Clusterningnew1_11.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "71d0107b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "d585803d",
   "metadata": {},
   "outputs": [
    {
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       "        -1.75297822e-01]])"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_excel(r'DataFinal17-20.xlsx',sheet_name='2018')\n",
    "df1=df.drop(['Total'], axis=1)\n",
    "df1=df1.iloc[:,2:15].values\n",
    "#scaler = MinMaxScaler()\n",
    "scaler= StandardScaler()\n",
    "# transform data\n",
    "df1 = scaler.fit_transform(df1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "c98a3819",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "#create a list for the wcss parameter\n",
    "wcss = []\n",
    "#test with 14 clusters\n",
    "for i in range(1, 15):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state =0)\n",
    "    kmeans.fit(df1)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "6ee64916",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[455.00000000000006,\n",
       " 151.88198373485318,\n",
       " 94.4962974367791,\n",
       " 52.36778125280657,\n",
       " 37.66311578686052,\n",
       " 27.85608724129876,\n",
       " 22.956380978779134,\n",
       " 18.411323303516994,\n",
       " 15.654980301497902,\n",
       " 13.505706826278605,\n",
       " 11.131631283310846,\n",
       " 9.189929945241067,\n",
       " 8.259155435971405,\n",
       " 7.249011347468659]"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wcss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "ed62a18b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Hifw0Kui13gCAE7yURURE5BQMO052ddkIXsoiIiJyBoYdJ+MaWURERM7FsONkXCOLiIjIuRh2nIzTz4mIiJzLZcJOUlISBEHA+PHjpW1FRUVITExEcHAw/P39MXToUGRmZtq9Lj09HYMGDYKvry/CwsIwefJklJaWOrl6x9mmn5++VACLVZS5GiIiIvfnEmFn9+7d+PTTT9GuXTu77RMmTMCqVauwfPlybNmyBefPn8eQIUOk/RaLBYMGDYLZbMb27dvx1VdfYfHixZg2bZqzP4LDIgJ9oFYpYC614tyVQrnLISIicnuyh528vDyMHDkSn332GerVqydtNxqN+M9//oP33nsPffv2RVxcHL788kts374dO3bsAAD8/vvvOHz4ML799lt06NABAwcOxFtvvYWPPvoIZrNZro90Q0qFgMbBZZeyOP2ciIio9skedhITEzFo0CD079/fbntKSgpKSkrstrds2RLR0dFITk4GACQnJ6Nt27YIDw+X2iQkJMBkMiEtLa3K9ywuLobJZLJ7OBOnnxMRETmPSs43//7777F3717s3r27wj6DwQC1Wo3AwEC77eHh4TAYDFKba4OObb9tX1VmzZqFN9988zarv3Wcfk5EROQ8svXsZGRk4KWXXsJ3330Hb29vp7731KlTYTQapUdGRoZT35/Tz4mIiJxHtrCTkpKCrKwsdOrUCSqVCiqVClu2bMGCBQugUqkQHh4Os9mMnJwcu9dlZmZCr9cDAPR6fYXZWbbntjaV0Wg00Gq1dg9nsvXsnGDPDhERUa2TLez069cPBw8eRGpqqvTo3LkzRo4cKX3v5eWFDRs2SK85duwY0tPTER8fDwCIj4/HwYMHkZWVJbVZv349tFotYmNjnf6ZHNWkfPp5Vm4xcou4ICgREVFtkm3MTkBAANq0aWO3zc/PD8HBwdL2J598EhMnTkRQUBC0Wi1eeOEFxMfHo0uXLgCAAQMGIDY2FqNGjcKcOXNgMBjwr3/9C4mJidBoNE7/TI7S+XghxF+Ni3lmnLqYj3aRgXKXRERE5LZkn411I/PmzcN9992HoUOHomfPntDr9VixYoW0X6lUYvXq1VAqlYiPj8djjz2G0aNHY8aMGTJW7RiO2yEiInIOQRRFj7+Nr8lkgk6ng9FodNr4nVf/ewDf787Ai32bYuKAFk55TyIiInfi6O9vl+7ZcWfSIOWL7NkhIiKqTQw7MuFlLCIiIudg2JFJTFhZ2Dl1MQ9WLghKRERUaxh2ZBJVzwdeSgFFJVZcMBXJXQ4REZHbYtiRiUqpQHSQLwAuG0FERFSbGHZkZLu5IMftEBER1R6GHRlxQVAiIqLax7AjoxjbjCxOPyciIqo1DDsyutqzw7BDRERUWxh2ZGQbs3MupxCFZovM1RAREbknhh0ZBfmpEejrBQA4xUtZREREtYJhR2ZNQsovZV3kIGUiIqLawLAjM04/JyIiql0MOzLj9HMiIqLaxbAjsyacfk5ERFSrGHZkFnPN9HNR5IKgRERENY1hR2bRwb5QCEBecSmyc4vlLoeIiMjtMOzITKNSIqp8QdATHKRMRERU4xh2XACnnxMREdUehh0XwOnnREREtYdhxwVw+jkREVHtYdhxAZx+TkREVHsYdlyAbfp5xuUCFJdyQVAiIqKaxLDjAkIDNPDXqGAVgTOXCuQuh4iIyK0w7LgAQRA4boeIiKiWMOy4CNv0c95rh4iIqGYx7LgITj8nIiKqHQw7LkK6jMUbCxIREdUohh0XEXNNzw4XBCUiIqo5DDsuonGIHwQBMBaW4HK+We5yiIiI3AbDjovw9lIiQucDgDcXJCIiqkkMOy6E08+JiIhqHsOOC4nhjCwiIqIax7DjQmw9O7zXDhERUc1h2HEhVxcE5WUsIiKimsKw40JsPTvplwpQYrHKXA0REZF7YNhxIXqtN3y8lCi1isi4zAVBiYiIagLDjgtRKAQ0DrHNyOK4HSIioprAsONiuGwEERFRzWLYcTFcEJSIiKhmMey4mJhQXsYiIiKqSQw7LobTz4mIiGoWw46LaVzes3MxzwxjYYnM1RAREdV9DDsuxl+jQrhWA4BrZBEREdUEhh0XJF3K4rgdIiKi28aw44I4/ZyIiKjmMOy4IE4/JyIiqjkMOy6oCaefExER1RiGHRcUUz5m59SlfFisoszVEBER1W0MOy6oQT0fqFUKmEutOHelUO5yiIiI6jSGHRekVAhoFOwLADjBQcpERES3hWHHRXH6ORERUc1g2HFRVwcps2eHiIjodjDsuChOPyciIqoZDDsuKoY3FiQiIqoRDDsuytazk2kqRl5xqczVEBER1V0MOy5K5+OFEH81AOAUL2URERHdMoYdFybNyOKlLCIiolvGsOPCbDOyTrBnh4iI6JYx7LgwTj8nIiK6fQw7Low3FiQiIrp9DDsuzNazc+piPqxcEJSIiOiWMOy4sKggX6gUAgpLLDCYiuQuh4iIqE5i2HFhXkoFossXBOWlLCIiolsja9j55JNP0K5dO2i1Wmi1WsTHx2PNmjXS/qKiIiQmJiI4OBj+/v4YOnQoMjMz7Y6Rnp6OQYMGwdfXF2FhYZg8eTJKS93nJnycfk5ERHR7ZA07kZGRSEpKQkpKCvbs2YO+ffviwQcfRFpaGgBgwoQJWLVqFZYvX44tW7bg/PnzGDJkiPR6i8WCQYMGwWw2Y/v27fjqq6+wePFiTJs2Ta6PVOOkZSPYs0NERHRLBFEUXWrka1BQEObOnYthw4YhNDQUS5YswbBhwwAAR48eRatWrZCcnIwuXbpgzZo1uO+++3D+/HmEh4cDABYuXIgpU6YgOzsbarXaofc0mUzQ6XQwGo3QarW19tluxbLd6Zjy34Po0SwE3zx5l9zlEBERuQxHf3+7zJgdi8WC77//Hvn5+YiPj0dKSgpKSkrQv39/qU3Lli0RHR2N5ORkAEBycjLatm0rBR0ASEhIgMlkknqHKlNcXAyTyWT3cFVc/ZyIiOj2yB52Dh48CH9/f2g0Gjz77LNYuXIlYmNjYTAYoFarERgYaNc+PDwcBoMBAGAwGOyCjm2/bV9VZs2aBZ1OJz2ioqJq9kPVoCYhZZexzhsLUVRikbkaIiKiukf2sNOiRQukpqZi586deO655zBmzBgcPny4Vt9z6tSpMBqN0iMjI6NW3+92BPmpofPxgiiW3W+HiIiIqkf2sKNWq9G0aVPExcVh1qxZaN++PebPnw+9Xg+z2YycnBy79pmZmdDr9QAAvV5fYXaW7bmtTWU0Go00A8z2cFWCIFyzbATDDhERUXXJHnauZ7VaUVxcjLi4OHh5eWHDhg3SvmPHjiE9PR3x8fEAgPj4eBw8eBBZWVlSm/Xr10Or1SI2NtbptdeWq8tGcPo5ERFRdankfPOpU6di4MCBiI6ORm5uLpYsWYLNmzdj3bp10Ol0ePLJJzFx4kQEBQVBq9XihRdeQHx8PLp06QIAGDBgAGJjYzFq1CjMmTMHBoMB//rXv5CYmAiNRiPnR6tRUs8OL2MRERFVm6xhJysrC6NHj8aFCxeg0+nQrl07rFu3DnfffTcAYN68eVAoFBg6dCiKi4uRkJCAjz/+WHq9UqnE6tWr8dxzzyE+Ph5+fn4YM2YMZsyYIddHqhUxXP2ciIjolrncfXbk4Mr32QGAvzJzMWDeVgRoVDjwxgAIgiB3SURERLKrc/fZoao1DPaFQgByi0uRnVcsdzlERER1CsNOHaBRKRFZr2xB0BNZHLdDRERUHQw7dcTVQcoct0NERFQdDDt1xNXp5+zZISIiqg6GnTqiCWdkERER3RKGnToixrYgKO+1Q0REVC0MO3WE7V47GZcLUFzKBUGJiIgcxbBTR4QGaOCvUcEqAumXCuQuh4iIqM5g2Kkjrl0Q9AQHKRMRETmMYacOaRLC6edERETVxbBThzQJ5fRzIiKi6mLYqUM4/ZyIiKj6GHbqEOnGgpx+TkRE5DCGnTqkcfmYnZyCElzON8tcDRERUd3AsFOH+KiVaBDoA4CXsoiIiBzFsFPHXB23w0tZREREjmDYqWNs089PcPo5ERGRQxh26hhOPyciIqoehp06htPPiYiIqodhp46x9eykXy5AqcUqczVERESuj2Gnjqmv9Ya3lwIlFhEZVwrlLoeIiMjlMezUMQqFgMa2mwvyUhYREdFNMezUQZx+TkRE5DiGnToohqufExEROYxhpw6yDVI+wZ4dIiKim2LYqYN4GYuIiMhxDDt1kG1B0It5xTAVlchcDRERkWu77bBz5swZHD58GFYr7/niLAHeXggL0ABg7w4REdHNOBx2vvjiC7z33nt2255++mk0adIEbdu2RZs2bZCRkVHjBVLlbJeyTmRxkDIREdGNOBx2Fi1ahHr16knP165diy+//BJff/01du/ejcDAQLz55pu1UiRVJK2RxRlZREREN6RytOHx48fRuXNn6fnPP/+MBx98ECNHjgQAzJw5E2PHjq35CqlSttXPeRmLiIjoxhzu2SksLIRWq5Web9++HT179pSeN2nSBAaDoWaroyrFhHH1cyIiIkc4HHYaNmyIlJQUAMDFixeRlpaGbt26SfsNBgN0Ol3NV0iViilfMuLUpXxYrKLM1RAREbkuhy9jjRkzBomJiUhLS8PGjRvRsmVLxMXFSfu3b9+ONm3a1EqRVFGDej5QqxQwl1pxPqcQUUG+cpdERETkkhwOO6+88goKCgqwYsUK6PV6LF++3G7/tm3bMGLEiBovkCqnVAhoFOyLvzLzcCI7j2GHiIioCoIoih5/DcRkMkGn08FoNNqNS3J1z36TgrVpBky7LxZPdG8sdzlERERO5ejvb4d7dipTVFSEZcuWIT8/HwMGDEDTpk1v53BUTdKyEZx+TkREVCWHw87EiRNRUlKCDz74AABgNpsRHx+PtLQ0+Pr64pVXXsH69esRHx9fa8WSPeleO5yRRUREVCWHZ2P9/vvvuPvuu6Xn3333Hc6cOYPjx4/jypUreOihh/D222/XSpFUOS4ISkREdHMOh5309HTExsZKz3///XcMGzYMDRs2hCAIeOmll7Bv375aKZIqZ5t+bjAVIb+4VOZqiIiIXJPDYUehUODascw7duxAly5dpOeBgYG4cuVKzVZHN6Tz9UKwnxoAcOoie3eIiIgq43DYadWqFVatWgUASEtLQ3p6Ovr06SPtP3PmDMLDw2u+QrohaUHQbA5SJiIiqky17rPzyCOP4Ndff0VaWhruvfdeNG58dbrzb7/9hjvvvLNWiqSqNQnxx+7TVzhuh4iIqAoO9+z84x//wG+//YZ27dphwoQJWLZsmd1+X19fPP/88zVeIN3Y1ennDDtERESVqdZ9dvr164d+/fpVum/69Ok1UhBVz9Xp57yMRUREVBmHe3aOHz+OESNGwGQyVdhnNBrx6KOP4uTJkzVaHN2crWfn1MV88GbYREREFTkcdubOnYuoqKhKb8es0+kQFRWFuXPn1mhxdHPRQb5QKQQUmC0wmIrkLoeIiMjlOBx2tmzZgoceeqjK/cOHD8fGjRtrpChynJdSgejyRUA5SJmIiKiiat1UMCwsrMr9ISEhyMjIqJGiqHqu3kmZ43aIiIiu53DY0el0OHHiRJX7//777zq1Yrg7sQ1SPsGeHSIiogocDjs9e/aUFgGtzIIFC9CjR48aKYqqp0kIp58TERFVxeGwM3XqVKxZswbDhg3Drl27YDQaYTQasXPnTgwdOhTr1q3D1KlTa7NWqgKnnxMREVXN4fvsdOzYET/++COeeOIJrFy50m5fcHAwfvjhB3Tq1KnGC6Sbs43ZOZdTiKISC7y9lDJXRERE5DocDjunTp3CfffdhzNnzmDdunU4fvw4RFFE8+bNMWDAAPj6+tZmnXQDwX5qaL1VMBWV4vSlfLTUc+wUERGRjcNhJyYmBg0bNkSfPn3Qp08fjBgxApGRkbVZGzlIEAQ0CfVHakYOTmYz7BAREV3L4bCzceNGbN68GZs3b8bSpUthNpvRpEkT9O3bVwpAXPVcPk1C/ZCakYMTWRy3Q0REdC2Hw07v3r3Ru3dvAEBRURG2b98uhZ+vvvoKJSUlaNmyJdLS0mqrVrqBGNsgZc7IIiIislOthUBtvL290bdvX3Tv3h19+vTBmjVr8Omnn+Lo0aM1XR85KIY3FiQiIqpUtcKO2WzGjh07sGnTJmzevBk7d+5EVFQUevbsiQ8//BC9evWqrTrpJq5OPy9bEFQQBJkrIiIicg0Oh52+ffti586daNy4MXr16oVnnnkGS5YsQf369WuzPnJQw2BfKAQgt7gU2XnFCAvwlrskIiIil+DwTQX/+OMPBAcHo2/fvujXrx/uvvtuBh0XolEpEVmPC4ISERFdz+Gwk5OTg0WLFsHX1xezZ89GREQE2rZti3HjxuHHH39EdnZ2bdZJDri6ICjDDhERkY3DYcfPzw/33HMPkpKSsHPnTly8eBFz5syBr68v5syZg8jISLRp06Y2a6WbaBLCZSOIiIiu53DYuZ6fnx+CgoIQFBSEevXqQaVS4ciRIzVZG1WT1LPD6edEREQSh8OO1WrFrl27MGfOHAwcOBCBgYHo2rUrPv74Y+j1enz00Uc4efJktd581qxZuOOOOxAQEICwsDAMHjwYx44ds2tTVFSExMREBAcHw9/fH0OHDkVmZqZdm/T0dAwaNAi+vr4ICwvD5MmTUVpaWq1a3EETTj8nIiKqwOHZWIGBgcjPz4der0efPn0wb9489O7dGzExMbf85lu2bEFiYiLuuOMOlJaW4rXXXsOAAQNw+PBh+PmV/eKeMGECfv31Vyxfvhw6nQ7jxo3DkCFDsG3bNgCAxWLBoEGDoNfrsX37dly4cAGjR4+Gl5cXZs6cecu11UW2GwtmXCmEudQKteqWO+6IiIjchiCKouhIw08//RR9+vRB8+bNa62Y7OxshIWFYcuWLejZsyeMRiNCQ0OxZMkSDBs2DABw9OhRtGrVCsnJyejSpQvWrFmD++67D+fPn5eWq1i4cCGmTJmC7OxsqNXqm76vyWSCTqeD0WiEVlt315USRRFtpq9DvtmC/03siaZhAXKXREREVGsc/f3t8H/9n3nmmVoNOgBgNBoBAEFBQQCAlJQUlJSUoH///lKbli1bIjo6GsnJyQCA5ORktG3b1m5droSEBJhMpiqXriguLobJZLJ7uAPbgqAAcIIzsoiIiADcxgDlmma1WjF+/Hh069ZNmtVlMBigVqsRGBho1zY8PBwGg0Fqc/0CpLbntjbXmzVrFnQ6nfSIioqq4U8jH04/JyIisucyYScxMRGHDh3C999/X+vvNXXqVBiNRumRkZFR6+/pLJx+TkREZO+WFgKtaePGjcPq1auxdetWREZGStv1ej3MZjNycnLsencyMzOh1+ulNrt27bI7nm22lq3N9TQaDTQaTQ1/CtfA6edERET2ZO3ZEUUR48aNw8qVK7Fx40Y0btzYbn9cXBy8vLywYcMGaduxY8eQnp6O+Ph4AEB8fDwOHjyIrKwsqc369euh1WoRGxvrnA/iQjj9nIiIyJ6sPTuJiYlYsmQJfv75ZwQEBEhjbHQ6HXx8fKDT6fDkk09i4sSJCAoKglarxQsvvID4+Hh06dIFADBgwADExsZi1KhRmDNnDgwGA/71r38hMTHRbXtvbqRxSFnYuVJQgiv5ZtTzu/lsNCIiIncma8/OJ598AqPRiN69e6N+/frSY9myZVKbefPm4b777sPQoUPRs2dP6PV6rFixQtqvVCqxevVqKJVKxMfH47HHHsPo0aMxY8YMOT6S7HzVKkToylY8P3mRvTtEREQO32fHnbnLfXZsHvt8J/78+yLmDGuH4Z3dZ6YZERHRtWr8PjtUd3D6ORER0VUMO26oSQgHKRMREdkw7Lgh212UOf2ciIiIYcct2S5jnbmUj1KLVeZqiIiI5MWw44YidD7w9lKgxCLi7JVCucshIiKSFcOOG1IoBDQKtt1JmeN2iIjIszHsuKkY27gdzsgiIiIPx7Djpmzjdk5wRhYREXk4hh03ZevZOcGeHSIi8nAMO26KNxYkIiIqw7DjpmwLgl7MK4apqETmaoiIiOTDsOOmAry9EBZQtuo7e3eIiMiTMey4sauXsjhImYiIPBfDjhtrwunnREREDDvuTFoQlDcWJCIiD8aw48Z4Y0EiIiKGHbdmG7Nz6mI+rFZR5mqIiIjkwbDjxiLr+UKtVKC41IpzOVwQlIiIPBPDjhtTKgQ0DPYFAJy8yEtZRETkmRh23BynnxMRkadj2HFznH5ORESejmHHzXH6OREReTqGHTfHnh0iIvJ0DDtuLqZ8zM4FYxEKzKUyV0NEROR8DDtuLtBXjSA/NQD27hARkWdi2PEAV8ftMOwQEZHnYdjxAJx+TkREnoxhxwNwkDIREXkyhh0PYLuMdYI9O0RE5IEYdjxAq/paAEDaeRPWHjLIXA0REZFzMex4gKggXzzZvTEAYNIPqfgrM1fmioiIiJyHYcdDTB3YEl1jgpFvtuCpr/fAWFAid0lEREROwbDjIVRKBT58tBMi6/ngzKUCvPD9PlisotxlERER1TqGHQ8S5KfGolGd4e2lwNa/sjF33TG5SyIiIqp1DDseJjZCi7nD2gMAFm45gVX7z8tcERERUe1i2PFA97ePwLO9YgAAk3/cj8PnTTJXREREVHsYdjzU5IQW6NU8FEUlVjz19R5czjfLXRIREVGtYNjxUEqFgAWPdESjYF+cyylE4nd7UWqxyl0WERFRjWPY8WA6Xy8sGt0Zfmolkk9ewszfjspdEhERUY1j2PFwzcMD8O/hHQAAX2w7hf+mnJW3ICIiohrGsEO4p40eL/ZrBgCYuvIgDpzNkbcgIiKiGsSwQwCA8f2aoX+rMJhLrXjmmxRk5xbLXRIREVGNYNghAIBCIWDewx0QE+qHC8YiPP9dCsylHLBMRER1H8MOSQK8vfDZ6M4I0Kiw+/QVzFidJndJREREt41hh+w0CfXH/BEdIAjAtzvSsXRXutwlERER3RaGHaqgb8twvDygBQBg2s+HkHLmsswVERER3TqGHarU871jcG9bPUosIp79di8MxiK5SyIiIrolDDtUKUEQMHdYe7TUByA7txjPfJuCohKL3GURERFVG8MOVclPo8KiUZ2h8/HC/owcTPv5EERRlLssIiKiamHYoRuKDvbFh492hEIAfthzFt/sOCN3SURERNXCsEM31aNZKKYObAUAmLHqMHacvCRzRURERI5j2CGH/LNHYzzYIQKlVhGJ3+3FuZxCuUsiIiJyCMMOOUQQBCQNaYfWEVpcyjfjmW/2oNDMActEROT6GHbIYT5qJT4dFYcgPzUOnTNh6ooDHLBMREQuj2GHqiWyni8+HtkJSoWAn1LP4z9/npK7JCIiohti2KFq69IkGNPuiwUAzPztCP48flHmioiIiKrGsEO3ZHR8QzwUFwmrCIxbuhfplwrkLomIiKhSDDt0SwRBwFuD26B9VCByCkrw9Dd7kF9cKndZREREFTDs0C3z9lLi08fiEOKvwVFDLib/uJ8DlomIyOUw7NBt0eu88emoTvBSCvjtoAEfbz4hd0lERER2GHbotsU1DMKMB9sAAN79/Rg2Hc2SuSIiIqKrGHaoRoy4Mxoj74qGKAIvfr8PJ7Pz5C6JiIgIAMMO1aDp97dG54b1kFtUiqe/SUFuUYncJRERETHsUM1RqxT4+LFO0Gu98XdWHiYs2w+rlQOWiYhIXrKGna1bt+L+++9HREQEBEHATz/9ZLdfFEVMmzYN9evXh4+PD/r374/jx4/btbl8+TJGjhwJrVaLwMBAPPnkk8jL4yUUuYQFeOPTUXFQqxT435FMzN9w/OYvIiIiqkWyhp38/Hy0b98eH330UaX758yZgwULFmDhwoXYuXMn/Pz8kJCQgKKiIqnNyJEjkZaWhvXr12P16tXYunUrnn76aWd9BKpE+6hAzPxHWwDA/A3HsS7NIHNFRETkyQTRRW6MIggCVq5cicGDBwMo69WJiIjApEmT8PLLLwMAjEYjwsPDsXjxYjzyyCM4cuQIYmNjsXv3bnTu3BkAsHbtWtx77704e/YsIiIiHHpvk8kEnU4Ho9EIrVZbK5/PE725Kg1fbjsNP7USPyV2Q7PwALlLIiIiN+Lo72+XHbNz6tQpGAwG9O/fX9qm0+lw1113ITk5GQCQnJyMwMBAKegAQP/+/aFQKLBz584qj11cXAyTyWT3oJr32r2tEN8kGPlmC576eg+MBRywTEREzueyYcdgKLv0ER4ebrc9PDxc2mcwGBAWFma3X6VSISgoSGpTmVmzZkGn00mPqKioGq6eAMBLqcCHj3ZEg0AfnL5UgBe/3wcLBywTEZGTuWzYqU1Tp06F0WiUHhkZGXKX5LaC/TX4dFQcvL0U2PJXNt79/ZjcJRERkYdx2bCj1+sBAJmZmXbbMzMzpX16vR5ZWfZ36y0tLcXly5elNpXRaDTQarV2D6o9bRroMHtoOwDAJ5tPYPWB8zJXREREnsRlw07jxo2h1+uxYcMGaZvJZMLOnTsRHx8PAIiPj0dOTg5SUlKkNhs3boTVasVdd93l9Jqpag92aIBnejYBAExefgCHzhllroiIiDyFrGEnLy8PqampSE1NBVA2KDk1NRXp6ekQBAHjx4/H22+/jV9++QUHDx7E6NGjERERIc3YatWqFe655x489dRT2LVrF7Zt24Zx48bhkUcecXgmFjnPK/e0RI9mISgsseDRz3Zg9+nLcpdEREQeQNap55s3b0afPn0qbB8zZgwWL14MURQxffp0LFq0CDk5OejevTs+/vhjNG/eXGp7+fJljBs3DqtWrYJCocDQoUOxYMEC+Pv7O1wHp547j7GwBP/8ajd2n74CjUqBDx/thLtjw2/+QiIious4+vvbZe6zIyeGHecqKrFg3JK9+N+RLCgVAmYNaYvhnTkjjoiIqqfO32eH3Je3lxILH4vDQ3GRsFhFvPLjAXyy+QSYu4mIqDYw7JAsVEoF5gxrh2d7xQAAZq89ird/PcKFQ4mIqMYx7JBsBEHAqwNb4l+DWgEA/vPnKUxavh8lFqvMlRERkTth2CHZ/bNHE7w3vD1UCgEr953DU1/vQYG5VO6yiIjITTDskEsY0ikSn43pDG8vBTYfy8bIz3fiSr5Z7rKIiMgNMOyQy+jTIgzf/bMLdD5e2Jeeg4c+Tcb5nEK5yyIiojqOYYdcSlzDevjx2XjU13nj76w8DP1kO/7OypW7LCIiqsMYdsjlNAsPwI/PdUVMqB8uGIswbGEy9qZfkbssIiKqoxh2yCU1CPTB8me7okNUIHIKSjDys53YdCzr5i8kIiK6DsMOuawgPzWWPHUXejYPRWGJBU99tQc/7Tsnd1lERFTHMOyQS/NVq/D56M54sEMESq0ixi9Lxed/nJS7LCIiqkMYdsjlqVUKzBveAU90awwAePvXI0hac5TLSxARkUMYdqhOUCgEvH5fK7xyTwsAwMItJzDlvwdQyrstExHRTTDsUJ0hCAKe790USUPaQiEAP+w5i2e/3YuiEovcpRERkQtj2KE655E7o/HJY3FQqxT435FMjP7PLhgLS+Qui4iIXBTDDtVJCa31+OaJOxHgrcKu05fx8KfJyDQVyV0WERG5IIYdqrPuahKMH56JR2iABkcNuRj6yXaczM6TuywiInIxDDtUp7Wqr8WK57qiUbAvzl4pxEMLk3HwrFHusoiIyIUw7FCdFxXkix+f64o2DbS4lG/GI4uS8efxi3KXRURELoJhh9xCiL8GS5/qgq4xwcg3WzB28S6sPnBe7rKIiMgFMOyQ2wjw9sKXY+/AvW31KLGIeGHpPnydfFrusoiISGYMO+RWNColPhjRCY91iYYoAtN+TsN76//i3ZaJiDwYww65HaVCwFsPtsH4/s0AAAs2HMf//XQIFisDDxGRJ2LYIbckCALG92+Otwa3gSAAS3amY9ySvSgu5d2WiYg8DcMOubVRXRriwxGd4KUUsOaQAY9/sRu5RbzbMhGRJ2HYIbc3qF19LB57J/zUSiSfvIRHFu1Adm6x3GUREZGTMOyQR+jWNATfPx2PYD810s6bMGzhdqRfKpC7LCIicgKGHfIYbSN1+PG5rois54MzlwowdOF2HD5vkrssIiKqZQw75FEah/jhv891RUt9ALJzizFs4XZMWJaKtYcMKDRz8DIRkTsSRN6ABCaTCTqdDkajEVqtVu5yyAmMhSV4+us92HnqsrTN20uBXs1DkdBaj34tw6Hz9ZKxQiIiuhlHf38z7IBhx1NZrSL2nLmCdWkGrD1kwLmcQmmfSiEgPiYYA1rrkRAbjjCtt4yVEhFRZRh2qoFhh0RRRNp5E35PM2BtmgF/ZeZJ+wQB6BgViHva6JHQWo+GwX4yVkpERDYMO9XAsEPXO5mdh3VpmViXZkBqRo7dvpb6ACS0Lgs+reoHQBAEeYokIvJwDDvVwLBDN2IwFuH3wwasSzNgx8nLdstORAf5IqF1OO5po0fHqHpQKBh8iIichWGnGhh2yFFX8s3YcDQLaw8Z8MfxbBSXWqV9oQEaDIgNR0JrPbo0CYZaxcmORES1iWGnGhh26FbkF5di61/ZWJtmwMYjWcgtLpX2ab1V6NcqHAmtw9GzeSh81SoZKyUick8MO9XAsEO3y1xqxfYTF7EuLRPrDxtwMc8s7fP2UqBns1Dc04ZT2omIahLDTjUw7FBNslhF7E2/gnWHymZ2nb3CKe1ERLWBYacaGHaotoiiiMMXTFh3yIB1aZk4lpkr7bNNabfN7GoUwintRETVwbBTDQw75CynLuZjXVrZzK596Tl2+xqH+KFDVCDaR+rQPioQsRFaaFRKeQolIqoDGHaqgWGH5GAwFmH94bIen+STl+ymtAOAl1JAbH0t2kcFloWgqEA0Dvbj9HYionIMO9XAsENyMxaUYF/GFezPMCI14wr2nzXicr65QjuttwrtowLRPjJQCkGhARoZKiYikh/DTjUw7JCrEUURZ68UYl9GDvaXPw6eM9rd18emQaAP2kfpyi+BBaJNAx38NJzqTkTuj2GnGhh2qC4osVhxzJCL/WfLwk9qRg6OZ+Xh+n/BCgFoHh4gXfrqEBWIZmH+UCl5k0Mici8MO9XAsEN1VV5xKQ6eNSLV1gN0NgcXjEUV2vl4KdG2gQ4dom2XwHRoEOjDdb2IqE5j2KkGhh1yJ5mmIin8pGbk4MBZI/KuubuzTYi/Bh2idGgfGYgO0YFoFxkInQ9veEhEdQfDTjUw7JA7s1pFnLyYh33pOeWXwIw4csGEUmvFf/pNQvzQPioQ0UG+0Ou8yx7askegrxd7gojIpTDsVAPDDnmaohIL0s6bpEtfqRk5OHOp4Iav0agUCC8PPuE6b+i1Guh1PmVhSKdBuNYbYQHeXACViJzG0d/fnLJB5IG8vZSIa1gPcQ3rSduu5Jux/2wODp414ryxEAZjEQymYmSainA534ziUivSLxcg/fKNQ1GIv1oKRbaeoXDd1efhWm9ovVXsJSIip2HPDtizQ3QzxaUWZJmKYTAV4YKxCJnGIhhMZQ/b91mmYpgtFafGV8bHS1kefDSor/MpD0caKQzpdd4I9ddwBhkR3RB7doioxmhUSkQF+SIqyLfKNlariCsF5rIwdF0QMpiKkWkswgVjIUxFpSgsseDUxXycuphf5fEUQtkg6jCtBuEB3gjTahAa4I2wAA3CAsovm2k1CPHXwIuhiIhugGGHiGqEQiEg2F+DYH8N2jTQVdmu0GwpC0DXhCLb97aglJVbDItVRFZuMbJyi3EIpiqPJwhAkK8aoQEahGm9ER5QFpDCbMGo/PvQAA28vbjWGJEnYtghIqfyUSvROMQPjW+wyrvFKuJSXrF0eaws9JSFoCxTMbJzi5BpKkZ2XlkoupRvxqV8M44acqs8JgDofLykABQe4I3Qa0ORLSxpNfBV80cjkTvhv2gicjlKhYAwrTfCtN43bGe1irhcYC4PREVXv5aHomu/N1usMBaWwFhYguNZeTc8rr9GhbAAjV1vUZC/GgHeXtB6qxDgrYLW2wsB3l4IKH/ur+GgayJXxbBDRHWWQiEgxL9s3E4sqh6cKIoijIUldiEo85owlH3NtsISC/KKS5FXXIqTNxhTVKEWoSwk2QKQ1scWjK5+DbA997mm3TXBydtLwcBEVAsYdojI7QmCgEBfNQJ91WgeHlBlO1EUkVdcaheKsnPLpt9fKShBblEJcotKYSr/mltUClNhCUqtIqwiYCoqhamo4t2qHaVSCFJQCvBWIUBjH4xswclfo4KPWgk/tQq+mrKvfholfNUqaRsHbRNdxbBDRFROEITyYOGFmFB/h14jiiKKSqzILSopDzu2IFQihSG75+XtbNtMhSXIKy6FVQRKrSKuFJTgSkHJbX8WtVIhBaGyYFQehjTXfVUr4atRSft9r3t+bXsfLyV7nqhOYtghIroNgiDAR62Ej1qJsFu8TZcoisg3W24QjOyDU4G5FPnFlrKvZgsKzRbkm0tRUGyR7nVktlhhLrAipwaC09XPCvh6XQ1DPmoV/DVK+GnKepv8NSr4lT8q2351v7Ksd4rhiZyEYYeISGaCIEih4HaZS61Xw095KMo3l5Zvs6CguNT+q7kUBeVfpQB1TZAqKC5FQYkFogiIIpBffpzsGvjcCgHXhaDykKRWwd/7upCkviY8eduHJ//ynifehJKqwrBDRORG1CoF1CoFdL41t4K97VKdrffIFqTyii3ILx/MnV/+uHab/fayEJVXXIp8cylEEbCKkMY+1QQvpQAfr7LLbj5qZfn3yuu+L79Up1bCu3ybbfu17X3LX+NTfjnPx0sJpYK9UHUVww4REd3QtZfq4NhQphuyWkUUlljsQlBucUlZL9Q1Ick+MFmq3G67dFdiEVFiub1B4jeiVinKwlF5CPJRK+HrpaoQjtQqBbyUCqgUAlRKBbxsX5VC2XalAC9F2Vfbfmn7ta9TClAprntdFft5OfDGGHaIiMipFApBumwVVgPHM5dar7kcZ0FRiUW6NFdotqCw/Hlh+f7CEgsKbe1LLCgq315Qvr1s/9W2thUkzaVWmEutyEHNjYOqKWUByD5Eqa8LSNeGMLWq7KuXUgEv1bWBSwG10v77smB1NXTZgpf6uu9V1+y/2vbqttAA+ZZ2YdghIqI6rezSnRqBVS/ddstEUURxqdXx8GS2oMRiRalVRInFWva9RUSJRUSp1fZ9JfutIkotV/eXWCu+zmyxotRihbWS5btLrSJKrSKK4NhivHL438ReaBpWA12Dt4Bhh4iIqAqCIMDbq2x8T5CfWu5yAJRdBrSFobKgdG2oujZIlQWoEsvVYFUitbGipPRqgCqxHaf0atgqKS0LWiUWK8zXfF/p8cq/mq8NbBb7QOellO9Sm9uEnY8++ghz586FwWBA+/bt8cEHH+DOO++UuywiIqIapVAI0CiUqIHJex7DLebpLVu2DBMnTsT06dOxd+9etG/fHgkJCcjKypK7NCIiIpKZW4Sd9957D0899RTGjh2L2NhYLFy4EL6+vvjiiy/kLo2IiIhkVufDjtlsRkpKCvr37y9tUygU6N+/P5KTkyt9TXFxMUwmk92DiIiI3FOdDzsXL16ExWJBeHi43fbw8HAYDIZKXzNr1izodDrpERUV5YxSiYiISAZ1PuzciqlTp8JoNEqPjIwMuUsiIiKiWlLnx3KHhIRAqVQiMzPTbntmZib0en2lr9FoNNBoNM4oj4iIiGRW53t21Go14uLisGHDBmmb1WrFhg0bEB8fL2NlRERE5ArqfM8OAEycOBFjxoxB586dceedd+L9999Hfn4+xo4dK3dpREREJDO3CDsPP/wwsrOzMW3aNBgMBnTo0AFr166tMGiZiIiIPI8gimIlq2x4FpPJBJ1OB6PRCK1WK3c5RERE5ABHf3/X+TE7RERERDfCsENERERujWGHiIiI3BrDDhEREbk1t5iNdbtsY7S5RhYREVHdYfu9fbO5Vgw7AHJzcwGAa2QRERHVQbm5udDpdFXu59RzlN1x+fz58wgICIAgCHKXUytMJhOioqKQkZHB6fXleE4qx/NSEc9JRTwnleN5qag2z4koisjNzUVERAQUiqpH5rBnB4BCoUBkZKTcZTiFVqvlP8Dr8JxUjuelIp6TinhOKsfzUlFtnZMb9ejYcIAyERERuTWGHSIiInJrDDseQqPRYPr06dBoNHKX4jJ4TirH81IRz0lFPCeV43mpyBXOCQcoExERkVtjzw4RERG5NYYdIiIicmsMO0REROTWGHaIiIjIrTHsuLlZs2bhjjvuQEBAAMLCwjB48GAcO3ZM7rJcSlJSEgRBwPjx4+UuRVbnzp3DY489huDgYPj4+KBt27bYs2eP3GXJymKx4PXXX0fjxo3h4+ODmJgYvPXWWzddh8edbN26Fffffz8iIiIgCAJ++uknu/2iKGLatGmoX78+fHx80L9/fxw/flyeYp3kRuekpKQEU6ZMQdu2beHn54eIiAiMHj0a58+fl69gJ7nZ35VrPfvssxAEAe+//75TamPYcXNbtmxBYmIiduzYgfXr16OkpAQDBgxAfn6+3KW5hN27d+PTTz9Fu3bt5C5FVleuXEG3bt3g5eWFNWvW4PDhw/j3v/+NevXqyV2arGbPno1PPvkEH374IY4cOYLZs2djzpw5+OCDD+QuzWny8/PRvn17fPTRR5XunzNnDhYsWICFCxdi586d8PPzQ0JCAoqKipxcqfPc6JwUFBRg7969eP3117F3716sWLECx44dwwMPPCBDpc51s78rNitXrsSOHTsQERHhpMoAiORRsrKyRADili1b5C5Fdrm5uWKzZs3E9evXi7169RJfeukluUuSzZQpU8Tu3bvLXYbLGTRokPjEE0/YbRsyZIg4cuRImSqSFwBx5cqV0nOr1Srq9Xpx7ty50racnBxRo9GIS5culaFC57v+nFRm165dIgDxzJkzzinKBVR1Xs6ePSs2aNBAPHTokNiwYUNx3rx5TqmHPTsexmg0AgCCgoJkrkR+iYmJGDRoEPr37y93KbL75Zdf0LlzZzz00EMICwtDx44d8dlnn8ldluy6du2KDRs24K+//gIA7N+/H3/++ScGDhwoc2Wu4dSpUzAYDHb/hnQ6He666y4kJyfLWJlrMRqNEAQBgYGBcpciK6vVilGjRmHy5Mlo3bq1U9+bC4F6EKvVivHjx6Nbt25o06aN3OXI6vvvv8fevXuxe/duuUtxCSdPnsQnn3yCiRMn4rXXXsPu3bvx4osvQq1WY8yYMXKXJ5tXX30VJpMJLVu2hFKphMViwTvvvIORI0fKXZpLMBgMAIDw8HC77eHh4dI+T1dUVIQpU6ZgxIgRHr8w6OzZs6FSqfDiiy86/b0ZdjxIYmIiDh06hD///FPuUmSVkZGBl156CevXr4e3t7fc5bgEq9WKzp07Y+bMmQCAjh074tChQ1i4cKFHh50ffvgB3333HZYsWYLWrVsjNTUV48ePR0REhEefF3JMSUkJhg8fDlEU8cknn8hdjqxSUlIwf/587N27F4IgOP39eRnLQ4wbNw6rV6/Gpk2bEBkZKXc5skpJSUFWVhY6deoElUoFlUqFLVu2YMGCBVCpVLBYLHKX6HT169dHbGys3bZWrVohPT1dpopcw+TJk/Hqq6/ikUceQdu2bTFq1ChMmDABs2bNkrs0l6DX6wEAmZmZdtszMzOlfZ7KFnTOnDmD9evXe3yvzh9//IGsrCxER0dLP3fPnDmDSZMmoVGjRrX+/uzZcXOiKOKFF17AypUrsXnzZjRu3FjukmTXr18/HDx40G7b2LFj0bJlS0yZMgVKpVKmyuTTrVu3Crck+Ouvv9CwYUOZKnINBQUFUCjs/0+oVCphtVplqsi1NG7cGHq9Hhs2bECHDh0AACaTCTt37sRzzz0nb3EysgWd48ePY9OmTQgODpa7JNmNGjWqwvjIhIQEjBo1CmPHjq3192fYcXOJiYlYsmQJfv75ZwQEBEjX0XU6HXx8fGSuTh4BAQEVxiz5+fkhODjYY8cyTZgwAV27dsXMmTMxfPhw7Nq1C4sWLcKiRYvkLk1W999/P9555x1ER0ejdevW2LdvH9577z088cQTcpfmNHl5efj777+l56dOnUJqaiqCgoIQHR2N8ePH4+2330azZs3QuHFjvP7664iIiMDgwYPlK7qW3eic1K9fH8OGDcPevXuxevVqWCwW6eduUFAQ1Gq1XGXXupv9Xbk+9Hl5eUGv16NFixa1X5xT5nyRbABU+vjyyy/lLs2lePrUc1EUxVWrVolt2rQRNRqN2LJlS3HRokVylyQ7k8kkvvTSS2J0dLTo7e0tNmnSRPy///s/sbi4WO7SnGbTpk2V/gwZM2aMKIpl089ff/11MTw8XNRoNGK/fv3EY8eOyVt0LbvROTl16lSVP3c3bdokd+m16mZ/V67nzKnngih60K1AiYiIyONwgDIRERG5NYYdIiIicmsMO0REROTWGHaIiIjIrTHsEBERkVtj2CEiIiK3xrBDREREbo1hh4hq1enTpyEIAlJTU+UuRXL06FF06dIF3t7e0jIH1eGKn4mIqsawQ+TmHn/8cQiCgKSkJLvtP/30kyyrD7uC6dOnw8/PD8eOHcOGDRvkLgeLFy9GYGCg3GUQuS2GHSIP4O3tjdmzZ+PKlStyl1JjzGbzLb/2xIkT6N69Oxo2bOhWizRaLBYuUkpUCYYdIg/Qv39/6PV6zJo1q8o2b7zxRoVLOu+//z4aNWokPX/88ccxePBgzJw5E+Hh4QgMDMSMGTNQWlqKyZMnIygoCJGRkfjyyy8rHP/o0aPo2rUrvL290aZNG2zZssVu/6FDhzBw4ED4+/sjPDwco0aNwsWLF6X9vXv3xrhx4zB+/HiEhIQgISGh0s9htVoxY8YMREZGQqPRoEOHDli7dq20XxAEpKSkYMaMGRAEAW+88UaVx5kzZw6aNm0KjUaD6OhovPPOO5W2raxn5vqes/3796NPnz4ICAiAVqtFXFwc9uzZg82bN2Ps2LEwGo0QBMGupuLiYrz88sto0KAB/Pz8cNddd2Hz5s0V3veXX35BbGwsNBoN0tPTsXnzZtx5553w8/NDYGAgunXrhjNnzlRaO5EnYNgh8gBKpRIzZ87EBx98gLNnz97WsTZu3Ijz589j69ateO+99zB9+nTcd999qFevHnbu3Ilnn30WzzzzTIX3mTx5MiZNmoR9+/YhPj4e999/Py5dugQAyMnJQd++fdGxY0fs2bMHa9euRWZmJoYPH253jK+++gpqtRrbtm3DwoULK61v/vz5+Pe//413330XBw4cQEJCAh544AEcP34cAHDhwgW0bt0akyZNwoULF/Dyyy9XepypU6ciKSkJr7/+Og4fPowlS5YgPDz8ls/byJEjERkZid27dyMlJQWvvvoqvLy80LVrV7z//vvQarW4cOGCXU3jxo1DcnIyvv/+exw4cAAPPfQQ7rnnHumzAEBBQQFmz56Nzz//HGlpaQgKCsLgwYPRq1cvHDhwAMnJyXj66ac99pIlEQCuek7k7saMGSM++OCDoiiKYpcuXcQnnnhCFEVRXLlypXjtj4Dp06eL7du3t3vtvHnzxIYNG9odq2HDhqLFYpG2tWjRQuzRo4f0vLS0VPTz8xOXLl0qiqIorQKdlJQktSkpKREjIyPF2bNni6Ioim+99ZY4YMAAu/fOyMgQAUgraPfq1Uvs2LHjTT9vRESE+M4779htu+OOO8Tnn39eet6+fXtx+vTpVR7DZDKJGo1G/Oyzzyrdb/tM+/btE0VRFL/88ktRp9PZtbn+/AYEBIiLFy+u9HiVvf7MmTOiUqkUz507Z7e9X79+4tSpU6XXARBTU1Ol/ZcuXRIBiJs3b67y8xF5GvbsEHmQ2bNn46uvvsKRI0du+RitW7eGQnH1R0d4eDjatm0rPVcqlQgODkZWVpbd6+Lj46XvVSoVOnfuLNWxf/9+bNq0Cf7+/tKjZcuWAMrG19jExcXdsDaTyYTz58+jW7dudtu7detWrc985MgRFBcXo1+/fg6/5mYmTpyIf/7zn+jfvz+SkpLsPldlDh48CIvFgubNm9udly1btti9Vq1Wo127dtLzoKAgPP7440hISMD999+P+fPn48KFCzX2OYjqIoYdIg/Ss2dPJCQkYOrUqRX2KRQKiKJot62kpKRCOy8vL7vngiBUuq06A2Xz8vJw//33IzU11e5x/Phx9OzZU2rn5+fn8DFvh4+PT7XaO3Lu3njjDaSlpWHQoEHYuHEjYmNjsXLlyiqPmZeXB6VSiZSUFLtzcuTIEcyfP9+u1usvUX355ZdITk5G165dsWzZMjRv3hw7duyo1mcicicMO0QeJikpCatWrUJycrLd9tDQUBgMBrtf2jV5H5lrf9mWlpYiJSUFrVq1AgB06tQJaWlpaNSoEZo2bWr3qE7A0Wq1iIiIwLZt2+y2b9u2DbGxsQ4fp1mzZvDx8XF4WnpoaChyc3ORn58vbavs3DVv3hwTJkzA77//jiFDhkgDudVqNSwWi13bjh07wmKxICsrq8I50ev1N62pY8eOmDp1KrZv3442bdpgyZIlDn0WInfEsEPkYdq2bYuRI0diwYIFdtt79+6N7OxszJkzBydOnMBHH32ENWvW1Nj7fvTRR1i5ciWOHj2KxMREXLlyBU888QQAIDExEZcvX8aIESOwe/dunDhxAuvWrcPYsWMrhICbmTx5MmbPno1ly5bh2LFjePXVV5GamoqXXnrJ4WN4e3tjypQpeOWVV/D111/jxIkT2LFjB/7zn/9U2v6uu+6Cr68vXnvtNZw4cQJLlizB4sWLpf2FhYUYN24cNm/ejDNnzmDbtm3YvXu3FPYaNWqEvLw8bNiwARcvXkRBQQGaN2+OkSNHYvTo0VixYgVOnTqFXbt2YdasWfj111+rrP3UqVOYOnUqkpOTcebMGfz+++84fvy49F5Enohhh8gDzZgxo8JlplatWuHjjz/GRx99hPbt22PXrl1VzlS6FUlJSUhKSkL79u3x559/4pdffkFISAgASL0xFosFAwYMQNu2bTF+/HgEBgbajQ9yxIsvvoiJEydi0qRJaNu2LdauXYtffvkFzZo1q9ZxXn/9dUyaNAnTpk1Dq1at8PDDD1cYh2QTFBSEb7/9Fr/99hvatm2LpUuX2k1pVyqVuHTpEkaPHo3mzZtj+PDhGDhwIN58800AQNeuXfHss8/i4YcfRmhoKObMmQOg7HLU6NGjMWnSJLRo0QKDBw/G7t27ER0dXWXdvr6+OHr0KIYOHYrmzZvj6aefRmJiIp555plqfX4idyKI119oJiIiInIj7NkhIiIit8awQ0RERG6NYYeIiIjcGsMOERERuTWGHSIiInJrDDtERETk1hh2iIiIyK0x7BAREZFbY9ghIiIit8awQ0RERG6NYYeIiIjcGsMOERERubX/B5pb92hja/ONAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, 15), wcss)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "359257a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "km=KMeans(n_clusters = 4, init = 'k-means++', random_state = 0)\n",
    "y_kmeans=km.fit_predict(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "bef15fb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 0, 1, 1, 0, 1,\n",
       "       1, 1, 2, 1, 1, 1, 1, 1, 0, 2, 1, 1, 1])"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "65cc50dc",
   "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>SrNo</th>\n",
       "      <th>State/UT/District</th>\n",
       "      <th>Homicide/Murder</th>\n",
       "      <th>Causing death by negligence</th>\n",
       "      <th>Hurt</th>\n",
       "      <th>Assault on woman</th>\n",
       "      <th>Kidnapping and abduction</th>\n",
       "      <th>Human trafficking</th>\n",
       "      <th>Rape</th>\n",
       "      <th>Offence against public tranquility</th>\n",
       "      <th>...</th>\n",
       "      <th>Others</th>\n",
       "      <th>Total</th>\n",
       "      <th>Population</th>\n",
       "      <th>Crime Rate</th>\n",
       "      <th>Sex Ratio</th>\n",
       "      <th>Literacy</th>\n",
       "      <th>Density</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Ahmednagar</td>\n",
       "      <td>308</td>\n",
       "      <td>799</td>\n",
       "      <td>1458</td>\n",
       "      <td>476</td>\n",
       "      <td>431</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>815</td>\n",
       "      <td>...</td>\n",
       "      <td>783</td>\n",
       "      <td>12613</td>\n",
       "      <td>4543159</td>\n",
       "      <td>0.277626</td>\n",
       "      <td>939</td>\n",
       "      <td>0.7905</td>\n",
       "      <td>266</td>\n",
       "      <td>19.094829</td>\n",
       "      <td>74.747979</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Akola</td>\n",
       "      <td>104</td>\n",
       "      <td>146</td>\n",
       "      <td>1203</td>\n",
       "      <td>202</td>\n",
       "      <td>133</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>125</td>\n",
       "      <td>...</td>\n",
       "      <td>187</td>\n",
       "      <td>5579</td>\n",
       "      <td>1813906</td>\n",
       "      <td>0.307568</td>\n",
       "      <td>946</td>\n",
       "      <td>0.8805</td>\n",
       "      <td>320</td>\n",
       "      <td>20.700216</td>\n",
       "      <td>77.008168</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Amravati</td>\n",
       "      <td>181</td>\n",
       "      <td>302</td>\n",
       "      <td>1325</td>\n",
       "      <td>419</td>\n",
       "      <td>236</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>145</td>\n",
       "      <td>...</td>\n",
       "      <td>381</td>\n",
       "      <td>9577</td>\n",
       "      <td>2888445</td>\n",
       "      <td>0.331562</td>\n",
       "      <td>951</td>\n",
       "      <td>0.8738</td>\n",
       "      <td>237</td>\n",
       "      <td>20.931982</td>\n",
       "      <td>77.752304</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Aurangbad</td>\n",
       "      <td>193</td>\n",
       "      <td>532</td>\n",
       "      <td>1852</td>\n",
       "      <td>419</td>\n",
       "      <td>281</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>389</td>\n",
       "      <td>...</td>\n",
       "      <td>364</td>\n",
       "      <td>15929</td>\n",
       "      <td>3701282</td>\n",
       "      <td>0.430364</td>\n",
       "      <td>923</td>\n",
       "      <td>0.7902</td>\n",
       "      <td>366</td>\n",
       "      <td>19.876165</td>\n",
       "      <td>75.343314</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Beed</td>\n",
       "      <td>173</td>\n",
       "      <td>318</td>\n",
       "      <td>944</td>\n",
       "      <td>249</td>\n",
       "      <td>145</td>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "      <td>421</td>\n",
       "      <td>...</td>\n",
       "      <td>346</td>\n",
       "      <td>5352</td>\n",
       "      <td>2585049</td>\n",
       "      <td>0.207037</td>\n",
       "      <td>916</td>\n",
       "      <td>0.7699</td>\n",
       "      <td>242</td>\n",
       "      <td>18.990088</td>\n",
       "      <td>75.753132</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SrNo State/UT/District  Homicide/Murder  Causing death by negligence  Hurt  \\\n",
       "0     1        Ahmednagar              308                          799  1458   \n",
       "1     2             Akola              104                          146  1203   \n",
       "2     3          Amravati              181                          302  1325   \n",
       "3     4         Aurangbad              193                          532  1852   \n",
       "4     5              Beed              173                          318   944   \n",
       "\n",
       "   Assault on woman  Kidnapping and abduction  Human trafficking   Rape  \\\n",
       "0               476                       431                   0    87   \n",
       "1               202                       133                   0    40   \n",
       "2               419                       236                   1    82   \n",
       "3               419                       281                   0    59   \n",
       "4               249                       145                   7    35   \n",
       "\n",
       "   Offence against public tranquility  ...  Others  Total  Population  \\\n",
       "0                                 815  ...     783  12613     4543159   \n",
       "1                                 125  ...     187   5579     1813906   \n",
       "2                                 145  ...     381   9577     2888445   \n",
       "3                                 389  ...     364  15929     3701282   \n",
       "4                                 421  ...     346   5352     2585049   \n",
       "\n",
       "   Crime Rate  Sex Ratio  Literacy  Density   Latitude  Longitude  cluster  \n",
       "0    0.277626        939    0.7905      266  19.094829  74.747979        0  \n",
       "1    0.307568        946    0.8805      320  20.700216  77.008168        1  \n",
       "2    0.331562        951    0.8738      237  20.931982  77.752304        1  \n",
       "3    0.430364        923    0.7902      366  19.876165  75.343314        0  \n",
       "4    0.207037        916    0.7699      242  18.990088  75.753132        1  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cluster']=y_kmeans\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "807c3d95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.scatter(df['SrNo'],df['cluster'])\n",
    "#for col in df.columns:\n",
    " #   print(col)\n",
    "plt.scatter(df1[y_kmeans == 0, 0], df1[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
    "plt.scatter(df1[y_kmeans == 1, 0], df1[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
    "plt.scatter(df1[y_kmeans == 2, 0], df1[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
    "plt.scatter(df1[y_kmeans == 3, 0], df1[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
    "#plt.scatter(df1[y_kmeans == 4, 0], df1[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n",
    "#plt.scatter(df1[y_kmeans == 5, 0], df1[y_kmeans == 5, 1], s = 100, c = 'black', label = 'Cluster 6')\n",
    "#plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n",
    "plt.title('Clusters of Districts')\n",
    "plt.xlabel('Annual Income (k$)')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "a6f8da60",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.cluster.hierarchy import linkage \n",
    "import scipy.cluster.hierarchy as sch # for creating dendrogram "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "fa2ebb69",
   "metadata": {},
   "outputs": [],
   "source": [
    "z = linkage(df1, method=\"complete\",metric=\"euclidean\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "fcc05a1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "plt.title('Hierarchical Clustering Dendrogram')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Crime')\n",
    "sch.dendrogram(z,\n",
    "    leaf_rotation=0.,  # rotates the x axis labels\n",
    "    leaf_font_size=8.,  # font size for the x axis labels\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "ed20af95",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = pd.read_excel (r'Path where the Excel file is stored\\File name.xlsx', sheet_name='your Excel sheet name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef309c0d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e67c31a7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bac9afad",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.0"
  },
  "vscode": {
   "interpreter": {
    "hash": "43972cab619b55ea9d63321d18bd0a8d9b574a0a3e89ad4f59bee985cd643a8d"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}