avm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Automate Value Machine  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AVM or Automated value machines are algorithms used to value houses based off of area, size, selldate and more. \n",
    "They are often used in mass valuations and are more accurate than human appraisors. The following AVM makes use house prices alone, and shows how machin learning can be used to plot value considerations for the end user. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import os \n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing housing price and area data to build a statistical model, find the average area for each house in the global data set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1020000  1500000   949000   500000   460000   630000   490000   860000\n",
      "   760000   460000   600000  1020000  1195000  1390000   745000  1190000\n",
      "  1300000  3500000   750000  1250000   665000   799000  1850000   835000\n",
      "   835000   550000   465000   470000   750000   410000   550000   695000\n",
      "   780000  2500000   750000  1295000   735000   700000  1700000   899000\n",
      "   825000  1490000  1050000   910000   380000  2650000  1352000   564590\n",
      "   835000  1020000   970000   830000   880000   850000  1249000   985000\n",
      "   860000   620000   730000  1250000   499000   731000   850000  1195000\n",
      "  1099000  1350000   291000   655000   725000  1600000   850000  1199900\n",
      "  3800000  1599000  2400000  1150000   895000  1895000  1680000   595000\n",
      "   560000   550000   600000  1800000   650000   895000   900000   120000\n",
      "   550000   720000   860000  1149000   680000  1475000   460000  1250000\n",
      "   490000  1260000   950000   999000   570000   280000   680000   850000\n",
      "   590000  2294000  2450000        0   699000   550000   750000   440000\n",
      "   650000   930000   710000   390000   450000   950000   425000  2400000\n",
      "  2999900  2360000  2189000   695000  1099000   650000  1100000  4499000\n",
      "   345000   720000   620000   335000   500000   520000  1069900  3090000\n",
      "  1190000   999900   550000  1199900   850000   790000   680000  1242000\n",
      "   980000   649000   585000  1050000   420000  1100000  1900000        0\n",
      "  1050000  1899000  1350000   450000   859900   575000   800000   695000\n",
      "   470000  1195000  2395000   699000   640000   450000   320000   400000\n",
      "  1250000  1320000   890000   500000  1050000   950000  1450000  1350000\n",
      "   920000   865000   860000        0 14950000   520000  1395000   445000\n",
      "  2450000   900000   740000  1450000  1100000   500000   570000   650000\n",
      "  1659900   920000  3100000  1250000   599999  1990000   899000   800000\n",
      "  1020000  2195000   590000   865000  1170000   420000   390000   750000\n",
      "  1200000   295000   455000   719000   680000   750000   499000  1395000\n",
      "   560000   380000   900000   710000   859000  1550000   623000  1890000\n",
      "  2450000   585000   850000   450000   700000   685000  1079900  1235000\n",
      "  1699000   520000   500000   635000   688000  1350000  3199900   820000\n",
      "   575000   820000   999000   599000   650000   870000  1490000   580000\n",
      "  3800000  1250000   800000   740000   835000   700000   550000   750000\n",
      "        0  1950000   700000  2650000   725000   850000   525000   530000\n",
      "   630000   730000   845000  1479900  1200000   590000  1850000  2189000\n",
      "   965000  1350000  1749000   750000  1050000  1350000   810000  1750000\n",
      "   859900   850650  2500000  1620000  2599000   959900   380000  1200000\n",
      "  1000000   960000   799000   599000   450000  1295000   450000   860000\n",
      "   500000   470000   485000  1099900   650000   745000   640000   649000\n",
      "   580000  1190000   800000   799000   800000   975000  1260000   875000\n",
      "   830000   639999   580000   560000   810000   850000   695000  3500000\n",
      "   830000   399000  1985000   800000   740000  1199000  1519900   760000\n",
      "  1190000   565000   590000  1350000   520000   835000   750000  4499000\n",
      "   445000   870000   699000  1500000   750000   710000   695000   899000\n",
      "   460000   899000   250000   390000   399000  1800000   550000   810000\n",
      "   750000   750000   630000  2650000   750000   930000   950000   580000\n",
      "   999900   360000   995000   999000   760000   750000  1200000  1795000\n",
      "   450000  1600000   800000  1200000   660000   575000   665000  2395000\n",
      "   765000] [   61    92    99    49    40    83    80    84    69    65   113    84\n",
      "    78    68    55    92    93    72   159    66   114    62    51    48\n",
      "   104    96    90    70    49    74    46    97    72   103    71   110\n",
      "    55   134   133   100    88    87   496   104   123    73    80   300\n",
      "    92   116   101    83    75    54    46    86    61    83    41    63\n",
      "    75    41    65    93    58    65    82    96    58   257    97    79\n",
      "   103    96    84   116    51   660    43    77   108    70    71    81\n",
      "    83    78    87   119   342    67    85    89   136   230    89    82\n",
      "    95   103    60    71    82    97    72    74    64    53    68    78\n",
      "   153    66    86    70   114    69    68    76    57    95   101    87\n",
      "    74    39    73    44    96   107    72    61    72    84    81    60\n",
      "    85    61   230    84    99   135   156    90    34    80   116   117\n",
      "    89    51    82   151    67    84    82    36    74    86    57    83\n",
      "    81    82    65    76   129    72    36    68    56   101    80    24\n",
      "    62    71    84    73    95    84    77    67   113    84    61    87\n",
      "    46    52    74    84    61    43   645    69   129    54    40   121\n",
      "    65   111   103    84    61    72   104   112    93   135    80    48\n",
      "   166    40   104    95   150    76    81    62    67   100   149   207\n",
      "   189    76    53   110    60    68    66    50    99    80    69    34\n",
      "   104    87    64    40    86    90   109    69    44    72    38    55\n",
      "    51    83   114    83    48   112    89    74   996    49    71    54\n",
      "    34    70    65    46    87    99    47    74    90    70    52    89\n",
      "    40    67   277    80   126    71    90    84    61   108    76    92\n",
      "    80    64   115    57   107   115    65    75    81   111    69    48\n",
      "   119    74    57    84    61    68    99    58    79    68   240    80\n",
      "    61    98    65    68   184    47    55    61   175    68    56    53\n",
      "   105    54    56    75    97    76   125    65    58   107    88    63\n",
      "    60    64   140   116    66    48    75    87    50    64    41    92\n",
      "    82    94   125   133    49    57    48    62    74   111    74    64\n",
      "    58    79   107    91    95   110    81    36    57    82    82    90\n",
      "    74    62    75    91    44    43    75    93 12874    82    59    78\n",
      "    60    95    50    80    91]\n"
     ]
    }
   ],
   "source": [
    "target_directory = r'C:\\GitHub\\Land-value-GIS\\backups'\n",
    "\n",
    "HOUSEDATA = \"house_price_and_area.csv\"\n",
    "\n",
    "path = os.path.join(target_directory, HOUSEDATA)\n",
    "\n",
    "listformatted = []\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "\n",
    "with open(path, \"r\") as r:\n",
    "    reader = csv.reader(r)\n",
    "    next(reader)\n",
    "    for row in reader:\n",
    "        if len(row)> 1:\n",
    "            listformatted.append(row)\n",
    "\n",
    "for sublist in listformatted:\n",
    "    sublist[0] = int(sublist[0])\n",
    "    sublist[1] = int(sublist[1])\n",
    "    x.append(sublist[0])\n",
    "    y.append(sublist[1])\n",
    "\n",
    "houses = pd.read_csv(path)\n",
    "\n",
    "X = numpy.array(x)\n",
    "Y = numpy.array(y)\n",
    "print(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wew1DExERUf1js9BUUlKCdu3aYefOnWjfvn2NC21IGJqIiIjqH5tNbuns7IzCwsIaFUdERERUHykeCD516lS89957KC0ttUU9RERERHZJ8TxNx48fx/79+7Fv3z507twZ7u7uFtu/+eYbqxVHREREZC8UhyYvLy888cQTtqiFiIiIyG4pDk0bNmywRR1EREREdk3xmCYiIiKie5HiK00A8NVXX2Hbtm1ITU1FcXGxxbb4+HirFEZERERkTxRfaVq5ciXGjx8PPz8/nDx5Er169YKPjw+Sk5Px8MMP26JGIiIiojqnODStXr0a69atw0cffQS1Wo3Zs2cjMjISr776KgwGgy1qJCIiIqpzikNTamoqevfuDQBwdXVFTk4OAGDs2LHYsmWLdasjIiIishOKQ5Ner0dWVhYAoFmzZjh69CgAICUlBdV4jB0RERFRvaA4NA0cOBA7duwAAIwfPx4zZszA4MGDMWrUKDz22GNWL5CIiIjIHih6YC8AmM1mmM1mODmVffBu69atOHLkCNq0aYMXX3wRarXaJoXaOz6wl4iIqP5Rcv5WHJqocgxNRERE9Y+S87fieZqioqLuur1///5KD0lERERk9xSHpv/7v/+rsE6lUklfm0ymGhVEREREZI8UDwS/deuWxZKZmYm9e/figQcewL59+2xRIxEREVGdU3ylSafTVVg3ePBgqNVqzJw5E3FxcVYpjIiIiMieWO2BvX5+fkhMTLTW4QCU3epbsGABgoKC4OrqilatWuHtt9+2mA9KCIGFCxfC398frq6uCAsLQ1JSksVxsrKyMGbMGGi1Wnh5eWHChAnIzc21aHPq1Cn069cPLi4uCAwMxLJly6zaFyIiIqrfFF9pOnXqlMX3QgikpaUhIiICXbt2tVZdAID33nsPa9aswaZNm9CxY0ecOHEC48ePh06nw6uvvgoAWLZsGVauXIlNmzYhKCgICxYsQHh4OM6dOwcXFxcAwJgxY5CWlobIyEiUlJRg/PjxmDx5Mr744gsAZSPnhwwZgrCwMKxduxanT5/GCy+8AC8vL0yePNmqfSIiIqJ6SiikUqmEg4ODUKlUFktoaKg4f/680sPd1bBhw8QLL7xgse7xxx8XY8aMEUIIYTabhV6vF8uXL5e2Z2dnC41GI7Zs2SKEEOLcuXMCgDh+/LjUZs+ePUKlUomrV68KIYRYvXq1aNSokSgqKpLazJkzR7Rt27bK2goLC4XBYJCWK1euCADCYDDUvONERERUKwwGg+zzt+LbcykpKUhOTkZKSgpSUlJw+fJl5Ofn48iRI2jXrp1VA13v3r2xf/9+/PbbbwCAX3/9FdHR0Xj44YelWtLT0xEWFibto9PpEBISgpiYGABATEwMvLy80LNnT6lNWFgYHBwcEBsbK7Xp37+/xcSc4eHhSExMxK1btyqtbenSpdDpdNISGBho1b4TERGRfVF8e6558+a2qKNSc+fOhdFoRLt27eDo6AiTyYR3330XY8aMAQCkp6cDKBtPdSc/Pz9pW3p6Onx9fS22Ozk5wdvb26JNUFBQhWOUb2vUqFGF2ubNm4eZM2dK3xuNRgYnIiKiBkxxaFq5cqXstuXjjqpr27Zt2Lx5M7744gt07NgRCQkJmD59OgICAjBu3LgaHbumNBoNNBpNndZAREREtUdxaPrggw9w/fp15Ofnw8vLCwCQnZ0NNzc3NGnSRGqnUqlqHJreeOMNzJ07F08//TQAoHPnzrh8+TKWLl2KcePGQa/XAwAyMjLg7+8v7ZeRkSENStfr9cjMzLQ4bmlpKbKysqT99Xo9MjIyLNqUf1/ehoiIiO5tisc0vfvuu+jatSvOnz+PrKwsZGVl4fz58+jevTveeecdaaxTcnJyjYvLz8+Hg4NliY6OjjCbzQCAoKAg6PV67N+/X9puNBoRGxuL0NBQAEBoaCiys7Mt5o86cOAAzGYzQkJCpDZRUVEoKSmR2kRGRqJt27aV3pojIiKie5DSUeYtW7YU8fHxFdafOHFCtGjRQunh7mrcuHGiadOmYufOnSIlJUV88803onHjxmL27NlSm4iICOHl5SW+//57cerUKfH3v/9dBAUFiYKCAqnN0KFDRbdu3URsbKyIjo4Wbdq0EaNHj5a2Z2dnCz8/PzF27Fhx5swZsXXrVuHm5iY++eQT2bUqGX1PRERE9kHJ+Vvx7bm0tDSUlpZWWG8ymSrc4qqpjz76CAsWLMDLL7+MzMxMBAQE4MUXX8TChQulNrNnz0ZeXh4mT56M7Oxs9O3bF3v37pXmaAKAzZs3Y9q0aRg0aBAcHBzwxBNPWIzN0ul02LdvH6ZOnYoePXqgcePGWLhwIedoIiIiIolKiDum15Zh+PDhuHr1Kj799FN0794dABAXF4fJkyejadOm2LFjh00KtXdGoxE6nQ4GgwFarbauyyEiIiIZlJy/FY9p+s9//gO9Xo+ePXtKnyDr1asX/Pz88Omnn1a7aCIiIiJ7pvj2XJMmTbB7924kJSXh/PnzAIB27dohODjY6sURERER2QvFoalcmzZt0KZNG5hMJpw+fRq3bt3iJ82IiIiowVJ8e2769On47LPPAJQN/h4wYAC6d++OwMBAHDp0yNr1EREREdkFxaHpq6++QpcuXQAAP/zwA5KTk3HhwgXMmDED8+fPt3qBRERERPZAcWi6ceOGNEv27t27MXLkSAQHB+OFF17A6dOnrV4gERERkT1QHJr8/Pxw7tw5mEwm7N27F4MHDwZQNnu3o6Oj1QskIiIisgeKB4KPHz8eI0eOhL+/P1QqFcLCwgAAsbGxaNeundULJCIiIrIHikPT4sWL0alTJ1y5cgVPPfUUNBoNgLJnws2dO9fqBRIRERHZA8UzglPlOCM4ERFR/WPTGcGJiIiI7kUMTUREREQyMDQRERERycDQRERERCSD4tAUHx9vMYnl999/jxEjRuAf//gHiouLrVocERERkb1QHJpefPFF/PbbbwCA5ORkPP3003Bzc8P27dsxe/ZsqxdIREREZA8Uh6bffvsNXbt2BQBs374d/fv3xxdffIGNGzfi66+/tnZ9RERERHZBcWgSQsBsNgMAfvrpJzzyyCMAgMDAQNy4ccO61RERERHZCcWhqWfPnnjnnXfw+eef4+eff8awYcMAACkpKfDz87N6gURERET2QHFo+vDDDxEfH49p06Zh/vz5aN26NQDgq6++Qu/eva1eIBEREZE9UPTsOZPJhOzsbERFRaFRo0YW25YvXw5HR0erFkdERERkLxRdaXJ0dMSQIUOQnZ1dYZuLiwucnZ2tVRcRERGRXVF8e65Tp05ITk62RS1EREREdktxaHrnnXcwa9Ys7Ny5E2lpaTAajRYLERERUUOkEkIIJTs4OPwvZ6lUKulrIQRUKhVMJpP1qqtHjEYjdDodDAYDtFptXZdDREREMig5fysaCA4ABw8erHZhRERERPWV4tA0YMAAW9RBREREZNcUj2kCgMOHD+PZZ59F7969cfXqVQDA559/jujoaKsWR0RERGQvFIemr7/+GuHh4XB1dUV8fDyKiooAAAaDAUuWLLF6gURERET2oFqfnlu7di3Wr19vMS9Tnz59EB8fb9XiiIiIiOyF4tCUmJiI/v37V1iv0+kqnfSSiIiIqCFQHJr0ej0uXrxYYX10dDRatmxplaKIiIiI7I3i0DRp0iS89tpriI2NhUqlwrVr17B582bMmjULU6ZMsUWNRERERHVO8ZQDc+fOhdlsxqBBg5Cfn4/+/ftDo9Fg1qxZeOWVV2xRIxEREVGdUzwjeLni4mJcvHgRubm56NChAzw8PKxdW73CGcGJiIjqHyXnb8W351544QXk5ORArVajQ4cO6NWrFzw8PJCXl4cXXnih2kUTERER2TPFoWnTpk0oKCiosL6goAD/7//9P6sURURERGRvZI9pMhqNEEJACIGcnBy4uLhI20wmE3bv3g1fX1+bFElERERU12SHJi8vL6hUKqhUKgQHB1fYrlKp8NZbb1m1OCIiIiJ7ITs0HTx4EEIIDBw4EF9//TW8vb2lbWq1Gs2bN0dAQIBNiiQiIiKqa7JD04ABAwAAKSkpaNasGVQqlc2KIiIiIrI3igeCN2/eHNHR0Xj22WfRu3dvXL16FQDw+eefIzo62uoFXr16Fc8++yx8fHzg6uqKzp0748SJE9J2IQQWLlwIf39/uLq6IiwsDElJSRbHyMrKwpgxY6DVauHl5YUJEyYgNzfXos2pU6fQr18/uLi4IDAwEMuWLbN6X4iIiKj+Uhyavv76a4SHh8PV1RXx8fEoKioCABgMBixZssSqxd26dQt9+vSBs7Mz9uzZg3PnzmHFihVo1KiR1GbZsmVYuXIl1q5di9jYWLi7uyM8PByFhYVSmzFjxuDs2bOIjIzEzp07ERUVhcmTJ0vbjUYjhgwZgubNmyMuLg7Lly/H4sWLsW7dOqv2h4iIiOoxoVDXrl3Fpk2bhBBCeHh4iN9//10IIUR8fLzw8/NTeri7mjNnjujbt2+V281ms9Dr9WL58uXSuuzsbKHRaMSWLVuEEEKcO3dOABDHjx+X2uzZs0eoVCpx9epVIYQQq1evFo0aNRJFRUUWr922bVvZtRoMBgFAGAwG2fsQERFR3VJy/lZ8pSkxMRH9+/evsF6n0yE7O7vGIe5OO3bsQM+ePfHUU0/B19cX3bp1w/r166XtKSkpSE9PR1hYmEUdISEhiImJAQDExMTAy8sLPXv2lNqEhYXBwcEBsbGxUpv+/ftDrVZLbcLDw5GYmIhbt25VWltRURGMRqPFQkRERA2X4tCk1+tx8eLFCuujo6PRsmVLqxRVLjk5GWvWrEGbNm3w448/YsqUKXj11VexadMmAEB6ejoAwM/Pz2I/Pz8/aVt6enqF+aOcnJzg7e1t0aayY9z5Gn+2dOlS6HQ6aQkMDKxhb4mIiMieKQ5NkyZNwmuvvYbY2FioVCpcu3YNmzdvxqxZszBlyhSrFmc2m9G9e3csWbIE3bp1w+TJkzFp0iSsXbvWqq9THfPmzYPBYJCWK1eu1HVJREREZEOypxwoN3fuXJjNZgwaNAj5+fno378/NBoNZs2ahVdeecWqxfn7+6NDhw4W69q3b4+vv/4aQNlVLwDIyMiAv7+/1CYjIwNdu3aV2mRmZloco7S0FFlZWdL+er0eGRkZFm3Kvy9v82cajQYajaaaPSMiIqL6RvGVJpVKhfnz5yMrKwtnzpzB0aNHcf36dbz99ttWL65Pnz5ITEy0WPfbb7+hefPmAICgoCDo9Xrs379f2m40GhEbG4vQ0FAAQGhoKLKzsxEXFye1OXDgAMxmM0JCQqQ2UVFRKCkpkdpERkaibdu2Fp/UIyIiontYLQxMr7Zjx44JJycn8e6774qkpCSxefNm4ebmJv773/9KbSIiIoSXl5f4/vvvxalTp8Tf//53ERQUJAoKCqQ2Q4cOFd26dROxsbEiOjpatGnTRowePVranp2dLfz8/MTYsWPFmTNnxNatW4Wbm5v45JNPZNfKT88RERHVP0rO3yohhFASsgoLC/HRRx/h4MGDyMzMhNlsttgeHx9vxUgH7Ny5E/PmzUNSUhKCgoIwc+ZMTJo0SdouhMCiRYuwbt06ZGdno2/fvli9erXF8/GysrIwbdo0/PDDD3BwcMATTzyBlStXwsPDQ2pz6tQpTJ06FcePH0fjxo3xyiuvYM6cObLrNBqN0Ol0MBgM0Gq11uk8ERER2ZSS87fi0DRmzBjs27cPTz75JPz8/Co8TmXRokXKK24AGJqIiIjqHyXnb8UDwXfu3Indu3ejT58+1S6QiIiIqL5RPBC8adOm8PT0tEUtRERERHZLcWhasWIF5syZg8uXL9uiHiIiIiK7pPj2XM+ePVFYWIiWLVvCzc0Nzs7OFtuzsrKsVhwRERGRvVAcmkaPHo2rV69iyZIllQ4EJyIiImqIFIemI0eOICYmBl26dLFFPURERER2SfGYpnbt2qGgoMAWtRARERHZLcWhKSIiAq+//joOHTqEmzdvwmg0WixEREREDZHiyS0dHMpy1p/HMgkhoFKpYDKZrFddPcLJLYmIiOofm05uefDgwWoXRkRERFRfKQ5NAwYMsEUdRERERHZN8ZgmIiIionsRQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJIPi0JSRkYGxY8ciICAATk5OcHR0tFiIiIiIGiLFUw48//zzSE1NxYIFC+Dv788H9hIREdE9QXFoio6OxuHDh9G1a1cblENERERknxTfngsMDITCJ68QERER1XuKQ9OHH36IuXPn4tKlSzYoh4iIiMg+Kb49N2rUKOTn56NVq1Zwc3ODs7OzxfasrCyrFUdERERkLxSHpg8//NAGZRARERHZN8Whady4cbaog4iIiMiuyQpNRqMRWq1W+vpuytsRERERNSSyQlOjRo2QlpYGX19feHl5VTo3kxACKpUKJpPJ6kUSERER1TVZoenAgQPw9vYGABw8eNCmBRERERHZI5XgpEtWYTQaodPpYDAYeIuSiIionlBy/uYDe4mIiIhkYGgiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRQPCM4AHz11VfYtm0bUlNTUVxcbLEtPj7eKoURERER2RPFV5pWrlyJ8ePHw8/PDydPnkSvXr3g4+OD5ORkPPzww7aokYiIiKjOKQ5Nq1evxrp16/DRRx9BrVZj9uzZiIyMxKuvvgqDwWCLGomIiIjqnOLQlJqait69ewMAXF1dkZOTAwAYO3YstmzZYt3qiIiIiOyE4tCk1+uRlZUFAGjWrBmOHj0KAEhJSQEnFyciIqKGSnFoGjhwIHbs2AEAGD9+PGbMmIHBgwdj1KhReOyxx6xeIBEREZE9UPzsObPZDLPZDCensg/ebd26FUeOHEGbNm3w4osvQq1W26RQe8dnzxEREdU/Ss7ffGCvlTA0ERER1T82f2Dv4cOH8eyzzyI0NBRXr14FAHz++eeIjo6uzuFki4iIgEqlwvTp06V1hYWFmDp1Knx8fODh4YEnnngCGRkZFvulpqZi2LBhcHNzg6+vL9544w2UlpZatDl06BC6d+8OjUaD1q1bY+PGjTbtCxEREdUvikPT119/jfDwcLi6uuLkyZMoKioCABgMBixZssTqBZY7fvw4PvnkE9x///0W62fMmIEffvgB27dvx88//4xr167h8ccfl7abTCYMGzYMxcXFOHLkCDZt2oSNGzdi4cKFUpuUlBQMGzYMDz30EBISEjB9+nRMnDgRP/74o836Q0RERPWMUKhr165i06ZNQgghPDw8xO+//y6EECI+Pl74+fkpPZwsOTk5ok2bNiIyMlIMGDBAvPbaa0IIIbKzs4Wzs7PYvn271Pb8+fMCgIiJiRFCCLF7927h4OAg0tPTpTZr1qwRWq1WFBUVCSGEmD17tujYsaPFa44aNUqEh4fLrtFgMAgAwmAwVLebREREVMuUnL8VX2lKTExE//79K6zX6XTIzs6ucYirzNSpUzFs2DCEhYVZrI+Li0NJSYnF+nbt2qFZs2aIiYkBAMTExKBz587w8/OT2oSHh8NoNOLs2bNSmz8fOzw8XDpGZYqKimA0Gi0WIiIiariqNU/TxYsXK6yPjo5Gy5YtrVLUnbZu3Yr4+HgsXbq0wrb09HSo1Wp4eXlZrPfz80N6errU5s7AVL69fNvd2hiNRhQUFFRa19KlS6HT6aQlMDCwWv0jIiKi+kFxaJo0aRJee+01xMbGQqVS4dq1a9i8eTNmzZqFKVOmWLW4K1eu4LXXXsPmzZvh4uJi1WPX1Lx582AwGKTlypUrdV0SERER2ZCT0h3mzp0Ls9mMQYMGIT8/H/3794dGo8GsWbPwyiuvWLW4uLg4ZGZmonv37tI6k8mEqKgofPzxx/jxxx9RXFyM7Oxsi6tNGRkZ0Ov1AMqujB07dsziuOWfrruzzZ8/cZeRkQGtVgtXV9dKa9NoNNBoNDXuIxEREdUPiq40mUwmHD58GFOnTkVWVhbOnDmDo0eP4vr163j77betXtygQYNw+vRpJCQkSEvPnj0xZswY6WtnZ2fs379f2icxMRGpqakIDQ0FAISGhuL06dPIzMyU2kRGRkKr1aJDhw5SmzuPUd6m/BhEREREiq40OTo6YsiQITh//jy8vLyk0GErnp6e6NSpk8U6d3d3+Pj4SOsnTJiAmTNnwtvbG1qtFq+88gpCQ0Px4IMPAgCGDBmCDh06YOzYsVi2bBnS09Px5ptvYurUqdKVopdeegkff/wxZs+ejRdeeAEHDhzAtm3bsGvXLpv2j4iIiOoPxWOaOnXqhOTkZFvUUi0ffPABHn30UTzxxBPo378/9Ho9vvnmG2m7o6Mjdu7cCUdHR4SGhuLZZ5/Fc889h3/+859Sm6CgIOzatQuRkZHo0qULVqxYgU8//RTh4eF10SUiIiKyQ4ofo7J3717MmzcPb7/9Nnr06AF3d3eL7ffqI0T4GBUiIqL6x6bPnnNw+N/FKZVKJX0thIBKpYLJZFJYbsPA0ERERFT/KDl/K/703MGDB6tdGBEREVF9pTg0DRgwoMptZ86cqVExRERERPZK8UDwP8vJycG6devQq1cvdOnSxRo1EREREdmdaoemqKgojBs3Dv7+/nj//fcxcOBAHD161Jq1EREREdkNRbfn0tPTsXHjRnz22WcwGo0YOXIkioqK8N1339l8ziYiIiKiuiT7StPw4cPRtm1bnDp1Ch9++CGuXbuGjz76yJa1EREREdkN2Vea9uzZg1dffRVTpkxBmzZtbFkTERERkd2RfaUpOjoaOTk56NGjB0JCQvDxxx/jxo0btqyNiIiIyG7IDk0PPvgg1q9fj7S0NLz44ovYunUrAgICYDabERkZiZycHFvWSURERFSnFM8IfqfExER89tln+Pzzz5GdnY3Bgwdjx44d1qyv3uCM4ERERPWPkvN3jeZpatu2LZYtW4Y//vgDW7ZsqcmhiIiIiOxaja400f/wShMREVH9U2tXmoiIiIjuFQxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERycDQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMdh2ali5digceeACenp7w9fXFiBEjkJiYaNGmsLAQU6dOhY+PDzw8PPDEE08gIyPDok1qaiqGDRsGNzc3+Pr64o033kBpaalFm0OHDqF79+7QaDRo3bo1Nm7caOvuERERUT1i16Hp559/xtSpU3H06FFERkaipKQEQ4YMQV5entRmxowZ+OGHH7B9+3b8/PPPuHbtGh5//HFpu8lkwrBhw1BcXIwjR45g06ZN2LhxIxYuXCi1SUlJwbBhw/DQQw8hISEB06dPx8SJE/Hjjz/Wan+JiIjIfqmEEKKui5Dr+vXr8PX1xc8//4z+/fvDYDCgSZMm+OKLL/Dkk08CAC5cuID27dsjJiYGDz74IPbs2YNHH30U165dg5+fHwBg7dq1mDNnDq5fvw61Wo05c+Zg165dOHPmjPRaTz/9NLKzs7F3795KaykqKkJRUZH0vdFoRGBgIAwGA7RarQ1/CkRERGQtRqMROp1O1vnbrq80/ZnBYAAAeHt7AwDi4uJQUlKCsLAwqU27du3QrFkzxMTEAABiYmLQuXNnKTABQHh4OIxGI86ePSu1ufMY5W3Kj1GZpUuXQqfTSUtgYKB1OklERER2qd6EJrPZjOnTp6NPnz7o1KkTACA9PR1qtRpeXl4Wbf38/JCeni61uTMwlW8v33a3NkajEQUFBZXWM2/ePBgMBmm5cuVKjftIRERE9suprguQa+rUqThz5gyio6PruhQAgEajgUajqesyiIiIqJbUiytN06ZNw86dO3Hw4EHcd9990nq9Xo/i4mJkZ2dbtM/IyIBer5fa/PnTdOXf/1UbrVYLV1dXa3eHiIiI6iG7Dk1CCEybNg3ffvstDhw4gKCgIIvtPXr0gLOzM/bv3y+tS0xMRGpqKkJDQwEAoaGhOH36NDIzM6U2kZGR0Gq16NChg9TmzmOUtyk/BhEREZFdf3ru5ZdfxhdffIHvv/8ebdu2ldbrdDrpCtCUKVOwe/dubNy4EVqtFq+88goA4MiRIwDKphzo2rUrAgICsGzZMqSnp2Ps2LGYOHEilixZAqBsyoFOnTph6tSpeOGFF3DgwAG8+uqr2LVrF8LDw2XVqmT0PREREdkHJedvuw5NKpWq0vUbNmzA888/D6BscsvXX38dW7ZsQVFREcLDw7F69Wrp1hsAXL58GVOmTMGhQ4fg7u6OcePGISIiAk5O/xvSdejQIcyYMQPnzp3DfffdhwULFkivIQdDExERUf3TYEJTfcLQREREVP802HmaiIiIiOoKQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0EREREcnA0EREREQkA0MTERERkQwMTUREREQyMDQRERERyeBU1wUQEVH9JYRAQYkJuYWlyCkqRW5hKXKLSpFTWIougTr461zrukQiq2FoIiIiFJWakJ1fgqy8YtzKL5a+zs4vxq38Ety6vd5YeGcwKkFuUSnMovJjfjS6G4Z3YWiihoOhiagWmMwCx1KykJlTCF9PF/QK8oajg6quy6IGzmwWuJlXjMycQlzPKUJmTlHZn8ZCXM8tQqaxbN3N3CLkFZtq9FoOKsBD4wRPF2d4aJzg4VK2EDUk/BdNZGN7z6ThrR/OIc1QKK3z17lg0fAOGNrJvw4ro/rMZBa4nlOEq9kFSDMU4Fp2Aa5lF+JadgHSDIXIMBbiZl4xTFVdBqqEgwpo5KaGl5szvN3V8HJTw9tNDS93ZzS6/bXW1RmeLk5SMPK8/aersyNUKv4iQA0bQxORDe09k4Yp/43Hn09b6YZCTPlvPNY8253BiSpVWGLC1ewCpGbl44+sfFwz3A5E2YW4ml2ADGMhSmUEIpUK8HFXo4mnC3w9NWjiqYHv7aWJpwt8tRo09tDA200NTxcnOPAKKFGVGJqIbMRkFnjrh3MVAhMACAAqAG/9cA6DO+h5q+4eJITAjdxipGbl40pWPlLvWK5k5SPdWAjxF5nI0UEFvdYFAV4uCPByhb/OFU29XOCvc4WftiwQ+bir4eTID0oTWQNDE5GNHEvJsrgl92cCQJqhEMdSshDayqf2CqNaYzYLpBkLkXI9Dyk3cpFyI98iJBWU3H0ckbvaEc183HFfI1c09XJFwO1AFOBV9n0TTw0DN1EtYmgispHMnKoDU3Xakf26lVeM5Bt5SLlRHo7ykHy97PuiUnOV+6lUQIDOFYHermjm7Va2+LhLXzdyc+Y4ISI7wtBEZCO+ni5WbUd1q7DEdDsU5d0RinKRfCMP2fklVe7n7KhCM283BDX2QFBjy1DU1MsVaifeOiOqLxiaiGykV5A3/HUuSDcUVjquSQVAryubfoDsR2GJCRczc3ExMxe/ZeTgt4xcXMzMQWpWfpXzEQFln4hs2cQdQY3dEdTYAy0bl319XyNXjikiaiAYmqjONdQ5jBwdVFg0vAOm/DceKsAiOJX3btHwDg2ir/VReThKysxBUkYufsso+zo1K7/KAdhaFye0bFIWiMoCkgeCGrujRWM3uKn5dkrU0PF/+T3KXoJKQ5/DaGgnf6x5tnuFPuobUB/tXUGxCb9fLwtEv2XkIklGOPJyc0awryfa+Hmgja8Hgv080drPA008NBxjRHQPUwnxVx9qJTmMRiN0Oh0MBgO0Wm1dl3NX9hJUqprDqPyU1JDmMLKXkNqQlYej3zJykJSZi6Tbf8oNR8F+nmjj64E2fp5o7KFmOCK6Ryg5fzM0WUl9CU32ElRMZoG+7x2o8iP55eN9oucMZLggC5WFo98ycnHlVtXhqJGbM9rcDkXBfuVXkBiOiEjZ+Zu35+4h9jTZIucwor9SUPy/MUflg7HlhqPg26Go/AqSjzvDERHVHEPTPURpULHlLSXOYUTlysORxZWjzBz8caugynDk7a6+fSvt9nij21eQGntoard4IrqnMDTdQ5QEFVuPe+IcRvee/OJS/J6ZV/Yx/swcXMzIlR2O7ryl1sbPg+GIiOoEQ9M9RG4AuXQjDx/+lGTTh8xyDqOGK7eoFL/fvnJUdnut7Bbb3cKRj7tauloU7OeB1r5lf/owHBGRHWFouofIDSpbjqXafNwT5zCq/4yFJUi6Pdao7GP8ZRNCXs0uqHIfH3e1dMUo2M9DGpzNcERE9QFDUwP353FJC4Z1wNQvqg4qTz/QDB/89FuVx7PmAG3OYVQ/ZOcX3x5rVHbF6OLtr9ONVd/ubeKpKRtz5OuB1n6eaN3Eg1eOiKjeY2hqwCobl+TtrsbEfkHYeSrNYn0jd2c81rUpSkx3f+p6OWsN0B7ayR+DO+g5h1EdE0Igw1iE5Ou5+P167h231XJxPaeoyv30Whe08fNAa9//jTdq4+sBLzd1LVZPRFQ7GJoaqN2nruHlL05WWJ+VV4z1h1MwqV8LDGynR+S5dHyXcA1ZecX47JdLso8vd3yUnE/gOTqoOK1ALSkoNiH5Ri6Sr5c9cDb5RllISrmeh7ziqgNzUy/X28HI43ZIKgtIWhfnWqyeiKhuMTQ1QLtPpWHaloqB6U7rD18CoMKGXy5VOn7pbvwrGaBdHo7SjYXIyi2Ct7saqVn52HIsFenGIot9eevNtoQQSDMU/i8UZeYi+UZZSLrbeCNHBxWaebuhZWN3tPb1kAZmt/L1gIeGbxVERHwnbGD2nknDy1/Ey2r76eEUxYEJADr4e+JYSpZ01aiy24BVseYn8O5lJrPAtewCXL6Zj8tZeWV/3iz/Mx8FJVVfNfJyc0bLxu5o1cSj7OGzTdzRqok7mnm7Q+3kUIu9ICKqXxia6pG/utVVXGrGP749I/t41X1+zv4L17H/wnW4ODlgYDtf7DmTLvtY5e3mf3sGA9v5yT5J34vPbisqNeGPWwUWYaj86yu38lFiqvqn7uSgQjMfN7Rs7IFWTdzRsok7WjbxQKsmHvB253gjIqLq4LPnrMRWz54rLjXj85hLOJx0HdEXb6DU/L9tjT3U+MfQ9riZX4Sdp67h3DUjSsxVH8veODsAAV6uuK+RGwK9XeHgoIIKgM5FDYfb45webOmDyHPpWLzjrMVtPr1Wg8V/64gBwb5Ysvsckm/kwdXZEa2beMDJUYWQFj5wcFThRm6R4pBV/jO/nJWP5t5uGBvaosZXYCoLfUWlJly9VYA/sgtw9VYBrmYX4I9bBbh6Kx9XswuQmVNU5bxGAKB2dEBjTzW83NRo5u2GXi28EdTEHc293RDo7QZnR141IiL6K3xgbx2wRWhauvsc1h9Ogfke/htyVzvedYCyXG5qRzzSSY8lj99faQAqDzXrD/+Og4nXLcKKgwqY1C8I8x7pUOmxKwtZAgKZxiJkGAux92w6vjx+BTmFpRbHlPP36q52RHMfdzT3cbvjTzdcupGHf/+UhIycO4OkCxb/jePFiKjhseXdBoamOmDt0LR09zl8EpVihcroz17sbxmA5I7JGn6/HhFPdMGt/GLczC3GzbwibPwlBVFJN6tdi6uzI5r7uOG+Rm64r5Ermnq5lv15+2vvSh40u/dMGl76b9Xj1tZyvBgRNSC2fqwXQ1MdsGZoKi41o+2CPXe9NUM1M7lfC8wY3A7fJfyBed/IHwemhOPtsGO6y1+kv84F0XMGyv6NyWQW6PFOJLLzS6ps08jNGSfeHNzgx3wRUcO390wapvw3vsK42fJ3N2t8qEjJ+ZuDHv5k1apVaNGiBVxcXBASEoJjx47Veg2bjlxiYLKxdYcvof3CvdUOTF6ufz0/kUmIuwYm4H+zq8t1NPnmXQMTANzKL8HR5Opf/SIisgcms8BbP5yr8rFeQNljvUy1OIaFoekOX375JWbOnIlFixYhPj4eXbp0QXh4ODIzM2u1jmMpPOHZO7MVU62S2dVjfpf3b0NuOyIie3UsJeuuwybufKxXbWFousO//vUvTJo0CePHj0eHDh2wdu1auLm54T//+U+t1pFvhYHP9Nc6+HtWe1/jHYO6a0ru7OpllE7uQERUP8n9hdJaj/WSg6HptuLiYsTFxSEsLExa5+DggLCwMMTExFRoX1RUBKPRaLFYS+f7dFY7FlWtNuYr8nRxQlUji1SofHb1uwlt2diq7YiI7JXcXyiV/eJZMwxNt924cQMmkwl+fn4W6/38/JCenl6h/dKlS6HT6aQlMDDQarX0a9PEaseiqk3q1xL+OpcqQ81fUf3Fjg4qYOmIzmVt/7zv7T8XDe+gaMD2g6184OV29/FUXm7OeJDP8iOieq5XkPdd36Or84tnTTE0VdO8efNgMBik5cqVK1Y79oMt//rESDXjrnFE3zZNsGh42dQDSoJT+X/UiX2D7tpuUr8gPNo1AGue7Q69zvI3Ib3OpVqf+nB0UCHi8c53bRPxeGd+co6I6j1HB1WV79HV/cWzphiabmvcuDEcHR2RkZFhsT4jIwN6vb5Ce41GA61Wa7FYi5wT44v9g+Cvq71LknVFr9Xgxf5B0Gs1Vj3uiqe6wNFBhaGd/CsNNS7Olf/XuPM/6vxhHfBi/yD8+f+rg8pyLqihnfwRPWcgtkx6EP9+uiu2THoQ0XMGVvtjskM7+WPts90r/Ez0Wg3naCKiBqWq9+jq/uJZU5yn6Q4hISHo1asXPvroIwCA2WxGs2bNMG3aNMydO/eu+9piRvC9Z9KweMc5pBsrn9CrfIbUdGMhrhsLsTkmGZezi63y2gDg4qiCo4MKPh5qNPVygbuLGmqVGbvP3ajQVu2ggofGEYHeLnB2ckJYez90DNAhK78Yjd01KDWb8d3JqzAWlUAlAL3OFc193NHOzxM38oqQlVcMbw8NfD01gABu5Fk+/uTO2WAbe2huz96dXGHQvLMD0K2ZN14Z2Bo5haX4586qf353qmy22chz6bImVLPFY1fkuBefx0dE9ybOCG6HvvzyS4wbNw6ffPIJevXqhQ8//BDbtm3DhQsXKox1+jNbPXtO6T+U4lIzNh1JwbGUWygoKUXnAB16t2oMB0cVMo2FUjjRa/93LKUnfXs5WZvMAkd/v4mY5BsA/vesujtrqWmt9tJXIiKyDYamGvj444+xfPlypKeno2vXrli5ciVCQkL+cj9bhSYiIiKyHYamOsDQREREVP/wMSpEREREVsbQRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjjVdQENRfkcoUajsY4rISIiIrnKz9ty5vpmaLKSnJwcAEBgYGAdV0JERERK5eTkQKfT3bUNH6NiJWazGdeuXYOnpydUKus+0NVoNCIwMBBXrly5Jx7Rwv42fPdan9nfhu9e63ND6q8QAjk5OQgICICDw91HLfFKk5U4ODjgvvvus+lraLXaev+PUwn2t+G71/rM/jZ891qfG0p//+oKUzkOBCciIiKSgaGJiIiISAaGpnpAo9Fg0aJF0Gg0dV1KrWB/G757rc/sb8N3r/X5XutvOQ4EJyIiIpKBV5qIiIiIZGBoIiIiIpKBoYmIiIhIBoYmIiIiIhkYmuzEqlWr0KJFC7i4uCAkJATHjh27a/vt27ejXbt2cHFxQefOnbF79+5aqtQ6lPR3/fr16NevHxo1aoRGjRohLCzsL38+9kbp32+5rVu3QqVSYcSIEbYt0AaU9jk7OxtTp06Fv78/NBoNgoOD69W/a6X9/fDDD9G2bVu4uroiMDAQM2bMQGFhYS1VWzNRUVEYPnw4AgICoFKp8N133/3lPocOHUL37t2h0WjQunVrbNy40eZ1WovS/n7zzTcYPHgwmjRpAq1Wi9DQUPz444+1U6yVVOfvuNwvv/wCJycndO3a1Wb11RWGJjvw5ZdfYubMmVi0aBHi4+PRpUsXhIeHIzMzs9L2R44cwejRozFhwgScPHkSI0aMwIgRI3DmzJlarrx6lPb30KFDGD16NA4ePIiYmBgEBgZiyJAhuHr1ai1XXj1K+1vu0qVLmDVrFvr161dLlVqP0j4XFxdj8ODBuHTpEr766iskJiZi/fr1aNq0aS1XXj1K+/vFF19g7ty5WLRoEc6fP4/PPvsMX375Jf7xj3/UcuXVk5eXhy5dumDVqlWy2qekpGDYsGF46KGHkJCQgOnTp2PixIn1Jkgo7W9UVBQGDx6M3bt3Iy4uDg899BCGDx+OkydP2rhS61Ha53LZ2dl47rnnMGjQIBtVVscE1blevXqJqVOnSt+bTCYREBAgli5dWmn7kSNHimHDhlmsCwkJES+++KJN67QWpf39s9LSUuHp6Sk2bdpkqxKtqjr9LS0tFb179xaffvqpGDdunPj73/9eC5Vaj9I+r1mzRrRs2VIUFxfXVolWpbS/U6dOFQMHDrRYN3PmTNGnTx+b1mkLAMS333571zazZ88WHTt2tFg3atQoER4ebsPKbENOfyvToUMH8dZbb1m/oFqgpM+jRo0Sb775pli0aJHo0qWLTeuqC7zSVMeKi4sRFxeHsLAwaZ2DgwPCwsIQExNT6T4xMTEW7QEgPDy8yvb2pDr9/bP8/HyUlJTA29vbVmVaTXX7+89//hO+vr6YMGFCbZRpVdXp844dOxAaGoqpU6fCz88PnTp1wpIlS2AymWqr7GqrTn979+6NuLg46RZecnIydu/ejUceeaRWaq5t9fk9yxrMZjNycnLqxXtWTWzYsAHJyclYtGhRXZdiM3xgbx27ceMGTCYT/Pz8LNb7+fnhwoULle6Tnp5eafv09HSb1Wkt1envn82ZMwcBAQEV3oTtUXX6Gx0djc8++wwJCQm1UKH1VafPycnJOHDgAMaMGYPdu3fj4sWLePnll1FSUmL3b8DV6e8zzzyDGzduoG/fvhBCoLS0FC+99FK9uT2nVFXvWUajEQUFBXB1da2jymrH+++/j9zcXIwcObKuS7GZpKQkzJ07F4cPH4aTU8ONFrzSRPVKREQEtm7dim+//RYuLi51XY7V5eTkYOzYsVi/fj0aN25c1+XUGrPZDF9fX6xbtw49evTAqFGjMH/+fKxdu7auS7OJQ4cOYcmSJVi9ejXi4+PxzTffYNeuXXj77bfrujSysi+++AJvvfUWtm3bBl9f37ouxyZMJhOeeeYZvPXWWwgODq7rcmyq4cbBeqJx48ZwdHRERkaGxfqMjAzo9fpK99Hr9Yra25Pq9Lfc+++/j4iICPz000+4//77bVmm1Sjt7++//45Lly5h+PDh0jqz2QwAcHJyQmJiIlq1amXbomuoOn/H/v7+cHZ2hqOjo7Suffv2SE9PR3FxMdRqtU1rronq9HfBggUYO3YsJk6cCADo3Lkz8vLyMHnyZMyfPx8ODg3r99mq3rO0Wm2Dvsq0detWTJw4Edu3b68XV8arKycnBydOnMDJkycxbdo0AGXvW0IIODk5Yd++fRg4cGAdV2kdDet/Zj2kVqvRo0cP7N+/X1pnNpuxf/9+hIaGVrpPaGioRXsAiIyMrLK9PalOfwFg2bJlePvtt7F371707NmzNkq1CqX9bdeuHU6fPo2EhARp+dvf/iZ96igwMLA2y6+W6vwd9+nTBxcvXpQCIgD89ttv8Pf3t+vABFSvv/n5+RWCUXlgFA3wcaD1+T2rurZs2YLx48djy5YtGDZsWF2XY1NarbbC+9ZLL72Etm3bIiEhASEhIXVdovXU8UB0EkJs3bpVaDQasXHjRnHu3DkxefJk4eXlJdLT04UQQowdO1bMnTtXav/LL78IJycn8f7774vz58+LRYsWCWdnZ3H69Om66oIiSvsbEREh1Gq1+Oqrr0RaWpq05OTk1FUXFFHa3z+rj5+eU9rn1NRU4enpKaZNmyYSExPFzp07ha+vr3jnnXfqqguKKO3vokWLhKenp9iyZYtITk4W+/btE61atRIjR46sqy4okpOTI06ePClOnjwpAIh//etf4uTJk+Ly5ctCCCHmzp0rxo4dK7VPTk4Wbm5u4o033hDnz58Xq1atEo6OjmLv3r111QVFlPZ38+bNwsnJSaxatcriPSs7O7uuuqCY0j7/WUP99BxDk5346KOPRLNmzYRarRa9evUSR48elbYNGDBAjBs3zqL9tm3bRHBwsFCr1aJjx45i165dtVxxzSjpb/PmzQWACsuiRYtqv/BqUvr3e6f6GJqEUN7nI0eOiJCQEKHRaETLli3Fu+++K0pLS2u56upT0t+SkhKxePFi0apVK+Hi4iICAwPFyy+/LG7dulX7hVfDwYMHK/0/Wd7HcePGiQEDBlTYp2vXrkKtVouWLVuKDRs21Hrd1aW0vwMGDLhr+/qgOn/Hd2qooUklRAO8FkxERERkZRzTRERERCQDQxMRERGRDAxNRERERDIwNBERERHJwNBEREREJANDExEREZEMDE1EREREMjA0ERERkV2LiorC8OHDERAQAJVKhe+++07R/osXL4ZKpaqwuLu7KzoOQxMR3dMWL16Mrl272uz4ly5dgkqlQkJCgs1ew9Y2btwILy+vui6D7mF5eXno0qULVq1aVa39Z82ahbS0NIulQ4cOeOqppxQdh6GJiAAAzz//PEaMGFFh/aFDh6BSqZCdnV3rNdWGWbNmVXiYrDUFBgYiLS0NnTp1qtFx7vztWKvV4oEHHsD3339vpSqJ7NvDDz+Md955B4899lil24uKijBr1iw0bdoU7u7uCAkJwaFDh6TtHh4e0Ov10pKRkYFz585hwoQJiupgaCKie5IQAqWlpfDw8ICPj4/NXsfR0RF6vR5OTk41PtaGDRuQlpaGEydOoE+fPnjyySdx+vRpK1RJVL9NmzYNMTEx2Lp1K06dOoWnnnoKQ4cORVJSUqXtP/30UwQHB6Nfv36KXoehiYgU+/rrr9GxY0doNBq0aNECK1assNhe2ZgDLy8vbNy4EQBQXFyMadOmwd/fHy4uLmjevDmWLl0qtc3OzsbEiRPRpEkTaLVaDBw4EL/++muV9ZTfAtu6dSt69+4NFxcXdOrUCT///LPUpvyK2Z49e9CjRw9oNBpER0dXenvuP//5j9Q/f39/TJs2rca1ld+eK69j//796NmzJ9zc3NC7d28kJiZWeYw7f4Z6vR7BwcF4++23UVpaioMHD0rb9+7di759+8LLyws+Pj549NFH8fvvv1eo5ZtvvsFDDz0ENzc3dOnSBTExMRavs3HjRjRr1gxubm547LHHcPPmTYvtv/76Kx566CF4enpCq9WiR48eOHHixF/WT2QLqamp2LBhA7Zv345+/fqhVatWmDVrFvr27YsNGzZUaF9YWIjNmzcrvsoEMDQRkUJxcXEYOXIknn76aZw+fRqLFy/GggULpEAkx8qVK7Fjxw5s27YNiYmJ2Lx5M1q0aCFtf+qpp5CZmYk9e/YgLi4O3bt3x6BBg5CVlXXX477xxht4/fXXcfLkSYSGhmL48OEVTvhz585FREQEzp8/j/vvv7/CMdasWYOpU6di8uTJOH36NHbs2IHWrVvXuLY/mz9/PlasWIETJ07AyckJL7zwgux9S0tL8dlnnwEA1Gq1tD4vLw8zZ87EiRMnsH//fjg4OOCxxx6D2Wyu8NqzZs1CQkICgoODMXr0aJSWlgIAYmNjMWHCBEybNg0JCQl46KGH8M4771jsP2bMGNx33304fvw44uLiMHfuXDg7OyvqP5G1nD59GiaTCcHBwfDw8JCWn3/+2eKXhnLffvstcnJyMG7cOOUvJoiIhBDjxo0Tjo6Owt3d3WJxcXERAMStW7eEEEI888wzYvDgwRb7vvHGG6JDhw7S9wDEt99+a9FGp9OJDRs2CCGEeOWVV8TAgQOF2WyuUMfhw4eFVqsVhYWFFutbtWolPvnkk0prT0lJEQBERESEtK6kpETcd9994r333hNCCHHw4EEBQHz33XcW+y5atEh06dJF+j4gIEDMnz+/0tepSW0nT560qOOnn36S2uzatUsAEAUFBZUeQ4iyn6mLi4twd3cXDg4OAoBo0aKFuHnzZpX7XL9+XQAQp0+ftqjl008/ldqcPXtWABDnz58XQggxevRo8cgjj1gcZ9SoUUKn00nfe3p6io0bN1b5ukS29Of3l61btwpHR0dx4cIFkZSUZLGkpaVV2H/gwIFixIgR1XptXmkiIslDDz2EhIQEi+XTTz+1aHP+/Hn06dPHYl2fPn2QlJQEk8kk63Wef/55JCQkoG3btnj11Vexb98+aduvv/6K3Nxc+Pj4WPzWmJKSUulvjXcKDQ2VvnZyckLPnj1x/vx5izY9e/ascv/MzExcu3YNgwYNqnR7TWr7szuvcvn7+0uvfzcffPABEhISsGfPHnTo0AGffvopvL29pe1JSUkYPXo0WrZsCa1WK129S01Nlf3a58+fR0hIiEX7O3+uADBz5kxMnDgRYWFhiIiIUNx3Imvq1q0bTCYTMjMz0bp1a4tFr9dbtE1JScHBgwerdWsOAGo+MpGIGgx3d3eLW1EA8Mcffyg+jkqlQtkvhP9TUlIifd29e3ekpKRgz549+OmnnzBy5EiEhYXhq6++Qm5uLvz9/S0++VLOGh97v9u8LK6urnfd15q13Xk7S6VSAUCF22h/ptfrpZPBhg0b8Mgjj+DcuXPw9fUFAAwfPhzNmzfH+vXrERAQALPZjE6dOqG4uLjGr32nxYsX45lnnsGuXbuwZ88eLFq0CFu3bq3yk01ENZWbm4uLFy9K36ekpCAhIQHe3t4IDg7GmDFj8Nxzz2HFihXo1q0brl+/jv379+P+++/HsGHDpP3+85//wN/fHw8//HC16uCVJiJSpH379vjll18s1v3yyy8IDg6Go6MjAKBJkyZIS0uTticlJSE/P99iH61Wi1GjRmH9+vX48ssv8fXXXyMrKwvdu3dHeno6nJycKvzW2Lhx47vWdvToUenr0tJSxMXFoX379rL75unpiRYtWlQ5BUFNarO2Xr16oUePHnj33XcBADdv3kRiYiLefPNNDBo0CO3bt8etW7cUH7d9+/aIjY21WHfnz7VccHAwZsyYgX379uHxxx+vdMAtkbWcOHEC3bp1Q7du3QCUXe3s1q0bFi5cCKDsk6XPPfccXn/9dbRt2xYjRozA8ePH0axZM+kYZrMZGzduxPPPPy+9VynFK01EpMjrr7+OBx54AG+//TZGjRqFmJgYfPzxx1i9erXUZuDAgfj4448RGhoKk8mEOXPmWFzd+Ne//gV/f39069YNDg4O2L59O/R6Pby8vBAWFobQ0FCMGDECy5YtQ3BwMK5du4Zdu3bhscceu+vttVWrVqFNmzZo3749PvjgA9y6dUvRAGug7CrKSy+9BF9fXzz88MPIycnBL7/8gldeeaVGtdnC9OnT8dhjj2H27Nnw9/eHj48P1q1bB39/f6SmpmLu3LmKj/nqq6+iT58+eP/99/H3v/8dP/74I/bu3SttLygowBtvvIEnn3wSQUFB+OOPP3D8+HE88cQT1uwakYX/+7//q3D1+k7Ozs5466238NZbb1XZxsHBAVeuXKlRHbzSRESKdO/eHdu2bcPWrVvRqVMnLFy4EP/85z/x/PPPS21WrFiBwMBA9OvXD8888wxmzZoFNzc3abunpyeWLVuGnj174oEHHsClS5ewe/duODg4QKVSYffu3ejfvz/Gjx+P4OBgPP3007h8+TL8/PzuWltERAQiIiLQpUsXREdHY8eOHYqvAI0bNw4ffvghVq9ejY4dO+LRRx+V5nqpSW22MHToUAQFBeHdd9+Fg4MDtm7diri4OHTq1AkzZszA8uXLFR/zwQcfxPr16/Hvf/8bXbp0wb59+/Dmm29K2x0dHXHz5k0899xzCA4OxsiRI/Hwww/f9WRF1FCoxN2iGxFRPXDp0iUEBQXh5MmTNn0kChHd23iliYiIiEgGhiYiIiIiGXh7joiIiEgGXmkiIiIikoGhiYiIiEgGhiYiIiIiGRiaiIiIiGRgaCIiIiKSgaGJiIiISAaGJiIiIiIZGJqIiIiIZPj/DAKMjvrLNG0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mymodel = numpy.poly1d(numpy.polyfit(X, Y, 3))\n",
    "myline = numpy.linspace(80000, 10000000, 10000000)\n",
    "\n",
    "plt.scatter(X, Y)\n",
    "plt.plot(myline, mymodel(myline))\n",
    "plt.xlabel(\"House price in Rands\")\n",
    "plt.ylabel(\"Area in meters squared\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following packages are used to data clean, organize and compute. CSV is used to open files commonly used in excel, oS is for use of the operating system, Matplot and Pandas are 2 data science modules that help with large data manipulation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "locations_price_paths = [\n",
    "    \"prop_westerncape_locations_price.csv\",\n",
    "    \"prop_eastern_cape_locations_price.csv\",\n",
    "    \"prop_freestate_locations_price.csv\",\n",
    "    \"prop_gauteng_locations_price.csv\",\n",
    "    \"prop_kwazulu_natal_locations_price.csv\",\n",
    "    \"prop_limpopo_locations_price.csv\",\n",
    "    \"prop_mpumalanga_locations_price.csv\",\n",
    "    \"prop_northern_cape_locations_price.csv\",\n",
    "    \"prop_northwest_locations_price.csv\"\n",
    "]\n",
    "\n",
    "target_directory = r'C:\\GitHub\\Land-value-GIS\\backups'\n",
    "\n",
    "pricelist = []\n",
    "\n",
    "for file in locations_price_paths:\n",
    "    full = os.path.join(target_directory, file)\n",
    "    with open(full, \"r\") as r:\n",
    "        reader = csv.reader(r)\n",
    "        next(reader)\n",
    "        for row in reader:\n",
    "            if len(row) > 1:\n",
    "                pricelist.append(row)\n",
    "    break\n",
    "\n",
    "for pair in pricelist:\n",
    "    pair.remove(pair[1])\n",
    "    \n",
    "\n",
    "for number in pricelist:\n",
    "    if number == 0:\n",
    "        pricelist.remove(number)\n",
    "    try:\n",
    "        int(number[0])\n",
    "    except:\n",
    "        pricelist.remove(number)\n",
    "\n",
    "pricelist = [int(price) for sublist in pricelist for price in sublist if price.isdigit()]\n",
    "\n",
    "def removeOs():\n",
    "    for number in pricelist:\n",
    "        if number == 0:\n",
    "            pricelist.remove(number)\n",
    "        elif number > 9999999:\n",
    "            pricelist.remove(number)\n",
    "        elif number < 400000:\n",
    "            pricelist.remove(number)\n",
    "\n",
    "for i in range(20):\n",
    "    removeOs()\n",
    "\n",
    "pricelist.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mymodel = numpy.poly1d(numpy.polyfit(X, Y, 3))\n",
    "myline = numpy.linspace(80000, 10000000, 10000000)\n",
    "\n",
    "plt.hist(pricelist, 15)\n",
    "plt.plot(myline, mymodel(myline))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400000 179.86481069050518 2223.8924804934923\n",
      "400000 179.86481069050518 2223.8924804934923\n",
      "400000 179.86481069050518 2223.8924804934923\n",
      "400000 179.86481069050518 2223.8924804934923\n",
      "400000 179.86481069050518 2223.8924804934923\n",
      "400000 179.86481069050518 2223.8924804934923\n",
      "403000 179.37532705960746 2246.6857990234116\n",
      "420000 176.62241464265088 2377.9541280180083\n",
      "429999 175.0197258222448 2456.8602080700302\n",
      "429999 175.0197258222448 2456.8602080700302\n",
      "430000 175.0195661479387 2456.8681631660206\n",
      "440000 173.4289227641888 2537.0624056649867\n",
      "449999 171.85062079748542 2618.5474216604316\n",
      "449999 171.85062079748542 2618.5474216604316\n",
      "450000 171.85046356001544 2618.5556365569314\n",
      "450000 171.85046356001544 2618.5556365569314\n",
      "450000 171.85046356001544 2618.5556365569314\n",
      "450000 171.85046356001544 2618.5556365569314\n",
      "450000 171.85046356001544 2618.5556365569314\n",
      "450000 171.85046356001544 2618.5556365569314\n",
      "450000 171.85046356001544 2618.5556365569314\n",
      "450000 171.85046356001544 2618.5556365569314\n",
      "450000 171.85046356001544 2618.5556365569314\n",
      "450000 171.85046356001544 2618.5556365569314\n",
      "450000 171.85046356001544 2618.5556365569314\n",
      "450000 171.85046356001544 2618.5556365569314\n",
      "450000 171.85046356001544 2618.5556365569314\n",
      "450000 171.85046356001544 2618.5556365569314\n",
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ave_price_area = []\n",
    "for price in pricelist:\n",
    "    print(price, mymodel(price), price/mymodel(price))\n",
    "    ave_price_area.append(price/mymodel(price))\n",
    "\n",
    "plt.hist(ave_price_area, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(377, 3)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houses.head()\n",
    "houses.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "Sunnyside            11\n",
       "Ferndale              9\n",
       "Montana               9\n",
       "Sandown               8\n",
       "Sandton Central       8\n",
       "                     ..\n",
       "Moregloed             1\n",
       "Krugersrus            1\n",
       "Northcliff            1\n",
       "Willow Park Manor     1\n",
       "Lynnwood Ridge        1\n",
       "Name: count, Length: 200, dtype: int64"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houses[\"location\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price        int64\n",
       "area         int64\n",
       "location    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houses.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = \"house_price_and_area_test.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "full = os.path.join(target_directory, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv(full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "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>price</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>240000</td>\n",
       "      <td>135</td>\n",
       "      <td>Melrose North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>899000</td>\n",
       "      <td>104</td>\n",
       "      <td>Klippoortje</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>720000</td>\n",
       "      <td>97</td>\n",
       "      <td>Floracliffe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>950000</td>\n",
       "      <td>82</td>\n",
       "      <td>Hatfield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>799000</td>\n",
       "      <td>76</td>\n",
       "      <td>Klippoortje</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    price  area       location\n",
       "0  240000   135  Melrose North\n",
       "1  899000   104    Klippoortje\n",
       "2  720000    97    Floracliffe\n",
       "3  950000    82       Hatfield\n",
       "4  799000    76    Klippoortje"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = test.dropna(how='all')\n",
    "# Drop rows with NaN values\n",
    "test = test.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "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>price</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>location_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>240000</td>\n",
       "      <td>135</td>\n",
       "      <td>Melrose North</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>899000</td>\n",
       "      <td>104</td>\n",
       "      <td>Klippoortje</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>720000</td>\n",
       "      <td>97</td>\n",
       "      <td>Floracliffe</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>950000</td>\n",
       "      <td>82</td>\n",
       "      <td>Hatfield</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>799000</td>\n",
       "      <td>76</td>\n",
       "      <td>Klippoortje</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    price  area       location  location_code\n",
       "0  240000   135  Melrose North             25\n",
       "1  899000   104    Klippoortje             21\n",
       "2  720000    97    Floracliffe             11\n",
       "3  950000    82       Hatfield             17\n",
       "4  799000    76    Klippoortje             21"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[\"location_code\"] = test[\"location\"].astype(\"category\").cat.codes\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "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>price</th>\n",
       "      <th>area</th>\n",
       "      <th>location</th>\n",
       "      <th>location_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1020000</td>\n",
       "      <td>61</td>\n",
       "      <td>Erand Gardens</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500000</td>\n",
       "      <td>92</td>\n",
       "      <td>Bergbron</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>949000</td>\n",
       "      <td>99</td>\n",
       "      <td>Montana</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500000</td>\n",
       "      <td>49</td>\n",
       "      <td>Oakdene</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>460000</td>\n",
       "      <td>40</td>\n",
       "      <td>Bardene</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>660000</td>\n",
       "      <td>60</td>\n",
       "      <td>Theresapark</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>575000</td>\n",
       "      <td>95</td>\n",
       "      <td>Bedford Gardens</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>665000</td>\n",
       "      <td>50</td>\n",
       "      <td>Pretoria Central</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>2395000</td>\n",
       "      <td>80</td>\n",
       "      <td>Magalieskruin</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>765000</td>\n",
       "      <td>91</td>\n",
       "      <td>Lynnwood Ridge</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>377 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       price  area          location  location_code\n",
       "0    1020000    61     Erand Gardens             50\n",
       "1    1500000    92          Bergbron             14\n",
       "2     949000    99           Montana            116\n",
       "3     500000    49           Oakdene            132\n",
       "4     460000    40           Bardene              7\n",
       "..       ...   ...               ...            ...\n",
       "372   660000    60       Theresapark            181\n",
       "373   575000    95   Bedford Gardens              9\n",
       "374   665000    50  Pretoria Central            144\n",
       "375  2395000    80     Magalieskruin            107\n",
       "376   765000    91    Lynnwood Ridge            105\n",
       "\n",
       "[377 rows x 4 columns]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houses[\"location_code\"] = houses[\"location\"].astype(\"category\").cat.codes\n",
    "houses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators=50, min_samples_split=10, random_state=1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = houses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor = [\"price\", \"area\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(min_samples_split=10, n_estimators=50, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(min_samples_split=10, n_estimators=50, random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(min_samples_split=10, n_estimators=50, random_state=1)"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(train[predictor], train[\"area\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = rf.predict(test[predictor])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "acc = accuracy_score(test['area'], preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5394736842105263"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined = pd.DataFrame(dict(actual=test['area'], prediction=preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "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>col_0</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0   prediction\n",
       "actual            \n",
       "34               2\n",
       "35               2\n",
       "36               1\n",
       "43               1\n",
       "44               1\n",
       "47               1\n",
       "48               2\n",
       "50               2\n",
       "54               2\n",
       "56               1\n",
       "57               1\n",
       "58               3\n",
       "61               2\n",
       "68               2\n",
       "70               1\n",
       "71               4\n",
       "72               1\n",
       "73               1\n",
       "75               1\n",
       "76               3\n",
       "81               2\n",
       "82               1\n",
       "83               1\n",
       "84               6\n",
       "87               1\n",
       "89               2\n",
       "92               1\n",
       "97               2\n",
       "99               2\n",
       "101              2\n",
       "104              3\n",
       "110              2\n",
       "111              1\n",
       "114              3\n",
       "116              2\n",
       "117              1\n",
       "120              3\n",
       "123              1\n",
       "129              1\n",
       "135              2\n",
       "140              1\n",
       "207              1\n",
       "342              1"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=combined['actual'], columns=[\"prediction\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>actual</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135</td>\n",
       "      <td>123</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
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       "      <td>97</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>114</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>207</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>120</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    actual  prediction\n",
       "0      135         123\n",
       "1      104         104\n",
       "2       97          97\n",
       "3       82          82\n",
       "4       76          76\n",
       "..     ...         ...\n",
       "71     114         116\n",
       "72     207         114\n",
       "73     116         116\n",
       "74      71          71\n",
       "75     120         111\n",
       "\n",
       "[76 rows x 2 columns]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined"
   ]
  }
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