13_kmeans_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "iris = load_iris()\n",
    "print(iris.feature_names)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "df = pd.DataFrame(columns=iris.feature_names, data=iris.data)\n",
    "df[\"target\"] = iris.target\n",
    "df.drop(['sepal length (cm)', 'sepal width (cm)'], axis='columns', inplace=True)\n",
    "display(df)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "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>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     petal length (cm)  petal width (cm)  target\n",
       "0                  1.4               0.2       0\n",
       "1                  1.4               0.2       0\n",
       "2                  1.3               0.2       0\n",
       "3                  1.5               0.2       0\n",
       "4                  1.4               0.2       0\n",
       "..                 ...               ...     ...\n",
       "145                5.2               2.3       2\n",
       "146                5.0               1.9       2\n",
       "147                5.2               2.0       2\n",
       "148                5.4               2.3       2\n",
       "149                5.1               1.8       2\n",
       "\n",
       "[150 rows x 3 columns]"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "scaler = MinMaxScaler()\n",
    "\n",
    "scaler.fit(df[['petal length (cm)']])\n",
    "df['petal length (cm)'] = scaler.transform(df[['petal length (cm)']])\n",
    "\n",
    "scaler.fit(df[['petal width (cm)']])\n",
    "df['petal width (cm)'] = scaler.transform(df[['petal width (cm)']])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "plt.xlabel('petal width')\n",
    "plt.ylabel('petal length')\n",
    "plt.scatter(df['petal width (cm)'], df['petal length (cm)'])\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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vsywmDQSSXiHpi5K+mbxeIOny4rtm9jtlpUaWLxrkosWDL1T9DEhctLj9JKs8+w/n7VfWFUvN0kqTGroN+BLwieT1z4Cv0qgmMitFnj178xjeNMq9G0ePWBDu3o2jDM07ses3Xc/6taKkqRqaFRH3AM/DC4vJuYzUSpVn4lYers6xOkoTCH4r6WUkT7uSltLYj8CsNHn27M3D1TlWR2lSQx+jsY/AqZIeAmYDFxfaK7Nx8lbNZC059d7AVkdpqoZ+ALwVeBPwH4DXR8QjRXfMrFmeqpk8e/Bm3YN42aknZmrP2y+zIk20ef2FY1/A+cDpNHYQe2/SZlaaPFUzefL9WfcgvvOKs4+66S879UTuvOLsjvbLrEgTpYbeO8F7QWOmsVlpslbN5Mn35/nMRDf9Tp3DrEhtA0FEfLDMjph12oxpUzjQYjB5ov2Eyxgj8Cxhq5o0VUNmPSnPfsJlzOD1LGGrmjRVQ2aVkLXSJs9+wmXs2+u9ga1qHAisJ+RZdC5vCqaMGbyeJWxV0jYQTFYZFBEeLLbSTFRp0+6GevW5px8RPMApGLNWXDVkPSFPpY1TMGbpuGrIekKV0zyeJWy9LtUYgaR/D7weOH6sLSKuL6pTZuNVNc3jvYStH6TZj+AW4P3AR2jst/E+YF7B/TI7QlXX4/csYesHaeYRvCkiPgA8HhH/HTibxlITZqUa2bmfxw4+RQCPHXyKkZ37u90lzxK2vpAmEIz9i35S0knAs8CriuuS2dFWDm/mjvW7jtgw5o71u1g5vLmr/Wo3RuFZwtZL0gSCb0iaCawCfgD8ErirwD6ZHeWuDbsztZfFs4StH6QZLP5MRDwN3CvpGzQGjJ8qtltmRzrcZiuydu1lcYmq9YM0geD7wJkASUB4WtIPxtrMyjAgtbzpD0y0cFBJPEvYet1E+xG8UtJiYJqkRZLOTL7eBkxP88MlnSdpm6Ttkq6Z4LiLJIWkoawXYPWwYsmcTO1mlt5ETwTnApcBJwN/09T+G+C/TfaDJQ0ANwPvBPYAD0taExFbxx13AvCfgQ2Zem61csPyhUBjTOBwBAMSK5bMeaHdzPKbaGbxl4EvS7ooIu7N8bPPArZHxA4ASXcDFwBbxx33V8CngatznMPMzI5RmqqhhyR9UdI3ASQtkHR5is8NAs0lHXuSthdIOhOYExH3T/SDJF0paUTSyL59rbcMtP5W1fJRs36QJhB8CVgLnJS8/hnwl8d6YkkvopFy+vhkx0bE6ogYioih2bNbbyJu/a2q5aNm/SBNIJgVEfcAzwNExHPA4Yk/AsAo0DySd3LSNuYE4AzgW5J+CSwF1njA2FqpavmoWT9IEwh+K+llNJaeRtJS4GCKzz0MnCbpFElTgUuANWNvRsTBiJgVEfMjYj6wHjg/IkayXoT1v3ZlolUoHzXrdWkCwcdo3MBPlfQQ8BUaC9BNKHlyuIpGWuknwD0RsUXS9ZLOP4Y+Ww25fNSsOIoUj9aSjgNOp7H66LaIaL/pa8GGhoZiZMQPDXW0cnizy0fNcpK0MSJapt4nDQSSjgc+BLyZRnroO8AtEdGVZSYcCMzMspsoEKRZYuIrwBPA3yWv/wS4nca+BGZm1uPSBIIzImJB0+t1ksZPCjMzsx6VJhD8QNLSiFgPIGkJ4NyM9QTvJ2w2uTSBYDHwPUm7ktdzgW2SNgMREW8orHdmx8D7CZulkyYQnFd4L8wKMNF+wg4EZr8zaSCIiJ1ldMSs07yfsFk6aSaUmfUk7ydslo4DgfUt7ydslk6aMQKznuT9hM3ScSCwvub9hM0m59SQmVnNORCYmdWcA4GZWc05EJiZ1ZwDgZlZzTkQmJnVnAOBmVnNORCYmdWcA4GZWc05EJiZ1ZwDgZlZzTkQmJnVnAOBmVnNORCYmdWcA4GZWc05EJiZ1ZwDgZlZzTkQmJnVnAOBmVnNFRoIJJ0naZuk7ZKuafH+xyRtlfSIpH+WNK/I/piZ2dEKCwSSBoCbgXcDC4AVkhaMO2wTMBQRbwC+DnymqP6YmVlrRT4RnAVsj4gdEfEMcDdwQfMBEbEuIp5MXq4HTi6wP2Zm1kKRgWAQ2N30ek/S1s7lwDdbvSHpSkkjkkb27dvXwS6amVklBoslXQoMAatavR8RqyNiKCKGZs+eXW7nzMz63HEF/uxRYE7T65OTtiNIegfwCeCtEfF0gf0xM7MWinwieBg4TdIpkqYClwBrmg+QtAj4e+D8iNhbYF/MzKyNwgJBRDwHXAWsBX4C3BMRWyRdL+n85LBVwEuAr0n6oaQ1bX6cmZkVpMjUEBHxAPDAuLbrmr5/R5HnNzOzyVVisNjMzLqn0CeCXja8aZRVa7fx6IFDnDRzGlefezrLF01U/VrNc5iZTcaBoIXhTaNce99mDj17GIDRA4e49r7NAB27UZdxDjOzNJwaamHV2m0v3KDHHHr2MKvWbuupc5iZpeFA0MKjBw5laq/qOczM0nAgaOGkmdMytVf1HGZmaTgQtHD1uaczbcrAEW3Tpgxw9bmn99Q5zMzS8GBxC8sXDTKycz93bdjN4QgGJC5aPDjpIG6WKqAyzmFmloafCFoY3jTKvRtHORwBwOEI7t04yvCmo5ZKOuIz1963mdEDhwh+VwXU7jNlnMPMLA0HghbyVPRk/UwZ5zAzS8OBoIU8FT1ZP1PGOczM0nAgaCFPRU/Wz5RxjjHDm0ZZdtODnHLN/Sy76UGnkszsCA4ELTxx6JlM7ZC9Cuic17beYKdde55zgMcVzGxyDgQt/Obpw5naoVEFdOOFCxmcOQ0BgzOnceOFC9tW9Kz7aestN9u15zkHeFzBzCZXi/LRlcObjyjTXLFkDjcsX9jVPuXN9y9fNHmJaSfOY2b10fdPBCuHN3PH+l1HlGnesX4XK4c3d/Q8WVMwUwaUqT0vz2A2s8n0fSC4a8PuTO15ZU3BPHM4MrXn5RnMZjaZvk8NjT0JpG3Pq6opmLE0kmcjm1k7fR8IBqSWN/0BdT4FM9ripl+FFEzWcQUzq5e+Tw29evb0TO15TZ/a+o+yXfuyU0/M1G5mVpS+DwQ79j2ZqT2vn+/9bab2O684+6ib/rJTT+TOK87uaL/MzCbT94GgrDGCPN43NPeIOQHvG5rb7S6ZWQ15jKBLvGexmVVF3z8RrFgyJ1N7WTzj18yqou+fCMZmEPfLzGIzs07r+0AAjWBQ9I0/awqqyuWmZlYvfZ8aKkvWFJRn/JpZVdTiiaAMWVNQnvFrZlVRi0CQdcP3vJVGWVNQnvFrZlXQ96mhPBuzVLXSyMysCH0fCPKUad6wfCGXLp37whPAgMSlS+d2vdLIzKwIhQYCSedJ2iZpu6RrWrz/YklfTd7fIGl+p/uQt0xzaN6JvHLG8Qh45YzjGZrnNYDMrD8VFggkDQA3A+8GFgArJC0Yd9jlwOMR8fvA/wQ+3el+5NmYxfv8mlmdFPlEcBawPSJ2RMQzwN3ABeOOuQD4cvL914G3S51d+yFPmaZn/ZpZnRQZCAaB5m3A9iRtLY+JiOeAg8DLxv8gSVdKGpE0sm9f+83dW8mz4btn/ZpZnfRE+WhErAZWAwwNDWVeNjRrmaZn/ZpZnRT5RDAKNNdbnpy0tTxG0nHADODXBfYpFc/6NbM6KTIQPAycJukUSVOBS4A1445ZA/x58v3FwIMR3d8oIE86ycysVxWWGoqI5yRdBawFBoBbI2KLpOuBkYhYA3wRuF3SdmA/jWBRCZ71a2Z1UegYQUQ8ADwwru26pu+fAt5XZB/MzGxifT+z2MzMJuZAYGZWcw4EZmY150BgZlZzqkC1ZiaS9gE7c358FvCrDnanF/ia68HXXA/Hcs3zImJ2qzd6LhAcC0kjETHU7X6UyddcD77meijqmp0aMjOrOQcCM7Oaq1sgWN3tDnSBr7kefM31UMg112qMwMzMjla3JwIzMxvHgcDMrOb6MhBIOk/SNknbJV3T4v0XS/pq8v4GSfO70M2OSnHNH5O0VdIjkv5Z0rxu9LOTJrvmpuMukhSSer7UMM01S/rj5O96i6R/KLuPnZbi3/ZcSeskbUr+fb+nG/3sFEm3Stor6cdt3pekv03+PB6RdOYxnzQi+uqLxpLXvwBeDUwFfgQsGHfMh4Bbku8vAb7a7X6XcM3nANOT7/+iDtecHHcC8G1gPTDU7X6X8Pd8GrAJ+L3k9cu73e8Srnk18BfJ9wuAX3a738d4zf8OOBP4cZv33wN8ExCwFNhwrOfsxyeCs4DtEbEjIp4B7gYuGHfMBcCXk++/DrxdkkrsY6dNes0RsS4inkxerqexY1wvS/P3DPBXwKeBp8rsXEHSXPMVwM0R8ThAROwtuY+dluaaA3hp8v0M4NES+9dxEfFtGvuztHMB8JVoWA/MlPSqYzlnPwaCQWB30+s9SVvLYyLiOeAg8LJSeleMNNfc7HIav1H0skmvOXlknhMR95fZsQKl+Xt+DfAaSQ9JWi/pvNJ6V4w01/wp4FJJe2jsf/KRcrrWNVn/f59UT2xeb50j6VJgCHhrt/tSJEkvAv4GuKzLXSnbcTTSQ2+j8dT3bUkLI+JANztVsBXAbRHxWUln09j18IyIeL7bHesV/fhEMArMaXp9ctLW8hhJx9F4nPx1Kb0rRpprRtI7gE8A50fE0yX1rSiTXfMJwBnAtyT9kkYudU2PDxin+XveA6yJiGcj4l+An9EIDL0qzTVfDtwDEBHfB46nsThbv0r1/3sW/RgIHgZOk3SKpKk0BoPXjDtmDfDnyfcXAw9GMgrToya9ZkmLgL+nEQR6PW8Mk1xzRByMiFkRMT8i5tMYFzk/Ika6092OSPNve5jG0wCSZtFIFe0osY+dluaadwFvB5D0OhqBYF+pvSzXGuADSfXQUuBgRPzrsfzAvksNRcRzkq4C1tKoOLg1IrZIuh4YiYg1wBdpPD5upzEoc0n3enzsUl7zKuAlwNeScfFdEXF+1zp9jFJec19Jec1rgXdJ2gocBq6OiJ592k15zR8HviDpozQGji/r5V/sJN1FI5jPSsY9PglMAYiIW2iMg7wH2A48CXzwmM/Zw39eZmbWAf2YGjIzswwcCMzMas6BwMys5hwIzMxqzoHAzKzmHAjMmki6TNJJKY67TdLFOX7+f5T0gRbt88dWm5T0xuYVNCV9StJ/yXous7T6bh6B2TG6DPgxBS1cltSBT+aNNJYBeaCIPpiN5ycC61vJb9k/lXSnpJ9I+rqk6cl7iyX9H0kbJa2V9KrkN/wh4E5JP5Q0TdJ1kh6W9GNJqydapVbSyyVtTL7/g2QPhLnJ619Imt78233Shx9J+hHw4aRtKnA98P6kD+9PfvwCSd+StEPSfyrqz8zqyYHA+t3pwOci4nXAb4APSZoC/B1wcUQsBm4F/joivg6MAH8aEW+MiEPA/4qIfxsRZwDTgD9qd6Jk6Y7jJb0UeEvys96ixiZAe5uWAR/zJeAjEfEHTT/jGeA6GvtFvDEivpq89VrgXBrLMn8yuQazjnAgsH63OyIeSr6/A3gzjeBwBvBPkn4IrKT9/gznqLGL3WbgD4HXT3K+7wHLaGwu8j+S/74F+E7zQZJmAjOTtecBbp/k594fEU9HxK+AvcArJjneLDWPEVi/G7+GStDY2WlLRJw90QclHQ98jsbOZrslfYrGgmYT+TaNG/884B+B/5qc81j3RGheLfYw/n/XOshPBNbv5iZr1AP8CfBdYBswe6xd0hRJY7/pP0FjCWv43U3/V5JeQmOl2sl8B7gU+HmyHv5+GguEfbf5oGR/gAOS3pw0/WnT2819MCucA4H1u23AhyX9BPg94PNJHv5i4NPJQO0PgTclx98G3JKkjJ4GvkCjimgtjSWRJxQRv6TxxDGW8vkucGBs68hxPgjcnJyreRB6HY3B4ebBYrPCePVR61uS5gPfSAZ6zawNPxGYmdWcnwjMzGrOTwRmZjXnQGBmVnMOBGZmNedAYGZWcw4EZmY19/8BwcO7x7FEgBIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "model = KMeans(n_clusters=3)\n",
    "y_pred = model.fit_predict(df.drop([\"target\"], axis='columns'))\n",
    "df['predicted'] = y_pred\n",
    "\n",
    "df1 = df.loc[df['predicted'] == 0]\n",
    "df2 = df.loc[df['predicted'] == 1]\n",
    "df3 = df.loc[df['predicted'] == 2]\n",
    "\n",
    "plt.xlabel('petal width')\n",
    "plt.ylabel('petal length')\n",
    "plt.scatter(df1['petal width (cm)'], df1['petal length (cm)'], color='red')\n",
    "plt.scatter(df2['petal width (cm)'], df2['petal length (cm)'], color='blue')\n",
    "plt.scatter(df3['petal width (cm)'], df3['petal length (cm)'], color='green')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "sse = []\n",
    "\n",
    "for k in range(1, 10):\n",
    "    km = KMeans(n_clusters=k)\n",
    "    km.fit(df[['petal width (cm)','petal length (cm)']])\n",
    "    sse.append(km.inertia_)\n",
    "\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Sum of squared error')\n",
    "plt.plot(range(1, 10), sse)\n",
    "plt.show()\n",
    "# Elbow rule"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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2yNiEbVqAXUSqQZjC/yHgMmCpu+8A6oEPlDRVlZnT2sii9ul0rdX1/CJS+cJc1ZOnaDZOd38eeL6UoapRtiPNvU/0asI2Eal4uhFrinRm0mweGGbd8zuijiIiMqF9Fn4z0yTzk7A0U5iwrUvTN4hIhZvojP/nAGZ2Z5myVLWXzG6mtTGlAV4RqXgT9fEnzOxzwEvN7BN7v+nuV5QuVvVJJIxspl1n/CJS8SY6438nMEqhcWgZ5yF76exI89eeAbZsH446iojIPu3zjN/dHwP+zcwecvff7etzstvY9fzd67bwumPmRpxGRGR8Ya7q+ZOZXWFmXcHj8rEJ22RPJyycQV3S1N0jIhUtTOG/DugH3h48+oDvlTJUtWqsS3LcgjYN8IpIRQtT+I9w9y+4+9+Cx78Ah5c6WLVammnnoQ3bGMqNRh1FRGRcYQr/TjN79dhGsBrXztJFqm6dHWmGc3lWb9CyBSJSmcIU/g8D3zSztWa2FvgGcNH+vmRm15lZj5mtLtrXbmZ3mNkTwXP6gJNXqM5ggHeZ5u0RkQq138Lv7ivd/QTgeOB4d1/i7g+FOPb3gXP32ncZcKe7HwncGWzXlFnNDRw+q0kTtolIxQo9V4+79wULsoT9/L3A3qOcbwKuD15fD7w57PGqSWdHmu51L+DuUUcREXmRck/SNtfdNwavnwP2ebG7mV04dglpb29vedJNkWwmzZYdIzzZuz3qKCIiLxLZ7JxeOB3e5ymxu1/t7ll3z86ePbuMyQ5eNpiwrXudLusUkcqz3/n4zSwJvAHIFH/+AOfq2WRm8919o5nNB3oO4BgV7/BZTbQ31bNs7RbesXRR1HFERPYQ5oz/18D7KSy0frBz9dwMvC94/T7gVwd4nIpmZkE/vwZ4RaTy7PeMHzjU3Y+f7IHN7AbgdGCWma0HvgB8GfipmX0IWEfhTuCalO1Ic8eaTfT2DzG7pSHqOCIiu4Qp/L8zs7Pd/fbJHNjdL9jHW2dO5jjVKpvZPWHbucfNiziNiMhuYbp6/gL8wsx2mlmfmfWbWejLOuPquAVt1KcSGuAVkYoT5oz/CuBkYJXrwvTQGlJJTji0TXfwikjFCXPG/wywWkV/8rKZdh5+dhuDI5qwTUQqR5gz/r8Bd5vZ74ChsZ1aenH/sh1prrrbWfnMVl5x+Myo44iIAOHO+J+iMK9OPVp6cVLGJmzTwiwiUkn2e8YfzL8vB2DG9HqOnNOshVlEpKKEuXP3LsaZWsHdzyhJohqTzaT57UMbyeedRMKijiMiEqqP/1NFrxuBtwG50sSpPdmOdm544Bme6BngqHnqIROR6IXp6unea9cfzeyBEuWpOWM3cnWte0GFX0Qqwn4Hd4NVs8Yes8zsHKCtDNlqwqL26cxqbtDCLCJSMcJ09XRT6OM3Cl08TwEfKmWoWmJmLM2k6dIdvCJSIcJ09RxWjiC1rLMjze9WP8emvkHmtjZGHUdEYm6fXT1mttTM5hVtv9fMfmVmXzOz9vLEqw1Lg4VZ1N0jIpVgoj7+7wDDAGZ2KoUplX8AbAOuLn202nHMIa1Mq0uqu0dEKsJEXT1Jdx+rVO8Arnb3G4EbzWxFyZPVkLpkghMWtumMX0QqwkRn/EkzG2sYzgT+q+i9MIPCUmRppp01G/vYPqRbIEQkWhMV/huAe8zsV8BO4D4AM3sJhe4emYTOjjSjeWfFM1ujjiIiMbfPwu/u/xv4JPB94NVF0zIngI+VPlptObEjjZkGeEUkehN22bj7X8bZ93jp4tSu1sY6jprbogFeEYlcmGmZZYpkM2kefHoro3mtaSMi0VHhL6OlmXYGhnI8+pyWLBaR6Kjwl9HYwizdWphFRCKkwl9GC2ZMY15roxZgF5FIqfCXkZmRzaTp1opcIhIhFf4yy3akeXbbIBu27ow6iojElAp/mWV3Tdims34RiUYkhd/M1prZKjNbYWZdUWSIytHzWmiqT2qAV0QiE+WcO691980R/n4kUskESxal+fOTzzMymqcuqT+6RKS8VHUicO5x83iiZ4Dzv/FHVq3XtEciUl5RFX4HbjezbjO7cLwPmNmFZtZlZl29vb1ljlda735lB99+dyfPDwzx5m/9kS/d8gg7h0ejjiUiMWG7514r44+aLXD3DWY2B7gD+Ji737uvz2ezWe/qqr2hgG07R/jSLY/wk2XPkJk5nS+99XhOPmJm1LFEpEaYWbe7Z/feH8kZv7tvCJ57gF8AJ0WRI2pt0+r48tuO58f/+AryDhdc8xc+e9Mq+gZHoo4mIjWs7IXfzJrMrGXsNXA2sLrcOSrJq14yi9suPZULTz2c/7fsac664h5uf/i5qGOJSI2K4ox/LvAHM1sJPAD81t1vjSBHRZlWn+Rz572MX158Cunp9Vz4w24u/vFyevuHoo4mIjUmkj7+yarVPv59GRnN8517nuRrd/6VafVJPv/GY3jbiQsws6ijiUgVqag+fplYXTLBR884klsueQ1HzmnmUz9byXuve4BnXtgRdTQRqQEq/BXsJXOa+elFJ/PFNx3L8nVbOOfKe7nuD09pIRcROSgq/BUukTDee3KG2z9xGicd1s4Xf7OGv//2n3h8U3/U0USkSqnwV4kFM6bxvfcv5cp3LGbt5u284Wv3ceXvH2c4l486mohUGRX+KmJmvHnJAn7/idM47+XzufL3T/DGr9/Hg09rwjcRCU+FvwrNbG7gP965hOven6V/MMdbr/oTX/z1GnYM56KOJiJVQIW/ip1x9Fxu//ip/MMrFnHdH5/i7K/ey31P1Na8RiIy9VT4q1xLYx3/+uaX89OLTqY+meA91z7Ap362kq07hqOOJiIVSoW/Rpx0WDu3XPIaLn7tEfziwQ287op7uWXVRqrhBj0RKS8V/hrSWJfk0+cczc0fPYV5bQ185EfLueiH3WzqG4w6mohUEBX+GnTsIW388iOn8NnXH809j/fyuivu4ScPPK2zfxEBVPhrViqZ4KLTjuC2S0/l2ENaueymVbzrmvtZu3l71NFEJGIq/DUuM6uJH//jK/nSW1/O6g3bOOfKe7n63ifJjerGL5G4UuGPgUTCuOCkRdzxidM49aWz+T+3PMpbvvUn1jzbF3U0EYmACn+MzGtr5Or3dPLNd53Ixm07Of8bf+D/3vYogyNa71ckTlT4Y8bMeMPx87nj46fxpsUL+OZdT3Le1+5j2doXoo4mImWiwh9T6aZ6Ln/7CfzggycxnMvz3779Zz7/y9X0a71fkZqnFbiE7UM5vnL7Y3z/T2uZXpfk0PR05rQ2MKelkbmtDcxtLTzPaW1kbmsjs5sbqE/pnEGk0u1rBa5UFGGksjQ1pPjC3x3L+Sccwk3LN7Cpb5Ce/iGe7NlMT/8QuXEWfmlvqmdOS1Gj0LJn4zC3tYFZzQ3UJdVAiFQaFX7ZZcmiNEsWpffYl887L+wYLjQGfUP09A+yqW+ITX2F557+QR59ro/NA8MvWhnMDGY21e/xl8Oc1sY9Goy5rY3MbKonpQZCpGxU+GVCiYQxq7lw9n7sIfv+3GjeeX77ED17NQqb+obo6RtkU/8gq5/tY/PAEHv3LiYMZjU3MKe1gbktjcFfDUFDETQSs1saaGlMMa0uqUXnRQ6SCr9MiWTCmNPSyJyWRo5b0LbPz+VG8zy/fXhX41D4S2J3Q7Fx2yAr129l88D4s4smE0ZzQ4rmhhQtjWOPul3bzY0pWou2W/Z4vXtbYxQSZyr8UlapZCLo5mmc8HPDuTybB4Z2jTdsHhiifzBH/+AIA4O5wuuhwnZP/yBP9uZ27R8OcVdyQyrx4kajobA91kiMbTePNRp7bTfXp0gk9NeHVB8VfqlI9akEh8yYxiEzpk36u0O5UfoHc0UNxEjR9ggDQ4X9fYO54HWhMVk3sIOBoRx9wWfCXPDWVJ+ksS5JQypBw97PqQQNqSSNdYXnhrrCvl2fTyX33K7b6/MTvJdUgyMHQYVfak5DKklDc5JZzQ0HfAx3Z/vw6K7Goj9oLIobj77BHNuHcgzlRhkcyTOUyzM0Mlp4DhqfzblhhnKjDI3ki57zof4qmUgqYXs1DC9udOqSCeqSRiqZoD6ZIJUw6lIJ6hJGXTIR7C+8P/bZwn7bczuRoD5lpBIv/lx9cJy6Xd8p2p8wkgnTmEwFUuEXGYfZ7rGEeW0Td0sdiHzedzUQhQYjX9SA7G48BvdqMMbbN1jU2Iwda2AoR27UGRnNBw8nN5pneNTJ5fOM5PKM5Avvl/JWHjOoS+xugOqShcYglUgEz0YieB5/O7Gf9wvP4Y+ZmPAYSSt6PdG+4mPbWE72+M7Yvj2ejYpoCFX4RSKQSBjT6pNMq09GHYXR/HgNRL6o4Sg85/J5hnNBw1G8f9T3+nzxe0FjM7Y/74yOOqPujOadXN4ZzecZzRdv7369c2S06DMwms/v8Zm9v1PY3n28cW5BidxYw1DcGKSSiaAB2bNhSRh86a3Hc9Jh7VOaIZLCb2bnAv8BJIHvuvuXo8ghImNnqYWxilqTz+9uZIobiVw+Tz7PrkYil3fyezciRd/b9Z7v2XC96DHe/r1+f+xYeX9xA1b83th2U8PU//9S9sJvZkngm8BZwHpgmZnd7O5ryp1FRGpbImEkMGqwTTsoUVzMfBLwV3f/m7sPAz8B3hRBDhGRWIqi8C8AninaXh/s24OZXWhmXWbW1dvbW7ZwIiK1rmJvX3T3q9096+7Z2bNnRx1HRKRmRFH4NwALi7YPDfaJiEgZRFH4lwFHmtlhZlYPvBO4OYIcIiKxVParetw9Z2YfBW6jcDnnde7+cLlziIjEVSTX8bv7LcAtUfy2iEjcVezgroiIlEZVrLlrZr3AugP8+ixg8xTGmSrKNTnKNTnKNTmVmgsOLluHu7/ossiqKPwHw8y6xltsOGrKNTnKNTnKNTmVmgtKk01dPSIiMaPCLyISM3Eo/FdHHWAflGtylGtylGtyKjUXlCBbzffxi4jInuJwxi8iIkVU+EVEYqZmC7+ZXWdmPWa2OuosxcxsoZndZWZrzOxhM7sk6kwAZtZoZg+Y2cog179EnamYmSXN7EEz+03UWcaY2VozW2VmK8ysK+o8Y8xshpn93MweNbNHzOzkCsh0VPDPaezRZ2aXRp0LwMw+Hvw7v9rMbjCzqV9k+QCY2SVBpoen+p9Vzfbxm9mpwADwA3c/Luo8Y8xsPjDf3ZebWQvQDbw56hXIrLACdJO7D5hZHfAH4BJ3/0uUucaY2SeALNDq7m+MOg8UCj+QdfeKuvHHzK4H7nP37wYTIU53960Rx9olWIVvA/AKdz/QGzOnKssCCv+uH+PuO83sp8At7v79iHMdR2GRqpOAYeBW4MPu/tepOH7NnvG7+73AC1Hn2Ju7b3T35cHrfuARxlmIpty8YCDYrAseFXFWYGaHAm8Avht1lkpnZm3AqcC1AO4+XElFP3Am8GTURb9ICphmZilgOvBsxHkAXgbc7+473D0H3AO8daoOXrOFvxqYWQZYAtwfcRRgV3fKCqAHuMPdKyIXcCXwGSAfcY69OXC7mXWb2YVRhwkcBvQC3wu6xr5rZk1Rh9rLO4Ebog4B4O4bgK8ATwMbgW3ufnu0qQBYDbzGzGaa2XTgPPZcx+SgqPBHxMyagRuBS929L+o8AO4+6u6LKSyOc1Lw52akzOyNQI+7d0edZRyvdvcTgdcDFwfdi1FLAScCV7n7EmA7cFm0kXYLup7OB34WdRYAM0tTWPP7MOAQoMnM3h1tKnD3R4B/A26n0M2zAhidquOr8Ecg6EO/EfiRu98UdZ69BV0DdwHnRhwF4BTg/KA//SfAGWb2n9FGKgjOFnH3HuAXFPpjo7YeWF/019rPKTQEleL1wHJ33xR1kMDrgKfcvdfdR4CbgFdFnAkAd7/W3Tvd/VRgC/D4VB1bhb/MgkHUa4FH3P2KqPOMMbPZZjYjeD0NOAt4NNJQgLt/1t0PdfcMhS6C/3L3yM/IzKwpGJwn6Eo5m8Kf55Fy9+eAZ8zsqGDXmUCkFw7s5QIqpJsn8DTwSjObHvy3eSaFcbfImdmc4HkRhf79H0/VsSNZiKUczOwG4HRglpmtB77g7tdGmwoonMG+B1gV9KcDfC5YnCZK84HrgysuEsBP3b1iLp2sQHOBXxRqBSngx+5+a7SRdvkY8KOgW+VvwAcizgPsaiDPAi6KOssYd7/fzH4OLAdywINUzvQNN5rZTGAEuHgqB+lr9nJOEREZn7p6RERiRoVfRCRmVPhFRGJGhV9EJGZU+EVEYkaFX+QAmNlA0evzzOxxM+uIMpNIWDV7Hb9IOZjZmcDXgHMqaNIxkQmp8IscoGBunmuA89z9yajziISlG7hEDoCZjQD9wOnu/lDUeUQmQ338IgdmBPgT8KGog4hMlgq/yIHJA2+nMH3156IOIzIZ6uMXOUDuvsPM3gDcZ2abKmQSQJH9UuEXOQju/oKZnQvca2a97n5z1JlE9keDuyIiMaM+fhGRmFHhFxGJGRV+EZGYUeEXEYkZFX4RkZhR4RcRiRkVfhGRmPn/XNTtJm32YuYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.9.1",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.9.1 64-bit ('base': conda)"
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  "interpreter": {
   "hash": "c2ca6912df4842e931c7c3a5a16a0959c1781efc9bb19bffe6a5f271c5364947"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}