k-means-clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>mean_dist_day</th>\n",
       "      <th>mean_over_speed_perc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3423311935</th>\n",
       "      <td>71.24</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423313212</th>\n",
       "      <td>52.53</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423313724</th>\n",
       "      <td>64.54</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423311373</th>\n",
       "      <td>55.69</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423310999</th>\n",
       "      <td>54.58</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean_dist_day  mean_over_speed_perc\n",
       "id                                             \n",
       "3423311935          71.24                    28\n",
       "3423313212          52.53                    25\n",
       "3423313724          64.54                    27\n",
       "3423311373          55.69                    22\n",
       "3423310999          54.58                    25"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"driver-data.csv\", index_col=\"id\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=4, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 49.98428468,   5.21441441],\n",
       "       [180.34311782,  10.52011494],\n",
       "       [177.83509615,  70.28846154],\n",
       "       [ 50.46127059,  32.42823529]])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 3, 3, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique, counts = np.unique(kmeans.labels_, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 2775, 1: 696, 2: 104, 3: 425}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_data = dict(zip(unique, counts))\n",
    "dict_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"cluster\"] = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sjangir\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\seaborn\\regression.py:546: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x143d7a21588>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdMAAAGoCAYAAAAdGw+vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvXmYZGlV5/8598aSmZVZlVXZ1dVNF00XTdNO0wI6NUCLjAi2TyuruC+AyIgzg8ug46gj83N5YMRBZVRwYUQFUUFaFARlYBrZFGgahaZoLOimF6uprq4ts3KJjOXe8/vjvTfiRmREZNyIuBGREefzPPlkxo27vDcyM0683/Oe7xFVxTAMwzCM/vHGPQDDMAzD2OtYMDUMwzCMAbFgahiGYRgDYsHUMAzDMAbEgqlhGIZhDIgFU8MwDMMYEAumhmEYhjEgFkwNwzAMY0AsmBqGYRjGgOTGPYBBueWWW/R973vfuIdhGIZhDIaMewCDsOdnpufOnRv3EAzDMIwZZ88HU8MwDMMYNxZMDcMwDGNALJgahmEYxoBYMDUMwzCMAbFgahiGYRgDYsHUMAzDMAbEgqlhGIZhDIgFU8MwDMMYEAumhmEYhjEgFkwNwzAMY0AsmBqGYRjGgFgwNQzDMIwBsWBqGIZhGAOSaTAVkT8UkYdF5ERi2yER+YCIfCn6fjDaLiLyWyJyt4jcKSJfm+XYDMMYnOrqWbZO3s7GnR9m6+TtVFfPjntIhjEWsp6Z/jFwS8u2nwVuU9XrgNuixwDfAlwXfb0M+N2Mx2YYxgBUV89SfuAuwkoZ/BxhpUz5gbssoBozSabBVFU/Alxo2fw84M3Rz28Gnp/Y/hZ1fAJYFpErsxyfYRj9Uz1zL4iH+D4igvg+iOe2G8aMkRvDNY+o6mkAVT0tIpdH268C/jWx36lo2+nWE4jIy3CzV66++upsR2sYRlvCcgn8lrcQz3PbI6qrZ6meuZewXMIrzpM/coz88uGmQ3rZp5f9u52n3+f6HaMxe0zSAiRps03b7aiqb1TV46p6/PBh+4M2jHHgFechDJs3hqHbTm8ycFqpuNP+2w/e3fE83a6RxRiN2WQcwfRMLN9G3x+Otp8CHpnY7yjwlRGPzTCMHskfOQYaokGAqqJBABq67fQmA6eVijvu//B9Hc/T7RpZjNGYTcYRTN8NvDj6+cXAuxLbXxSt6n0KsBbLwYZhTB755cMUr74Br1CEoIZXKFK8+oa6/BmWS+C1vMW0yMC97JOk0/4EQcfzdLtGFmM0ZpNMc6Yi8ufA04HLROQU8AvAa4C/EJGXAg8A3xnt/rfAtwJ3A1vAS7Icm2EYg5NfPtwxd+gV5yNp1G9sTMjAve7Tyznx/cb3Nufpdo1hj9GYTTINpqr6vR2eemabfRV4eZbjMQxjdOSPHKP8wF1ogJvZhWGTDNzrPj2d8/JrqJ1/sON5ul1j2GM0ZpNxrOY1DGMGcDPWG7qugu1ln17PWd13oMt5ul1juGM0ZhNxE8K9y/Hjx/WOO+4Y9zAMw9hDWKnLRNKuomPPMEmlMYZhGJljpS5GFlgwNQxjprBSFyMLLJgahjFTWKmLkQW2AMkwZoxZzxdaqYuRBTYzNYwZwvKFuzs3GUY/WDA1jBnC8oW7OzcZRj+YzGsYM0QvnV72AoNK1d2cm4Z9LWM2sJmpYcwQu3V62QuMUqo2WdzoFQumhjFDTEO+cJRStcniRq9YMDWMGWIa8oWjLG2xMhqjVyxnahgzRpp84SQyytIWK6MxesVmpoZh7ClGKVVPgyxujAYLpoZh7ClGKVVPgyxujAaTeQ3D2HMMS6rupexlr8vixmiwmalhGDOJlb0Yw8SCqWEYM4mVvRjDxGRew5hCzLVnd6bFDcqYDGxmahhThsmXvTENblDG5GDB1DCmDJMve8PKXoxhYsHUMKYMc+3pDSt7MYaJ5UwNY8rYzbVnVPnUvZC3tbIXY1jYzNQwpoxu8uWo8qmWtzVmDQumhjFldJMvR5VPtbytMWuYzGsYU0gn+XJU5SDTXnayFyRsY7TYzNQwZohRlYNMc9mJSdhGOyyYGsYMMapykGkuOzEJ22iHybyGMUM4KfKGzCXKUV2nE1nKsNMuYRv9YcHUMGaMUZWDjKvsJJZhEa9JhoXh1JBaw3CjHSbzGoYxVWQtw06zhG30jwVTwzCmiqwdoMw5yWiHybyGYUwVo5Bh97pzkpX2DB+bmRqGMVWYDNsdK+3JBgumhmFMFSbDdsdKe7LBZF7DMCaGbvJjGmlyr8uwWWKlPdlgwdQwjImgW0kLkGm5yyxhpT3ZYDKvYRgTQTf50aTJ4WE55WywmalhGBPBrvKjSZNDYdzuVNOKBVPDMDIjTZ5zN/lxEGnSSkGasZzy8DGZ1zCMTEhbgtFNfhxEmrRSEGMUWDA1DCMT0uY5u5W0DFLuYvlWYxSYzGsYRib0U4LRTX7s9NxuEq6VghijwGamhmFkwigahPci4U5zo3JjcrBgahhGJoyiBKMXCddKQYxRYDKvYRiZMEgJRq+rb3uRcK0UxBgFFkwNw8iMfkow0jT37tXNx0pBjKwxmdcwjIkizepbk3CNScGCqWEYE0Wa5t7WIcaYFEzmNQxjokhrxB5LuHGetfzAXVTPWF7UGC02MzUMY6LoR7o1lyNj3FgwNQxjouhHujWXI2PcmMxrGMbE0br6trp6lq2Tt5vLkTGx2MzUMIyJxlyOjL2ABVPDMCYaczky9gIWTA3DmGh6KZWxEhlj3FjO1DCMicZcjoy9gM1MDcOYaEzCNfYCFkwNw5hoTMI19gJjk3lF5BXAfwAU+BzwEuBK4G3AIeCfgBeqamVcYzQMY/z02kFmHEzy2IzRMpaZqYhcBfw4cFxVbwR84HuAXwVep6rXAReBl45jfIZhTAaT7Gw0yWMzRs84Zd4cMC8iOWABOA08A7g1ev7NwPPHNDbDMCaASXY2muSxGaNnLDKvqj4oIr8GPACUgPcDnwZWVbUW7XYKuKrd8SLyMuBlAFdffXX2AzYMoy8GlUE7ORsFW+tdHZGyJL6nYP0iiEBhDonHaK5LM8u4ZN6DwPOAY8AjgH3At7TZVdsdr6pvVNXjqnr88GHLTxjGJDIMGbSds5FWKxAGY5FXm+7J80AVrZTQIJoDmOvSzDIumfebgHtV9ayqVoF3Al8HLEeyL8BR4CtjGp9hGAMyDBm0bVlMUIVcfizyavKeyBVAAAWtbFvJzowzrmD6APAUEVkQEQGeCdwF/D3wHdE+LwbeNabxGYYxIGmafHeiXVkMfg7JFQY6b78k78nL5SE/V5+hWsnObDOunOknReRWXPlLDfhn4I3Ae4G3icirom1vGsf4DGMWqa6epXzqJFreAsCb20fhqsf2HRzSNvnuRKuz0dbJ24dy3n5ovScvl0fFwysUWbj+SZlf35hcxraaV1V/QVW/SlVvVNUXqmpZVb+sqk9S1ceo6neqanlc4zOMWaK6epbte+9EtzdBFVQJS+ts3/e5vnORWTkXjdMRydyYjE6YA5JhGC4XGAYuB+iJ+xIPglrfucisnIvG6YhkbkxGJ8zo3jCMKN/YZvG8hgPlIpMSbVxSUn7groHLWcZpam+G+kY7bGZqGEaUb5SdT4g3lFykuQUZ044FU8MwXM7P893kNFT3pSH4uaHkA80tyJh2TOY1DMPJlsce37Kad3Gg1bxJOjkZtUrIozSO7+VaZmRv9IoFU8MwgGxzgb2UycRSMOI1ScEw/AU+vVxrlOMx9j4m8xqGkTm9lJSMUgru5VomTRtpsGBqGEbm9FJSMgzHpF7p5VqjHI+x9zGZ1zCMTGiXb+zmEpSUgjWoodWyq331c1RXzw4srSbHo0EVwhApFBs7tMjOw3JwGtaYLWc72djM1DCModNPKUwsBYfVslsEFYaAgPgDl9G0jgfPh1qZsFLuKDuP2+3Iyon2FhZMDcMYOv3kG2MpmDAAxEmshTknDQ+Yq2wdj5cvQr4IGnSUncftdmQ5272FybyGYQydYGvdzeoqoVsN6/kQ1AjKW2ydvL2jXJlfPkzZz0NhHtdQKiJFrrKdNNquNEdyBQhqLD7+Gzqeq76yNzpfHMhGEVB7LScyJgObmRqGMVSqq2fd7DJu6h2GUKs4EwjP21WubNcQvNdcZSdpVPxcX+ccp9Q6yOtgjB4LpoZhDJXqmXshl4/cCaPu2TG5wq5y5SC5yk7SqKr2dc5xSq3jztka6bBgahjGUAnLJSRXQArzkJRqEddQG7rKlYPkKjuVsxAGfZ1znOUx487ZGumwnKlhGEMlLikRP4f4OcLtTSdXJoPSLnJlv25M3cpZ+jnnKMpjupW/WIeavYPNTA3DGCqt8qRbRKPg5TKXK4ctjWYttVr5y/RgwdQwjKHSKk/6c/vIX/kY/Pl9mcuVw5ZGs5ZarfxlejCZ1zCMoTNqeTJLp6As78XKX6YHm5kahrGn2ctSqZW/TA8WTA3D2NPsZanUyl+mB5N5DcPom93k1Szk19ZzBlvrSGGueac9IpW61+IGM7OfAiyYGobRF7s1z86iuXa7cxIGaLXStQPMJGPlL9OBybyGYfTFbvJqFvJr23Pm8hBUTSo1xooFU8Mw+mI3d6As3IPanVNyBfBz5hRkjBWTeQ3D6Ivd3IE6PS9+js3P/wPh9gao8+2VuUWKR6/vGADjPKlWy1Ato/liw5owDPHnF+uNx+N9yw/c1TUHaY23jWFiM1PDMPpit5Wo7Z7XoEpY3iIsrdcDKYBub7B9751ty1maSl/yRdd9prJNWK3suGavZTJ7uZzGmEwsmBqG0Re7uQO1e15yhaYg2kQYtM2nJvOkXi6PFBecgX6tsuOaveZp93I5jTGZmMxrGEbf7LYStfX5jTs/7GaWbdG2+dRWlyDxczDnrAljabfTvkDbPG1yPw1qTj4OA4JKierqWZN7jdTYzNQwjJHhFeddWUtbpG05SxqXoF73jffToIZWStFs2bWLM7nX6AcLpoZhjIz8kWM7Z44xnt+2nCWNS1Cv+9b3q2xHvcvVxdJ80eReoy8smBqGMTLyy4eZu+ar8eaXmhqHy9wic8ce31ZeTdO5pdd94/3cxXGz5fycWyG8R9yTjMnCcqaGYYyUfhx/0hzT67755cNUF5cJSpsQ1qC6TVirgJdz7eIMIwU2MzUMY2bxFg9BrezyrIr7Xiu77YaRApuZGoYxs4QbF1yeNKi5Vcax5+/GhXEPzdhjWDA1DKMto3AIyvIavZw7LJeQXAHJN0zyVduX6BhGN0zmNQxjB6NwCMryGr2e25pzG8PCgqlhGDsYhUNQltfo9dzWnNsYFibzGsaM004O7dVJaBCyvEav57bm3MawsGBqGDNMpwbe4ufQMOzYEWYY7NZ1ZlTntubcxjDoWeYVkTeLyHLi8UER+cNshmUYxijoJIeqaubyZ5YSq8m3xqhJkzN9vKquxg9U9SLwNcMfkmEYo6JTA2/CoGfXoX5J42w0Sec2jHakkXk9ETkYBVFE5FDK4w3DmDC6yaGjkD+zukacBw621gElKAUQLT6ygGpkQZpg+OvAP4rIrTivkO8CXp3JqAzDGAn5I8coP3AXGhDNSMM9L4fGeWANQ2fGAECNYHuT8IG7AJuhGsOnZ5lXVd8CfDtwBjgLvEBV/ySrgRmGkT3TKIfGeWDCmjOx98Q9DmrWEcbIjJ5mpiLiAXeq6o3AXdkOyciCyj0nKH/8A4Sr5/GWVyjedDOFa28c97CMCWDaVrPWy2Jam5BrOLaOMKNwkzLGS08zU1UNgc+KyNUZj8fIgMo9Jyi97+2EG2swt0C4sUbpfW+ncs+JcQ/NMIZO3dWotQm5eGNxNxqFm5QxftKs5r0S+LyI3CYi746/shqYMTzKH/8A+D6SL7ryh3wRfN9tN4wpIy6LwctFnWBcmU88Wx11PngUblLG+EmzAOmXMhuFkSnh6nmYW2jemCu47YYxZSRdjYKtEFAQD39u31jk1VG4SRnjp+dgqqofFpFHAdep6v8TkQXA3+04Y/x4yytO4k10xqBWwVteGd+gDCND2uWB47xl+YG7Rpq3zNLpyZgc0jgg/TBwK/D70aargL/OYlDGcCnedDMEAVotOzeYahmCwG03jBlgnHlLc2OaDdLkTF8OPBW4BKCqXwIuz2JQxnApXHsj87d8N97iAdjewls8wPwt322reY2ZYZx5y2ksPzJ2kiZnWlbViogAICI5XHrf2AMUrr3Rgqcxs4w7bzlt5UfGTtLMTD8sIv8dmBeRm4F3AH+TzbAMwzCGhzUBN7ImTTD9WZzz0eeAHwH+FnhlFoMyDMMYJpa3NLImzWreUETeDHwSJ++eVFWTeScUczwyZpXObkOz0QTc3JbGQ8/BVESeBfwecA/O8fKYiPyIqv5dVoMz+iN2PML3mxyPuAULqMZU06nZeWxuP+1BZbf7N7Ijjcz768A3qurTVfUbgG8EXpfNsIxBMMcjY1aZdbehWb//cZImmD6sqncnHn8ZeHjI4zGGQLh6HnKF5o3meGTMAJ2anc+K29Cs3/84SRNMPy8ifysiPygiL8at5P2UiLxARF6Q9sIisiwit4rIv4jIF0TkJhE5JCIfEJEvRd8Ppj2v4RyPqFWaN5rjkTEDzPqq3Vm//3GSJpjO4XqZfgPwdNzK3kPAc4Bn93Ht3wTep6pfBTwB+AJuxfBtqnodcFv02EiJOR4Zs8qsr9qd9fsfJzKsBbki8nOq+is97rsf+Czw6OSKYBE5CTxdVU+LyJXAh1T1+m7nOn78uN5xxx2DDH0qsdW8xqwy66tZ9/D9y7gHMAhpHJB24zuBnoIp8GjczPaPROQJwKeBnwCOqOppgCigtrUrFJGXAS8DuPrq6WyxOo5gaAHYSMOkvmnPwqrdbsz6/Y+LNDLvbqT5VJEDvhb4XVX9GmCTFJKuqr5RVY+r6vHDh6fvj2bQZt79HG8NxI00WMNrw2hmmME0jV58Cjilqp+MHt+KC65nInmX6PtMrhYetLSln+OtnMZIg5VgGEYzY5mZqupDwL+KSJwPfSZwF/Bu4MXRthcD7xri+PYMg5a29HO8ldMYabASDMNoZpg503ek3P/HgD8VkQKuZvUluOD+FyLyUuABXB525hi0mXc/x1sDcSMVno9ub6Koc9sRD8IAgK2Tt480f7r94N1UH74PggB8n/zl1zB31WNGcm3DiNk1mIrIb9NFwlXVH4++/880F1bVzwDH2zz1zDTnmUaKN91M6X1vRym72WKtkqq0pZ/jB72mMTtUV8+itQpoCAhoALhASmF+pBZ22w/eTfX03dSFsSCIHmMB1Rgpvci8d+BW287h8ppfir6eSP0/yBgmgzbz7ud4ayBu9Er1zL2In0eKC81Sr3h4ufxI86fVh+8DBLzEFxJtN4zRsevMVFXfDCAiP4jz5q1Gj38PeH+mo5thBm3m3c/x1kA8Oya1jKQf4kbbbuFRjrC0HmlXCQGrJX+a2f0HHT7Pd9reB9P0uzOyI80CpEcAS4nHi9E2wzC6MG1lJDss68Rr/g5NFnaZ3r/vp9uekmn73RnZkSaYvgb4ZxH5YxH5Y+CfgFR5UsOYRaatjKTVsg4vByj4ubYWdlnef/7ya9y1w8QXGm0fnGn73RnZkaY5+B+JyN8BT442/WxU4mIYRhdiWbSJjMtIspQmWxtt+/P78BYfSbhxoX49b/EQ1TP3Un7gLucNnS8iJGaLnkdQ2mDr5O0DjTFeZJRczevtP0zt4kOsP/Rld6m5fRSuemxf9z+O352xN0nTHFyAb8L56f6yiFwtIk9S1duzG55h7H284nwkEyaCSYadPEbRILqbZV3r9amWoVJCoxwr4FYDB7Ud8mk/Y5y76jGNoLp6lu1774SgVn8+LK2zfd/n4JqvTn3uUf/ujL1LGpn3d4CbgO+NHq8Dbxj6iAxjyhh1J49xS5Ot13e1y4JWthv3X6uCnx/6GKtn7nX1rkJjda94ENT6Ord1YTF6JU0wfbKqvhzYBlDVi0Ch+yGGYeSXD1O8+ga8QhGCGl6hSPHq7Gowx+1O1Hp9L5dvmIFE94/nI/mWt48hjNEd36YsXsO+zj3q353RGyLyiyLyX/s4bllE/nMWY0rjgFQVEZ/oL1VEDgNh90OMrKncc4LSbX9FeMHZGHsrR5h/xvO7lrhYd5jRM8pOHuOWJttdXzwfb3GZheufBDiXpKC0iYY1Z/4gHng5/Pl9A187qJbZEVDF6/v+rQvLVLEM/Gec0toTUYpTVLVrvEszM/0t4K+AIyLyauBj2GresVK55wSb734L4fkzgIIq4bmH2PqbP+nY7cW6w0w/45Yme7m+t3gIamVXYqO477Wy2z7otT0/Ome0uldD8HMmze5hRORFInKniHxWRP6k5bkPicjx6OfLROS+6OfHicjtIvKZ6NjrcFUp10bbXhvt99Mi8qlon1+Ktl0jIl8Qkd/BVa48crcxplnN+6ci8mmc3Z8Az1fVL/R6vDF8yh//AFS2QaK8kODexMolyh//QNvZZrI7DAD5Ikq54/7G3qN1te2ojQZ6uX64ccFJv0FiZurn3PZBr33s8ZRPnUTLWwB4c4t9r+Y1xo+IPA74eeCpqnpORA4BP97Dof8R+M0odhUAH9fq80ZVfWJ07m8GrgOehHsHfbeI/HucN/z1wEtUtSdZOK3R/WXAVlQmc1hEjqmqFVyNiXD1vPtEHxfLh24GgCq1U1+mcs+JHQEyXD0PcwvNJ7LuMFPHuKXJ3a4flktIrtD4UAeo6lDyuuO+d2PoPAO4VVXPAajqBae87srHgZ8XkaPAO1X1S22O++bo65+jx4u44PoAcL+qfqLXQfYs84rILwA/A/xctCkPvLXX443h4y2vRAs91AXSMACNckUibeVbb3nFmdgnse4wxojZ4aIEVnJidELo3i+7RiOWzcUbVfXPgOcCJeD/isgzOpz7V1T1idHXY1T1TdFzm2kGmSZn+m3RwDajgX6FZntBY8QUb7oZCnMugIYJL1LPw1tabtvcu3jTzRAEaLXs8lnVsnWHMUbOuPO6xp7iNuC7RGQFIJJ5k9wH/Nvo5++IN4rIo4Evq+pv4XplPx5X0pmMW/8X+CERWYyOuUpELu9nkGmCaUVVnVeXu+hgy+6MgSlceyP7nvsivJUjjY25PN6BFaQ431a+te4wxiRgJSdGr6jq54FXAx8Wkc8Cv9Gyy68B/0lE/hGXioz5buCEiHwG+CrgLap6HvgHETkhIq9V1fcDfwZ8XEQ+B9xKn5NEUe02e07s6Gp6rgNuBn4F+CHgz1T1t/u58LA4fvy43nHHHeMcQl8Muzxl/a2vI9xYa85BVZ27jDe/b6DrWCmNMQiDWhta15aZoadE6KTSczAFEJGbcclagPer6ge67T8K9mIwjctT8P2mRtyDzBDbnVO3S6gq3vxC39fJYqzG7NBkLeh5USlM2PMsdNDjjT3Fng6maWRegM8BHwU+Ev1s9EGyPEVE3GyyTX4zDe3kW1lYxJtfGOg6WYzVmB0GtTYctzWiYfRKGqP7/wD8f8AHcZ8gfltEfllV/zCrwU0rWZWntDb3Xnv9Kwe+jpXSGIMwaNeVrLq2TIN0PA33ME2kmZn+NPA1qvqDqvpi3Oqpn8lmWNPNqMpThnEdK6UxBmHQEpgsSmimoeH3NNzDtJEmmJ7CLSuOWQf+dbjDmQ1GVZ4yjOtYKY0xCIOWwGRRQjMN0vE03MO0kcYB6UHgkyLyLlx5zPOA20XkJwFUtXW5stGBwrU3wi1ktkI2ufqW4hzhdgkiWbapjGYCxmrsDfqVFNNYG3a+xnCtESel4fcgMu2k3IPRIE0wvSf6inlX9N2MG/qgNb85LJpW384tEG5egq11ZN9+ZGEJahX3/C30fP2sxmrsDQZtNt6Lvd9u1xhmLnDcXXVg8Nd0Eu5hmhGRW4DfxPn5/oGqvma3Y9IY3f9S4kIesKiql/oZqJEdO4zsK9uAoOUS3r79ZmxvpCYpKQLg+2jgtg8ryI3iGjH5I8coP3AXGtBUbjNK96VB73cS7mFaiVqNvgHnqXAK+JSIvFtV7+p2XJrVvH+Gc+EPgE8DB0TkN1T1tf0P2xg2O1bf1mquq0xQa2yz1bhGCkYhKY5Sthx3Vx0Y/H4n4R4mhVf+ceUW3ALZY8C9wGtf9YOF9w1wyicBd6vqlwFE5G24tOZwgilwg6peEpHvB/4Wt5L304AF0xHQqwuRt7ziepXGM9NcDqpVQAnOPgh+DinO4x9qbz9pbkdGK6OQFL3iPMH2JtrSks2fy8a1dBDpeBglKcN4Ta07Tj2QvgEoAxeAK4E3vPKPKy8fIKBeRfPi2lPAk3c7KM1q3ryI5IHnA+9SVfcObWROmoberatvXaPkEFcaLFCroZuX8B/12IGuY8wOozCl9xYPQbWlWXh18Gbhw2ZYJSlm9D80fhoXSLeix1vR458e4JztnJh2jXVpgunv49z59wEfEZFHAZYzHQFpXIhanZBEFYoLkM+77jK5HCwsEdz/xYGuY8wOozClDzcuQK7o8n+C+54rDtwsfNgMqyTFjP6HxjEagTRmK9reL6eARyYeHwW+sttBaRYg/RbwW/FjEXkA+MbE4xer6pt7Pd8sMCzJNFw9j4oQrp9xuU8/hywsdcx7Jlffrr3+lcjcAsmmuKra9thB3Y5MIp58BilxyeqNvrp6lmBjFVAn7+bn8HL5gZuFZ+EQNMzcrsm0Q+FenLSbDKgL0fZ++RRwnYgcw5WEfg/wfbsdlNabt446Eqta+Il+zzWNDFUyLc6hly5CELg3myBwj4tzux6axsFoELcjk4gnn0l0zamXiIAT0jSE6jZhrTpQXjare7Wm5hPHa4EiLoASfS8ywFqeKK79KK7X6ReAv4jawHWl72Dahj3t+D9shiqZJjv7aIftHUjjYDSI25FJxJPPJLrm1MdUmIveQcT9jVfLA+UQs7pXy3VOFtEio5cDp4FD0fdBFh8BoKp/q6qPVdVrVfXVvRyTZjXvrtcf4rn2PEM1iK+UYekglNZdqUsuB/NLbnsvFIqE588A4B26nPmbv6Ot/DqI25EZ4k8+k+iaE49JRKAw7z7ARQvmBskhZnWvVpIyeUSBc6DgOQyGGUyZ+QBYAAAgAElEQVRtZppgR4kK9G0Q7y2vwMYacqhhBajVsltk1IWkG5J32ZVOwq1Wuh7Tr9vRMO/XyIZJdM1Jjkn8HOLn0CDAKxQHClBZ3qvlOo129CTziognIt+1y27/MITxTA3DNIjv91yjlF7NEH/ymUSJMqsxTeK9GtNNT8FUVUNcQrbbPl2fnzXaNeuev+W7+5r19XuucPU85ArNGzOSXod5v0Y2TGI5RlZjmsR7NaYb0R4WsQCIyP8ASsDbgc14u6qOtRDs+PHjescdd4xzCBPL+ltfR3DhYbRcciU14tXzUbmjj+4pH2rlLoaRHdbgu4k9nSpMs5r3h3Crpj6CsxH8NGBRbILxH/VYdPOSW7SkQK3qymuK8z2Vrli5i2FkxySWKhn903MwVdVjbb4eneXgjMEI7v8iLCy51b9hAIibndYqPeVPrdzFMLJjEkuVDIeI/KGIPCwiPc8c0nSNWQB+ErhaVV8mItcB16vqe/oYq9EnnWTXeHvt7FeQIIBcDt0uweIy/qEDBA8/6AKpUO8gE9YCwlNfZu31r2wr4Vq5i9GJpDyJ54KBBjWTKlMwiaVKRp0/Bl4PvKXXA9KUxvwRTtr9uujxKeAdgAXTEbGj8Xcku9Yefz/VOz/hXGPKJeflUK24wLl+kVBws9MgcHKvnyPc3oL1i84DNXGuZNNwK3cx2pFsbK2qsL2JolCYT93kepaZxFKlvciHP7+1owXbNzxuYVDTho+IyDVpjkmTM71WVf8XUI0uVmKPJ4z3Gp1k18onb3P/kFEjcDzf9TAV9+vVjTVn8qAKqsjCktsGeEvLHSVcK3cx2tHU2DqoRg2JovSBSZU9Y+U7gxMF0jfg/HnrLdii7SMlTTCtiMg8kdORiFyLa3VjjIhOpS5aKbvttRr1zzcioCGy/yCo4qF4K0fwLrvCdZJRRfYfRJKfglskXCt3MdrhpN3orUMTPrXxzyZV9oSV7wyFLFqw9UUamfcXcZZNjxSRPwWeCvxgBmPaM/RTNpI8hkLRBb3ydk/Ht5Ndw0sXQEPCM3Ev26AxK83lIKghuTxaq8D2Fur75A4/wpnnlzYJLiQ60bRpGt6vI5IxvTTJk/VyK+pKSKtUOevlH93u39yUBuYYbkaaZNAWbH2RZjXv+4EX4ALonwPHVfVD2Qxr8umnbCR5TIgQnj9DeO4h116th+NbZddg7TyUNnfuqBqt3gXdvISKoKUttFqBcona+TOEaxec1BvPZrs0DTeMJEl5Ej/f6PaSK+yQKme9/GPW738E3EujY0zMoC3Y+qLnYCoi7wa+GfiQqr5HVc9lN6zJp5+ykeQxlNajGaSgW+s9Hd8qu7Idpa2lTepaBKpVVxoTRrNVz3f7V7YjEwdxs9ddmoYbRpKkPCkiyNw+vPkl19O7Raqc9fKPWb//ETD0FmwAIvLnwMeB60XklIi8dLdj0uRMfx14GnCXiLxDRL5DRHZvqDml9GPV13RMreb6IoYBVMoEF864UpU0ZScautyVKtEqkPpT3uVH3fd9+51ZQxg4o/ug5rrNRP0iZWEJ//Kr8A8dwdu338pejJ7ILx8mf+RY1N8zQHJ5ilffwML1T2qSLZvyqzEzlFOd9fvPmmjV7o4WbENYzfu9qnqlquZV9aiqvmm3Y3rOmarqh4EPi4gPPAP4YeAPgf19j3gP00/ZSNMxInUpFnBlK+sXYeVIx+NbS2O4dKFhxkAcUHHnrlWQQhHdWodQ6dQhT9cvEgLe3IKVvRg9kyyPScqXrSUxs17+Mev3PwqiwDn2FmypmoNHq3m/HfiPwL8D3pzFoPYC/ZSNJI9pqipKFm63k2wjWqVl5hejZ7T5e2EegoDCk5+Jbm10PqfngYJurlvZi5GKXuXLWS//mPX7nyXSOCC9HXgy7hPAG3C507D7UdNLP420C9feSO3x97u60KCKqwmNZpXRSsjw7GnW/s+rnHRbKTedt9WRyCvOEW5tUA+iIi4whzXCtQuU/+F9TtbN5SEgserS5Vm9A9FMuVbFWzzQs4m9md8bvbr37JVm2lmtON4r928MTloHpO9T1WDXPWeEtGUjlXtOUL3zE8jifjQMnLQrwNw+t6BIFXyf8NxD7oClg5BwJkrKxFouEV6KHIz8HN7+ZcLSVj0H62ajUZCt1fAOXka4ecldE9wMtziP53l4iwdY+oFX9HwP7VyYks5JxvSTRr6c9PKPXiXrfpn0+zeGQxqZ9zbg5SJya/T1YyKSz2pg00hSpvUWD0TGCsDWugukQvQ9WqFbal7lm5SJw421+jGyb8ntV9l2QTleuSt+tIJXCddXd7og9SHtmvm9AdMlX9qKW2MYpAmmvwv8W+B3oq+vjbYZPZJczSvFebz9B90ne1VnmrB0ECf5Ritza86QPl4l3FQaU6vVj/Fi6TdMyLgx4kWF9TtdkPpxNBplw3Fjcpkm9x5bcWsMgzQy779T1SckHn9QRD477AFNM97yCsGFh90/aew6NLfgum4s7kfyRYKtdSfFhiGgrtuL5+EddG9SceALHvpXtLKNrp0nWD0PhQJ1WTee3boH4PvkrjrWJOXGec/Nd/w+m1EXGXCz3IXnvKhjgDXzeyNmWuRLW3FrJBGRR+K6xVwBhMAbVfU3dzsuzcw0iPx44ws+GresxeiRpmbdSdeh6766Lt/KwlLU3SVssmfTrQ0q95yo5yw1ri/VqOylUnE/5wsNByQN3M+FuSYpNz5H7cH76u3YYnRznc2//D8dnZjM/N6YNqZJsjaGQg34KVX9N8BTcOnNG3Y7KM3M9KeBvxeRL+Oye48CXtLPSGeVerPuyrYLqLkcFOZgfZX5W767vkJW8/lGDaqfc8YLntfIS/q+C4Ke74JunDtdPIDM7wMgPH8GAG/lcuaf+W1NM80470mt0n6g1Qrlj3+g7ey0n1XMhjHJ2Irbvc36He/b0YJt6fgtfdedquppnPkDqrouIl8ArgLu6nacqLYv5m+7s0gRuB4XTP9FVcuJ525W1ZGvQjl+/Ljecccdo75sX6y9/pUwt+BqRCNUFd1Yxb/synpwCs6dRhaXd+zHdtQYYW6B8OyDiZkpkTVgHqoV1wkml8O/7EpYWib40ufQShkpFCk8+ZmU7/iQsxqsdmn6Iy1dQfwcxafewsLTnjXEV8SYVUZhfj/rBvt7kNQtPaNA+gYanWNiO8GXDxJQ6wNyPU0/Atyoqpe67ZvKtEFVy6p6p6p+NhlII3411ShnEG95ZcdsULfW0fJ2k2G+lredc1GSKC/pLa80XI3qNoK4n6vu3FqtoKUtal+5n9qJ212LNs9DqxXKH30vbG06O8FuaNjcXiuoUf7Ie9j66HsHfBWMWWcU5u9mMD8zZNaCTUQWgb8E/stugRRSBtPdrj3Ec00l7fKNurXhZqvJUpO5BXRro21esnjTzS2uRi3Kguc3moPXZ56RKYTnN3KsffptVD55W7+3bxjAaEpRrNxlZjhGI5DGDNyCLSr7/EvgT1X1nb0cM8xg2rteHCEivoj8s4i8J3p8TEQ+KSJfEpG3i0hht3PsJdo126Y453KiCbx9+932Nk25C9feCMU5J+mKlwiqsS9v9CtNlse0k/I9r3mfHtGK9YM3BmMUpShW7jIzDL0Fm7j82puAL6jqb/R6XJoFSFnwE8AXaJjl/yrwOlV9m4j8HvBSpqyWtdU1af2tr2tfarKvc/+A3OFHUDt/xsXPWg0IGx9lgg7ybetio/oslZYVvQnnpDZIoTFOsxU0+mEUpShW7jIzvBaXM4XmnOkgLdieCrwQ+JyIfCba9t9V9W+7HTTMmel9aXYWkaPAs4A/iB4LrhvNrdEubwaeP8TxTSTtpN+wtIVubXRsPO4/6rHONalWaxg8pBUGglrjq4nu5/Gv+2qgv+bohgGjKUWxcpfZIFpktKMF24CreT+mqqKqj1fVJ0ZfXQMppJyZisjXAdckj1PVt0TfX5ByzP8b+G/AUvR4BVhV1fjd/RRuOfJU067UBD8HQc3lT8F58VKul6sE938R2bcfjc0f8nnnx9srsSF+chGSiJOIwy6lw3MLsL4KtDQ6bzNGw+jEKEpRrNxldogC59hbsKXpGvMnwLXAZ2iYNSjOKSIVIvJs4GFV/bSIPD3e3GbXttMkEXkZ8DKAq6++Ou3lJ45W6TcuoQm3t9CoqwtAeOEslXtOUDt9v1u5GzsdFQvpgqlqcyCNFywF7QJpYtYbBNQeuJv1t76O2tmv4C0dbN7VbAWNHhmFe9I0ODSdX69x6nyNUkWZLwhHV3KsLI07O2e0I81v5Thwg6YpTO3MU4Hnisi3AnO4nOn/BpZFJBfNTo8CX2l3sKq+EXgjuDrTIYxnovCWV1xOdGu94bcLoCGb73hjc15U1e03CN1mo8nPM4Ezmgg31qC8Tehdwl880HjebAUNY2icX69x9+mqKyH3oFxV7j7t/vctoE4eaXKmJ3BehQOjqj+nqkdV9Rrge4APqur3A38PfEe024uBdw3jenuN4k03O4OGsKV8xfPbLDAaYUWSCN7iAVfGs7AI21tmK2gYGXHqfM1lZDxBRKLvbrsxeaT5eHMZcJeI3I4rigVAVZ87xPH8DPA2EXkV8M+45clTSXIlLIWik1nL2/VVsVKcQ2MpNm6phlBX2D0/mlGOaGKeLyDFedcT9dIFd30/h7d4wFbzGjNJqwR7YF5YK+nQJNlSRcm1VveI225MHml+07+YxQBU9UPAh6Kfvww8KYvrTBLJBtshApGPruw/WF8VKwtLaHk7you2WPtBQprtYyVvGvwc3soRtLTpzCLAjSdwJvoWQI1ZpFWC3dwOWd2EQg7yvgxFkp0vuPP4CfEpVLfdmDx6/i2r6oezHMgs0bQSdv1M3TxBt9bxDh1BKYMIUpxHS5uu+0scM/18i9SbYSD1PCfnqrpAGgf2uJH53IKt3jVmkqQECxBGS0mCEAo5wRcIQuXU+VrfwfToSo67T1cJQsWThoPo0RXLl2aJiMzh/HiLuBh5q6r+wm7H9ZwzFZGniMinRGRDRCoiEojIrn6Fxk6aGmzH7dhEGjWfuQKUt1l4zgvxLruiXkvqrRxh33e+rCELg/u+sNTuMjvJ5Xe6woArk/F3/oN6B1ZcS7hK2bku+Tk3O/Z915R8335bvWvMJKWKC3AxYeg+X4aJz7aDSrIrSzkec2WeYl6ohVDMC4+5Mm+Lj7KnDDwj6t/9ROAWEXnKbgel+a28HrdY6B24lb0vAq7rY6BTT5wPDc6ddj1LNUTEa3RyKRSdI1G+6NqwBYGbYIpHcOEM1KpIvkj5xKcIL56tB9nw7FcoffCv8ZYva6pDDbe3dhrjpyEI2loLhhfPuu37D5I7/Ai3ijcMCTcvoZcuoGEIns/6W19ncq8xU7RKsJ7nZqXDlmRXlqwUZjcuvvo/7WjBdvDnf3cQ0wYFopwW+ehr109FabvG3A34qhqo6h8BT085zqknzocGFx5GS1su71kpo5UyWtpy27c2nMtRtQzzS41m3kGt7mqkQUDtxO31TjAx4cMPEq5dQLdLaLVMUNpCL13sbXC16s4VwkBX43tVuHQRlpbR7RLh2gXXvi0+j4bUzp8x9yNjpji6kkPVSbmqjVmq77l2iW67SbJZEwXSNwBXAhei72+ItvdN5Bv/GeBh4AOq+sndjkkTTLci4/nPiMj/EpFXAPv6HOvUEudDtVyKXIXiZ5zBgpZLyNw83uJ+vMUDeCjeypHIuF4gl0OWDnb22AU3K11YdEb4G6t9m9b3hJ9zM+Yvfc7lTz0vCrzS6FBT2QbfbzQvN4wpp1WC3Tfn8ajLfBaKnkmyoyWTFmzRhPGJOL+DJ4nIrrJbmt/0C3HB90eBVwCPBL69n4FOIsMybQ9XzzvbvaDWWKwDje+VciSfesx/54/Ur9HaODxYPdf5IrUq4dmvEPqRRDyMRUj1UpsWIpclrZTRzUuJrjTxDuJm0+Z+ZAyZSXf/MQl2IjiGm5EmGbgFW4yqrorIh4BbcF4LHel5Zqqq9+PeQq9U1V9S1Z+MZN89zzBN2+sNwP1cw+4PgHiBUbTYSKTpGjsah/cy0wxqDCWQinR2QQoDF7BzOShvNxZJxdJ0GD1n7kfGEIlLT8pVbXL/Ob9uhgVGE1m0YDssIsvRz/PANwH/sttxaVbzPgfny/u+6PETReTd/Q13skiWqtQbdPcpW8ZdYKQ47wJOPdZFP0SraWXxQNM1WrvHUBhhq6hdA7fCwqKTeesGEvFTIRTmzP3IGCrm/mP0yGtxJSxxQB1GC7Yrgb8XkTuBT+Fypu/Z7aA0OdNfxBkqrAKo6mdwHWT2PE2lKjF9ypZxA3D/0OXI/AJSnHOrd4m6skQ5UW9uoekarY3Dc1ccJXfjkyDfY3/0QXKm4nUvrynO4yHIwhLe/oNuJhqX2IiQWzlSb15uGMOgtfQEzP3H2Em0andHC7YBV/PeqapfE7Vgu1FVf7mX49II/jVVXZOsFrqMEW95pX2D7j5ly9YuMJBoAh6VlgRr5+t51Iuv+TG8Q5cz/8xvY+kHXuFk5w/+NeGpL7vxHX4EAOG509ExbVyP+u4/EP0+k6U1nucCrO/jLS27AA+EG2tIcR4/arCs1TLe4gGWfuAVfV7bMNpj7j9Gr0SBc+wt2FIZ3YvI9wG+iFwnIr8N/GNG4xop7Rp0D1u2LN50c6O0pFJpDn6qhOfPsPnut7D10fey9Td/QnjuoWgf91x46SJ48WefIX46z+d3NggPQ7diuDhffx1G8RoZRkxr6YmVmhiTTppg+mPA43DLjv8MWAN+IotBjZpWidVbPDB02bJw7Y2N0pIds8rQybSVbSqfvK1RVuP5IHGv0RreyuWw/1D3C/l+9+dbqXUuwfEPXV5/HUbxGhlGjLn/GHuNNH+ZN0RfuejrecBzgcdnMK6R006aHYQdLki+D+VtZGnZmSyEAXWJVSPTzaDW6BQDzd1igoDw/BnXpaXbhZMNvnN5vKVlV4rTibbysJORY8k5bgYu0ape/7IrzfHIyJw4cMblMfHiIwuoxiSS5q/yT4H/iqu16fp+PuvEpTYa1NBSVEscV5Rcuuhmp63t0zrVeNbrVUM3S51bgLUeF0bVqoS97tt8YQC2Pvpeqnd+grBWhXLJxd1qheDCw67rzS1YQDUyw5pjG3uJNDLvWVX9G1W9V1Xvj78yG9keZocLUr0XaTQTDYP2hvPQ2CdJ5DgkiwdIvQCsrX1gD8zvo/LJ25xsXNmm7ngUuTiZ45GRNVYeY+wl0ny8+wUR+QPgNpqbg79z6KPa4zS5IMXmBrGcurAE5ZLrwlKtOFm2aVbaaXGRousXuzsjDQsR5ycc1NDKdmPscWeZyMUpXD1P5Z4TNjs1MsGaYxt7iTTB9CXAV+Ec9OPpjgIWTFuol9rQxlloewtv5QgHXvY/ACcJb/7F73V2IEoS9LDPMIjl5fjn+vXjGUGzi5PJvUYWWHmMsZdIE0yfoKpfndlIpojiTTe7INNK7ByUkGqdVLrHPmm3cXGyYGoMG2uObewl0uRMPyEiN2Q2kikiLiMB6jM4vMj9aP9B53Eb4RyQInekdvlQL2WpSzfaNADv6ZjWcXVwcTKMYWLlMcZeIs1f5dcDLxaRe3E5U8H1UZ2K0phhU7j2RspHH+1cgxLOSrFrUIy3vEK4eSkyxY8+29SqRN3CXTDVcACHowSB65Vad1HK5V2rt27nFs995PJ9d3zg+q7q1joafUgwg3sjKyatM8ukd7IxxkeamektwHXANwPPAZ4dfTc60ItrUPGmm51RvCpotBgpORHsJZeahnrg1N3PHXeTUXVBPQgSAb9Wb1JuLkjGLGCdbIxupGrB1u4ry8HtdXpxDSpceyP7nvsi1yA8WtjjHX4ExX//bLzLH9EorfH8LuU0aYgidb4QzVK77yv5gjPsV0WWlpEDhxpysechC4uWLzVmAivVMbph+kTGxIEmbjwe12YWrr2xYWh//gxA3ey+9pX7E7aCnus6UylH+dNE3Whdsq1vYNfFTNEu3sISB370Vay9/pWE66stOVs3a/WWDnLgR18FtDQvn3PdjoLSFuGFh1l7/SsHaqg+rMbshpElVqpjdMOCacbEbkj4flPj8drj76fy6Y+gpU23o+DM7m99Y1SfGp1AQ1eXChC0GDD0k0eNjym4PG5TzjZpb+j5TbnQ1s464fYWrEduTon7Slsm0+n1sXIbY9KwUh2jG8PQDY0udGo83tHQvhYtCKr/f6b5R20JrtLh1xuvMKZDzlYVKc7vzO0m878bawB4S8sDNVQfZmN2w8gS62RjdMOCacZ0ajyulXK02CgZLBM/9zrrzBddDrO1iXgu377UJpdHDqzUy3Pa5mwvu4KF57xwR243mf9FFdl/0LVpS9xX2jKZYTZmN4wssVIdoxv2VzBEkrk/im62F26sRXKo73xyczkozCGFopuZ7uqd25oTxQVKDevNu+Na1PDCw27FrRetwm137loVXT2HAhd/++eRoIpuNhqDy4EV5p/x/KZA2prTnP+W73GPo9lp49yuoXqaHOiwG7MbRpZMWqmOMTnYzHRIxLm/cGMNFSE895BbWOTnXFCLW6vVarC1ju4/2MdVIvk37jhTmCMsbRFeuhgtYooCbxj2ZnB/6UJTIAXQtfNs/vUfUbnnxI77SuY0/Uc9tm3Zj/+ox7bdPz5fK9Z03DCMacCC6ZBI5v50a72Rl6zGq3Cj2WIuh+zbD+ceinKaKXKi+QKysIjkC1CcJ7dyBG9xf8OMQfx0KdZObG/Vc5adcprB/V9sW/YT3P/FVDlQazpuGMY0YHrFkKh3ioFED1IaC3sivH37oTCHrq9GVn0e1Cp0LGvJFdz2yH1IS1t1s4Xav94TXTx05xqWU1J8P0Bw7jS6XWqY3OfyyOIBwtXzbRuql/7ubY3XIXEP3XKgw27MbkwvvTgQmUuRMQ5sZjokvOWVKCgSBTaN+pAmEcJLF6OZq9cIfNIaSCWxHRcs41ZuSdei+uM42A6xeLw4R+WeEy54J89bq6JrUU64DU2vQ/0Yy4Eag9OLA5G5FBnjwoLpkEjm/mRhqRH8kngugOrWBrnHHadu6ZcsYSkkgpR4OwNoR4ZZOO7MIDp2tFHtOAO2HKiRFb04EJlLkTEuTPsYEoVrb4RbGk5HctkVhGdPN3KnIg05Np9n6XkvYevQ5a7etFJ2pS25AlS2h2QbOAhKePY0YbcAXSk3P0yuZC4U3f1ub5mjkdE3rXLt5nZIIde8KKDVgchcioxxYcF0iLTm/tbf+rquXWMWnvYsFp72LCDhBLSwzwXVWgXdLqGqLsDGq4HTsv8QB3/s1QCs/tpPotVKoxNNUr4VL5KlY8m5y5tPS6eYVhcjahUIAua/5XssiBp9Ecu1ItTl2iCEaqBNAbXVgchcioxxMe4p0FSTRvJst2pWyyWobLsm3P3OVksb9R8LT34mdWm5Nb+a7CYDdF8WLE33YC5GxrBpJ9f6HtSC7g5E5lJkjAv7C8uQVum3m+TZtBq4vjEABG9ugRCchV+aGarnu7rWiPosOGlluLCEVygSXjzbfFyXPK3MLzTdQ9uxm4uRMQDt5NpCzs06i3npuFI3/tlW8xqjxv7Chkgn559epM62TkBRnjV4+EHnnFScTxdMo4B48Vd/3NW21ipoebsxK1V1JvqFovuq1ZzU286GMIGWtlh/6+vq9zcMFyPrHGMk6STXLs57POGa9ivJY2bJpcjKgCYHk3mHRCenoE7OP620SsLh5qWoJCbaoVaDzUv9DS4qZ9HN9YaRfuK5cPVcZCyhLmDvtno4DAkuPFy/v0FX8A762hnTh8m1u2NlQJOFBdMhMWjecIeRfKUM+/bjHbzMzUp7Ko/pE1VEleLXfyu5I0cbpTrtZqieD77vZOLo/gZ1MbKcq9GKmcrvjpUBTRb2l9mGfiTHQfKG7YzkS3/3NiRqxu0X5wnOPuhM7DNCK2Wqn/24k2bn5vGWDkJluzmXGiPipOLE/Q3iYmQ5V6Md0yDXZinDWhnQZGEz0xb6lRz7df7pdD2Kc83n8zN+U9GQsFZ14yhvE66dJ1xrE8zCwAV1Pzc0ZyNzTTKmkaxl2PmCELbETSsDGh8WTFvoV3LsN2/Y6XouYdQ4nxTnd10YNDBb624cC4uw3aU9nIZuPENyNjLXJGMayVqGtbzyZGGvegv9So5pymBar6cihOtnnHTq59yq2NJmw7bP85ClZVg6CJcuDHqLnVFFyyV0e4uupg0i+IcuH9qK235fO8OYZLrJsMOQf60MaLKwV72FQco8+sobFufQcw9FloOeW7UbW/X5PqiAhuilC+Dn0507LeKM+N1q3w5dbPwcuaOPZukHXjHUS1vnGGPa6FTe43u6w93p7tOu5K2fgGrBczIwmbeFkUuOyTIVpXnVrviR9V+0X2tecdiIM7hH6Cwph6HJr4bRA51kWEFsFe4UYsG0hZE3q66UnXzr+21atsWMaHWeAn4OWTrYmCkn8XLQ4n5kGEZ7OpX31EIn9yaxVbh7H9MH2pCl5NhaBkNxDi+oIYeOoOWSM1CIZ6sa7gxoWaIhVMvoaoWmAO7nXLCvVZEgoHLPCQuoM4C56wxOOxn21PmamfFPITYzHSHtymDCjUvodolw8xLh2gWaDOaDWvbSbltaPiEHtcjjV6BQNHeiGcDcdbLDVuFOJxZMR0i7MhhvfsGVolTKgEI+DwtL2deV7qDdp+LEtlwO78AhvH37zZ1oBjB3newwd6fpxH57bcjCdL1yzwlqp77sJNxcDllYwptbcL1Lt7dQ34cw3+jyUpyH8jYEffYxTU27fE1jm3/oSGNzolTIDOqnk0HddbKQiKdJdrZVuNOHzUxbyMJ0vd48O14hGwTo+kXC7S0n4xaKUeCsNcpjttZHGEh3R8ulxoOoVMgM6qeXQdx1spCITXY2Jh0Lpi1kYboen9NbWm4qOdGNNWfNJ+KkXj17+LYAACAASURBVIlqO5Oreke5AKkL4frqjlIhM6ifXgbJ62UhEZvsbEw6k/FOPUGEq+ed9JpkQNP1+JxSnMfbf7BhF6jK/C3fDeVtJ/smn4vJMHcqB3r1vo3qT1tKhbJ4rYzJYJC8XqmiQy/9yOKchjFMTLRvoV8HpG65w+Q5pTiPX5xHq2W8xQMUrr2Rcvx8OzJsvaZb640+prm88+KtNx+XyATJmTh4K0c48MOvbDp+GE3BjcklTV4vmc8MAiUMwfOUWuD+rERgrtDb8e3yoZ3chCalnGSa8rlJpvW+ssBmpi3044C0W+5wt3MWb7rZlcesXXD5Us9vnDyrYCoC1Yo7fxi66wZR+Qvg5Gat/xhuXNqRCzWDegN25jM9D6oBlKuN/vahQrVG2xxnL/nQSS4nmdZ87rTeV1ZYMG2hHwek3XKHu52zcO2NLmfqRb+OXA727c/wLls+zYu4a8bfk9Kyn0OWV/DmF3bkQkfuFmVMJK35zELOa/yFCfgezBWEnC9tc5y95EMnuZxkWvO503pfWTH+v8QJZDcHpFZJt3b2K66ZdpKW3GG3c1buOUF44eHGzLBSbpjdI/hXPJLgoQcGvKsk2lwJE89Alw7hRU+EiOtQE9TQ1XMoQri+umPcVhYzmyTlv0pNKeTAT3xIi1sl7Cs2Pq+ratscZ69lOO1k535kyHbHwO7dVzpdq934Q1XWtpRPfLG0Z+VRaz6eDpuZpqSdpEt5m3DzUvOOPeYO62Uz9RW8rX+o6kposu5lGgSwftGV6RSKsHauxStYoVJm66PvbRq3lcXMHq3yn+Ak3VqilsaTnX+ynXKc/Zbh9CNDtjvm5KkKJx+sdD1Pt2u1jr8WKuWqe132sjxqzcfTYcE0Je0kXddMe6uv3GF8vqY8aQu6seZckbIiWX4jbd4FE1Q+eRuQTQmRsTdolf/y0YSrUm3kM30PfOktx9lvPrQfGbLdMYFCEHY/T7drtY6/UnURKJ9jT8ujk5ynnkTG8qqIyCOBtwBXACHwRlX9TRE5BLwduAa4D/guVb04jjF2ol3zcFlYgjDAWzyQWvKsny9s0zHGa5TQeLk8emAF3bzUWHHr+9EKjwFlFw3du4l46OYlpLXcJblrJD/320TdGA7jXGW5UQoJ1cm2ntfIwIcKm2VFgPkiHF7yWSvprmPst8l1qeL+Nyq1ePWwmwmWIjvrdq9RUrqsBqFbbRz9+9RCJRfV37TKmW2PC2G7qhxdgcdcma9fS4FiDvJ+40PqbvLoJK6atebj6RjXq1IDfkpV/0lEloBPi8gHgB8EblPV14jIzwI/C/zMmMbYlk7lIP5lV/bVMLt+vlwOKsleptEM0fPIXXWs6dzrb30d4cYaGgSuafigwTRGQ7S0hVy2v9HbNLm6F5BCsXncVhYzcmLJcRjNpfu5dqguAAnuM1grCtQCOLMW9rxIqB97Pd9TtsqJYB5COYSFonZ8jXxPCVUIw5BKi8FYuaqQh5wnO+TMuDQneZzipL27T1d5zJV5nnDNHACfvW/bnStBN3l0nL/P3TDbw94Zi8yrqqdV9Z+in9eBLwBXAc8D3hzt9mbg+eMYXzeGXQ4Sn4/CXIvc6rlgVpjbce76GDbW2s9oByIqiakHSaWexxWh8ORnNo/BymJGzjhXWZ46XyPnR5mANrEhzpUGUW1plmOSeADSPB5BOr5GgqBRmU58bIyqk6rbyZmx5Jk8TgQK+Z2vfVp51FbNTgdjz5mKyDXA1wCfBI6o6mlwARe4vMMxLxORO0TkjrNnz45qqMDwy0Hi8+VWjsD8AuQLTt4VwVs5wr7nvmjHueNj6jPSdo28+0E84tXE+17wUth/iPq7Tb5A8WnPYuFpz2oag5XFjJ5xugGVKkreF4p52TEGSXwPNfsxuRIZdx3FfS/m3fZOr1EtdJJs/BHRE1e2E58n1PZlN3FpTvK4Yl7IebLjPtOW8Zi703Qw1vm7iCwCfwn8F1W9JD2uWFXVNwJvBDh+/PjI/+KG3Tz83uK/4WOXXc/FOeXgonDsCrj3Ibi4oRz8kvD1xYBj5S9Q+uBfE54/A4B36HK8lSOupEa1EUw1dEYMnheZ5qcwy4/djpZX3D3+2Ku77p5lE3WjM+NyA7rvTJlKVYmLtgrRu0cs9SrNGYdqoCwUu3/IS+YKcx4oShBK08/JXF2r05IIzBca1whCF5jKVbei1vfcYqhkbvTeM1UKOTfLdGYSSj7nAmoxL3W5tpWVpRwHFlxj71CVak0ph9rW3SmNPDrp7k5Gb4wtmIpIHhdI/1RV3xltPiMiV6rqaRG5Enh4XOMbFSdPBbznEwG+D/MFOHdJuf8MLM7DvjlYLyn//P8+x8r5P8OvbLmDBBdUc3n3VSmDBpH9H+Dloo4zad2TXP2p/6jHDvcmjaFydCXH3aer9cARRsp8lqss7ztT5v5zzX9PlRpNAaCVSg2uXO68QzJXCMpmFKVznguEAMWc+/nu01XWtwLOrIX13KKquwaEFHIuz1kL3N+wSCOn25rX3Sw3Ir6AW81bUfI+XHtFvuvrcHQlx8lTFaph498tln/Pr9f6yi+O4/dpDJ+xyLzipqBvAr6gqr+ReOrdwIujn18MvGvUYxs1HzsR4vtQyLl8Sbni3gi2K0RuMsLjL34QLW9HC5J8kChpFQZ4S8t4l11RTxp5K0fwVi5vuCmlQQRZPEBw/xeHfp/G8BiHG9CDF1wgbZUjQ4VHXebvKO73Pcj7sFbqLBwlc4W1oBEAa2Hzz3EO8cELwQ6npULOBZ74dcjnIJcTinmPQpu4mBy/QH01sieulGW313BlKUchlrhld3enXphkdyejd8b123oq8ELgcyLymWjbfwdeA/yFiLwUeAD4zjGNb2Rc3FDmExJREO5cJblUPY9o4IJoHXGLjyplHnr+L/GxE6GThReF5538xSY3mvgzdLu3tab3Rs9DFpZ6Lm85eSrg/Z8OOL/mTrSyBN983Of6o51rZo3hMIxVlr2WY5xfr1GL/h5DbZQha2SkFQfMfUUX5GJaHY9ar7e57WaU9fPSsCyJfw5DV4pSqUVBs6J4ohSifGXeF2oC10WlKVtloufb33PShEBpSMSqWr/H3ajUdv4nteY4W+XraqBUo4n9QkE4dmRnTtaC595mLL89Vf0YbdcCAvDMUY5l3BxcFNZLWs8/+V5cQN7YZz2/wlxtPcppJkpVPI/t+UNNMvF6STkTrHCFrOMREr89dZofNIpfxHny9ljecvJUwF/9Q8DWdqMN69k1eOdHA17wNCygTji9lmPE+yVprcRyOUQXZIr5xr91Mu/X7npB6IJMISd1ebN+jfgHoS75Js9brigUGi5D8bnj82z3uHhnuxIyV/BSNT4PwsYCq1DdveR96vnhTvJ1zGZZOflgheuvGn/pizE8xr6ad9b5+hs9gsC9EakqxYJ7s5oruE/LlZpy58FnIMU590QYuPxoVDZz++IzmmTiQk74/GXPoEyi/rNjKE0gIMX5nstbPnYiZLviZDJPGiURlZp7zphsei3HiPfLd/hslPfdOXJ+nJ9sXw7S7nrxwqAgVHJ+Y6Yb50NbFzQlUWLHJae5xOfO59ihyXSjVh9z743Pfa/RnTCuJKsF7e+11rJsIVk6ZKUv04UF0zFz/VGfZz/FZ2le2K7AZfuFpz9BWNnvHi/NC1/zTV/N/ue/aEdudN9zX8RJ/4Ydb3Rnl2/gI1d8P97hK+vlLiE+G7lDbOQPEeLXl/iHCOQLyMIi/qHLey5vubjhXGda37hChdUNW9I/6fRajhHvV8x7O/7O8r7b7n52wbFT3q/d9eIZqZvNCvuKwkIRfL/xc0yc10yiuDKXIGyU6eT95lyp5zVWHSdJnipt4/NCTijkG8sS4g+T7e611ds2vnaoVvoybZjGMEJOngqacptff6PH9Uf9+len/f/yowGhPhZ/6b9x+SMbxwEc/FK1SSYG10ty84rHceCWJ9a3vel98X6Nt5H1klKtuVlwcjy93MPGFlFhekOSjuvvlheHs6S/0+tlDE6v5RjOnk9RtF6TWY5mhNXA5TPj2ZbArs29NSopiXOkyQCdzwlHV5qD2mfv2+bSVljXVpJysCp8/oEKClG5TnNwcouKhGqbHGcyN9spV3zfmTIPXgiohe51uOqQX78PZxUYUlHqjlCfunuLRx8pNL22yTxw8rqe9C4rm53f3sBmpiMiLoFZL2k9t/meTwScPNW+fCXe/9wlVxpQrUGpDOcvNR/XKhNXakoQuO1JWvdbLymbJfepvZfxtN7DUmTLG2qjhk/Vna/12qN4vYx09OLSc369RiXKhxJ9cNqu6A7pNYyeQzp3STm6kqMWKNsVlyslCkDlGmyVw67H+ZHsGwetJN3mdhrlTttZHsaI0LajS1wKVAsbq4rvPxeQ99z9l6sh5WqzDL1VhpOnKhyYl6hsJ9w53uj/xPd2l5WtOffewoLpiGgtgSnkBN/vnF+M9y9Hpt2eOFlpu0LTca0y8dK88Oyn7Jzptu5Xrbla1sX53sbTeg/zReHgUmNWKgKHD8ALnjac1bxpXy8jHb2UY5w6X4vKTGgytO+EWx/Xubl3PhflDInOF53QWQ92Pu76qwrRSuF09xgrJd1YKHptc8XJUqB4YRPAxU11TkiJIBk/H9esrpXcPnEapHXcnsD1VxV2nWGazeDewvSCEXFxw3XTOLepdWl031wjv3jyVMD77wg4v4771A4cWHBvNBq6zgDx/29pG1bXlV+7tVKXP196S/dic6BJTv61WytNJTngJLdu+c7WMh5nw6ZsV+CnvqNzp5l+aL1WL+ObVYYlBa5vBU3n2Si5DzSx8uB57Y3tY4IQSpWQfE7w25SKlCrEi9DJ+VCpNsrANrYbJy5VlI/etYUnsDjvcWBeqIU7Z8S9sNsx8XW3K8o//stWff9kmUxyIVS83fel3r81XiwV71yqKCtLOXy/SjHPjnKhWtjbKt7dmnObBDxZ2Mx0RBRzsLbZXEe6tulk0ZOnAt750YCza41/zDDk/2/v/GPsuK77/j0z837sLpdLaSnTBimbiqXQlf+QFBOuaiOuaqSK4gRx7SKIkyJ1AiNOAadNgaKoWxStm6JAgLRxkbYJ6sSuE8RV6iZx4jiG48I/6koCHTG2flC0KBEiJS1NUeSSS+6v9+a9mdM/zr1v7ps3M2/ee/t+7O75AOTuzpu5c9998+bMPfec78HapvxMElyEzaZd3xre/XnbAerkvVlaUfF65zDHDMskz7WbGdYVmD5usxHj5WtRl8s1iiUtxa5vZgXTpLGpIq2Ie9NikFzbbuWVvHbaMXBrS/qVTo8pS1n7y5Drq51STLJuWZfzl1u5VlpyV+V9j1pcu+h4dQHPHmpMJ4X5/tjKap2HVRLXZdhO3EXWpdv1xJtC3GTDuz/LrrWOesywTPJcu5lhXYHp4+xN23W5drAGpYxlKkgVsRVW+vqLHcoY8J3Evm0nm7uLwLin0w96FhvQBIxeXLvoeHUBzx7qExiCYaJMmy1gaQHYbCTlqYiA19dEjDuK5IsboVvmjNEbEQhI1TaL6/4s27cTx3zgQTHCaxsSDexXgD8/FeGxA3HmceljDo0xwnaS59oN5Ln0hnEFrm9FWNu0D19yvF3fizkpfs2dPRICD4VKQXbfmIGzr4ZgDjvbKj6MGtBIQzFW+hlv308iiit+9ns5d0neNwBUK1L2zdY3JQJeuhLiwpUW2jFKFU1/6UqILRM7MW9mtZN0Aas7uRzEO1VYekqcPHmST58+PbHzucL09ssURcgM+nFxU1MaIePmpmy3ikfWteQaTt8TvdBKAGw1ZJvrgrr9AFCvEcI2Y3FOjM0wfRv2PSmTwVXUcYXQ7zYSes0Wd80ko1iUiKyAunucVSvKw500pndLP9RlPeTtVdxxsQIVVlrRCjikH3zJ7GtfCHxCHDOaZvIoZd+o81nmSTlmffaex2Cm0p970TmKKLr2xmBQd/UajvrMBmTYKFPXbbm+lWw/UO+O9kvfnN51L2HbyJFZ9y8gx2w00OX+HLZvGjk72xS59AZ1BVpDmhsZS6mfGa9R0T57FeNJYogh7eTHcvdyDKM7+jdmU5XGCPbbVBsicYX3c8/2K3I+bhewupPLo8Z0QG5scKYSTL8oUzc1pW2ieZfmZWbJ3BvCH/hAvQK89/5KV+HiwAMW54y7LepOhRm2b8Mep0yGIrWiohSXrOMsWSkbbzksF0FeSoktvs2cFOUeN0TSr1r/YPWRz5O53fx0C5D7nhxQC7KLM7lNxXGSI2v/tvvYbUWFwPsVOS/7uQ9bbFwLl5dHHd8DkhamB8pHmdrUlLQake9J3qd9ovU9oFYVaUEAeMMh6lEvqlXFteumxAzSNzcVpx0BawAIjEogs2XP18jZWaFIrahoPSvrOBdrUO1D2vEjNVxb38Zms1vowK7hVwJCaNb+mq3JTEyZgVdXo05EsecBi3MewnaMrWbfwwc6T+Z287OzDEPAQl0s6GYjJ6KXe93h1iB5Zn3afWApivAt+uzzKs3sZLFxLVxeHp2ZDshORJmm2/C97nWWKAY2t4G73jjYOcvu56bixG4aAMSor21KLqtGzs4Gea7cpTkqTI/IOs69gbszpqO3+5IL2uw1EGz+a4Td662Tmpt05AOR5KQOmyqzE32JoxhLc5K6lc67zVpHtq7fKJbPiSFjL2uvxRG+w0QEjxpFPK629jo6IgOyE1Gm6TYYwHzNVtCwBYeBC68Nds6y+7mpOHHqZsCQ8x+Y1zJqs4KdfaRnoO56FgD45oa9strumrW4x731jRWsb0U9mrPHj9Tw9MVGblRRZJYi2DFs06IdJcpE04ifXG8Ani8eIBuIZGdu6e+T78m/wMQkbIfAfI1BSNyzRdGxeZ99UfDPMMdMoq29jo7IEOQJ0w/bhlUjSiulvL7G+PRXWrlpLl9/qoUnzop2b60iwUrvvV/cvjY95rEzMS5di3HhNXTauXIjERp3Z8TWkN6xJLKFyuyQ5dJ70YgHhG2p4ON5Vpi++LjlxQDHj/SeY9vo7hIAZBiqStBbWzTNpCJ87fU7DRgyVhWfupZemBmbTUYtQCdi10bqh23gB44kn4XrnrfBPEUGdVTjlVa3GsQgauHycugIzQBZa52bDblxpYXe8aAY4q8/1cI3nzZ1HEncs998mnHtZohXr6JTLPzaLcbLV0SHd6Eu7YTmi+6G8rvpOKo0tDvwPcZWMzEqcQw0Y5n5DMNclToVXbIsYhnX6qQmiuli4pOEkL+W6CExpGnOvhri3jvl9zKF2YclXYh9sxFjzaitVXza8fMpgi6KzQBZa51bTWCulp+u8sRZTgS2nZ9nLqIrzaUZyvZGmCgmzdcSmTQ3itEjCXxSpaHdAXVyVTplbru3D8ix5SA3WGnW8Kd4eR5epNy1xEpB1HHM4i4dd7pJGXUrTW/ZefSOOQNkVX6pmqhaFzddJSua0obbu2kurhawZaEuhvqOJXENSt1HmckePphddUaZPWTNrTtto1YpVigqYnkxwIlj3dUFbJv9mLRxO3F0ZwsrlOWORcK9b57LTUnq53zeDnns6Sbp9l11q3GcTxF0jj8jpNdhP/2VFlZvJfUfbVDS8kHCuZWoU2PRpgsAicG8dhNYnGPUa9RRWHJvdq1I0m2yKs3YlJlHvyF6cstLwMPvUOM6i4irsTtNwarglCUrtebQgtelqtTKKBWT5Wad1HopAJx5ZbKL+h4Bdy5LoJbFXUu8eKWJ51fCvg8y1q0rLnMZLd880NqUm1FJu6BttR9NbxkvOjOdUe56I7Cx3T2z3NgWI/mlU1FntmDTBez9rlaR39c2ge0mo1YVd269ir6pPFnVa66uAV94XItyzyKjpi3kVR6xxa2jmNFqZ6eh9FQziZ119z14j45ZioNfvNKb3OoWEi+CgKTYukMUywPu0tzODFz6urCzVCmyrukt40KN6Yxy4TVgYQ6dHFTfk79fuCRrorctUo8U4YG6zFwPHZCn0fVtcds+dB9h+WBx8XAgv3pNI1RpwVmkTIHvIvLW7mxx61qFEJo0lHqVUK9Sl/uQIEEtogiUuIQX572prmkWMWiB8TS2aHjWNvudyaIWyNJKEGQXOa8GMu47Qfq6WKh7eMthH/M1b6jrRCmHjuYYKVPBJW+fGxuMA3UxfhZmxuXrUgsyTuUteCai9/ur3dtXrjEuvCY3voPzMuN97EyMPz8VoRYAoGQG7KbDuNVrohh49Wp3MXJ1+84Go6QtFFUese2eemHblB2T6zCoUScFZL5Gne3tmNEMxX3ZbMWdtqYVcZvHqHmpWbNPuy3rvb7lsI/FeR8XrrSw2ewOk7aGlyFRtuli6lmpLGUruGg6y+TR0R4TbiWWrNSWfvtkpctsmMoxWV/amLND8ltOGszNLeDrTzEW52XtxrpzgWyNUetCtuS9D2V3UkYqLm+fwEvECqwhTV+Ws2ZId4rV9XZXvmgRL1+LII+mvdjhsQ8dPcXUU6kz61sRrtyMx5ZSo4zGjDpjdj9lKrEU7ZOVLrM9pBapkzUBAGiGksdKzpNxP9Hyg/OjFSNXZo8ya655+xy93e9sD1u9hnSv4nvdKSVl00ts/WIXm54ma5m9xdTT7vdL1yOt4DLD6OPMmLixIWILlkaTcXMLuHaT8YnfD7G8BKxvScFwF5v+cuKYj0t3x/i/z3SLjg9D+kYXZs1g+8QXXV/vbmltnXFuJdLZ6ZQYtGBz0f52e+ABTIwXL7ewstrG0hzh5jajHcnnTiQPfu2YzYxr/xHFwK2tGE9fbGA7lIfcMuS5lys+MF/zuj6PPPd7OwZqY0ypUUZDjemYcN20jSbjxkZ3zcOra0lN0sW55DirPnRuJcITZ0c3pOMiZqi7d0rkuQGBbHdfv/3tWpzsQz2qObWKJP63I8mR3Kvu27LELAZ1rkrIqAtQGo8kXzb9mZVxrbvbNcVlNlA375hw3bQbjW65PrfI93Yzu8rLY2fimdbHjRnq7p0SgyrolNm/jGqOFXVXjDYwUd/lkSICP/szK+Na1xSX2UON6ZjoKgZuPGJu6DwhKbLsKh+5hb5nHS0gPh0GVdAps38Z1Rw1pL2MEh2cjuC15KU8HT9SGykVShkv+imMATfdpRbIDC5u21qI3ftuNoALr0lR7gfulm2//vkQN7cm3+9BuXxdfv6XPw3x8Mnu3NW8lJ8y6UJKMYMWbM7avxVJpZlTL2xjrkrwPSmAEDP3CDA0wnhoicK9zEYjGRRComDkbi9is8kgAE88v4WFutcVgLQdMnyPEXjUU1lm2sZz0PX6/QLxNAoC7iAnT57k06dPT7sbHdx0lygCbm4O9kTvAdiN962FGvDBH/Y7BtOOQcWXdeAoAh64m/Dd89yzXbWAB8NdA7VpFczInaWk929FYjgrJpI8ZllHtdfpJGUB9xK1QOq+toeIzaoGZpbLIuwQx9xJdRP9ZSr8jCfFoNfegOzqxV918+4wbrqLm35Slt1oSAGJELbrp3kpP0+c5b7pQkp/BlU+Su8fx2JIaxWvsx4KyJ3MqhnZotZKeULzcDgIttqTlQSNWNatre42kRjnWUmDGXfFm92Mzs0dyrog7X6vr8kTvv0C+b6IJBCJWtF+ohWJu/pff1aipgJfxsD3ROawVoURZQeubSbi/Qt1XXcdhnRayyAFpq2qkYvVYg7MNVwminyhRkbVRwH6r5/a8XKVoWLunuEBKTcxi4HdDmMEfnfh92lQpJq139FnT4N1TaaLcacF3u1+q7cYWw258cQs/zpqQ3pdoR3BRB6K8tJGQ0L7b252i/ffNOkXymDkidT3U+QBZA21p+KL8aA0W+WXJZolcyz3C/0ie21Oak+RAO7v34xj+WzShmzSZF07mp4jqDE1lFEscveb5bSVWcE+dQOSArRg8mmtGkzHBa7fw4EZxd2WlXrh02Afg+8Ntza4l6lWKPfBMPDl4THwzbWfer3fA3iSoz7dB5hRKxXtZdSYGm5scFdRbSA79cPuF8UapFEGa1BtybilhcSt5ZH8HWaU+FKKGaXAdNaa64ljVQSp6z/rpk/mPItzeutIE3iEauB13UfsOvSBulTSqQWEWoV6tbBJApiKqAVAFE/3yXPUSkV7GR0BQ5awvFUjspxbidAIgVubakjLEsUi823lCn0PWJoH6kYXLWxzV2UcpRz90mP6pS+kUyxW19u566QeAQfnPdx3vN7VdhGHFsRa3NqK901+ajolZr4GLFQJNzYZa5vyWiviruAuu+TBLN8Rj5L1a4t9qGlFMjN9+mIjNx2lTNrKqKkts5CeM4vo46UhS1jeLaJt10orweg1EfczUQzcMIXLiwqVK8UUudsGXU9dXW/j3KWwx+jZm7pPiRvPbTvtybF4pn+2j/uVrSZwdZ17cnSjWKLf7cOLHSIbe5EeMubktaqf/3mW+dxHWWtXitG7mMFVLMoqom3XShfnCEvzyL2RKP1hlsLlRYXKlWKK3G3DyA2KbGDvax6AE8eqXdHDtu1axetZI6wGwL1vrnZmL7vdlo4iFzjIOWwajD1fYGRHAy9xrdcqQCXwcj/PYWQjNbVl59C5usOJY/k3drcKjLgoGdc3Jte3vYSsuQEfeaQy7a7savLcbZuN2Kzpy7pqJSD4znpq2s23sR131rA7ZfmMFfRMybEXL7cwVyXc2orNjFV2SNbDCfe8qYKV1TaeXwkBhLvekALiNm/H4rGq+pJLutOz7U40LwNzNTnfgz+YVL9IF2gHstfHy6StaGrL+NCZaUluO0CddT9bTk0ZHILcoN21aGXnsGuf9gZt1Y1akRjOLDefde/2uBed4wMP2GrGPW5IeV32Pn+51THk7bhcruqsI3m0IvvXbI8v7Y0h34usNJOy6Shl9tPUlvGhxrQk6SowyvDUq7pOOi5WVtvwvSSoBUYMoB3JOmaWmy/wk/2teABDtgV+sm+RcWy1kgp8igAAFJRJREFUZFa7F4ON2pGkvYwbG3yUTjMpm44ySrF3TW0ZHR3Bkpw45gMPAl/96yizuLZSEhId3zxUCH80tkNGNSB4HqMdSbK/Z8R2V1bbWNuMjeuXUTEhpRWfwCzHbTW5J5LUUmRMY5iUpz2YMia1RAm1CptZeDa+lzyQEInaV0DAeqPcmFix+7TrPq12lReBW2a/sm0pg6MjOCBhS4KPYt4bbqxJwwxshdmFxV2BfFeFSguQl8emzFT8JN+x2RLXa7OVSNlJbm+Miu8h5uRG7oqYb4diPIi4UB7Tlhbci7NSIAkKcofAo8RAVnxgvpakDlmsS71eTcYzb4wWatRzvEvZdJQy+2lqy3hQX9sAJBG90+7J7qYZZhcWL6tCpeST5cazyju+R6hWJHqTYTV4Ezdf2gVcMffbsMUdqcwsfEoKV+9FeyrqRTLTt5G3WS70NOnxLHIVT1vZSBkdNaYDYNWP6jVJj1GGI4qL1aVctAD5YGSlzPieuHIB666kzkzSTalJqypVfA+1IAlE8khSX9x9CJI6YwtXzzJE2TrQ1UBmhukqOYEn24FkzOaqhGoFHQUjOyvPmumlxzPIyLMhmg1lI2V0dK4/AK5KUr1G8LdYXb1DEMVOYfE/C/HwOyQlqYwK1awzrcLJRed9+mKjSy0p8AhUEUPquhZdVaV2zGiGyXzJI5mhVQMPnsdotbmTm/rSlRAvXm5NRYTdAwDHxWzzNbPcqczoiXdYqBHuOpLI4dlxvLUlBdHbtioOM2oViYR1XehRLNvTrK63EUXiJvc9RiUgBB51jOt8LRmsrDa0APfuQ2emA+BG9G4389c/lHIQgKtrwBcel+o8/VSoZp1pqcv0O++g0aBhO0Yj5J4UmLANNFrymn2IjFmUfqKIp1KOLUa34WQMtna72WScuxRidb3dGcf17V4JxGZb9m1H3Hcc3QLaBHl4bISMsB2LlCAVfxaqUrQ72R13qRnBVUla39YCyjuB50kFnsfOxH1VqGadaanL9DtvWXFyu1/seFtsMJKN6o2iRLzdzgIJkleaJYw/KVz1oEGPi2IZKzuORd6mSoC+42jbqVU8VCvJWMUxcOJoFSeOVQvbUJWi3Yn6DQx5KRl2+5UbMhP1PeANhwi1CnBwHnh9bdo9352YbI3Ok7tdFy1SoZp1pqUuk3XeOGbc3GKcemG74yYsiha1LC8G8P0WIjM169gnR9jBqio1W9z5HO2+9jNdqHtgFl1aux9ofKIHwxpxGzS1sR3D96mvq3qrCRAY9xRUSnE/D+sStmNhj1leDDqu3Bcvt7Cy2u64crdDiWwK2yypTZ6s3242ROReXb+ziX4SyE/JuHR3jO+etzcE2TdkYPWWrIVsqnjD0NibrlV+2U3ronn0q+QyqfO2ohjNdqLtat2EQHagTFZ7rTZ3DJ8JXO1gVZEotb2zvuol+81VCVFkxN7H+Ewx6pJLzEDVY8Tc/7OyruETR7PHs8x14LqC05+R77Ex2ubYGGia2XLa9QuU+0yV8aNOSuSnZDxxluH7ksoBmMg945acq5m1Ig1AGh6T5L9XFJGmpS6TPq9NY5E0mMHdhMeWA/gmfYa5ezZphdftdjuzC7zu3933fvT22fY0WKUnApWaObuu4SzKXAdFrlyyZtS615M/1fU7w+z+O9gOkJeSYQUabM1BIHFLHqj3L+ar9OI+9/sE3HEI+MC7d69r12VahZPT52WI+LybijGIu3l5McCJY1XM15DcyEnarFdlHdCKFizUCPM1wPep8zuIut778SO1zBvNTszX7Xqk22Z9QE9ArUqo+DJ2d7+pUlhi0eaZxpw/nmWug6Li7nJMMsZ5a8EqUD9bqDlAfmHwakV++p5TxBdyY7l6U/bby8ov46ASALUqcPgg7cmqMdNSl3HPa1NhXAZ1N2e155u7esX34BH3pNYUcXDB67Sx2Yi7XMf2e+UWzLa43y/7ug2QspG7RMB8lTrruL5H8L3yaWuBR4hi7oyPT0DERng+Qx7RGrii8cy7Duw6adhmtCD3GPvQ435GzVbyeztmRCa6ejuMEfjoKFfZ4gWaRjN9dGaK/MLg77qXEEVy8weMhGAk/+JYlJBqe88ejJUoBja3gbveOO2e7F122t28E+3ZNpqtONM4AWIsg5SDIi3hF3ipNVy28oiMpTnq9LNszqvrkl6aI5y/3IJnzhFlGFJbCMD3Bh9PN+WlaiRJGyGjFcVdY+qOdyuSVCRLFIvHrNmKu/qsaTTTRx9fkIjYP3YmxtoG45ATzXv0sETzvh4xIpNr55MY0nqNMFcDmtc157QfBHnS9z1ZI73w2rR7tHfZaTHznWjP7iu1Th29W8i1UQmAuaqH7ZCxUAPaESM0JQ/nq8Adiz5ubrO4Rx3xPc8YYI8IN7cZd5uaqtshMF+T2akVavDNLNeqOVV8dFzSrpxi1e8WprCCFa1Of7qFHsrirpP6HgEUo9WW/i3N947pymobN7dsTVqpZ2r7xIzOe7Vt2vcYxYyV1bbOTieMjrYhLyUjvf0//lGIuWp3oV4imeK7uW72i7efOXII2GhICpE7XsysEoFjZlB3s3UVbmyLb9QaOUAMVjtKaqS2WowVJDmsRe25xhdIXLhpoYWmmW0BQBMi8ff2O6td7ds2rWC/1Q6W6jiMRotxbBm473i96/yuoXK3g8VgNluMje2wy80aeAT2uWN8W5FE+v/AEXFTvXQlxJlXwk7fPALuXPZx/Eh+SaR0ClPF9xB4skaadpfb9722GXbGh8ior5m12OXFIFN5StdSp4Ma0wHJWl/1CGBK1nfGlUu327Dryr4HHHCKA+w2icC9jnU/WlH8nsvXWXu0LtZbWzHOh9mpGVlpH+cuhYii8tkxYRs4+0qIe9+c5GS6FW2SouROihUgCkZbEa7cjHvSTtztnXQdg/290RLj34ri7nJrRunp7KuhuJbTQ8TAy9ciAM1cgzpI6tTqehvnVsKubWzec+wzFurewG0q40XXTAcka321Gojrsl6VtVRNlxHsZHSruXslAvcD1lU4yFKFDfzJSs3ISvuI4l4D1PccSNp327SVbzqYFBK7/dL1KDPtxN3eTnXGepSi2KxVOm+LKFF3ijMMqcul6/kuqUHWnldW24hyPo8oTo7RYt+zw8zd0YjoESI6R0Tniejj0+5PmizJuw/+sI8PvNvH8kFZQ1VkPcpNOl/bEB1e38OukgjcD9g0jXhAa5fnTnTTPtoxY7PRq3U7SN/SbdrKNxab7xrHsk877k0nydvuYl+ygvZ22yBzvLSRdhkkdWo7FKNI6C7QDsh3yFVSmkY6ltLLTI04EfkA/huAvwtgBcCTRPRFZj473Z51U7S+em4lwqPfiNDWNdPOelPgA3csiXs3bPU/Tpks1lVoU0HKkudOtO0xc1ck6rB9c9u07sw4wzqHbSBmWZeMGT2uz6ztLlZw4r7jdTx9sYFbW92Rx2XeSb8o4rJr2a4KlTWodh3bungHbVMZL7M2M30ngPPM/BIzhwD+EMD7p9yngXjsTIz52vQEv2cFa0iBJABJi33PJtZVOIhQvEfIdSd2qs+0RjOkti23TevOTJdSs7SjpFB52vXpbu/RMjZdtWpNx5YD+EbVaZA4iJ1Se0qrUI2SkqNMhlkzpkcBvOr8vWK2dUFEHyWi00R0+urVqxPrXBlubDAW6sBti70VZfaCgV1aQNfDgpVYtNGV8zUjOWcinG870K1Io8W+Zw/rKlyoe5LraVKYAvPPLQhOkM/14LyX60607bmfsl13dPGouOpSxc93ZxZhC5WnXZ/udt8n1Jz3FXjAWw4n0bjLiwFOHK1ioUZdkn6iAkU932WPuo8flbQKFZGoTZ04WtVZ6Iwya59Klr3pufMy86cAfAoATp48OVN3ZhvtW68S6kbsIWwzNrYlovXWZneBYgLgm4fZO++Qty/RwslQhG3G4pwoBn36Ky2sbzNubTqpCjkuZSLgTbdT53jb9uqt/CdtG6VsCz97BBxeoq4+lMX21UUjeWeTnXYVLi8GWJqXItvWVQlADAPEGNt0kLTCEpBdMNvt4+Pf2+pZA7Wu3KL3M8j7TO/r9jOoU1c/yypBDYK6b3cXszYzXQFwp/P3MQDfn1JfhqKMmlLXLYK6xd77Fci2r9vI4fTSkdv2fBVdx3eOLbCH9SqwULcBHfL7sBG4u73YtzIaZV2Vw0SkWneqbTPtph3X+9HIWSWPWbsKngRwDxHdBeASgA8B+NnpdmkwyqgpxZGs+UQsTzPLS8DD73CCmnKOT7dv8wLrRqFlKxSpw8AXd6tHEm3sHm+P/f6q9MHOGg4uAO+4h3DhNXHD3rEEgCRgqKeNHRgLZe8jrkoRONgO5TrLUg8aRmFJ3KlNXLoeoR3LjPTo7TvnZs17P4P2U9k/EM+YwgARvQ/AfwbgA/gMM/+Hov1PnjzJp0+fnkjfFEVRlLGxq9d/Zu6Ripm/DODL0+6HoiiKopRFF68URVEUZUTUmCqKoijKiKgxVRRFUZQRUWOqKIqiKCOixlRRFEVRRkSNqaIoiqKMiBpTRVEURRkRNaaKoiiKMiJqTBVFURRlRNSYKoqiKMqIqDFVFEVRlBFRY6ooiqIoIzJzVWMGhYiuAnh52v2YQQ4DuDbtTswwOj7F6PgUo+NTzDDjc42ZHxlHZybBrjemSjZEdJqZT067H7OKjk8xOj7F6PgUsx/HR928iqIoijIiakwVRVEUZUTUmO5dPjXtDsw4Oj7F6PgUo+NTzL4bH10zVRRFUZQR0ZmpoiiKooyIGlNFURRFGRE1pnsAIrpIRM8S0VNEdNpsu52I/g8RvWh+3jbtfk4KIvoMEb1ORGecbZnjQcJvEtF5InqGiH5oej2fHDlj9AkiumSuo6eI6H3Oa//SjNE5IvrR6fR6MhDRnUT0DSL6HhE9R0S/YrbrNWQoGKN9ew2pMd07/B1mvt/J7fo4gK8x8z0Avmb+3i98FkA6+TtvPH4MwD3m30cB/PaE+jhtPoveMQKAT5rr6H5m/jIAENG9AD4E4O3mmN8iIn9iPZ08bQD/jJn/BoAHAXzMjIFeQwl5YwTs02tIjene5f0Afs/8/nsA/t4U+zJRmPlbAK6nNueNx/sB/D4LpwAcIqI3Taan0yNnjPJ4P4A/ZOYmM18AcB7AO8fWuSnDzJeZ+Tvm93UA3wNwFHoNdSgYozz2/DWkxnRvwAC+SkR/TUQfNduOMPNlQC58AG+YWu9mg7zxOArgVWe/FRTfFPY6v2xclZ9xlgb27RgR0XEADwD4NvQayiQ1RsA+vYbUmO4N3s3MPwRxN32MiN4z7Q7tIihj237NF/ttAG8FcD+AywD+k9m+L8eIiA4A+GMA/5SZbxXtmrFtz48PkDlG+/YaUmO6B2Dm75ufrwP4AsR9csW6mszP16fXw5kgbzxWANzp7HcMwPcn3LeZgJmvMHPEzDGA30Hihtt3Y0REFYiR+Bwz/4nZrNeQQ9YY7edrSI3pLoeIFoho0f4O4GEAZwB8EcCHzW4fBvBn0+nhzJA3Hl8E8A9NROaDAG5aV95+I7XO9wHIdQTIGH2IiGpEdBck0OavJt2/SUFEBODTAL7HzL/hvKTXkCFvjPbzNRRMuwPKyBwB8AW5thEA+J/M/BUiehLA54noIwBeAfBTU+zjRCGiRwE8BOAwEa0A+LcAfg3Z4/FlAO+DBERsAfiFiXd4CuSM0UNEdD/E/XYRwC8BADM/R0SfB3AWEsX5MWaOptHvCfFuAD8H4Fkiesps+1fQa8glb4x+Zr9eQyonqCiKoigjom5eRVEURRkRNaaKoiiKMiJqTBVFURRlRNSYKoqiKMqIqDFVFEVRlBFRY6ooiqIoI6LGVFFmHCJ6iIi+ZH7/SSLKrQBERPe7Za9Ktn+RiA6P2k9F2c+oMVWUXQQzf5GZf61gl/shAgKKokwQNaaKkoKIjhPR80T0u0R0hog+R0Q/QkSPm8LQ7zQyjp8hoieJ6LtE9H7n2P9HRN8x/95ltj9ERN8koj8ybX/OSLLl9eERs99jAD7obP95Ivqv5vefMv17moi+RURVAL8K4KdNYeafzml7mYi+avr93+GIkBPRn5rqQ8/ZCkRE9BEi+qSzzy8S0W9kNK0o+xZVQFKUFKak1HlIWannADwJ4GkAHwHwkxC5uLMAzjLzHxDRIYjO6AMQGbWYmRtEdA+AR5n5JBE9BNFyfTtE4PtxAP+cmR/LOH8dwIsA3mv68b8AzDPzTxDRzwM4ycy/TETPAniEmS8R0SFmXnNfL3h/vwngGjP/KhH9OIAvAbiDma8R0e3MfJ2I5sz7/tsAGgCeAfA2Zm4R0RMAfomZnx1mfBVlL6IzU0XJ5gIzP2uqXzwH4GssT57PAjgOKSjwcaNL+k0AdQBvBlAB8DvG0P1vAPc6bf4VM6+YNp8y7WTxNnP+F805/yBnv8cBfJaIfhGAP8B7e49tk5n/AsAN57V/QkRPAzgFqfJxDzNvAvg6gJ8gorcBqKghVZRuVOheUbJpOr/Hzt8x5HsTAfj7zHzOPYiIPgHgCoD7IA+rjZw2IxR///q6jJj5HxHR3wTw4wCeMgLjZelp38yefwTA32LmLSL6JuQhAQB+FyJk/jyA/zHAeRRlX6AzU0UZjr8E8I/tuicRPWC2LwG4bGafP4fBZoyW5wHcRURvNX//TNZORPRWZv42M/8bANcgM8l1AIt92v8WgH9g2vgxALc5fb9hDOnbADxoD2Dmb5v2fxbAo0O8J0XZ06gxVZTh+PcQl+4zRHTG/A0AvwXgw0R0CsAPAtgctGFmbgD4KIC/MAFIL+fs+utE9Kw5/7cg67rfAHBvUQASgH8H4D1E9B2Iu/oVs/0rAAIiesa8n1Op4z4P4HFmvgFFUbrQACRFUUphcl0/ycxfm3ZfFGXW0JmpoiiFENEhInoBwLYaUkXJRmemijJFiOgLAO5Kbf4XzPyXO9D2LwD4ldTmx5n5Y6O2rShKN2pMFUVRFGVE1M2rKIqiKCOixlRRFEVRRkSNqaIoiqKMiBpTRVEURRmR/w/jU/dQRxYS8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 474.375x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('mean_dist_day', 'mean_over_speed_perc', data=data, hue='cluster', palette='coolwarm', size=6, aspect=1, fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "719601.5383469037"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inertia is the sum of squared error for each cluster. \n",
    "# Therefore the smaller the inertia the denser the cluster(closer together all the points are)\n",
    "\n",
    "kmeans.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method KMeans.score of KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=4, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>mean_dist_day</th>\n",
       "      <th>mean_over_speed_perc</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3423311935</th>\n",
       "      <td>71.24</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423313212</th>\n",
       "      <td>52.53</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423313724</th>\n",
       "      <td>64.54</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423311373</th>\n",
       "      <td>55.69</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423310999</th>\n",
       "      <td>54.58</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423313857</th>\n",
       "      <td>41.91</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423312432</th>\n",
       "      <td>58.64</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423311434</th>\n",
       "      <td>52.02</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423311328</th>\n",
       "      <td>31.25</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423312488</th>\n",
       "      <td>44.31</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423311254</th>\n",
       "      <td>49.35</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423312943</th>\n",
       "      <td>58.07</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423312536</th>\n",
       "      <td>44.22</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423311542</th>\n",
       "      <td>55.73</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423312176</th>\n",
       "      <td>46.63</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423314176</th>\n",
       "      <td>52.97</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423314202</th>\n",
       "      <td>46.25</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423311346</th>\n",
       "      <td>51.55</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423310666</th>\n",
       "      <td>57.05</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423313527</th>\n",
       "      <td>58.45</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423312182</th>\n",
       "      <td>43.42</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423313590</th>\n",
       "      <td>55.68</td>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423312268</th>\n",
       "      <td>55.15</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423314255</th>\n",
       "      <td>43.84</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423311976</th>\n",
       "      <td>59.26</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423312669</th>\n",
       "      <td>37.14</td>\n",
       "      <td>41</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423310697</th>\n",
       "      <td>64.30</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423312113</th>\n",
       "      <td>45.75</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423313343</th>\n",
       "      <td>45.97</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423311431</th>\n",
       "      <td>56.04</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423313079</th>\n",
       "      <td>180.29</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423312932</th>\n",
       "      <td>222.08</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423311728</th>\n",
       "      <td>165.10</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423314434</th>\n",
       "      <td>212.75</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423311292</th>\n",
       "      <td>170.64</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423314315</th>\n",
       "      <td>196.20</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423310889</th>\n",
       "      <td>189.99</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423310851</th>\n",
       "      <td>208.96</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423313381</th>\n",
       "      <td>219.39</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423311448</th>\n",
       "      <td>188.25</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423311551</th>\n",
       "      <td>187.13</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423313834</th>\n",
       "      <td>187.28</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423312123</th>\n",
       "      <td>192.74</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423310590</th>\n",
       "      <td>211.20</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423312146</th>\n",
       "      <td>189.88</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423312648</th>\n",
       "      <td>165.58</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423310473</th>\n",
       "      <td>191.88</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423312226</th>\n",
       "      <td>194.22</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423310647</th>\n",
       "      <td>167.22</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423311832</th>\n",
       "      <td>185.37</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423311103</th>\n",
       "      <td>203.80</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423311192</th>\n",
       "      <td>167.05</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423314043</th>\n",
       "      <td>177.48</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423312391</th>\n",
       "      <td>170.22</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423312567</th>\n",
       "      <td>209.76</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423310685</th>\n",
       "      <td>160.04</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423312600</th>\n",
       "      <td>176.17</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423312921</th>\n",
       "      <td>170.91</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423313630</th>\n",
       "      <td>176.14</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423311533</th>\n",
       "      <td>168.03</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean_dist_day  mean_over_speed_perc  cluster\n",
       "id                                                      \n",
       "3423311935          71.24                    28        3\n",
       "3423313212          52.53                    25        3\n",
       "3423313724          64.54                    27        3\n",
       "3423311373          55.69                    22        3\n",
       "3423310999          54.58                    25        3\n",
       "3423313857          41.91                    10        0\n",
       "3423312432          58.64                    20        3\n",
       "3423311434          52.02                     8        0\n",
       "3423311328          31.25                    34        3\n",
       "3423312488          44.31                    19        3\n",
       "3423311254          49.35                    40        3\n",
       "3423312943          58.07                    45        3\n",
       "3423312536          44.22                    22        3\n",
       "3423311542          55.73                    19        3\n",
       "3423312176          46.63                    43        3\n",
       "3423314176          52.97                    32        3\n",
       "3423314202          46.25                    35        3\n",
       "3423311346          51.55                    27        3\n",
       "3423310666          57.05                    26        3\n",
       "3423313527          58.45                    30        3\n",
       "3423312182          43.42                    23        3\n",
       "3423313590          55.68                    37        3\n",
       "3423312268          55.15                    18        0\n",
       "3423314255          43.84                    22        3\n",
       "3423311976          59.26                    32        3\n",
       "3423312669          37.14                    41        3\n",
       "3423310697          64.30                    29        3\n",
       "3423312113          45.75                    16        0\n",
       "3423313343          45.97                    23        3\n",
       "3423311431          56.04                    39        3\n",
       "...                   ...                   ...      ...\n",
       "3423313079         180.29                    20        1\n",
       "3423312932         222.08                    11        1\n",
       "3423311728         165.10                    13        1\n",
       "3423314434         212.75                     9        1\n",
       "3423311292         170.64                    13        1\n",
       "3423314315         196.20                    14        1\n",
       "3423310889         189.99                    12        1\n",
       "3423310851         208.96                    10        1\n",
       "3423313381         219.39                     1        1\n",
       "3423311448         188.25                    10        1\n",
       "3423311551         187.13                    12        1\n",
       "3423313834         187.28                     8        1\n",
       "3423312123         192.74                    13        1\n",
       "3423310590         211.20                     8        1\n",
       "3423312146         189.88                     9        1\n",
       "3423312648         165.58                     6        1\n",
       "3423310473         191.88                     7        1\n",
       "3423312226         194.22                    12        1\n",
       "3423310647         167.22                     2        1\n",
       "3423311832         185.37                    14        1\n",
       "3423311103         203.80                    22        1\n",
       "3423311192         167.05                    10        1\n",
       "3423314043         177.48                    19        1\n",
       "3423312391         170.22                    16        1\n",
       "3423312567         209.76                    18        1\n",
       "3423310685         160.04                    10        1\n",
       "3423312600         176.17                     5        1\n",
       "3423312921         170.91                    12        1\n",
       "3423313630         176.14                     5        1\n",
       "3423311533         168.03                     9        1\n",
       "\n",
       "[4000 rows x 3 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}