDCsRVCKozQOo95iJMXSiQM+WiSum1kQBdoblaGlxY04+/Rxc9d9+rjP+ZyDvqP7Hj1KH1H7juzLtSCSsBy0VJJg6UQIZkFUiAm4OX1fCpQfDt1vChdSWEp7o3xKCabyHd0PHlz6iDrsMtF0TeywFkGh4Lykp+9OgqUTA2qazbVcRo4cqUuXLo32Iq24/Ea+D/4m/Kpl+PTbAKzE1ZEO294oj9ZWpwS2F/jCe/WCz30O5s93D+3GRnjnHT8F0dTkYhlKoanJKaow7bdu9bunhgb3gMx86Le2Osf17Nl77vO889wUTBKVgxH/bK6JYSAwDyiScw0IN4JP99tAZ8sgFeyfx56Hfdj2Rnn4WALvvAM//3lH68LXN1BOsJhPEsA0lbAIMoPz2tvd64wZphy6AaYgfBgDLKZ4odWwy0vH4Eb8k+kYST052J9t5YZtb5SOT7I0cIqhFMpZQumTBDBNtrPVplaMENgUUxgW4dJptNExJiEVbPOwh3RXwbdATymkUu6BPGNGaedPnQp3311cSfTo4aa/7KFvZGFTTFFgI/juQ0NDdH2Xu4Ryzhw/C6JXr66rHOKcqrsLYRaEYeRi6FBYtaqyfVagVCRg5UcjLMXZXTALwjDKYf368vtIpaKZ509KqogoSFpRooRjCsIwclFoKagPaT9DFCt/4pIKuhYkIVV3F8IURCa5ajOMxwWp1aqCnFEbyh19R5mqoTuniohJKc7ugimINItwgWgzgW2ABq8/B36WtW9m0Nbqr3ddfEbp+ejRI9oI464c4VyMpBQl6iKYggBnDYwld7W4XISpIFfrGtZGafiM0vOxcyeMG+e3qqbU1TjdNZ6hO/tfaoFPwqa4bhVL1jdFO1d189mKFeaJQw1ro3TyJXTz3YolfuvmCeNKwlJ1VwQsWV8I5hCuKFCaNiDfVGchq6SYBVLI6jCLpHpkj9LDUqwutK3GCU939r/UAFMQEL7mg8+5YSvGpcnnC5kJDAm2XMfMJxINmXmIhgwprY9cq2psNU5pdGf/Sw2wQDlwo/AQyTE70JjnXN8+M7O/hs0cm41lc42Wxkb/Mp/ZZGdv9c3IWk7W166MZZgtCwuUC0PYmg+ZvEPukXvYinEQvv51NrksEqNylBMbkb2qxlbjlIdlmK0KpiAALqd0BbGT3L4E30UUme1K9YWkKeQTMcqnnJUx2efaahwjAZiCgMK1FnzINXIvpQJcJQaLNuCMjlJjI3JFNXfnaGgjMZiCSJMvU6vP8yDXyN3HKsmuH1GJwaINOKOj1HdeIjAAAAqHSURBVNgIEVi82L2mt9//vnjRH1uNY9QYUxCZDARm4JzG7cHrTs9zs0fupVSAK8cXku7XBpzRUWgFTS5SKRdVvWMHrF3b8djata4iXToDafZ5thrHiAGmIIpRii8hTdj6EeX4QiB8RTsjPPkimCdMgPHjO+477TQXVV2ItjbXrjtFQxuJwZa5FmMqLs6gkPM4hXvol1ggrAOFqtal1fmuHMesol388K0pMXQoPP109PIYRkAil7mKyL+KyHMi0iIiV9VaHqA0X0I5FLI6VgWbVbRLBr4Fh555Jlo5DKNEYmNBiEg98DzwCWAD8ARwjqo+m++cqlWUs1rURimI+LeNyf+h0T1IogVxHNCiqi+o6g7gF8BnayyTw2pRG4bRDYmTgjgIeCnj84ZgXwdEZLKILBWRpRs3bqyacDlXOM3A0loY+fHN3TR0aLRyGEaJxElB5LLHO9ndqnq3qo5U1ZH77bdfFcQyjBK57Ta/drfeGq0chlEicVIQG4CDMz73A/5eI1kMo3xGj4Zp0wq3mTbNtTOMGBInBfEEMEhEDhWRvYCzgfk1lskwyuOaa+CRRzpPIw0d6vZfc01t5DIMD3rUWoA0qrpTRL4MPAzUAz9WVc91goYRY0aPtjgHI5HERkEAqOpCYGGt5TAMwzDiNcVkGIZhxAhTEIZhGEZOTEEYhmEYOYlNqo1SEJGNwIs1FGFfYFMNr18Jkn4PSZcfkn8PSZcfkn8PYeXvr6pFA8kSrSBqjYgs9clnEmeSfg9Jlx+Sfw9Jlx+Sfw9RyW9TTIZhGEZOTEEYhmEYOTEFUR5311qACpD0e0i6/JD8e0i6/JD8e4hEfvNBGIZhGDkxC8IwDMPIiSkIwzAMIyemIDwRkYNF5A8islpEVonIJcH+viLyOxFZG7y+r9ayFkJE6kXkKRH5TfD5UBH5ayD/fUEm3dgiIu8VkXkisib4LUYl6TcQkUuDv59nROReEekV999ARH4sIq+KyDMZ+3J+5+K4Lagrv1JEjq6d5LtlzSX/9OBvaKWI/FJE3ptx7OpA/udE5NTaSN2RXPeQcewKEVER2Tf4XLHfwBSEPzuBy1X1SOB44CIR+SBwFfCIqg4CHgk+x5lLgNUZn28Cbgnkfw24oCZS+XMr8FtVHQwMx91LIn4DETkI+AowUlWH4rIWn038f4OfAv+atS/fdz4GGBRsk4E7qiRjIX5KZ/l/BwxV1WbgeeBqgOB/+mxgSHDOD0Wkvnqi5uWndL4HRORg4BPA3zJ2V+43UFXbStiAXwc/zHPAAcG+A4Dnai1bAZn74f6ZRwO/wVXx2wT0CI6PAh6utZwF5G8C1hEsrsjYn4jfgD1ldfviMin/Bjg1Cb8BMAB4pth3DtwFnJOrXZzkzzr2b8DPgvdXA1dnHHsYGFVr+fPdAzAPN1BaD+xb6d/ALIgSEJEBwFHAX4F/UdVXAILX99dOsqL8APgasCv4vA/wuqruDD7nrAMeIw4DNgI/CabJZopIbxLyG6jqy8D3cKO9V3CVzZeRrN8gTb7v3Ku2fMz4D2BR8D4x8ovIZ4CXVXVF1qGK3YMpiJCISCPwAPBVVX2j1vL4IiKfAl5V1WWZu3M0jfO65x7A0cAdqnoU8BYxnU7KRTBP/1ngUOBAoDduOiCbOP8GxUjU35SIfBM3ffyz9K4czWInv4g0AN8EcpUkrNg9mIIIgYikcMrhZ6r6YLD7nyJyQHD8AODVWslXhBOBz4jIeuAXuGmmHwDvFZF04ai41wHfAGxQ1b8Gn+fhFEZSfoOPA+tUdaOqtgEPAieQrN8gTb7vPDG15UVkEvApYLwGczEkR/6BuIHGiuB/uh/wpIjsTwXvwRSEJyIiwI+A1ar6/YxD84FJwftJON9E7FDVq1W1n6oOwDnhlqjqeOAPwNigWWzlB1DVfwAvicgHgl2nAM+SkN8AN7V0vIg0BH9PafkT8xtkkO87nw9MDFbSHA9sTU9FxQkR+Vfg68BnVHV7xqH5wNki0lNEDsU5eh+vhYyFUNWnVfX9qjog+J/eABwd/I9U7jeoteMlKRvwYZyZthJYHmyn4ebxHwHWBq99ay2rx72cDPwmeH8Y7h+gBZgL9Ky1fEVkHwEsDX6HXwHvS9JvAEwD1gDPALOBnnH/DYB7cT6TtuBBdEG+7xw3vXE70Ao8jVuxFUf5W3Dz9On/5Tsz2n8zkP85YEyt5c93D1nH17PHSV2x38BSbRiGYRg5sSkmwzAMIyemIAzDMIycmIIwDMMwcmIKwjAMw8iJKQjDMAwjJ6YgjFgjIv8WZKocXINrr09nyPTZHxdE5CgRmRm8v05ErijQ9rqsz/uJyG8jFtFICKYgjLhzDvAnXHCf4cc3gP9TqIGIfFBEHgOmiMiTInIOgKpuBF4RkROrIKcRc0xBGLElyHt1Ii6w6eyM/SeLyB9lT12InwWRyenR/bTgofd02vLIHkmLq8cwIHj/KxFZJq5Ow+QQ8g0QV5Pif4JzF4vI3sGxw0Xk9yKyIpBlYBDZOj249tMiMi7jfh4VkftF5HkRuVFExovI40G7gUG7/UTkARF5Itg6PcRFpA/QrJ0TuCEiXxSRRYGM1wH34FJBnwg8kdH0V8B43+/B6LqYgjDizBm42g/PA1uyCp8cBXwV+CAuEjnzYblJVY/GPfzyTq9k8B+qegwwEviKiOwTQsZBwO2qOgR4HfhcsP9nwf7huHxLrwBn4iLBh+PyMk1P5zMK9l0CDAPOA45Q1eOAmcDFQZtbcXUjjg2uMzOHPCNxUdodEJEvA58GzlDVt4EduAysdar6tqq2ZDRfCnwkxHdgdFFMQRhx5hxcYkGC13Myjj2uqhtUdRcuVcKAjGPpRIrLsvbn4ysisgL4Cy7J2aAQMq5T1eWZ1wtG8Qep6i8BVPUddfl+Pgzcq6rtqvpP4FHg2ODcJ1T1FVV9F5ciYXGw/+mMe/g4MENEluPy7TQF18rkAFxK9EzOw2WN/VzQP7g8RMOAL4vIAhEZntH+VVy2WaOb06N4E8OoPsEofjQwVEQUV31NReRrQZN3M5q30/Fv+d0c+3fScUDUK7jOybgH7yhV3S4if0wf8yRbjr3JnW6ZAvuz+9mV8XkXe+6hLpDz7QL9vE1n+Z/BWS79cAWXUFeb4hwR+TZueulBXIZQgvMLXcPoJpgFYcSVscA9qtpfXcbKg3EPtw+X2N96XGpwgqmqQ4P97wFeC5TDYFw52bJQVydkg4icEVyvp7j8/Y8B48TVBd8P+CjhMoUuBr6c/iAiI3K0WQ0cnrXvKeBLwHwROTA4d0hwbBfO8umd0f4IckxTGd0PUxBGXDkH+GXWvgeAc0vs7wGgbzA9MwVXhxjgt0APEVkJ/BdumqkSnIebuloJ/C+wP+5+VgIrgCXA19SlZ/blK8BIcYXonwUuzG6gqmuA92RPPanqn3D+mIeCJbpnishfcNXUFgd9p/kY8FAIuYwuimVzNYwuhohcCmxT1VxO7Oy216nqdVn7HgM+q6qvRSSikRDMgjCMrscddPRpFOKPmR+Cqa/vm3IwwCwIwzAMIw9mQRiGYRg5MQVhGIZh5MQUhGEYhpETUxCGYRhGTkxBGIZhGDn5/3uImOmBG1rPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x[y_hc==0].iloc[:,0],x[y_hc==0].iloc[:,1],s=100,c=\"red\",label=\"cluster 1\")\n",
    "plt.scatter(x[y_hc==1].iloc[:,0],x[y_hc==1].iloc[:,1],s=100,c=\"blue\",label=\"cluster 2\")\n",
    "plt.scatter(x[y_hc==2].iloc[:,0],x[y_hc==2].iloc[:,1],s=100,c=\"green\",label=\"cluster 3\")\n",
    "plt.scatter(x[y_hc==3].iloc[:,0],x[y_hc==3].iloc[:,1],s=100,c=\"cyan\",label=\"cluster 4\")\n",
    "plt.scatter(x[y_hc==4].iloc[:,0],x[y_hc==4].iloc[:,1],s=100,c=\"magenta\",label=\"cluster 5\")\n",
    "plt.title(\"clusters of customers\")\n",
    "plt.xlabel(\"Annual Income (k$)\")\n",
    "plt.ylabel(\"Spending Score (1-100)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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