Air Quality Index - KNN Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4aeaaf0a",
   "metadata": {},
   "source": [
    "# KNearestNeighbors Regression on the Collected Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee645a6e",
   "metadata": {},
   "source": [
    "In this Jupyter notebook we will be applying the KNearestNeighbors Regression on the data that we have extracted (Real_Combine.csv) and try to make predictions for the AQI (PM 2.5) based on different factors :\n",
    "\n",
    "- T\t:   Average temperature (°C)\n",
    "- TM:\tMaximum temperature (°C)\n",
    "- Tm:\tMinimum temperature (°C)\n",
    "- SLP:\tAtmospheric pressure at sea level (hPa)\n",
    "- H\t:   Average relative humidity (%)\n",
    "- VV:\tAverage visibility (Km)\n",
    "- V :   Average wind speed (Km/h)\n",
    "- VM:\tMaximum sustained wind speed (Km/h)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "783841a7",
   "metadata": {},
   "source": [
    "## Importing the important libraries required"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "636e9696",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn import metrics "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbed86a8",
   "metadata": {},
   "source": [
    "## Reading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "57a310c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Data\\Real-Data\\Real_Combine.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "93a3b33a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>TM</th>\n",
       "      <th>Tm</th>\n",
       "      <th>SLP</th>\n",
       "      <th>H</th>\n",
       "      <th>VV</th>\n",
       "      <th>V</th>\n",
       "      <th>VM</th>\n",
       "      <th>PM 2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.9</td>\n",
       "      <td>21.6</td>\n",
       "      <td>8.8</td>\n",
       "      <td>1017.4</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>7.6</td>\n",
       "      <td>172.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.7</td>\n",
       "      <td>21.6</td>\n",
       "      <td>10.2</td>\n",
       "      <td>1018.1</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>9.4</td>\n",
       "      <td>146.791667</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.0</td>\n",
       "      <td>16.8</td>\n",
       "      <td>13.6</td>\n",
       "      <td>1017.5</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>7.6</td>\n",
       "      <td>113.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.8</td>\n",
       "      <td>17.6</td>\n",
       "      <td>6.4</td>\n",
       "      <td>1016.6</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>11.1</td>\n",
       "      <td>227.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.1</td>\n",
       "      <td>14.5</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1019.2</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>7.6</td>\n",
       "      <td>231.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1076</th>\n",
       "      <td>13.9</td>\n",
       "      <td>23.2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1017.2</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>375.791667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>13.0</td>\n",
       "      <td>23.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.4</td>\n",
       "      <td>223.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>13.3</td>\n",
       "      <td>22.4</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1016.5</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>5.2</td>\n",
       "      <td>9.4</td>\n",
       "      <td>248.958333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>15.0</td>\n",
       "      <td>21.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1013.5</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>11.1</td>\n",
       "      <td>441.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>10.7</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1018.7</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>9.4</td>\n",
       "      <td>NaN</td>\n",
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       "<p>1081 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "         T    TM    Tm     SLP     H   VV    V    VM      PM 2.5\n",
       "0     14.9  21.6   8.8  1017.4  76.0  0.8  0.9   7.6  172.750000\n",
       "1     14.7  21.6  10.2  1018.1  97.0  0.6  3.1   9.4  146.791667\n",
       "2     15.0  16.8  13.6  1017.5  94.0  0.5  0.9   7.6  113.875000\n",
       "3      9.8  17.6   6.4  1016.6  90.0  0.5  3.9  11.1  227.000000\n",
       "4     10.1  14.5   7.2  1019.2  90.0  0.3  3.3   7.6  231.250000\n",
       "...    ...   ...   ...     ...   ...  ...  ...   ...         ...\n",
       "1076  13.9  23.2   5.5  1017.2  69.0  1.0  4.1  13.0  375.791667\n",
       "1077  13.0  23.1   4.6  1017.0  74.0  1.1  3.5   9.4  223.250000\n",
       "1078  13.3  22.4   4.4  1016.5  72.0  1.1  5.2   9.4  248.958333\n",
       "1079  15.0  21.9   6.0  1013.5  68.0  1.0  5.4  11.1  441.416667\n",
       "1080  10.7  17.0   3.3  1018.7  82.0  0.5  4.8   9.4         NaN\n",
       "\n",
       "[1081 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizing the data\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dbcd664",
   "metadata": {},
   "source": [
    "Here we can see that we are having 1081 rows and 9 columns (features). Where T, TM, Tm, SLP, H, VV, V, VM are the independent features and PM 2.5 is the dependent feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0bcaec50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T         0\n",
       "TM        0\n",
       "Tm        0\n",
       "SLP       0\n",
       "H         0\n",
       "VV        0\n",
       "V         0\n",
       "VM        0\n",
       "PM 2.5    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for null values in Data\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25b9f2eb",
   "metadata": {},
   "source": [
    "Here we can see that we are having 3 null values in the Target feature that needs to handled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0cc373a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\prank\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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WaZwE3O/uNe6+CljOodMzJgAr3H1N0q5ARERERESkHZKZYL2/AGModnHGQ/o0WaQxnmMnA/e19OJad0RERERERLpaSha5MLM84GLgOy310bojnXfv/LXtPuayU4YlIRIRERERkdSQzBGsxgUYG8UuznhInyaLNLZ17AXAa+6+OcExi4iIiIhIN1FRAeXlYAY5OcHP8vKgvbtKZoL1CjDazEaEI06TCRZljNXSIo1zgMlhlcERwGjg5ZjjPksr0wNFRERERCR1VVRA//5w+eWwJqy4UF8f/FyzBqZO7b5JVtISrPCequuAx4AlwIPuvsjMfmBmF4fdml2k0d0XAQ8Ci4FHgWvdvR7AzHoSVCZ8OFmxi4iIiIhINCoqggSqsrLlPnv3wk03dV1M7ZHUe7DcfS4wt0nb92Ket7hIo7vPAGY0015NUAhDRERERETSzE03BQlUW9a2v1xAl0jqQsMiIiIiIiLtEW/iNKyb1lZTgiUiIiIiIt1GPIlTYSHMOGSuW/egBEtERERERLqNGTOCBKqprDBzGT4cZs2CKVO6Nq54KcESEREREZEu1Vz59cafV10V3IOVnR30HT4cZs8Oqgi6w+rV3Te5ghRdaFhERERERFJTY5XAxkIWjeXXm/vZOBWwOydUTWkES0REREREuky8VQKhe5djb4kSLBERERER6TLtLa/eXcuxt0QJloiIiIiIdJn2llfvruXYW6IES0REREREukxLVQKb053LsbdECZaIiIiIiHSZKVOCMuvDhwfbjdUCm/7s7uXYW6IqgiIiIiIi0qWmTEm9xCleGsESEREREZEu07gGVlZW8LOiIuqIEksjWBKXBnfcITvLog5FRERERFJU0zWw1qwJtiF9RrSUYEmrKqtq+PMbG1i/Yx8OnHXEAM4Y1Z/cbA1+ioiIiEj8KirgqqsOLiTcqHGtq3RJsPQpWVpUWVXD755byaZd+zlxWF9G9u/JPxZv5o7nV1FX3xB1eCIiIiKSIhpHrpomV41Sba2r1mgES5pVV9/A/724mroG5+ozRzC4TwEAb6zbwYML1vOXNzfy6RPLMNOUQRERERFp3U03HZwW2JxUW+uqNRrBkmbNX7Wd7dW1XDpu6PvJFcAJQ/ty9pEDeXXNDt5YtzO6AEVEREQkZbQ2QpWKa121RgmWHGL/gXqeWrqFUQOLGD2o1yH7Jxw9kCF9C3hs0XvU1mmqoIiIiIi0rqURquzs1FzrqjVKsOQQLyzfxt7aes4be1iz+7PMuPDYwezeX8dzy7d2cXQiIiIikmpmzAhGqmIVFsJdd6VXcgVKsKSJBndeXbOD0QOLKCsuaLHf8JKejC3tzbPvbqW6pq4LIxQRERGRVDNlSjBSNXw4mAU/023kqpESLPmAVduq2bnvACcN69tm34lHD+JAvTN/1fYuiExEREREUtmUKbB6NTQ0BD/TMbkCJVjSxOtrd5Kfk8XRg3u32XdQ7x4cMaiIf66s5IDKtouIiIiIKMGSg2rrGnh74y6OKetDXk58/zTOHDWAqpo63lRFQRERERERJVhy0Lub91Bb18AJQ4vjPmbkgJ4c1rsHL62sxN2TF5yIiIiIpJSKCujfP7jnyiyoGGgG5eXBvnSlBEve9+7mPfTIzaK8pGfcx5gZ40f0Y9Ou/WzcuT+J0YmIiIhIqqiogM9/HiorD7Y1hHeUrFkDU6emb5KlBEsAcHeWbali5IAisrOsXcceP6SYnCxjwRoVuxARERERuOkmOHCg5f179wZ90lFSEywzO9/MlprZcjO7sZn9+Wb2QLh/vpmVx+z7Tti+1MzOi2kvNrOHzOwdM1tiZqcl8xoyxZY9Nezad4AjBh66sHBbCvKyOaasD2+u38m+2vokRCciIiIiqWTt2sT0SUVJS7DMLBu4FbgAGAN81szGNOl2NbDD3UcBNwM/CY8dA0wGxgLnAzPD8wHcAjzq7kcBxwNLknUNmWTZ5j0AjB5U1KHjxw3vy/4DDTy6aFMiwxIRERGRFDRsWGL6pKJkjmCNB5a7+0p3rwXuByY16TMJuCt8/hAwwcwsbL/f3WvcfRWwHBhvZn2AjwC3A7h7rbvvTOI1ZIx3t1QxoFc+xYV5HTp+RP+eFBfm8pc3NiY4MhERERFJNTNmQG5uy/sLC4M+6SiZCVYZsC5me33Y1mwfd68DdgElrRw7AtgK/J+ZvW5mvzezZisymNlUM1tgZgu2bt2aiOtJW3X1DazeVs3ogR0bvYKg2MVxZX14ftk2tlfXJjA6EREREUklFRVw/fWH3oOVFWYew4fDrFlaaLi7yAFOAm5z9xOBauCQe7sA3H2Wu49z93EDBgzoyhhTzsad+6hr8HZVD2zOcUOKqWtw/v62pgmKiIiIZJqKCigqgssv/2D1QIC8PLj7bnCH1avTN7mC5CZYG4ChMdtDwrZm+5hZDtAHqGzl2PXAenefH7Y/RJBwSSes2b4XgOElhZ06z+A+PRg5oCdzNE1QREREJCPErnV1+eVQXd18v9ra9K0a2FQyE6xXgNFmNsLM8giKVsxp0mcOcFX4/DPAkx6sVjsHmBxWGRwBjAZedvf3gHVmdmR4zARgcRKvISOsqdxLv5559OrRykTZOJgZFx1fysurt/PeLq2JJSIiIpLOmlvrqjXpWjWwqaQlWOE9VdcBjxFU+nvQ3ReZ2Q/M7OKw2+1AiZktB24gnO7n7ouABwmSp0eBa929sf73V4AKM3sLOAH4YbKuIRO4O2u372VYv86NXjW6+PhS3OGRtzSKJSIiIpKuKirgyitbX+uqqXStGthUTjJP7u5zgblN2r4X83w/cEkLx84ADqkt4u5vAOMSGmgG215dS1VNXaenBzY6fEARx5T15q9vbeKLHz48IecUERERke6jceSqoSH+Y/Ly0rdqYFOpVuRCEmxteP9VokawAC46rpQ31+1kTWULk3BFREREJOU03m91+eXtG7kqKoI77kjvwhaxlGBluDXb95Kfk8Wg3j0Sds5PHF8KwCNvqZqgiIiISDqYPr356oCtKSqC2bNhz57MSa5ACVbG27BjH2V9C8gyS9g5y4oLOLm8r6oJioiIiKS46dODCoG33Rb/MSUlmZlYNVKClcHqG5z3du+nrE9Bws994bGDWbp5D8u37En4uUVEREQk+aZPb19ilZcXJFbbtmVmYtVICVYG27JnP/UNTmlx4hOsC44djBn87a33En5uEREREUm+WbPi75uVlVn3WbVGCVYG27hzH0BSEqxBvXtw8vB+zF2o+7BEREREUlF9fdt9IBi5uvtuJVeNlGBlsA0795OXk0VJUV5Szn/hcZomKCIiIpKqsuLIFDKtQmA8lGBlsI079zG4T4+EFriIdcExh2maoIikDDPrYWYvm9mbZrbIzP4zbB9hZvPNbLmZPWBmeWF7fri9PNxfHukFiIgkUEVF6/sztUJgPJRgZagGdzbt2peU6YGNBvbuwcnl/fjbQlUTFJGUUAOc4+7HAycA55vZqcBPgJvdfRSwA7g67H81sCNsvznsJyKSFq65pvmFhM2UWLVFCVaG2ranhgP1npQKgrEuPHYw726uYtlmTRMUke7NA1XhZm74cOAc4KGw/S7gk+HzSeE24f4JZkmaEiAi0oWmT4eqqpb3K7FqXU7UAUg0Nu7aD8Dg4sQtMAxw7/y1H9jed6AeA37893eYcPSgQ/pfdsqwhL6+iEhnmFk28CowCrgVWAHsdPe6sMt6oCx8XgasA3D3OjPbBZQA27o0aBGRBGuteuAwfXRrk0awMtTm3fvJMhjQKz+pr9O7Ry7DS3qycMOupL6OiEgiuHu9u58ADAHGA0d19pxmNtXMFpjZgq1bt3b2dCIiSdda9cAZM7oujlSlBCtDvbdrPwN65ZMTT3mYTjp2SB+27Klh8+79SX8tEZFEcPedwFPAaUCxmTXO+BgCbAifbwCGAoT7+wCVzZxrlruPc/dxAwYMSHboIiKdlp3dfLuZpgfGQwlWhtq8Zz+Deid2emBLjintjYFGsUSkWzOzAWZWHD4vAM4FlhAkWp8Ju10F/CV8PifcJtz/pLt7lwUsIpIkU6c2337NNV0bR6pSgpWB9h+oZ+feAxzWRQlWrx65lPfvydtKsESkexsMPGVmbwGvAI+7+yPAvwI3mNlygnusbg/73w6UhO03ADdGELOISEJNnw6/+c0H27KyYNo0mDkzmphSjYpcZKAt4VS9rhrBAjimrA9/fXMjm3d33ciZiEh7uPtbwInNtK8kuB+raft+4JIuCE1EpEtMnAhPPHFoe1YWnHFG18eTqjSClYHe210DdHGCpWmCIiIiIt3S9OnB/VXNJVcAdXVw001dG1MqU4KVgd7bvZ+8nCyKC3O77DUbpwkuXL8L3aIgIiIiEp2KCigqCpIqM7jttraPWbu27T4SUIKVgTbv3s+gXvlkdfF6mMcN6cPWqpr31+ASERERka5VUQFXXAHV1e07TutfxU8JVoZx98jugzqurJjsLOONtTu6/LVFREREBK6/Hto7mSgnR+tftYcSrAxTVVPH3tr6SBKsgrxsjhzUizfX76K+QdMERURERLpSRQVUHrJaX+t69IA779T6V+2hBCvDbN0TFLgY2Cs/ktc/YWgxVTV1rNhaFcnri0hmMbMsM+sddRwiIlGoqID8/IP3Wl1+efuOnzAB9u1TctVeSrAyzJYwwRoQUYJ11GG96JGbxRvrdkby+iKS/szsXjPrbWY9gbeBxWb2rajjEhHpShUVQUJVW9ux46dNg3nzEhtTplCClWG27qkhLzuLPgVdV0EwVk52FseWFbNo4y5q6uojiUFE0t4Yd98NfBL4OzACuCLSiEREutj117f/mKIimD07uEdLiwp3nBYazjBbq2oY0Csf6+IKgrFOHFrMK6u3s3jj7shiEJG0lmtmuQQJ1q/d/YCZ6cZPEckYY8e2714rM2hoSF48mUYjWBlm656ayKYHNhpWUkjfwlxNExSRZPktsBroCTxrZsMBfaMjIhlh4kRYvLh9x1xzTXJiyVRJTbDM7HwzW2pmy83sxmb255vZA+H++WZWHrPvO2H7UjM7L6Z9tZktNLM3zGxBMuNPNzUH6tm170BkBS4aZZlx/NBilm+pYsturYklIonl7r909zJ3/7gH1gBnRx2XiEhXeOKJ9vWfMEHTARMtaQmWmWUDtwIXAGOAz5rZmCbdrgZ2uPso4GbgJ+GxY4DJwFjgfGBmeL5GZ7v7Ce4+Llnxp6OtVdEWuIh10tC+OPDQa+ujDkVE0oyZDTKz283s7+H2GOCqiMMSEelWGu+3UiGLxEvmCNZ4YLm7r3T3WuB+YFKTPpOAu8LnDwETLLg5aBJwv7vXuPsqYHl4PumEqCsIxurfK58R/Xty/8vraNCaWCKSWHcCjwGl4fa7wNeiCkZEpKtUVLTdp7GIxZ49Kr+eLMlMsMqAdTHb68O2Zvu4ex2wCyhp41gH/mFmr5rZ1JZe3MymmtkCM1uwdevWTl1Iuti6p4Ysg5Ke0SdYAONH9GPt9r08v3xb1KGISHrp7+4PAg3w/vuLypaKSNpr616qadOUVHWFVCxycaa7n0Qw9fBaM/tIc53cfZa7j3P3cQMGDOjaCLupLXtqKCnKJzsrugqCscYO7k2/nnncO39t1KGISHqpNrMSgi/kMLNTCb7AExFJW9OnQ1VVy/tnz9a9Vl0lmQnWBmBozPaQsK3ZPmaWA/QBKls71t0bf24B/oSmDsZtW1UN/Yu6x+gVBGtiXfKhITy+ZLOKXYhIIt0AzAFGmtkLwN3AV6INSUQkeSZOhNtua3l/drZGrrpSMhOsV4DRZjbCzPIIilbMadJnDgdvPP4M8KS7e9g+OawyOAIYDbxsZj3NrBeAmfUEPga8ncRrSBv1Dc726lr698yLOpQPmDx+GPUNzoML1rXdWUQkDu7+GvBR4HTgy8BYd38r2qhERJJj4sS2KwfWa5J0l0raQsPuXmdm1xHcaJwN3OHui8zsB8ACd58D3A7cY2bLge0ESRhhvweBxUAdcK2715vZIOBP4SK5OcC97v5osq4hnWzcuY/6BqekG41gAYzo35PTR5Zw38vrmHbWqG4zfVFEUpeZXdmk6SQzw93vjiQgEZEkqaiIryz78OHJj0UOSlqCBeDuc4G5Tdq+F/N8P3BJC8fOAGY0aVsJHJ/4SNPf6spqAEqKutcIFsBlpwzjuntf59llWzn7yIFRhyMiqe/kmOc9gAnAawRTBUVE0sL06a1PC4w1Y0bbfSRxkppgSfexeluQYHWne7AafWzMYfQvyuOel9YowRKRTnP3D9xvZWbFBEuFiIikhYqK+JOrkhLdf9XVUrGKoHTAqm17yc02evXofjl1Xk4Wl586nCff2cLyLXuiDkdE0k81MCLqIEREEuWmm+Lrl5MDt9yS3FjkUEqwMsTqympKeuaTZd3zHqcrTh1Ofk4Wtz+/KupQRCTFmdlfzWxO+HgEWEpQdVZEJC2sWdN2n6IiuPNOjV5FofsNZ0hSrN5W3S3vv2pUUpTPp08awh9fW883PnZkt5zKKCIp42cxz+uANe6+PqpgREQSqays7T4TJsC8ecmPRZqnEawMUFffwNrte7t90vLFD4+gtq6Bu1+K42sZEZEWuPszMY8XlFyJSLqYOBE2bmy9T2mpkquoKcHKABt27qOuwSnpZmtgNTVyQBETjx7I7H+uYV+tFmwQkfYxsz1mtruZxx4z2x11fCIinTF9enwl2TdsSH4s0jolWBlg1bbGEu3dewQL4IsfPpzt1bX88TV94Swi7ePuvdy9dzOPXu7eO+r4REQ6Kt6S7NOmJT8WaVtcCZaZPWxmF5qZErIUdLBEe/cewQI4ZUQ/jhvSh9ufX0V9g0cdjoikMDMbaGbDGh9RxyMi0lGzZrXdJzsbZs5MfizStngTppnAZcAyM/uxmR2ZxJgkwVZX7qVnXjZF+d2/pomZMfUjh7NqWzVzF26KOhwRSUFmdrGZLQNWAc8Aq4G/RxqUiEgn1Mdx58RddyU/DolPXAmWu89z9ynASQRvVPPM7EUz+7yZ5SYzQOm8VduqKe/fE+umJdqbuuCYwYwaWMSvnlxGg0axRKT9/gs4FXjX3UcAE4B/RhuSiEjHVFS03Wf2bJVj707invJnZiXA54AvAq8DtxAkXI8nJTJJmNWVQYKVKrKzjK+cM4p3N1fx2KL3og5HRFLPAXevBLLMLMvdnwLGRR2UiEh7TZ8Ol1/eep8JE5RcdTfx3oP1J+A5oBC4yN0vdvcH3P0rQFEyA5TOOVDfwPod+xhRkjoJFsAnjivl8P49+eWTy3HXKJaItMtOMysCngUqzOwWoDrimERE2qWiou3CFnl5KsneHcU7gvU7dx/j7j9y900AZpYP4O76VrAbW7d9L/UNnlIjWBCMYl179iiWbNrNvCVbog5HRFKAmV1iZj2AScBe4OvAo8AK4KIoYxMRaa9rrmm7zx13JD8Oab94E6z/bqbtpUQGIsmxujL40nZE/8KII2m/SSeUMqxfIb98YplGsUQkHpcBa4HfAOcD7u53ufsvwymDIiIpo6qq7T6aGtg9tZpgmdlhZvYhoMDMTjSzk8LHWQTTBaWbW7VtLwDlKTZFECAnO4vrzh7Fwg27eGqpRrFEpHXu/ilgFDAP+Aqw3sx+Y2YfjTYyEZH2mTix7T4TJiQ/DumYtup2n0dQ2GII8POY9j3Ad5MUkyTQ6m3V9OqRQ7+e3X8NrOZ86qQyfvXUMn4xbxlnHzkwZSohikg03H03cBdwV1ic6TPAL82sn7sPjTY6EZG2TZ8OTzzRep/iYt171Z21OoIVTq04G/icu58d87jY3R/uohilE1ZXVjMihUq0N5WbncVXzh7NW+t38YTuxRKROJlZX+DTwKVAP+ChaCMSEWlbPIUtSkthx46uiUc6pq0pgo2FIcvN7Iamjy6ITzpp1bbqlJweGOtTJ5UxrF8hv3jiXd2LJSItMrMiM7vCzOYCiwlKs/8XMMzdvx5tdCIibbv++rb7bNiQ/Dikc9oqctH4ybwI6NXMQ7qxmrp6Nu7cl3IVBJvKzc7iK+eM4u0Nu3l88eaowxGR7ms1wdT2mQRJ1Zfd/SnXNzMikiIqVY4nLbR6D5a7/zb8+Z9dE44k0rrte2nw1Kwg2NSnTizj1qeWc/O8ZUw8ehBZWak55VFEkmqou++LOggRkfaaPr3tqYEA06YlPxbpvHgXGv6pmfU2s1wze8LMtsZMH5RuKpUrCDaVk53FVyeMZsmm3fxj8XtRhyMi3ZCSKxFJRfEmV2PGwMyZyY9HOi/edbA+FlZm+gTBFIxRwLeSFZQkxuptjWtgpX6CBXDx8aUc3r8nv5i3jIYGzfgRERGR1BZPUYtGixYlNxZJnLbKtDftdyHwB3fflapV6TLJqspqigtzKS5MzRLtTTWOYn3tgTe46c9vc2xZn7iOu+yUYUmOTERERKR9yspg48b4+mpqYGqJN8F6xMzeAfYB08xsALA/eWFJIqzamvoVBJu66PhSZvxtCU8s2czY0t5kKdEXkZCZ/RVocXjb3S/uwnBERFpUWAj74pzUXFysqYGpJq4pgu5+I3A6MM7dDwDVwKRkBiad17gGVjrJzjLOOXogW/bU8PaGXVGHIyLdy8+A/wVWEXwh+LvwUQWsiDAuEZH3jR0bf3JVUKA1r1JRvCNYAEcRrIcVe8zdCY5HEmRfbT2bdu1PuxEsgGPL+vDUO1t44p0tHFPWR6NYIgKAuz8DYGb/6+7jYnb91cwWRBSWiMj7Kipg8eL4+paWas2rVBVvFcF7CL4ZPBM4OXyMa/Wg4LjzzWypmS03sxub2Z9vZg+E++ebWXnMvu+E7UvN7Lwmx2Wb2etm9kg88WeiNduDAhflaVCivaksM845aiBb99SwcL1GsUTkED3N7PDGDTMbwcF1HUVEInPVVfH1Ky5WcpXK4h3BGgeMac9ijWaWDdwKnAusB14xsznuHpu3Xw3scPdRZjYZ+AlwqZmNASYDY4FSYJ6ZHeHu9eFx1wNLgN7xxpNp0q2CYFPHlPVh0NJgFOvYIRrFEpEP+DrwtJmtBAwYDnw52pBEJNNNnAj19W3307TA1Bdvmfa3gcPaee7xwHJ3X+nutcD9HHrf1iTgrvD5Q8AEC8oTTgLud/cad18FLA/Ph5kNIahm+Pt2xpNR3l8DK00TrCwzJhw1iG1VNby5bmfU4YhIN+LujwKjCb6M+ypwpLs/Fm1UIpLpnnii7T4TJsDevcmPRZIr3gSrP7DYzB4zszmNjzaOKQPWxWyvD9ua7ePudcAuoKSNY38BfBtoaO3FzWyqmS0wswVbt25tI9T0s3pbNf2L8ujdIzfqUJJmTGlvDuvdgyff2UK91sUSkZCZFRKs1Xidu78JDDOzT0QclohksIqKtvtMmwbz5iU/Fkm+eKcIfj+ZQcQrfIPc4u6vmtlZrfV191nALIBx48Zl3KfvVZWpUaL93vlrO3xslhkTjh5Ixfy1vLl+JycN65vAyEQkhf0f8CpwWri9AfgDoPt2RSQS11zT+v7sbJViTyfxlml/BlgN5IbPXwFea+OwDcDQmO0hYVuzfcLqhH2AylaOPQO42MxWE0w5PMfMZsdzDZlm9bbqtJ0eGGvM4N6U9unBU+9soSH+WwRFJL2NdPefAgcA3H0vwb1YIiJdrqICqqpa73PXXa3vl9QSbxXBLxHcI/XbsKkM+HMbh70CjDazEWaWR1C0oum0wjlAYz2VzwBPhoU05gCTwyqDIwjm0r/s7t9x9yHuXh6e70l3vzyea8gk1TV1bNlTk7YFLmKZGWcdOZDK6lqtiyUijWrNrIBw0WEzGwnUtHWQmQ01s6fMbLGZLTKz68P2fmb2uJktC3/2DdvNzH4ZVrx9y8xOSuZFiUjq6dsXLm/jk2pJCUyZ0jXxSNeI9x6sawlGj3YDuPsyYGBrB4T3VF0HPEZQ8e9Bd19kZj8ws4vDbrcDJWa2HLgBuDE8dhHwILAYeBS4NqaCoLRhdWVYoj0FpggmwpjS3vQvyueZd7fSjkKXIpK+/oPgvWOomVUATxDcu9uWOuAb7j4GOBW4NqxqeyPwhLuPDs/VuOzIBQRfAI4GpgK3JfQqRCSlFRbCzp1t97vllqSHIl0s3nuwaty91sJS2OF0vjY/ybr7XGBuk7bvxTzfD1zSwrEzgBmtnPtp4Om2Q888q9+vIJh+a2A1J8uMj4zuz8Ovb2DZliqOGNQr6pBEJCJmlgX0BT5NkCQZcL27b2vrWHffBGwKn+8xsyUEMzYmAWeF3e4ieO/517D97nDmxT/NrNjMBofnEZEMNnYs7NsXX1+NXqWfeEewnjGz7wIFZnYuwc3Cf01eWNIZmTaCBXDCsGL6FOTyzLuZVzFSRA5y9wbg2+5e6e5/c/dH4kmumgoXvj8RmA8Mikma3gMGhc/jqZYrIhlm+nRYvLjtfhBMD5T0E2+CdSOwFVhIsFjjXODfkhWUdM6qbdUM7JVPz/x4ByhTX05WFmeO6s+qbdWsDRNMEclY88zsm+E9Vf0aH/EebGZFwB+Br7n77th94WhVu+YiZ/qyISKZ5rZ2TBbW9MD0FG8VwQaCohbT3f0z7v47180u3VamVBBsalx5Xwpys3lao1gime5SgnuHnyUo1/4qsCCeA80slyC5qnD3h8PmzWY2ONw/GNgStsdTLRd3n+Xu49x93IABAzpwOSKSKsaOjb/v7NmaHpiuWk2wwgpJ3zezbcBSYKmZbTWz77V2nERrdWU1IzJoemCj/JxsTh9Zwjvv7eG93fujDkdEIuLuI5p5HN7WcRbcaHw7sMTdfx6zK7bi7VXAX2LarwzfK08Fdun+K5HMVVER39TAMWPAXclVOmtrBOvrBNUDT3b3fu7eDzgFOMPMvp706KTd9uw/wLaq2owcwQI47fAS8rKzeFajWCIZy8wKzezfzGxWuD06XKi+LWcAVxCssfhG+Pg48GPgXDNbBkwMtyGYLr8SWA78Dpie6GsRkdRx/fVt95k2DRYtSn4sEq22btK5Ajg39gZhd19pZpcD/wBuTmZw0n6NFQRHZEgFwaYK83M4ubwvL62sZOLRg+jXMy/qkESk6/0fwbTA08PtDQTFmR5p7SB3f56WFySe0Ex/J5iKKCJCZWXr+0tLYebMrolFotXWCFZuc9WX3H0rkJuckKQzVjVWEMzQESyAM0cPwDCeW6ZRLJEMNdLdfwocAHD3vbScOImIdMrYsWBx/IXZcMgdmpKu2kqwaju4TyKyeluQYA3vl7kJVp+CXE4cVsyra3ZQVVMXdTgi0vVqzayAsNqfmY0EaqINSUTS0dix8d13NW1a8mOR7qOtBOt4M9vdzGMPcGxXBCjts3pbNYP79KAgLzvqUCL14dEDqG9wXlrR7uVvRCT1/QfwKDDUzCqAJ4BvRxuSiKSjeNe70tTAzNLqPVjuntmf0lPQqsrqjFpguCUDeuVz9ODe/HPldqpr6jJqTTCRTOfuj5vZa8CpBFMDr+/IYsMiIq3p2ze+fj31sSzjxLvQsKSITF0DqzkfOWIA+w7Uc/8r66IORUS6gJmd1PgAhgObgI3AsLBNRCQhxo6FnTvj6/vb3yY1FOmG9LV+Gtm19wA79h7I2AqCTQ3rV0h5SSG3P7eSK08bTm62vk8QSXP/G/7sAYwD3iQYwTqOYKHh0yKKS0TSSLzrXUFw75XWu8o8+sSZRt6vIKgpgu/7yBED2LhrP4+8tTHqUEQkydz9bHc/m2Dk6iR3H+fuHwJOJCjVLiLSaddc03YfM5g9W/deZSqNYKWRxgqCIzRF8H1HDOrFEYOK+O0zK/nkCWVYPHVURSTVHenuCxs33P1tMzs6yoBEJH1UVbXdp6Eh+XFI96URrDSyals1ZjC0n6YINsoy48sfGck77+3h6Xe1LpZIhlhoZr83s7PCx++At6IOSkQyg0qyixKsNLJqWzWlfQrokavij7EuOr6UwX168JunV0Qdioh0jc8Bi4Drw8di4PNRBiQi6WH69Nb3jxmjaYGiBCutrK6s1vTAZuTlZHH1mSOYv2o7r6/dEXU4IpJEZpYN/N3db3b3T4WPm919f9SxiUjqmzWr9f2LFnVNHNK9KcFKE+7Oqm1KsFoyefwwevfI4bfPrIw6FBFJInevBxrMrE/UsYhI+qmvb3mf1ruSRipykSa2V9eyZ3+d1sBqQVF+DleeVs6tTy9n5dYqDh9QFHVIIpI8VQT3YT0OVDc2uvtXowtJRFJdWwsLa70raaQRrDSxurKxgqAKXLTkqtPLyc3O4nfPaRRLJM09DPw78CzwasxDRKRDpk9ve2FhrXcljTSClSZWbA0SrMP7a2SmJQN65XPJh4bwhwXr+fq5RzCwV4+oQxKR5HgAGBU+X677r0Sks267LeoIJJVoBCtNrNxaTW62MaRvQdShdGtf+vDh1DU08H8vrI46FBFJMDPLMbOfAuuBu4C7gXVm9lMzy402OhFJVdlxFGfOy0t+HJI6lGCliZVbqxjWr5CcbP0nbU15/55ccMxgZv9zDXv2H4g6HBFJrP8B+gEj3P1D7n4SMBIoBn4WZWAikpry8uJbNPiOO5Ifi6QOfRpPE6u2VatwQ5y+/NHD2bO/jvteXht1KCKSWJ8AvuTuexob3H03MA34eGRRiUhKKiuDA3F8Fzttmu6/kg/SPVgp6t75B5ODBndWbqtmcJ8eH2iX5h03pJjTR5Zw+/Or+NzpI8jL0fcMImnC3d2baaw3s0PaRURaMn06bNzYdr/SUi0sLIfSJ8s0sKO6lvoGp39RftShpIwvf3Qkm3fX8Oc3NkQdiogkzmIzu7Jpo5ldDrwTQTwikqLiLWqxQR8jpBlJTbDM7HwzW2pmy83sxmb255vZA+H++WZWHrPvO2H7UjM7L2zrYWYvm9mbZrbIzP4zmfGnim1VtQBKsNrhI6P7c/Tg3sx6diUNDfpiWyRNXAtca2ZPm9n/ho9ngK8STBMUEWlRRQWYBY94TNNfFWlB0hIsM8sGbgUuAMYAnzWzMU26XQ3scPdRwM3AT8JjxwCTgbHA+cDM8Hw1wDnufjxwAnC+mZ2arGtIFduqagDo30sJVrzMjGs+ejjLt1TxxDtbog5HRBLA3Te4+ynAD4DV4eMH7j7e3fU9s4i0qKICLr88/v4TJmhqoLQsmSNY4wnWH1np7rXA/cCkJn0mEZTSBXgImGBmFrbf7+417r4KWA6M90BV2D83fGT88MPWqhp65GbRMy+OOqLyvguPHUxZcQG/eWZF1KGISAK5+5Pu/qvw8UTU8YhI93fTTfH3HTMG5s1LXiyS+pKZYJUB62K214dtzfZx9zpgF1DS2rFmlm1mbwBbgMfdfX5zL25mU81sgZkt2Lp1a+evphvbtqeGAUX5WLxj2gJATnYWUz9yOK+u2cFLKyqjDkdEREQismZNfP0KCmDRouTGIqkv5YpcuHu9u58ADAHGm9kxLfSb5e7j3H3cgAEDujTGrratqkb3X3XQpScPZVDvfG6e9y7NFB8TERGRNDdxYvx99+5NXhySPpKZYG0AhsZsDwnbmu1jZjlAH6AynmPdfSfwFME9Whmrpq6e3fvrdP9VB/XIzWb6WaN4edV2XlqpUSwREZFM80ScE4n1PazEK5kJ1ivAaDMbYWZ5BEUr5jTpMwe4Knz+GeDJcA2TOcDksMrgCGA08LKZDTCzYgAzKwDOJcNL76qCYOddevJQDuvdg188vkyjWCIiIhmkrOnNK80oLlZyJe2TtAQrvKfqOuAxYAnwoLsvMrMfmNnFYbfbgRIzWw7cANwYHrsIeBBYDDwKXOvu9cBg4Ckze4sggXvc3R9J1jWkgsYKggOUYHVYj9xspp89kpdXb9e9WCIiIhli4sS2FxMuLYUdO7omHkkfOck8ubvPBeY2aftezPP9wCUtHDsDmNGk7S3gxMRHmrq27anBgJKivKhDSWn/Mm4oM59awc3z3uW0kSUqGCIiIpLm4pkaqIWEpSOSmmBJ8m2tqqFPYS652SlXr6TL3Dt/bVz9xo/ox5w3N/KDRxbzHxeNTXJUIiIiEpWKirb7jGm6eqtInPSpPMVtq6rR9MAEGTe8L8WFufxj0WYaGjTZWkREJB3Fu6iwyrFLRynBSmHuzraqWkqUYCVETnYW5x49iA079/HIwk1RhyMiIiIJNn16fMmVRq+kMzRFMIXt2V9HbV0DA3T/VcIcP7SY55dv4/tzFrFzby05WfF9B3HZKcOSHJmIiIh0xvTpcNtt8fXV6JV0hkawUtjWsIKg1sBKnCwzzht7GNura3l51faowxEREZEEqKiIP7maNi25sUj6U4KVwlSiPTlGDyzi8AE9efKdLew/UB91OCIiItJJV1wRf9+ZM5MXh2QGJVgpbNueGnKzjd4FuVGHklbMjAvGDmZvbT3PLtsadTgiIiLSCWPHxr9Q8OzZyY1FMoMSrBS2eU8NA3rlk6U1mxKurG8Bxw/pw/PLtrG9ujbqcERERKQDCgth8eL4+s6eDVOmJDceyQxKsFLYlt37GdirR9RhpK3zjxlMlhl/e6uNZd5FRESk2ykshH374uvrruRKEkcJVoraV1vP7v11DFKBi6TpU5DLOUcNZMl7e1j63u6owxEREZE49e3bvuRKJJGUYKWoLXv2AzCwt0awkun0USUMKMrnr29t4kB9Q9ThiIiISBsKC2Hnzvj6KrmSZFCClaK27A4qCA5SgpVUOVlZXHR8Kdura3lu2baowxEREZFWZGfHP3JVXJzUUCSDKcFKUVv27Cc32yguVAXBZBs1sIhjSnvzzLtbqAxL44uIiEj3YgYN7ZhssmNH8mKRzKYEK0WpgmDXuvC4UrKzjD++toEGzScQERHpVgoL4+9bUKCpgZJcSrBS1Jbd+xmkCoJdpk9BLhceW8rqympeWlEZdTgiIiISmj49/mmBBQWwd29y4xFRgpWCdu07wO79dSpw0cVOGlbMUYf14rFF77F1j6YKioiIdAe33RZfv9xcJVfSNZRgpaDlW/YAMFAl2ruUmfHJE8vIzc7ioVfXaaqgiIhIxPr2ja9fQQHU1iY3FpFGSrBS0LubqwBVEIxC7x65XHx8Ket27OOZd7dGHY6IiEjGMouvHHtpqUaupGspwUpB727eowqCETpuSB+OG9KHeYs3s3JbVdThiIiIZJSxY4PkKh6lpbBhQ3LjEWlKCVYKWr6lShUEI2RmfPKEMvr1zOOBV9axZ/+BqEMSERHJCGaweHH8/ZVcSRSUYKWgdzfvUQXBiPXIzeayU4ax/0A9FfPXUlNXH3VIIiIiaa293yuPGZOcOETaogQrxezad4DNu2tUQbAbGNyngM98aChrt+/lOw8vxFX0QkREJOEqKtqfXGVlwaJFyYlHpC1KsFLMss1BBcFBqiDYLRxb1ocJRw/k4dc28LN/LI06HBHpBDO7w8y2mNnbMW39zOxxM1sW/uwbtpuZ/dLMlpvZW2Z2UnSRi6Svigq4/PL2HZObC/WaWCIRUoKVYpZtCYoqaASr+zjnyIF8dvwwbn1qBb9/bmXU4YhIx90JnN+k7UbgCXcfDTwRbgNcAIwOH1OBOFfiEZF4FRa2P7kqLVU5domeEqwU8+7mPRTkZquCYDdiZvz3J4/hwmMH899/W8LvnlWSJZKK3P1ZYHuT5knAXeHzu4BPxrTf7YF/AsVmNrhLAhXJAGawb1/7jpk2TUUtpHvIiToAaZ9lm6sYNbBIFQS7mews4xeTTwCDGXOXsGvfAb7xsSMw/XcSSXWD3H1T+Pw9YFD4vAxYF9Nvfdi2CRHplI4Us9D9VtKdJHUEy8zON7Ol4Rz1G5vZn29mD4T755tZecy+74TtS83svLBtqJk9ZWaLzWyRmV2fzPi7o3c372H0wKKow5Bm5GZncculJzD55KH8+qnl3PDgm+w/oEngIunCg0o27a5mY2ZTzWyBmS3YulULlIu0Ji+vff1nz1ZyJd1P0hIsM8sGbiWYpz4G+KyZNS2YeTWww91HATcDPwmPHQNMBsYSzIefGZ6vDviGu48BTgWubeacaauyqoYte2o4enDvqEORFuRkZ/GjTx/LNz92BH96fQOfnvkiq7dVRx2WiHTc5sapf+HPLWH7BmBoTL8hYdsh3H2Wu49z93EDBgxIarAiqSw7Gw60Y2nJ2bNhypTkxSPSUckcwRoPLHf3le5eC9xPMGc9Vuzc9oeACRbMqZoE3O/uNe6+ClgOjHf3Te7+GoC77wGWEEzJyAhLNgUVBJVgdW9mxnXnjOb/PncyG3bu4+O/fI57/rmGhgaVcRdJQXOAq8LnVwF/iWm/MqwmeCqwK2YqoYi0U2EhNDTE31/JlXRnyUywWpqf3mwfd68DdgEl8RwbTic8EZjf3Iun45SMJZt2A3D04F4RRyLxOPuogfz9+g9z0rC+/Puf3+bTt73Ia2t3RB2WiLTAzO4DXgKONLP1ZnY18GPgXDNbBkwMtwHmAisJvgD8HTA9gpBF0sL06e0raKHkSrq7lCxyYWZFwB+Br7n77ub6uPssYBbAuHHj0mLoYMmm3QzslU9JkdbAShWlxQXcc/V4Hnp1PT99bCmfnvkiFx9fyrfPP5IhfQujDk9EYrj7Z1vYNaGZvg5cm9yIRDLDbXEucqDESlJFMhOseOanN/ZZb2Y5QB+gsrVjzSyXILmqcPeHkxN697R4027GlGp6YKoxMy4ZN5SPHzuY255ewe+eW8nchZuYdEIZ13z0cEYP0oikiIhkpsI4v2scM0bJlaSOZE4RfAUYbWYjzCyPoGjFnCZ9Yue2fwZ4MvxWcA4wOawyOIJgIceXw/uzbgeWuPvPkxh7t1Nb18CKrVW6/yqF9czP4ZvnHclT3zyLK04bztyFmzj35meZevcCXtfUQRERyTB9+8Y3NbCgQJUCJbUkbQTL3evM7DrgMSAbuMPdF5nZD4AF7j6HIFm6x8yWEyzuODk8dpGZPQgsJqgceK2715vZmcAVwEIzeyN8qe+6+9xkXUd3sWzLHg7UuxKsNFBaXMB/XDSWr5wzmjtfXM1dL67mH4s3c9rhJVx79ijOGFXS7PpZ985f2+7XuuyUYYkIWUREJKHiXeuquBh26DtISTFJvQcrTHzmNmn7Xszz/cAlLRw7A5jRpO15ICNXbm2sIDhGBS7SRr+eedxw7hFM/cjh3Dd/Lb9/fiWX3z6f44f0YfrZo/jYmEFaqFhERNJOe97alFxJKkrqQsOSOEs27SY/J4vykp5RhyIJVpSfw5c+cjjPfvtsfvipY9mx9wBfvudVPjXzRV5ZvT3q8ERERBIiL699ydW0acmLRSSZlGCliLc37OLowb3JydZ/snSVn5PNZacM48lvfJSf/r/j2LRrH5f85iW+dPcCLVYsIiIpq7AwSKzas4jwhAkwc2byYhJJppQs055pGhqcRRt386kTM2ZN5ZSTyPujcrKz+JeTh3LR8aXc8cIqbnt6Bef94lnOPnIgZ4zqT3aWpg2KiEj3V1EBl1/e/uNyc2HevMTHI9JVNBySAlZVVlNVU8exZX2iDkW6UEFeNteePYonvvFRPnrEAB5d9B63PbOcjTvbsRqjiIhIBDqaXGVlQW1t4uMR6UpKsFLA2xt2AXCMEqyMNKh3D357xYf47Phh7N5Xx21Pr+CF5dsIVjQQERHpfq64ov3HlJZCfX3iYxHpakqwUsDC9bvIy8li9KCiqEORiJgZx5b14WsTRjN6UBF/W7iJivlr2VerdyIREek+Jk4M7rdq73eAEybAhg3JiUmkq+kerBSwMCxwkasCFxmvMD+HK04dzgsrKnn07U386qllTDllOGXFBVGHJiIiGa6wML6Fg2MVFMDevcmJRyQq+sTezTUWuDi2TAsMS8DMOHNUf778kZG4w6xnV7AwnEYqIiIShbKy9iVXubnBKJeSK0lHGsHq5laHBS6OKyuOOhRJsI5UHow1tF8h088aScX8tdz38lq2HDWQs48aSJYWJxYRkS6Ul9e+EuyzZ8OUKcmLRyRqGsHq5t5arwIX0rJePXL54pkjOHFoMU+8s4X7X1lHbV1D1GGJiEgGKCtr//pWY8YouZL0pwSrm3t97Q4K87I5QgUupAU52Vl85kNDOH/sYSzasItZz61g1752vNuJiIi0Q2Mhi40b23dcbi4sWpScmES6EyVY3dwb63Zy3JA+5KjAhbTCzPjIEQO44tThbKuqZeZTy1m3XRPbRUQksczgiSfaf9yYMVrfSjKHPrV3Y/sP1LNo425OHNY36lAkRRw1uDfXfHQkOdnG755byV/eUM1bERFJjI7c4ttYzEIjV5JJlGB1Y4s27qKuwTlhaHHUoUgKOax3D6adNYohfQu4/v43+NljS2lo0KLEIiLSMY1TAjtCo1aSiZRgdWOvr90JwIlKsKSdivJz+MKZI7h03FB+/dRyple8RnVNXdRhiYhICpk+veNTAouL27/YsEi6UJn2buz1tTspKy5gYO8eUYciKSgnK4sf/79jGT2oiB/OXcLFv97DrVNO4qjDtKaaiIi0rm9f2LmzY8cqsZJMpxGsbuz1tTs4cVhx1GFICjMzvvjhw5l99Sns2lfHpF+/wP0vr8X17iciIs0oLAxGrTqSXE2bpuRKBJRgdVsbd+5j4679nKQCF5IAp4/qz9+v/zAnl/fjxocXct19r7OtqibqsEREpJtoXNNq3772HztmTJBYzZyZ+LhEUpESrG7qldXbARg/ol/EkUi6GNArn7u/MJ5vnXckjy/azMSfP8MfFqzTaJaISAYbO7Zja1o1GjNGFQJFmtI9WN3U/FXb6ZWfw9GDdb+MJE5WlnHt2aM4b+wgbvzjQr710Fv86fUN3HTh0Ywt7dOhc947f227+l92yrAOvY6IiCTG2LGweHHnz6PkSqR5GsHqpl5ZtZ0PlfclO6uDdVFFWjFqYC8e/PJpzPjUMby9YRcX/vJ5rr33NZZvqYo6NBERSZLGqoCdTa4apwQquRJpnkawuqHt1bUs21LFJ08sizoUSWNZWcaUU4bzieNK+f1zK7n9+VX8feEmLjyulM+fUa77/0RE0kRZWcenAMbKzdW6ViLx0AhWN9R4/9Upuv9KukCfgly+8bEjee7bZ/PFDx/O0+9s4dMzX2TSrS/wp9fXs/9AfdQhiohIOzUuDtyZ+6tiTZig5EokXhrB6oZeWbWdvJwsjh3SsXtiRDqipCif7378aK6fMJo/vraeO19YzdcfeJMf/HUxl4wbymfHD2NE/55RhykiIi3ozNpVzdGIlUjHaASrG3pxRSUnDSsmPyc76lAkA/XMz+HK08qZd8NHmX31KZx6eAm3P7+Ks3/2NJf/fj5/X7iJA/UNUYcpIiIcLK/e0bWrWjJtmpIrkY7SCFY3U1lVw+JNu/nWeUdGHYqkgc5U+MvKMs4c3Z8zR/dn8+79PPjKOu57eS3TKl5jQK98rjh1OJ87ozzBEYuISFsmToQnnkjOuSdMgHnzknNukUyR1BEsMzvfzJaa2XIzu7GZ/flm9kC4f76Zlcfs+07YvtTMzotpv8PMtpjZ28mMPSovrKgE4IxR/SOOROSgQb178JUJo3nuX8/h9qvGMba0Nz9//F3O+PGTzFuymX21uk9LRCSZKiogKysYqUp0cjVtWlAV0F3JlUgiJG0Ey8yygVuBc4H1wCtmNsfdY4uDXg3scPdRZjYZ+AlwqZmNASYDY4FSYJ6ZHeHu9cCdwK+Bu5MVe5ReWLaNXj1yOLZM919J95OdZUw4ehATjh7E2xt28csnlvGPxZt5ccU2Jh49iFNGlGhpARGRBKqogCuuCJKfRNM6ViLJkcwpguOB5e6+EsDM7gcmAbEJ1iTg++Hzh4Bfm5mF7fe7ew2wysyWh+d7yd2fjR3pSifuzvPLt3H6SH1IlWi0d0rhWUcO5MjDevH3he/xyFubeHXNDi4+vpThJSqGISLSUcmcAmgG99wDU6Yk5/wiktwpgmXAupjt9WFbs33cvQ7YBZTEeWyrzGyqmS0wswVbt25tZ+jRWF25lw0793Hm6AFRhyISt8F9Cvj8GeV8dvww9tbW89tnV/KXNzZQW6dCGCIibamogJycg4UqkjEFEIJ7q9yhoUHJlUiypW2RC3efBcwCGDduXBIG1hPv2XeDRPBM3X8lKcbMOLasD0cMKmLe4s28sKKSFVur+JdxQxnStzDq8EREuo3p0+G225L/OqWlsGFD8l9HRA6VzBGsDcDQmO0hYVuzfcwsB+gDVMZ5bNqZt2QzI/r31FpDkrLyc7K58LhSrj5zBAfqnd88s4Knlm6hIRk3D4iIpIjYUapkJlfZ2TB7djBSpeRKJDrJTLBeAUab2QgzyyMoWjGnSZ85wFXh888AT7q7h+2TwyqDI4DRwMtJjDVyVTV1zF+5nQlHDYw6FJFOGzmgiK+eM5pjyvrw+OLN3P3SavbW1kUdlohIl6iogPz8g1P+Lr8c6pNYbLWxCmBdnab/iXQHSZsi6O51ZnYd8BiQDdzh7ovM7AfAAnefA9wO3BMWsdhOkIQR9nuQoCBGHXBtWEEQM7sPOAvob2brgf9w99uTdR1d5fllW6mtb2DC0YOiDkUkIQrysrl03FDKS3ryt7c28eunljNl/PCowxIRSbhkFqVoiSoAinRfSb0Hy93nAnObtH0v5vl+4JIWjp0BzGim/bMJDrNbmLdkC7175DCuvG/UoYgkjJlx6uEllBUXcO/La/nNsysY0q+AyScPJSgYKiKSmrrqXqqmlFiJdH9JXWhY4lPf4Dz1zhbOOnIgudn6TyLpZ2i/Qq47exSH9+/Jdx5eyDf/8JYWJxaRlFFRAUVFH6z015XJVexCwEquRLq/tK0imEpeXrWdyupaPjZW0wMlffXMz+Gq08vZsqeGXz25jEUbd3Hb5R9SURcR6bYqKuALX4Da2q57zQkTYN68rns9EUk8DZd0A4+8tZGC3GzOUYELSXNZZtxw7hH83+dO5r3d+7n4V8/z6Nubog5LROQQEycGxSmSnVzFjk65K7kSSQdKsCJWV9/A399+j4ljBlGYpwFFyQxnHTmQR75yJocP6Mk1s19jxt8Wc6BeCxOLSLSmT0/uYr+NYpOqmTOT9zoiEg0lWBF7cUUl26tr+cRxg6MORaRLDelbyIPXnMaVpw3nd8+t4rLf/ZPNu/dHHZaIZKiJE5N3X1XTUSolVSLpTQlWxP765kZ65efw0SMGRB2KSJfLz8nmB5OO4ZbJJ/D2ht1c+MvneGH5tqjDEpEMk8gy6xMmfDCZUkIlknmUYEWoqqaOvy3cxAXHHkaP3OyowxGJzKQTyphz3Rn0Kcjl8tvn86O5S6ipU5VBEUmuigrIyel8chWbVOkeKhFRghWhR97cyN7aei49eVjUoYhEbvSgXvz1K2dy2fhh/PbZlXx65oss37In6rBEJE1VVMCVV0J9B77LUWEKEWmNEqwI3ffKOo4YVMRJw4qjDkWkWyjMy2HGp47ld1eOY9Ou/XziV89zzz/X4O5RhyYiaeaaa6ChHbV1cnJg9mxN+RORtinBisiSTbt5c91OLj15GGYWdTgi3cq5Ywbx6PUf5uTyfvz7n9/mC3e+wnu7VABDRBJj+nSoqoqvr1kwYnXgAEyZkty4RCQ9qC54RO56cTV5OVl86sSyqEMR6ZYG9u7BXZ8fz10vreaHc5dw1s+e4uPHDOZDw/u2+aXEZado2q2ItOy3v227jxncc4+SKhFpP41gRWDLnv08/NoGLvnQEPr1zIs6HJFuKyvL+PwZI/jqOaM5rHcBD7++gTtfXM3OvUle+VNE0sr06ZCVdXCNq7amBubkKLkSkY7TCFYE7npxNQcaGvjihw+POhSRLnfv/LXtPqakKJ8vfngE81dW8tiizdzyxDLOP+YwTi7vR5am2EqEzOx84BYgG/i9u/844pCkienT27e+1bRpusdKRDpHI1hdbM/+A9zz0hrOH3sYI/r3jDockZSRZcZpI/vz1QmjKetbwF/e2Mhvn1nBxp37og5NMpSZZQO3AhcAY4DPmtmYZL1eRQWUlwcjMeXlwXaijm1pf9P26dNb3u7fP3i09NwsGBlq7mfs6FJ29qFtnXkouRKRrqYRrC4269mV7N5fx/SzRkUdikhK6tczj6vPGMEb63Yyd+Embn1qOaePLOGcowZRkKf15KRLjQeWu/tKADO7H5gELE70C1VUwNSpsHdvsL1mTbANbU9ja+vYlva/8ALcddcH22OTlabblZVtP28sid70Z2yh0Mbpe1EUD1VyJSKJoBGsLrRl935+/9wqLjq+lGOH9Ik6HJGUZWacOKwvN5x7JCeP6MeLKyr52T+W8uKKbdS1p+6ySOeUAetitteHbQl3000HE51Ge/cG7Z09tqX9s2Yd2p7OSkqijkBE0oVGsLrQzx9/l7qGBr71sSOjDkUkLRTkZfPJE8oYX96Pv7+9iUfe2sRLKyrp3SOXTxw3mJxsfYck0TOzqcBUgGHDOlbhcm0Lty621N6eY1va35EFeFNVdjbcckvUUYhIutCnjy7y8qrt3P/KOq46rZxhJYVRhyOSVkqLC/jCGSO46rRycrOz+NoDb3DWz57mnpdWs/9ABn1KlK62ARgasz0kbPsAd5/l7uPcfdyAAQM69EIt5WXx5GttHdvS/uwMmXFbUhJMhVTFQBFJFI1gdYH9B+r51z++xZC+BdzwsSOiDkckLZkZRx7Wi9GDihjUuwczn17Ov/9lEf/7+LtcdFwp/+9DQzh+SJ8W19Bqb3VDrbUlwCvAaDMbQZBYTQYuS8YLzZjxwfukAAoLg/bOHtvS/quu+uA9WOkiJwfuvFMJlYgkj0awusAP5y5h1bZqfvzp4yjMU04rkkxZZpw7ZhAPTzudB6aeyodHD+DBBev45K0vMPHnz/CjuUt4ftk2jWxJp7l7HXAd8BiwBHjQ3Rcl47WmTAnuiRo+PKiMN3x4sB1PktDWsS3tnznz0PZp01reLikJHi09h4OjYk1/xn7vkZV1aFuiFBUpuRKR5DOPokxPFxs3bpwvWLAgktd+6NX1fPMPb/KlD4/gpgsTV723I2sJiWSKpqNLu/cfYO5bm/jLGxtZsGY7B+qd/Jwsjh9azDGlfTh2SG9Wbd1LSVEeuXHet6URrK5jZq+6+7io40iEKN+PRESk8+J5T9JwShK9tKKS7/5pIaePLOFfzz8q6nBEMlbvHrlMHj+MyeOHUV1Tx8urtvPssq28sW4nFfPXUPNCQ0zfHEqK8inpmUdJzzz6xTzPz82Qm1JERESkw5RgJcn8lZV84c5XGN6vkF9fdpKqmYl0Ez3zczj7qIGcfdRAAOrqG1i5rZrbn1tFZXUN26trqayq5Z339lBVU/eBY3vl59CvKI/+PfPZsbeW8pKelPcvpLykJz3z9edURERElGAlxcOvrefGhxcytG8B937pVPr1zIs6JBFpQU52FkcM6sXxQ4sP2VdzoJ7K6loqq2vZXlXDtupaKqtqeHfzHl5du+MDfQf1zufw/kUcPqAnhw8Ifo7sX0RZ3wKys5JwM4mIiIh0S0qwEmjn3lp+NPcdHliwjlMP78etl51ESVF+1GGJSAfl52ZTWlxAaXHBIfsmnVDK6spqVm/by+rKalZsrWLl1mr++uZGdu8/OPKVl5NFeUkhh/cvYt+BegYU5dO/Vz4DivIpyGt9yqHu8xIREUk9SrASoLqmjvteXsttT69g574DXPPRkXzjY0fEfbO8iKSenvk5jC3tw9jSPh9od3cqq2tZubWalVurWLkt+Pnu5j2srqymIaauUEFuNn175lJckEffwlyKC/PoW5hHcWEufQs18i0iIpKKkppgmdn5wC1ANvB7d/9xk/35wN3Ah4BK4FJ3Xx3u+w5wNVAPfNXdH4vnnF2lqqaOl1dV8ujb7/H3he+xp6aO00eWcNOFRx/ygUtEulaUVTbNjP5F+fQvymf8iH4f2HfPS2vYXl3Ltqoatu6pYfveWnburWVrVQ3LtuzhQP0Hq7rePO9dBoSjXf175dG/KP/9EbCSnnn0Lsild49cehfk0Kcgl555OWRpOqKIiEikkpZgmVk2cCtwLrAeeMXM5rj74phuVwM73H2UmU0GfgJcamZjCBZsHAuUAvPMrHGF3rbOmVBb99Tw9sZdbNy5j40797Fhxz7eeW8Py7ZUUd/g9MzL5oJjB3PZKcM4aVjfZIUhImkgO8uChKlXPkcP/uA+d6e6tp6de2vZsfcAO/fWsnPfAar217G1qoaV26qpqjnA/gMNzZ8cMAiSroIcevcIEq6CvGx65mdTkJtDYV52+AieN92Xl5NFbnYWedlZ5ObYwefZWeRm2/v7s8wwC9YcM4L1ilpawFlERCTTJHMEazyw3N1XApjZ/cAkIDYZmgR8P3z+EPBrC96lJwH3u3sNsMrMlofnI45zJtTTS7fwrYfeAoIPR4f17sGogUVMPHoQp40s4UPD+9JDpZtFpJPMjKL8HIrycxjSync1dfUNVNXUUV1Tz/66evbV1rP/QPDYd6CB4SWF7Np3gN37DlBdW8fOvbVs2Bn021tbR3VtPbV1LSdpnZEVJlpZBoaFiRc8PO0MxpT2TsprioiIdDfJTLDKgHUx2+uBU1rq4+51ZrYLKAnb/9nk2LLweVvnBMDMpgJTw80qM1vagWs4xErgxUScqPP6A9uiDqILZdL1ZtK1Qope75SOHZaS19pB71/r2P/u9LmGd/oM3cSrr766zczWdPHLZsK/O11j6kv36wNdYzroTxzvSWlb5MLdZwGzoo4jWcxsQVurSKeTTLreTLpWyKzr1bWKuw/o6tfMhP8WusbUl+7XB7rGdBBeX3lb/ZJZ5m4DMDRme0jY1mwfM8sB+hAUu2jp2HjOKSIiIiIiEolkJlivAKPNbISZ5REUrZjTpM8c4Krw+WeAJ93dw/bJZpZvZiOA0cDLcZ5TREREREQkEkmbIhjeU3Ud8BhBSfU73H2Rmf0AWODuc4DbgXvCIhbbCRImwn4PEhSvqAOudfd6gObOmaxr6ObSdvpjCzLpejPpWiGzrlfXKlHIhP8WusbUl+7XB7rGdBDX9VkwYCQiIiIiIiKdlcwpgiIiIiIiIhlFCZaIiIiIiEiCKMFKQWZ2vpktNbPlZnZj1PF0lpndYWZbzOztmLZ+Zva4mS0Lf/YN283Mfhle+1tmdlJ0kbefmQ01s6fMbLGZLTKz68P2dL3eHmb2spm9GV7vf4btI8xsfnhdD4RFawgL2zwQts83s/JIL6ADzCzbzF43s0fC7XS+1tVmttDM3jCzBWFbWv5bTnVm9l/h7/0NM/uHmZVGHVOimdn/mNk74XX+ycyKo44pkczskvDvaIOZpVUZ7HT7XNNUc59z0klLn23SSUufZ1qiBCvFmFk2cCtwATAG+KyZjYk2qk67Ezi/SduNwBPuPhp4ItyG4LpHh4+pwG1dFGOi1AHfcPcxwKnAteF/v3S93hrgHHc/HjgBON/MTgV+Atzs7qOAHcDVYf+rgR1h+81hv1RzPbAkZjudrxXgbHc/IWbdk3T9t5zq/sfdj3P3E4BHgO9FHE8yPA4c4+7HAe8C34k4nkR7G/g08GzUgSRSmn6uaepODv2ck05a+myTTlr6PNMsJVipZzyw3N1XunstcD8wKeKYOsXdnyWoIhlrEnBX+Pwu4JMx7Xd74J9AsZkN7pJAE8DdN7n7a+HzPQQfxMtI3+t1d68KN3PDhwPnAA+F7U2vt/H38BAwwcysa6LtPDMbAlwI/D7cNtL0WluRlv+WU527747Z7Enw/2Facfd/uHtduPlPgrUy04a7L3H3pVHHkQRp97mmqRY+56SNVj7bpI1WPs80SwlW6ikD1sVsryfN/hGHBrn7pvD5e8Cg8HnaXH84JexEYD5pfL3hlLk3gC0E3zCvAHbGfBCKvab3rzfcvwso6dKAO+cXwLeBhnC7hPS9VgjeXP5hZq+a2dSwLW3/Lac6M5thZuuAKaTnCFasLwB/jzoIiYv+NqSRJp9t0krTzzPu3uI1KsGSbi9cfDqtvm01syLgj8DXmnyznHbX6+714bSkIQTfVB4VbUTJYWafALa4+6tRx9KFznT3kwim9lxrZh+J3Zlu/5a7OzObZ2ZvN/OYBODuN7n7UKACuC7aaDumrWsM+9xEMGWpIrpIOyae6xPprlr7bJMOmn6eMbNjWuqbtIWGJWk2AENjtoeEbelms5kNdvdN4TSiLWF7yl+/meUS/AGqcPeHw+a0vd5G7r7TzJ4CTiOYHpYTjtzEXlPj9a43sxygD1AZScDtdwZwsZl9HOgB9AZuIT2vFQB33xD+3GJmfyJIoNP+33J35e4T4+xaAcwF/iOJ4SRFW9doZp8DPgFM8BRc6LMd/w3Tif42pIEWPtukpZjPM+cT3Bt5CI1gpZ5XgNEWVCbLAyYDcyKOKRnmAFeFz68C/hLTfmVYkexUYFfMdKRuL7zH5nZgibv/PGZXul7vgMZKXmZWAJxLMDf7KeAzYbem19v4e/gM8GSqfEhy9++4+xB3Lyf4//JJd59CGl4rgJn1NLNejc+BjxG80aTlv+VUZ2ajYzYnAe9EFUuymNn5BFN0L3b3vVHHI3HLlM81aauVzzZpo4XPMy3+HbUUej+XUPgN+S+AbOAOd58RbUSdY2b3AWcB/YHNBN+q/hl4EBgGrAH+xd23h/8T/5rgW4O9wOfdfUEEYXeImZ0JPAcs5OB9Ot8lmKucjtd7HEGhg2yCL3QedPcfmNnhBDcy9wNeBy539xoz6wHcQzB/ezsw2d1XRhN9x5nZWcA33f0T6Xqt4XX9KdzMAe519xlmVkIa/ltOdWb2R+BIgr87a4BrGkcg04WZLQfyOTgS/E93vybCkBLKzD4F/AoYAOwE3nD38yINKkHS7XNNU819znH32yMNKoFa+mzj7nOjiyqxWvo802J/JVgiIiIiIiKJoSmCIiIiIiIiCaIES0REREREJEGUYImIiIiIiCSIEiwREREREZEEUYIlIiIiIiKSIEqwRLoBM6s3szfM7G0z+4OZFYbtbmazY/rlmNlWM3ukmXOca2avmtnC8Oc5LbzW981sQ/h6b4TlcUVEpIuZWUnM3+L3Yv427zSzxV0cyyfNbEzM9g/MrN0LH5tZuZk1u/hqVzCz7zbZfjH8GWlcklmUYIl0D/vc/QR3PwaoBRrXbqkGjgkXtYNgYbuW1q7ZBlzk7scSLPB6Tyuvd3P4eiek0zoVIiKpxN0rG/8WA78h/NsMnMDB9YQSxsxyWtn9SeD9BMvdv+fu8xIdQxf4QILl7qdHFYhkLiVYIt3Pc8ComO25wIXh888C9zV3kLu/7u4bw81FQIGZ5SctShERSaZsM/udmS0ys380ftFmZiPN7NFwpsJzZnZU2F5uZk+a2Vtm9oSZDQvb7zSz35jZfOCnzR1vZqcDFwP/E46gjQyP+0x4jpPN7EUze9PMXjazXuHrPWdmr4WPVhMZC/zazJaa2Twzmxtz/tVm1j98Ps7Mng6fjzezl8zs9fD1jwzbP2dmD4fXsczMfhq2/5jgve8NM6sI26qaiSXbzP7HzF4Jf19fDtsHm9mzMTNKPtzJ/4aSoZRgiXQj4beLFxCsht7ofmCymfUAjgPmx3Gq/we85u41Ley/LnxTucPM+nYqaBERSYbRwK3uPhbYSfB3HWAW8BV3/xDwTWBm2P4r4C53Pw6oAH4Zc64hwOnufkNzx7v7i8Ac4FvhiNqKxgPNLA94ALje3Y8HJgL7gC3Aue5+EnBpk9drzqeAIwlGya4E4hlZegf4sLufCHwP+GHMvhPC1z0WuNTMhrr7jRycETKllfNeDexy95OBk4EvmdkI4DLgsXAU8XjgjThiFDlEa0PFItJ1CszsjfD5c8DtjTvc/S0zKycYvWpzOp+ZjQV+AnyshS63Af8FePjzf4EvdDRwERFJilXu/kb4/FWg3MyKCBKTP5hZY7/GmQqnAZ8On98D/DTmXH9w9/o2jm/JkcAmd38FwN13A5hZT+DXZnYCUA8c0cZ5PgLc5+71wEYze7KN/gB9gLvMbDTBe1ZuzL4n3H1XGMtiYDiwLo5zQvD+eFzjCFr4OqOBV4A7zCwX+HPM71+kXZRgiXQP+8JvzFoyB/gZcBZQ0lInMxsC/Am4MvYbyFjuvjmm/++AQwpmiIhI5GJnINQDBQQzj3a28X7RnOrwZ0ePb87Xgc0EIz1ZwP5OnKuOg7OqesS0/xfwlLt/Kvyi8emYfU1/P+35TGsEo3iPHbLD7CME0/LvNLOfu/vd7TivCKApgiKp4g7gP919YUsdzKwY+Btwo7u/0Eq/wTGbnwJUVUlEJAWEo0erzOwSeP++puPD3S8Ck8PnUwhmQ7Tn+D1Ar2Zedikw2MxODo/pFU5n70MwstUAXAFktxH+swRT+bLD96GzY/atBj4UPv9/Me19OFjY6XNtnL/RgXAEqjWPAdMa+5nZEWbW08yGA5vd/XfA74GT4nxNkQ9QgiWSAtx9vbu3Nb/9OoLiGN+zg2V/BwKY2e/NbFzY76cWlHJ/i+AN7uvJi1xERBJsCnC1mb1JUNBoUtj+FeDz4d/2K4Dr23n8/cC3woISIxs7u3stwb1OvwqPeZxglGkmcFXYdhQHR8la8idgGbAYuBt4KWbffwK3mNkCgtGoRj8FfmRmrxP/CNUs4K3GIhct+H0Yx2sWlG7/bXj+s4A3w9e7FLglztcU+QBz96hjEBEREZEMYmZ3Ao+4+0NRxyKSaBrBEhERERERSRCNYImIiIiIiCSIRrBEREREREQSRAmWiIiIiIhIgijBEhERERERSRAlWCIiIiIiIgmiBEtERERERCRB/j+s/9TVHbhRZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# observing the distribution of 'PM 2.5' target value\n",
    "\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(1,2,1)\n",
    "sns.distplot(df['PM 2.5'])\n",
    "plt.title('Distribution Plot')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "stats.probplot(df['PM 2.5'], dist=\"norm\", plot=plt)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79d94405",
   "metadata": {},
   "source": [
    "It can be seen that the target variable 'PM 2.5' have right skewed value, we can handle the null values by two ways \n",
    "- dropping the records as there are only three null records\n",
    "- replacing the null values with the median value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "87b0a691",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing the null values with the median value\n",
    "\n",
    "df['PM 2.5'].fillna(df['PM 2.5'].median(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f4ba53d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T         0\n",
       "TM        0\n",
       "Tm        0\n",
       "SLP       0\n",
       "H         0\n",
       "VV        0\n",
       "V         0\n",
       "VM        0\n",
       "PM 2.5    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking if the null values are replaced successfully\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f164d6fe",
   "metadata": {},
   "source": [
    "## Observing the correlations between the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "290d0827",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(df.corr(), cmap=\"YlGnBu\", annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45dd7531",
   "metadata": {},
   "source": [
    "From the heatmap we can easily identify the correlations between the features and understand their importance in predicting the values for 'PM 2.5'."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35ca58a1",
   "metadata": {},
   "source": [
    "### Dividing the data into dependent and independent features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6375647b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# independent features\n",
    "x = df.iloc[:,:-1]\n",
    "\n",
    "# dependent features\n",
    "y = df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "88504820",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1081, 8)   (1081,)\n"
     ]
    }
   ],
   "source": [
    "print(x.shape,' ',y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba98c6cc",
   "metadata": {},
   "source": [
    "### Understanding the feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "410dbf6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ExtraTreesRegressor()\n",
    "model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "97da57ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.17721673, 0.11420575, 0.19933231, 0.17263996, 0.07541564,\n",
       "       0.15789559, 0.05321369, 0.05008033])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# identifying the importance of features\n",
    "\n",
    "model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8ae5be33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing the feature importance graphically\n",
    "\n",
    "feature_importance = pd.Series(model.feature_importances_, index = x.columns)\n",
    "feature_importance.nlargest(10).plot(kind = 'barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6367a625",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "121e2c03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(864, 8)   (864,)\n",
      "(217, 8)   (217,)\n"
     ]
    }
   ],
   "source": [
    "# splitting the data into train and test\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 0)\n",
    "\n",
    "print(x_train.shape,' ',y_train.shape)\n",
    "print(x_test.shape,' ',y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acb695c9",
   "metadata": {},
   "source": [
    "## Creating the KNearestNeighbors Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a7192280",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(n_neighbors=1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating model\n",
    "\n",
    "regressor = KNeighborsRegressor(n_neighbors = 1)\n",
    "regressor.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6eb5a735",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Score : 1.0 for train data\n"
     ]
    }
   ],
   "source": [
    "# coefficient of determination of the prediction\n",
    "\n",
    "print('Model Score : {} for train data'.format(regressor.score(x_train, y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b89c7c54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Score : 0.115675945270314 for test data\n"
     ]
    }
   ],
   "source": [
    "# coefficient of determination of the prediction\n",
    "\n",
    "print('Model Score : {} for test data'.format(regressor.score(x_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "87988147",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cross validating the model\n",
    "\n",
    "score = cross_val_score(regressor, x, y, cv = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e54e07e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.061220989201742036"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean accuracy obtained on cross validation\n",
    "\n",
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b4dadd8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# making predictions for test data\n",
    "\n",
    "pred = regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3d4ad677",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\prank\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='PM 2.5', ylabel='Density'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# observing the 'y_test - pred' graphically\n",
    "\n",
    "sns.distplot(y_test - pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5175fb3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE :  52.62455837173579\n",
      "MSE :  5626.946083349335\n",
      "RMSE :  75.01297276704433\n"
     ]
    }
   ],
   "source": [
    "# model evaluation\n",
    "# observing 'MAE', 'MSE', 'RMSE'\n",
    "\n",
    "print('MAE : ', metrics.mean_absolute_error(y_test,pred))\n",
    "print('MSE : ', metrics.mean_squared_error(y_test,pred))\n",
    "print('RMSE : ', np.sqrt(metrics.mean_squared_error(y_test,pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af0b459d",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning for K value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "227dcb54",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_rate = []\n",
    "# finding accuracy rate for different K values\n",
    "for i in range(1,40):\n",
    "    knn = KNeighborsRegressor(n_neighbors = i)\n",
    "    score = cross_val_score(knn, x, y, cv = 10, scoring = 'neg_mean_squared_error')\n",
    "    accuracy_rate.append(score.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "292a4d52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Accuracy Rate')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting Accuracy Rate vs K value\n",
    "plt.figure(figsize = (10,6))\n",
    "plt.plot(range(1,40), accuracy_rate, color = 'blue', linestyle = 'dashed', marker = 'o', \n",
    "         markerfacecolor = 'red', markersize = 10)\n",
    "\n",
    "plt.title('Accuracy Rate vs K value')\n",
    "plt.xlabel('K value')\n",
    "plt.ylabel('Accuracy Rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eda8a3da",
   "metadata": {},
   "source": [
    "Here it can be seen that till K = 10 the Accuracy rate was increasing and after that it became nearly constant so we can consider K = 10 for our model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7597bea",
   "metadata": {},
   "source": [
    "## Model for K = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0e0fc201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(n_neighbors=10)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating model\n",
    "\n",
    "regressor = KNeighborsRegressor(n_neighbors = 10)\n",
    "regressor.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1fc1228a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# making predictions for test data for optimized model\n",
    "\n",
    "pred = regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "eb66e6e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\prank\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='PM 2.5', ylabel='Density'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# observing the 'y_test - pred' graphically for optimized model\n",
    "\n",
    "sns.distplot(y_test - pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81110154",
   "metadata": {},
   "source": [
    "## Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b082f10d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE :  42.10979646697388\n",
      "MSE :  3604.6352284370196\n",
      "RMSE :  60.038614477992574\n"
     ]
    }
   ],
   "source": [
    "# observing 'MAE', 'MSE', 'RMSE'\n",
    "\n",
    "print('MAE : ', metrics.mean_absolute_error(y_test,pred))\n",
    "print('MSE : ', metrics.mean_squared_error(y_test,pred))\n",
    "print('RMSE : ', np.sqrt(metrics.mean_squared_error(y_test,pred)))"
   ]
  }
 ],
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