clustering_usarrests.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import KMeans\n",
    "from pyclustertend import hopkins\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "from yellowbrick.cluster import KElbowVisualizer\n",
    "from sklearn.metrics.cluster import adjusted_rand_score\n",
    "from scipy.cluster.hierarchy import linkage, dendrogram\n",
    "from sklearn.cluster import AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"USArrests.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>8.8</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Murder  Assault  UrbanPop  Rape\n",
       "Alabama       13.2      236        58  21.2\n",
       "Alaska        10.0      263        48  44.5\n",
       "Arizona        8.1      294        80  31.0\n",
       "Arkansas       8.8      190        50  19.5\n",
       "California     9.0      276        91  40.6"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 4)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 50 entries, Alabama to Wyoming\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Murder    50 non-null     float64\n",
      " 1   Assault   50 non-null     int64  \n",
      " 2   UrbanPop  50 non-null     int64  \n",
      " 3   Rape      50 non-null     float64\n",
      "dtypes: float64(2), int64(2)\n",
      "memory usage: 2.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.78800</td>\n",
       "      <td>170.760000</td>\n",
       "      <td>65.540000</td>\n",
       "      <td>21.232000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.35551</td>\n",
       "      <td>83.337661</td>\n",
       "      <td>14.474763</td>\n",
       "      <td>9.366385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.80000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>7.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.07500</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>54.500000</td>\n",
       "      <td>15.075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.25000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>20.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.25000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>77.750000</td>\n",
       "      <td>26.175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.40000</td>\n",
       "      <td>337.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Murder     Assault   UrbanPop       Rape\n",
       "count  50.00000   50.000000  50.000000  50.000000\n",
       "mean    7.78800  170.760000  65.540000  21.232000\n",
       "std     4.35551   83.337661  14.474763   9.366385\n",
       "min     0.80000   45.000000  32.000000   7.300000\n",
       "25%     4.07500  109.000000  54.500000  15.075000\n",
       "50%     7.25000  159.000000  66.000000  20.100000\n",
       "75%    11.25000  249.000000  77.750000  26.175000\n",
       "max    17.40000  337.000000  91.000000  46.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42664111087352724"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hopkins(df, df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40078928515447915"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hopkins(scale(df), df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_means = KMeans(n_clusters=2).fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters = k_means.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Murder', ylabel='Assault'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RIH5oOI4/F61z//xuxW58W1eOly+9DUmaKOESIxIZdq0TUcA1W1vxUnmhV7y6rQHHW2oFyIhIvHhFTkQBZ3HaUdPW6HNbg80U2GSoW7E77agyN6LF3oYoRRiSNdHdfrU+FnIiCrhIRRjyE3KwubLIa1uGlhPQ0IVptJiw6dQevHrkS1iddugUGjzQfzrGxPeDSq4QOj2/Ydc6EQWcUibHvMyxSFDpPOJ/6DUOPcJiBMqKxK6k8QT+cbgQ1v8+AdFsM+Phkjdx1NS9b9fwipyIBJERHo/Vl96BYy01qGlpQL/YNKSFxSJCoRE6NRKhNocNbx7/xivuggu79YeRE5kqQFaBwUJORIJJCYtGSlg0iiuL0b9XD6HTuShN1lZUtBpQ2VqPWFU40rXxiFfrOt6RuogLzna2OF3tbekeWMiJSHBOp///0J5oqcNPzZVotpkxICoN6WFx0CrUXXLsBqsJLx/+DO9V7HXHsnUpWD7oJqTyVkFAqGVKzE4fheKGY17bRsb1FSCjwGEhJ6Ju72DTKSzYswatDqs7tjh7Kqb3HAGNTHnRxz9irPEo4hJIIAGwv+EEC3kADY7OwPysK7Du6NewuxzQylVY0v9aZEZ07wGULORE1K0121rx7KEtHkUcAF4s+wQj4vogKyLxot/j+/qj7n8Pj8nClJRLUNJwAt/pDyNWFYFsXQp0yrCLfh86v2hVOH7f6wpMSR4Eo92MaGU4UjRRkEq697huFnIi6taarWbsbzzpFXfChZq2pi4p5GdnousRFoPRCdn4a+l77m0fV/+ARX0LcEP6KChl/JPrb0qZHBnh8UKnEVB+/a167rnnsHv3bthsNtx2223Yt28fiouLodVqAQDz58/HuHHjUFhYiDVr1sBisWDevHmYNWuWP9MiIhFqsrbCBReilNrftJ9SJkesKgIGi9FrW3gX3SPPi0pHuFyNq1OH4bUj2722v3T4M1yekI2M8O7dxdsVTPY2WBw26BRhkEtlQqcjCn4r5Hv37sWhQ4fw1ltvobGxEddccw1Gjx6NJ598Ejk5Oe7XtbS0YMWKFdi0aRPkcjlmzJiBgoICd7EnotBWb2nBLn051h/bAbvLget7jsQViQOQoI7s1P4J6kjc3fdKPL5/o0d8cHQm0jRdc/86IzweL4+4DT+3nEajj5np7C4HGqytyOiSd+uezA4rDjRW4JWfv8DptgaMTxyIGWkj0JOL6HTIb4V88ODBeOGFFwAAOp0ONpsNRqP3N+L9+/cjNzcXERERAIAhQ4Zg3759yM/P91dqRCQSdqcD71Xsxuqft7ljzx7agp+aKnFf/2uglas6dZzLE/rh2SE3419HvkKzrRVXpw7F5ORLEK0K77Jc++iSoZYpECFXw2hv89gmk0gRyefjz2t/40nctfdf7p9fP74TX9UcxD9G/JGL6HRA4nK5XP5+k7feegvFxcWoq6uDUqmE0WhEYmIili5diq+//holJSVYunQpAODZZ59FRkYGZs6c6fNYFosFpaWl/k6ZiIKAMjkKC0rXweJjrfKX8/4AZ3VTp48lk8mg0mnhkgFOowUWi6UrUwUAqFQqHNa24KmyzR7xW9PHIl+ajraW1i5/z+4gLFqHp2sKcaDplNe2pwfOQUSNTYCsgs/AgQOhUnl/efX7yIvPP/8cb7/9NtauXYvdu3cjMzMTvXv3xurVq7Fy5UoMHz7c4/Uul6vTE9wPHTrUHykHraKiIrY5BLDNvyhvrvJZxAFAopYH5f+nDJsF6ZEJeO/kHlicNsxIG4H+uh6IUnneLgzF8wz4bnddWzOqjzf6fL0RNowT+f+niz3XHV3A+nVM/o4dO/DSSy9hzZo10Ol0mDRpEnr37g0AmDBhAsrLyxEfHw+DweDeR6/XIyGBA0KICIhWhiNJHeUVD5MpEaeKCHxCnRCuUGFITC8su2Q2Vgyei8vis72KOHmKVITh8vhsn9syOUCwQ34r5EajEcuXL8fq1asRHR0NAFiwYAFOnTrTdbJnzx706dMHeXl5KCsrg9FohMlkQklJCYYNG+avtIhIROLVOjyaOxMKyS+jlyWQ4JGBM5EcFi1gZh2TSCTd/vnlrnJmEZ0xXl/ObkwfjZ5hsQJlJR5+61r/+OOP0dTUhMWLF7tj1113HRYvXgyVSgWtVounnnoKSqUSixYtwty5cyGVSrFw4UKo1V3zSAgRid/gmEysv+wu/NRcBbvLgRxdKnpq4yFjkexWMsIT8Mqld+CwsRp1bc3op0tFT20cJ9LpBL8V8tmzZ2P27Nle8RkzZnjFCgoKUFBQ4K9UiEjEZBIpekUkolcXTNxCwS01LIZT2l4AfqUlIiISMRZyIiIiEePEvxQy2hxWHG2pxfaag7A67RiXOABZ4UkIV3RuUhEiomDEQk4h45vaMjxY8h/3z68f34m/9L8W1/YYxjmdSXScLicsDjtUMjlHx4c4FnIKCdWtDVh+8AOv+As/fYThsVmcz5lE5VhLLT6uKkaR4SiGxGRiWuoQPm8dwljIKSQ0281osnlPj2lx2tFgMbGQk2gcb6nD7btXu3+fS5sq8MGpvVh96R0s5iGK/TEUErRyFcJkSq+4TCKFTsnFLEg8dtYe8vpS2mQzY3vNQYEyIqGxkFNISNZE466+V3rFb8kci1RNcM8QRvRr+xsr2omfDHAmFCzYtU4hQSaRYnLKJUjTxuLtE7tgc9oxq+dI5Eb3hFKmEDo9ok67LL4vvqw94BUf3c5c5dT9sZBTyNApNLg0rg+GxvSCC4CCI9VJhIbFZiFDG4/jpjp3LD0sDpfG9REwKxISCzmFHD5qRmKWGhaDF4b+DqVNFShtrED/yB7Ii+qJFE5tGrJYyImIRCYlLAYpYTGYnHyJ0KlQEOBgNyIiIhFjISciIhIxFnIiIiIRYyEnIiISMRZyIiIiEWMhJyIiEjEWciIiIhFjISciIhIxFnIiIiIRYyEnIiISMRZyIiIiEWMhJyIiEjEWciIiIhFjISciIhIxFnIiIiIR43rkREREXeSkSY+fjadhtLUhW5eMnto4v78nCzkREVEXKG+uwv/sWQOjvc0du6ffNAzURvv1fdm1TkREdJFabG144aePPYo4ALzw08dwRKn8+t4s5ERERBepydaKffVHveJOuGCwtfj1vVnIiYiILpJSKkecKsLnNo1M6df3ZiEnIiK6SPFqHe7qO8Ur3l/XA5F2gQt5c3OzV6yiosIvyRBRx+xOO06bG1FjboLT5RQ6HSL6r8vj++HpwfPQNyIZiepIzO91BZ4cNAfmuka/vu95R607nU4sXLgQ//73v+FyuQAAbW1tuOuuu/DBBx/4NTEi8lbV2oD/HN+J90/tg0Iqw40Zo3Ftj2FIUEcKnRpRyNMpwzAusT+GxGTA6nAgRqWFVCLFaRzz6/u2W8i3bNmCVatW4cSJE8jJyXHHpVIpLr/8cr8mRUTemm2teOrAJuw2/AwAsDhteOXnbWi0mvCn7KlQyvg0KVEw0CnCAEXg3q/dT/5VV12Fq666CqtWrcLdd98duIyIyKeq1gZ3Ef+19yr24Ib0UUjXxguQFXWFNocNVeYGmO1WxKrCkaSJEjolEpF2C/k777wDAEhOTnb/+9dmzZrlv6yIyEub0+Yz7nA5YXXaA5wNdZXatib868iXeL9iL5xwIVYZjifybsCQmEzIpTKh0yMRaLeQFxUVnXfHzhTy5557Drt374bNZsNtt92GESNGYMmSJTAajUhKSsIzzzwDpVKJwsJCrFmzBhaLBfPmzeOXBCIfElQ6RCo0aLKZPeK9tAmIVfp+7IWC37bTpXivYo/7Z4O1BYuL1mHD6LuRGZ4gYGYkFu0W8qeeeuqiDrx3714cOnQIb731FhobG3HNNddg1KhRmDlzJqZOnYoVK1Zg8+bNuPLKK7FixQps2rQJcrkcM2bMQEFBAbRa7UW9P1F3k6yJxpOX3Ih7v/83LP+9AtcpNHgsbxZiVOECZ0cXwmAx4j/Hv/GK21wOHGmpYSGnTulwdEx+fj4kEolHzOl04uuvvz7vfoMHD8YLL7wAANDpdLDZbPjuu+/wxBNPAAAmTJiADRs2IDU1Fbm5uYiIOHNFMWTIEOzbtw/5+fkX0h6ibksikWBobC9sGH03KlvrIZNI0SMsFqlhMUKnRkQC6rCQv/HGG+5/OxwO/Pjjj6iuru74wHI55PIzh9+4cSPy8/PxxRdfQK1WAwBiYmKg1+tRV1eHmJhf/hDFxsZCr9d3KvmOuv+7I7Y5NHTU5rMzN59Gs98fbQmUUDzP1YdPYFbqCPz9yGcecblEhgRXWLf9f9Jd23U+/mxzh4U8NTXV4+eePXvi0Ucf7fQbfP7553j77bexdu1a7Nixwx13uVyQSCRQKDzH6J+Nd8bQoUM7nUd3UFRUxDaHALY5NBQVFWHgwIFIaGtCpaURH5z6ZbDb43nXo39MBuTJ3W+wW6ie64tps8ViQWlpabvbOyzku3bt8vi5vr4e+/fv79Sb79ixAy+99BJeffVV6HQ6aLVamM1maDQa6PV6JCQkID4+HgaDwb2PXq/HyJEjO3V8IiKxS1BHYnHONMzOuAxmuwWxqgg+fka/SYeF/KWXXnL/WyKRIDIyEn/5y186PLDRaMTy5cuxbt06REefWYt1zJgx2LZtG6666ioUFhYiPz8feXl5KCsrg9FohFQqRUlJCR5//PELbxERkcioZQr04sA2ukAdFvL169d7xQoLCzs88Mcff4ympiYsXrzYHVu+fDkeeOABrF27FpmZmZg6dSrkcjkWLVqEuXPnQiqVYuHChe776ERERHR+HRbyqqoqbNiwAQ0NDQAAq9WKPXv2YNKkSefdb/bs2Zg9e7ZX3NcXg4KCAhQUFHQ2ZyLyA4fLCb3FCCkkiFNFdHqsChEJq8PVz5YsWYLIyEj88MMP6NevH2pra7FixYpA5EZEAVJtbsA/Dxdi7jcrcfO3f8eG4ztQ1+a98iERBZ8OC7lMJsMdd9yBuLg4/O53v8OaNWvw2muvBSA1IgoEo82Mpw9sxrqj29FsM6Pe2oJVZZ/i9eM7YXVw6tdgVWNuwhFjDWramoROhQTWYde62WzGsWNnnlM9duwYUlNTUVtb6/fEiCgwqswN+EZf5hV/+8QuXJc2Aj21cQJkRe1pc1jxnf4wnj64GXqLEXGqCNyXczVGxfeFRqYUOj0SQIdX5HfeeSdKSkrwu9/9Dtdddx0uvfTSkHsGkKg7MzusPuN2lwMWh++FWkg4PxtPY0nx69BbjAAAvcWIB354A4ebO56oi7qnDq/Ix48f7/737t27YTabERkZ6dekiChwElQ6RMjVMNrbPOI9tXGIVXExlmDzUWWxz/iWyiLkRacHOBsKBh1ekX/++ed455134HK5sGjRIkyaNAnvv/9+AFIjokBI1kRj2SWzoZD8MouYVqbC47lcjCUYtZzzheuXuCXAmVCw6NSEMP/+97/x5ZdfQiqV4sMPP8SiRYswffr0AKRHRP4mkUgwIrY3Xh99NypaDZBKpEgLi+W98SA1LWUwtlaXeMWvSh0iQDYUDDos5FqtFuHh4dixYweuu+46JCYmQqVSdbQbEYmIXCpDRngCMji7WNDrp0vFrb3y8e+jX8MJF6SQYF7mGORE9hA6NRJIh4XcZrPhlVdewRdffIH77rsPZWVlaGvz3bVDRET+FaXS4vdZV2BK8iA0WFsQpdQiVRMDjZwj1kNVh/fIly1bhtraWixfvhxarRZ79+7FPffcE4jciIjIB41MiayIRAyLzULviCQW8RDX4RV5WloabrnlFqSlpaG4uBhOpxM5OTmByI2IgoDZYoVcJoNC3v2W1CTqDjo1RWt5eTkqKyvx5z//GeXl5Z1a/YyIxK2mwYj3vynF/7z4Hpa8sgVF5afQavH9zDkRCafDQn769GlMmDABW7duxbx58/DXv/4VJpMpELkRkUAaW8xY/uYX+H8bCvHjsWps//Eobnt+I3YfOil0akR0jg4LOQC4XC73+uHAmQFwRNR9naprwvYfj3rFn9m4HXVNLQJkRETt6bCQjxgxAkOGDEFERAT69u2L1atXc73wbsJssaGitgGn6hphtTuEToeCSJPJ7DNeXd+MNisXUiEKJh0Odrvvvvtw++23Q6VS4cMPP8TOnTtRXl4eiNzIj07WNmDl+zvx5Q8/Qy6TYfrogbh18jAkx+iETo2CQHyU7xnd+qUlIELDeSSIgkmHhbykpATvvvsuPvnkEzgcDixbtgyTJ08ORG7kJw1GMx7618c4eOLMKnY2uwMbt5fAYrXjgTlXQK1UCJwhCS01LhJ/uHIE/vXpHndMKZdhyewrEBWuETAzIjpXu4V8zZo1eO+992A2m3Httdfi3XffxaJFizBt2rRA5kd+UGVochfxX/to90HcOnkYMpJiBMiKgolWrcS8CUMwqn86vj1wHDERGlzaLx2ZyfzdIAo27Rby559/HtnZ2Xj88ccxYsQIAGfmZCbxa+9+uMPpgt3pDHA2FKyiwjUY2qcHhvbh1J9EwazdQv7VV19h06ZNWLp0KZxOJ2bMmMHR6t1EUnQEoiM0aDB6DmjKzUhCfCRXuyIiEpN2R63Hx8fj9ttvx9atW/Hkk0/ixIkTqKysxJ133ont27cHMkfqYsmxOjx7+9WICPtl0FJSdASWzpuESC2fSCAiEpMOB7sBZx5BGzFiBJYuXYoPP/wQf//7393PlJM4XZKVgjcemotqfTNkMglSYiORGB0hdFpERPQbdaqQnxUeHo4bb7wRN954o7/yoQCRSCRIjY1Eamyk0KkQEdFF6NTMbkRERBScWMiJiIhEjIWciIhIxH7TPXIi8s3lcqGusQUuAHGRWsik/I5MRIHBQk50kWobjdjy3SG8/sX3sDucmDUmD7PG5CE5lvPWE5H/sZBTUKlrbEFzaxs0KoUoFnCx2OxY++levLW9xB1bu3UvjlYbsOzWKxHeiQVGGoytqDe2QqVUIDmGjwAS0W/DQk5BwWZ3oOjwKSzb8Dmq65uhC1PhTzPGoE9scBe2akMz3t2x3yu+/cejqDQ0I7tHfLv7ulwulB4/jSfWf4aj1fXQqBSYP2UELs2K9WfKRNTN8EYeBYWj1Qbc/fdNqK5vBgA0t1qw7PXPUWd2CZzZ+Vls9nbnpzdbzj+l8cnaBtz54rs4Wl3vfv3fN3+Dg9XNXZ4nEXVfLOQUFHb/dBIOp3fR3rijFBabXYCMOicuUou0eO9JdSK1aiS0s6b3WUeq6n0W+w3bfkCDsbXLcjzLYrPjaLUBe346ibKKWhhb27r8PYgo8Ni1TkGhvavXVqsNziBekS1Wp8WyW6/EgpXvofW/bVDKZfjf+VOR0sFgN6vd9xcUi83u80vNxWgxW/DBtwfw4ns73D0I0y7Nwd3TL+/wCwcRBTcWcgoKI3PS8fJH33nFp4/MgUalFCCjzsvNTMYbD81FRV0jHA4X0hKi0DMhqsP9spJjIZEArnNq9rUjcxCrC+vSHI9WG/DsO56LHX20+xBGZKfh6lEDuvS9iCiw2LVOQSErJRb3XZ8PueyXX8kZoweiT2JwD3YDzsxb3zMhGqMHZGJsXi9kJsV06jnynonReGr+VKgVv3yfHj0wA/kD0yCRSLo0x12HTviMb/z6R7S2Wbv0vYgosHhFTkEhXKPCzDF5GJWTAX2zCZFaNVJjdfjpYKnQqfmNSiHH+EF90K9HAmqbWqBVK5EaG4nDPx0AkN6l7xWu9v0YnFatgFTatV8aiCiwWMgpaKgUcmQmxyAzOUboVNplsztwpNqAL4p/RkNLKyYM7oPsHvGIjriwrnC5TIqeidHomRjdxZl6Gp6dBrlMCrvDc7zBzROHQa1U+PW9ici//Nq1Xl5ejokTJ2LDhg0AgGXLluG6667DzTffjJtvvhlfffUVAKCwsBCzZ8/G9OnT8c477/gzJQowu8MBU5sVrnNvBIvUD0cqcfPyN7Dmk914d8d+LFj5HtZu3QuT2SJ0aueVmRyD/7v7OqTFRwEAorRqPH7zZORmJgmbGBFdNL9dkbe2tmLZsmUYNWqUR+zJJ59ETk6OO9bS0oIVK1Zg06ZNkMvlmDFjBgoKCqDVav2VGgWAw+nE4Uo93vzyBxyu1GNcXi9MGZ6Nngn+vfL0p3pjK576zxdeI8o3bPse0y7NQXZagkCZdUwhk2F4dhpevfcGUc2cR0Qd89sVuVKpxCuvvIKEhF/+uJlMJq/X7d+/H7m5uYiIiIBGo8GQIUOwb98+f6VFAXK4Uo/fPf0mNu86gEMna/CPLbuwYOV7qNQ3CZ3aBWsxW3C8psHnNkNz1z/37Q9xkVr0So5lESfqRvx2RS6XyyGXex7eZDJh5cqVMBqNSExMxNKlS1FXV4eYmF/uicbGxkKv13fqPYqKiro0ZzEQQ5vDtOFYv/Nn2OwOj3iVoRk/HK5AbcXR3/RseLC0WaOLQUqsDlUG75nXVFJXl+YZLG0OJLY5dIRiu/3Z5oAOdpszZw4yMzPRu3dvrF69GitXrsTw4cM9XuNyuTr96M3QoUP9kWbQKioqCpo21ze3orm1DWqVAknRno+INbe24XD1bp/7Vda3YNq0kZ1+n2BqMwAsueEK/PkfH3jErro0B33Sk6ELy+yS9wi2NgcC2xw6QrHdF9tmi8WC0tL2n+AJaCGfNGmS+98TJkzAY489hqlTp8JgMLjjer0eI0d2/g89BZbd4cQPP1fir298jpO1jYiJCMM9s8ZibG4v90pfWrUSl/VPx5Eqg9f+AzPEPbhqRL80vHb/HLzzdQkMRhOmX5aLQb1ToAtTC50aEYWogE4Is2DBApw6dQoAsGfPHvTp0wd5eXkoKyuD0WiEyWRCSUkJhg0bFsi06Dc4Wm3AglXv4WRtI4AzA8AeWfspDpyocb9GJpVixuhcxOk8ByyOzOmJPqlxgUy3y6mVCuT1SsYTv5uClQtmYNLQvoiP5BSnRCQcv12Rl5aWYsWKFaisrIRcLsfWrVsxb948LF68GCqVClqtFk899RSUSiUWLVqEuXPnQiqVYuHChVCreXUTrHb/dNLrWWQA2PB5EQZlpUD131nKMpJi8Oq9N+CHI5U4Wm3A4D490C8tHvHdZF5viUQCmYwTqRCR8PxWyAcOHIj169d7xadMmeIVKygoQEFBgb9SoS5kbPX9vLSx1eI1gC0tIQppnZhznIiILhznWqff5LIBvqcOnTkmN+gXNyEi6o5YyOk3yUqOxaIZl0P6qycLCob3w6U5XTs3OBERdQ7nWqffJCJMjTnjBmHMwEzUNZkQqdWgR5wOERy1TUQkCBZy+s3USgWyUuKQlSLuEehERN0Bu9aJiIhEjIWciIhIxFjIiYiIRIyFnIiISMRYyImIiESMhZyIiEjEWMiJiIhEjIWciIhIxFjIiYiIRIyFnIiISMRYyImIiESMhZyIiEjEWMiJiIhEjIWciIhIxFjIRcbucMBktsDlcv3mfdusdpitNj9kRRfCZnfA1GYVOg0iEjmuRy4SdocT0vA4LNvwOX6u0iM/LwtXDs9Gz4ToDvdtbDGj5GgVXt/2PewOJ+aMG4ShfXsgVqcNQOZ0LovNjrKKOmzYVoRKfTMKhmfjikG9kRoXKXRqRCRCLOQicbiyDnf+/QPY7A4AwKGTtdi86wBe/vOs8xYAu8OJD74txYubdrpjPxypwi0Th+LOq0dBrVT4PXfyVHrsNG5/YSPOdqocOlmDT/b+hOf/51okRIULmxwRiQ671kXAanNgfWGRu4ifVWVoxqGTNefdt9rQjJe3fOcVf/2L71FlaO7SPKljza1tWPXBTpx7Z+TQyVqcqKkXJikiEjUWchFos9pQXqn3ue3Y6fP/8W9ps6LNZveKO5wuNLdauiQ/6jyzxYbj7ZwzfVNrgLMhou6AhVwEwtRKjB6Q7nPbwPSk8+4bFa5GdITGK65WyhETEdYl+YmRy+VClb4JP1XUosrQdEGDBy+ELkyNYdlpPrelxukCkgMRdS8s5CIgl0kxfXQu4s4ZnDYypyd694g7777JMTo8ctNESCSe8Qdmjw/ZwmFqs2LLdwdx41Ov46b/PfPfJ3t/QmsARpBrVArcPnUkwjUqj/g1o/p3auAiEdG5ONhNJDKTYvDcH6fgRL0JR6vrMbh3KrJ7xCM+suPBUZcNyMDrD8zF7p9Owu5wYGROOjKTYyCThub3uPJTdXjs35+5f25uteCRtZ/iX/fNxqCsFL+9r8vlQpWhGRarDf9YdB1+qqhFdb0RI7LT0Ds1DlHh3j0nREQdYSEXEUuzHtMuHfqb91Mp5OjXMwH9eib4ISvx2fTNfp/xj7476LdC7nS6sK+8Ave/sgXG/45NGN0/Aw/PnYCkmNDsGSGirhGal2QU0qw2h++43Xe8K5zSN+LP//jAXcQB4JuDx/F/m7+FxcdgRCKizmIhDzF1TS3YW1aB978pxb7yCuibTEKnFHDTRw8EAKgVcqTE6qBWnOmYmnZpjt/es1LfhDard8HeurcsJM8BEXUddq2HkNP1zXjktU/x/eFKd2xkTk88dvNkJEZHCJhZYOX0TMCLC65FRV0jKvXNSI3ToWdCNPql+e/WQ3vjEeRyKaTnjkTsRsxWG45X1+P7I5VQymS4JCsFmUkxUMhlQqdG1G2wkIeQfeWnPIo4AHx36CR+PFqNSUNDp5Cb2qx4afMulJ2qdcf6pyfib7ddBZ1W7Zf3TIuPQpxOC32z59X3TeMHI76bzuZmdzix7fvDeHTdVndMLpXixYXXYlT/DOESI+pm2LUeIlwuF7buK/O57YvinwOcjbD2lFV4FHEAOHiiBt//7Pklx2Kzw+7omvvmybE6/N/dMzAgPRHAmUcK54wbhOvHXgK5rHt+DKsMTfjf/2zziNmdTjyxvhA1DUaBsiLqfnhFHiIkEgl6JcfgmwPHvbZlJscEPiEBfdnOF5evSo5g2qU5qGkwuscRxOrCMOeKwejXMwGai5yXvk+PePz97uugbzZBIZMiKUYHZTfuYjY0m3yOC6htbEGjqS2kbucQ+RMLeQiZdml/vPVVicfobLVCjvGDeguYVeD165mAHaXHvOI5PRPQ2GLG029/hS9/+KXYF35/GC8uuBZjcntd9HtHatWI9FP3fbAJ16ghkcBrXnm1Uo4wLtZD1GW6Z58e+ZSVEotX77sBlw/MQKwuDPl5vfDqfTcgKyVW6NQCavLQvghTeRaScLUS4y7Jwqm6Jo8iftbf3v4KhmaOLv8tesTpMGN0rlf89mkjkRzLZ+eJugqvyEOITCrFgPQkrPjjNLSYrQjXqKBRdf2VUZvVBn2TCRKJBIlR4ZAHWfdxr+RYrL1/Nt74ohg/Hq3G4KwUzLliMHolx2Knjyt1ADilb4LZYgtwpuKmUSlxx7SRyM1MxptfFkOpkOHmiUMxtE+PbjsugEgILOQhSKNSQqNS+uXYFXWN+Mfmb1H4fTlkUimuz8/D3PFDkRQTPPdDJRIJ+qTG46EbJ6DVYoVWrYRcdubLRnyk1uc+/dISvOZHp47FR4Xj2ssGYPyg3pBKJdCq/fN7RxTK+LWYukxjSyse+den+HRfGRxOF6x2B17fVozVH+8KytnLFHIZIrUadxEHgNS4SNwycajX6+6/YRznQr8IEWEqFnEiP/FrIS8vL8fEiROxYcMGAIDBYMD8+fNxww03YNGiRbBaz6w2VVhYiNmzZ2P69Ol45513/JkS+VGloRn7j1d7xT/cdRDV9c0CZPTbhWtUuHXKcLz851mYO2EIFs24HOv/ciPyeiULnRoRkU9+61pvbW3FsmXLMGrUKHfs6aefxsyZMzF16lSsWLECmzdvxpVXXokVK1Zg06ZNkMvlmDFjBgoKCqDV+u7ipODV3hzmDqcLdrszwNlcuKhwDYZnp2F4O+uGExEFE79dkSuVSrzyyitISPhl2ss9e/Zg/PjxAIAJEyZg586d2L9/P3JzcxEREQGNRoMhQ4Zg3759/kqL/CgpOsJn93NOzwTER/GLGRGRP/jtilwul0Mu9zy8yWSCWn3mGdqYmBjo9XrU1dUhJuaXCUliY2Oh1+s79R5FRUVdl7BIBHObFQoFnvzdJPzl1U/R0nbmtkl8pBZLZo3B0fKf4HRe2FV5MLfZX9jm0BCKbQZCs93+bHNAR60rFL886uRyuSCRSDxiv453xtChv31tbjErKioK+ja7XC688dBcVBmaIZNKkRqn++962z0v6HhiaHNXY5tDQyi2GQjNdl9smy0WC0pLS9vdHtBCrtVqYTabodFooNfrkZCQgPj4eBgMBvdr9Ho9Ro4cGci0qAtJJBL0iI9Cj/gooVMhIgoJAX38bMyYMdi27cwiCoWFhcjPz0deXh7KyspgNBphMplQUlKCYcOGBTItIiIi0fLbFXlpaSlWrFiByspKyOVybN26Fc888wzuu+8+rF27FpmZmZg6dSrkcjkWLVqEuXPnQiqVYuHChe776ERERHR+fivkAwcOxPr1673ivmIFBQUoKCjwVypERETdFmd2IyIiEjEWciIiIhFjISciIhIxrn5GHaprbEG9sRVqpQIpsToogmxZUiKiUMZCTu1yOJ0o/rkSj63biup6IxRyGeaOH4ybxg9BXDvLfRIRUWCxa53adbKmAXet2oTqeiMAwGZ34LXP9mH7j0cEzoyIiM5iIRdQbWMLig6fwncHT+BETQPsjuBaIexwpR5Wu/eKZv8uLEKDsVWAjIiI6FzsWhfIsep6LHrpfVTqmwAAaoUcT99+FUb1T4dMGhzfr5wul8+43eFsdxsREQVWcFSMEGNqs+L5d7e7izgAtNnsWLJ6Cyrrms6zZ2D1TomDTOq9gM3scZcgVhf898hP1jRgW/FhfLjrAH46WQuzxSp0SkREXY5X5AIwNJuw88Bxr3ibzY7q+mb0TIwOfFI+pCdE4W+3X41H121Fi9kCACgY0Q9ThmULnFnHDp2swR3Pv+NeThUAHr5pAq4a2R8qBX/tiaj74F80AchlUmhUCpgtNq9tyiAqMgqFHPl5vfCfh+airskErVqBlNhIaNVKoVM7r+bWNjz91pceRRwAlr/5BQb3TkWv5FiBMiMi6nrsWhdAQlQEbp7ovTZtRmI0esRFCpBR+yQSCVLjIjEoKwV9UuODvogDQFOLGSVHq73iDqcLpxuMAmREROQ/wXP5F0LkMilmXp4LqUSCfxfuQ5vVjnGXZOGu6aMRHxUudHqip1TIERWuQWOL2WtbuFolQEZERP7DQi6Q+KhwzC8YgatG9ofT6USsTguNSiF0Wt1CYnQEFl5zGZ58Y5tHfFBWCtLig6vHg4joYrGQC0gmlSIlVid0Gt3ShMF9EBGmxpqPv0OL2YprRvXH1aMGIDoiTOjUiIi6FAs5dUtR4RpMHtoXI7LT4HA4ER0RBqmPR+mIiMSOhVykmkxm1DWZoJTJkBSjg1LBhUx8iQrXCJ0CEZFfsZCLUFlFLZ5YX4ifKmrdA+dunTIcidERQqdGREQBxsfPRKbK0IQFq97DTxW1AM5Ml/rW9hK8u2M/HM7gmqudiIj8j4VcZCrqmtBg9H6s6vUvvkddY4sAGRERkZBYyEXG0c4KaTa7gwuZEBGFIBZykUmNi4Ra6T204aqR/RGn42QyREShhoVcZNLio/DC/1wLXdgvM5QNykrBH6YM58h1IqIQxFHrIiOVSjA8Ow1vPDQXNfVGqJQKpMTpEKXlY1ZERKGIhVyEJBIJUmIjkRLL6UaJiEIdu9aJiIhEjIWciIhIxFjIiYiIRIyFnIiISMRYyImIiESMo9YFZLbYUF3fDJvdgYSocK6VTUREvxkLuUCq65ux6v2d2LqvDC4XkJUSi4dvmoiocDWSY3RQKXhqiIioY+xaF4DV7sBrn+7Fp3vPFHEAOFJlwJLVW/DVD0fwysffod7YKmySREQkCizkAqhtNOL9XQe84vpmEzQqBf716V4U/1wpQGZERCQ2LOQCcLkAZztrh7v+e4n+ny+L0Wa1eWzT6GJQdPgUvig+jIMnamBsbfN7rkREFNx4I1YA8ZFaTBmajY/3/uQR14WpYLU7AAAyqRQSicS9rVLfhCff+RaHTta6Y/MmDMEfrhyBqHDOs05EFKp4RS4AtVKBO68ehaF9erhj8ZFa3Hv9OLzxRTEA4Kbxgz0GvH3+fblHEQeADdu+x7HT9YFJmoiIglJAr8hLS0uxYMECpKenAwD69u2LBQsWYMmSJTAajUhKSsIzzzwDpVIZyLQE0SM+Cs/ecTUq9U0wGFtxuFKPlZt2wGS24k/XjcElvVLcr21ubcNHuw/5PM73hysxuHdqoNImIqIgE9BC3traiilTpuDhhx92x/7yl79g5syZmDp1KlasWIHNmzdj1qxZgUxLMDqtGjqtGgDQNzUOY3N7QaNSICEqHHLZL50lSrkMCdER+LnK4HWMhChtwPIlIqLgE9CudZPJ5BXbs2cPxo8fDwCYMGECdu7cGciUgsKJmga8/+0BPLH+M6z7bC+OnTa4B70BZ7rifz95uNd+4RoVcnslBzJVIiIKMgG/Ii8qKsLvf/972Gw2LFy4ECaTCWr1mavSmJgY6PX6QKYkuFN1jbjzxXdQ09ACACg9fhoffncQ6+6fgz494t2v65+egGdvn4p/bNmNirpGjMjuiQXXXIaMxBihUheUsdUCs9UKXZgaaqVC6HSIiAQjcf360s/Pjhw5gp9//hlTpkzBiRMncOutt6KlpQV79+51b3/88cexfv36do9hsVhQWlraJfnI5XIowiIAiQwShxVmkxEB/N8BmUyGY0YJlr3xhde2GaMHYObQnjC3mjxer9SeyRd2K9paWwKabzBQqjUwOlV4+ZM9qKhtxPDsNMy9Ig8uUwPsdrvQ6RER+c3AgQOhUqm84gG9Is/KykJWVhYAID09HXFxcaitrYXZbIZGo4Fer0dCQkKnjzd06NALzqW1zYq95RV4bv12VOqbMLRvDyy+biz69ez8+3eFr9792me87JQeGbPGIUz9y8C/oqIi9O+TFajUgkJRUZHHeS45UoW7n33LPSNe4feHsfunk1i35EakJ0YLlGXXOrfNoYBtDh2h2O6LbXNHF7ABvUe+adMmvPbaawAAg8EAg8GAWbNmYdu2bQCAwsJC5OfnBySXAydOY/E/NqOirhFOlwt7yypw2/MbA/441yVZKT7jlw/IYJfxOSw2O177bC/O7YRobrXgx6NVwiRFRCSwgF6RT5w4Effffz8+++wz2O12PPbYY8jJycG9996LtWvXIjMzE1OnTvV7HmarDf/6ZK9X3NRmxf6j1chMCtx95/7pibikVzJKjla7Y/GRWlw5oh+kUsl59gw9VpsdVYZmn9uq640BzoaIKDgEtJBHRETgn//8p1f8fPfE/cFmc8Bg9B5BDwCGAC9WkhgdgeV/nIaDJ2rw/c+V6Jsah0G9U5EWHxXQPMQgXKPC5GF9cbjSe0DkED5LT0QhKiSnaNVp1Zg2sj9efG+H1zYhCkJidAQSoyNwxaDeAX9vMZFIJJgyNBuf7SvD4cpfnqmfMXogeqXECpgZEZFwQrKQA8CkIX2wveQIfjjyy73VPxZcGtBudfrtesRHYeXCGTh+uh51TSakJ0QjLSGK880TUcgK2UKeEhuJv91+FSpqG9HU2oak6AikxkUiXOM9tJ+Cy9keDCIiCuFCDgCxOi1idZzilIiIxIurnxEREYkYCzkREZGIsZATERGJGAs5ERGRiLGQExERiRgLORERkYixkBMREYkYCzkREZGIiW5CGNev1rC0WCwCZiIMtjk0sM2hIRTbDIRmuy+mzVarFYBn/fs1iau9LUHKaDSivLxc6DSIiIgCqm/fvoiI8J6eWnSF3Ol0wmQyQaFQQCLhet1ERNS9uVwu2Gw2aLVaSKXed8RFV8iJiIjoFxzsRkREJGIs5ERERCLGQk5ERCRiLOREREQixkJOREQkYkE/IcyLL76IXbt2wWq14oknnkBubq57W3FxMVasWAGLxYJJkyZhwYIFAmbadZ577jns3r0bNpsNt912GwoKCtzbpk+f7vEc4TPPPIPExEQh0uwypaWlWLBgAdLT0wGceVZy6dKl7u3d9Txv3LgRmzdvdv9cWlqK4uJi98/d6VyXl5djwYIFuPXWWzFv3jwYDAYsWbIERqMRSUlJeOaZZ6BUKj32Od9nXwzObXNNTQ0efPBBWK1WSKVS/O1vf/M4nx19DsTg3DYvW7YMxcXF0Gq1AID58+dj3LhxHvuI/TwD3u1etGgRGhoaAACNjY0YNGgQli1b5n59l59rVxDbtWuXa/78+S6Xy+UqKytz3XTTTR7bJ0+e7KqqqnI5HA7XrFmzXCdOnBAizS61Z88e1x//+EeXy+VyNTQ0uMaMGeOx/dprrxUgK//avXu3669//Wu727vjeT7X3r17XY8++qhHrLuca5PJ5Jo3b57rkUceca1fv97lcrlcS5YscX300Ucul8vlWr58uWvjxo0e+3T02Q92vtr8wAMPuNu8YcMG1/Llyz326ehzEOzaa/PBgwfb3Ufs59nl8t3uX3vwwQddxcXFHrGuPtdB3bW+e/duTJgwAcCZbyy1tbUwm80AgIqKCkRGRiI5ORlSqRTjxo3Dzp07hUy3SwwePBgvvPACAECn08Fms8HpdLq3m0wmgTLzn/O1qbue53OtWrXKq6ehu5xrpVKJV155BQkJCe7Ynj17MH78eADAhAkTvM7p+T77YuCrzY888ggmT54MAIiOjkZLS4vHPmI/377a3FGbxH6eAd/tPuvYsWNoaGjAoEGDPOJdfa6DupDX1dUhJibG/XNMTAz0ej0AoLa21mNbbGyse5uYyeVydzfUxo0bkZ+f7zGTT2NjIxYvXowbb7wRzz//fLtz74pJa2srioqK8Pvf/x7z5s3Drl273Nu663n+tR9//BGJiYle3ebd5VzL5XKo1WqPmMlkcsd+/bk+63yffTHw1WatVgu5XA6Hw4E33ngD06ZN89h+vs+BGLR3nleuXIl58+bh3nvvRWNjo8d2sZ9nwHe7z1q3bh1uueUWr3hXn+ugvkeuUCg8fna5XO5pWc+3rTv4/PPP8fbbb2Pt2rUe8cWLF2Pq1KnQarW4++67sXXrVlx55ZUCZdk1+vXrhzvuuANTpkzBiRMncOutt2Lr1q1QKpXd/jwDwNtvv42pU6d6xbvjuT7r1+fV1zntrufd4XBgyZIlGDFiBEaOHOmx7XyfA7GaM2cOMjMz0bt3b6xevRorV67Eo48+6t7eXc8zAJjNZuzatcujvWd19bkO6ivy+Ph4GAwG98/19fWIi4sDACQkJHhs0+v1Prs2xGjHjh146aWXsGbNGuh0Oo9tN910E6KioqBQKDB27NhusYBMVlYWpkyZAgBIT09HXFwcampqAHTv83zW3r17MWrUKK94dzzXZ2m1WncXqq9zer7Pvpg9+OCDSE1NxaJFi7y2ne9zIFaTJk1C7969AZy5hXLu73B3Pc8A8P3332PIkCE+50bv6nMd1IV87Nix2LZtGwDgwIEDSEtLc3dhJCUlwW63o6qqCg6HA19++SXGjh0rZLpdwmg0Yvny5Vi9ejWio6M9tjU2NmL+/Pmw2WwAzhSAPn36CJFml9q0aRNee+01AIDBYIDBYHB3M3fX83zW6dOnoVQqoVKpPOLd9VyfNWbMGPdnu7CwEPn5+R7bz/fZF6vNmzdDKpXinnvu8bn9fJ8DsVqwYAFOnToF4My4iHN/h7vjeT6rpKQEffv29bmtq891UHetDxw4EP369cOMGTMgk8nw5JNP4r333kNERAQmTZqEhx56CAsWLIBEIsE111yD5ORkoVO+aB9//DGampqwePFid+zSSy9FdnY2Jk2ahDFjxmDOnDlQKBQYMGBAt+hqnThxIu6//3589tlnsNvteOyxx7Bly5ZufZ7Pqq2t9bga/fXvd3c516WlpVixYgUqKyshl8uxdetWPPPMM7jvvvuwdu1aZGZmum8tLF68GE899ZTPz76Y+GqzwWCASqXCzTffDODMVdnjjz/ubrOvz4GYutV9tXnevHlYvHgxVCoVtFotnnrqKQDd5zwDvtu9atUq1NXVYdiwYR6v9de55upnREREIhbUXetERER0fizkREREIsZCTkREJGIs5ERERCLGQk5ERCRiLOREIeTUqVPIzs7GW2+95REvKipCdnY2du/efUHHve+++/Dee+91RYpE9BuxkBOFmIyMDI/lU4Ezk5VkZmZ2av9fL+JzIS52fyLyFNQTwhBR10tISIDNZkNFRQXS0tJgs9mwb98+5OXlobKyEmPHjsXXX38N4MyqbHa7HYsXL8bQoUMxe/ZstLW1YenSpXjooYdw+PBh9OzZE01NTe7jb9myBa+//rp7AaAnn3wSsbGxHvv7mn+aiC4Mr8iJQtDVV1+NDz74AACwfft2XHbZZT7nhP41k8mEyy67DI8++ii++eYbHDt2DBs3bsSyZcvcc2jX1NRg9erVWLduHdavX48xY8bgpZde8tqfiLoOCzlRCJo6dSo+/vhjAGe61a+55poO93G5XBgyZAgAoLy8HIMGDYJEIoFWq8Ull1wCANi/fz/q6uowf/583Hzzzfjwww9RV1fntT8RdR12rROFoOjoaKSnp+Prr7/GiRMnkJubCwBeS0ja7XaPn8/OB33uzM6/vu+dl5eHl19+2ef7imnucCKx4BU5UYi69tpr8fTTT2Py5MnumE6ng9FohNVqBXBmQQhfevfujZKSErhcLrS0tOCHH34AAOTm5uLHH3+EXq8HAHz22WfYunWrfxtCFOJ4RU4UosaPH49HH33Uo1s9PDwcM2fOxPz585GVlYWkpCSvq28AuPzyy7F582Zcf/31SElJweDBg+F0OpGYmIiHH34Yd9xxB9RqNZRKJZYvXx7IZhGFHK5+RkREJGLsWiciIhIxFnIiIiIRYyEnIiISMRZyIiIiEWMhJyIiEjEWciIiIhFjISciIhKx/w8OecaUT0I6dwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df.Murder, y=df.Assault, hue=clusters, palette=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "centroids = k_means.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Murder', ylabel='Assault'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/1R3HnwrXun9+v2wnfqgpxT+vuAspmpjgFUYkMhxaJ6KAa7SZ8WppgVd7ZXMdjjdVB6EiIvHiGTkRBZzV5UBVc73PbXV2U2CLoU7F4XKgwlKPJkczYhQRSNXEdvqn9THIiSjgohURGJeUg03lhV7bMrWhvwCN1W6G3WmFILggkUihkKmgUkQEu6ywV281YeOpXXj9yNewuRzQKTR4pO9NGJPYByq5Itjl+Q2DnIgCTimTIz9rLH7UH0a1tdHd/vvu49ElIi6IlbXOYmtCvbkK+qYymKwNcAlOSCUyaFXRSIjMQExEMjTKyGCXGbaK60/g74fPXbJptFvwePHbeGPUPOREpwexMv9ikBNRUGRGJmLVFffgWFMVqprq0Cc+AxkR8YhSaIJdmk9NzXU4pi+GsbnWo90lOGFsroWxuRZR6jhkJQxCpDomOEWGsWanHW8f/96rXYCAnfrDDHIiIn9Ii4hFWkQsisqL0Ld7l2CX0yKLrclniJ/P5nTgRMNJVFsa0SdlBJSKCCSqdQGsMtwJcLWwxSW0tKVzYJATUdC5XP7/h/ZEUw1+aSxHo92CfjEZ6BaRAK1C3a59682nWw1xq9OBgw2ncMx0ZsZ9k1SF/RYzftt9HNJD+FJBZ6KWKTGr2ygU1R3z2jYyoVcQKgocBjkRdXoHGk5h3q7VMDtt7raFvafipq4joJEpW93XajdD33Sq1dc02s3uEAeAUw3HMCBhEPbVnWCQB9Dg2EzMzb4aa49+B4fghFauwqK+NyIrKvQnUF4OBjkRdWqNdjNeOLjZI8QB4JVDn2FEQk9kRyW3ur/daYXJ2tDqa/RWo/vPiSodEuUKZGhisPn0fsSrotBblwadkrPa/S1WFYnfdb8aU1IHweiwIFYZiTRNDKSSzr1kSufuHRGFvUabBfvqT3q1uyCgqrn1gAYAQXDBJThbfU3Ef8/qtXI1UjQx2GP4FTXWBnxTfQDz9/wLm04VwubkWvKBoJTJkRmZiNyYrugSEdfpQxzw8xn5iy++iJ07d8Jut+Ouu+7Cnj17UFRUBK1WCwCYO3cuxo8fj4KCAqxevRpWqxX5+fmYOXOmP8siIhFqsJkhQECMUntR+yllcsSromA476z5rMh2XCOXSKSQSmSthnmcKgoKiRzdtAk41FgJuVQOAWcWvgGAVw9/gauSeiMzsnMP8XYEk6MZVqcdOkUE5FJZsMsRBb8F+e7du3Hw4EG88847qK+vxw033IDRo0fj2WefRU5Ojvt1TU1NWLZsGTZu3Ai5XI7p06cjLy/PHfZEFN5qrU3YoS/FumPb4BCcuKXrSFyd3A9J6uh27Z+kjsb9va7F0/s2eLQPjs1Chqbt69cKmQpaVXSrk92iFGqMSeqDRrsZNpcdmdHdUNnchGanHQDgEJyos5mR2a6Kw5PFacP++jK89utXON1chwnJ/TE9YwS68iE6bfLbmMPgwYPx8ssvAwB0Oh3sdjuMRu9vxPv27UNubi6ioqKg0WgwZMgQ7Nmzx19lEZGIOFxOfFC2E8/sew9Hm6pw0qTHCwc34++lX8DksLb7fa5K6oMXhtyOftFn7lWf13MynsqdiVhV24u3qBQRSIjMaPN10coIxCojoZDIkRGdjR2152ZPyyRSRIfo/fGhYl/9Sczb/TqK6o6h0lKPN49vxx/3vIHTlvpglxbyJIIgCP7+kHfeeQdFRUWoqamBUqmE0WhEcnIyFi9ejO+++w7FxcVYvHgxAOCFF15AZmYmZsyY4fO9rFYrSkpK/F0yEYUAZWoM5pWsdQ9Rn++fA34PV2Xb17jPkslkUOm0EGSAy2iF1dq+LwISiQQp6Qk4WlOEWmNVq6+VyqRQRifAok7E8kNb3HXf2W0sxkm7obnJ3O56w0lErA7PVxVgf4P33QHP95+NqCp7EKoKPf3794dKpfJq9/us9S+//BLvvvsu1qxZg507dyIrKws9evTAqlWrsGLFCgwfPtzj9YIgtHuB+6FDh/qj5JBVWFjIPocB9vmc0sYKnyEOABK1PKD/n3TRUW0uChOljkNGfC4qrGaMS+oLq8uO6Rkj0FfXBTEqz8uF4XicAd/9rmluROXxep+vN8KO8SL//3S5x7qtE1i/Tufbtm0bXn31VaxevRo6nQ6TJk1Cjx49AAATJ05EaWkpEhMTYTAY3Pvo9XokJXFCCBEBscpIpPhY7jRCpkSCKiqgtUSqY9EjaRiyEgYiSh0HqeTMRCypRPbfpVkHokfSMMRo4tE3JgNLBs7CssFzcGVib68QJ0/Righcldjb57YsThBsk9+C3Gg0YunSpVi1ahViY2MBAPPmzcOpU2eGTnbt2oWePXtiwIABOHToEIxGI0wmE4qLizFs2DB/lUVEIpKo1uHJ3BlQSM7NXpZAgif6z0BqRGzA69EoI5Eak41eySPQP30sctPHoX/6WPRKGYHUmGyPB6ZIJJKwuPWpI5x5iM4Yry9nt3Ybja4R8UGqSjz8NrT+6aefoqGhAQsXLnS33XzzzVi4cCFUKhW0Wi2ee+45KJVKLFiwAHPmzIFUKsX8+fOhVrdv2UQi6vwGx2Vh3ZX34ZfGCjgEJ3J06eiqTYQsiCGpUkTwsaUdLDMyCa9dcQ8OGytR09yIPrp0dNUmcCGddvBbkM+aNQuzZs3yap8+fbpXW15eHvLy8vxVChGJmEwiRfeoZHRvYwU2Er/0iDguaXsJOO5DREQkYgxyIiIiEeNDUyhsNDttONpUjW+rDsDmcmB8cj9kR6YgUuF9XyYRkVgwyClsfF99CI8W/8f985vHt+PPfW/EjV2GcU1nEh2X4ILV6YBKJufs+DDHIKewUGmuw9IDH3m1v/zLJxgen831nElUjjVV49OKIhQajmJIXBampQ/h/dZhjEFOYaHRYUGD3Xt5TKvLgTqriUFOonG8qQZ371zl/vtc0lCGj07txqor7mGYhymOx1BY0MpV7mdGn08mkUKn5MMsSDy2Vx/0+lLaYLfg26oDQaqIgo1BTmEhVROL+3pd69V+R9ZYpGsCv0IY0aXaV1/WQvvJAFdCoYJD6xQWZBIpJqcNRIY2Hu+e2AG7y4GZXUciN7YrlDJFsMsjarcrE3vh6+r9Xu2jW1irnDo/BjmFDZ1CgysSemJoXHcIABScqU4iNCw+G5naRBw31bjbukUk4IqEnkGsioKJQU5hh7eakZilR8Th5aG/RUlDGUrqy9A3ugsGxHRFGpc2DVsMciIikUmLiENaRBwmpw4MdikUAjjZjYiISMQY5ERERCLGICciIhIxBjkREZGIMciJiIhEjEFOREQkYgxyIiIiEWOQExERiRiDnIiISMQY5ERERCLGICciIhIxBjkREZGIMciJiIhEjEFOREQkYgxyIiIiEePzyImIiDrISZMevxpPw2hvRm9dKrpqE/z+mQxyIiKiDlDaWIH/2bUaRkezu+2BPtPQXxvr18/l0DoREdFlarI34+VfPvUIcQB4+ZdP4YxR+fWzGeRERESXqcFuxp7ao17tLggw2Jv8+tkMciIiosuklMqRoIryuU0jU/r1sxnkRERElylRrcN9vaZ4tffVdUG0I8hB3tjY6NVWVlbml2KIqG0OlwOnLfWosjTAJbiCXQ4R/ddViX3w/OB89IpKRbI6GnO7X41nB82Gpaber5/b6qx1l8uF+fPn49///jcEQQAANDc347777sNHH33k18KIyFuFuQ7/Ob4dH57aA4VUhlszR+PGLsOQpI4OdmlEYU+njMD45L4YEpcJm9OJOJUWUokUp3HMr5/bYpBv3rwZK1euxIkTJ5CTk+Nul0qluOqqq/xaFBF5a7Sb8dz+jdhp+BUAYHXZ8dqvW1FvM+GPvadCKePdpEShQKeIABSB+7wWf/Ovu+46XHfddVi5ciXuv//+wFVERD5VmOvcIX6+D8p24TfdRqGbNjEIVVFHaHbaUWGpg8VhQ7wqEimamGCXRCLSYpC/9957AIDU1FT3n883c+ZM/1VFRF6aXXaf7U7BBZvLEeBqqKNUNzfgX0e+xodlu+GCgHhlJJ4Z8BsMicuCXCoLdnkkAi0GeWFhYas7tifIX3zxRezcuRN2ux133XUXRowYgUWLFsFoNCIlJQXLly+HUqlEQUEBVq9eDavVivz8fH5JIPIhSaVDtEKDBrvFo727NgnxSt+3vVDo23q6BB+U7XL/bLA1YWHhWqwffT+yIpOCWBmJRYtB/txzz13WG+/evRsHDx7EO++8g/r6etxwww0YNWoUZsyYgalTp2LZsmXYtGkTrr32WixbtgwbN26EXC7H9OnTkZeXB61We1mfT9TZpGpi8ezAW/Hg3n/D+t8zcJ1Cg6cGzEScKjLI1dGlMFiN+M/x773a7YITR5qqGOTULm3Ojhk3bhwkEolHm8vlwnfffdfqfoMHD8bLL78MANDpdLDb7fjxxx/xzDPPAAAmTpyI9evXIz09Hbm5uYiKOnNGMWTIEOzZswfjxo27lP4QdVoSiQRD47tj/ej7UW6uhUwiRZeIeKRHxAW7NCIKojaD/K233nL/2el04ueff0ZlZWXbbyyXQy4/8/YbNmzAuHHj8NVXX0GtVgMA4uLioNfrUVNTg7i4c/8QxcfHQ6/Xt6v4tob/OyP2OTy01eezKzefRqPfb20JlHA8zpWHT2Bm+gj87cgXHu1yiQxJQkSn/X/SWfvVGn/2uc0gT09P9/i5a9euePLJJ9v9AV9++SXeffddrFmzBtu2bXO3C4IAiUQChcJzjv7Z9vYYOnRou+voDAoLC9nnMMA+h4fCwkL0798fSc0NKLfW46NT5ya7PT3gFvSNy4Q8tfNNdgvXY305fbZarSgpKWlxe5tBvmPHDo+fa2trsW/fvnZ9+LZt2/Dqq6/i9ddfh06ng1arhcVigUajgV6vR1JSEhITE2EwGNz76PV6jBw5sl3vT0QkdknqaCzMmYZZmVfC4rAiXhXF28/oorQZ5K+++qr7zxKJBNHR0fjzn//c5hsbjUYsXboUa9euRWzsmWexjhkzBlu3bsV1112HgoICjBs3DgMGDMChQ4dgNBohlUpRXFyMp59++tJ7REQkMmqZAt05sY0uUZtBvm7dOq+2goKCNt/4008/RUNDAxYuXOhuW7p0KR555BGsWbMGWVlZmDp1KuRyORYsWIA5c+ZAKpVi/vz57uvoRERE1Lo2g7yiogLr169HXV0dAMBms2HXrl2YNGlSq/vNmjULs2bN8mr39cUgLy8PeXl57a2ZiPzAKbigtxohhQQJqqh2z1UhouBq8+lnixYtQnR0NH766Sf06dMH1dXVWLZsWSBqI6IAqbTU4R+HCzDn+xW4/Ye/Yf3xbahp9n7yIRGFnjaDXCaT4Z577kFCQgJ++9vfYvXq1XjjjTcCUBoRBYLRbsHz+zdh7dFv0Wi3oNbWhJWHPsebx7fD5uTSr6GqytKAI8YqVDU3BLsUCrI2h9YtFguOHTtzn+qxY8eQnp6O6upqvxdGRIFRYanD9/pDXu3vntiBmzNGoKs2IQhVUUuanTb8qD+M5w9sgt5qRIIqCg/lXI9Rib2gkSmDXR4FQZtn5Pfeey+Ki4vx29/+FjfffDOuuOKKsLsHkKgzszhtPtsdghNWp+8HtVDw/Go8jUVFb0JvNQIA9FYjHvnpLRxubHuhLuqc2jwjnzBhgvvPO3fuhMViQXR0tF+LIqLASVLpECVXw+ho9mjvqk1AvIoPYwk1n5QX+WzfXF6IAbHdAlwNhYI2z8i//PJLvPfeexAEAQsWLMCkSZPw4YcfBqA0IgqEVE0slgycBYXk3CpiWpkKT+fyYSyhqOmCL1zn2q0BroRCRbsWhPn3v/+Nr7/+GlKpFB9//DEWLFiAm266KQDlEZG/SSQSjIjvgTdH348yswFSiRQZEfG8Nh6ipqUNxpbKYq/269KHBKEaCgVtBrlWq0VkZCS2bduGm2++GcnJyVCpVG3tRkQiIpfKkBmZhEyuLhby+ujScWf3cfj30e/gggApJMjPGoOc6C7BLo2CpM0gt9vteO211/DVV1/hoYcewqFDh9Dc7Htoh4iI/CtGpcXvsq/GlNRBqLM1IUapRbomDho5Z6yHqzavkS9ZsgTV1dVYunQptFotdu/ejQceeCAQtRERkQ8amRLZUckYFp+NHlEpDPEw1+YZeUZGBu644w5kZGSgqKgILpcLOTk5gaiNiEKAxWqDXCaDQt75HqlJ1Bm0a4nW0tJSlJeX409/+hNKS0vb9fQzIhK3qjojPvy+BP/zygdY9NpmFJaegtnq+55zIgqeNoP89OnTmDhxIrZs2YL8/Hz85S9/gclkCkRtRBQk9U0WLH37K/y/9QX4+Vglvv35KO56aQN2HjwZ7NKI6AJtBjkACILgfn44cGYCHBF1XqdqGvDtz0e92pdv+BY1DU1BqIiIWtJmkI8YMQJDhgxBVFQUevXqhVWrVvF54Z2ExWpHWXUdTtXUw+ZwBrscCiENJovP9sraRjTb+CAVolDS5mS3hx56CHfffTdUKhU+/vhjbN++HaWlpYGojfzoZHUdVny4HV//9CvkMhluGt0fd04ehtQ4XbBLoxCQGON7Rbc+GUmI0nAdCaJQ0maQFxcX4/3338dnn30Gp9OJJUuWYPLkyYGojfykzmjBY//6FAdOnHmKnd3hxIZvi2G1OfDI7KuhViqCXCEFW3pCNH5/7Qj86/Nd7jalXIZFs65GTKQmiJUR0YVaDPLVq1fjgw8+gMViwY033oj3338fCxYswLRp0wJZH/lBhaHBHeLn+2TnAdw5eRgyU+KCUBWFEq1aifyJQzCqbzf8sP844qI0uKJPN2Sl8u8GUahpMchfeukl9O7dG08//TRGjBgB4MyazCR+LV0Pd7oEOFyuAFdDoSomUoOhPbtgaE8u/UkUyloM8m+++QYbN27E4sWL4XK5MH36dM5W7yRSYqMQG6VBndFzQlNuZgoSo/m0KyIiMWlx1npiYiLuvvtubNmyBc8++yxOnDiB8vJy3Hvvvfj2228DWSN1sNR4HV64+3pERZybtJQSG4XF+ZMQreUdCUREYtLmZDfgzC1oI0aMwOLFi/Hxxx/jb3/7m/uechKngdlpeOuxOajUN0ImkyAtPhrJsVHBLouIiC5Su4L8rMjISNx666249dZb/VUPBYhEIkF6fDTS46ODXQoREV2Gdq3sRkRERKGJQU5ERCRiDHIiIiIRu6hr5ETkmyAIqKlvggAgIVoLmZTfkYkoMBjkRJeput6IzT8exJtf7YXD6cLMMQMwc8wApMZz3Xoi8j8GOYWUmvomNJqboVEpRPEAF6vdgTWf78Y73xa729Zs2Y2jlQYsufNaRLbjASN1RjNqjWaolAqkxvEWQCK6OAxyCgl2hxOFh09hyfovUVnbCF2ECn+cPgY940M72CoNjXh/2z6v9m9/PopyQyN6d0lscV9BEFBy/DSeWfcFjlbWQqNSYO6UEbgiO96fJRNRJ8MLeRQSjlYacP/fNqKythEA0Gi2YsmbX6LGIgS5stZZ7Y4W16e3WFtf0vhkdR3ufeV9HK2sdb/+b5u+x4HKxg6vk4g6LwY5hYSdv5yE0+Ud2hu2lcBqdwShovZJiNYiI9F7UZ1orRpJLTzT+6wjFbU+w3791p9QZzR3WI1nWe0OHK00YNcvJ3GorBpGc3OHfwYRBR6H1ikktHT2arbZ4QrhJ7LF67RYcue1mLfiA5j/2welXIb/nTsVaW1MdrM5fH9BsdodPr/UXI4mixUf/bAfr3ywzT2CMO2KHNx/01VtfuEgotDGIKeQMDKnG/75yY9e7TeNzIFGpQxCRe2Xm5WKtx6bg7KaejidAjKSYtA1KabN/bJT4yGRAMIFmX3jyBzE6yI6tMajlQa88J7nw44+2XkQI3pn4PpR/Tr0s4gosDi0TiEhOy0eD90yDnLZub+S00f3R8/k0J7sBpxZt75rUixG98vC2AHdkZUS1677yLsmx+K5uVOhVpz7Pj26fybG9c+ARCLp0Bp3HDzhs33Ddz/D3Gzr0M8iosDiGTmFhEiNCjPGDMConEzoG02I1qqRHq/DLwdKgl2a36gUckwY1BN9uiShuqEJWrUS6fHROPzLfgDdOvSzItW+b4PTqhWQSjv2SwMRBRaDnEKGSiFHVmocslLjgl1Ki+wOJ45UGvBV0a+oazJj4uCe6N0lEbFRlzYULpdJ0TU5Fl2TYzu4Uk/De2dALpPC4fScb3D7NcOgVir8+tlE5F9+HVovLS3FNddcg/Xr1wMAlixZgptvvhm33347br/9dnzzzTcAgIKCAsyaNQs33XQT3nvvPX+WRAHmcDpharZBuPBCsEj9dKQcty99C6s/24n3t+3DvBUfYM2W3TBZrMEurVVZqXH4v/tvRkZiDAAgRqvG07dPRm5WSnALI6LL5rczcrPZjCVLlmDUqFEebc8++yxycnLcbU1NTVi2bBk2btwIuVyO6dOnIy8vD1qt1l+lUQA4XS4cLtfj7a9/wuFyPcYP6I4pw3uja5J/zzz9qdZoxnP/+cprRvn6rXsx7Yoc9M5IClJlbVPIZBjeOwOvP/gbUa2cR0Rt89sZuVKpxGuvvYakpHP/uJlMJq/X7du3D7m5uYiKioJGo8GQIUOwZ88ef5VFAXK4XI/fPv82Nu3Yj4Mnq/D3zTswb8UHKNc3BLu0S9ZkseJ4VZ3PbYbGjr/v2x8SorXonhrPECfqRPx2Ri6XyyGXe769yWTCihUrYDQakZycjMWLF6OmpgZxceeuicbHx0Ov17frMwoLCzu0ZjEQQ58jtJFYt/1X2B1Oj/YKQyN+OlyG6rKjF3VveKj0WaOLQ1q8DhUG75XXVFKhQ+sMlT4HEvscPsKx3/7sc0Anu82ePRtZWVno0aMHVq1ahRUrVmD48OEerxEEod233gwdOtQfZYaswsLCkOlzbaMZjeZmqFUKpMR63iLWaG7G4cqdPvcrr23CtGkj2/05odRnAFj0m6vxp79/5NF23RU56NktFbqIrA75jFDrcyCwz+EjHPt9uX22Wq0oKWn5Dp6ABvmkSZPcf544cSKeeuopTJ06FQaDwd2u1+sxcmT7/6GnwHI4Xfjp13L85a0vcbK6HnFREXhg5liMze3uftKXVq3ElX274UiFwWv//pninlw1ok8G3nh4Nt77rhgGowk3XZmLQT3SoItQB7s0IgpTAV0QZt68eTh16hQAYNeuXejZsycGDBiAQ4cOwWg0wmQyobi4GMOGDQtkWXQRjlYaMG/lBzhZXQ/gzASwJ9Z8jv0nqtyvkUmlmD46Fwk6zwmLI3O6omd6QiDL7XBqpQIDuqfimd9OwYp50zFpaC8kRnOJUyIKHr+dkZeUlGDZsmUoLy+HXC7Hli1bkJ+fj4ULF0KlUkGr1eK5556DUqnEggULMGfOHEilUsyfPx9qNc9uQtXOX0563YsMAOu/LMSg7DSo/rtKWWZKHF5/8Df46Ug5jlYaMLhnF/TJSERiJ1nXWyKRQCbjQipEFHx+C/L+/ftj3bp1Xu1TpkzxasvLy0NeXp6/SqEOZDT7vl/aaLZ6TWDLSIpBRjvWHCciokvHtdbpolzZz/fSoTPG5Ib8w02IiDojBjldlOzUeCyYfhWk591ZkDe8D67I6di1wYmIqH241jpdlKgINWaPH4Qx/bNQ02BCtFaDLgk6RHHWNhFRUDDI6aKplQpkpyUgO03cM9CJiDoDDq0TERGJGIOciIhIxBjkREREIsYgJyIiEjEGORERkYgxyImIiESMQU5ERCRiDHIiIiIRY5ATERGJGIOciIhIxBjkREREIsYgJyIiEjEGORERkYgxyImIiESMQS4yDqcTJosVgiBc9L7NNgcsNrsfqqJLYXc4YWq2BbsMIhI5Po9cJBxOF6SRCViy/kv8WqHHuAHZuHZ4b3RNim1z3/omC4qPVuDNrXvhcLowe/wgDO3VBfE6bQAqpwtZ7Q4cKqvB+q2FKNc3Im94b1w9qAfSE6KDXRoRiRCDXCQOl9fg3r99BLvDCQA4eLIam3bsxz//NLPVAHA4XfjohxK8snG7u+2nIxW445qhuPf6UVArFX6vnTyVHDuNu1/egLODKgdPVuGz3b/gpf+5EUkxkcEtjohEh0PrImCzO7GuoNAd4mdVGBpx8GRVq/tWGhrxz80/erW/+dVeVBgaO7ROalujuRkrP9qOC6+MHDxZjRNVtcEpiohEjUEuAs02O0rL9T63HTvd+j/+Tc02NNsdXu1Ol4BGs7VD6qP2s1jtON7CMdM3mANcDRF1BgxyEYhQKzG6Xzef2/p3S2l135hINWKjNF7taqUccVERHVKfGAmCgAp9A34pq0aFoeGSJg9eCl2EGsN6Z/jclp6gC0gNRNS5MMhFQC6T4qbRuUi4YHLayJyu6NElodV9U+N0eOK2ayCReLY/MmtC2AaHqdmGzT8ewK3PvYnb/vfMf5/t/gXmAMwg16gUuHvqSERqVB7tN4zq266Ji0REF+JkN5HISonDi3+YghO1JhytrMXgHuno3SURidFtT466sl8m3nxkDnb+chIOpxMjc7ohKzUOMmnofI+z2s2wO60QBBckEikUMhVUighILvwG0gFKT9XgqX9/4f650WzFE2s+x78emoVB2Wkd/nlnCYKACkMjrDY7/r7gZvxSVo3KWiNG9M5Aj/QExER6j5wQEbWFQS4i1kY9pl0x9KL3Uynk6NM1CX26JvmhqstjsTWh3lwFfVMZTNYGuAQnpBIZtKpoJERmICW99RGHS7Hx+30+2z/58YDfgtzlErCntAwPv7YZxv/OTRjdNxOPz5mIlLjwHBkhoo4ROqdkFHaamuvwa/UeHNMXw9hcC5dwZla+S3DC2FyLY/piHK0pQlNzfYd+rs3u9N3u8N3eEU7p6/Gnv3/kDnEA+P7Acfzfph9g9TEZkYiovRjkYaamoQm7D5Xhw+9LsKe0DPoGU1DqsNia3AHemlpjFY7pf4LF1tRhn33T6P4AALVCjrR4HdSKMwNT067I6bDPuFC5vgHNNu/A3rL7UNCOARF1DhxaDyOnaxvxxBufY+/hcnfbyJyueOr2yUiOjQpoLfXm022G+FnG5lrUm6ugUXbMYik5XZPwyrwbUVZTj3J9I9ITdOiaFIs+Gf679NDSfAS5XAqpH+YBhAqLzY7jlbXYe6QcSpkMA7PTkJUSB4VcFuzSiDoNBnkY2VN6yiPEAeDHgyfx89FKTBoauCC32s3QN526qH30TWWI06ZCpbj8W+ZMzTa8umkHDp2qdrf17ZaMv951HXRa9WW/vy8ZiTFI0Gmhb/Q8+75twmAkdtLV3BxOF7buPYwn125xt8mlUrwy/0aM6psZvMKIOhkOrYcJQRCwZc8hn9u+Kvo1oLXYnVaYrA0XtY/J2gC7s2MWsNl1qMwjxAHgwIkq7P3V80uO1e6Aw9kx181T43X4v/uno1+3ZABnbimcPX4Qbhk7EHJZ5/w1rDA04H//s9WjzeFy4Zl1BaiqMwapKqLOh2fkYUIikaB7ahy+33/ca1tWalxAaxEEl3tiW3u5BCcEwdUhn/91C19cvik+gmlX5KCqzuieRxCvi8DsqwejT9ckaC5zXfqeXRLxt/tvhr7RBIVMipQ4HZSdeIjZ0GjyOS+gur4J9abmgF/OIeqsGORhZNoVffHON8Ues7PVCjkmDOoR0DokEimkEtlFhblUIoNE0jFnrn26JmFbyTGv9pyuSahvsuD5d7/B1z+dC/uCvYfxyrwbMSa3+2V/drRWjWg/Dd+HmkiNGhIJvNaVVyvliODDeog6TOcc0yOfstPi8fpDv8FV/TMRr4vAuAHd8fpDv0F2WnxA61DIVNCqLu6RnVpVNBQyVdsvbIfJQ3shQuUZJJFqJcYPzMapmgaPED/rr+9+A0MjZ5dfjC4JOkwfnevVfve0kUiN573zRB2FZ+RhRCaVol+3FCz7wzQ0WWyI1KigUXX8mVGzzQ59gwkSiQTJMZGQXzB8rFJEICEyo92z1gEgITKjQya6AUD31HiseXgW3vqqCD8frcTg7DTMvnowuqfGY7uPM3UAOKVvgMVq75DPDxcalRL3TBuJ3KxUvP11EZQKGW6/ZiiG9uzSaecFEAUDgzwMaVRKaFRKv7x3WU09/r7pBxTsLYVMKsUt4wZgzoShSInzvB4aE5GMKHVcu8I8Sh2HmIjkDqtRIpGgZ3oiHrt1IsxWG7RqJeSyM182EqO1Pvfpk5HktT46tS0xJhI3XtkPEwb1gFQqgVbtn793ROGMX4upw9Q3mfHEvz7H53sOwekSYHM48ebWIqz6dIfX6mUaZSSyEgYiSt36RLu4qGRkJQzqsHvIz6eQyxCt1bhDHADSE6JxxzVDvV738G/Gcy30yxAVoWKIE/mJX8/IS0tLMW/ePNx5553Iz8+HwWDAokWLYDQakZKSguXLl0OpVKKgoACrV6+G1WpFfn4+Zs6c6c+yyE/KDY3Yd7zSq/3jHQdwx6RhyEz2DO1IdSx6JA1rda11wapApDomQD0AIjUq3DllOEb3z8J3+44iXheBK/tmBnweARFRe/ktyM1mM5YsWYJRo0a5255//nnMmDEDU6dOxbJly7Bp0yZce+21WLZsGTZu3Ai5XI7p06cjLy8PWq3vIU4KXS2tYe50CXA4fN86plFGQqOMRJw21fPpZ3IVVPII7N27F2nJXf1ZtpeYSA2G987A8BaeG05EFEr8NrSuVCrx2muvISnp3LKXu3btwoQJEwAAEydOxPbt27Fv3z7k5uYiKioKGo0GQ4YMwZ49e/xVFvlRSmyUz+HnnK5JSIxp/YuZShGBSHUsojTxiFTHQiU/M7FNuPDeJSIi8uC3M3K5XA653PPtTSYT1Ooz99DGxcVBr9ejpqYGcXHnhlzj4+Oh1+vb9RmFhYUdV7BIhHKfFQoFnv3tJPz59c/R1GwDcGby2KKZY3C09Be4XJe2oEso99lf2OfwEI59BsKz3/7sc0BnrSsU5251EgQBEonEo+389vYYOvTin80tZoWFhSHfZ0EQ8NZjc1BhaIRMKkV6gu6/z9u+tOFxMfS5o7HP4SEc+wyEZ78vt89WqxUlJSUtbg9okGu1WlgsFmg0Guj1eiQlJSExMREGg8H9Gr1ej5EjRwayLOpAEokEXRJj0CUxJtilEBGFhYDefjZmzBhs3XrmIQoFBQUYN24cBgwYgEOHDsFoNMJkMqG4uBjDhg0LZFlERESi5bcz8pKSEixbtgzl5eWQy+XYsmULli9fjoceeghr1qxBVlYWpk6dCrlcjgULFmDOnDmQSqWYP3+++zo6ERERtc5vQd6/f3+sW7fOq91XW15eHvLy8vxVChERUafFld2IiIhEjEFOREQkYgxyIiIiEePTz6hNNfVNqDWaoVYqkBavg+KCx5ISEVHwMMipRU6XC0W/luOptVtQWWuEQi7DnAmDcduEIUho4XGfREQUWBxapxadrKrDfSs3orLWCACwO5x444s9+PbnI0GujIiIzmKQB1F1fRMKD5/CjwdO4ERVHRzOS1uL3F8Ol+thc3g/0ezfBYWoM5qDUBEREV2IQ+tBcqyyFgte/RDl+gYAgFohx/N3X4dRfbtBJg2N71euFp485nC6WtxGRESBFRqJEWZMzTa89P637hAHgGa7A4tWbUZ5TUMrewZWj7QEyKTeD7CZNX4g4nWhf438ZFUdthYdxsc79uOXk9WwWG3BLomIqMPxjDwIDI0mbN9/3Ku92e5AZW0juibHBr4oH7olxeCvd1+PJ9duQZPFCgDIG9EHU4b1DnJlbTt4sgr3vPSe+3GqAPD4bRNx3ci+UCn4156IOg/+ixYEcpkUGpUCFqvda5syhEJGoZBj3IDu+M9jc1DTYIJWrUBafDS0amWwS2tVo7kZz7/ztUeIA8DSt7/C4B7p6J4aH6TKiIg6HofWgyApJgq3X+P9bNrM5Fh0SYgOQkUtk0gkSE+IxqDsNPRMTwz5EAeAhiYLio9WerU7XQJO1xmDUBERkf+EzulfGJHLpJhxVS6kEgn+XbAHzTYHxg/Mxn03jUZiTGSwyxM9pUKOmEgN6pssXtsi1aogVERE5D8M8iBJjInE3LwRuG5kX7hcLsTrtNCoFMEuq1NIjo3C/BuuxLNvbfVoH5SdhozE0BrxICK6XAzyIJJJpUiL1wW7jE5p4uCeiIpQY/WnP6LJYsMNo/ri+lH9EBsVEezSiIg6FIOcOqWYSA0mD+2FEb0z4HS6EBsVAamPW+mIiMSOQS5SDSYLahpMUMpkSInTQangg0x8iYnUBLsEIiK/YpCL0KGyajyzrgC/lFW7J87dOWU4kmOjgl0aEREFGG8/E5kKQwPmrfwAv5RVAzizXOo73xbj/W374HSF1lrtRETkfwxykSmraUCd0fu2qje/2oua+qYgVERERMHEIBcZZwtPSLM7nHyQCRFRGGKQi0x6QjTUSu+pDdeN7IsEHReTISIKNwxykclIjMHL/3MjdBHnVigblJ2G308ZzpnrRERhiLPWRUYqlWB47wy89dgcVNUaoVIqkJagQ4yWt1kREYUjBrkISSQSpMVHIy2ey40SEYU7Dq0TERGJGIOciIhIxBjkREREIsYgJyIiEjEGORERkYhx1noQWax2VNY2wu5wIikmks/KJiKii8YgD5LK2kas/HA7tuw5BEEAstPi8fht1yAmUo3UOB1UCh4aIiJqG4fWg8DmcOKNz3fj891nQhwAjlQYsGjVZnzz0xG89umPqDWag1skERGJAoM8CKrrjfhwx36vdn2jCRqVAv/6fDeKfi0PQmVERCQ2DPIgEATA1cKzw4X/nqL/5+siNNvsHts0ujgUHj6Fr4oO48CJKhjNzX6vlYiIQhsvxAZBYrQWU4b2xqe7f/Fo10WoYHM4AQAyqRQSicS9rVzfgGff+wEHT1a72/InDsHvrx2BmEius05EFK54Rh4EaqUC914/CkN7dnG3JUZr8eAt4/HWV0UAgNsmDPaY8Pbl3lKPEAeA9Vv34tjp2sAUTUREISmgZ+QlJSWYN28eunXrBgDo1asX5s2bh0WLFsFoNCIlJQXLly+HUqkMZFlB0SUxBi/ccz3K9Q0wGM04XK7Hio3bYLLY8Mebx2Bg9zT3axvNzfhk50Gf77P3cDkG90gPVNlERBRiAhrkZrMZU6ZMweOPP+5u+/Of/4wZM2Zg6tSpWLZsGTZt2oSZM2cGsqyg0WnV0GnVAIBe6QkYm9sdGpUCSTGRkMvODZYo5TIkxUbh1wqD13skxWgDVi8REYWegA6tm0wmr7Zdu3ZhwoQJAICJEydi+/btgSwpJJyoqsOHP+zHM+u+wNovduPYaYN70htwZij+d5OHe+0XqVEht3tqIEslIqIQE/Az8sLCQvzud7+D3W7H/PnzYTKZoFafOSuNi4uDXq8PZElBd6qmHve+8h6q6poAACXHT+PjHw9g7cOz0bNLovt1fbsl4YW7p+Lvm3eirKYeI3p3xbwbrkRmclywSg8qo9kKi80GXYQaaqUi2OUQEQWNRDj/1M/Pjhw5gl9//RVTpkzBiRMncOedd6KpqQm7d+92b3/66aexbt26Ft/DarWipKSkQ+qRy+VQREQBEhkkThssJiMC+L8DMpkMx4wSLHnrK69t00f3w4yhXWExmzxer9SeqRcOG5rNTQGtNxQo1RoYXSr887NdKKuux/DeGZhz9QAIpjo4HI5gl0dE5Df9+/eHSqXyag/oGXl2djays7MBAN26dUNCQgKqq6thsVig0Wig1+uRlJTU7vcbOnToJddibrZhd2kZXlz3Lcr1DRjaqwsW3jwWfbq2//M7wjfvf+ez/dApPTJnjkeE+tzEv8LCQvTtmR2o0kJCYWGhx3EuPlKB+194x70iXsHew9j5y0msXXQruiXHBqnKjnVhn8MB+xw+wrHfl9vntk5gA3qNfOPGjXjjjTcAAAaDAQaDATNnzsTWrVsBAAUFBRg3blxAatl/4jQW/n0Tymrq4RIE7D5Uhrte2hDw27kGZqf5bL+qXyaHjC9gtTvwxhe7ceEgRKPZip+PVgSnKCKiIAvoGfk111yDhx9+GF988QUcDgeeeuop5OTk4MEHH8SaNWuQlZWFqVOn+r0Oi82Of32226vd1GzDvqOVyEoJ3HXnvt2SMbB7KoqPVrrbEqO1uHZEH0ilklb2DD82uwMVhkaf2yprjQGuhogoNAQ0yKOiovCPf/zDq721a+L+YLc7YTB6z6AHAEOAH1aSHBuFpX+YhgMnqrD313L0Sk/AoB7pyEiMCWgdYhCpUWHysF44XO49IXII76UnojAVlku06rRqTBvZF698sM1rWzACITk2CsmxUbh6UI+Af7aYSCQSTBnaG1/sOYTD5efuqZ8+uj+6p8UHsTIiouAJyyAHgElDeuLb4iP46ci5a6t/yLsioMPqdPG6JMZgxfzpOH66FjUNJnRLikVGUgzXmyeisBW2QZ4WH42/3n0dyqrr0WBuRkpsFNITohGp8Z7aT6Hl7AgGERGFcZADQLxOi3gdlzglIiLx4tPPiIiIRIxBTkREJGIMciIiIhFjkBMREYkYg5yIiEjEGOREREQixiAnIiISMQY5ERGRiIluQRjhvGdYWq3WIFYSHOxzeGCfw0M49hkIz35fTp9tNhsAz/w7n0RoaUuIMhqNKC0tDXYZREREAdWrVy9ERXkvTy26IHe5XDCZTFAoFJBI+LxuIiLq3ARBgN1uh1arhVTqfUVcdEFORERE53CyGxERkYgxyImIiESMQU5ERCRiDHIiIiIRY5ATERGJWMgvCPPKK69gx44dsNlseOaZZ5Cbm+veVlRUhGXLlsFqtWLSpEmYN29eECvtOC+++CJ27twJu92Ou+66C3l5ee5tN910k8d9hMuXL0dycnIwyuwwJSUlmDdvHrp16wbgzL2Sixcvdm/vrMd5w4YN2LRpk/vnkpISFBUVuX/uTMe6tLQU8+bNw5133on8/HwYDAYsWrQIRqMRKSkpWL58OZRKpcc+rf3ui8GFfa6qqsKjjz4Km80GqVSKv/71rx7Hs63fAzG4sM9LlixBUVERtFotAGDu3LkYP368xz5iP86Ad78XLFiAuro6AEB9fT0GDRqEJUuWuF/f4cdaCGE7duwQ5s6dKwiCIBw6dEi47bbbPLZPnjxZqKioEJxOpzBz5kzhxIkTwSizQ+3atUv4wx/+IAiCINTV1Qljxozx2H7jjTcGoSr/2rlzp/CXv/ylxe2d8ThfaPfu3cKTTz7p0dZZjrXJZBLy8/OFJ554Qli3bp0gCIKwaNEi4ZNPPhEEQRCWLl0qbNiwwWOftn73Q52vPj/yyCPuPq9fv15YunSpxz5t/R6Eupb6fODAgRb3EftxFgTf/T7fo48+KhQVFXm0dfSxDumh9Z07d2LixIkAznxjqa6uhsViAQCUlZUhOjoaqampkEqlGD9+PLZv3x7McjvE4MGD8fLLLwMAdDod7HY7XC6Xe7vJZApSZf7TWp8663G+0MqVK71GGjrLsVYqlXjttdeQlJTkbtu1axcmTJgAAJg4caLXMW3td18MfPX5iSeewOTJkwEAsbGxaGpq8thH7MfbV5/b6pPYjzPgu99nHTt2DHV1dRg0aJBHe0cf65AO8pqaGsTFxbl/jouLg16vBwBUV1d7bIuPj3dvEzO5XO4ehtqwYQPGjRvnsZJPfX09Fi5ciFtvvRUvvfRSi2vvionZbEZhYSF+97vfIT8/Hzt27HBv66zH+Xw///wzkpOTvYbNO8uxlsvlUKvVHm0mk8nddv7v9Vmt/e6Lga8+a7VayOVyOJ1OvPXWW5g2bZrH9tZ+D8SgpeO8YsUK5Ofn48EHH0R9fb3HdrEfZ8B3v89au3Yt7rjjDq/2jj7WIX2NXKFQePwsCIJ7WdbWtnUGX375Jd59912sWbPGo33hwoWYOnUqtFot7r//fmzZsgXXXnttkKrsGH369ME999yDKVOm4MSJE7jzzjuxZcsWKJXKTn+cAeDdd9/F1KlTvdo747E+6/zj6uuYdtbj7nQ6sWjRIowYMQIjR4702Nba74FYzZ49G1lZWejRowdWrVqFFStW4Mknn3Rv76zHGQAsFgt27Njh0d+zOvpYh/QZeWJiIgwGg/vn2tpaJCQkAACSkpI8tun1ep9DG2K0bds2vPrqq1i9ejV0Op3Htttuuw0xMTFQKBQYO3Zsp3iATHZ2NqZMmQIA6NatGxISElBVVQWgcx/ns3bv3o1Ro0Z5tXfGY32WVqt1D6H6Oqat/e6L2aOPPor09HQsWLDAa1trvwdiNWnSJPTo0QPAmUsoF/4d7qzHGQD27t2LIUOG+FwbvaOPdUgH+dixY7F161YAwP79+5GRkeEewkhJSYHD4UBFRQWcTie+/vprjB07Npjldgij0YilS5di1apViI2N9dhWX1+PuXPnwm63AzgTAD179gxGmR1q48aNeOONNwAABoMBBoPBPczcWY/zWadPn4ZSqYRKpfJo76zH+qwxY8a4f7cLCgowbtw4j+2t/e6L1aZNmyCVSvHAAw/43N7a74FYzZs3D6dOnQJwZl7EhX+HO+NxPqu4uBi9evXyua2jj3VID633798fffr0wfTp0yGTyfDss8/igw8+QFRUFCZNmoTHHnsM8+bNg0QiwQ033IDU1NRgl3zZPv30UzQ0NGDhwoXutiuuuAK9e/fGpEmTMGbMGMyePRsKhQL9+vXrFEOt11xzDR5++GF88cUXcDgceOqpp7B58+ZOfZzPqq6u9jgbPf/vd2c51iUlJVi2bBnKy8shl8uxZcsWLF++HA899BDWrFmDrKws96WFhQsX4rnnnvP5uy8mvvpsMBigUqlw++23AzhzVvb000+7++zr90BMw+q++pyfn4+FCxdCpVJBq9XiueeeA9B5jjPgu98rV65ETU0Nhg0b5vFafx1rPv2MiIhIxEJ6aJ2IiIhaxyAnIiISMQY5ERGRiDHIiYiIRIxBTkREJGIMcqIwcurUKfTu3RvvvPOOR3thYSF69+6NnTt3XtL7PvTQQ/jggw86okQiukgMcqIwk5mZ6fH4VODMYiVZWVnt2v/8h/hcisvdn4g8hfSCMETU8ZKSkmC321FWVoaMjAzY7Xbs2bMHAwYMQHl5OcaOHYvvvvsOwJmnsjkcDixcuBBDhw7FrFmz0NzcjMWLF+Oxxx7D4cOH0bVrVzQ0NLjff/PmzXjzzTfdDwB69tlnER8f77G/r/WniejS8IycKAxdf/31+OijjwAA3377La688kqfa0Kfz2Qy4corr8STTz6J77//HseOHcOGDRuwZMkS9xraVVVVWLVqFdauXYt169ZhzJgxePXVV732J6KOwyAnCkNTp07Fp59+CuDMsPoNN9zQ5j6CIGDIkCEAgNLSUgwaNAgSiQRarRYDBw4EAOzbtw81NTWYO3cubr/9dnz88ceoqanx2p+IOg6H1onCUGxsLLp164bvvvsOJ06cQG5uLgB4PULS4XB4/Hx2PegLV3Y+/7r3gAED8M9//tPn54pp7XAiseAZOVGYuvHGG/H8889j8uTJ7jadTgej0QibzQbgzAMhfOnRoweKi4shCAKamprw008/AQByc3Px888/Q6/XAwC++OILbNmyxb8dIQpzPCMnClMTJkzAk08+6TGsHhkZiRkzZmDu3LnIzs5GSkqK19k3AFx11VXYtGkTbrnlFqSlpWHw4MFwuVxITk7G448/jnvuuQdqtRpKpRJLly4NZLeIwg6ffkZERCRiHFonIiISMQY5ERGRiDHIiYiIRIxBTkREJGIMciIiIhFjkBMREYkYg5yIiEjE/j/WleDL4deCrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df.Murder, y=df.Assault, hue=clusters, palette=\"viridis\")\n",
    "sns.scatterplot(x=centroids[:,0], y=centroids[:,1], s=200, alpha=0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ssd = []\n",
    "\n",
    "K = range(1, 10)\n",
    "\n",
    "for k in K:\n",
    "    kmeans = KMeans(n_clusters=k).fit(df)\n",
    "    ssd.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Inertia')"
      ]
     },
     "execution_count": 15,
     "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": [
    "plt.plot(K, ssd, \"bx-\")\n",
    "plt.xlabel(\"K values\")\n",
    "plt.ylabel(\"Inertia (Error)\")\n",
    "plt.title(\"Inertia\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Distortion Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='distortion score'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's use yellowbrick to visualize the elbow method.\n",
    "\n",
    "kmeans = KMeans()\n",
    "visualizer = KElbowVisualizer(kmeans, k=(2,10))\n",
    "visualizer.fit(scale(df))\n",
    "visualizer.poof()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=4).fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"pred_labels\"] = labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "      <th>pred_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>8.8</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Murder  Assault  UrbanPop  Rape  pred_labels\n",
       "Alabama       13.2      236        58  21.2            1\n",
       "Alaska        10.0      263        48  44.5            1\n",
       "Arizona        8.1      294        80  31.0            1\n",
       "Arkansas       8.8      190        50  19.5            3\n",
       "California     9.0      276        91  40.6            1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Score(n=4): 0.5017569886048432\n"
     ]
    }
   ],
   "source": [
    "print(f\"Silhouette Score(n=4): {silhouette_score(df, labels)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If our silhouette score is close to 1, it means that clusters are separated from each other\n",
    "# very well (means clusters are sort of far from each other without data points getting mixed\n",
    "# in different clusters) and integration within same clusters are accomplished more successfully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.scatterplot(x=df.Murder, y=df.Assault, hue=labels, s=50, palette=\"viridis\")\n",
    "sns.scatterplot(x=kmeans.cluster_centers_[:, 0], y=kmeans.cluster_centers_[:,1], s=200, alpha=0.6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df.sample(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "      <th>pred_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>6.0</td>\n",
       "      <td>115</td>\n",
       "      <td>66</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>2.7</td>\n",
       "      <td>72</td>\n",
       "      <td>66</td>\n",
       "      <td>14.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Mexico</th>\n",
       "      <td>11.4</td>\n",
       "      <td>285</td>\n",
       "      <td>70</td>\n",
       "      <td>32.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>4.3</td>\n",
       "      <td>102</td>\n",
       "      <td>62</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>12.2</td>\n",
       "      <td>252</td>\n",
       "      <td>81</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>9.0</td>\n",
       "      <td>178</td>\n",
       "      <td>70</td>\n",
       "      <td>28.2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</th>\n",
       "      <td>5.9</td>\n",
       "      <td>238</td>\n",
       "      <td>72</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>5.3</td>\n",
       "      <td>46</td>\n",
       "      <td>83</td>\n",
       "      <td>20.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>6.8</td>\n",
       "      <td>161</td>\n",
       "      <td>60</td>\n",
       "      <td>15.6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>17.4</td>\n",
       "      <td>211</td>\n",
       "      <td>60</td>\n",
       "      <td>25.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>4.4</td>\n",
       "      <td>149</td>\n",
       "      <td>85</td>\n",
       "      <td>16.3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>3.2</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>6.3</td>\n",
       "      <td>106</td>\n",
       "      <td>72</td>\n",
       "      <td>14.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vermont</th>\n",
       "      <td>2.2</td>\n",
       "      <td>48</td>\n",
       "      <td>32</td>\n",
       "      <td>11.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>10.4</td>\n",
       "      <td>249</td>\n",
       "      <td>83</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Murder  Assault  UrbanPop  Rape  pred_labels\n",
       "Kansas            6.0      115        66  18.0            0\n",
       "Minnesota         2.7       72        66  14.9            2\n",
       "New Mexico       11.4      285        70  32.1            1\n",
       "Nebraska          4.3      102        62  16.5            0\n",
       "Nevada           12.2      252        81  46.0            1\n",
       "Missouri          9.0      178        70  28.2            3\n",
       "Delaware          5.9      238        72  15.8            1\n",
       "Hawaii            5.3       46        83  20.2            2\n",
       "Wyoming           6.8      161        60  15.6            3\n",
       "Georgia          17.4      211        60  25.8            3\n",
       "Massachusetts     4.4      149        85  16.3            3\n",
       "Utah              3.2      120        80  22.9            0\n",
       "Pennsylvania      6.3      106        72  14.9            0\n",
       "Vermont           2.2       48        32  11.2            2\n",
       "Illinois         10.4      249        83  24.0            1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df[\"true_labels\"] = [3,1,1,2,3,1,1,1,2,2,2,3,1,1,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "      <th>pred_labels</th>\n",
       "      <th>true_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>6.0</td>\n",
       "      <td>115</td>\n",
       "      <td>66</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>2.7</td>\n",
       "      <td>72</td>\n",
       "      <td>66</td>\n",
       "      <td>14.9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Mexico</th>\n",
       "      <td>11.4</td>\n",
       "      <td>285</td>\n",
       "      <td>70</td>\n",
       "      <td>32.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>4.3</td>\n",
       "      <td>102</td>\n",
       "      <td>62</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>12.2</td>\n",
       "      <td>252</td>\n",
       "      <td>81</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>9.0</td>\n",
       "      <td>178</td>\n",
       "      <td>70</td>\n",
       "      <td>28.2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</th>\n",
       "      <td>5.9</td>\n",
       "      <td>238</td>\n",
       "      <td>72</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>5.3</td>\n",
       "      <td>46</td>\n",
       "      <td>83</td>\n",
       "      <td>20.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>6.8</td>\n",
       "      <td>161</td>\n",
       "      <td>60</td>\n",
       "      <td>15.6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>17.4</td>\n",
       "      <td>211</td>\n",
       "      <td>60</td>\n",
       "      <td>25.8</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>4.4</td>\n",
       "      <td>149</td>\n",
       "      <td>85</td>\n",
       "      <td>16.3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>3.2</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>6.3</td>\n",
       "      <td>106</td>\n",
       "      <td>72</td>\n",
       "      <td>14.9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vermont</th>\n",
       "      <td>2.2</td>\n",
       "      <td>48</td>\n",
       "      <td>32</td>\n",
       "      <td>11.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>10.4</td>\n",
       "      <td>249</td>\n",
       "      <td>83</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Murder  Assault  UrbanPop  Rape  pred_labels  true_labels\n",
       "Kansas            6.0      115        66  18.0            0            3\n",
       "Minnesota         2.7       72        66  14.9            2            1\n",
       "New Mexico       11.4      285        70  32.1            1            1\n",
       "Nebraska          4.3      102        62  16.5            0            2\n",
       "Nevada           12.2      252        81  46.0            1            3\n",
       "Missouri          9.0      178        70  28.2            3            1\n",
       "Delaware          5.9      238        72  15.8            1            1\n",
       "Hawaii            5.3       46        83  20.2            2            1\n",
       "Wyoming           6.8      161        60  15.6            3            2\n",
       "Georgia          17.4      211        60  25.8            3            2\n",
       "Massachusetts     4.4      149        85  16.3            3            2\n",
       "Utah              3.2      120        80  22.9            0            3\n",
       "Pennsylvania      6.3      106        72  14.9            0            1\n",
       "Vermont           2.2       48        32  11.2            2            1\n",
       "Illinois         10.4      249        83  24.0            1            3"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's calculate the adjusted rand score now. Please keep in mind that to calculate this,\n",
    "# we need an expert with the domain knowledge to point out clustering mistakes in a sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11764705882352944"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjusted_rand_score(new_df[\"true_labels\"], new_df[\"pred_labels\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# When ARI score is close to 0, it means the machine's clusterings are terrible.\n",
    "# When ARI score gets closer to 1, the better clusterings the machine achieved.\n",
    "# ARI score has a scale of -1 to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's try hierarchical clustering now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "hc_complete = linkage(df, \"complete\")\n",
    "hc_ward = linkage(df, \"ward\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.title(\"dendrogram\")\n",
    "plt.xlabel(\"Observations\")\n",
    "plt.ylabel(\"Distance\")\n",
    "dendrogram(hc_complete, leaf_font_size=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.title(\"dendrogram\")\n",
    "plt.xlabel(\"Observations\")\n",
    "plt.ylabel(\"Distance\")\n",
    "dendrogram(hc_ward, leaf_font_size=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 2, 1, 2, 0, 1, 1, 2, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 2, 1,\n",
       "       0, 1, 2, 0, 0, 1, 0, 2, 1, 1, 1, 0, 0, 2, 2, 0, 2, 1, 0, 2, 2, 0,\n",
       "       0, 2, 2, 0, 0, 2])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster = AgglomerativeClustering(n_clusters=3, affinity=\"euclidean\", linkage=\"ward\")\n",
    "cluster.fit_predict(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 2 1 2 0 1 1 2 0 0 1 0 0 0 0 1 0 1 2 1 0 1 2 0 0 1 0 2 1 1 1 0 0 2 2\n",
      " 0 2 1 0 2 2 0 0 2 2 0 0 2]\n"
     ]
    }
   ],
   "source": [
    "print(cluster.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f21ba16ecd0>"
      ]
     },
     "execution_count": 41,
     "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": [
    "plt.scatter(df.iloc[:,0], df.iloc[:,1], c=cluster.labels_, cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df.Murder, y=df.Assault, hue=cluster.labels_, palette=\"viridis\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Score(n=3): 0.5320745032866294\n"
     ]
    }
   ],
   "source": [
    "print(f\"Silhouette Score(n=3): {silhouette_score(df, cluster.labels_)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}