Clustering NewsPaper example - Students.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K Means Documentation\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "news = pd.read_csv('np.csv')\n",
    "news.columns = ['time','sections','persons']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>sections</th>\n",
       "      <th>persons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   time  sections  persons\n",
       "0     0         0      370\n",
       "1     1         0        9\n",
       "2     1         1      127\n",
       "3     1         2      119\n",
       "4     1         3       55"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "news.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53 entries, 0 to 52\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype\n",
      "---  ------    --------------  -----\n",
      " 0   time      53 non-null     int64\n",
      " 1   sections  53 non-null     int64\n",
      " 2   persons   53 non-null     int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 1.4 KB\n"
     ]
    }
   ],
   "source": [
    "news.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2939"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "news['persons'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# expanding the dataset to get rid of the count value\n",
    "newsupdated = pd.DataFrame(news.values.repeat(news.persons, axis=0), columns=news.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>sections</th>\n",
       "      <th>persons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>370</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370</td>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370</td>\n",
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      "text/plain": [
       "   time  sections  persons\n",
       "0     0         0      370\n",
       "1     0         0      370\n",
       "2     0         0      370\n",
       "3     0         0      370\n",
       "4     0         0      370"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newsupdated.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time        2939\n",
       "sections    2939\n",
       "persons     2939\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing using count\n",
    "newsupdated.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>sections</th>\n",
       "      <th>persons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2939.000000</td>\n",
       "      <td>2939.000000</td>\n",
       "      <td>2939.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.370534</td>\n",
       "      <td>3.394012</td>\n",
       "      <td>103.913916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.369500</td>\n",
       "      <td>2.253787</td>\n",
       "      <td>104.837641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>103.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>370.000000</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "              time     sections      persons\n",
       "count  2939.000000  2939.000000  2939.000000\n",
       "mean      3.370534     3.394012   103.913916\n",
       "std       2.369500     2.253787   104.837641\n",
       "min       0.000000     0.000000     1.000000\n",
       "25%       1.000000     2.000000    47.000000\n",
       "50%       3.000000     3.000000    67.000000\n",
       "75%       6.000000     5.000000   103.000000\n",
       "max       7.000000     7.000000   370.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newsupdated.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In the next few statements we will try some visualizations to understand the dataset better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.kdeplot(newsupdated['persons'])\n",
    "#analyse the distribution of the sections using probability (y axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f8aba95f10>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = 'sections', y = 'time', data = newsupdated, size= 'persons', sizes = (20, 500),legend = False) #actual values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1f8abb24be0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Another option to visualize a joint distrbution \n",
    "\n",
    "sns.jointplot(x = 'time', y= 'sections', data= newsupdated, kind=\"hex\",height=7, space=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The below visualization gives us some insights about the different newspaper habits that might exist in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x266287fab20>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1M93ocoLBNz1MDOdp+KCuC1f+7zs4f1YmvrCq1OhyiE4zOOxH5bEe7G7oxu6G7g971W6nA3NzEzErJxFlGXEoy4hHVmI00uPdSIlzI8r5z+D2ByT6vD70DI6goWsQjV2DqG0fQFVTL/Y39aC2fQASgEMAJelxOCM/GWcUJGNGRjynrNXYsC+Af3vyA8R7XHjpm6tPep8UxQMgTAznEI34A7js9xvR0jOEO646g1dnkym093lR3dKHgy19ONTah4auQfQO+U77PacQcDoEhAC8vsBp/y4AZCdFoyA1FkWpsSjPSsCMzHhecR0BW2s78MtXDuC7l87Gzeco3ylgOIeJyRKiu9+qQVVTL759YTmDmUwjLd6DtHgPVpxwP3HP0AiauofQOTCMnsER9Az54PNLBOTon+goJ2KinIh1O5EW70F6nBtp8R5eX2GQxYUpOKMgCb95rRqXL8xDRoK95h23G/acQ1DT2oeLfvM2FhUm45sXlBtdDhHZzLGuQfzbk7twxaI83HHVGUaXMxn2nMPEr8BBCgQkbn9qN6KcAjeuLDa6HCKyoZzkGFw8LxtPbKvH9qOdRpdDOmI4B+mvW+vw/uEOfHZ5kdITPhCRtV25KB8psVH472f2IhBQerCRwsBwDkJzzxB+8o9KzMlJxJqKDKPLISIbi3E78ZnlRdjV0I3Ht9YZXQ7phOEchO8/swdeXwA3n1PCh1oQkeHOLkvDrOwE/Pyl/egeGDG6HNIBw3kKL+05hpf2NuOTZ+YhJ8n4aRGJiIQQuGFlMboHR/Dr1w4YXQ7pgOE8ie7BEXzvmb0oTovFJQtyjC6HiOhDxWlxWDs7C3/eVIv9TT1Gl0MaYzhP4mcv7kd7nxe3nFMKl4O7iojUctXifMS6nfj+M3uh2G2xFCYmzgQ21bTjL+8fxSXzc1DKR0ESkYISoqNw1eICbD7cged3HTO6HNIQw3kcQyN+3P7kLmQlevCpxflGl0NENKG1szJRkh6HH79QiYHh06dkJXNiOI/jd+urcaR9AF9YVQqPi3MGE5G6HA6BG84qRlP3EP74+kGjyyGNMJxPsaehG3e9WYNzyzMwPy/J6HKIiKZUkZ2AVTPScfdbNTjS1m90OaQBhvMJvD4/bnt8JxJjXLhueZHR5RARBe3aZYVwORz44fP7jC6FNMBwPsFvX6vGgeY+3HxOKeKj+cQpIjKP1Dg3rliUhw37W/Dqvmajy6EwMZyP21nXhTvfPIRzyzNwZmGK0eUQEYXsknnZKEyNxX/8fTdnDjM5hjOAwWE/vv34TqTGuXH9WRzOJiJzcjkd+NK5ZWjv8+IHz+01uhwKA8MZwA+e24ua1n6sW12GWDeHs4nIvErS43DFojw8taOBw9smZvtwfvaDRjy2pQ4fX5jLq7OJyBKuXJiHorRY/MdTu9HRP2x0OTQNtg7n2vZ+fOepXSjPiudkI0RkGWPD250Dw/j24zv53GcTsm04D4348ZVHtwMAvnreTM6dTUSWUpwWh8+tKMLrVa24660ao8uhENkykaSU+NcndmFvQw++dG4ZMhI8RpdERKS5C+dkYXlJKu54uQpbjnQYXQ6FwJbh/IcNB/HcB424ZmkBlhSlGl0OEZEuhBBYt7oUGQkefOWR7WjpHTK6JAqS7cL5xd3H8MtXD+CcGen4+Bm5RpdDRKSrWLcL37hgJroHR/D5B7ag38uHY5iBrcJ5Y3UbvvHYTszMjMfN55RCCGF0SUREuitOi8PX185E5bEefPXR7fD5A0aXRFOwTThvqmnHzQ9uQXZSNP71oxVwu2yz6UREOLMwBTedXYLXq1rx3af3QEpewa0yW8y4sa22A5//vy1IS/DgPy6ZjYToKKNLIiKKuLWzs9DWN4zHttTB4RD40eXz4HBwBFFFlg/nV/Y24RuP7URybBT+85LZSIphMBORfV29JB8BKfHo5qMY8Prwi6vOQJSTI4mqsWw4Sylx38bD+PE/KlGWGY9vX1iO5Fi30WURERlKCIFrlxUixu3EX7fUoc/rw28+vQjxHsvGgSkJxc47aFJMv9eHHzy3F49vrceyklTcuqYMHpdTi0UTEVnGK3ub8OB7R1CcFoc7P7cY5VkJWi2aY+Vhslw4bz3SgW89vhP1HYO4fGEerlqSDwevyiYiGte+xm78fsNBeH0B/PDyufjU4nwt7mRhoxsmy4Rze58Xv99wEA+9dwTp8R58+dwyzMpJ1LA0IiJr6hwYxh82VGPfsV6cVZqGH1w+N9xeNMM5TKYP5z6vDw9sPIw73zqEwWE/zp+Vhc8cP59CRETBCQQk1u9vxuNb6zE04sd1K4pw8zklyE+Jnc7iGM5hMm04Vx7rwSOba/H3HQ3o9/qxtDgF1ywpRF5KjJ71ERFZWs/gCP66tQ5vVLVAQODSBTm4YWUxzixMDmW4m+EcJtOE89CIHx/UdWFDVQvWV7bgYEsfopwCK0rT8NG52SjLiI9knUREltbW58VLe5qwYX8LBkf8yEmKxsXzcnD+rEwsLEye6upuhnOYlArn5p4h2TvkQ/fgCBq7BlHfOYja9n7sbuhGVVMvfAEJp0Ngdk4CFhemYNWMDMRH8/J/IiK9DAz7sK22E5sPd+CDui74AhIOAczKTsSc3ESUpMehOC0OmYkepMRGISnGjYwED8M5TEqFc/Ht/zitmMRoF4rS4lCSHocZGfGYm5eIWDcDmYgo0gaGfahu7sOBll5UN/ehvnMAnQMjp/3ekZ9dynAOk1Ip9421MxDncSHO7UJmogdZiR4GMRGRMtwoSovFBXMyP/zJ4LAfjd2D6OwfRu+QD4MjfKiGFpTqOaelp8uCwmKjy9BNe1sr0tIzjC5DedxPweF+Cg7309S03kdVXRLP/+MFrJ2dpdkyLWrCEQaluqUFhcVYv3Gz0WXoZu2q5ZbePq1wPwWH+yk43E9T03of5ZbNwsCwX7Pl2RFnOyciIs31e31Gl2BqDGciItJcW5/X6BJMjeEcQdffdLPRJZgC91NwuJ+Cw/00Na33kUMINPUMabpMu1HqgrCFZy6RPDdERGRuxeVzcPWPHsZ9Ny41uhTVTXhBGHvORESkqSinAweae40uw9QYzkREpCm3y4G6zkEMDPOisOliOBMRkabcrtFoqTzWY3Al5sVwJiIiTUUfD+dNNR0GV2JeDGciItKU0yFQkBqD9w61G12KaTGciYhIc3Nzk7DlSAcnI5kmhjMREWlueUkqvL4AXtnXZHQppqRrOAshviWE2CuE2COE+IsQIlrP9RERkRrKsxKQHu/GMzsajS7FlHQLZyFEHoCvA1gipZwHwAng03qtj4iI1OEQAmfPSMfb1W2o6xgwuhzT0XtY2wUgRgjhAhALgF+hiIhs4sLZWRACuPftGqNLMR3dwllK2QDgDgBHARwD0C2lfOXU3xNCrBNCbBVCbG1va9WrHCIi0tGD99+DtauWY+2q5ejsGL1KOy3eg1Uz0vHYljo+CCNEeg5rpwC4HEAJgFwAcUKI6079PSnl3VLKJVLKJXwgOhGROd1w0y1Yv3Ez1m/cjJTUtA9//rEzcjHiD+B366sNrM589BzWvgDAYSllq5RyBMBTAFbquD4iIlJMbnIMLpidhYc31WJvY7fR5ZiGnuF8FMAKIUSsEEIAWAugUsf1ERGRgq5aUoD4aBe+9/QeBALqPAlRZXqec94M4AkA2wHsPr6uu/VaHxERqSne48JnlhVh+9Eu3MOLw4Ki69XaUsrvSylnSSnnSSk/J6XkFQFERDa0emY6lpek4hcvV2FbbafR5SiPM4QREZHuhBC45ZxSpMa58bVHt6NrYNjokpTGcCYiooiI87jw9bUz0dLrxbqHtmFoxG90ScpiOBMRUcSUZcTjy2vK8P6RDnzrrzt5gdgEGM5ERBRRK8vScd3yIry4pwk/fH4fpGRAn8pldAFERFZR1Tb+Na8V6Z4IV6K+SxfkoGNgGP/37hE4HQLfvXQ2Ru+6JYDhTER0molCVq/l2TW8r1teiICUuG/jYQz7AvjBx+fC4WBAAwxnIrIRrUNXKyfWZaegFkLg+hVFiHII/HlTLYZ9Afz4ynlwOXnGleFMRJaiagAHy25BLYTAtcsKEeV04K9b69DW78Xvr12EWLe948neW09Epmb2IJ7K2PZZPaSFELhqSQGSYqPw4LtHcM1dm3D/jUuRkWDt7Z4Mxw6IyDSq2rwn/bELu2zvR+Zk47YLK3CguRdX/vEdVDf3Gl2SYRjORKQ0O4bxROywDxYXpeB7l81Bn9eHK/73Hbyyt8nokgzBcCYi5TCQJ2aH/VKWEY8fXTEP2YnRWPfnbfj1qwdsN1kJw5mIlMBADo3V91NavAf/ddlcrJ6Zjt+ur8a6P29F79CI0WVFDMOZiAzDQA6P1feb2+XAl84tww1nFWPD/hZc8cd3cKi1z+iyIoLhTEQRx0DWjtX3oxACF83Lxn9eMhutvV5c/od3sL6y2eiydMdbqYgoIqwSIpWt4W/H7AxtbxGqavNa/narOblJ+PGV8/GrVw/g5ge34rYLy/GV82ZYdkYxhjMR6casgaxFAIeyfK3D2qrS4z3474/NxT1v1+CXrx7AnsZu/PLqhYj3WC/KrLdFRGQoMwWy3iEcrLE6wglpO/SegdHz0LeuKUNJehwe2VyLK/74Du65fglK0uOMLk1TDGcihB4odmgEQ6F6IKsSwlMJN6TtEtBCCFwyPweFqbH43YZqfPwPG/GHz5yJc8szjC5NMwxnsg0tA4SPBhylciibJZDHU9nq5VB3EOblJeHHV8zDL185gJse2IIfXD4X160oMrosTTCcyXKMDIxT123FsGYgR8Z0A9ouvecxGQnR+P7H5uL3r1fju0/vwZG2fnznktlwmvxCMYYzmZbKITHGKk8YUnlfWymQT8UedHBi3E58+8IK/HlTLe7deBhHOwbwu2sXITrKaXRp08ZwJqWpHAqhMluvWvV9b+VQPtF0AtpuvWcAcDoEblxZjOzEaDz03hF84cEtuOf6JaZ99KQ5qyZLUj0MtKZir9os74FdgnkMe9DBu2heNmLdTtz11iFcf9/7eODzS5EQHWV0WSFjOJNhzBIEkWBkr9pM74PdQvlEoQa0HXvPY1aXZ8DtcuAPrx/EdfduxiO3rDDdvdDmqpZMz0xBYKTx9pNWDa1Z3wM7BzOFbkVpGlxOgV+/egC3PrwN9924FFFO88xYzXAm3Zk1DFRj5/2oajBH+v54Dm+HZklRKr6wqhT3vF2D7z29Bz/9xHwIYY6ruBnOpBs7hwlpR7VgDue4HnttpIab7Ty0Peb8WZlo6/PisS11KE6Pw5fOLTO6pKAwnElzDGXSikrBrMckNtMJTvaeQ3fV4nw0dg3ijpersGpGOublJRld0pTMMwBPyuNjAElLKgSz3s+b5uclMoQQuHlVKRKiXbjt8Z3w+vxGlzQlhjOFjaFMWjM6mCN5TPOzExnx0S7cck4pDjT34ffrDxpdzpQYzjRtDGWyIh7T1rWoMAUry9Jw38bDaO9T+31mONO0sAEjvRjZa+ZxbX2fODMfQyN+3LfxsNGlTIrhTCFhb5msSqUHpkwllC8w/LyeLC85BstLU/Hge0fQMzRidDkTYjhTUBjKFAlG9Zp5bNvLxfNy0O/14/X9LUaXMiGGM02JDRdZGY9v+5mRGY/kmCi8sq/Z6FImxHCmSbHhIiKrcQiBxUUpeH1/C4Z9AaPLGRfDmSbEYCYiq5qdk4iBYT8OtfYZXcq4GM50Gp5fJiKrK0yNBQBUNfUaXMn4GM50EoYyGcXoiUfIXnKSo+FyCOxnOJPqGMxkR1Z/MITVt2+6XA4HUuLcaOkdMrqUcTGciYgMFmqA8sEX2ohzO9E9oOa9zgxnAsBeM9kbe5f2FO9xoXNg2OgyxsVwJgYzKcHo3iAD2n7iPC50sedMKmIwE/2TEQHNLwXGife40D3IcCbFMJiJTsewtI/46NFwllIaXcppGM5ERKeIVEDzi4Cx4j0u+AISfV6f0aWchuFsU+w1E01O7+Cc7vKNPjdvJXEeFwAoed6Z4WxDDGai4LBna23xDGciInOqSPdoHtIcNldDSqwbANDUo95EJAxnm2GvmWh6tAo6BqY60uJHw/lY96DBlZyO4ZNdyagAACAASURBVGwjDGai8IQbrAxmtSTFRMHlEGjoZDgTEZnadIe5tQhmXgymLYcQyE2OQXWLeo+NZDjbBHvNRNoKJaTZY1ZXQWosKo/1GF3GaVxGF0D6YzAT6efE4D3xs8ZANofC1Fi8c7ANnf3DSIlzG13Oh9hzJiLSyFhvmsFsHuWZ8QCArbWdBldyMoazxbHXTGZR2cpjlSKvNCMeLofA+4fbjS7lJAxnIiKyLbfLgRmZ8XivhuFMEcJeMxHR1M7IT8aehh60KDQZCcOZiMgEeBuVfhYVJgMAXq9qMbiSf+LV2hbFXnNoJjrfyQYxMni+mYxUmBqL9Hg3XqtswTVLC40uBwDDmWwolCA48XcZ1GQmvGI8eEIILCpMwcbqNgyN+BEd5TS6JA5rWxF7zSerbPWe9Cfc5RCR9ZxZmIzBET82KXJhGHvOZBmRCs7KVi970RriFx5SwZycJHhcDqyvbMGaikyjy2E4W40Ve80qNt5jNTGkiazB7XJgfl4S1lc244eXz4UQwtB6GM6kHBXDeCLsRYfHTO81Wd+iwhRsre1EVXMvZmUnGloLzzmTMnhOl4iMNHZL1Yb9xt9SpWs4CyGShRBPCCH2CyEqhRBn6bk+Miezh7KZayeif0qJdaMgNQbvHGwzuhTde86/BfCSlHIWgDMAVOq8Plsz2/lms4fyiayyHZHEfUYqmpuThK1HOuH1+Q2tQ7dwFkIkAlgN4D4AkFIOSym79FofmQsbZiJS0dy8RHh9AWyvNTau9Ow5lwJoBfCAEGKHEOJeIUTcqb8khFgnhNgqhNja3taqYzmkCqsGs1W3Sw/cV9bz4P33YO2q5Vi7ajk6O9S4V3g65uQkwiGAdw8ZO7StZzi7AJwJ4E9SykUA+gHcfuovSSnvllIukVIuSUvP0LEcazPLkDYbZSJruuGmW7B+42as37gZKalpRpczbbFuF0oz4g0/76xnONcDqJdSbj7+9ycwGtZkU3YIZjtsI5HVzc1NxK76bvR5fYbVoFs4SymbANQJISqO/2gtgH16rY/UxtCiMTwWIsMso2kqmpebBF9A4v3Dxg3P63219tcAPCKE2AVgIYCf6Lw+UhAbYyIyk/KsBEQ5Bd49aFw46zpDmJRyJ4Aleq6D1GbHYOasYUTm5nY5UJ6VYOh5Z84QZgEcviKzsOOXNTKnublJqGzqRXufMccsw5l0w4aYSDv8PEXWvNzRubU31XQYsn4++IJ0wYaETmXVY2KikauKdJ7aMLPSjHjEup1480ALLl2QE/H1s+dMmrNqIxwK7gPrq2rzTnpKiaebzM3pEDgjPxnrK1sQCMiIr5/hTJpiKJGVjQVysMEbyu+SehYXpaC9fxg76yM/lSfD2eRU+uAzmGkiZj42Qg3kiZZB5rOwIBkuh8BzHzRGfN0MZyKdmDmQtGTW/cBeL8V5XFhclIK/72jAsC8Q0XUznEkTZm2Aicac2EPWI5QZ9Oa0piITXQMjeK2yOaLrZThTWKz0TGY92H3fqL79eobxROsLh+r704oW5CUhPd6NB945HNH1MpxNzOhv4mwoaDKqHh+RDuTx1k/m4XAIXDo/F1uOdOL9w5G755nhTNOiasOrIu4rNah0DlmVOig4583KQGKMC3/YUB2xdTKcKWQMG5qKSseISqEcaXbdbq15XE5cOi8Hb1W34d0IzbfNcKaQqNTomomd9psq26p6KKtcG53uonk5yEjw4IfP74M/ApOSMJxNKtIfbF74FT477D9VttEswTedOlXZx3bjdjnwmWWF2N/Ui8e2HNV9fQxnmhIbAwqGCseJ6r3l8ZitXjtbXpKK2TkJ+PmL+9HSM6TruhjONCkVGlwrser+VGG7GHKkNyEEbllViqGRAL73zB5Iqd/wNsOZxsVhbP1Yab+qcpyYPZjNXr+d5CTH4JOL8/Hy3ma8sLtJt/UwnE1I7w+yCo2t1VlhH6uyDXYMtlD2vR33j94unZ+D0vQ4/Ncze9DZP6zLOhjOdBJVGlw7UKXXOR2q1G2l4LHStlid0yGwbnUpugZH8MPn9+myDoYzfUiVBtduzLbfVamXYUZGKkqLw+ULc/H3HQ3YsF/7ebcZzgRAnQbXrsyy/1Wp06rBbNXtsqorF+YhPyUG33t6LwaGfZoum+FsMnp8eFVpcO1O9WFulWuzI74fxnM5HfjC2SVo6BrE79Yf1HTZDGeb4wdcPaqFtGr1WL13afXts5pZOYlYU56Be9+uwYHmXs2Wy3AmUpQKoWj0+k/F4CIVXbu8EB6XA796pUqzZTKcbUy1hpfGZ9T7xOPDOvilRl+J0VG4aF42XtrbjL2N3Zosk+FMZAJjvehIBaaKwWyngLHTtlrFxfNyEOd24vfrtXmsJMPZRLT8wKrY+FJw9A5pHhtEoYvzuHDerEy8VtmC7oGRsJfHcCYyKT1CmsFsLny/1LK8JBW+gMSGqvDve2Y4E5mcHRpoDvOSGZRmxCM1zo2X9zCcaRrs0JjbjRbvKY8LovA4hMDMzHhUaXBLFcOZyCLCGeZmMBNpIzEmCh0aPAyD4UxERKSRxGgXugdH4PMHwloOw5mIMDvDY3QJRJbQP+xHTJQTDiHCWg7DmchCGLJExqrrGEB5VjwcDoYzEWlA5WCvSFe3NqIxASlR1zGAiuyEsJfFcCayCC3CVeWAJlLdrvou9Az5sGpmRtjLYjjbEBtga5md4dH0PVX1+GDv+XSqvld29fLeZmTEe3DR3Oywl8VwJjIxvRpnNvrG4hcR86nvHMDOui58dkUh3K7wo9WlQU1EFGGRCM+xdah0D3RFuoezhU0Dw15fASlx38bDSIqJwudWFGmyTPacTUTLDxh7Ruak9RB2sOtUCYOGVLO+sgX7m3rxn5fORlq8Nscne85EilMhHFXrRVu5B80vH+bS3DOEx7YcxVmlabhqcb5my2XP2cZUaPRpYkb0kqeiUk12DzFV3gc78/r8+PVrB+ByCPzPpxZAhDnxyInYcyZSjBkaXVV60lbrQdv9C4eZSClxz1s1ONo+gAc+vxQFqbGaLp/hbHOzMzyGN7BkjkAejwohPRZoZg9pBrO5PLfrGN451I5/+Ug51lRkar58DmubjB4fYLMGgxWoNEwcDhW2oyLdY5uAC2Vf22WfRNK7h9rwl/eP4tL5Obh1zQxd1sFwJgAMaCNYcZ8zpKfHbPXa2b5jPfjTG4ewtDgFv7z6jLDn0J4Iw5k+ZHSjahcqBJjeVNhGs4S0GWqkUfWdA/jVq1UoTIvFPdcvQXSUU7d18ZwznYTnoPVjdFgZQaVz0mNUOTc93VC243Gkgo7+Yfz8pf2IjXLhwc8vQ3KsW9f1MZxNSO8rVBnQ2rN7g6pCSI+ZKBQjGdqR6i2zV66NwWE/fvHyfvQP+/G3L56l+ZXZ42E407gY0NqweyifSqWQPlUkQjvcsOTxFHn+gMRvNxxAXccg7rtxCeblJUVkvQxnmpDKDakZsCGdmJm+/E0WqMEEN3uv5vbI5lp8UNeNn1w5X5dbpibCcKYpmakhVQFDOThW+PIXyeDlcRV56/c348U9Tfj82cX4zPLCiK6bV2ubVKS/jbNhCA73U+hUuLLbithjD8+B5l488M4RnFuegf+8ZHbE189wpqCxAZ0c9094uP8mxn0TWX1eH36/oRo5SdH43bWL4HJGPio5rE0hscJQpNbYcGqHxxcZTUqJu948hK6BETzx5aVIiokypA72nGlaGEijuB/0wf1KRnmjqhVbaztx+8WzsLAg2bA6GM40bXZuQHmeVH/cv6Omsx94vnl6ugdH8Oj7R7GsOBU3nV1iaC0MZxNT4QNoxwbUjttsFLt/CbLzthvh4U21GBrx4yefmK/bnNnBYjgTBcnuQWEk7nfS2/6mHmw82IZb15RhRma80eUwnCl8dmg47bCNqrPbe2C37TXaE9vqkRHvwZd1egRkqBjOpAmrNiTsLauF78fUVDjdZTZ7G7uxt7EHt55Xhhi3fk+aCgXDmTRjpUaTIaA2q783Vt8+1Ty9swGZCR5cuyyys4BNhuFMdAo2jObA94m00Ng1iD0NPbhhZbGuz2cOFSchITqOjb35WHHSEh6HkbW+shkuh8BVS/KNLuUk7DmbnGrnl8zYsHAI2/z4/o1SrT1QnS8QwFvVbfjo3GxkJkQbXc5JGM5ka2zUrcMKX7LMXr/Z7G3oQZ/Xh8sX5hpdymkYzqQ5szQwZqmTQmPW99WsdZvZ5sPtiPM4sbo8w+hSTqN7OAshnEKIHUKI5/VeF1Gw2BBaG99fmkogILGtthNrZ2UpdSHYmEj0nL8BoDIC6yEKChtuezDTMLcWdfJ8c2gOt/ejZ8iH82dlGl3KuHQNZyFEPoBLAdyr53qIgmWWxpq0o3pIq1yble2q7wYArJqZbnAl49O75/wbAP8GIKDzeoimxEbQ3lQPaYqsPQ3dmJubiPR4NY8J3cJZCHEZgBYp5bYpfm+dEGKrEGJre1urXuUQEQFQK6RVqUMLD95/D9auWo61q5ajs6Pd6HIm5fMHcLC1D8tL0owuZUJBhbMQ4iohRMLx//+uEOIpIcSZU7zsbAAfF0IcAfAYgPOFEA+f+ktSyrullEuklEvS0tW7Yo6swUqNIGljLKSNOjasdkzecNMtWL9xM9Zv3IyUVHVDDwCOtPdj2BfA0uIUo0uZULA95+9JKXuFEKsAfBTAgwD+NNkLpJTfkVLmSymLAXwawAYp5XVhVUtEpINIB7XW6+HFYKE50NwHAFhcpG44Bzt9p//4fy8F8Ccp5TNCiP/WpyQibVmth0L6OvF40XpaUB6Lajjc1o/MBA8yE9WaFexEwYZzgxDiLgAXAPi5EMKDEM5XSynfAPBGyNURERlIq6BmKKvlSHs/FuQnGV3GpIIN56sBXATgDilllxAiB8C/6lcWEZFaxgvYyQKbgaymYV8AjV2D+MSiPKNLmVRQ4SylHBBCPAMgSwgx9sDL/fqVRcGqarPO03iIzEaFAOb55tA09wwhIIGyzHijS5lUUOEshPgagO8DaMY/71mWABboVBcREZHmmrqHAAAl6XEGVzK5YIe1vwGgQkqp9s1rpAQrPVuXiKylpXe0fSpKVTucg72oqw5At56FEBFRaDikHbqeoRG4HAKJMcH2TY0RbHU1AN4QQvwDwIfdIinlr3SpikyLvWYiUln34AjS4t0QQhhdyqSCDeejx/+4j/8hOo2qwVzZ6lXiwh0iMt7AsA9JMVFGlzGlYK/W/gEAHJ/CU0op+3StioiIJsUh7emREnAo3msGgp9be54QYgeAPQD2CiG2CSHm6lsaTUWl26hU7TWPUb0+Iooc9aM5+AvC7gZwm5SySEpZBODbAO7RrywyE7MEn1nqJCL9OB0CXr/6TzEONpzjpJSvj/3l+HScal+HThFhtsAzW71E4+GQ9vQlxUShrVf9diDoq7WFEN8D8Ofjf78OwGF9SiIzYMgRkRklx7rRM+TD0Igf0VFOo8uZULA955sAZAB4CsDfj///5/UqiqZm5PlmswdzZavX9NtARNOTHj96w1Fdx4DBlUwu2Ku1OwF8XedaSHFWCzTeYkVmxCHt8BSnjZ6R3dvYg5lZCQZXM7FJw1kI8Rsp5TeFEM9hdC7tk0gpP65bZaQUqwXzmLHtYkgT2UNucgzcLgd2N3TjCoWfTDVVz3nsHPMdehdCarJqKJ+KvWgie3A6BErS47D5sNqPipj0nLOUctvx/10opXzzxD8AFupfHo0nEueb7Xhe1o7bTGRHC/OTsaehBy29Q0aXMqFgLwi7YZyf3ahhHaQQuwcUQ5pUxfPN2lhUmAwAeKOq1eBKJjbVOedrAXwGQIkQ4tkT/ikBgNpjAhQyBtLJeD6ayJoKU2ORmeDBMzsacPWSAqPLGddU55zfBXAMQDqAX57w814Au/Qqiiam15A2g3liDGkiaxFCYE1FJh7fWoea1j6UZsQbXdJppjrnXHt8NrDPAth8wvnmSgD5EaiPIoDBHBzuJyLrWFORAadD4C/vHzW6lHEFe875cQAnTkbqB/A37cuhSGPghIbno8koPN+srZRYN5YUpeBvW+sxNOI3upzTBBvOLinl8Nhfjv8/n+scYVoPaTNkpo/7jsj8Lpidha7BEby0p8noUk4TbDi3CiE+nHBECHE5gDZ9SqJIYLiEj71ocxh7n/h+0anm5CYiJykaj2yuNbqU0wT74IsvAXhECPFHjM4UVg/get2qIl2xgdIWJzBR00TH+Yk/5/tmbw4hcP6sTDyy+SiqmnpRka3OdJ5B9ZyllIeklCsAzAYwV0q5Ukp5UN/SSA8MZn2wV6aOUN4Lvme0ujwDUU6BRxXrPQcVzkKILCHEfQD+JqXsFULMEUJ8Qefa6ARanG9mQ6Q/7mNjTWf/8z2zt8ToKCwvScNTOxowMOwzupwPBXvO+f8AvAwg9/jfDwD4ph4FEZkdG3tjhLPf+Z7Z29pZmegd8uH5D44ZXcqHgg3ndCnlh7dTSSl9GL2dikyCjU9kcX9Hlhb7m++ZfVVkJyA/JQYPKzS0HWw49wsh0nD8sZFCiBUAunWrisgCeB6atMJ7nPUlhMDaWVnYVd+NPQ1qRFuw4XwbgGcBlAkh3gHwEICv6VYVnSTc880MCGNx/+tLy/3L98q+zpmZDrfLgUc2qzFjWLDhXAbgYgArMXruuRrB34ZFBmJjowa+D/rgfiWtxHlcWFmahqd3NqB3aMTocoIO5+9JKXsApAC4AMDdAP6kW1VEFsQgIVLb2tlZGBz24+mdjUaXEnQ4j138dSmAO6WUz4DTdyqPYaAenofWjl77ke+PfZVlxKEkPQ6PbKqFlNLQWoIN5wYhxF0ArgbwghDCE8JrKQx6PSKSjMUAIFLP6IVhmdjf1IsddV2G1hJswF6N0XPNF0kpuwCkAvhX3aqisLHxVx/fIyL1rCxLR0yUE49sMvbCsGCn7xyQUj4lpaw+/vdjUspX9C2NyPoY0Gri+2JfMW4nzp6Rhud3NaJrYHjqF+iEQ9MWZKaGparNO+4fOzHT+6UK7jPS0wWzs+D1BfDk9gbDauDtUAqzYkgFu00n/p4dJmAYCxs+JYnIeEVpcZiZGY9HNtXi8yuL4XCIiNfAnjNFRDg9Yjv1ptkjJFLDR+Zmo6atH28eaDVk/Qxni1Gpcdd6mJoBTUSRsqI0FWlxbtz15iFD1s9wJs3p2dO1Sy+aAT2xSO0bld4DOxzzqnE5HLhoXjY2He7ATgNuq2I4W4gRjYlRF3PZobHihCVExlo7KwuJ0S7c8XJVxNfNC8IUpWL4qFbTWD1Wv2CsstXLC8WIDBDjduLyhXn486ZavHOwDWfPSI/Yutlztgg9elhmub1J9fq0wB70KO4HirQLZmchPd6Nn7xQCX8gclN6MpzpJGYI44mYte5gMZgij/uc3C4Hrl1WiL2NPXjw3SMRWy/DWUGhBowWDYiVgs0sPf7pYFgQRd5ZpWlYWJCMX7xShYauwYisk+Fsc1YMsFNNNAuZWbfbrheKGbXNdtzXdDIhBG46uwSBgMTtT+5CIALD2wxnkwun4TBrOGnJzMHN0LAXMxyTVpaR4MF1K4rwdnUb7nqrRvf1MZxtyCzhYzQzhDUDOjK4nwkA1s7KxIrSVNzxchW2HunQdV0MZ8XoHQKqhowZqBrUdggOO2wjqU8IgVvOKUV6ghtfeXQ7mnuGdFsXw9nEQm2wVAsVM1MtpBle+uM+JgCIdbvwrQvK0TPow+cf2II+r0+X9TCcbUKlILESlULaquFh1e0i8ypKi8PX187A/qYefO3R7fD5A5qvg+GskFAa+VAaLFXCw8pUCWmrBZlq22N0PSocYzRqYUEKbjq7BK9XteL7z+6FlNpewc1wtjh+mCNLhf1tdIBYHfcvjVk7OwsfPyMXj2w+qvkV3AxnE2LjoDYVetFWOEZU3gaVa6PIumZpAVaWpeFnL+7H87saNVsuw9nCjA4IuzN6/zNAiPTnEAJfXF2GWdkJuO2vH2h2ixXDWRFaN+RGBwONMvp9MGtAm6Fuo2o0+pii07ldDtx2YTnS4t34woNbcbitP+xlMpxNxgyNFp3M6MbUbMeMmeo1U62kr4ToKPz7RbMgpcQN97+P9r7wjg2GswUZHQZ0OqPfE4aIfrhvaUxWYjS+/ZEKNHUP4UsPb8NIGLdYMZwtRosQmGy+aaNDxsyM3ndmCBEz1Eg0mfKsBKxbXYotRzrxkxcqp70cl4Y1kc70bLhCCY7xfrci3aNlOZZV1eblvpqAmYO5stWL2Rl8X2nU2TPScbClDw+8cwQLC5Jx+cK8kJfBnrMCjOxRadUbZu86eEbuHzMHoOoiuW/5GVPfZ1cUYlZ2Am5/cjfqOwdCfj3D2UJC/cDq+QHncLi6VAxoFWsiCofL4cCta2YgICV+8Ny+0F+vQ02kA60bLyPCcrJ12m2ol8Pb1sThbTpRRoIHn1iUh79sqcNr+5pxwZysoF/LnrNFhHvO2Gh27GVzeJvI+i6Zn4P8lBj85MXKkObfZjjbjFmCz04hTUTW5XI6cMn8HNS09mNHXVfQr2M4k9Ii2ZvmlwFjsBdPVre8JBVulwNPbKsP+jW6nXMWQhQAeAhANoAAgLullL/Va31mFclAMHv4jNWvxbnaifbFqT/neWEiCles24XFRSl4ZW8TfnLl/KBeo2fP2Qfg21LK2QBWAPiKEGKOjuuzLbOHbqjstr1EZH65STFo6xsOetYw3cJZSnlMSrn9+P/3AqgEEPqd2ERkaby6mewgKSYKANDeNxzU70fkViohRDGARQA2j/Nv6wCsA4D8gsJIlGM6WpyTY2/zn4LdFxzSplDZ+YvGg/ffg4fuvxcA0NnRbnA16omOGu0L93lHAERP+fu6XxAmhIgH8CSAb0ope079dynl3VLKJVLKJWnpGXqXQxbBLxtEarnhpluwfuNmrN+4GSmpaUaXo5y6jgFEOQUKUmOD+n1dw1kIEYXRYH5ESvmUnusiewmnV1uR7pny9VbvNavWw1OtnumwwjaQfmra+jErOxEelzOo39fzam0B4D4AlVLKX+m1HrIXLUPT6AA2av2qhsjsDI9pb6uK9D41+til0PQOjeBgSx8+vbQg6Nfo2XM+G8DnAJwvhNh5/M8lOq6PLI4NEqlI1S87pI6X9jbB6wvgsyuKgn6Nbj1nKeVGAEKv5VPwrHB+1mrBzF7z+MzWe1Z9f5LxBof9eGVvMz4yJwvlWQlBv44zhJHSgjk/TMExS5CwTrKSv22rQ5/Xh1vPmxHS6/hUKlKSlQPZiG0zW5CM1atiL9rofWnlz4bV7Gnoxot7mnD9WUVYWJAc0mvZcyalWL2nzGAOjWq1q1YPqavf68Odbx5CSXocvnPx7JBfz54zKcHKgTwm0ttolSBRoRdtlX1JkeELBPDb9dXoHhzBfTcuRYw7uNunTsRwJkPZIZQBBrMWjLhYTLX9aJfPi5lJKXHv24exu6Ebv/jUgpCHs8cwnG2gIt2j3BXbdmpkIrmtqoWJ1iLRi7b6PiR9Pbm9AW8eaMU31s7EVUuCv6/5VAxniji7BDN7y/o5cVu1CGoz7Du7fG7M7M0DLXhyez0+eWYevnnBzLCWxXCmiLJLA8PecuScuv3BhLXd9xlpb3dDN+55+zDOnpGGn35iAUYnyZw+hjNFhF1CGYjctjJgxmfF/WKnz48Z1bb349evHsCMjHj86brFcLvCvxGK4UykEYYy6YHBrLb2Pi/+5+UqJES78H83LUVidJQmy2U4G0i1i7T0YvXGhaFMerH6Z8fsBoZ9+MXLVfD6/Hj4iyuRkxSj2bIZzianesBbuXHheWUi+/IFAvjNa9Wo7xzEA59fijm5iZoun+FsA0YFuFWDmaFMkWDVz49VPLzpKHY3dOPnn5yP1eUZmi+f4UwUBN4WRZHEYFbbG1UteHlvE76wqgTXLC3UZR0MZ8WpOPF/MMzeuPCRjmQUs392rO5gSy/u2zh6y9R3Lp6l23oYzmR7qjSGDGZS5Vik8Q0O+/H7DQeRnRiNP1x7JlxO/Z4dxXA2iOoXclmZag0gQ5lUOyZpfH/edARtfV48/sWzkBLn1nVdDGeyPFUbPoYyAeoen3SybbWdeL2qFbeuKcOS4lTd18dwJs2p0tioUsd4GMwEqH2M0j8N+wJ48L0jqMhKwDcvKI/IOhnOBgh2SHuqi8GCWY4dh89Vb/AYzKT6MUone2H3MbT2evHbTy/UZGrOYDCcI8yOYRlJKjd6DGVS+fik8XUNDOOZDxrw0blZWFmWHrH1MpzJMlRu+BjM9qbysUmTe3FPE4Z9Adx+8eyIrpfhHEGh9JrNen+zEVRu+OwSyhXpHo4KjUPlY5OmNjjsx/r9zfjo3GyUpMdFdN0M5whhw6UPlRs/KwRzKPt3qt+1y2dA5WOSQvPmgRb0e/24ZXVpxNfNcI4APRolVS8GY8M0yszBrNd7ON5yrRLYPO6t6Y2qVizIT8KZhSkRXzfDWWfTaXw4pB0cFRtEs4ayUfvyxPWaLahVPP5IO3UdA6jtGMB/nz3HkPUznHXEYNaPig2jGYNZpf1ohqBWaX+Rvt451AanELjsjFxD1s9w1oHeDYvdGy4VG0gzBbOK++9UKgW1GfYXaW97bSdWlKYiPd6Y95/hrKFwGxH2mqemWkPJUNafUUFt1v1F4Wvv86KucxA3rCw2rAaGswa0aDCCDWajexFGUq2xZDBHnp5BbZV9ROHbWd8FADi3PMOwGhjOYYhkKIcq0iGud8OmUsPJUFbDqdsWyjFv5f1C4dt/rBcZ8R7MyIw3rAaG8zRoFXyhBrOqvWa7BDNDWW123GbSx6HWPiwqTIYQwrAaGM4hMCqUtVy31uwQzAxlIvvoHRrBse4hXH9WsaF1MJyDYGQoa7l+szE6aBjKRPZzqLUPALCwINnQOhjOk1DhnLLKwRzJmaQiiaFMZF8HW/rgEMCC/CRD62A4TyCcXfyM8AAAEn9JREFUUNTiIi87hrLey54KQ5mIqlv6UJ6VgDiPsfHIcD4FQ3lyVuwtM5SJaEx95yDOq8g0ugyG84mMmm5T9UAeY6VgNlMgAwxlokgYGPaho3/Y0FuoxjCcjws1IMMJZbOE8RiGsnEYykSR09g1BAAMZxVEIpTNFsZjGMrGYSgTRV5r72hbXZAaY3AlNg/nUEIzlFA2axgD1rvYi6FMRMHqHBgGAOQkMpwNE2yA2mHOaytOJMJQJqJQtfcPIzrKgcQY46PR+AoMoGUwhxPKegb6WGN/4joiGQBGhQ1DmYima3DYh8ToKEOn7Rxju3DWKphDCVYje9WRbvzZSw4OQ5lIPV5fADFup9FlALBZOAcTklqEsgpD3FVtXsv3lBnKRKSlEX8AHpfD6DIA2Cicww3mqV6vQiAbIdJhY8ZABhjKRGbgEAK+gDS6DAA2CuepTDeYQw3lwy3dE/5bSaaxc7mGIpJhw0AmokiIjnJiwOs3ugwANgnnqQJ0OsE81TInC+FQXqNaYEcqcMwayABDmcisoqOc6B/2GV0GABuEs9bBPNnyphPIUzlxmUYGdSQCh4FMREZKiY1C75APfV4f4vngCzWFEsx6hPJE64l0QOsZOmYOY4CBTGQ1mQnRAIC6jgHMzkk0tBZLh/N0e83BBvNUodxz7Mik/z6exJziSf99bJ16h7RewcNAJiJVZSWOfr6PtPUznPViVDBPJ5Anev1kQa1XL1qP8DFzIDOMiewjPyUWTofAB/XduHh+jqG1WDacJxNOMOsVypMtc6KQniqgQ73XWcsgMmMgM4iJ7M3tcqAkPQ7bj3YaXYo1w3k69xxPN5gnC2VvY1XQ6/fkVkz4bz3Hjkw7oIOlRTCZLZAZxkR0qpmZ8diwvwUj/gCinMZNSGLJcJ7MeL1mLYM5lECe6HXjBfVkAR2ucEPKLKHMMCaiqZRnJeDFPU3Y19iDMwqSDavDduEcjKmCWctQHs/Ysk4N6YkCOpzecziBpXooM4yJKFQzM+MBANtqOxnOWppsSDuYXrPWwTxVaE82nO1trAo6oCNJ1VBmGBNRuNLiPUiPd2P70U7chBLD6rBcOOsp2GAOpRc91XC2ngEdapipFsoMYyLSw8zMBGyrNfaiMDUev6ERvXvNpzo1hL2NVWENb0/02mCWqfdEKCoEc0W656Q/RER6KM+Kx7HuITR2DRpWA3vOE5hqOHu8YJ6Mt6ESnrzZU653ovPNp/agw+09BxtuRoUyw5eIjDIzKwEAsLOuC7nJMYbUYNtwnqrXfKLpBrO3oXLSvwOYMLDHG86OtEgGM8OYiFSRdzyQa1r7DKvBFuE82cMtQhVMMI8XwhMu7/jvBturjlRg6x3MDGMiUlV0lBNpcW7UtPUbVoNlzjlPZ+KRiZw4pB3qJCOhBPNUr5tqqPzU2oI972xEMPJ8MRGZSWaiB0fbBwxbv2XCORzBBnsw55XDMZ2A1oNWvWaGMRGZVZTTAV9AGrZ+W4azFs9kPm14e4pgDvZK7mCWoyctgpmBTEQUHlucc54uLR5mMdk5aqMv+NIaA5mIrGLEH0BslHERacue83ScGKrB9pqnHAYP9irvCA1tT7fXzJ4yEVmJlBJHOwZQnp1gWA26hrMQ4iIhRJUQ4qAQ4nY91zURLa/UDkWwgRrpc8pahyhDmYispqlnCP1eP87ID/+Jf9OlWzgLIZwA/gjgYgBzAFwrhJij1/ooPNPpNTOYiciKdtePXnu0sNC4B1/o2XNeBuCglLJGSjkM4DEAl+u4PoogBjMRWVEgIPHinmM4Iz8JFVnWHNbOA1B3wt/rj//sJEKIdUKIrUKIre1trTqWYz96BSiDmYhO9eD992DtquVYu2o5OjvajS5n2rYc6UBTjxdfOrcMQgjD6tAznMfbqtNuGpNS3i2lXCKlXJKWnqFjOTSRUIa0GcxENJ4bbroF6zduxvqNm5GSmmZ0OdMyOOzHY1vqUJwWi4/MzTa0Fj2vE68HUHDC3/MBNOq4PlMK5naqYG+5Ksmc/OKFcIOVwUxEVvbAO4fR0juEv9yyAk6Hcb1mQN+e8xYAM4UQJUIIN4BPA3hWx/WNy6inKoVzD/Nk82wbdW80g5mIrOzt6la8fbANX187E8tLje/56xbOUkofgK8CeBlAJYDHpZR79VpfJJ0akBOF6WRB6smtCDtoJ3tkpOqPhCQiUsWu+i7c83YNlhWn4mvnzzS6HAA6zxAmpXwBwAt6riNSPLkV07oneSyAJ3pO82m/f0rQa9VTDqfny14zEVnVrvou/PKVA5iREY+7PrfY8OHsMZwh7BQnnredrGd6qqke+ahFT3my1091vnm6GMxEZFVjwVyaEYdHblmBlDi30SV9yJbhfGrgTCeAxgvKYJ7JPOkyQ+g1c0ibiGj61lc2439erkJJehwevWUFUhUKZsBC4axXD+/EEDztXLNGAe3Jmx1Uz3siel2lzV4zEVmNLxDA/e8cxr0bD2PVjHQ8/qWzlAtmwELhPJlQe4iThZ3WAT2di8lCGW6fCHvNRGQ3PYMj+OkL+/HqvmasW12K+29ciqSYKKPLGpdtHxlZke456bnOp/79RIk5xZM+PnK8i8WmO8Q9btiH0GvWYsg+nNcREano/cMduG9jDYZGAvjV1WfgE2fmG13SpGzRc56OUHrPE/0sVMEsl71mIqLg9Q358PsN1fj1awdQkBqL5762SvlgBiwWzpP19sYLpFB6maeG4kRBOp2Qnuh1UwUze81ERBPbXtuJf33yA2w+3IFvXVCOp79yNioMfEZzKGw7rB2MkswkHG7p/vDvpw5vT3Tv84mhOtG90VPe7xxmME+EvWYisrp+rw8PvXcEb1W3oSIrAY/cfAbm5hr3bObpsFU4z87woLL15PPKU517DiaggemHcDC/P1Uwj4e9ZiKyow/qRmf76hoYwVfOK8M31pbD7TLfILHlwnmyC7umu4ypAhqYOqSnMlGIBxPM7DUTkd0NDvvx8OZabNjfgrKMONx/41KcUZBsdFnTZrlwnkowvefxjBfQACYM6TGThfVUverpBvN4PwsmmNlrJiIz2tPQjbvfqkFbnxfrVpfitgvLER3lNLqssFgynPXoPQOnBzQQ3G1WoRrviuxwgjkYDGYiMpuhET/+8v5RvLKvGUVpsXjic2dhcVGq0WVpwpLhPJVge8+hBPSYyYJ6MpPdIhVuMHM4m4isZn9TD+588xCae7z4/NnF+LePzkKM29y95RNZNpyn6j2HG9AATgtp4PSQnSisg7lfeaILv7QOZvaaicgshn0B/HXLUby4pwl5KTF4bN0KrFDg+ctas2w4T9dEAQ0gpJAeM51JQya7GlvLoexwX0tEFEkHW3rxpzcPobFrCNetKMR3Lp6NOI81Y8yaW3XcdHrPk71uop8HE9LBCDWUJ/s5MHWvmcFMRGbg8wfwxPZ6PPdBI7ISo/HwF5Zj1cx0o8vSlaXDGdAnoIHTe9HA+OE6WWCHe79yOMFMRGQGjV2D+OPrB1HT1o+rl+Tju5fNQWK0mg+r0JLlwzkYkwU0MH4QT/ZvJwomgCeiZzCz10xEKpNS4vWqVjz03hFERzlx53WLcdG8bKPLihhbhHMwt1ZNFNBTvT7YkA7WVKE51b8zmInI7AaGfbjrrRq8f7gDZ5Wm4dfXLER2UrTRZUWULcIZ0CaggYlD+NTACzasQwlKLYaxGcxEpLL6zgH8+tUDaOn14vaLZ2HdOaVwOITRZUWcbcIZCD+gx5YBTB2+WoVgMMthMBORFWw+3I673jyEWLcLj96yAstKrDGhyHTYKpyB4AMagCYhPV1az5fNYCYiVUkp8fjWejy9swELC5Jx53WLbTeMfSrbhTMQ/PSeU/Wix5Y1RospQ4MVytXYDGYiUpU/IHHP2zV480ArrllSgB9eMRcel3Vm+pouW4YzEFpAA5P3ok9cpt5CvUWKwUxEqhr2BfC7DdXYVtuJb6ydiW9eMBNC2O/88nhsG85AaA/ICCWk9TCd+5YZzESkqqERP/7n5f3Yf6wXP/j4XNywstjokpRi63AGQn+CVaRDmqFMRFbj+//t3VuM3GUdxvHvr12KLUpbCyFpS0vbkNpa6IGKHAwmwgWKUo1RqQeiUS88IBqVoPHCGxONSmqChzSoNyIaKzGKRgmxGENM5VBJKaW1ByitLaxAy2JLu7vz82JGrSt0l53Zed/B7+dqZtqdPn2zs8++/8P7DjdYd9cOth8cYN01K1izYk7pSNX5vy9nGN/FXSNLs5Nl3c7qXhazpJo1MvneH3bx4L7DfO2d51nML8JyPkE7+0CPt6w7tcympSypF/x4017u2fUUN1y5mPe8bl7pONWynEfo1C1S3Vzb2mKW1Av+vOdpfr3lANdePJ+PvXFR6ThVm1Q6QK16ofAWn3FqT+SUpP6B51n/x12cP3c6X7pqqVdlj8KZ80l08h7mTrKQJfWSRiO5eeNOguDmtauY0ue8cDSW8xhN9IpgY/33JanX3L2jnx1PPMdN717OvFnTSsfpCZbzS9TN2bSFLKnXHTk+xE/v28sF82fyjpVemT1WlnMbxrsT1VjfT5J63S8272fg6BBffttrPc/8EljOHWS5StJ/HDk+xF3bnuTqFbM5b+700nF6imflJUkT4u7t/RwdHOYjb1hYOkrPsZwlSR3XyOTOhw+yev5MZ83jYDlLkjpud/9zPPHsMdZe6Cpg42E5S5I67t5Hn6FvUnDFkrNKR+lJlrMkqePuf+wZXr/w1UyfdkrpKD3JcpYkddRwI9l/6CiXnXtm6Sg9y3KWJHXU80MNAFbNn1k4Se+ynCVJHXVscJi+ScGy2V6lPV6WsySpowaHG8ybNY2pUyaXjtKzLGdJUkcNDicLZp1WOkZPs5wlSR012Gi4+1SbLGdJUkdlwlmnv6J0jJ5mOUuSOm7GVO9vboflLEnquBkuPtKWyMzSGf4tIvqBx0rnmEBnAH8vHaIHOE5j4ziNjeM0uk6P0fzMdAWSNlRVzi93EXFfZq4unaN2jtPYOE5j4ziNzjGqj4e1JUmqjOUsSVJlLOfuWl86QI9wnMbGcRobx2l0jlFlPOcsSVJlnDlLklQZy1mSpMpYzl0QEVdGxPaI2BkRN5bOU6OIODsiNkbEtojYGhHXl85Us4iYHBGbI+KO0llqFREzImJDRDzS+r66uHSmGkXEZ1qfuYci4raIcN3NCljOEywiJgPfBt4MLAXWRsTSsqmqNAR8NjOXABcBn3CcTup6YFvpEJX7FvDbzHwNsBzH639ExBzgU8DqzFwGTAauKZtKYDl3w4XAzszcnZnHgZ8Aawpnqk5mHsjMB1qPB2j+IJ1TNlWdImIucBVwS+kstYqI04HLgO8DZObxzDxUNlW1+oCpEdEHTAP+VjiPsJy7YQ7w+AnP92HpnFREnAOsBDaVTVKtdcANQKN0kIotBPqBH7YO/98SEW4wPEJm7ge+AewFDgCHM/POsqkElnM3xAu85v1rLyIiXgn8HPh0Zj5bOk9tIuKtwJOZeX/pLJXrA1YB383MlcA/AK/3GCEiZtI8krcAmA2cFhHvL5tKYDl3wz7g7BOez8XDRi8oIk6hWcy3ZubtpfNU6lLg6oh4lOYpkjdFxI/KRqrSPmBfZv7r6MsGmmWt/3YFsCcz+zNzELgduKRwJmE5d8O9wLkRsSAiptC82OKXhTNVJyKC5vnBbZl5U+k8tcrML2Tm3Mw8h+b30u8z05nOCJl5EHg8Iha3XroceLhgpFrtBS6KiGmtz+DleOFcFfpKB3i5y8yhiPgk8DuaV0L+IDO3Fo5Vo0uBDwBbIuIvrde+mJm/KZhJve064NbWL8W7gQ8VzlOdzNwUERuAB2jeMbEZl/Ksgst3SpJUGQ9rS5JUGctZkqTKWM6SJFXGcpYkqTKWsyRJlbGcpTa1dj/6eOvx7NatKZI0bt5KJbWptRb4Ha1dfSSpbS5CIrXvq8Ci1uIpfwWWZOayiPgg8Haai88sA74JTKG52Mox4C2Z+XRELKK5reiZwBHgo5n5SPf/G5Jq4WFtqX03ArsycwXw+RF/tgx4L82tQ78CHGltxPAn4NrW31kPXJeZFwCfA77TldSSquXMWZpYG1v7Uw9ExGHgV63XtwDnt3bhugT4WXNpYwBO7X5MSTWxnKWJdeyEx40Tnjdofv4mAYdas25JAjysLXXCAPCq8Xxha8/qPRHxLmjuzhURyzsZTlLvsZylNmXmU8A9EfEQ8PVxvMX7gA9HxIPAVmBNJ/NJ6j3eSiVJUmWcOUuSVBnLWZKkyljOkiRVxnKWJKkylrMkSZWxnCVJqozlLElSZf4JxUkQXQmxavoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x = 'time', y= 'sections', data= newsupdated, kind=\"kde\",height=7, space=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2939 entries, 0 to 2938\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype\n",
      "---  ------    --------------  -----\n",
      " 0   time      2939 non-null   int64\n",
      " 1   sections  2939 non-null   int64\n",
      " 2   persons   2939 non-null   int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 69.0 KB\n"
     ]
    }
   ],
   "source": [
    "newsupdated.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now we will run the k means clustering using sklearn library "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=5)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model5 = KMeans(n_clusters = 5)\n",
    "news1 = newsupdated.iloc[:,0:2]\n",
    "model5.fit(news1)\n",
    "#news1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The attribute cluster_centers_ gives the centroid of cluster centers for the 5 clusters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.3647541 , 2.38729508],\n",
       "       [1.98693759, 2.31640058],\n",
       "       [6.28120516, 5.67718795],\n",
       "       [0.27897839, 0.24950884],\n",
       "       [2.51618705, 5.6294964 ]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model5.cluster_centers_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The labels_ attribute gives the cluster label for k clusters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 3, 3, ..., 2, 2, 2])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model5.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding the lables to the dataset\n",
    "newsupdated['cluster'] = model5.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x266288e06a0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QJDWxMCRJTSwMSdrDJNknyTeTXNstb/6mlu16Xd5ckrQ6Ww4/YtHy5kfcsGW1N+5t+4C7u7olQr6S5DNV9fXlNvIMQ5IGqiuLRcubd+MrttIPuLMwJGm4elvePMmGJNcAtwGXjfuAux1ZGJI0XL0tb97yAXc7sjAkabh+wGiF2oX27cbXxK58wJ2FIUnDdS6j5cy3lcZaLW++og+4szAkaaC62VCLljdfg1lSjwG+mOQ64EpG72F8emcbuby5JK0flzeXJO3+LAxJUhMLQ5LUxMKQJDWxMCRJTSwMSVITC0OS9lDdelLfSrLTezDA5c0ladDOf+Xli5Y3P+09x632xr1tTge2AI9oebJnGJI0UF1ZLFrevBtflSQHAycBF7ZuY2FI0nD1trw58E7gTOCB1g0sDEkarl6WN0/yAuC2qrpqV7azMCRpuPpa3vxY4EVJbgI+AhyX5AM728jCkKTh6mV586p6fVUdXFWbgJOBy6vqZTvbzsKQpIHqZkMtWt58DWdJ7ZLeljdPcgjwfuCfM3pT5YKqOm+5bXZ1efNbzjiTuy69FO6/HzZsYL8TT+SQc9+2qtxr7uwDxozdsf45ljL0fGDGNXD+Ky9fNHbae46bQJKl7SEZXd58CfcBf1xVRwC/DpyW5ClrtfNbzjiTuz71qVFZANx/P3d96lPccsaZa3WI1Rv3IrLc+Hobej4w4xoY9yK33PgkmHE69FYYVXVrVV3d/Xwno5tDDlqr/d916aWjH5IHvxaOS5LW1Lq8h5FkE/AM4Btjfndqkrkkc/Pz8+073XZm0TouSVqV3gsjyX7Ax4HXVNXPdvx9VV1QVbNVNTszM9O+4w0bdm1ckrQqvRZGkr0YlcUHq+qv1nLf+5144uiHqge/Fo5LktZUb4WRJMBFwJaqesda7/+Qc9/Gfi984YNnFBs2sN8LXzisWVJLzZIZyuyZoecDM66BpWbxDGkGkhmnQ5/Tan8T+DKwmQfXKnlDVS35rvSuTquVpCkz1dNqe1vevKq+wpT/x5EkPcg7vSVJTSwMSVITC0OS1MTCkCQ1sTAkSU0sDElSEwtDktTEwpAkNbEwJElNLAxJUhMLQ5LUxMKQJDWxMCRJTSwMSVITC0OS1MTCkCQ1sTAkSU0sDElSEwtDktTEwpAkNbEwJElNLAxJUhMLQ5LUxMKQJDWxMCRJTSwMSVITC0OS1MTCkCQ1sTAkSU0sDElSEwtDktRkY187TvJe4AXAbVV1ZB/H2PL0o+Geex4ceOhDOeLaa/o41IptOfyIRWNH3LBlAkmWcPYBY8buWP8cyzHjqp3/yssXjZ32nuMmkGRpZhy+Ps8w3gcc39fOF5UFwD33jMYHYlxZLDe+7sa9yC03PglmXLVxL3LLjU+CGadDb4VRVV8CftLX/heVxc7GJUmrMvH3MJKcmmQuydz8/Pyk40iSljDxwqiqC6pqtqpmZ2ZmJh1HkrSEiRfGij30obs2LklalaktjCOuvWZxOQxsltRSs6EGM0tqqVk8A5rdY8bVW2oWz5Bm95hxOqSq+tlx8mHgOcCjgR8Db6yqi5bbZnZ2tubm5nrJI0kDkEkHWI3e7sOoqpf2tW9J0vqb2ktSkqT1ZWFIkppYGJKkJhaGJKmJhSFJamJhSJKaWBiSpCYWhiSpiYUhSWpiYUiSmlgYkqQmFoYkqYmFIUlqYmFIkppYGJKkJhaGJKmJhSFJamJhSJKaWBiSpCYWhiSpiYUhSWpiYUiSmlgYkqQmFoYkqYmFIUlqYmFIkppYGJKkJhaGJKmJhSFJamJhSJKaWBiSpCYb+9x5kuOB84ANwIVVdc5a7n/L4UcsGjvihi1reQhJUqe3M4wkG4DzgROApwAvTfKUtdr/uLJYblyStDp9XpI6Bvh+Vd1YVfcCHwFe3OPxJEk96rMwDgJuWfB4aze2nSSnJplLMjc/P99jHEnSavRZGBkzVosGqi6oqtmqmp2ZmekxjiRpNfosjK3AIQseHwz8qMfjSZJ61GdhXAk8McmhSfYGTgY+uVY7X2o2lLOkJKkfvU2rrar7krwa+ByjabXvrarr1/IYloMkrZ9e78OoqkuBS/s8hiRpfXintySpiYUhSWpiYUiSmlgYkqQmFoYkqYmFIUlqYmFIkppYGJKkJqlatB7gxCSZB25ewaaPBm5f4zhrbegZh54PzLgWhp4Pdu+Mt1fV8WsdZr0MqjBWKslcVc1OOsdyhp5x6PnAjGth6PnAjEPmJSlJUhMLQ5LUZHcpjAsmHaDB0DMOPR+YcS0MPR+YcbB2i/cwJEn9213OMCRJPbMwJElNprowkhyf5LtJvp/krEnn2VGS9ya5Lcm3J51lKUkOSfLFJFuSXJ/k9ElnWijJPkm+meTaLt+bJp1pKUk2JPlWkk9POss4SW5KsjnJNUnmJp1nnCQHJvlYkhu6/yf/5aQzbZPkyd1/u21fP0vymknnWk9T+x5Gkg3A94DnAVsZfYb4S6vqOxMNtkCSZwN3Ae+vqiMnnWecJI8BHlNVVyfZH7gKeMlQ/jsmCfDwqroryV7AV4DTq+rrE462SJLXArPAI6rqBZPOs6MkNwGzVTXYm+KSXAx8uaouTLI3sG9V/XTSuXbUvf78EPi1qlrJzcZTaZrPMI4Bvl9VN1bVvcBHgBdPONN2qupLwE8mnWM5VXVrVV3d/XwnsAU4aLKpHlQjd3UP9+q+BvdXTpKDgZOACyedZVoleQTwbOAigKq6d4hl0Xku8Ld7UlnAdBfGQcAtCx5vZUAvdNMoySbgGcA3Jptke92lnmuA24DLqmpQ+TrvBM4EHph0kGUU8PkkVyU5ddJhxjgMmAf+vLu0d2GSh0861BJOBj486RDrbZoLI2PGBveX57RIsh/wceA1VfWzSedZqKrur6qjgYOBY5IM6vJekhcAt1XVVZPOshPHVtUzgROA07pLpkOyEXgm8O6qegbwc2CI703uDbwI+MtJZ1lv01wYW4FDFjw+GPjRhLJMte69gY8DH6yqv5p0nqV0lyeuAIa2eNuxwIu69wg+AhyX5AOTjbRYVf2o+34b8AlGl3WHZCuwdcEZ5McYFcjQnABcXVU/nnSQ9TbNhXEl8MQkh3aNfzLwyQlnmjrdm8oXAVuq6h2TzrOjJDNJDux+fhjw28ANk021vap6fVUdXFWbGP1/eHlVvWzCsbaT5OHdpAa6yzzPBwY1e6+q/h64JcmTu6HnAoOYfLGDl7IHXo6C0SngVKqq+5K8GvgcsAF4b1VdP+FY20nyYeA5wKOTbAXeWFUXTTbVIscCfwBs7t4nAHhDVV06wUwLPQa4uJuV8hDgo1U1yGmrA/erwCdGfx+wEfhQVX12spHG+s/AB7s/Am8EXj7hPNtJsi+jmZn/cdJZJmFqp9VKktbXNF+SkiStIwtDktTEwpAkNbEwJElNLAxJUhMLQ3uMJJuS/P6Cx7NJ3jXJTNI0cVqt9hhJngO8bogryUrTwDMMTY3ubuVLus/G+HaS30vyrCR/0y2o97luuXaSPCHJ/+mee3WSxwPnAP+q+yyD/5LkOds+uyLJI5P8dZLrknw9ydO68bO7zzW5IsmNSf5oqSyT+u8irZepvdNbe6TjgR9V1UkASQ4APgO8uKrmuxfttwCvAD4InFNVn0iyD6M/js5iwRlGd8axzZuAb1XVS5IcB7wfOLr73eHAbwH7A99N8u4lski7NQtD02Qz8PYk/x34NPAPwJHAZd2SFxuAW7s1kw6qqk8AVNU/AnTPWcpvAv+6e/7lSR61oAQuqap7gHuS3MZomY3tslTVl9f2nyoNj4WhqVFV30vyLOBE4K3AZcD1VbXdx3h2H8Szq5ZbLv+eBWP3Axt3zJLk81X15hUcV5oavoehqZHkXwB3V9UHgLcDvwbMbPvc5yR7JXlq93keW5O8pBt/aLdo3J2MLiuN8yXg33bPfw5w+3KfCzImyxCX4ZbWlGcYmiZHAecmeQD4JfAq4D7gXd3lo42MPvnuekYr8P5Zkjd3z/03wHXAfUmuBd4HfGvBvs9m9Elv1wF3A6esIIu0W3NarSSpiZekJElNLAxJUhMLQ5LUxMKQJDWxMCRJTSwMSVITC0OS1OT/AxryTtHLMKBAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Very useful plotting function from seaborn . \n",
    "# Read https://seaborn.pydata.org/generated/seaborn.lmplot.html\n",
    "sns.lmplot( x=\"sections\", y=\"time\", data=newsupdated, fit_reg= False, hue='cluster', legend=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2bf96d0f0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://seaborn.pydata.org/generated/seaborn.scatterplot.html\n",
    "# students can try to add different shape of the markers \n",
    "# style = 'cluster'\n",
    "sns.scatterplot(x = 'sections', y = 'time', data = newsupdated, size= 'persons', legend = False , sizes = (20, 500), hue ='cluster')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## attribute inertia_ gives the  SSE for the final clusters for k = 5  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4247.132351999837"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model5.inertia_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      0\n",
       "2      1\n",
       "3      2\n",
       "4      3\n",
       "5      4\n",
       "6      5\n",
       "7      6\n",
       "8      7\n",
       "9      8\n",
       "10     9\n",
       "11    10\n",
       "12    11\n",
       "13    12\n",
       "14    13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sse\n",
    "sse = pd.Series(range(14),index = range(1,15))\n",
    "sse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1     31419\n",
      "2     14236\n",
      "3      9019\n",
      "4      5910\n",
      "5      4258\n",
      "6      3627\n",
      "7      3011\n",
      "8      2471\n",
      "9      2109\n",
      "10     1858\n",
      "11     1741\n",
      "12     1630\n",
      "13     1468\n",
      "14     1382\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,15):\n",
    "    model5 = KMeans(n_clusters = i)\n",
    "    model5.fit(news1)\n",
    "    sse[i] = model5.inertia_\n",
    "print(sse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a2bbbb4748>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sse.index, sse, linewidth=2.0)"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}