Analysis of the primary complaint type.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Science and Machine Learning Capstone Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1: Which type of complaint should the Department of Housing Preservation and Development of New York City focus on first ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents   \n",
    "    1.Data processing\n",
    "    2.Finding most Frequent complaint\n",
    "    3.Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Proessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "bringing in the data and libraries required for processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib as mlt\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### analyzing the 311 HPD data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>created_date</th>\n",
       "      <th>unique_key</th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>incident_zip</th>\n",
       "      <th>incident_address</th>\n",
       "      <th>street_name</th>\n",
       "      <th>city</th>\n",
       "      <th>borough</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-11-01 07:35:46</td>\n",
       "      <td>44202740</td>\n",
       "      <td>HEAT/HOT WATER</td>\n",
       "      <td>10075.0</td>\n",
       "      <td>416 EAST   78 STREET</td>\n",
       "      <td>EAST   78 STREET</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>MANHATTAN</td>\n",
       "      <td>40.771400</td>\n",
       "      <td>-73.952597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-11-01 17:17:03</td>\n",
       "      <td>44206980</td>\n",
       "      <td>ELECTRIC</td>\n",
       "      <td>10467.0</td>\n",
       "      <td>3324 PERRY AVENUE</td>\n",
       "      <td>PERRY AVENUE</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>40.877661</td>\n",
       "      <td>-73.874858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-11-01 12:46:43</td>\n",
       "      <td>44204492</td>\n",
       "      <td>HEAT/HOT WATER</td>\n",
       "      <td>11219.0</td>\n",
       "      <td>1254 42 STREET</td>\n",
       "      <td>42 STREET</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>40.639556</td>\n",
       "      <td>-73.989068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-11-01 05:31:13</td>\n",
       "      <td>44205138</td>\n",
       "      <td>HEAT/HOT WATER</td>\n",
       "      <td>10459.0</td>\n",
       "      <td>1116 HOE AVENUE</td>\n",
       "      <td>HOE AVENUE</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>40.826121</td>\n",
       "      <td>-73.890890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-11-01 07:40:20</td>\n",
       "      <td>44202027</td>\n",
       "      <td>HEAT/HOT WATER</td>\n",
       "      <td>11225.0</td>\n",
       "      <td>1700 BEDFORD AVENUE</td>\n",
       "      <td>BEDFORD AVENUE</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>40.665006</td>\n",
       "      <td>-73.957127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         created_date  unique_key  complaint_type  incident_zip  \\\n",
       "0 2019-11-01 07:35:46    44202740  HEAT/HOT WATER       10075.0   \n",
       "1 2019-11-01 17:17:03    44206980        ELECTRIC       10467.0   \n",
       "2 2019-11-01 12:46:43    44204492  HEAT/HOT WATER       11219.0   \n",
       "3 2019-11-01 05:31:13    44205138  HEAT/HOT WATER       10459.0   \n",
       "4 2019-11-01 07:40:20    44202027  HEAT/HOT WATER       11225.0   \n",
       "\n",
       "       incident_address       street_name      city    borough   latitude  \\\n",
       "0  416 EAST   78 STREET  EAST   78 STREET  NEW YORK  MANHATTAN  40.771400   \n",
       "1     3324 PERRY AVENUE      PERRY AVENUE     BRONX      BRONX  40.877661   \n",
       "2        1254 42 STREET         42 STREET  BROOKLYN   BROOKLYN  40.639556   \n",
       "3       1116 HOE AVENUE        HOE AVENUE     BRONX      BRONX  40.826121   \n",
       "4   1700 BEDFORD AVENUE    BEDFORD AVENUE  BROOKLYN   BROOKLYN  40.665006   \n",
       "\n",
       "   longitude  \n",
       "0 -73.952597  \n",
       "1 -73.874858  \n",
       "2 -73.989068  \n",
       "3 -73.890890  \n",
       "4 -73.957127  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_selected = ['created_date','unique_key','complaint_type','incident_zip','incident_address','city','borough','latitude','longitude','street_name']\n",
    "df_hpd = pd.read_csv('311.csv',parse_dates = ['created_date'],usecols=columns_selected)\n",
    "df_hpd.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "created_date        datetime64[ns]\n",
       "unique_key                   int64\n",
       "complaint_type              object\n",
       "incident_zip               float64\n",
       "incident_address            object\n",
       "street_name                 object\n",
       "city                        object\n",
       "borough                     object\n",
       "latitude                   float64\n",
       "longitude                  float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#examining the data types of the columns\n",
    "df_hpd.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df_hpd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Each column's datatypes matched, and Use the.isnull() or.notnull() functions to find any missing values in each column.\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m null_data_hpd \u001b[38;5;241m=\u001b[39m \u001b[43mdf_hpd\u001b[49m\u001b[38;5;241m.\u001b[39mnotnull()\n\u001b[1;32m      3\u001b[0m null_data_hpd\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m5\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df_hpd' is not defined"
     ]
    }
   ],
   "source": [
    "# Each column's datatypes matched, and Use the.isnull() or.notnull() functions to find any missing values in each column.\n",
    "null_data_hpd = df_hpd.notnull()\n",
    "null_data_hpd.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True    5892615\n",
      "Name: created_date, dtype: int64\n",
      "\n",
      "True    5892615\n",
      "Name: unique_key, dtype: int64\n",
      "\n",
      "True    5892615\n",
      "Name: complaint_type, dtype: int64\n",
      "\n",
      "True     5811990\n",
      "False      80625\n",
      "Name: incident_zip, dtype: int64\n",
      "\n",
      "True     5839784\n",
      "False      52831\n",
      "Name: incident_address, dtype: int64\n",
      "\n",
      "True     5839784\n",
      "False      52831\n",
      "Name: street_name, dtype: int64\n",
      "\n",
      "True     5812401\n",
      "False      80214\n",
      "Name: city, dtype: int64\n",
      "\n",
      "True    5892615\n",
      "Name: borough, dtype: int64\n",
      "\n",
      "True     5812016\n",
      "False      80599\n",
      "Name: latitude, dtype: int64\n",
      "\n",
      "True     5812016\n",
      "False      80599\n",
      "Name: longitude, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#count missing values in each column\n",
    "for column in null_data_hpd.columns.values.tolist():\n",
    "    print(null_data_hpd[column].value_counts())\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Finding Most Frequent complaint"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "There are no missing values in created_data, complaint_type, or unique-key.\n",
    "In order to respond to this inquiry, we must address the complaint_type field.\n",
    "Enter the name of each sort of complaint in the complaint_type column.\n",
    "Look up the number of complaints for each sort of complaint in the complaint_type column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HEAT/HOT WATER            1168990\n",
       "HEATING                    887869\n",
       "PLUMBING                   705513\n",
       "GENERAL CONSTRUCTION       500863\n",
       "UNSANITARY CONDITION       442858\n",
       "PAINT - PLASTER            361258\n",
       "PAINT/PLASTER              342991\n",
       "ELECTRIC                   304842\n",
       "NONCONST                   260890\n",
       "DOOR/WINDOW                201875\n",
       "WATER LEAK                 190130\n",
       "GENERAL                    148236\n",
       "FLOORING/STAIRS            136102\n",
       "APPLIANCE                  110903\n",
       "HPD Literature Request      52830\n",
       "SAFETY                      50616\n",
       "OUTSIDE BUILDING             7074\n",
       "ELEVATOR                     6555\n",
       "Unsanitary Condition         5499\n",
       "CONSTRUCTION                 5078\n",
       "General                      1163\n",
       "Safety                        424\n",
       "STRUCTURAL                     16\n",
       "Plumbing                       11\n",
       "AGENCY                          9\n",
       "VACANT APARTMENT                8\n",
       "Outside Building                6\n",
       "Appliance                       4\n",
       "Electric                        1\n",
       "Mold                            1\n",
       "Name: complaint_type, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_hpd['complaint_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6L0zzPQocmNp3KSV8lwL3SZoD/AQ4ISKeqdP2d8BzTV6HmZmZNcEPa7UeqVQqUa1Wu25oZma2GtCKfFirmZmZmfU+J2FmZmZmJXASZmZmZlYCJ2FmZmZmJXASZmZmZlYCJ2FmZmZmJXASZmZmZlYCJ2FmZmZmJfDDWq1HNEDBqLKjsL4sxvi/YWa24pT+sNb03sWHC2VjJZ2eji+T9LSkddL5Zuml20haQ9L3JT0saZakByW9JzdOq6SQdFBh/JA0Lnd+uqSx+bklXZxegfSIpMXFVylJ6ifpOUnnF8aenF4FNCPF05rKf5l/b6akPSTNlLRWof9aks6XNFfSdElTJB2c6jaUdIWkxyXNS8cb5r7HkPTZ3FgTJI3IfY/zU1yPpb5b59o+IWnL3HU+m7732vnakl7JtR8k6Y50rXMlfTX3Ls0Rkt6UtGuu/cMdvE7KzMzMemh5LkcuAT5Vp/xYYACwa0R8gOy9jf/M1bcB96Q/814FjpJUfAflUhFxSkS0Ah8F5kVEa/pcl5ocQPZuyGNyL8muOT4idiN7ofUFqewLwBmSNk/vdpwA/E9EvF7o+7/AVsAuETGY7H2bG6S6nwF/jogdImJ7YD7w01zfvwOfk7R2B5d1RoprJ+Ah4I5C2yW16yR71+b43HW/VmskaT3gRuD8iNgJ2A3YC/if3FgLgLM7iMPMzMx60fJMwi4ie0l3v0L5VsAzEfEmQEQsiIh/AKTE6Biyl2AfIGndXL83yF5wPboHMbUB3wOeAvbsoM0UYGCK7W9kL9D+DnAyMDMi7sk3ltQf+G/gsxHxaq1fRFwraQdgCFmSVnMuUJG0fTp/DrgdOLGzwCMzHngWOLixy32b/wLujYjb0niLgFOBL+Xa3AwMkrRTN8Y3MzOzJizPJOwpsjta/7dQfi1wWFouGydp91zdXsD8iJgHTAYOKfS9GDi+tpzXjJTQ7Q/cBFzFsnfaag4CJuXOLwF2Bs4AzqzTfgfgqYh4qU7dzkB7RCypFaTjdmBQrt23gdMlrdnApUwH3tdAu6JBwLR8Qfqe15f0zlT0JlnC+ZVujG9mZmZN6G4S1tFO2GL5t8iSl6XzRMQCsqW1L5P9T/92Sful6jbg6nR8NYVEKSU6VwCndSPmQ4E7I2IxcD1wRCHp+aWk+WTLcRfn5nwT+DFwS0Q83415uxQRfwYeILtb1ZXiMmpvuxL4UH6f3jIBSCMlVSVVWbScozEzM1tNdTcJex7YuFC2CbAwXxARc8nu+ny8UP5qRNwSEWcA3+SthOhjwNfSJv4fAAdJ2oC3uwg4CXhHkzG3AfunsacBmwL75uqPB7YDLk9z572ZPvU8Drw7dzcp7xGgNe0nA7IfJgCtqS7vm8BZdJ1k7Q482kWbeh4hWxpdStJ2wCv5u3gR8QYwLsVSV0RMjIhKRFTo341IzMzMrHtJWES8AjwjaV8ASZuQLePdU6f5ecDptRNJgyUNSMdrALsCTwL7ke252iYiWiJiW7I7VkcW5n6BbEnzpEbjTQnS3sC709gtwCkse6ctgK+S3QlqaMkv7a36GfC92ob5tJH/mIh4nGwz/Tm5LucA01Ndfpw5ZInSYR1cgySdRran7tZGYiv4JTBU0v5pvPWA75MtPxZdRrZ0u3k35jEzM7MG9GRP2CeAr0pqB+4Avp72GL1NRMwm28dUswVwk7JHXMwk23A/gSwhuqHQ/Xrq790aB3T4K8k6jgTuqG2cT35DtjdtnUK8i9P4ZzQx/jlkG+wfSdd1M1C7u3QS8N70eIp5wHvpOIE8D9i6UHaBpBlkv+r8P8Dw/K8eG5Wu63DgHEl/AmYBD5J998W2r5ElaFs0O4+ZmZk1xg9rtR6pVCpRrVbLDsPMzGyFUNkPazUzMzOznnESZmZmZlYCJ2FmZmZmJXASZmZmZlYCJ2FmZmZmJXASZmZmZlYCJ2FmZmZmJXASZmZmZlYCP6zVekQDFIwqOwpbGcQY/7fEzFZ/flhrgyQtkdQu6WFJv5LUP1d3hKTIvyNSUkt67RCShqX6w3L1N6fyG9K4j0t6MR23S9qridiekDRL0kxJt0l6V6687iuZJE2SdH+hbCdJk9P8j0qaKOnAXEyvSPpTOr4ixZ+PuT33Psn893WTpI0avR4zMzNrzmqdhAGLI6I1InYBXgNOztW1kb1wvN67KWsWAGcXCyPiyIhoBT4N3J3maI2I+5qMb3hE7ApUga901jAlREOADSVtl6v6PjA+zf9+4AcR8ftaTGns49P5J1KffMytEfHHVJ7/vl4ge8m5mZmZLQerexKWdzewA4Ck9YGhZC/SPq6TPjOAFyUdsJxju6sWWyeOAm4CrubtMW9FliwCEBGzeimmKcDAXhrLzMzMCvpEEiapH3AwUEtQDgdujYjHgOclDemk+3nAOcs5xENzsXWkDbgqffJ378YDd0i6RdLoBpcQ9y4sR26fr5S0JrAfcGPjl2BmZmbNWN2TsPUktZMtyT0F/CyVt5HdUSL92eGSZETcBSBp6HKI784U3zuBb3XUSNKWwI7APSlxfF3SLim+nwPvB34FDAPul7ROF/MWlyPnpfLa9/UssCXwhw7iGSmpKqnKooav1czMzHL6lR3AcrY47YtaStImwL7AByQFsCYQks7oZJza3bA3Gp1Y0jZky4cAl0TEJXWaDY+IhQ0M93FgY2C+JMiStjbSfrWI+CtwKXBp+mHBLsC0RmPNWRwRrekHDL8n2xP2/WKjiJgITIT060gzMzNr2up+J6yeo4FfRMS2EdESEdsA84G9O+oQEbeRJUG7NjpJRPwld6epXgLWjDbgoBRvC9kG/eMAJB0kaa10/C5gU+DpnkwWEYuA04AvpqVcMzMz62V9MQlrA24olF1P57+ShOxu2DbLJaJlzZS0IH1+DWwLLH00RUTMJ/vBwB7AfwAPS5pBdvfqjIh4tovxi3vCji42iIiHgJl0/b2YmZlZN/hhrdYjlUolqtVq2WGYmZmtEH5Yq5mZmdkqzkmYmZmZWQmchJmZmZmVwEmYmZmZWQmchJmZmZmVwEmYmZmZWQmchJmZmZmVwEmYmZmZWQn8sFbrEQ1QMKrsKGxFijH+b4aZ9V1+WGsvkLQkvbLnYUm/Si+trtUdISkkvS9X1pJejo2kYan+sFz9zan8hjTu45JezL0aaK/U7jhJZ0saIem5VPeIpP9O9SMkTegg5tY070GF8rMlzZY0M423R2dxSJos6U+5suvSOGMlPZ2Lya8sMjMzW076bBIGLE4v194FeA04OVfXBtxD5+9NXACcXSyMiCMjohX4NHB37iXe96UmBwO3puNrUtthwDclbdlFzMvEJWlP4FBgcETsCuwP/KWBOI7PleXfHTk+9Tsc+HHt5eBmZmbWu/pyEpZ3N7ADgKT1gaHAScBxnfSZQfYS7QManUSSgFZger48Iv4OzCN7UXdnfY8BRgAHSFo3VW0FLIyIV9NYCyPir43G1JGImAssAjbu6VhmZma2rD6fhEnqR3Z3alYqOhy4NSIeA56XNKST7ucB5zQx3e7AjChsxJO0HbAd8HgnffcC5kfEPGAycEgqvw3YRtJjkn4oaZ8GY/llbjnygmKlpMHA3JQgmpmZWS/ry0nYepLagSrwFPCzVN4GXJ2Or6aTJcmIuAtA0tAG5zwIuCV3fmyK4SpgVES80EnfunFFxCvAEGAk8BxwjaQRDcSSX448I1c+WtJs4AGyJHMZkkZKqkqqsqiBmczMzGwZ/coOoESL096npSRtAuwLfEBSAGsCIemMegMktbthbzQw538AH8udXxMRp3bVSdKaqd/hks4GBGwqaYOIeDkilpDdHZssaRZwInBZA/HUMz4iLpT0n8DPJG0fEf/ON4iIicBESL+ONDMzs6b15Tth9RwN/CIito2IlojYBpgP7N1Rh4i4jWzf1K6dDSxpQ6BfRDzfjbj2A2ZGxDYprm2B64EjJe0kacdc21bgyW7M8TYRcSPZXcITezqWmZmZLctJ2Nu1ATcUyq6n819JQnY3bJsu2hwA/LHBOEZIWlD7dBHX+sDl6ZESM4GdgbENzJHfE9ZRXOcCX5DkvydmZma9zA9rXUEk/RT4aUTcX3YsvckPa+17/LBWM+vLevNhrU7CrEcqlUpUq9WywzAzM1sh/MR8MzMzs1WckzAzMzOzEjgJMzMzMyuBkzAzMzOzEjgJMzMzMyuBkzAzMzOzEjgJMzMzMyuBkzAzMzOzEvhhrdYjfmL+qstPvjcza54f1roKkrQk967GdklfSuWTJVUKbYdJerHQfv9U9y5JV0uaJ2mapN9J2i3X7gVJ82vvhJTUImlxOn9E0hWS1srNc3Nu3oMlVVO7hySNW5HfkZmZWV/Sr+wA+pDFEdHaRPu7I+LQfIEkkb3I+/KIOC6V7Qa8sza2pMuAmyPiunTeAsyLiFZJawJ/AD4O/LIw9i7ABOCQiJiT2o5s+irNzMysIU7CVi3Dgdcj4pJaQUTMaLRzRCyRNBUYWKf6TOC8iJhTawv8qIfxmpmZWQe8HLnirFdYXjy2i/Z7F9pvD+wCTOtuAJLWBfYAbq1T3fDYkkamZcsqi7objZmZWd/mO2ErTm8sR3Z37u0ltQPvAX4bETO7OxBAREwEJkLamG9mZmZN852wVctsYEg3+s1LCeD2wBBJ/9mLY5uZmVk3OAlbtdwBrCNp6YZ5SbtK2ruRzhGxEPgS8OU61RcAX5H03jTuGpJO7oWYzczMrA4nYStOcU/Y+bm630pakD6/SmXFPWFHR/ZQtyOB/dMjKmYD3wKebSKOSUD/YuKWlig/D1wl6VHgYWC77l6smZmZdc4Pa7UeqVQqUa1Wyw7DzMxshfDDWs3MzMxWcU7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgh7Vaj2iAglFlR9E3xRj/u2tmtqL5Ya0rGUkhaVzu/HRJY3PnIyXNSZ+pkobm6iZLqubOK5Im584/KOkuSX+S9JCkn0rqn+qOkDRT0qOSZkk6ItfvMklPS1onnW8m6Yl0vIak70t6OPV7UNJ7JD2QXpH0lKTncq9MalkOX5uZmVmf1q/sAFYTrwJHSfpWekn2UpIOBUYBQyNioaTBwCRJH4yI2jsft5B0cETcUui7JfAr4LiImJLKjgY2kLQjcCFwQETMl/Qe4A+S/pzeAwmwBPgU8KNCvMcCA4BdI+JNSVsD/4qIPdIcI4BKRJza86/GzMzM6vGdsN7xBjARGF2n7izgjFpyFhHTgcuBU3JtLgDOrtP3FODyWgKW+l8XEX8DTge+GRHzU/l8spd5n5HrfxEwWlIx2d4KeCYi3kx9F0TEPxq9WDMzM+s5J2G952LgeEkbFsoHAdMKZdVUXjMFeE3S8EK7Xer0bWbcp4B7gP9baHctcFhaahwnafcO5qgrLa9WJVVZ1ExPMzMzq3ES1ksi4iXgCuC0bg7xDeCc3otoqdrdsaX/rCNiAbAT8GXgTeB2Sfs1OmBETIyISkRU6N/b4ZqZmfUNTsJ610XAScA7cmWPAEMK7YYAs/MFEXEHsB7woVzx7Dp9mx13LtAOfLxQ/mpE3BIRZwDfBI7AzMzMVhgnYb0oIl4gW+o7KVf8HeDbkjYFkNQKjAB+WGeIbwBn5s4nACdK2qNWIOmotGH/QuDLtV8upj+/AoxjWeeR7SGrjTFY0oB0vAawK/BkwxdqZmZmPeZfR/a+ccDSXxVGxI2SBgL3SQrgZeCEiHim2DEififpudz53yQdB1woaQuypcO7gFtT3VnATZLWAl4HzoyI9jrjzpY0HRicirYAflJ7fAUwlSzhMzMzsxXED2u1HqlUKlGtVrtuaGZmthrww1rNzMzMVnFOwszMzMxK4CTMzMzMrAROwszMzMxK4CTMzMzMrAROwszMzMxK4CTMzMzMrAROwszMzMxK4Ie1Wo9ogIJRZUexcosx/nfMzGx14Ye1dpOkJZLaJc2WNEPSF9O7E2v1QyVNlTQnfUYW+o/M1U2VNDRXN1nSn9K4D6Z3ROb7fknS8ZKel6RUtqekkLR1Ot9Q0guS1pB0maSjc2NXc2NVJE1Ox8MkvSjpoTT/XZIObSRuSYdLmpRr92VJj+d39gxAAAAgAElEQVTOD5N0Yw++cjMzM+tAn0rCgMUR0RoRg4ADgIOBMQCS3gVcCZwcEe8DhgKjJB2S6g8FRgFDU/3JwJWpX83xEbEb2cu5LyjMfSBwG/AM8P5UthfwUPoT4EPA1Ih4s07sW0g6uIPrujsido+InYDTgAmS9msg7vvSnDV7Ai+l91TW4ruvgznNzMysB/paErZURPwdGAmcmu5MnQJcFhHTU/1C4EzgS6nLWcAZqZzU7vLUr2gKMLB2IumdwNoR8RxZUlNLuvYCxhfO7+0g5AuAsxu4rnbgXN56iXiHcad4XpK0Q2o7ELi+wXjMzMysB/psEgYQEX8G1gS2AAYB0wpNqqmcBurzDgIm5c73B25Px/fyVpKzHfAroLa23NmdpynAa5KGd1CfNx14X4Nx3wvsJWknYC5wfzrvB+wGPFgcPC1vViVVWdRANGZmZraMPp2ELQe/lDSf7I7Vxbnyg4Bb0vF9ZEnOe4AnIuLfgCStDwwBHuhk/G8A5zQQh5qIuXZnbi+yRG8qsAewOzAnxfc2ETExIioRUaF/EzOZmZnZUn06CZO0HbAE+DvwCFkSlDcEmJ2Ou6oHOJ7s7tblwA9y5R8kS26IiLnARsBhZEkPZHeqPkmWlL3SUbwRcQewHm/fx1XP7sCjDcZduzO3FzAlIl4G1gWG4f1gZmZmy02fTcIkbQ5cAkyI7DkdFwMjar9qlLQp8G3gO6nLd4Bvp3JSuxFkm/CXSmN9FfiQpPdJGkR2R2lJrtn9wOd4KwmbAnyexvZffYNsr1pH17Vrmr92J66ruB8FBpD9EOGhVNZOtoHf+8HMzMyWk35lB7CCrSepHVgLeAP4BfBdgIh4RtIJwE8kbUC2pHdRRNyU6m+UNBC4T1IALwMnRMQzxUkiYrGkccAZZEnOrYUm9wIfJdubBVkSth0N3HmKiN9Jeq5QvLekh4D+ZHf1TouI2xuJOyJC0gPAhhHxei6ekY3EY2ZmZt3jh7UuZ5L+AHyiXrK2OqhUKlGtVrtuaGZmthrozYe19rU7YStcRBxQdgxmZma28umze8LMzMzMyuQkzMzMzKwETsLMzMzMSuAkzMzMzKwETsLMzMzMSuAkzMzMzKwETsLMzMzMSuCHtVqPaICCUWVHsXKJMf53ysxsddWbD2v1nbAGSBov6fO5899L+mnufJykL+TOPy/p35I2TOcHSmpPn1ck/SkdXyFpmKQXc/XtkvZP/Zak84cl3SRpow7iW+al35LGSnq6MO5GufqLUv0aubIRkiak4zUkXS7pUknq2TdoZmZmRU7CGnMvsBdkyQmwGTAoV78Xb3/PYhvwIHAUQET8PiJaI6KV7H2Rx6fzT6T2d9fq0+ePqXxxOt8FeAE4pcm4xxfG/WfuGo4E/gLsU+yUkq5LyN6x+enw7VIzM7Ne5ySsMfcBe6bjQcDDwMuSNpa0DvB+YDqApO2B9YFzyJKx3jIFGNhLYw0DZgM/on6M3wc2JXvn5Zu9NKeZmZnl+N2RDYiIv0p6Q9K7ye561RKiPYEXgVkR8VpqfhxwNXA3sJOkLSPib11Msbek9tz5xyJiXu1E0prAfsDPmgx9tKQT0vE/ImJ4Om4DrgJ+A3xT0loR8Xqq+y/gUWBYRLxRb1BJI4GRAGzYZERmZmYG+E5YM+4jS8BqSdiU3Pm9uXZtwNXpDtL1wDENjF1cjqwlYOul5OxZYEvgD03GnF+OHA4gaW3go8CkiHgJeAA4MNdnOrAt8MGOBo2IiRFRiYgK/ZuMyMzMzAAnYc2o7Qv7ANly5P1kd8KW7geT9AFgR+APkp4guyvWkyXJxWkf2baAaH5PWD0HAhsBs1KMQ3l7jHOAjwPXSBq0bHczMzPrDU7CGncfcCjwQkQsiYgXyJKZPXlrU34bMDYiWtJnADBA0rY9mTgiFgGnAV+U1NMl5DayzfYtEdECvAc4QNLSe1oRcR/wGeDmtARrZmZmvcxJWONmkf0q8v5C2YsRsTCdHwfcUOh3QyrvzN6FR0kcXWwQEQ8BM6l/Z62/pAW5T+1xGaML4+4MHAT8Njfuv4B7gMMK890EnAvcKmnTLuI3MzOzJvlhrdYjlUolqtVq2WGYmZmtEH5Yq5mZmdkqzkmYmZmZWQmchJmZmZmVwEmYmZmZWQmchJmZmZmVwEmYmZmZWQmchJmZmZmVwEmYmZmZWQn8sFbrEQ1QMKrsKHpfjPG/F2Zmtiw/rHUVJWlLSVdK+rOkaZKmSDpS0jBJLxZeMbR/6hOSxuXGOF3S2HQ8VtLThX4bFcabI+nCOrFMknR/oWyspNOX89dgZmZmOAlbYSQJmATcFRHbRcQQsndKbp2a3B0RrbnPH1P5q8BRkjbrYOjxhX7/zI8H7A4cKunDuVg2AoYAG0rarpcv1czMzBrgJGzF2Rd4LSIuqRVExJMR8YMu+r0BTARGd2fSiFgMtAMDc8VHATcBV9P1y8XNzMxsOXAStuIMAqZ3Ur93YVlx+1zdxcDxkjas0290rs+dxUpJGwM7AnflituAq9KnrekrMTMzsx7rV3YAfZWki4GhwGvAGWTLh4fWaxsRL0m6AjgNWFyoHh8Ry+z5IkvqZpAlYBdFxLNp3i1T2T0REZJel7RLRDzcROwjgZEA1EsLzczMrEu+E7bizAYG104i4hRgP2DzBvtfBJwEvKPB9ndHxG5kd+BOktSayj8ObAzMl/QE0EKTd8MiYmJEVCKiQv9mepqZmVmNk7AV5w5gXUmfyZU1nMJExAvAtWSJWMMiYj5wPnBWKmoDDoqIlohoIdug731hZmZmK5iTsBUksgeyHQHsI2m+pKnA5byVHBX3hB1dZ5hxQPFXkqML/Vrq9LsE+Eiq2xZY+miKlKS9KGmPVHSOpAW1T/eu1szMzLrih7Vaj1QqlahWq2WHYWZmtkL4Ya1mZmZmqzgnYWZmZmYlcBJmZmZmVgInYWZmZmYlcBJmZmZmVgInYWZmZmYlcBJmZmZmVgInYWZmZmYl8MNarUc0QMGosqPomRjjfwfMzKwxflhrjqQlxdf2SBom6eY6bdeWdJGkxyXNlfQbSVvn6rdOZXMlzZP0PUlrp7phkl5Mc8yRdGGu3whJE9LxWEmLJG2Rq38ld7ylpCsl/VnSNElTJB1ZiHOapHUkfUrSLEkzJT0s6XBJF6cYHpG0uPiaI0n9JD0n6fzCmJMlVdLxE7lx/5+kbXPtzpY0O9W1515nZGZmZr1olU/CgMUR0Zr7PNFJ228CGwA7RcSOwCTg10qAXwOTUt17gfWB83L9746IVmB34FBJH+5gnoXAF4uFaY5JwF0RsV1E1F6enU8E3wM8DWwOnA0MjYhdgQ8BMyPilBTDR4F5ueu+Lg1xAPAYcEyaryPD07iTgXPS3HsChwKDU93+wF86GcPMzMy6aXVIwhoiqT/wSWB0RCwBiIifA68C+6bPv1MZqc1o4FOp71IRsRhoBwZ2MN2lwLGSNimU7wu8FhGX5MZ6MiJ+kGtzEHArsAXwMvBKavdKetl2V9qA7wFPAXs20H5K7jq2AhZGxKtpzoUR8dcGxjAzM7MmrQ5J2Hq5JbkbOmm3A/BURLxUKK8Cg9JnWr4itX0q9V1K0sbAjsBdHcz1Clki9rlC+SBgeicxwltJ2Azgb8B8ST+XdFgX/ZC0Ltndq5uAq8gSsq4cRHZ3DuA2YBtJj0n6oaR9GuhvZmZm3bA6JGH55cgju27eI3tLmkG2XPj7iHi2k7bfB06UtEFHDdL+rhmSHkznawNbR8Sf0524g4CjyZYXx0sa20V8hwJ3pjt11wNHSFqzg7Z3SnoaOJgsYSMiXgGGACOB54BrJI2oE/dISVVJVRZ1EZGZmZnVtTokYY2aB7y7TlI0BJgNPJKOl5L0TuDdwOOp6O6I2I3sjtZJklo7miwi/glcCZySK54NDM61OQXYj2z/F8DewD25+oiIqRHxLbK9Yx/r4hrbgP0lPUF2V29TsiXQeoYD25Itq349N+eSiJgcEWOAU+vNGRETI6ISERX6F2vNzMysEX0mCYuIfwGXA9+t3R2S9AmgP3AHcDvQP5WR2owDLouIRYWx5gPnA2d1Me13gVFAv3R+B7CupM/k2uTTmIOAW9L8AyQNztW1Ak92NFFKGPcG3h0RLRHRQpYAdrgkGRFvAJ8HPiFpE0k7Sdqx0TnNzMys+1bnJGw/SQtynz2BLwP/Bh6TNBc4Bjgy3XEK4EiyXxXOJVsC/DfwlQ7GvwT4iKSWjgKIiIXADcA66TyAI4B9JM2XNJUsMawlc8OA/5eO1wIuTI/DaAeOZdk9ZnlHAnfUNtUnvwEOk7ROJzE+Q7YceQrZr0EvT4+/mAnsDIztZE4zMzPrJj+sdSWRnlf2k4g4uOxYmlGpVKJarZYdhpmZ2QqhXnxYa7+um9iKEBELyDbJm5mZWR+wOi9HmpmZma20nISZmZmZlcBJmJmZmVkJnISZmZmZlcBJmJmZmVkJnISZmZmZlcBJmJmZmVkJ/LBW6xENUDCq7Cg6FmP899vMzHpPbz6s1XfCepmkIySFpPel8xZJiyW1p9cBXSJpjS7KH64z7mWSjs6dbybpdUknF9o9Ien63PnRki7LnR8sqZrmfEjSuFQ+VtLTKZ7aZ6Pl8BWZmZkZTsKWhzbgHt7+4ux5EdEK7Er2PsYjuihvxDHA/dR/QfcQSTsXCyXtAkwAToiInYEK8HiuyfiIaM19/tlEPGZmZtYEJ2G9SNL6wFDgJOC4Yn1EvAHcB+zQSHkX2oAvAgPTeyfzxgFn1+lzJnBeRMxJ8y6JiB81MaeZmZn1Eidhvetw4NaIeAx4XtKQfKWk/sB+wKxGyjsiaRtgq4iYClwLHFtoci0wWFIxqdsFmNbJ0KNzS5F3NhKLmZmZdY+TsN7VBlydjq/mraXC7SW1A/cCv42IW7oo78qxZIlWcZ6aJcAFwJebjD+/HDm8o0aSRqZ9ZVUWNTmDmZmZAdCv7ABWF5I2AfYFPiApgDWBAC7mrb1fRR2Vd6UNeJek49P5AEk7RsTcXJtfkCVh+U3+s4EhwIxuzLlUREwEJkL6daSZmZk1zXfCes/RwC8iYtuIaImIbYD5wDa9OYmk9wLrR8TANE8L8C0Kd8Mi4nVgPDA6V3wB8JU0BunXmG/7daWZmZmtGE7Cek8bcEOh7HqaXxIE2EnSgtznmAbmqfcryZ+Ru9sZETOBzwNXSXqU7C7Zdrn2+T1h7ZJauhG7mZmZNcAPa7UeqVQqUa1Wyw7DzMxshfDDWs3MzMxWcU7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgJMzMzMysBE7CzMzMzErgh7Vaj2iAglG9M1aM8d9FMzNbua3UD2uV9ErhfISkCel4rKSn0ytxHpb0n3XK50r6taSdOxj/MklHF8oGSLouHbdK+mgvXs9Gkv6nt8arM/4ISc+la58jaXTXvZav3v4OzczMbFllLEeOj4hW4BjgUklr5MsjYkfgGuAOSZs3MmBE/DUiaolZK9BUAiGpXyfVGwFNJ2GS1myi+TXpO/kwcLakXn3pdzc0/R2amZlZc0rbExYRjwJvAJvVqbsGuA34r0bGktSS7qytDZwLHJvuLB0r6R2SLpU0VdJDkg5PfUZIulHSHcDtktaXdLuk6ZJm1doB5wPbp/EukDRM0s25uSdIGpGOn5D0bUnTgWMkbS/pVknTJN0t6X1dfCfPA48DW6XxNpd0vaQH0+fDqXxTSbdJmi3pp5KelLRZ7XvIxXa6pLHpuG4sko5J390MSXfV+w4b+WdgZmZmzensDlB3rSepPXe+CXBjsZGkPYA3gec6GGc60GnSUhQRr0n6GlCJiFPTPN8E7oiIT0naCJgq6Y+py2Bg14h4Id0NOzIiXpK0GXC/pBuBLwG7pDtVSBrWRRjPR8Tg1PZ24OSImJuu94fAvh11lPRuYF1gZir6HtkdwntS3e+B9wNjgHsi4lxJhwAnNfD1TOwglq8BB0bE05I2qvcdmpmZWe9bHknY4lrCAtkdJyC/gW20pBOAl4FjIyIk1RunbmE3/Afwn5JOT+frAu9Ox3+IiBdy831T0kfIksOBwJbdmO8aAEnrA3sBv8pd3zod9Dk2zfs+4NSI+Hcq3x/YOdf/nWncjwBHAUTEbyX9o7OAuojlXuAySdcCv27kAiWNBEYCsGEjPczMzKxoeSRhXRkfERc20G53oNoL8wn4WET86W2F2d2gf+WKjgc2B4ZExOuSniBL2Ire4O3LuMU2tTHXAP6ZT0g7cU1EnCqpAtwm6caIeDaN8aFcUlaLvaNxOoqtw1gi4uT0XRwCTJM0pKtgI2Ii2Z217NeRZmZm1rSV8jlhkj5Gdgfrqm50fxnYIHf+e+CzSpmLpN076Lch8PeUgA0Htu1gvCfJ7k6tk5Y396s3WES8BMyXdEyaV5J26yzwiKgCvwA+l4puAz5bq5dUS6LuIu2Xk3QwsHEq/xuwRdoztg5waFexSNo+Ih6IiK+RLQ1vU+eazczMrJetTEnY6LQRfC5wArBvRHS0X+zHkhakz5RC3Z1kSVJtU/n/AmsBMyXNTuf1/BKoSJoFfAKYA0s3y9+bNq9fEBF/Aa4FHk5/PtTJNR0PnCRpBjAbOLyTtjXfBj4paQPgtBTTTEmPACenNl8HPpKu5yjgqRTr62Sb6qcCf6hdQxexXJB+iPAwcB8wg2W/QzMzM+tlfljraiAtnVYiYuGKnrtSqUS12hurxmZmZis/rcwPazUzMzOzrpWxMd96WUS0lB2DmZmZNcd3wszMzMxK4CTMzMzMrAROwszMzMxK4CTMzMzMrAROwszMzMxK4CTMzMzMrAROwszMzMxK4CfmW49ogIJRPR8nxvjvoZmZrfz8xPzVnKSzJc1O74xsl7RHKt9M0uuSTi60fyK9/7E9ffaS1CJpca6sXdInJD2Qjp+S9Fyu7peSPpMbc480/1or+vrNzMz6Aj8xfyUjaU/gUGBwRLwqaTNg7VR9DHA/0AZcUug6PP/uSEktwLyIaC20uyLVjyB73+Sp6XxLYIqk64DngQnA/6SXgpuZmVkv852wlc9WwMKIeBUgIhZGxF9TXRvwRWCgpK17c9KI+BtwIfAd4GRgZkTc05tzmJmZ2VuchK18bgO2kfSYpB9K2gdA0jbAVhExFbgWOLbQ7860rPhArmz7wnLk3l3MfQmwM3AGcGZHjSSNlFSVVGVRs5dnZmZm4OXIlU5EvCJpCLA3MBy4RtKXgM3Iki+Aq4FLgXG5rm9bjkzqLUd2Nvebkn5Mtkz5fCftJgITIW3MNzMzs6Y5CVsJRcQSYDIwWdIs4ERgAPAuScenZgMk7RgRc3t5+jfTx8zMzJYjL0euZCTtJGnHXFErsCawfkQMjIiWiGgBvkW2R8zMzMxWQU7CVj7rA5dLekTSTLI9Wg8ANxTaXU/XSVhxT9hpyyFeMzMz6wY/rNV6pFKpRLVaLTsMMzOzFcIPazUzMzNbxTkJMzMzMyuBkzAzMzOzEjgJMzMzMyuBkzAzMzOzEjgJMzMzMyuBkzAzMzOzEjgJMzMzMyuBH9ZqPaIBCkZlxzHGf5fMzGz11icf1ippa0m/kTRX0jxJ35O0dqobIWlCof1kSRVJD6RX9jwl6bncK3xaJH1K0ixJMyU9LOnw1PcySUfnxvlTajNH0gRJG+XmWVJ4NdCX6sR+maT5qX6OpDG5uickbZY7Hybp5uJ1SRor6fQ6Y9fmny1phqQvSlqjg7HelLRrru/DklrS8fqSfpS+2+mSpkn67+b/SZmZmVkjVokkTJKAXwOTImJH4L1k71g8r6u+EbFHRLQCXwOuiYjWdP4GcDYwNCJ2BT4EzOxgmONTm12BV4Hf5OoW18ZMn/M7GOOMNG8rcKKk93QVe4Nq8w8CDgAOBsZ00HYB2TXX81PgH8COETEYOAjYpJdiNDMzs4JVIgkD9gX+HRE/5/+3d+fhdlR1use/rwwmyCwRQYYAAjZhiGSjKKioqKAoDmlJGoU43DigjfbFFgRtuFeuIiqKIHQcAJFJQZBGWpyIIjLtA0kIYRAIKIKa0DIEImB47x+1DhSbM+fk1EnO+3me/eyqNa86Rfy5qmoXYHsZ8Eng/ZLWGmKbLwAeBpaUNpfYXthXBduPA/8ObCFplyH2O658PzLE+r2y/VdgJvCxErh2ugSYJGn7eqKkbYCXAUfZfrK0tcj2ccM9xoiIiKisLEHYJKCrnmD7IeAPwIuH2OZc4C/AQkmnSXrrQCqVAHAu8JKSNL7jcuQBvVQ9XtIcqtWoc0vANOxs3wmsRhVkdnoS+BLwmY70ScDc7gCsP5JmSmpLavPocg03IiJizFpZgrD+9HZHeK93ipdgah9gKnAbcIKkowfYX32VqfNy5Hm91Om+HPlC4PWSXtnHGFfkHe5nA7v3dTlU0pEloLy3p3zbs2y3bLcY6jpkRETEGLeyBGELgCn1BEnrAlsAtwP3Axt01NkQWNxXo65ca/sLwDTgXf0NRNJqwE7AzQMe/TP7XALMBvYsSZ1j73fc/Yxva2AZ0ONKm+1/AF8BPl1LXgDs0n1Dv+1jS8C47lDHEREREX1bWYKwXwJrSToIngqEvgKcbvtR4DpgD0kvLPkt4LnAH3trUNKmknatJU0G7u5rEJLWAL4A/NF2bzfx90nS6sDLgTtK0mzgvSVvNeA9wOVDbHsCcCpwkvv+7ZHTgb2BCQC2bwfawOfLGJA0jmeu+EVERMQwWr3pAQyEbUt6B/BNSZ+lCh4vpdzbZPsvkg4FLi2rOUuA6f3c47QG8GVJmwJ/BxYBH+6l7FmSHqMK7H4B7F/LG1/u9er2U9vP+pkKqnvCjgLWpAoqf1TS/y9wiqS5VEHPT4Hv9zKOoyR9onvH9ma1/tegeuLzTOCrvdTvrve4pBOBr9eSPwgcD9wu6X5gKdVDCBEREbEC5MdaY7m0Wi232+2mhxERETEixuSPtUZERESsShKERURERDQgQVhEREREAxKERURERDQgQVhEREREAxKERURERDQgQVhEREREAxKERURERDQgQVgsl657u9AxebtRRETEYCUIW0EkLZM0p/Y5vKTPLu+2rJfdS9KDHeX3lnS5pDd1lP2EpFM69v8uab2y/6ZaG0sk3Vq2v1fy95R0raRbymdmra2jJf2plF8gafqKPEYRERFj2Urx7siV1FLbkwdR/grb+9UTJG0NTAMuqyVP45nvdJxO9QLzdwKn2b6su7yk2cBhtttl/4XA2cDbbV8vaSPgMkl/sv2T0t4Jtr8saVugS9L5tp8YxDwiIiJiALISNrqdD7xF0poAkiYCmwJXlP1tgLWBo6iCsf4cApxu+3oA24upArpnvXDc9u+BR4ENlncSERER8WwJwlac8R2XFw/op/yrOspvY/t/gGuBfUuZacAP/PRb16cB51IFZdtL2rifPiYBXR1p7ZL+DJJ2BX5v+6895M2U1JbU5tF+eoyIiIge5XLkirPclyOLc6iCrR+X7w/U8qYD77D9pKQLgH8GThrqgItPSnofsB3w1p4K2J4FzALQpnJPZSIiIqJvWQkb/X4MvL6sTK1luwtA0k7AtsDPJd1FFaD1d0lyATClI20KcFNt/wTbk4B3Ad+RNG75pxARERGdEoSNcraXAJcD36VaFes2HTja9sTy2RTYVNKWfTR3MjBD0mQASc8HjgO+1EO/F1Ndqjx4eGYSERERdQnCVpzOe8K+WMv7iaR7yueHJa3znrCptfLnALvwzCBsGnBhR58XlvQe2b4PeA/wLUm3AL8Dvmv7v3qp8n+Af5OU8yQiImKY6el7vCMGr9Vqud1uNz2MiIiIESGpy3ar/5L9ywpHRERERAMShEVEREQ0IEFYRERERAMShEVEREQ0IEFYRERERAMShEVEREQ0IEFYRERERAMShEVEREQ0IEFYLJeue7vQMWp6GBERESudxoMwSRMlze9IO1rSYSPU/4clHVS2Z0jadAX2dZCk+ZJulHTDcM1R0mxJrbJ9qaT1y+ejtTKbSjp/OPqLiIiI5dd4ENY026fa/l7ZnQEMKgiTtPoAy+0LfAJ4o+2dgN2BBwfT10DYfrPtB4D1gY/W0u+1PbX3mhERETGSRn0QVlZ5jpN0raTbJL2qpE8qaXMkzZO0bUm/SFKXpJskzay1s0TSsZLmSrpa0sYl/WhJh5UXZreAs0qb4yV9TtJ1ZfVqliTVxvQ1SW3gSEkLJa1R8tat79ccARxm+14A24/Z/lapM7mMaZ6kCyVt0M/cx0s6V9LNki4ExtfmeZekjYAvAtuUuRxfX3GUNE7SabUVudeW9BmSfiTpp5J+L+lLw/m3jIiIiKeN+iCsWN32y6hWkv6jpH0Y+LrtyVTB0z0l/f22p5S0f5X0/JL+POBq27sAvwH+V70D2+cDbeBA25NtLwVOsr2b7R2pAp39alXWtN2yfQwwG3hLSZ8G/Mj2Ex1z2BHo6mV+3wM+bXtn4MbaHHub+0eAR23/U0mb0kObhwN3lLl8qiPvkGrK3gmYDpwhaVzJmwwcAOwEHCBp886GJc2U1JbU5tFeZhQRERF9Gg1BmAeQ/qPy3QVMLNtXAZ+R9GlgyxI0QRV4zQWuBjYHti3pjwOX9NBOX14r6RpJNwKvAybV8s6rbX8beF/Zfh9w2gDaBkDSesD6tn9dks4AXl0r0tPcXw18H8D2PGDeQPsr9qzVvwW4G9iu5P3S9oO2/w4sALbsrGx7VglAW6w1yJ4jIiICGB1B2P3ABh1pGwKLa/uPle9lwOoAts8G3gYsBS6V9DpJewF7A68oK143AN0rPE/Ydmc7vSkrQ98EppYVo2/V2gJ4pHvD9pXAxNL/araf8aBBcRM9r1j151lzX8Eeq22PVJ8RERFjTuNBmO0lwH2SXgcgaUNgH+C3fdWTtDVwp+0TgR8DOwPrAX+z/aikl1Dd/D4YDwPrlO3ugGuxpBDw4aUAACAASURBVLWB/m5q/x5wNr2vgn0BOF7SC8v415T0QdsPAn/rvt8LeC/w617a6PYb4F9KOztSzb2vuXS6Ajiw1N8O2AK4tZ8+IyIiYhiNllWOg4CTJX217B9j+45+6rwbeK+kJ4A/A/+PanXqw5Jupgoqrh7kOE4HTpW0FHgF1erX/NL+df3UPQv4PHBOT5m2Ly0PA/yi3OBv4Lsl++DS71rAnTx9abM3pwCnlXneTA/3mtm+X9KV5Wb8/wZOrmV/EzilXGb9BzDD9mPluYOIiIgYAXr6Cl0sj/J05f6239v0WEZSq9Vyu91uehgREREjQlKX7dZwtDVaVsJWapK+AewLvLnpsURERMTKIUHYMLD98abHEBERESuXxm/Mj4iIiBiLEoRFRERENCBBWEREREQDEoRFRERENCBBWEREREQDEoRFRERENCBBWCyXrnu70DH5pf2IiIjBGlNBmKQXSjpX0h2SuiRdKmk7SZMk/UrSrZJ+L+mz5dVCSJoh6UlJO9famS9pYtl+v6QbJc0r6ftLOlnSHEkLJC0t23MkTZV0uqSFZX+upNfX2r1L0ka1/b0kXVLb31dSu7R7g6SvSDqy1v6y2va/Sjpa0mGlriQdVeZ3m6TLJU3q6PuC2v5USaevkD9EREREjJ0fay1B1YXAGbanlbRdgI2p3hn5Eds/K+9vvAD4KE+/b/Ee4EjggI42Nyvpu9p+sLzoe4LtH5f8icAltifX6uwHfMr2+ZJeC8wCth3A+HcETgLeYvsWSasBM22fAhxbyizp6OvoWhOHAK8EdikvOH8jcLGkSbb/XspMkbSD7QX9jSciIiKWz1haCXst8ITtU7sTbM8FtgOutP2zkvYo8DHg8FrdS4BJkrbvaPMFwMPAklJ3ie2FgxjTVcCLBlj234Fjbd9S+lpWArCB+jTwsTI/ynx/BxxYK/MVqqAyIiIiVrCxFITtCHT1kD6pM932HcDaktYtSU8CXwI+01F3LvAXYKGk0yS9dZBj2ge4aIBlext/v8o8nmf7zo6sNtX8u/0A2FXSi4fST0RERAzcWArCltfZwO6StupOsL2MKpCaCtwGnNBxCbA3x0u6rbR5XC3dPZTtKW1FWQYcDxzRVyFJM8u9aW0eHZmBRURErGrGUhB2EzClh/QFnemStgaW2H6oO832P6gu1326XtaVa21/AZgGvGsAY/mU7e1KW9+tpd8PbFDb3xBY3M/4+1Xm8UiZV92U0m7dmcCrgc37aG+W7ZbtFmsNZUQRERExloKwXwHPlTSzO6E88XgrsKekvUvaeOBEqsuPnU4H9gYmlLKbStq1lj8ZuHsQYzoJeI6kN5X92cB7S9urAe8BLi95xwOfkbRdyX+OpA8Poq/jgRPL/Cjz3ZNqNe4ptp8ATgA+OYi2IyIiYpDGTBBm28A7gL3LT1TcBHwB+DOwP3CUpFuBG4HrqAKkzjYepwrQXlCS1gC+LOkWSXOonp48dJBj+jzVTfcA/xd4saS5wA3A7cD3S9l5wCeAcyTdDMwHOle2+vKNMq8byzw/C+xve2kPZb/DGHpyNiIiogmq4oCIoWm1Wm63200PIyIiYkRI6rLdGo62xsxKWERERMRokiAsIiIiogEJwiIiIiIakCAsIiIiogEJwiIiIiIakCAsIiIiogEJwiIiIiIakCAsIiIiogEJwiIiIiIakCBsFJG0saSzJd0pqUvSVZLe0dBYZkh61qubIiIiYngkCBslJAm4CPiN7a1tTwGmAZutwD7zfsiIiIiGJAgbPV4HPG771O4E23fb/oak1SQdL+k6SfMkfQhA0l6SZks6v7xE/KwSzCFpiqRflxW1yyRtUtJnS/qapDZwqKS3SrpG0g2SfiFp4yYmHxERMdZkJWT0mARc30veB4AHbe8m6bnAlZJ+VvJeWureC1wJ7CHpGuAbwP62F0k6ADgWeH+ps2b3y0clbQDsbtuSPgj8O/C/V8D8IiIioiZB2Cgl6WRgT+Bx4G5gZ0lTS/Z6wLYl71rb95Q6c4CJwAPAjsDPy8LYasB9tebPq21vBpxXVsrWBBYOYGwzgZkAW2yxxdAmGBERMcblcuTocROwa/eO7UOA1wMTAAEftz25fLay3b0S9litjWVUgbWAm2rld7L9xlq5R2rb3wBOsr0T8CFgXH8DtT3Ldst2a8KECUOYakRERCQIGz1+BYyT9JFa2lrl+zLgI5LWAJC0naTn9dHWrcAESa8o5deQNKmXsusBfyrbBw959BERETEoCcJGCdsG3g68RtJCSdcCZwCfBr4NLACulzQf+E/6uJRs+3FgKnCcpLnAHOCVvRQ/GvihpC5g8TBNJyIiIvqh6n/7I4am1Wq53W43PYyIiIgRIamr++G25ZWVsIiIiIgGJAiLiIiIaECCsIiIiIgGJAiLiIiIaECCsIiIiIgGJAiLiIiIaECCsIiIiIgGJAiLiIiIaECCsIiIiIgGJAhbiUg6UtJNkuZJmiPp5X2UfUkpc4Okbfoo95kVM9qIiIjoS4KwlUR5Gfd+wK62dwb2Bv7YR5W3A+fbfqntO/oolyAsIiKiAb2+BDpGnU2AxbYfA7C9GEDS54C3AuOB3wEfAvYFPgEsk/R626+V9B7gX4E1gWuAjwLHAuMlzQFuAu4A/sf210rbxwJ/tf31kZtmRETE2JCVsJXHz4DNJd0m6ZuSXlPST7K9m+0dqQKx/WxfCpwKnFACsH8CDgD2sD0ZWAYcaPtwYKntybYPBL4LHAQg6TnANOD7IzrLiIiIMSIrYSsJ20skTQFeBbwWOE/S4cDDkv4dWAvYkGpF6786qr8emAJcJwmqYO2vPfRxl6T7Jb0U2Bi4wfb9neUkzQRmAmyxxRbDNMOIiIixJUHYSsT2MmA2MFvSjVSXHncGWrb/KOloYFwPVQWcYfuIAXTzbWAG8EKqlbGexjELmAXQarU8uFlEREQE5HLkSkPS9pK2rSVNBm4t24slrQ1M7aX6L4Gpkl5Q2tpQ0pYl7wlJa9TKXgjsA+wGXDZsE4iIiIhnyErYymNt4BuS1gf+AdxOdUnwAWA+8Gfgup4q2l4g6SjgZ+VeryeAQ4C7qVa05km63vaBth+XdDnwQFl5i4iIiBVAdq4mxdNKkHY98M+2f99f+Var5Xa7veIHFhERMQpI6rLdGo62cjkyniJpB6oVtl8OJACLiIiIocvlyHiK7QXA1k2PIyIiYizISlhEREREAxKERURERDQgQVhEREREAxKERURERDQgQVhEREREAxKERURERDQgQVhEREREAxKEDYCkIyXdJGmepDmSLi/ft0t6sGzPkfRKSbMl3SpprqTrJE2utbOko90Zkk6q7R8kab6kGyXdIOkwSSeXthdIWlrra2rpq1WrP1HS/LK9V21st0j6cg/zukjS1R1pR0s6bDiPX0RERDxbfqy1H5JeAewH7Gr7MUkbAWvavlfSXsBhtverlQc40HZb0vuA44E3DKCffYFPAG8sbT8XOMj2ISV/InCJ7XpQ97F+mr3C9n6SxgM3SLrQ9pWl7vrAFGCJpK1t3zmgAxIRERHDIith/dsEWGz7MQDbi23fO8C6VwEvGmDZI6gCuntLP4/Z/tagR9sD20uBOR1jeSfwX8C5wLTh6CciIiIGLkFY/34GbC7pNknflPSaQdTdB7hogGV3BLoGPboBkLQBsC3wm1rydOCc8pm+IvqNiIiI3iUI64ftJVSX7WYCi4DzJM3op9pZkhYCRwIn99fF8gyvn7RXSZoL/Am4zPafASRtTBWU/db2bcATknYcaKeSZkpqS2ovWrRoOYYfERExdiUIGwDby2zPtv0fwMeAd/VT5UCqF2GfAXyjlr5U0pq1/Q2BxWX7JqpgbzDuBzbopT2o7gnbBZgEfKD2kMC7S72Fku4CJjKI1TDbs2y3bLcmTJgwyCFHREQEJAjrl6TtJW1bS5oM3N1fPdsGPgvsLuklJfnXwHtKu+OpgqHLS94XgOMlvbDkrynpg/10Mxt4j8rTAMDBtfbqY1kIfBH4dEmaDuxje6LtiVTBX+4Li4iIGEEJwvq3NnBG+YmIecAOwNEDqVhuiP8K8KmSdCjwTklzgKuBH9r+TSl7KXAS8AtJNwHXA+v208Us4GFgbrnsuDbwrJ+iKE4FXl2estyy9N89zoXAg5JeXpKOknRP92cgc42IiIjBUbVgEzE0rVbL7Xa76WFERESMCEldtlv9l+xfVsIiIiIiGpAgLCIiIqIBCcIiIiIiGpAgLCIiIqIBCcIiIiIiGpAgLCIiIqIBCcIiIiIiGpAgLCIiIqIBCcIiIiIiGpAgLCIiIqIBCcJGkKRlkuZImi/ph5LWKulLhqn9vSRd0kvepZLWH45+IiIiYvklCBtZS21Ptr0j8Djw4ZHq2PabbT8wUv1FRERE3xKENecK4MX1hM6VLEknSZpRtu+S9IWyktaWtKukyyTdIakezK0r6SeSbpV0qqTn1OpvJGmipJslfUvSTZJ+Jml8KbObpHmlj+MlzV/hRyEiImKMShDWAEmrA/sCNw6y6h9sT6YK4E4HpgK7A8fUyrwM+DiwA7AN8M4e2tkWONn2JOAB4F0l/TTgQ6WPZX2Mf2YJBNuLFi0a5BQiIiICEoSNtPGS5gBt4A/AdwZZ/+LyfSNwje2HbS8CHqvd73Wt7TttLwPOAfbsoZ2FtueU7S5gYqm/ju2rSvrZvQ3C9izbLdutCRMmDHIKERERAbB60wMYY5aWVabe/INnBsbjOvIfK99P1ra797v/lu6o07lfbweqFa/xfYwpIiIiVoCshI0udwM7SHpuWZl6/RDaeJmkrcq9YAcAvx1IpXLT/sOSXl6Spg2h74iIiBigrISNIrb/KOkHwHxgIXDDEJq5DjiJ6qb/y4ELB1H3A8C3JD0J/Bp4cAj9R0RExADI7ulqVYxFkta2vaRsHw5sYvvQvuq0Wi232+0RGV9ERETTJHXZbg1HW1kJi7q3SDqC6ry4G5jR7HAiIiJWXQnC4im2zwPOa3ocERERY0FuzI+IiIhoQIKwiIiIiAYkCIuIiIhoQIKwiIiIiAYkCIuIiIhoQIKwiIiIiAYkCIuIiIhoQIKwUULS2yVZ0ktqadtKukTSHZK6JF0u6dUlb4akRZLm1D47SJpY2vl4rZ2TJM2o7R8m6ZZS5zpJB0k6VtJxtTJbSrqzvMMyIiIihlmCsNFjOtXLtqcDSBoH/ASYZXsb21OAjwNb1+qcZ3ty7bOgpP8VOFTSmp2dSPow8AbgZbYnU70kXMDngbdL+qdS9OvAZ8uLvSMiImKYJQgbBSStDexJ9QLtaSX5QOAq2xd3l7M93/bpA2hyEfBL4OAe8j4DfMT2Q6XNh2yfYXsp8EngZElvBtaxfdZQ5xQRERF9SxA2OuwP/NT2bcD9kqYAk4Dr+6l3QMflyPG1vOOAwySt1p0gaV2q4OrOnhqzfSnwN+AM4KO9dSpppqS2pPaiRYsGNMGIiIh4pgRho8N04NyyfW7ZfwZJF0qaL+lHteTOy5FLuzNKoHUN8C+DHMvJwHW2b+2tgO1Ztlu2WxMmTBhk8xEREQF5gXfjJG0IvA7YSZKB1QADxwCv7i5n+x2SWsCXB9H8/wPOB35d2nhI0hJJW/e2GgY8WT4RERGxAmUlrHlTgTNtb2l7ou3NgYXA7cAekt5WK7vWYBq2fQuwAHhrLfkLVPd9rQvV/WiSDlquGURERMSgZSWsedOp7t+qu4DqBv39gK9K+hrwF+BhqqcYux0gac/a/keBezvaOha4obZ/CrA2cJ2kJ4AngK8s7yQiIiJicGS76THESqzVarndbjc9jIiIiBEhqct2azjayuXIiIiIiAYkCIuIiIhoQIKwiIiIiAYkCIuIiIhoQIKwiIiIiAYkCIuIiIhoQIKwiIiIiAYkCIuIiIhoQIKwiIiIiAY0HoRJulzSmzrSPiHplI79v0tar6PcyyT9RtKtkm6Q9G1Ja9XyL5J0dUedoyU9KukFtbQlkp4vaU75/FnSn2r7a/Yw7smSLGmfjvRlpc58ST/sGM/bS52X1NImSlpa6iyQ9D1Ja0h6U63/JWWOc0r+XqWdD/YwnsPK/umSFtba+F1JnyHpSUk71+rOL+O4ppT9g6RFtboT+/obRkRExOA1HoQB51C9J7FuWknvNh24Dnhnd4KkjYEfAp+2vb3tlwI/BdYp+esDU4D1JG3d0f5i4H/XE2zfb3uy7cnAqcAJ3fu2H+9h3NOB35bvuqWlzo7A48CHB1DnjtLvTsBmwLttX1YbTxs4sOx3v2x7PvDujrbndrT7qdocXllLvwc4snNCtl9e+vsccF6t7l09zD8iIiKWw2gIws4H3tK92lRWXTYFrij721C9cPoonhm8HAKcYfuq7gTb59v+S9l9J/BfwLk8O8j7LtXLrzccyoAlCfhnYAbwBknjeil6BfDiUmdtYE/gAz2Mp3v8y4BrgRcNYBh3A+MkbVzGsw/w3wOcwiXAJEnbD7B8REREDLPGgzDb/0MVeOxbkqYBP/DTbxafRhVIXQFsX1bAAHYEuvpoejrVato5PHvlaQlVIHboEIf9SmCh7TuA2cBbOgtIWp1qTjeWpP2Bn9q+Dbhf0pQe6owDXk61ojcQ51MFg68Ergce68g/vnZJ8axa+pPAl4DPDLCfznHOlNSW1F60aNFQmoiIiBjzGg/CivolyZ4uRZ5r+0ngAqqgo08lUNsW+G0Jep6QtGNHsROBgyWtM4TxTqcKDCnf9SBvvKQ5VJcQ/wB8ZwB1til1/gLcZ3veAMfxA6rj0R1wdqpfjjywI+9sYHdJWw2wr6fYnmW7Zbs1YcKEwVaPiIgIYPWmB1D8GDhB0q7AWra7ACTtRBVM/by64saawELgJOAmqnu+ftxDe+8GNgAWlnrrUgUqT90HZfsBSWdTXdYcMEmrAe8C9pd0JCDg+ZLWsf0w5Z6wjjobAq8DdpJkYDXAkj5Vitxhe7KkjYArJb3N9sX9jcX2nyU9AbyBalXvlf1Uqdf9h6SvAJ8eaJ2IiIgYPqNiJcz2EuByqkuEnatgR9ueWD6bAptK2pIqEDtY0su7C0t6Z1kFmw7s012PKljr6T6srwIfYnDB6OuBebY3L+1vSbVC944+6kwFzrS9ZamzOVUw+aqO47AYOBw4YhDj+RzVwwnLBlGn2+nA3kCWsyIiIkbYqAjCinOAXXhmEDYNuLCj3IXAtHID/jTgy+XnG24G3gQ8H9gSeOqnKWwvBB6sB2wlfXFp77mDGOf0HsZ0Ac++72yodS4C1pL0qh7ynsX272xf1Et2/Z6wZ/3URnnq80TgBT1Xj4iIiBVFT9//HjF4rVbL7Xa76WFERESMCEldtlvD0dZoWgmLiIiIGDMShEVEREQ0IEFYRERERAMShEVEREQ0IEFYRERERAMShEVEREQ0IEFYRERERAMShEVEREQ0IEFYRERERANW6SBM0maSfizp95LukPT1zlf39FLvMwMo821JO/SQPkPSSYMY416SHiyvFZon6ReS+n2NkKTfle+Jkub3Uma2pFbZvlTS+gMdV0RERKxYq2wQJknAj4CLbG8LbAesDRw7gOr9BmG2P2h7wfKN8ilX2J5se2fgOuCQAfT/ysF0YPvNth8Y6gAjIiJieK2yQRjwOuDvtk8DsL0M+CTwfklrda5YSbqkrEp9ERhfVqbOkvQ8ST+RNFfSfEkHlPL1Vab3SbpN0rXAHrU2J0i6QNJ15bMHfSiB4zrA38r+0ZIOq+XPlzSxbC/pof54SedKulnShcD4Wt5dkjYqK2c3S/qWpJsk/UzS+FJmt7IaN0fS8b2tsEVERMTyW5WDsElAVz3B9kPAH4AX91bJ9uHA0rIydSCwD3Cv7V1s7wj8tF5e0ibAMVTB155A/RLl14ETbO8GvAv4di/dvkrSnDK2vYHvDniWz/QR4FHb/wT8BzCll3LbAifbngQ8UMYGcBrwIduTgWW9dSJppqS2pPaiRYuGONSIiIixbVUOwobLjcAbJB0n6VW2H+zIfzkw2/Yi248D59Xy9gZOKgHWxcC6ktbuoY/uy5GbUwVCXxriWF8NfB/A9jxgXi/lFtqeU7a7gInlfrF1bF9V0s/urRPbs2y3bLcmTJgwxKFGRESMbatyELaAjpUgSesCWwC3A//gmfMf11Mjtm8DdqUKxj4v6XODGMNzgN1LgDXZ9otsP+syYoeLqYIpBjrGIXistr0MWH2Y2o2IiIgBWpWDsF8Ca0k6CEDSasBXgNNtPwrcBUyW9BxJmwMvq9V9QtIapd6mVJf4vg8cTxWQ1V0DvEbS80udf67l/Qz4ePeOpMkDGPeewB1l+67u/iTtCmzVT93fAP9Syu8I7DyA/gAoN+0/LOnlJWnaQOtGRETE4K2yKyC2LekdwDclfZYq4LyUp598vBJYSLVidjNwfa36LGCepOuB7wHHS3oSeILqvqt6P/dJOhq4iur+qjm17H8FTpY0j+pY/wb4cA/D7b4nTMCDwAdL+gXAQZJuogr2butn2qcAp0m6ucypq5/ynT4AfKvM9ddlLBEREbECyHbTY4hRQtLa3ZdLJR0ObGL70L7qtFott9vtERlfRERE0yR12W4NR1ur7EpYDMlbJB1BdV7cDcxodjgRERGrrgRh8RTb5/HMpzsjIiJiBVmVb8yPiIiIGLUShEVEREQ0IEFYRERERAMShEVEREQ0IEFYRERERAMShEVEREQ0IEFYRERERAMShI0QSW+XZEkvWY42Tpc0tWx/W9IOwzfCiIiIGEkJwkbOdOC35Xu52f6g7QXD0VZERESMvARhI0DS2sCeVC/InlbS9pL0G0k/kXSrpFMlPafkLZF0gqSbJP1S0oQe2pwtqVW2T5HULuWPqZW5S9Ixkq6XdGP3KpyktSWdVtLmSXpXSX+jpKtK+R+WcUdERMQKkCBsZOwP/NT2bcD9kqaU9JcBHwd2ALYB3lnSnwe0bU8Cfg38Rz/tH1leJroz8BpJO9fyFtveFTgFOKykfRZ40PZOtncGfiVpI+AoYO9Svg3829CnHBEREX1JEDYypgPnlu1zefqS5LW277S9DDiHarUM4Emefofj92vpvXm3pOuBG4BJVEFdtx+V7y5gYtneGzi5u4DtvwG7l3pXSpoDHAxs2VNnkmaWlbf2okWL+hlaRERE9CQv8F7BJG0IvA7YSZKB1QADPynfdZ37/aUjaSuqFa7dbP9N0unAuFqRx8r3Mvr+ewv4ue1+71mzPQuYBdBqtXodW0RERPQuK2Er3lTgTNtb2p5oe3NgIfAq4GWStir3gh1AdeM+VH+XqWX7X2rpPVkXeAR4UNLGwL4DGNPPgUO6dyRtAFwN7CHpxSXteZK2G+gkIyIiYnAShK1404ELO9IuKOnXAScBN1MFZt3lHqEK0OZTraL9n94atz2X6jLkLcDZwJUDGNPngQ0kzZc0F3it7UXADOAcSfOAq4Ah/5xGRERE9E12riY1QdJewGG29+shb4ntleLJxFar5Xa73fQwIiIiRoSkrvIw3HLLSlhEREREA3JjfkNszwZm95K3UqyCRURExNBlJSwiIiKiAQnCIiIiIhqQICwiIiKiAQnCIiIiIhqQICwiIiKiAQnCIiIiIhqQICwiIiKiAQnCRiFJyyTNqX0OL+mzJQ36V3olTZb05j7yW5JOXJ4xR0RExODkx1pHp6W2Jw9je5OBFnBpZ4ak1W23gbx7KCIiYgRlJWwlJemNkq6SdL2kH0pau6TvJul3kuZKulbSelQvAD+grKodIOloSWdKuhI4U9Jeki4p9deWdJqkGyXNk/SuBqcZERGxykoQNjqN77gceUA9U9JGwFHA3rZ3pVrF+jdJawLnAYfa3gXYG3gE+Bxwnu3Jts8rzexQ6k/v6PuzwIO2d7K9M/CrFTbLiIiIMSyXI0en/i5H7k4VRF0pCWBN4Cpge+A+29cB2H4IoJTpdLHtpT2k7w1M696x/bfOApJmAjMBtthiiwFMJyIiIjolCFs5Cfh55yqWpJ0G0cYjQ+3c9ixgFkCr1fJQ24mIiBjLcjly5XQ1sIekFwNIep6k7YBbgU0k7VbS15G0OvAwsM4A2/45cEj3jqQNhnXkERERASQIG6067wn7Yj3T9iJgBnCOpHlUlyJfYvtx4ADgG5LmUgVU44DLgR16ur+sB58HNpA0v7Tx2uGdWkRERADIztWkGLpWq+V2O79uERERY4OkLtuD/s3OnmQlLCIiIqIBCcIiIiIiGpAgLCIiIqIBCcIiIiIiGpAgLCIiIqIBCcIiIiIiGpAgLCIiIqIBCcIiIiIiGpAgLCIiIqIBCcJWEZIs6fu1/dUlLZJ0ST/1Zkg6qZe8JcM9zoiIiKgkCFt1PALsKGl82X8D8KcGxxMRERF9SBC2arkUeEvZng6c050haUNJF0maJ+lqSTt3Vpa0laSrJN0o6fMjNOaIiIgxKUHYquVcYJqkccDOwDW1vGOAG2zvDHwG+F4P9b8OnGJ7J+C+FT3YiIiIsSxB2CrE9jxgItUq2KUd2XsCZ5ZyvwKeL2ndjjJ78PTq2Zm99SNppqS2pPaiRYuGY+gRERFjToKwVc/FwJepXYocJPdbwJ5lu2W7NWHChCF2ExERMbYlCFv1fBc4xvaNHelXAAcCSNoLWGz7oY4yVwLTyvaBK3KQERERY12CsFWM7Xtsn9hD1tHAFEnzgC8CB/dQ5lDgEEk3Ai9acaOMiIgI2f1efYroVavVcrvdbnoYERERI0JSl+3WcLSVlbCIiIiIBiQIi4iIiGhAgrCIiIiIBiQIi4iIiGhAbsyP5SLpYeDWpscxym0ELG56EKNYjk//coz6l2PUvxyjvg30+Gxpe1h+JHP14WgkxrRbh+spkVWVpHaOUe9yfPqXY9S/HKP+5Rj1rYnjk8uREREREQ1IEBYRERHRgARhsbxmNT2AlUCOUd9yfPqXY9S/HKP+5Rj1/hpU4wAABUxJREFUbcSPT27Mj4iIiGhAVsIiIiIiGpAgLIZE0j6SbpV0u6TDmx7PiiBpc0mXS1og6SZJh5b0DSX9XNLvy/cGJV2STizHZJ6kXWttHVzK/17SwbX0KZJuLHVOlKS++hiNJK0m6QZJl5T9rSRdU+Z0nqQ1S/pzy/7tJX9irY0jSvqtkt5US+/xPOutj9FI0vqSzpd0i6SbJb0i59AzSfpk+W9svqRzJI0b6+eRpO9K+quk+bW0xs6bvvpoQi/H5/jy39k8SRdKWr+WNyznxlDOvz7ZziefQX2A1YA7gK2BNYG5wA5Nj2sFzHMTYNeyvQ5wG7AD8CXg8JJ+OHBc2X4z8N+AgN2Ba0r6hsCd5XuDsr1Bybu2lFWpu29J77GP0fgB/g04G7ik7P8AmFa2TwU+UrY/CpxatqcB55XtHco59Fxgq3JurdbXedZbH6PxA5wBfLBsrwmsn3PoGcfnRcBCYHztbztjrJ9HwKuBXYH5tbTGzpve+hhlx+eNwOpl+7ja2Ift3Bjs+dfvPJo+0fJZ+T7AK4DLavtHAEc0Pa4RmPePgTdQ/TjtJiVtE6rfSgP4T2B6rfytJX868J+19P8saZsAt9TSnyrXWx+j7QNsBvwSeB1wSfkHenHtH8KnzhXgMuAVZXv1Uk6d5093ud7Os776GG0fYD2qAEMd6TmHnh7zi4A/UgUKq5fz6E05jwwwkWcGGY2dN731MZqOT0feO4Cz6n/z4Tg3Bnv+9TeHXI6Moej+R7PbPSVtlVWWnF8KXANsbPu+kvVnYOOy3dtx6Sv9nh7S6aOP0eZrwL8DT5b95wMP2P5H2a/P6anjUPIfLOUHe9z66mO02QpYBJym6pLttyU9j5xDT7H9J+DLwB+A+6jOiy5yHvWkyfNmZft3//1UK3cwvOfGYM+/PiUIi+iHpLWBC4BP2H6onufq//Ks0EeMR6KPoZC0H/BX211Nj2UUW53qkskptl8KPEJ1iecpY/kcAij3HO1PFbBuCjwP2KfRQa0Exvp50xdJRwL/AM5qeiz9SRAWQ/EnYPPa/mYlbZUjaQ2qAOws2z8qyX+RtEnJ3wT4a0nv7bj0lb5ZD+l99TGa7AG8TdJdwLlUlyS/DqwvqfuVaPU5PXUcSv56wP0M/rjd30cfo809wD22ryn751MFZTmHnrY3sND2IttPAD+iOrdyHj1bk+fNSvHvvqQZwH7AgSWIhOE9NwZ7/vUpQVgMxXXAtuXpkTWpbk68uOExDbvytNB3gJttf7WWdTHQ/ZTRwVT3inWnH1SeItodeLAs618GvFHSBuX/9b+R6v6C+4CHJO1e+jqoo62e+hg1bB9hezPbE6nOgV/ZPhC4HJhainUen+45TS3lXdKnlaeOtgK2pbppuMfzrNTprY9RxfafgT9K2r4kvR5YQM6huj8Au0taq8yh+xjlPHq2Js+b3voYNSTtQ3V7xNtsP1rLGs5zY7DnX9+avKkun5X3Q/WkzG1UT4Ac2fR4VtAc96Raip8HzCmfN1Nd//8l8HvgF8CGpbyAk8sxuRFo1dp6P3B7+byvlt4C5pc6J/H0Dyj32Mdo/QB78fTTkVuXf3xuB34IPLekjyv7t5f8rWv1jyzH4FbKU1p9nWe99TEaP8BkoF3Oo4uonlLLOfTMY3QMcEuZx5lUT5iN6fMIOIfqHrknqFZUP9DkedNXH6Po+NxOdV9W97/Xpw73uTGU86+vT34xPyIiIqIBuRwZERER0YAEYRERERENSBAWERER0YAEYRERERENSBAWERER0YAEYRERERENSBAWERER0YAEYREREREN+P99uRssE1EwFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visuvalizing to verify high-complaint categories\n",
    "#keeping complaint type in df_hpd_complaint_type, a different data frame\n",
    "df_hpd_complaint_type = df_hpd['complaint_type'].value_counts()\n",
    "#storing in the same dataframe and sorting in ascending order based on the frequency of complaint kinds\n",
    "df_hpd_complaint_type.sort_values(ascending=True,inplace=True)\n",
    "#plotting using a bar graph\n",
    "df_hpd_complaint_type.plot(kind='barh',figsize=(8,10),color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### By analyzing and visualizing HPD data, it is possible to concentrate on HEAT/HOTWATER issues, as the top 3 complaints account for 50% of the total."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scripted by : Venkat Alapati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}